Differentiated Understanding

Jaksot

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  Autonomous Computer Agents, Token Economics, and Dual Use-case of LLMs with Simular AI Ang Li 17.09.2026 57min

  In this episode, I speak with Ang Li, co-founder and CEO of Simular AI, about the rise of computer use agents and his vision for computers that can increasingly do work on behalf of humans. We discuss why computer use matters beyond the current wave of API-based agents, particularly for the vast amount of enterprise work still carried out through legacy desktop software that was never designed to be accessed programmatically.We explore where these agents could have the most immediate impact, from processing invoices and extracting information from unstructured documents to navigating financial, healthcare and other enterprise systems. Ang argues that the goal is not necessarily to remove humans from the workflow, but to shift the balance between execution and judgment, with agents handling repetitive tasks while people remain responsible for decisions and sign-off.The conversation also gets into the economics and technical challenges of computer use agents. Ang explains why relying on a frontier model for every click can be expensive, slow and difficult to control, and how Simular’s neurosymbolic approach turns repeated workflows into executable playbooks. We discuss the role of smaller and open-weight models, the changing economics of AI agents, and why Ang sees computer use as a way to make automation more accessible beyond the highest-value technical work.Finally, we look at what this shift could mean for SaaS and the future of work. Ang distinguishes between systems of record and software that primarily serves as a portal or interface, and argues that these categories may face very different levels of disruption from agents. We end with a broader question about human agency: if AI increasingly handles execution, will the more valuable skill become knowing how to identify the right problems to solve in the first place?The AI Proem Podcast is part of the AI Proem newsletter, which has ~13k followers globally. To learn more about China AI, the business of AI, and how AI is impacting society, please check out the newsletter here and more insightful conversations here.Chapters00:00 Introduction to Simular and Its Vision05:14 The Future of Work and Human-AI Collaboration10:21 Technological Landscape and Market Demand15:31 Real-World Applications and Use Cases20:40 Challenges and Competitive Landscape29:55 The Dual Nature of Work: Content Creation vs. Execution33:50 The Competitive Landscape: Frontier Labs vs. Open Weight Models37:17 The Rise of Computer Use Agents43:30 Empowering the Average Person: High Agency Through Technology48:46 Understanding SaaS: Infrastructure vs. Portals52:32 The Future of AI: High-End Jobs vs. Repetitive TasksAI-generated Transcript (for reference only)Grace Shao (00:00)Ang, thank you so much for joining us today. Really excited to have you. To start with, could you tell us a bit more about yourself and why you built Simular? What is your long-term vision for the company? And what really brought you here along your academic journey?Ang Li (00:13)Yeah, thank you so much, Grace, and thank you for having me here. My name is Ang Li. I’m the co-founder and CEO of Simular. We’ve been working on this company for almost three years.In short, we call ourselves the autonomous computer company, meaning we are building autonomous computers. The goal of Simular is basically this: everyone has computers right now, but we have to work on them manually by moving the mouse, typing on the keyboard, looking at a screen, and understanding what’s going on ourselves.We envision a future where computers will do the work on behalf of humans, on their own. That’s really the technology that we’re building towards. Nowadays people also call them computer-use agents. It’s basically a general-purpose agent that can use the computer just like a human.A bit about my background: I’ve spent almost 20 years researching AI. My personal research direction has basically been trying to figure out what people sometimes call AGI, artificial general intelligence. The goal is to have a general-purpose system that can learn like humans and perform actions just like humans.I see this autonomous computer problem as the first possible realization of AGI technology that could have a huge impact on society.Grace Shao (01:29)It’s quite interesting. You mentioned computer-use agents. It seems like there’s been quite an influx of capital going into this space right now in Silicon Valley. Has there been a genuine technological shift here? Why is everyone suddenly so hyped up about CUAs?Ang Li (01:44)Yeah, so that’s the interesting part. We started three years ago, and when I told people we were building agents, people didn’t understand it. I was telling people, “Okay, we’re building agents that use computers.” And people would ask, “Why? Why are you building agents that use computers? Why not just use APIs?”Agents aren’t actually a new concept. It’s a word that has been around for tens of years in the research community. We already talked about agents within DeepMind when we were doing research towards AGI. It just wasn’t very familiar to the broader public.Three years ago, we had the first version of ChatGPT, and we knew the scaling laws for foundation models were working. Foundation models were becoming very powerful.We looked at the trajectory of the technological shift and realized that, for a computer to work like a human, you need APIs. If you want a computer to go into your Gmail, look at an email, and send an email on your behalf to someone else, there’s already a Gmail API. So if you want to do those kinds of tasks, you just let the agent call the Gmail API.Three years ago, that was basically the case for tool calling: basic APIs.Then we asked: suppose we have all the APIs readily available to agents, what’s remaining?The answer became very natural. What’s remaining is all the software that has no APIs.For example, lots of companies have legacy software on Windows computers, like old ERP systems that nobody is maintaining anymore. That software has been running for many years, and people don’t really want to change because they’re so familiar with it.The problem is that because the software is outdated, people still have to manually work on it. There are no APIs.If our goal is to liberate human labor, we don’t want people spending so much time sitting in front of computers doing repetitive, tedious tasks. Nobody likes that. Everyone thinks, “Why can’t I do something more interesting with my life? Why should I spend eight hours every day doing repetitive stuff that doesn’t require much cognitive load, just looking at a spreadsheet and filling out the same form over and over again?”Those problems cannot be solved by agents that only have APIs.So then we realized this is actually a harder problem. It requires technology that can look at a screen, decide, “Should I click this button here? Should I type something?”, move the mouse to the coordinates of the button, click on it, and move to the next page.It feels a bit like a self-driving car in the digital world. A self-driving car looks at the streets and decides, “Should I turn left or right? Should I press the gas?” In this case, we’re looking at a screen and deciding where to click.When you combine the two, API agents and computer-use agents, you cover the full spectrum of computers. There’s nothing else remaining.Once you have API agents and computer-use agents and combine the two, computers can become autonomous. That’s the AGI that everyone is striving for.Grace Shao (05:25)It’s pretty crazy. Your vision of the future of work is essentially that computers run themselves.I get that vision. But wouldn’t work itself, by nature, just change? So much of our work right now, like you said, filling out PowerPoints or spreadsheets, you can almost call it performative because it’s ultimately for humans to view.But if it’s agents or computers viewing the output, do we still need to fill out those PowerPoints and forms?Ang Li (05:50)Yeah. I think first we have to look at what the bottleneck of work is right now.If we still have a lot of people performing manual data entry, that’s the bottleneck right now. If we remove that bottleneck, people’s productivity could be 100x in the future, and they can spend more time on strategic decision-making instead of doing this performative work.That’s our first goal as a company. Why not just remove that repetitive work from people?It doesn’t mean that in the future computers do all the work and humans never look at a screen. It’s more like when you hire an intern. You delegate some tasks to the intern and say, “Can you fill out this form?” The intern comes back and says, “I finished it. Do you want to take a look?”You still take a final look and make sure everything is correct and according to the company’s policies.Computer agents will do the same. It’s not going to be that agents just finish the work, make some random mistake, and walk away.In the end, the human will still be the final gatekeeper for everything. Humans just don’t have to be involved throughout the entire process. You still need a human to sign off.Grace Shao (07:23)Right. You don’t need to be hitting enter, enter, enter the whole time when Claude keeps prompting you.Ang Li (07:26)Yeah, exactly.Grace Shao (07:30)But then my question for you is: do you think the future will still look like the desktop we know today?What would the interface be? Are we still going to use the current desktop applications we use today? Or do you have a different vision for how humans even interact with AI?Ang Li (07:47)To answer this question, I think we have to clarify two concepts.One is what kind of device humans use. The second is what kind of device agents use. Those two devices don’t have to be the same.First, we have to look at the natural way for humans to receive information. This seems like a relatively simple problem.Humans invented paper, and society has been using roughly this size of paper to view documents, do approvals, and sign off on things.For computer screens, we envision the future as something more like an iPad.You don’t necessarily have to have a keyboard or trackpad. You can just have a big screen.Grace Shao (08:36)You don’t even necessarily have to have a screen or interact with it, right? It could just be like a box.Ang Li (08:42)Yeah. I mean, you can still have interaction. You can still click on it.But there’s a fundamental size that humans are comfortable with. It’s roughly the size of a piece of paper.Some people say the future is the mobile phone. I disagree with that because the size is limited. It’s hard for me to read books or documents on it.An iPad-sized screen is actually a good size. It’s kind of like current computers, just removing the keyboard and trackpad.The second question is: what kind of device do agents use?That device doesn’t necessarily need a screen. It’s just a machine with computational power, and that’s good enough.Whether the machine is a laptop or a desktop doesn’t really matter. What’s important is the computing power. You could have GPUs in there. You could host models in there. You should think about it more like a piece of metal sitting in a data center. That’s the machine the agents use.Then there’s another question along that line: what kind of operating system runs on the machine? Is it still desktop, mobile, iPadOS?Our view is: why not have a common infrastructure for all operating systems?For now, the most powerful one is the desktop because the majority of the killer applications for computer use are legacy desktop software.That becomes the primary direction we’re tackling right now.But it’s possible that in the future, if a lot of apps run on mobile phones, then we need Android machines in the cloud that help you offload that kind of work.It’s definitely possible. It’s just that right now, we see the biggest bottleneck as Windows desktops.Grace Shao (10:40)So in the future, would you guys look at creating the operating system you were just talking about? Or would you even go into the hardware?Ang Li (10:48)Yeah. I mean, we are an autonomous computer company, so basically we’re working on computers. It’s definitely possible.The future trajectory could go beyond software.But right now, we already have cloud infrastructure where, if you say, “I need a Windows desktop,” we can give you a Windows desktop in the cloud. If you need an Android phone, we can give you an Android phone. If you need a Mac, we can give you a Mac.We have infrastructure that can allocate any operating system in the cloud for your agents to use.It’s general-purpose, and the agents can choose how many devices they want.Some people might want to use 10 Windows desktops for their own purpose. Some companies may need 100 to run massively parallel jobs for certain software.That’s already possible today.Grace Shao (11:37)I want to double-click on something you mentioned earlier.You sound more optimistic about the idea that, as AI advances, we’ll be freed up to work on more creative work.I’m going to play devil’s advocate here. Some people would argue not everyone wants to work on strategic work. Not everyone has that creativity, not everyone wants to do that, and frankly, not everyone has the capability to do that.Some people have been part of the execution chain for the last three decades. That’s how the workforce has trained them.So it leads me to this broader conversation. There’s a bit of fear-mongering in Silicon Valley saying AI is going to take our jobs. AI will certainly take over many of the administrative and executional tasks you mentioned.But others are saying jobs don’t equal tasks.It sounds like what you’re proposing is that CUAs will help with tasks but not necessarily replace the entire job.Help me understand your more philosophical view on this and how you see the future of work.Ang Li (12:34)Yeah. We talk to customers, and people also reach out to us.I can give you an example. There was a general manager of a car dealership who reached out to us. It’s a small family-style business in the U.S., only three to five people.They go into QuickBooks and generate hundreds of invoices every day for their customers, and they don’t like it.Even though people are willing to do this job, they don’t like it. That’s the real problem for society right now.They earn their wages through this kind of work, but it’s not really a job they like. If those people had the opportunity to do something else, they would do it.The real problem is not that they’re trained to do this, therefore they want to do it. It’s because this is the way for them to earn wages.Suppose we had a way to give these people the same amount of salary and the opportunity to do something else they’re interested in. Everyone would do that.The real question is: do we have enough productivity that allows people to explore their interests?My view is that this division of labor exists because we are in a constrained economy.When you only have a certain amount of money and resources to distribute, you have to make trade-offs.But if society’s productivity becomes 100x higher, meaning we produce 100x more goods and resources with the same population size, then we can allocate more funding and resources to each individual.In that scenario, people aren’t going to say, “Because I was trained to do repetitive work, that’s what I want to do.”People will ask, “Can I use Claude Code to create apps?”A lot of people are already doing that. People in non-technical industries who used to do tedious work are turning to coding agents and asking, “What kind of thing can I create?”Everyone becomes a creator. You’re creating something new. That’s what people find interesting.I wouldn’t doubt that.I feel like the main problem is that our society has a limited economy. That’s waiting for us to amplify it by 100x.This digital workforce is an opportunity for society to amplify the economy because, in the future, every company could have 100x more digital workers who aren’t human.Naturally, the speed at which you produce goods or run operational pipelines becomes much faster. Companies run faster and produce more resources for society.That’s the opportunity I see. This might be a little controversial, but I see agents helping industry become much more productive, and in return giving humans more opportunity to do creative work or whatever work they’re passionate about.The problem right now is that we are constrained, so people are forced to do a lot of manual work.Grace Shao (15:56)No, I actually agree with you on this.Technology has always disrupted jobs, but that disruption has also led to replacement and redirection of people’s interests.Even looking at the last generation of workers, think about people who worked in car manufacturing or factory jobs. As automation replaced some of those jobs, people found new work.What you’re saying is that now our minds are doing these laborious jobs. They’re almost mental labor jobs.Once our minds, or at least our time, are released from that, potentially we find new ways to use them.But that may take a decade or two, or even a generation, to figure out what the new way of living is.I’m in that optimistic camp as well.It’s just interesting when you talk about 100x supply: will there also be 100x demand? And how might that affect the economy?But we’re not going into that today. That’s a whole rabbit hole we could go down.Ang Li (16:54)Yeah. In short: use the new technology, find new opportunities, and make the pie bigger.You want to make the pie bigger so everyone can be relieved from some of the stress and burden of labor.Grace Shao (17:09)I feel stressed out covering AI right now because it’s moving too fast. There’s too much to follow every day.But look, what are some real-life use cases you’re seeing?I liked your dealership example, but what are some more enterprise-facing use cases where people are already using Simular?I think on another podcast you were talking about healthcare and pharmaceuticals. Are those verticals something you guys are focused on, or have they naturally become high-demand industries for your kind of technology?Ang Li (17:44)It’s organic. They naturally become high-demand industries. Financial services as well.We’re working on this general-purpose horizontal platform that allows people to take our technology, build on top of it, and solve their own problems.We have this market pull where people come and talk to us.We’ve found a pattern across almost all the use cases.The pattern is basically: “I have a form in a certain shape.”It could be a physical receipt. It could be handwritten or machine-printed. It’s basically unstructured data, often an image.The agent needs to look at the image, parse the information on that paper, and move the data into another system.And that system usually doesn’t have APIs.It’s often a desktop environment. Many of them aren’t browser applications. They’re old desktop applications running on Windows computers.This kind of manual data entry is one of the biggest bottlenecks in industry today.And it’s not just healthcare. People from many other industries have exactly the same problem.Grace Shao (18:55)So that’s currently the biggest use case.Help me understand the technicalities of this. Am I giving you access to my PC? How does this work?Ang Li (19:05)There are two types of scenarios.In regulated industries, people worry about giving a third party access to private information.Suppose you’re a healthcare provider. You already have your own computer with the software installed.What you want is: “Can you install an agent on my computer so the agent takes the image, fills out the form, clicks through the portal, and does some calculations?”That’s the option we call BYOD, bring your own device.Basically, you have your own device. You have everything installed there. I don’t touch any of it. I give you the agent, and you install the agent as software on your computer.Then there’s another set of users who want to scale.They say, “Okay, I have one computer. I can make it an autonomous computer. But what if I want to run 100 autonomous computers for this type of task in parallel?”It’s impossible for them to stack 100 computers in their office and manage all of them.So they come to us and say, “Can you provision 100 virtual computers in your cloud?”That becomes the advantage of having cloud infrastructure for all these desktop computers.In that scenario, you don’t need to provide the computer. You come to us, and there’s already a computer there with an agent installed.You just prompt the computer and say, “Okay, do this task. Fill out this form and test our computer,” and then you walk away.You don’t have to manage anything.Grace Shao (20:40)I see. So a lot of it is actually outsourced to you guys to manage.The elephant in the room for all the newer players is obviously Big Tech and the frontier labs: OpenAI, Anthropic, Google and others.These companies are becoming very good at almost everything. They’re going around eating everyone’s lunch.What happens when they become very good at computer-use agents?What is the lasting moat for a company like yours? You’re three years old, you’ve obviously done very well in your vertical and were very early to it, but how do you create a lasting moat?Ang Li (21:16)First, I want to thank the frontier labs for working on this problem.When we started three years ago, I had a really hard time convincing people this technology was useful. People thought it wasn’t useful.Another group of people would always ask me, “What if OpenAI does the same thing?”It feels natural because we are working on a very general technology. It feels natural for OpenAI to do the same thing.And I didn’t really have an answer to that.I would literally tell them, “Okay, it’s definitely possible.”I came out of DeepMind and I’m working on this general technology. I believe there are a few sets of people in the world who share the same vision and are pushing the same technology toward the future.About a year after that, OpenAI started working on this problem. Then other labs started working on it too.Even though we were one of the only companies focused on it at the time, we released our first open-source computer-use agent, which ranked number one on a public benchmark called OSWorld. That happened in October 2024.I still remember that one week later, Anthropic released its first computer-use product.That was the moment where it became real: frontier labs were working on the same problem as us.The interesting part is that it actually helped us educate the market.More people became familiar with the technology. Whenever they talked about computer use, they knew there was a company called Simular that had been working on this for years.They thought, “It seems like promising technology. We want to try it and see how it can help us as a company.”So that’s the first answer: having other people working on the same thing may not be a bad thing.I want to say this to founders because everyone worries about a big company taking over.Actually, it may be the contrary. If a big company starts working on the same problem, it validates the product-market fit for your technology.Then we have to think about what’s next.Our philosophy has always been: if there is something other people can work on, why does the world need us to work on it?In the beginning, we wanted to build the whole computer agent. We knew it was relatively easy for people to build API agents, so why should we spend time on API agents? Why not spend time on the remaining problem?That’s why we worked on computer-use agents.Now the big labs are working on computer-use agents as well. So we have to look at what kind of technology they’re focusing on.The big labs’ business model is based on the idea that AGI is a single model.Their business model is to sell APIs for that single model.Computers are a vertical on top of their model business. Computers are not the whole thing for frontier labs.They’re still trying to train the models. Computer use is an application for them.So the way they do computer use is to take the model, treat it as an API, and have the agent ask the model, “What do I do next?” Then the model produces the result.That approach has three problems.We were actually one of the first open-source agents using that paradigm, where the agent repeatedly asks the model and gets the result. That helped us become number one on the benchmark.But we realized there are three problems with that framework.First, it’s expensive.For every move, every click, you need to take the whole screen and pass all of that screen information to the model.Sometimes if you go to Wikipedia, it could be 100,000 tokens. Every step, you’re passing all of that information to a frontier model and asking, “What’s next? What should I do?”It becomes very easy to spend $100 on a certain task.It’s expensive.Even though model prices are dropping in some areas, we can still see relatively steady pricing for frontier models because the model intelligence keeps getting better.The second problem is speed.For every step and every move, I need to package everything on the screen and pass it to the model. I may have to wait five or ten seconds for each move.It’s slow.The third problem is even worse: it’s not stable.Foundation models are neural networks, so fundamentally they are probabilistic models.Every time you ask the same question to ChatGPT, it can give you a different result.Have you ever copied and pasted the same prompt into a large language model ten times? It can give you ten different results.That reflects the fact that the model is probabilistic.But in the agent space, we’re talking about an agent asking the model what to do at every single step.Grace Shao (26:17)And in your use case, you actually want exactly the same thing every time.Versus wanting it to feel like a different human talking to me, your use case makes more sense if it gives you the exact same answer every time, right?Ang Li (26:29)Exactly. I just want the exact result.I don’t want creativity in terms of which button to click.If I deploy this agent on my server, I need to be able to anticipate what’s going to happen.That isn’t necessarily the case with agents today.For chatbots, that variability is okay because sometimes that’s what I want.But these three problems are fundamental problems with using a single model to build agents.We have a solution for these three problems, and it isn’t a single-model solution.We have an architecture that we call a neuro-symbolic approach.Thirty years ago, when nobody was talking about neural nets, everyone in AI was working on symbolic approaches: logic, reasoning, if-else statements, programs.We try to combine the two.We take some inspiration from what humans do.For example, when I learn to ride a bike, the first time is really hard.But if I ride a bike 100 times, it becomes muscle memory. I don’t even need to think about what I should do. I’m balancing myself without thinking.Humans have this characteristic called the power law of practice.As you practice, your efficiency at performing the job becomes extremely high, your consumption of brainpower becomes extremely low, and your reliability becomes extremely high.Today’s agents powered by frontier models don’t have those characteristics.Every time I ask them to do the same job, it costs me the same amount of tokens. I may pay the same $30 for one simple task. And every time, it can still give me some surprises and failures.That’s the problem.Our solution is basically something like note-taking.Suppose I’m performing a job for the first time. At the same time, I write a playbook. That playbook is represented by code.The agent has a notebook that remembers what it did to perform the job.Next time, it refers to the notebook and tries to reproduce it.Grace Shao (28:46)That’s brilliant. Okay, so you save a lot of token usage in this process.Ang Li (28:50)Yeah.One of our early experiments showed that for some tasks, we can save 90% of token usage.So it’s 10x cheaper than a typical agent.At the same time, you get higher reliability because most of the actions are code. It’s deterministic.When I run it, I know the result.It also helps make the system more transparent.In an enterprise setting, when I look at the system, I know exactly what this thing is going to do every day.I’m not worried about the AI suddenly jumping out of the sandbox and attacking other companies.It’s much more controlled.Those benefits help people adopt the technology in production environments.Grace Shao (29:32)That makes a lot of sense. That’s really interesting.On the token-spend point, I want to ask you as a founder: how do you balance which models you use?How do you decide which model to route to, and how do you decide on token spend?Explain to us the pain point you face right now as a founder, and whether you have any solutions or suggestions for other founders.Ang Li (29:55)It really depends on the work you’re performing.One observation is that there are basically two types of work.One is content creation. The other is execution, which is what we focus on.Content creation can be generating images, generating videos, generating code, generating apps.I view programming and engineering as creation.That kind of work requires the most intelligent models.When a significantly better frontier model shows up, a lot of people will immediately move to it because they feel, “Okay, this model is smarter for writing my apps or doing programming work.”It’s difficult to do the same type of job with the same efficiency using a much lower-tier model.That’s one category.The second category is something like, “I want to go on LinkedIn and see what’s going on, see how many people reached out to me, and click around.”We don’t need an IMO gold-medalist-level model to do that kind of task.For computer use, it’s highly likely that you only need a good open-source model to accomplish a lot of these jobs when it’s paired with the harness we’ve built, because most of the job gets translated into code.When you don’t ask the model what to do at every single step, your dependency on the model becomes lower.That means smaller models have the opportunity to produce equivalent performance for this type of work.Computer use is a category that shows up a lot in non-technical corporate functions like finance, marketing, and GTM teams.They go to different websites and extract information.It’s not a small category. I would say 80% of what we do on a computer is actually looking at a screen and clicking things.As a founder, you have to balance the tools.Do you really need your GTM team to use the most expensive frontier model to click around LinkedIn or go to websites and extract information?You don’t have to. You don’t need a PhD to do that kind of work.It’s related to how you manage a team: put the right person in the right position, and put the right agent on the right work.For computer use, we have an opportunity to drop costs dramatically. That’s technology you can already use to maximize productivity in non-technical domains.For coding agents, I still think it’s important for engineers to use the best model because you’re doing content creation.You want the agent to be smart in terms of interpreting your intention, getting feedback, and revising the code.That’s an area where it’s currently very hard to say, “Let’s just use a worse model and the team will still have the best performance.”My strategy is to make sure the team has all the tools available to them.That space is moving very fast. Today we have one model; tomorrow we may have a better model.As a startup, you want to move fast, and one way you move fast is by using the best technology available.But I think the part most people overlook is that a majority of work doesn’t actually need the best model.We have a solution for those workloads, and people should consider reducing their costs there.Grace Shao (33:50)That’s a very clear way of putting it, and I really appreciate that.How do you view the way frontier labs are essentially incentivizing this race toward token maxing?Over the last couple of months, we’ve also seen a lot of open-weight models push token costs down.To your earlier point, that really changes the competitive landscape for agent companies like yours, which can be big consumers of tokens.How do you view the incentives of open-weight models versus frontier models right now?The frontier labs are clearly still chasing token maxing and more expensive tokens, while the open-weight models can continue compressing costs.How do you see that dynamic playing out?Ang Li (34:35)Suppose you are Sam Altman or Dario. What would you do? Would you give your models away for free?If I were managing a frontier lab, I would feel like I had no choice.Their valuations are very high, and those valuations need revenue to justify them.How do they get revenue?People use their models, and they charge for token usage.So you have two choices. One is to increase your price. The second is to increase usage.That’s basically what’s happening.People have been saying frontier-model prices will drop over time. But over the last few years, for the highest-end models, you still see premium pricing.The most intelligent tokens can become more expensive.Grace Shao (35:27)They justify the premium.Ang Li (35:29)Exactly.Then you have token maxing.Those dynamics make sense in the current industry because these companies have extremely high valuations, they’re competing with each other, and they have to raise money and generate revenue to make the companies sustainable.From our perspective, though, we’re not a frontier lab. We’re not a foundation-model company.What we want is to serve normal people.We want people to have more affordable and accessible options, so that everyone has the opportunity to automate their work.We don’t want only rich people to have this opportunity.We want to bring this kind of luxury to everyone.Even for very tedious work, they should be able to automate it so people can be freed.In that scenario, we don’t have to do token maxing.We’re doing the complete opposite. We try to minimize the number of tokens you use.And we don’t always have to use a premium model because this type of work doesn’t require a Math Olympiad gold-medalist model.An open-source model can work.That’s exactly why I find this category so interesting. It has the opportunity to allow everyone to offload their work.But if we’re talking about coding, that’s a different story.Coding agents need the best models. That means you have to pay the premium. They are going to be expensive.That’s also why the frontier labs are still focused so much on coding agents. It makes sense for them because that’s one of the fastest ways to drive revenue.I wouldn’t say it’s a simple question. It’s a complex societal problem where you have to consider economics and how startups work.After all that analysis, their behavior makes sense.Grace Shao (37:18)That makes sense.Touching on coding agents, last year was really interesting. It felt like the year of the coding-agent wars. Everyone was in an arms race, and that’s still ongoing.Coding agents were arguably the first major breakout use case for agents.And now we’re seeing more and more companies move into computer-use agents. Manus was one of the earlier examples.Why do you think the market is mature enough now to actually consider computer-use agents as the next big area of competition and focus?Ang Li (37:57)Because there’s nothing else remaining.I used to tell people: when the frontier labs start competing on computers, that means we’re getting close to AGI.Grace Shao (38:09)But how would you define AGI?AGI feels vague, right? Everyone gives a different version.If we’re really so close to AGI, it doesn’t feel like the world is suddenly going into doom, all our jobs are lost, we’re irrelevant, machines are taking over.It doesn’t feel like that, which is the narrative Silicon Valley has been telling.How do you define AGI?Ang Li (38:32)My definition is basically this: for my whole workday, I don’t need to sit in front of the computer. I just talk to my agents through my phone, and they do my job.That’s it. It’s pretty simple.I wouldn’t worry too much about humans losing their jobs.You still need humans to sign off.You still need someone to be responsible for the outcome. You can’t let the agent itself be responsible.Grace Shao (38:56)But then would your value still be as high?If your job becomes so easy — and your job technically isn’t supposed to be easy as a founder or technical person — but it becomes much easier, are you still worth what you used to be worth?Does your market value drop?Ang Li (39:11)You’ll focus much more on high-level strategy.Even what kind of prompts you give the agents becomes important.That’s one interesting thing about coding agents.People say, “Okay, agents can write code.”But if you tweak your prompt a little bit, the outcome can be very different.If you understand the algorithm behind a coding agent, one of the first things it does when you send a prompt is extract keywords and search through your computer.If you directly give it the right keywords, it becomes massively more efficient for the agent to do the work.If you don’t give it the right keywords, or you use language that doesn’t appear anywhere in your files, it becomes less efficient.So there are still subtle differences in what kind of prompts you give agents, what kind of decisions you make, and what kind of problems you choose to solve.Those things become more important.It’s kind of like the product manager’s job.People keep saying product managers won’t be needed anymore.But actually, that kind of strategy becomes extremely important once you remove all the other work from the process.The good part is that this kind of work doesn’t require you to sit in an office anymore. It doesn’t require you to give up your physical freedom.Grace Shao (40:29)So should companies be training employees on how to prompt, essentially?Is that going to become part of training — how to make you more efficient at your job?Ang Li (40:39)Yeah.This kind of training is essentially what teachers have always tried to teach students in school: ask the right question.I did my PhD at Maryland, and my advisor was a very senior person.The biggest thing I learned from my PhD advisor was to ask the right question. Find the right problem to solve.I spent five years constantly asking myself, “How do I find the right problem to solve?”It’s an extremely hard problem.I still remember him telling me, “If you find the right problem, 50% of the problem is already solved.”Once you can write a problem statement clearly, 50% of the job is already done.The remaining 50% is relatively easy because you follow that problem statement and search for results.That’s happening in the AI space right now.If humans have the capability to ask the right question, 50% is done. Agents can handle a lot of the remainder.The reality is most of us aren’t trained this way.Most education today trains people to solve problems.Solve math problems. Take exams where somebody gives you a problem and asks, “Can you solve it?”There’s no exam that asks, “Can you come up with an important problem and then solve it?”I feel like the whole education system will evolve in that direction, pushing everyone to think, “What’s the right problem?”I only have, I don’t know, 60 years of my life. What kind of problem is important enough for me to solve during my lifetime so I can make the maximum contribution to society?Most people don’t have the opportunity to think about that because they are given homework every day at school, and then they’re given homework every day at work even after they become adults.Now more and more people are starting to think: what should I prompt the agent to do?Suppose I come up with an interesting prompt and it creates an interesting app. That’s the excitement people are getting from the current technology.I view this as a positive change for society.This could potentially take society’s creativity and innovation to the next level with these agent tools helping everyone.But the important part is: how do we have a solution, an agent, that’s ready for everyone to use?Not just people already in this camp. Not just people in Silicon Valley.Can we let people running car dealerships, accountants in family businesses, dentists, solopreneurs, a five-person dental practice — can all these people use the technology and become maximally productive?Then they can start asking bigger questions about their lives: what kind of thing can I do to make my life more meaningful to society?Grace Shao (43:30)That’s a very interesting way of putting it.It makes me think that you’re essentially creating a tool that enables the average person to become a high-agency person.Silicon Valley loves talking about high agency, but high agency, like you said, is also kind of a luxury.Most people have agency. It’s just that you’re so bogged down by day-to-day mundane tasks, duties, and the work you need to do to earn wages that you can’t actually act on that agency.If you have someone executing a lot of that tedious work for you, you suddenly have the mental capacity to put your energy elsewhere.I really appreciate that.I want to ask you another question.Earlier this year, we saw the “SaaSpocalypse,” and that was quite wild. It was a wild ride for the market.More recently, people have started saying that reaction wasn’t very sensible.SaaS companies have existed for decades because there’s industry know-how, workflows, processes, and systems in place. These things can’t necessarily be replaced overnight by something vibe-coded.However, what you’re saying is that the agents you’re building can actually operate these SaaS products on the desktop.How do you view SaaS companies going forward?Over the last six to eight months, we’ve definitely seen a bit of a flip-flop.Now people are saying, “Actually, ServiceNow cannot be easily disrupted. My God, how could the market have reacted that way?”What’s your sensible take here?Ang Li (45:09)I feel like the industry has a pattern of viewing something as just one thing.I always say that when you look at something, there are usually two angles.When we look at SaaS, we also have two categories.The first category of SaaS companies stores the data. They are the systems of record.They record things in their own databases and serve that information to users.Another set of SaaS companies doesn’t really store the core data. They build a portal on top of other systems.So there are two types of SaaS companies.My principle is that infrastructure tends to stay.Over the past 50 years, infrastructure doesn’t just disappear. Humans are really good at building layer upon layer of infrastructure.The first set of companies, the ones that have the data, are infrastructure.Data is kind of like electricity in the digital world.The data is important because it powers further innovation.For example, Salesforce has the data.That’s important.Those companies won’t simply disappear because they form part of the infrastructure.Agents can sit on top of these systems of record.Agents can replace a lot of the human-made portals in between because now everyone can create a portal with an agent, in real time.You don’t necessarily need a company to spend 10 years building a portal and then sell it to customers.You can have an agent build a portal on top of the system of record in a day.So that second type of SaaS company may face existential risk because it’s competing with the agent layer.At least half of them are fine. There’s nothing to worry about.This also goes back to the question people always ask: will GUIs still be there? Why not rebuild everything around APIs?We should view it partly as an infrastructure problem.If software and its GUI have existed for 20 or 40 years, we should probably view that as infrastructure.Then you can build agents on top of it to modernize that infrastructure.But if you have a SaaS startup that built a portal used by a small fraction of users, and the portal has only existed for three years, it may not be strong enough to become infrastructure.If it isn’t strong enough to be part of the infrastructure, then it becomes easier for a company like ServiceNow or Salesforce to say, “Okay, I’ll build an API for that.”Then the agent can connect directly to the underlying system, and your portal may no longer be useful.We really have to identify what is truly infrastructure and what isn’t.Even within GUIs, there are two parts.Long-lasting legacy software that is difficult to move away from has already become infrastructure.But modern software produced by many Silicon Valley startups hasn’t necessarily achieved that status yet.That kind of software can potentially be removed by a system-of-record company producing an API and connecting directly with agents.Grace Shao (48:46)I see. That makes sense.I just want to ask you two questions I always ask everyone.One is: what do you think people really misunderstand about your industry? In this case, CUAs.And the other one I’m going to throw to you now so you can think about it: what is the differentiated view you hold?It could even be something non-AI-related, if you feel really strongly that the Earth is flat or something.Ang Li (49:08)I think on the first question, I’m always being misunderstood because people keep telling me the future will be all APIs.I hope the future is all APIs, but I think it’s actually impossible for society to become entirely API-based because APIs are not transparent.It’s hard for you to see what’s going on.GUIs are more transparent.You can see exactly what the agent is doing.I actually feel safer when I look at a screen and I can see, “Okay, the agent is moving the mouse, clicking on a button.”I appreciate having the opportunity to go in and stop it if I see something going wrong.I also appreciate the fact that it isn’t necessarily too fast.If the entire digital world becomes 100x faster than human processing speed, I don’t think that’s necessarily a good thing for us.It’s nice that everything becomes fast, but you don’t want humans to become overwhelmed by intelligent systems.That’s one of the things frontier labs are dealing with.Humans have a limitation on processing speed, so we want to maintain some kind of balance.That’s why I feel like computer use is actually a relatively safe road for society to move down.It operates around human speed. Sometimes it’s even slower than a human.Grace Shao (50:35)That’s very interesting.Ang Li (50:37)Yeah.And we won’t be spamming the whole internet because it’s slower.Worst case, I’m just performing a task like a human. Why is that necessarily a bad thing?People worry about spam, fraud, and security because you could have something processing at 100 times the speed of a normal human.That can overwhelm the entire infrastructure of the digital world.That’s why I feel like working on computers isn’t just a technology problem.It’s also about asking: what’s the best way to make sure AI is safe, manageable, and transparent while still solving real problems and freeing people’s time?These views didn’t all come from day one.Over the course of developing the technology and talking to customers, we gradually realized that while frontier labs worry about their agents being unsafe, we’re actually pretty confident deploying this into the real world.We think it can benefit people without causing damage because people can always look at what it’s doing, and they have time to react if something goes wrong.Grace Shao (51:50)That’s really interesting.Your thoughts are probably also evolving as these scenarios play out in real life.It’s interesting that you’re saying even what looks like a disadvantage — the latency, the lag, the controlled environment — can actually become an advantage in this scenario.Ang Li (52:08)An advantage, yes.The frontier labs are now saying, “Okay, maybe we should pause. We should slow things down.”For us, we’re basically maintaining this speed already, and it isn’t creating those kinds of problems.Grace Shao (52:24)That makes a lot of sense.All right, last question. What is one differentiated view you hold? Something wild or non-consensus?Ang Li (52:32)I’m not sure if this is really a differentiated view.My view is that in AI development there are two camps.A lot of people are focused on the first camp. We’re in the second.Everything is ultimately about how to do human work.The first camp is trying to do more and more high-end work, gradually moving towards scientists’ jobs.Initially, everyone thought AI was going to replace all human work, and a lot of blue-collar workers would lose their job security.But the reality is that some of the easiest jobs to replace may actually be scientific jobs. Even AI researchers’ jobs.That’s an interesting pattern.When I talk to my friends — we used to be researchers training models — I ask researchers at big labs, “Do you think going into a portal and clicking around is easier to automate, or replacing your model-training pipeline is easier?”Most people will say replacing the model-training pipeline is much easier because the whole procedure is so routine.There’s already a playbook.Suppose you want to train a model. There’s already a playbook. You follow the playbook and do it every day.It’s actually a very tedious job.I used to be on the hiring committee at DeepMind, and we would hire machine-learning engineers into the company.Everyone was so excited. They thought, “DeepMind is exciting. It’s a frontier AI lab. If I go there, I must be doing something incredibly exciting.”That was before they joined.After they joined, a lot of people realized, “Why am I always cleaning dirty data? Why am I always doing tedious engineering work to build around the system, maintain data quality, and feed it into the model? Am I supposed to be training models? Am I supposed to be changing the model architecture?”But in reality, for many AI researchers, you spend less than 5% of your time actually looking at the model.The majority of your time is spent on tedious things like data cleaning, data labeling, organizing data, and moving files around.That’s the interesting part.When I started working on computer use, initially I saw it as a challenging problem that nobody else was working on.Over time, I realized it might actually be one of the hardest problems, even compared with these so-called high-end jobs.If you look at the frontier labs’ strategies, they are moving toward high-end work: having AI write code, having AI train models.Basically, they’re trying to automate more and more of an AI researcher’s job.We’re trying to come from the other direction.We’re looking at all of this repetitive work and asking: if it’s so repetitive, why is it actually harder to automate than an AI researcher’s job?The reason is that most of the AI researcher’s work is already handled through code.When you handle something through code, the data is structured.But when you look at a screen, the screen is extremely unstructured.It’s similar to understanding video content. You have a real human in the real world, and understanding that kind of environment is a much harder problem than solving a coding problem.Grace Shao (56:03)That’s really, really insightful. Thank you so much for your time.I just want to end on one thing you’re saying, which is basically that nobody’s job is as glamorous as it looks from the outside.It reminded me of when I still worked in broadcast TV.People would say, “You must just be talking to four or five Fortune 500 CEOs and looking pretty on TV.”No.Most of the time, we were squatting in a corner waiting for the guest to come out, eating takeout — or not getting to eat or go to the bathroom for 10 hours — and just waiting around.We were waking up at three in the morning, doing research and makeup at the same time.Nothing is as glamorous as it looks from the outside.So much of the work is actually preparation.Ang Li (56:39)Exactly. Yeah, exactly.It’s like founders.Grace Shao (56:43)Thank you so much for your time today.Ang Li (56:46)Thank you so much. Nice chatting with you.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  DeepSeek’s fundraising, unique Chinese market ticks, and what’s ahead with The Information 09.09.2026 1t 7min

  Hello everyone, I've been on the road, so I'll be delaying this week’s written post and next week’s podcast episode. Apologies in advance.In this episode, I’m joined by Asia Bureau Chief Jing Yang and Senior Reporter Juro Osawa from The Information to unpack DeepSeek’s surprising shift toward external capital, its unique investor structure, and what its rapid revenue growth reveals about the economics of AI. We also explore why China’s AI labs remain fiercely competitive despite a far smaller capital market than the US.The conversation moves from DeepSeek and Huawei to accusations of distillation, ByteDance, Alibaba, and Tencent, and the growing race to turn AI models into real businesses. We also look at China’s emerging advantage in robotics, from its dense hardware supply chain to the vast amounts of real-world data being generated through deployment.Finally, we discuss the next frontier: world models. As the race moves beyond language models, can China’s hardware and data advantages translate into an edge in embodied AI? And in a provocative final take, Jing argues that Chinese frontier models may never fully overtake their US counterparts.The AI Proem Podcast is part of the AI Proem newsletter, which has ~13k followers globally. To learn more about China AI, the business of AI, and how AI is impacting businesses, please check out the newsletter here and more insightful conversations here.Chapters00:52 The Rise of Chinese AI Models04:06 DeepSeek’s Unique Position in the Market12:31 Capital Structure and Investor Dynamics16:12 The Landscape of Chinese AI Labs19:17 DeepSeek and Huawei: A Strategic Partnership25:43 The Competitive Landscape of AI Labs33:51 Challenges in the Chinese Capital Market36:44 ByteDance’s Unique Approach to Model Training41:32 The Future of BAT Companies in AI48:22 China’s Robotics Landscape and Advantages53:51 Impact of US Regulations on Robotics54:43 Unitree’s IPO and Market DynamicsGrace Shao (00:00)Hey Jing, hey Juro thank you so much for joining AI Proem today.Juro (00:03)HeyJing Yang (00:03)Yes.Grace Shao (00:04)to start, you know, let’s start with Ox alpha. That was such a teaser. It turns out to be ZAI again, but I think it was very much expected by people who do watch space closely. It was no surprise in that sense. But I think what’s shocked a lot of people is that, you know, the inference was running on Chinese domestic ships and potentially GLM five point three flash.The price, it’s priced at about like one fortieth of Opus 4.8. That’s quite significant of a gap, right? Tell us about what you think about that, the implication on these continued Chinese open weight models that are coming out strong but cheap, and how that’s impacting US frontier models.Juro (00:43)Yeah, maybe I can start on this one, but I think those flash models are coming out, like Deep Seek had the V4 flash, which is a smaller size one, and that became very popular. And then this one comes out. Alibaba just had a three point eight flash as well. So those are smaller size, you know, lower cost models that can still handle a lot of like AI Asian type of tasks really well. Soknow, this is like a sort of sweet spot for demand, right? So a lot of Chinese companies with open source models are coming out. So that’s one, you know, one thing about this, you know, that’s part of that broader trend. But I think as you pointed out, the chip part, you know, it kind of shows how far the Chinese chips have come in terms of inference, right? AndThey have become more capable, especially Huawei chips, being used, you know, more and more for inference. And you know, Deep Seek came out with V4 and you know it was also optimized for you know to run on Huawei chips as well. And we’re gonna see more and more of this for sure. But I mean to what to keep in mind that when it comes to training, it’s not the same story yet, not quite yet. So, you know, a lot of Chinese companies are using, still using the most advancedNVIDIA chips and trying to gain access to that and which we also wrote about recently.Jing Yang (02:03)Yeah, I mean the only thing I I’ll add to that is I think the bigger picture is that there is a shortage of inference trips in both the US and China, but in China the shortage is much more runs much deeper and you knowA part of that is because of government policies, right? We still have not seen H two hundred actually officially being allowed to export into China. So the right course for all the major leading Chinese AI labs is they have to adapt the inference to domestic trips and to you know this concept of homogeneous compute, which has been discussed in Silicon Valley, but as well as in China.And then the hemogeneous part in the Chinese context is very different. It’s about being able to run your inference on a combination of NVIDIA and and and say five different other Chinese GPUs. Right. So we saw that Kimi K3 had to suspend our new customer subscription shortly after it was released, and that was directly a result of not having enough inference chips.to to to you know to to meet the surging demand.Grace Shao (03:14)I think Kimi wasn’t the only one that faced this kind of issue. And Deep Seek and ZAI throughout last year, I think, had to like, you know, manage their demand as well. But I wanna bring the conversation to Deep Seek. You know, you guys broke the story on well, actually Juro just broke the story on Deep Seek’s revenue. And I believe Jing Yu wrote the story about Deep Seek’s fundraising. It’s quite fascinating because DeepSeek, quite secretive, you know, their investor letter or investor conversation was leaked.Apparently the founder was not happy that it was leaked, But, you know, they are fundraising now. And that’s shocked a lot of people because for the longest time they didn’t want to take any external capital. Jing, do you want to take that first and just talk about, you know, their capital structure, their fundraising, how unique it was, and then maybe we’ll pass it to Juro to talk about, you know, his recent findings about the money they’re making.Jing Yang (04:03)Sure. Yeah, so both Juro and I and our colleague Qianer as well, we’ve been reporting Deep Seek very closely since the beginning of last year. So I think one thing I like to sort of take the credit for is that I think most media allies sort of moved on after the initial buzzy period around Deep Seek early last year, but we stayed. I think we immediately recognize that this company is gonna be a very uniquelike sort of existence in China AI landscape for the time being. and we reported, I mean I was just as shocked as anyone when we got a tip when we had our very first fundraising story. Because knowing the company the way we knew it for the last eighteen months, we were I don’t know if you were about you, but I was quite shocked. I think I edited it, I wrote that story in like huge disbelief. Like it’s a funny moment for ourjournalism because a lot of times you kinda expect things will happen in a certain way or the direction of travel, at least you have a sentence on the post, but this one I think it completely surprised me. And then so in the two months that we’ve been chasing every step of the way of the first ever fundraising, one question that always lingered in my head is whatPrompted this change of this dramatic change of attitude toward external money. So eventually we were able to do a sort of the deeper story in which we revealed that it was Anthropic’s mythos preview that contributed a lot toCEO Liang Wenfeng thinking, because I think there was a period of time, if you remember, like in the second half of last year, people were in AI research community, people were doubting or casting skepticism on whether the scaling law still exists, if if it’s still going to yield kind of you know progress. and then I think mythos showed that scaling law still exists. And then that’s when Liang realized that okay,even if we have done innovation when it comes to you know in cr improving model efficiency. To really get to the next level, like Mythos demonstrated, we need to fully embrace the competition and we fully in not the competition, but fully embrace the game of, you know, you know, gathering resources. We need a lot more data and a lot more compute and therefore you need money. And and it’s amoney at a scale at a magnitude that Leon himself cannot fully fund anymore. That’s essentially the reason. And I think the broader takeaway while having covered that story is it is kinda like, you know, how the the peer pressure and the the the competition really makes it very difficult for any any labto just you know be stay on the sidelines because you know up until April when DeepSeek D came out with the first funding pitch, they were I think I believe the only holdout right around the world’s major AL labs to not have done any funding, fundraising.So that’s quite striking and I’d like to keep reminding people that I’m not passing judgment, whether it’s a good thing to join this arms race, but just that, you know, when the last holder had to succumb to the broader pressure, it tells us something.Grace Shao (07:08)It’sjust a very, very capital intensive game that they’re playing. but I wanna kinda throw this to Juro then. Tell us about the recent story you just broke.Juro (07:17)Yeah, so we wrote about their finances. So their revenue you know for the first seven months of this year was about seventy million dollars. and that’s you know seems very small still, but that’s still you know, like about ten x compared to all of last year. So just considering how this company last year, you know, seemed like you know they had no interest in really making money. And so this year they’re just really getting started.Right. And they’re starting to have this growth. And looks like a lot of the revenue also came, you know, quite recently as well, in the past few months. And so even though the number is small, also another remarkable thing is that their gross margin is quite solid, so forty four forty four point six percent. And if you look at their API sales for models, that’s like eighty three percent. And so they’re maintaining this like a very highquite high margin, even though you know what they charge is you know they’re charging very low prices. And so this also shows what they’ve been doing to kind of lower the running cost, running costs of their models, right? And so a lot of those numbers are telling us, you know, kind of where they are. But I think this is important because they’re raising money and even you know talking about an IPO as well. SoNo, investors will be looking at those, you know, this progress very, very closely.Grace Shao (08:43)I think that’s really interesting.Jing Yang (08:43)And and Grace you asked earlierabout the capital structure. I I’ll justGrace Shao (08:48)Mm mm.Jing Yang (08:48)briefly talk about this because I think it’s also quite important. soBear in mind that I think between Juro and I, we have probably 15 to 20 years of recovering venture capital as a journalist. And this is a unique deal for both of us. The way I like to talk about it is that if you categorize the types of investors in deep sea first around, I would categorize as four types. For number one is Leon himself. U number two is the sort of strategic, you know, in you know, the tens and the end of the net, right? And say it’s all. And then third type is the VC firms. And then the third the fourth.type is the national air fund. so we don’t care, you know, Leon, he’s his money, he is he’s putting, you know, you know, in he’s putting his money in his in his own company. We don’t care what he used he he does with it. But for this following three types of investors, only the national air fund can invest in the SIC corporate entity directly. And then the other two types of investors have to input have to wire their money into a SPV structure.like a LP structure that’s actually managed by Leon himself. And as part of that arrangement, they also do not have voting shares. They have to essentially make Leon the proxy for their votes. and they also have to agree to lock up their shares for five years. I guess with the exception of you know, events like an IPO. This is all very unusual, right? Because you are putting money intonot into the company or funding. And well, you are putting mu money into the controlling shareholder of the company are funding and at the same time you are giving up on any voting rights. and the reason for that sort of rigorous or strange structure is because as we reported that Leon wants to make sure that he only attracted in you know investors who are there for the long run, not for a quick exit. he is I guess in today’s popularspeech in Silicon Valley, a very AGI appealed. So he wants to keep Deep Seek open source and he does not like commercialization or generating revenue despite as Juro you know mentioned that they generated a pretty quick and revenue growth but that’s not their goal, right? so you can debate whether the end justifies the Minx.but this is the structure that they came up with. And I know that maybe maybe a lot of us would tend to have an impression that while this is such a hot company and when they finally come to the market with the fundraising obviously investor would like to would just like, you know,I’m swarming and then regardless of the terms, but actually that’s not the case. I know definitely investors who passed off on the deal exactly because of the structure, they feel like they could not accept those terms. and I think Deep Seek has actually taken that feedback because in the current, you know, the second funding round, they actually are loosening some of those requirements that were in the first round.Grace Shao (11:39)That’ssuper interesting because I think, okay, just let me process both what both of you said. On one half, what Jing said is that he still wants control of the company and like he doesn’t want to run it like a commercial business, right? Like it’s a very unique setup. And then on the other hand, there’s a clear sentiment change or at least a strategic shift that is being pushed by the fact that he has to get money because everyone else has money. And if he doesn’t have the money, he can’t do the RD that is required, right? And then obviously the IPO that’s imminent, or at least, you know, in the pipeline. NowI do want to ask one question. You know, he’s openly talked about not wanting to frankly make money for the sake of making money, but he said that I want to make money, but that use that money for continued RD. He’s kind of openly talked about, but even then, the margins, like Juro said, are still extremely high. So what does that say about the other labs?Jing Yang (12:29)Maybe I’ll I’ll try to take a stab and thenI just point out as we reported that they have this really high API sales margin, like 80% plus, because there is actually real renovation behind it. The industry term is that they have a very strong infar team. But basically what that means is that they have managed to bring down the inference cost of their models so that for the same number of tasks or to tokens consumed by you know, you you you need it, you need less chips.fewer chips. and then whether that’s the way to go to get a higher margin. For example, Juro Feel determined by like, you know, we saw Minimax results just recently and then they have a much lower level margin for example, right? SoJuro (13:14)Yeah, I think definitely Deep Seek, people have written about this too, but some researchers have pointed out that their, you know, innovation is in how they run their AI, you know, with like just chip requirements, are very low. Like memory chips, especially, a lot of companies are, you know, dealing with that cost right now. And so yeah, that’s where Deep Seek seems to be able to make a difference. Yeah.Jing Yang (13:40)Yeah, so so I think I’m trying to sort of like try nochannel my Wenfeng here not that I’ve never ever met him or anything but based on enough that I’ve heard about from people who do know him right he believes that AI should benefit all any advancement in technology and AI should benefit all. And then to make it benefit all the first thing you need is to make it cheap, make it affordable. And then and then therefore you need to first make the deployment of AI cheap, cheaper and more efficient. That’s why they have this, you know improvements on infra.And then as a result, as long as you have something that’s both good and cheap, then obviously you are gonna generate a lot of revenue, right? Right, and profit eventually. I think for people who know him, they would say this is the train of logic. that not that it’s this high margin shows the company is so profit driven, butWhen you are pursuing the greater good, money will naturally follow.Grace Shao (14:40)No, I think that’s definitely something you’re hearing more and more coming from the AI builders. Like I think it was Neil Mova that recently we just saw Patrick Shaughnessy. The goal is to make AI as cheap as possible. And this ‘cause there’s definitely a sentiment shift from like even a year ago where the narrative is really about like how much money can we make with AI or like, do we pay premium for intelligence, et cetera.Let me bring the conversation back to the Chinese labs. like you just mentioned Jing, you covered Minimax as well, obviously Moonshot, Zai these are some of the leading labs in China. What’s their current structure? there seems to be a lot of them. Should we be expecting some consolidation? I guess this is two two questions separately, but just kind of give us a high level landscape breakdown.Jing Yang (15:20)Yeah, I’m happy to do a micro thing and then I’ll let Juro talk about the individual labs since he knows them better than me. It’s just something that I’ve been actually discussing with my own, you know, contents and investors a lot these days. If you look at in the US, we have it’s been consolidated to like only just two to three major labs left. And in China we still have, depending on who you include or not include the list general, you have about six to seven or seven to eight.AI model makers that are still firmly fully in the race and any kind of consolidation is not on the cards anytime soon. And this is sort of like a characteristic of the Chinese economy and the Chinese market. If you look at EV, how many years has the EV battle been happening?And how many companies are still in the race, right? So I think there are several structural reasons. Number one is that you know, the capital markets in China are highly regulated. It’s very difficult to do once you are already a public company. It’s very difficult for a public company to acquire another public company. Or or or in an and I transaction, if any either party is public, the transaction will becomea lot harder. and then we are still sort of we’re just a few years in the aftermath of the antitrust crackdown, right? I think so that makes a lot of the tech incumbents, you know, the Alibaba and the Bidowns of the world also a bit hesitant about that. And then number two is is sort of fundamentally the the the both the private and the public markets in China lacklike the kind of breaths and depths l as in the US. And that made up that created and the lack of, you know, when you do not have enough capital, either in private or pa or public markets going around, that makes potential acquirers very hesitant or reluctant about paying a premium. Right? there were opportunities for there were discussions.that say for example I can tell you that one of the major, you know, tech companies was seriously considering acquiring one of the smaller labs, that then by theAnd then that kind of talks actually happened several times. It was involving different sort of combination of companies, about two to three years ago. And then none of that happened because even back then, the the established tech companies are not willing to pay what they consider a premium, right? that’s number two. and so I thinkI think it’s gonna be really quite hard to see any real consolidation and I I think with if Deep Seek does go public and we’re also look if Deep Sea Moonshot and Stepfund, all three of them say go public next year, then it’s just gonna gonna make it even harder for any kind of consolidation to happen. And the world is gonna be in a place where companies continue to compete.and then the fight to the competition would just have to drag on for years, for a lot more years.Grace Shao (18:25)Veryinteresting take. Juro, do you have anything to add on that?Juro (18:28)Yeah, I mean, I guess it’s also quite difficult to imagine one of those founders working for another one of the founders. Or also, you know, the idea of even Alibaba or ByteDance or Tencent acquiring them because I mean they do also have their own labs and you know they brought in Tencent brought in Yao Xing Yu from OpenAI and you know ByteDance has the biggest AI team in China, so it doesn’t really have the rationale, you know, for those companies to just suddenlyyou know, turn around to buy one of those, right? SoJing Yang (19:01)Yeah, the the thirdreason actually, sorry, I forgot to mention, I gave you two before. The third one kinda touches on what Juro mentioned is talent. There’s just so much more talent in China. So it’s very easy for someone like Jiang Y Ming or or I don’t know, Polima or whoever, where they would think, Okay, if you don’t want to sell to me at a discount, then I’ll do this on my own. I’ll roll up my sleeves and do it on my own. I can’t hire people. SoGrace Shao (19:26)Feel like, yes, I agree with you.Jing Yang (19:27)Yeah.Grace Shao (19:27)But then you look at you read the book, the Google Deep Mind story, right? It’s like no one thought Demise would go in and work for a big tech either, but eventually it happened. I mean, now that it’s clearly proven that it didn’t work out, but it was an interesting phase where I wonder if at one point like you just can’t keep on Juan right? Like there’s like you hit a limit of the Juan and then you just like you’re forced also to consolidate for resources for talent and for the like.For the lack of compute out there. So if you believe in AGI, the some as some claim they do, and your goal is really to have the resources to pursue that scientific research and breakthrough, then isn’t it in some ways going to the company with the most resources the best outcome? I’ve heard people in the industry argue that, right? Like, I don’t know. What are your thoughts on that?Jing Yang (20:14)No, I think what you said is a very sensible, rational take. But the problem is for I think the three reasons I mentioned earlier in China, this kind of rational, you know, economy of scale, right? Economics one, right? Just somehow does not apply to China that people would rather trend thanGrace Shao (20:35)They’re not rational. They’re not making rational decisions, is what you’re saying.Juro (20:37)Well I think there’s anotherAnother factor is that you know Alibaba for example has invested in so many of those AI startups, right? And some of them have gone public as well. And for them, because they have Alibaba Cloud, which has a big business with a lot of those AI labs as well. So, you know, like they would rent chips from Alibaba Cloud and you know use their infrastructure. So yeah, I mean for them to kind of decide to just buy them, I meanwhen they can keep them as major customers and, you know, also hold a stake. So I guess that’s another logic for, you big cloud platforms, right?Grace Shao (21:15)Yeah, and they have exposureJing Yang (21:16)And and theGrace Shao (21:17)anyway. Mm.Jing Yang (21:18)yeah, and then the last reason I w I would really I think also important is is the enterprise market is stillvery massively underdeveloping China, which which has so far limited the the commercial, the revenue prospect of all these companies. And then when when you are projecting your revenue, that is only like 10X in the next five years instead of 100 X, then then of of course you are gonna be very you’re gonna be stingy when it comes to MA. Right. I think we can see the kind of consolidation that happened in the US. It’s it’s because they these companies actually you know can generate a lot of revenue from it.enterprise market so they can afford to spend on MA.Grace Shao (21:57)Fair. Well, we’re all speculating. Let’s watch and see and what happens the next few years. Sometimes there’s a wild card thrown at us. I I do wanna ask you guys, give us some color on what happened between Deep Seek and Huawei. I mean, we started a conversation with Juro telling us about, you know, how GLM five point three Flash was tr you know, inference on domestic trips. But I think Deep Seek really took one for the team there and made the first step over. What was I guess an incentive? Was it really just because, you knowThey wanted to show the ecosystem that this could be done. was there a commercial reason on the Huawei side? Did they approach Deep Seek? Was there a top-down mandate from Beijing? Like, how did that play out?Jing Yang (22:35)Yeah, I’ll take this one. so we and some other outlets reported throughout twenty twenty five that Deep Seek has been adapting to How Richards.And then we’re trying to and just by the way, in order to make the inference you know adaptable to domestic chips, you have to basically reengineer your training process, right? Once you’ve done the training within video chips, then you have to then recreate that process to domestic chips. to like I’m butchering this, but to to oversimplify. and so I thinkUs including many other observers, Chubash, John Warder, Elam, masses, just assumed that this is because Gypsy has been asked by the government that you now are a national champion. You have to take a lead in adapting to domestic semiconductors. and which is why you know when we reported, I think that was a story in June that we did.That it was actually DeepSeek that took the initiative in adapting to Huawei chips. And Huawei didn’t and it wasn’t until DeepSeek spent some time tinkering with Huawei GPUs that the Huawei engineers got a wind of it. And then they approached DeepSeek and said, how can we support you? So why did DeepSeek do this?The reason we report it is that as I said earlier, Leon is someone who believes in AGI and believes that AI should be inclusive. And so inclusion in here means you know you need to have a diversity of technologies in both AI and the semiconductor, right? He based on the leaked investor call, we can see that that’s the case, but bear in mind I I’d like to point out that we when we reported this thethe investor call map was not leaked, right? Had it not been leaked. So we reported that, you know, he believes that NVIDIA should not be, there shouldn’t be just one dominant GPU designer. There should not just be one dominant technology. And then and then Deep Seek wants to play its part in increasing that diversity. That’s why they did it. There’s nothing sort of forced or you know political behind it.Grace Shao (24:35)Yeah. Juro, so this question’s for you. I think that’s a really good context and understanding why DeepSeek did what DeepSeek did, but help us understand just who plays what role in the ecosystem right now, if you had to generalize in one or two sentences about each.Juro (24:51)I mean each AI company in China.Grace Shao (24:53)AI lab at this point. We’re talking about I guess just the labs. You can include the big tech if you want, but just like the let’s just a h eight companies that keep on pushing out models.Juro (25:04)So yeah, I think for Moonshot, you know, Kimi, we can say that they are trying very hard right now to be the sort of anthropic of China and really focusing on the you know coding, API sales and and especially they are you know really expanding globally, right? And so they try to show that they can, they are the ones that can really compete at the frontier level.And the frontier coding kind of you know capabilities. So and they know that they can really build a big business like Anthropic did. So that’s the kind of path that they see, I think. And I think Jupu is you know ZAI, what they call it now. so they are trying to also you know focus on that kind of market, but they also have a you know kind ofmuch stronger domestic background with the Xinhua University, you know, researchers that this you know that studied it. So I mean they have been trying to kind of transition from the previous business focus was you know doing a lot of work for domestic companies including like state owned companies and you know helping them deploy their AI models. And but then they want to also become this moreMore globalized, you know, also selling AI models, you know, and also like a little bit like Anthropic, you know. I mean, that’s the path that they a lot of those guys see because that is the path to revenue right now, right? And I think deep seek, as we discussed, you know, stands in a you know a little bit different position, interesting. I mean because they do you know, they do seem to kind of, you know, they do keep their prices very low.And also not just chasing like Frontier in the same way that Kimi does, but you know, how their models can be also easily deployed, right? And by many, you know, I mean there are many different requirements from you know different kinds of users. And so they are going for, you know, like they may not necessarily be the most powerful, but you know, they are kind of trying tolike you know AI for everybody kind of approach right now as at least with the pricing they have, right? And I think they also do have a lot of, you know, do handle a lot of domestic demand as well. Like so, that’s the national champion aspect. And then the minimax I think they have chased you know both video and LLM. And I think recently they haveThey have kind of made a comeback with the latest video model. But I think it’s still quite difficult to see and they do seem a little bit confused to me that in terms of deciding which direction. And because it’s very resource intensive to try to chase both markets because video market you’re competing against ByteDance, which has you know just infinite amount of money and resources. So butYou know, they’re definitely not out of the race. You know, they are, you know, trying to keep up. So yeah, that’s and and the big labs, you know, they try to do everything. And Alibaba, especially with the cloud, you know, they try to, you know, be in every segment of the market. And you can say that about byte dance, but maybe we can talk about byte dance later when we talk about distillation stuff. But yeah. Yeah.Grace Shao (28:24)I like how you’ve setset the tone. We’re gonna be talking about distillation stuff later. All right.Juro (28:28)Yeah.Grace Shao (28:29)No, no, that’s really good context.Jing Yang (28:29)There there is a popular saying now inThe AI space in China is the, I don’t know if you have a smart translation for that into English, but DeepSeek Jansen.So basically the Deep Seek has set the survival line for everybody else. Either you go for Solta, Solta, Solta, or you go cheap, cheap, cheap. and then but you cannot go cheaper than Deep Seek. So that’s the survival line. And I like to just one more thing, I like to sort of compare, you know, Kimi and Memex a little bit because I’m sure you guys all remember, right? Kimi, I think back in 24,you know, actually spend quite a bit of an amount of money marketing, advertising for the chatbot, right? And then I think at some point we realized there’s no way that they could compete against Dobao and by Bay Dance. So then they switch to, you know, working on SOTA models. And whereas Minimax continues to beYou know, Juro, actually I wanna ask you, like ‘cause they continue to still be dabbling in everything. Yeah, I think I think yeah.Juro (29:26)So actuallyOn that point I guess Minimax has kind of moved a little bit away from consumer apps, especially something like if you remember talkie, like that was the early sort of hit.Jing Yang (29:38)I remember talking.Juro (29:40)But I think internally Yeah, yeah. But internally that’s that’s definitely notGrace Shao (29:40)Yes, and High Law. Like they had so many products. Sorry, go on.Jing Yang (29:42)Yes.Juro (29:46)That’s definitely not the priority now. I think over the past year, the priority has been, you know, really the models, right? And models and API. So I think that’s definitely and you know some would argue that Kimi has kind of moved ahead more quickly. and they had big success with K three. But essentially that’s the direction that you know, all these guys want to move in. So I’m I think Minimax is definitely trying tomove in the same direction as well.Grace Shao (30:15)So then my question is if every single lab is somewhat doing the same thing, like it kind of goes back to what we were talking about earlier, it’s just gonna be like a price for a never-ending duet, right? Then are we seeing any interesting application innovation right now you think that is being a bit undercovered or underappreciated? Maybe not coming out of these labs.Jing Yang (30:36)Juro, do you want toJuro (30:37)I think the application side battle for consumer apps is definitely, you know, very fierce. But we just happen to pay more attention, like I guess everybody’s been paying more attention to the SOTA models. But I think Dobao from ByteTance and Qwen and Alibaba’s app, I mean that competition continues, right? And yeah, it’s not like they don’t emphasize that. So yeah.It’s just that the people I think have been talking more about something like Chimi K three, but that domestic competition for apps is definitely there and it’s gonna just keep going for sure.Jing Yang (31:12)I think there’s been you know increasing appreciation for model rappers. I I remember when Manners just became rebel viral in March last year, people were like, this is just another rapper, and there’s no yeah, and then there’s yeah, there’s no modesGrace Shao (31:24)It used to be a dirty word. Now people are like harnesses. LikeJing Yang (31:29)if you are just a rapper and now yeah, now people call it a harness. but I think that is sort of on the right.Course, I think something that I would like to write about more that I have not been able to for various reasons is that actually after the Manners Wave and then the open call of friends in China, what we have seen is actually there’s a lot of flying under the radar Chinese startups that actually have built really smart harnesses targeted at what I call small business like small enterprise customers. and then there’s actual mode.in doing that in offering that business and there’s actual demand and they are offering this to not just a male and Chinese customers, actually a lot of them offering them to like you know overseas customers. and imagine if you are a company that has a loyal employee size of like about 50 to 100 in like a non-tech, non-digital industry, you want to use AI, you don’t know how, you don’t even know where to start. It’s not possible for these kind of companies to go come to go on like I don’t know, openRouter and say, okay, I’m gonna use the one, two, three, four, these models for this different task, right? They don’t have that know-how. and this is why the kind of you know AI, I think they call it AI employees, right? This AI employee business, which essentially harnesses built on top of you know models, it actually are our take our gaining traction actually proven to be quite useful. soSo I think that’s something that gives people, including me, a little bit of hope and the optimism for the enterprise market in China in the future. Yeah.Grace Shao (32:59)Yeah,a lot of very strong vertical products coming out. I wanna direct the attention back to Jing for a few questions on, you know, the capital market. So the secondary market in the US for A Labs obviously been incredibly active. But we’re seeing nothing like that in China. And valuation obviously has it’s like digits away in terms of like the gap. How do you view that? Why is that? Obviously, kinda touched on it or alluded to already, just in general, China’sYou know, Deep Seek and P space is not as vibrant, but is there other reasons behind this phenomenon?Jing Yang (33:33)When you say secondary, you mean like the secondary share sale market, not the public market, right? I think the number one reason still is what I mentioned earlier, there’s just not enough money, right? You know, I think the American LPs have, I think a big part, big chunk of the American money has left, right? And then that void still has not been fully replaced and may never be. and that hasHas really suppressed reven you know, funding, therefore valuation. but then if you actually look at let’s say you know I actually wrote a column several months ago back when Drupal, Z.I.A. and Minimas just went public. Even though that was around the time that ZIA was less than 100, I think their highest point it was about $100 billion US, right, in valuation. That was before that. It was lower than that. But still, if you look at the price tocells multiples, they are a lot, lot higher than the open A and Anthropic. Anthropic Open A in my memory was around thirty X andmini mass and z.ai are like a hundred or two hundred X. So I think just looking at the pure valuation numbers are kinda like not kinda misleading or not really useful. You need to look at the multiples. And if you look at the multiples actually the Chinese labs, the ones that have been published are a lot more expensive.so I don’t think at first that, you know, it’s just because it’s the order of magnitude less, right? Fifty billion versus five hundredGrace Shao (34:56)That’s interesting.Jing Yang (34:57)billion. That does not mean that Chinese labs are cheaper. So we you can easily calculate based onthe numbers reported on Deep Second to get their PS modules. And then you can see it’s also very high, right? So and then you c if you look at the P and S and then it’s very high, not because the P is high, it’s because the S is too small.Okay, that’s why the modules are a lot higher. the multiples are actually one order of magnitude higher than the US ones. And then that’s because the S is too small and the S is too small again, going back to my earlier point, I don’t want to repeat myself, but it’s because the enterprise market has not been developed. It’s just a lot more limited mm compared to the US.Grace Shao (35:38)No, that’s interesting. I think usually we just only read the headlines and just think Chinese models are very undervalued, but you provide a lot of context there. Juro, I I wanna go back to you. You wanna talk about distillation, right? Let’s talk about distillation.Juro (35:52)yeah, soYeah, one thing that kind of is interesting about Byte Dance is that you know we wrote about how Jiang Yi Min, the founder, you know, said in the internal meeting about how you know ByteDance is not going to rely on this and they haven’t. And so you know, unlike you know many other Chinese labs that you know have really relied on distillation to as a shortcut, right, in their and to improve their models.And that I think this is one of the reasons is that you know f if they really want to compete at the frontier level and you know, distillation, you know, like putting distillation as you know as your using that as your main strategy is not gonna really get you there. So, you know, you do need to kind of really properly train your model using, you know, your own data and you know, the data that you can actually, you know.use for you know really advanced training. And that’s really the path to really kind of come up with your own frontier models that really sort of match or even surpass US models. And so that seems to be part of the thinking. Also another reason we heard is that they also are you know aware that you know byte dance is under so much scrutiny already with TikTok in the US andYou know, distillation has become such a sensitive topic between the US and China, right? I mean the US has accused a lot of Chinese labs of distilling American models. So if Python did that, you know, what kind of you know, what kind of criticism they would get in the US, right? And and would TikTok, you know, face any, you know, more challenges because of that or so those concerns also were, you know, we heardwere behind that kind of approach. But yeah, this definitely makes them quite interesting because a lot of Chinese labs do, you know, when we talk to people, they may not say publicly, but they do acknowledge that yes, distillation does help. And so yes, I mean but I mean there are indications that other labs also, I mean, we are starting to see more progress that maybe cannot be explained, you know.solely based on distillation, right? Like Kimi K3 achieving, you know, really you know, strong performance, right? So but yes, what ByteNAS is doing is quite interesting. And whether they can really get to the sort of frontline and you know really emerge as the you know leader that way is gonna be also very interesting.Grace Shao (38:26)Yeah, I think I think you like hit the nail with that. It’s like distillation and genuine innovation are not mutually exclusive. Like you can get the benefits of distillation but still innovate on top of a certain engineering techniques. One thing I also heard was that, you know, with distillation is you inherit the good but also the bad. And apparently Jiang Ming is a bit like sensitive or anal about the fact that potentially he can always still inherit the bad or thevalues or whatever of you know a distilled model. I mean it obviously that story went viral domestically as well. I think a lot of people were like, Jiang is coming out and saying we’re like we we we are not gonna go distill. We’re gonna do it differently. However, the flip side of the argument’s like, well buddy, you’re not gonna distill but n your seat is not actually Soto or anywhere near the last few iterations, right? SoHow do you view that? Do you think at this point for a company as big as Spite Dance and as as much pressure they’re faced with and as much capex it put into this, is it better to stick to the principles right now and hope or continue to work hard on potentially coming out with something completely original, completely innovative on their own? You know, they want to go for the best of the best in the world, or you know, the other kind of strategy, let’s not name names, but maybe other labs or certain big tech.I would say we catch up first and at least then it helps with diffusion and then AI becomes a flywheel in our existing business and we can let this money churn and then help put more money into AI and et cetera. How do you view that?Juro (40:04)Well, I think it would be hard for them to make that shift now. Having s you know, after Emin said that in the meeting and we wrote about it and if they do make that shift, you know, we or somebody else is gonna probably write about that shift. So then then that would make them look, you know, pretty bad, I think, if theyJing Yang (40:22)There therebea massive off ramp for that to happen, I think.Juro (40:26)Yeah. Right.Grace Shao (40:27)It’s not the first day one of the big techs decided to change a strategy.Juro (40:31)Mm.Grace Shao (40:31)I love how you also refer to him as E Ming Juro. You guys must be best buddies. to my firstJing Yang (40:35)Yeah.Grace Shao (40:36)name bases. okay, let’s talk let’s talk about the BATs though. Let’s talk aboutWhereyou see the BATs right now. Juro, you’ve been covering them in terms of their businesses for a long time. You’ve covered the executives. You’ve done like, you know, extensive profiles of these some of these leaders. Where do you see these big tech going? Like what is their end game here? You kind of alluded to Baba, you know, really going in for cloud, but is that enough in the AI air?you know, is Qwen still a priority? is embedding AI into commerce still very important? How important is really Yao Shen Yi? Is he really the kind of savior to Ten Sen, Huen Yuan, for supposedly coming out very soon? How do you view that? And then of course, seed and seed dance. I mean, everyone knows seed dance is good, everyone knows seed dance has a lot of data, butThey’re very secretive, very hush hush, right? and given that they’re not public, they’ve actually had the luxury to not have disclosed what they’re doing publicly. Can you give us your views on this? This is an open ended question. Just see however you want to take this.Juro (41:40)Yeah, so for Alibaba I think Qwen AI being really center and front is that’s pretty obvious. And also how much of their investment is going into it as well. So they have been kind ofspending more kind of ahead of their plan. Like they had a three year plan. And so and their CapEx, you know, was really increasing at a very I think 10 billion dollars for the was it the quarter recent quarter they reported and so yeah it’s it’s growing at a very fast pace and so it it’s very clear that it’s their priority. And obviously if you look at the revenuestill the biggest part does come from e-commerce. But there are very few questions that come up during earnings call about e-commerce anymore, right? So a lot of the questions about the future and also whatever they want to emphasize is really about, you know, Qwen and especially cloud, because that’s how you know the main platform for selling the models. So yeah, they even Alibaba’s trying to be a bit like anthropic, right? In that sense thatYou know, we can sell a lot of models. And and plus they have the cloud, so they, you know, they think that we’re gonna sell everything, you know, the infrastructure. And so that has definitely become yeah the priority. And that shift is pretty clear. And e-commerce, I think they are saying that you know they are incorporating AI into e-commerce, and that’s definitely another big theme. But the future, the big part of it.depends on how successful this you know AI model push is gonna be. Yeah.And others ten cent. So yes we’ve we’ve written about them and you know Yao Xing Yu’s role and definitely I mean general view is that they their model has improved and I think you know because the perception before he joined was pretty, you know, kind of negative, right? And a lot of there are people who are even saying that, you know, they’re kind of not part of the race anymore, you know, like in terms of you knowthe leading models, but I think they have you know come back. And what they do have is the huge you know platform for applications and you know consumers with WeChat. And we have written you know previously about the WeChat agent and that was a scoop earlier. But I think something like that, you know, they could still kind of make a huge impact byyou know, rolling out something because they have the ability to really, you know, engage their users, right? And that’s still very powerful. So we still can’t, I mean and no matter how this model race goes, Tencent will be very important. And and then I guess yeah byte dance of course yeah we talked about it but theyThey are still the biggest AI lab in China and I think the amount of effort that goes into it, I mean they are trying to do everything as well. And they also just like Alibaba, they are really going for, you know, their cloud business, you know, selling models. That has become the biggest sort of emphasis for the cloud business. So previously, before this AI boom, people didn’t really talk about byte dance as a cloud player, right?But now they do because of that. The model business, you know, that’s really elevated the position in that area too. So yeah, I think all three of them, I mean, are definitely very important. And AI is really the center of what they do now.Grace Shao (45:16)For sure.Jing Yang (45:16)Just have one thing too quickly to add. I thinkBut I’ve been pretty much looking forward to the release of the Xiao Wei, right? With the Witch Hat or the Wasting Agent. Not everyoneGrace Shao (45:25)Did you try it?Jing Yang (45:26)I’ve s what do you think?Grace Shao (45:27)I tried the beta and it’s like okay,It’s on beta. I tried it honestly, cause this is I think exactly to Juro’s point. Like it’s really convenient, so you can’t see it. Okay, there’s a glare. But basically, it’s really convenient. It’s at the top of your like B chat interface, so there’s a lot of functional adjacency. That sounds like it’s really easy to get there. You don’t have to log out to another app. But like the wholeexperience it’s more like what do I need it for, you know, and how good is it? At least for our jobs I would assume, you know, or desktop jobs, AI is seen as a very, very strong research tool at this point, if you’re not doing it like if you’re not running your own agents. But for the day to day SAO it just feels like a supercharged search engine. And I don’t even know if it adds that much value, frankly, if I’m just like, likeI don’t know, when does this shop open? Like that’s when I think a consumer application, you would open up a consumer application. Do you know what I It’s not I’m gonna open up Sellway, be like, tell me about you know tree’s IPO and like what’s your assessment on this? Do you know what I mean? It’s like I feel like it’s not it’s kind of sounds good, but right now they haven’t found a really clear use case. And then obviously on top of that, Selway doesn’t even use Huen Yuan, they use their own model, which is this other wild piece of the poll.Tencent strategy. Look, guys, it’s almost like an hour in, and I still have a lot of questions for you guys. I’m so sorry. I’m gonna like redirect the conversation. We spent a lot of time on labs. I want to ask you guys about the other side of the hype or the international interest right now, which is all about the world models, the robots, the neol labs in China. Juro, you recently wrote about this topic. I actually just spoke to Many Core CFO for this podcast like two weeks ago.And it was very interesting to hear about, you know, a spatial, like a 3D data company or 3D intelligence company is now pivoting to spatial intelligence and thus building world models. It just seems like a lot of companies are in this space. It’s very crowded. Can you give us a high level thought an overview of China’s strongest advantage in the space, China’s current landscape, you know, in the hardware space as well as the software space?Juro (47:30)Yeah, sure. Yeah. So I think for robotics, China definitely has a very broad, you know, supply chain and covers almost all kinds of components, right? And like motors, sensors, you know, structural components, batteries, camera, you know, everything. And this also partly because there’s a big overlap between you know, supply chains for electric cars or drones or you know, other products, right? AndAnd then there are also like a huge concentration of those suppliers, like in you know, Shenzhen or you know, Shanghai, you know, Yanzi River Delta kind of areas. They have a lot of different suppliers all in one place. So like when I was talking to a robotics you know founder in Shanghai, the same person Jim had lunch with, I think, today, so he was telling telling me about how his team can just, you know, like take a DD oryou know, even ride a bike to, you know, many of their suppliers. They’re just in the neighborhood, right? So this obviously speeds up everything, right? So I think that’s the major advantage. And then the bottleneck I think a lot of people point out, but it is the software part. And I think a lot of robotics founders also agree that you know securing that kind of talent, I mean becauseThat same talent can go work somewhere else that pays more. So, you know, in China that talent is still kind of limited for those guys, whereas the hardware talent is so plentiful, right for this area.Jing Yang (49:05)but there’sI want to add about China’s advantage in robotics. I think it’s very easy to just say it’s a supply advantage. I think there’s also actually there’s more to that.I’m going to talk about data advantage.We have not seen real AI powered, real intelligent robots or robot robotic models is exactly because the lack of data.lack of spatial 3D environment manipulation data. Right? And so even though so that in China when you have you know so many pilots happening in the warehouses and the fa on the and on the factory floors, when these robots get deleted and get deployed in these pilots, they are gathering a lot of data that can be then used to improve the model. And this is something that is not happening.I think with very few exceptions of maybe Tesla and a couple others. But in China, this is happening in every you know, major logistics you know, Korea company, warehouse company, and car EV makers. This is why you see CATL, company like CATO and JD and Metron have invested in so many robotic startups. And this will create this data flywheel.That will eventually, hopefully, at least the people in the industry believe, right? That makes China maybe ahead in the breakthrough of the brain, the real intelligence of the robots. That’s something I think that’s less appreciated, but I should bring it.Grace Shao (50:33)Yeahno, that’s really interesting. And think just kind of adding to that, what I’ve also been thinking a lot about is just like because like you said, Jing, there’s so many moving parts in this ecosystem and they’re all done in China, whether it’s data collection with the data fine-tuning or actually the understanding of the manufacturing of these robotic parts and all that. There’s also a lot of talent that’s in this space. That again is being underappreciated. I think people don’t realize that the hardware self-integration is actually the bottleneck for a lot of these products, it’s not really just the manufacturing.Even some say you can manufacture these products like say in Vietnam or wherever. But like you know, I think Patrick McMickey wrote about it in his Apple book. It was a lot of it is just understanding how to hardware or software or like these more like a niche, like the one percent of the top hard blue collar actually is even very, very hard to replace and train up. And that’s been done through decades. So guys, let me ask you a relatively kind of sensitive question.So we saw that DC’s been talking about banning robotic imports from China, but from all of us, I’m sure we’ve talked to a lot of robot companies in the US, like I would say like easily like 90, 90% of them have 90 to 95% of them have some kind of arm in China, be either supply chain or certain even some parts, right? So like if this ban really comes to force, what first of all, what’s driving that decision? Second of all, how practical can that be and what kind of impact?Will it have American robots? Because American robots at this point are already priced significantly higher than these Chinese robots.Juro (52:03)Yeah, so what they cited when they made that announcement about the ban was the you know national security concerns, right? So the robots can, you know, gather data and for surveillance or you know, whether they could be controlled remotely from China or you know, that was the kind of concerns that were cited behind the decision. And I think the definitely the immediate impact is that a lot of US startups oruniversity labs by Unitry robots or you know other robots from China because you know they need to use them for software development or research, right? And so this means that they have to find it somewhere else, but it’s really hard to find, you know, anywhere else because most of today’s humanoids that are affordable and also available are really from Chinese companies. And I think that’s the most kind of obvious impact.Grace Shao (52:58)Right.Jing Yang (52:59)I I the one only thing I’ll add, I think the impact on the US robotics development would be thatthe leading companies that I can produce at scale, you know, can assemble, like I should say, because you know, they can produce all the parts, right? That can assemble in the US at scale would be fine. And I think this regulation of this ban will, whether it’s intended or unintendedly, strengthen the leading players’ position. And then those who have not been able to assemble at scale will have an even harder time.Grace Shao (53:30)I see. Let’s talk about the Unitree IPO. Unitree at the time of recording was late August. Unitree just went public like a week ago. Jing, what are your thoughts on that? And I think you guys wrote something quite fascinating. To be honest, for people who are familiar with A Share, it was nothing too shocking, but I think you exactly pointed that out. You’re chuckling. Why don’t you tell us about it?Jing Yang (53:51)No, I mean, okay, that story was not something that I pitched. I was sort of asked to do a piece that sort of explains why Unitry’s first day like a debut popped so much at 416%. I did not think maybe I’m too close to this because I covered, you know, capital markets before. And so I’m like, okay, this is notNews. This is to me it’s just like another Wednesday. You know, I mean they went public on Wednesday, right? And and thenGrace Shao (54:16)It’s just how Ashare works. It’s just how Ashare works, yeah.Jing Yang (54:20)yeah, and then and then then I had to then I realized okay, okay, actually maybe that’s exactly why I should write the piece. And so then my boss was my editor was initially not convinced. I told him I say, you know, this is because of the John Tin butt, the cap on the stock prices ceiling and floor.that is only exempted on, you know, the first day or the first couple of days of IPO. And then he did not think that this was relevant. We’re talking about first day pop. I’m saying what are you talking about? Of course this is relevant. It’s because if you don’t a lot of people believe it, if you do not get in on the first day when there’s no cap on the stock movement, then you will not be able to get into the stocks for days in a row because though immediately the RP market opens, they’ll hit the ceiling.Right? And then say, okay, then do you have data to back it up? I said sure. And then I went to Bookflow Data. And then there were a hundred and nineteen IPOs on the HR market last year. The mean of the first day share price was two hundred twenty-five percent. So let’s just put that in context. And then in the US is about twenty-five percent or so, right? So so this is why I thinkfor you know, for anyone who’s not familiar with the Asian market, this looks like really striking. But I think for people who are used to this it’s quite normal. And then do you know why people call you you must know this term in Mandarin called that sing, right? And then f like fight for new is what I sort of loosely translated. But why was it called that sing? Because actually inThis is a dialogue in s the you know, like southern part of China, like in Shanghai when China first had a stock market, right? So in the eighties. And the people would be there without digital training, right? People would be lining up on the street outside of the stock exchange, waiting for it to open so they can get in and then and then put place an actual paper, like a hand in their money and get the the the the the the stock order placed. soSo now that whole phenomenon still exists, it’s just not happening in the digital world. But because of that, you know, like sort of fighting tooth and nail, that sort of hardship we are willing to endure to just get into an IPO that people call it DASI. so that’s a big reason for why we see these POPs. The highest first day pop last year in Asia was 1211% and/or 12 times.Grace Shao (56:45)Which companyWas this?Jing Yang (56:46)thatThat was a company called HAR being Big Bird Industrial or in Chinese Da Pong Yi. And it’s a company that does what they call high precision industrial cleaning. So they offer solu cleaning solutions to, you know, like machines that produce EV components. I mean you can debateGrace Shao (57:03)So they’re also part of the autonomousrobot kind of wave as well. They’re just not that sexy.Jing Yang (57:08)Yeah, but you you can debatewhether this is like a very high tech but butGrace Shao (57:12)Mm.Jing Yang (57:13)but definitely, you know, if you compare a company like that that had a twelve times first day pop and you and Uniture is like only a four point six, I was like, Okay, now I get it, right? So yeah, that’s my point.Grace Shao (57:25)No, that II thought that because I saw on your I think it was your WeChat moments. I was like, yes, Ding, this is like quite correct. Like and I think it’s a relevant context to people who are not familiar with Ashare.Okay, guys, let’s talk about China robotics. I think because like Jing said, we’re so close to it. Sometimes I completely normalize like seeing robots on the streets in China. And like you have like cleaning robots, you have like hotel delivery robots, you have like and these things have been around for like I’d say at least three to five years. So when you look at the big trend right now with the robotics hype, and let’s just take a step back, go look beyond humanoids.Where do you think the whole industry is going? Like what are some interesting use cases you’re seeing that’s really scaling robots? and that that is maybe being overlooked by the rest of the world.Juro (58:10)Yeah, I think humanoids, a lot of the applications they’re talking about, especially like industrial applications or even like a home household, you know, chores kind of thing, there’s still a lot of you know, hurdles, right? And but as you said, there if you are talking about sort of robotics and automation broadly, there are a lot of very impressive, like a very fast growing applications andAnother one I can think of is like as those sort of logistics vans kind of thing and that are you know autonomous and so those are kind of sometimes in the robotics kind of category, but I think those are growing very quickly as well. And then what you mentioned about cleaning robots, yeah. And but I think with humanoids we’ve seen so many impressive sort of you know demos and robot performances.And those are very impressive in terms of how robots can move so well, like what they call locomotion, like both software and hardware, right? But I think there’s still a lot of problems to be solved for like robots interacting with the environment and interacting with other objects and people, and especially if they involve like you know unpredictable situations or kind of you know, situations that cannot be really controlled.So because a lot of real world applications contain those kinds of unpredictable, you know, situations. So when it comes to real, you know, I mean there’s still a lot of hurdles. And I think that’s where there is a little bit of a or maybe not a little bit, but there is a hype you know, as to you know, looks like those robots can do all the work tomorrow, right, in factories, but it it’s not that simple. AndA lot of people do say that the AI brain part of the robot is still where they need so much more, like so many breakthroughs are necessary for a lot of those applications to actually happen. And that’s another reason why, you know, I wrote about world models, but that’s, you know, a big part of the discussion because that’s where they need a lot of breakthroughs.Grace Shao (1:00:21)Who are the main players in the world models right now?Jing Yang (1:00:22)I think I want toSorry, before we talk about that, I just the only one thing I want to say ‘cause I just learned about this. I actuallySomething new that I learned. I don’t know if your audience would know, but I want to share. So there’s a big difference between embodied AI and humanoids. And I think somehow these two terms have been used interchangeably. And then the difference is that humanoids are just robots that are shaped like humans. embodied AI actually are robots that have real intelligence. and if you look at the Chinese government’s fifth 15th five-year plan where they supported the sector, they actuallyactuallyspelt out both say, you know, support the development of embodied AA and humanoids. That level of sophistication from the Chinese policymakers, I I don’t think that’s been widely appreciated, right? I just want to point that out.Grace Shao (1:01:11)That’s an interesting take. Look, I wanna ask a question, what do you think is something you still want to share with the audience that, you know, you guys are focusing on in the near future? something that’s exciting you as you cover China Tech, Asia Tech?Jing Yang (1:01:24)Sure, do you wanna go first?Juro (1:01:26)yes, I think I mean just the speed of you know changes and you know I’ve written about some you know trends like for example earlier this year when open claw you know briefly became a huge you know phenomenon in China, right? And then last year there was like a AI agent manace and all kinds of you know clones of manace, right? And yeah, I thinkThese kinds of things will keep happening and I always find them after all these years. I’m still fascinated just how quickly they move. And all those founders jump in and you know I mean there’s so much of the domestic competition that’s also you know not fully appreciated, just how intense everything is. And you know, those guys launched something, you know, when OpenClaw came out, everybody, you know, immediately worked on something, you know, product andbecause they know that everybody else is gonna do the same and if they don’t do it now, you know, they’re gonna look like they’re falling behind. And so yeah, I feel like sometimes like you know part of my beat is just like you know the new hype beat and but you know where I’m still fascinated by just how intense you know things change and you know like yeah how intense the competition is.Grace Shao (1:02:44)Juro, it can be very exhausting, can’t it? I feel like there were like eight models in just the summer. It’s very hard to keep up at times.Juro (1:02:52)Yeah. Yeah, I mean weWe were talking about world models and you know Jean was talking to also some investors and how they were talking about, you know, even founders with backgrounds that have little to do with world models also, you know, starting those, you know, launching those startups now. And yeah, just how that happens so quickly, right? And everybody kind of jumps in.Grace Shao (1:03:17)It’s kind of the new gold rush.Jing Yang (1:03:17)I made this prediction before I madethis prediction before but I’ll make it again. I think we’re gonna assume we’ll see the War of A hundred War models. we saw the War of A hundred L Ms back in twenty ninety three. And I think this year will be the year where we see the beginning of the War of A hundred.or models and and the scary part of this is that at least LM is built on something that has like a a c a piece of technology or fundamental like architecture that has a consensus riched upon and and word model is nothing close to that. This is sort of the scary part.Grace Shao (1:03:50)No, I agree with that. Guys, one last question I ask every single guest that comes on and Jing you’re familiar with this. What is one difference you view you hold or something non consensus?Jing Yang (1:03:59)Okay, maybe I’ll say this. I hope it doesn’t get me into trouble, but I don’t think Chinese models are gonna like the frontier Chinese models are gonna really be able to catch up with the US frontier models. Is sort of my maybe differentiated view becauseGrace Shao (1:04:14)Well, if you put that out there, youI have to elaborate now. Why?Jing Yang (1:04:17)If we assume that a lot of the progress or distillation contributed to a lot of the progress we’ve seen recently, then that just means if you continue to distill, then you continue to be playing catch-up. you may reachyou may reach, you know, to be very close to be on par, but you can never overtake. like a student can never be better at than a teacher, right? So I think that’s just simple logic. I mean I’m making this prediction or this sort of differentiated view based on the fact that distillation continues to be rampant and continues to be commonplace, right? Whenever that’s changed, then obviously my view will change too. So that’s just yeah what I think.Grace Shao (1:04:56)I think that was one of the arguments people made about why Zhangyiming was so against distillation, because he was under the impression that you can only catch up or distill as good as the model that you’re distilling from, right? Jira, what is yours?Juro (1:05:08)Huh. I haven’t really thought about this, but I I guess maybe this is not necessarily non consensus anymore, but a lot of the US China, you know, restrictions on tech and you know with chips or yeah, I mean all kinds of you know, especially US measures for China and yeah wEvery time just what I see is that how there are always you know ways to get around it and and you know people involved or you know Chinese companies kind of even talking about it as if those restrictions didn’t exist or so you know for example like access to Claude or you know or Blackwell chips or so yeah, I mean those would be like I’ve kind of stoppedseeing them as, you know, like when new restrictions come in, the sort of first instinct is how are they gonna get around it this time? Because I’m sure they will, right? So yeah.Grace Shao (1:06:07)It’s like a when there’sa w will there’s a way kind of thing. Yes.Juro (1:06:10)There was a wheel there’s a way. So that’s the kind II mean, maybe this is well known, I don’t know. But yeah.Grace Shao (1:06:18)no, I appreciate both of your time. I really appreciate your insights today. We covered a lot. We’ll love to have you guys back on another time, but thank you so much.Jing Yang (1:06:26)Thank you.Juro (1:06:26)Thank you.Grace Shao (1:06:27)Okay.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  A closer look at the economics of the Chinese Iabs with Bernstein’s Robin Zhu 01.09.2026 1t 9min

  In this episode, I’m joined by Robin Zhu, one of the sharpest observers of China’s technology and AI landscape.We talk about some of the biggest names in Chinese AI, from Z.ai, Moonshot and DeepSeek to Alibaba, Tencent and ByteDance. But rather than just looking at who has the biggest or most talked-about models, we get into what each company is actually good at, how they’re approaching the frontier, and what Robin looks for when trying to separate real progress from the hype.We also touch on how Chinese AI companies are operating under very different constraints than their US counterparts, yet they’ve continued to make impressive progress through techniques such as model compression and reinforcement learning. This raises a bigger question around where the value in AI ultimately sits: if models become increasingly capable, cheaper and more commoditized, who actually captures the economics?From there, we get into the business of AI — how open-weight labs can make money, what AI monetization might look like, and whether the biggest opportunities will sit with the models themselves or with the applications, infrastructure and orchestration layers built around them.Finally, we zoom out to the bigger picture: what China’s progress in AI could mean for geopolitics, model sovereignty and international adoption, and how investors should think about valuing these companies when the technology is moving faster than traditional financial metrics can keep up.The AI Proem Podcast is under the AI Proem newsletter, which has over 12k followers globally. To learn more about China AI, the business of AI, and how AI is impacting businesses, please check out the newsletter here and more insightful conversations here.Chapters00:00 Introduction01:06 China’s AI Race and the Rise of New AI Labs06:39 The Compute Bottleneck — And How China Is Closing the Gap11:37 AI Monetization: Who Captures the Value?13:06 Why China Has So Many AI Labs — And Who Will Survive16:10 When Does an AI Model Become “Good Enough”?19:10 Token Rationalization, AI Harnesses and the Future of Work25:21 Models vs. Applications: Where Will AI Value Accrue?30:31 How Open-Weight AI Labs Can Make Money33:07 Can Chinese AI Capture 30–35% of Global AI Revenue?45:02 How Should Investors Value AI Companies?49:56 Robin’s final thoughts on AI’s future in China and globallyTranscript(AI-generated, for reference only)Grace Shao (00:01)Hey Robin, good to have you.Robin (00:03)Thanks for having me. Good to be here.Grace Shao (00:05)Yeah, yeah. Tell us about your coverage and your recent initiation on Z.ai and MiniMax. I think that was quite exciting. It was a huge report — 50 pages or 80 pages, was it? What made you decide that now was a good time? And what is your main takeaway there?Robin (00:23)Sure. Yeah, look, you know, I’ve been covering internet at Bernstein for a long time now. I’ve been covering gaming for the last number of years in Japan. Year to date, I think something like 80% of our research has been about some form of AI or other. I’ve been using Z.ai and MiniMax as the examples to effectively fill my exhibits and illustrate different points. The stocks kinda ran away from me as we were doing that. We initially thought, okay, we were gonna you know, work out what AI does or what these businesses do and then they all went vertical. there came a point in the summer I was just like, All right, you know, the stocks can do whatever they want given such small free floats and we’ll wait a little bit and the lockup expiries were coming up at that point and yeah, we picked a week Shortly after. I was in the US for a month to kinda network and do different things. and I think we got lucky on the timing, to some degree. But yeah, you know, now there’s more price discovery. There’s, you know, it seems to be a new model launching every other week. So yeah, fun times.Grace Shao (01:34)Yeah, sorry, we were just talking about how there’s such AI fatigue. Like, there were literally eight models over the summer and there was no summer for any of us covering AI, right?Robin (01:44)Are you not excited about Ox Alpha?Grace Shao (01:47)Everyone’s excited about Ox Alpha, but we all have different conspiracy theories, right? Well, because like I don’t wanna like you know go into these conspiracy theory holes today. Let’s focus on some of the big pictures. I do want to askRobin (01:58)Okay.Grace Shao (01:59)You are one of the rare people who gets access to these labs and their executives. When I last spoke to you, you said you were hanging out in Beijing, meeting with some of the executives at Z.ai, MiniMax and whatnot.Robin (02:09)Mm-hmm. Mm-hmm.Grace Shao (02:12)Obviously not sharing anything sensitive, but what’s the vibe? What are the cultural differences?Robin (02:16)Yeah.Grace Shao (02:17)You know, do you think any of their personalities or cultural makeup actually, you know, differentiates them from how they go to market, how they build their products or technology, or maybe even their philosophy on AI?Robin (02:31)Yeah, sure. I mean, I think it’s kind of interesting, you know, I deal with investors day to day a lot. you know, the debate there is, are these labs raising prices? Are we gonna get competition? Do models get commoditized and pricing goes to you know, gets hammered and so on. you talk to the guys at these labs and it’s a very kind of singular focus on, you know, everybody thinks they’re changing the world. AGI is very much top of mind for everybody and Iterating the model is much more of a focus, obviously, compared to investors. But the vibe is very much: yeah, just keep going, keep cranking and see where we can go. Culturally, there are some quite big differences. You know, Z.ai came out of Tsinghua University. Dr. Tang is still kind of on both sides of the fence in some ways. You know, somebody else described it as being monastic. I’m not sure I’d go that far, but it is a much more kind of academic and nerdy organization. MiniMax and all the dealings I’ve had with them seem to be more commercial. You know, they’ve had a couple of pivots in terms of what the main focus has been. certainly more international than Z.ai. but yeah, Kimi’s kind of I guess in some ways halfway in between. You know, they are More international than Z.ai but yeah, you know, you’ve got kind of the more si how do I how’d you describe it? More kind of science based aspect of you know what they’re doing. So yeah, you know, these are they show through in how these companies behave, and the results that you’re seeing in terms of model progress. And yeah.Grace Shao (04:24)How do you think they’re defining AGI? Is it different from what SF is saying?Robin (04:31)What’s SF saying? It seems to be different every few weeks.Grace Shao (04:34)Huh.Robin (04:35)I don’t know if there is a single kind of monolithic, you know what — like we’re going to do AGI and it’s this thing. you know, I think the common analogy is summoning the machine god, which... But I think it’s a little bit narrower than that. I think it’s, you know, how do we get AI to iterate our models for us? How do we get into kind of, you know, I guess some definition of loose RSI or narrow RSI? I don’t tend to get into discussions about broad RSI with people, you know, where it does actually just become a little bit more religious. But yeah, I think, you know, everyone is just focused on kind of iterating the next generation of models.Grace Shao (05:17)And they’re all kind of facing the same issue, right? End of the day it’s compute. But potentially there’s domestic compute becoming more abundant. do you think that’s really gonna change the game here? Or is that even something that’s happening in the near term?Robin (05:33)Yeah, I think it is a bottleneck. Everybody is short on compute. Everybody makes comments on, you know, if you compare the FLOPs per engineer here versus in SF, there is a big difference, and it does hold back some of the progress. But despite that, you’ve seen some of the Chinese labs come up with cache-compression tricks, RL tricks to Close the gap, and I think it has been quite remarkable to see where they’ve gotten to on quite limited resources. I mean, Z.ai in particular, getting to what they’ve done with a 750B pre-train has certainly surpassed what I thought was possible without getting to a bigger model.Grace Shao (06:22)Okay, well then let’s take some. I wanna double click on that later for sure on what the potential implications of all that progress means later. But first, start big, high level. What’s your sense on each of the labs? Like who’s good at what? What are they each gunning for? What’s a good mental framework for us to when we’re evaluating these different labs? Because the one thing I wanna lead to the next question really is we just have so many Chinese like lab providers, I sorry, model providers right now. Like, There’s Z.ai, MiniMax, DeepSeek, let’s just call them somewhat the first tier labs. Then we have like the BAT here, I mean like ByteDance, Alibaba, Tencent. Then like randomly over the last like three to six months, we get Xiaomi, Meituan, RED, Huawei, all crowding that space. And then you have like StepFun and a few others kind of dabbling in this. Well, not dabbling, but they’re also doing this. Well, maybe considering them second tier. How do we understand this landscape andRobin (07:18)NiGrace Shao (07:18)How do we evaluate? These labs.Robin (07:21)Yeah, I think the way that we kind of framed it and when we launched coverage was, you know, I think there are three frontier labs if you look at the latest models. The concept of the Pareto frontier is quite important because there isn’t sort of a monolithic kind of first place. You either have to be the smartest model at your price point or vice versa, the cheapest model for a certain level of intelligence, however you define that. So you can have different spots on the frontier. For example, when Kimi K3 came out, it obviously was smarter than the latest GLM, but it was also something like three times as expensive. So I happen to use both in my day-to-day. and they’re not kind of direct substitutes immediately to each other. but let’s say, you know, we said and I stand by this view that there are three frontier labs: Z.ai, Kimi/Moonshot and DeepSeek. I think amongst themselves, the consensus is that Z.ai is good at post-training, Kimi has the biggest pre-training scale and is good at pre-training, and DeepSeek has this crazy infra capability, so they can run super-high tokens per second and so on. So, you know, they are good at different things based on their different backgrounds. The internet companies — look, Alibaba’s probably spent the Most time and effort to try and develop a frontier suite of models. You know, Qwen is frontier-ish. It’s kind of up there. And then Tencent’s come back really into the conversation in the last six months with Hunyuan 3. I think initially people ignored the preview and then more recently it’s become clear that, you know, the machine that builds the machine is now working and they’re iterating towards Hunyuan 4 sometime this year. So you know that’s that.Grace Shao (09:13)I think Hunyuan 4 preview is coming out this Friday actually. It is. Somebody just told me. I think that’s public information soonRobin (09:17)Is that right? can I quote you on that? Yeah. Cool.Grace Shao (09:24)Or now. Anyway, yes, go on.Robin (09:26)It is now. Yeah. well, You know, they said it’s coming this year. I kind of assumed it was coming in Q4, but cool. That sounds encouraging. ByteDance is funny, right? Because they kinda raced out into a big lead with Doubao. It was, you know, it was considered to be the — well, it’s, I guess it still is the chatbot app in you know, compared to Qwen or compared to Yuanbao. And then I think they ran into the issue of, well, okay, we’ve grabbed the users that we can grab, and then when they tried to monetize it through subs, the paying ratios were very low. and so now I think there’s been a bit of a pivot towards, do we do enterprise? My understanding is they’re gonna start doing ads within Doubao in the second half of the year. so there’s a couple of ways that they’re going through. But yeah, I would say that Tencent and ByteDance are more focused on applying AI through their ecosystems. Alibaba’s much more focused onGrace Shao (10:22)But they want to use their own models, Right? They want to use their own models. So it’s like without really strong models, how do they apply? And this brings me, sorry, I’m just hijacking this whole conversation, but it brings me backRobin (10:30)Mm.Grace Shao (10:31)To like Ben Thompson’s recent interview with Patrick Shonas. He was really interesting. He just goes on about, you know, end of the day, it’s advertising and consumers will not pay. So it seems like, you know, all these AI application companies will just become like they will just monetize the same way the internet companies monetize. And then it’sRobin (10:45)Everybody sells ads in the end, C Yeah, I think that’s I think that’s A plausible yeah, I think that’s a plausible end outcome. where I would differ a little bit is something like a WorkBuddy, where people are just paying for effectively tokens and task completion. but yes, on the kind of base consumer layer then yeah, advertising, monetizing merchants who Effectively pay for access to people doing stuff on WeChat or elsewhere ends up being you know, Douyin started to monetize some of the local service recommendations recently. again through a kind of quasi ads model. So yeah, I think we do move in that direction medium term.Grace Shao (11:27)Then what about all the rest like I just talked about? I think like 10 other companies are serving up models these days. How do you make a or actually let me re-reshift the question? How whyRobin (11:34)I tend to think of those asGrace Shao (11:40)Let me reframe the question: why are there so many? Should we be expecting some calls consolidation? Like, because I don’t expect you to comment on every single one of them, how they’re different. But the fact that we just have like 20 different labsRobin (11:50)Mm-hmm.Grace Shao (11:52)Offering models at this point. It doesn’t seem very economical, but and also aren’t they all just fighting for the same compute, same talent at this point? How do you view all Of that?Robin (12:00)Yes, there’s only so many PhDs that you can hire out of these places. Look, I think there will be fewer players at the frontier or frontier-ish. you know, I’ll pick on Meituan since it’s a company I cover. But they came out with LongCat-2.0. I think initially there was some excitement, and then people realized that it’s got the reasoning capabilities of Hunyuan 3, which has got a quarter as many parameters or something. So or less. and I do think that over time, you know, if you think about what’s important here, it’s access to a data pipeline, it’s the ability to turn that into RL environments and then train these models. and I do assume that the you know, the labs and the very biggest internet companies will kind of, you know, be thereabouts. Some of these smaller names that you’ve just mentioned, I think you know, in the Meituan example, like what’s the difference between having your own model versus using DeepSeek or something? Like, just You know, fine-tuning DeepSeek or something. I do think there’s a kind of open debate there. and you know, certainly costing them a decent amount if you look at their accounts. So I do think there will be fewer players at the frontier. You know, I thinkGrace Shao (13:14)SoRobin (13:14)If you want to have some kind of basic search engine that is AI powered, then so be it. Do you need a complicated long horizon agentic model to underpin every internet platform? No.Grace Shao (13:27)What is driving all this kind of effort to even build their own models? Just FOMO.Robin (13:33)I think it’s partly, “we want our own thing.”. I think it’s innately because there’s so much competition in the internet space that there is this kind of insecurity around you know, if we don’t have a model then do our peers who do then find a way to get an upper hand somehow. And so we need to at least understand the technology. That bit I get. I just don’t think that translates into long term commitment into, you know, training ever larger models infinitely.Grace Shao (14:02)Why don’t we see that kind of phenomenon in the US as much? Like the internet players aren’t just all rolling out their own models.Robin (14:11)They’re all too busy serving compute to the two guys at the front. Seems to be what’s happening. Yeah. I think generallyGrace Shao (14:16)All right, let’sRobin (14:19)Generally, there’s a lot more duplication in China thanGrace Shao (14:24)Mm.Robin (14:24)In the US where the you know these players kinda keep to their own lanes a bit more.Grace Shao (14:29)Yeah, yeah. Well, on that topic, this is like relevant, but much of a conversation around AIs that these models are being commoditized, but you do have a more nuanced view. reading your recent report, you know, you’re sayingRobin (14:40)Mm-hmm.Grace Shao (14:40)That, you know, frontier models versus good enough models kind of have different value within the long term ecosystem. At what point do you think a model becomes good enough that the user simply stops caring whether another model is theoretically more intelligent?Robin (14:55)Yeah.Grace Shao (14:55)Do you think it’s use case dependent or you know? Client-, customer-, end-use-dependent or how do we understand that?Robin (15:04)Yeah, I think initially, you know, agentic AI kind of exploded in Q1, and then everybody got super excited and you know went both feet in to try and work out what they could do with it, hence the token maxing movement. and then subsequently I think as especially as we started using agentic AI more and more, the framework that I kind of zoomed in on was more around User perception, right? And you can define that however you want in terms of use cases, in terms of you know who the user is. And I guess the point that we tried to make was instead of there being some kind of objective standard by which AI models become good enough, they become you know, AI becomes good enough when you can solve a task that you’re trying to solve. And If you then have more than one AI model or if you have multiple AI models able to solve the same task, then the arbiter of who you give the task to then moves away from reasoning capabilities because at that point, you know, by default, if multiple models can solve the problem, then you move on to cost and availability and you know, in some cases UI/UX and your kind of taste of one model versus the other in some cases. So yeah, I you know, I think If you kind of lead on to that, then we do think in a couple of the companies have talked about this in different ways, is that you will have a frontier where people will pay higher and higher ARPUs for more and more specialized and longer and longer horizon task completion. you know, you go from ordering bubble tea to doing agentic commerce to doing, you know, more serious work stuff to frontier science. With the number of tasks that you solve probably get fewer and fewer and the ARPUs just scale infinitely almost. And meanwhile you’ve got this kind of behind the frontier bit where yeah you can solve a task with a good enough model. you know, we were in discussions with or talking to execs at Tencent who said that on WorkBuddy there’s probably thirty percent you know performance-driven tokens and versus seventy percent what I think was said was value-driven tokens where it’s like, you know, you can solve the task with A cheaper model. the way that Z.ai tries to frame it is, you know, you have thirty percent and then maybe fifty percent, which you can monetize and then the last twenty percent is just given away for free on Doubao. but yeah, you know, you have a split in the market as a result of you know, using models to solve what it can versus solving problems that you know multiple models can do versus the really simple stuff.Grace Shao (17:50)And is that the responsibility of the, I guess, the provider of the tokens or the user? As in, should I be routing my own models left, right, center to optimize my cost or what? Or should the platform beRobin (18:02)Yeah.Grace Shao (18:03)Doing that for me?Robin (18:04)I think people ran into that themselves because I mean the irony for me is that Fable being as expensive as it was was probably the catalyst for people to realize, you know, maybe we shouldn’t ask Fable for the weather because it costs you five dollars to do that or something. and and then at the same time, I think in June, July there was this explosion of open-source Chinese models that you know meant that the number of alternatives Then expanded and people started to kind of think about, maybe I should rationalize or, you know, at least match the value of whatever task I’m solving with the cost of the tokens that you know, that I’m spending. In the first half there were these crazy kind of anecdotes like folks working for the internet companies having quotas of upwards of like a thousand dollars a month and, you know, me Saying to them like I know what you do, you make slides for your boss. You don’t need a thousand dollars of tokens a month. and eventually I think the internet companies backed away from that. So yeah, you know I think we’re seeing that kind of rationalization movement happen.Grace Shao (19:13)Yeah, yeah, there’s definitely been a cap I’ve heard for especially what they call like people who are just working with documents, definitely definitely don’t need to be token maxing at all. I am an advocate for not using AI for simple things. Like when you’re asking about the weather, maybe you should just turn on your weather app, like honestly, and not like burn computeRobin (19:29)Yeah.Grace Shao (19:30)On that. Anyway, you use the term token rationalization in your recent report. Tell us about that, because you kind of alluded to it already.Robin (19:39)Yeah, yeah, yeah. So I think it’s, you know, again, it’s this idea of matching the value of what you’re doing versus the cost of the tokens that you’re spending. and it kind of ties back to this Pareto frontier where you know, you have different levels of complexity, you have different levels of cost associated with different models based on their scale and other factors. And you know, you try and be smart about You know, how much you spend, I guess. Like one of the kind of ways I’ve settled in is to have worker bots. I run my own Hermes setup with different bots and I have my worker bots that are relatively cheap and do what they do. and then I have a checker of homework at the end that makes sure that nothing stupid happens and they red team each other and so on. So I think, users kind of find their own ways to do that. Maybe You know, outside of the kind of pro users, then there is the role of the harness or of the kind of you know, the WorkBuddy-like app that then decides for you, this goes to this model, that goes to that model. That makes the overall experience more optimized.Grace Shao (20:49)Why don’t we just jump it straight into how you use AI for research? Like I wanna hear more about it. Like, how do you use Hermes? How do you run your own models? Like it’s not a VeryRobin (21:00)Yeah.Grace Shao (21:00)Common sell-side analyst approach. I think you’re definitely like AI-pilled to the max out of all the cellRobin (21:06)I amGrace Shao (21:07)Sell-side analysts I speak to. Why don’t we talk about that first beforeRobin (21:12)Yeah.Grace Shao (21:12)I get more into the analysis? Like I’m curious.Robin (21:15)God, how long do you have? Look, I started with OpenClaw, round about the same time as when everybody got excited about OpenClaw. And then I very quickly fell out of love with it and then almost by accident happened upon Hermes around the same time. I use it for a number of things now. I use it for kind of work research as a way to gather information. these bots have a way of scraping data from websites. That I can’t manually. there was one instance of a website where you know the website would show you the last 12 months of data and then the bot went inside and after a while said, here’s an API that allows me to download the last 10 years of data, which is great. maybe it’s less great for the guy operating the website. But it there are things like that, or I can now I’ve set up bots to monitor Twitter and Reddit sentiment when a new game comes out, or you know, I’ve built You know, reasoning loops to try to forecast game sales, look at or actually one of the biggest time savers of all has probably been the ability to summarize podcasts. Like in the old days, pre AI somebody sends you a two and a half hour podcast, you’re like, great. Like I’m sure this is super interesting, but I’ve just lost my Saturday morning. Whereas now I can basically have the Hermes bot gonna summarize it, give me key quotes, key insights, timestamps, you know I probably then go back and listen to what was actually said on maybe 10–20% of the two and a half hours. So yeah, it’s been, you know, a good time saver. The other thing is you know, just the ability to knock around ideas on my phone while I’m walking around. Like there was one time when, you know, my wife and I took our daughter to some tourist spot that I’d been A lot of times and they were kind of going around sightseeing. I was behind them having a chat with my Hermes bot and by the end of the four hours of walking around I developed most of a note to write down. So yeah, it’s it’s you know, there’s different ways that I’ve tried to use it. I’m sure we’ll find more over time.Grace Shao (23:26)So you just exposed yourself For not actually listening to my AI Proem podcasts when you do say you listen to them. You’re probably just getting Hermes to summarize them for you.Robin (23:36)No, I listened to you.Grace Shao (23:38)All right. You listen to this episode.Robin (23:39)Hahaha.Grace Shao (23:41)Okay, let’s get back to the serious stuff. Okay. I think I think that’s really interesting because I think someone who actually uses it, like understands it differently from just pure observer. But do you think then, just going back to the last conversation, but do you think then frontier model providers still capture the most of the economics? Or do you think the value is gonna move to applications, orchestration layers? Because early in the conversation, you know, we were talking about like, you know, people are like more mindful of the costs now. but this is how these frontier labs make money. So doesn’t it then challenge their existing business model?Robin (24:15)Yeah, I mean there is this ongoing debate about value capture, you know, between the semis industry where all the stocks have gone vertical this year and then less so in the last couple of months. and then the hyperscaler layer, I mean that you know, you if you look at the US internet companies, spending AI capex is alternately good and bad every other few months, you know, depending on what gets reported. And then the labs themselves, and then, you know, increasingly you’ve had these kind of harness type debates. The one that has struck me as being very interesting lately is that the competition between first and third party harnesses. If you’re an AI lab, then you know the harness is something that basically puts — if you think of the model as being an answering machine or a reasoning machine, the harness actually adds persistent memory, reference files,, you know, tool calls and The ability to do stuff on a kind of recurring and ongoing basis next to the model. And that’s actually what makes it so for example, the Hermes construct is what makes these bots be able to work with you much more like a human worker can. Right. And so there’s been the debate of, well, you know, it’s strategically necessary for every AI lab to have its own harness, whether it’s Claude Code, whether it’s Codex, whether it’s Z Code and Kimi Code and so on. Or do you just end up with kind of WorkBuddy and that acts as an orchestrator across multiple models? or do the first party models then allow other models into their own kind of space? So that’s a debate I think is not going to be solved anytime soon. I think it’s gonna be useful to kind of see how it plays out. but at the end of the day, especially in the Chinese context, I do think that, you know, if the strategic need is for Frontier reasoning capabilities, then on some level these labs have to survive. Right. If you assume that essentially they get zero part of the value, then they will cease to function or they will stop being able to fund themselves and fund the next training run. So I think they will have to retain some of the value in order to keep doing that. and if you think about which layer of the Chi of the tech stack in China that’s most likely to get overbuilt, it’s probably the compute layer. Which also argues in favor of the labs getting, you know, some of the value. So I think it’s yeah, I think it’s an ongoing debate.Grace Shao (26:41)Interesting, you just brought up actually like a lot of these labs will have to create their own products to actually still capture some of the value beyond just infrastructure layer, right? But actually, I think Sam Altman was just on a podcast like a few days ago. He was talking about how he’s like actually bringing everything back. they’re cutting products, right? Like no longer doing a bunch of different products. They’re saying that they’re a platform business instead they only want to use ChatGPT’s interface, they’re even like renaming Codex or something. Like, do you think that is the future for like the kimmies and the mini sac max of the world ‘cause then my argument or my challenge against that is then how could they compete with the big tech and channel where they have just like such broad reach, I guess. They’re like with these super apps and everything.Robin (27:27)Yeah, so that’s that’s the interesting part where if you’re an AI lab, you know, right now you’re using, you know, some of these third party harnesses as distribution. over time, you know, do you have to retain some parts of the reasoning capabilities like, you know, for example, cyber is one of these niche things that you can do with an AI model. Does that need to go into a you know, power user grade or consumer grade AI harness? Or, you know, there are different Flavors of model that do different things, not you know, maybe you don’t put all of them onto the generic kind of third party harness. but that is something to work out. I mean in Sam’s case, I think there’s a kind of semantic thing here where you can name it what you want. to me the accumulation of personal context behind the model through repeated engagement with it and it kind of learning what you do and You training it to do different things as skills, setting up a network of expert tool calls that you can kind of call on. Like that stuff is actually in my mind, where a lot of the long term value lies, or where the usefulness of the AI kind of goes and resides in the you know, as you use it repeatedly. So yeah, and you know, there are a few ways where I think this can play out. One is potentially, you know, there is obviously the The version of the world where everybody goes on to a third party harness within you know that’s run by one of the big internet companies. The alternative is that, you know, if you are a more pro user, more specialized user, maybe you do need some of the specialized functionality that, you know, the AI labs keep to themselves. and then you have a range of these outcomes. yeah, very complicated question. I don’t know if I have a fully formed answer at this point.Grace Shao (29:18)Fair enough. but let’s look at monetization in general for the open-weight labs. You know, obviously I think from the Western perspective, it’s like the biggest questionRobin (29:25)Mm-hmm.Grace Shao (29:25)Is always like how do they make money? How do open-source models make money? How do you see like the current API sales? Is that just a durable revenue pool? or is that not gonna be enough to sustain the capital they need to continue to train?Robin (29:43)Yeah, I mean right now, you know, back to your earlier point about compute constraints. you’ve trained these models, there’s been this explosion of interest in them. I think one of the biggest constraints that they have faced is the lack of silicon is constraining their ability to serve up APIs. You know, I feel this pain every morning Asia time where, you know, I ask a Chinese model to do something at nine AM Hong Kong time and it’s rate limited all the time because everybody else is trying to do that at the same time. And as that gets resolved, presumably, you know, that induces some demand and your ARPU goes up. yeah, I and there are probably lumps around new model releases and whatnot that means that you know it’s spiky rather than that being this smooth curve upwards. But yeah, I do assume that expands over time. You know, all of these companies distribute via the internet platforms as well through, you know, ModelScope and workbody and You know, other things. and then I think to me the interesting thing that’s kind of happened recently is this idea that they are now starting to try and charge or take rate from the global inference providers, right? Like Kimi has gone and signed these deals with different inference providers, essentially charging them you know, you can call it a royalty fee or a or it’s some kind of licensing agreement. But essentially altering the license so that if you are Looking at the weights for academic use or whatever, then that’s fine. If you’re using it to actually just host it and make money, then you have to pay them something, which I think makes sense. So that’s a route that they can kind of pursue to try and capture some of the global economics.Grace Shao (31:26)Yeah. So going towards like a bit more controlled commercial economics. Okay, you estimated Chinese models to be able to address roughly thirty to thirty-five percent of global AI revenue, despite the current headwinds with geopolitics and whatnot. Walk us through that thinking. Thirty to thirty-five percent is quite a large pie. I think even a year ago when I spoke to some of the labs, they were jokingly saying, even if we get five percent, that’s enough money for our business, you know.Robin (31:54)Yeah.Grace Shao (31:55)I mean, but that’s one lab. I guess cumulatively it’s it’s it adds up. So tell us about your thinking on that.Robin (32:01)Sure. I mean it was more of a top down kind of estimate based on what was Attainable or what was kind of in a you know in the context of geopolitical realities what was realistically kind of accessible to these labs. And you know we had made these TAM estimates by region. I think US was like half of global or you know maybe even a little bit more than that. And then China obviously we assumed was a captive market. We ended up assuming a very, very limited access to the US market because of the geo issues. and you know The thing that there’s been a few things that have happened since, like, you know, potentially well, Ramp, I think at one point said that the like f you know, five percent of their highest engagement AI users were playing around with AI models from the Chinese labs. and then you had Microsoft that was allegedly thinking about using different AI models from the Chinese labs to power copilot. So, you know, have we been Conservative, have we been kind of you know, is it that there truly is no access to the US market? Question mark. But you know, in Europe you know, we’ve assumed some access, you know, not unconstrained access. I guess if you’re Airbus, you’re probably never gonna use a Chinese model for obvious reasons. But then you’ve also had you know Mistral Mistral’s kind of turned itself from being a you know frontier lab to something that now helps Z.ai to distribute GLM. And so, you know, there are these kind of future permutations I think are gonna be interesting. That means that, you know, that Europe is accessible to the Chinese AI labs to an extent. and then in the rest of the world, I mean you’ve got, you Z.ai, for example, Z.ai doing sovereign projects with Malaysia, with the Middle East. that presumably then acts as a bit of a kind of beachhead for them to go and do other stuff within these markets. So Yeah, you know, we assume more access to these other markets where, you know, the geopolitical picture is probably you know, more more kind of open to the Chinese labs. So it’s a top down estimate. You know, that it doesn’t mean we think the Chinese AI labs will have thirty, thirty five percent market share. You’re still kind of contesting these markets with OpenAI, Anthropic and others. but yeah, it was more of a kind of, you know, how much of the TAM is actually open to you?Grace Shao (34:24)And let’s just say like geopolitics side, like you mentioned, like obviously government agencies are not going to use Chinese labs, but like a lot of companies might, like, you know, companies with less like strict compliance on this, then what does continued progress with China’s open-source models mean for like what would it mean for these US frontier labs, especially as of now realistically, there’s really two labs left at the kind of frontier really fighting it out. and they’re not you’re not seeing them lowering their Cost or opening up their weights.Robin (34:53)You’re gonna get hate mail from Elon Musk after you upload this.Grace Shao (34:59)Well, if he watches this, it’ll be great.Robin (35:03)No, look, I think you know, I think there’s going to be a a mix of model use in in the market, right? Like isn’t something I think a lot of people have underweighted is what Alex Karp has been saying, where, you know, companies need sovereignty over their own data, you need ownership of what you’re doing. He’s obviously talking about his book, but you know, I do think that there will be a variety of solutions. you see, you know, folks from Databricks and DoorDash posting about testing Chinese models on Twitter and I think it’s interesting that’s, I think that will continue. I think you know these companies will find ways to orchestrate across different models. And the fact that certainly compared to the US labs, these are generally smaller models. if you can get most of the reasoning capability from for much lower token costs, then yeah, I do I do think that you know in a growing section of Use cases that you need, that these will be good enough. Like within the home market, obviously they fight to be frontier. but outside of China in the global market, they are that kind of, you know, eighty percent cheaper for or, you know, much cheaper for most of the capability kind of market positioning.Grace Shao (36:21)All right, let’s zoom in on the companies themselves. I want to kind of touch on the labs, especially the two companies you just wrote about in your initiation report, and then we can talk about your long-term coverage of ATs. Just start with what’s your bull case onRobin (36:33)Okay.Grace Shao (36:34)Z.ai? It’s no secret that you love them. Why? Well, what’s your thinking on that? Like, do you think they wouldRobin (36:43)Yeah.Grace Shao (36:43)Just be like the leading research engine research lab in China?Robin (36:48)Yeah.Grace Shao (36:49)Frankly, not nationalized the same way that DeepSeek is likely more to commercialize. Like, I don’t know, but you just raise your eyebrows. Maybe I’m understanding that incorrectly. Help us understand. What do you think of Z.ai these days?Robin (37:00)Yeah, look, I’ll save the DeepSeek comment to the last. But you know, I do like their ability to iterate these models. You know, they came out of Tsinghua University and I think that relationship helps on some level when it comes to expert domain training data. You know, when you talk to them they emphasize that they have this data advantage, which I think you’ve seen through some of the RL progress that they’ve shown. and, you know, the ability toGrace Shao (37:27)Sorry, what Is their data advantage? What is their data advantage? They’ve said thatRobin (37:30)As anGrace Shao (37:31)To me too, but I don’t know what that means.Robin (37:33)Yeah, I think it’s, you know, if you can buy data and you can, you know, acquire data from experts, you are effectively paying people to write down what they know. but there is also the process of turning that into verifiable you know, like RL environments where you have verifiable kind of end goals or checkpoints that the model needs to hit, or how do you verify correctness or not? And Turn it into something that’s a lot more structured and you can feed it into the RL pipeline to actually train models with it rather than just you know, I sit there and write a hundred page thing on how to do equity research or something. Like I you know, there it has to be there yeah, there has to be kind of reasoning gates and a way to kind of let the model kind of as assimilate that information. so you know, the I think in the GLM-5.3 Release paper is actually really interesting in the sense that they emphasize look RL is all we did and effectively they’ve taken you know data and you know translated that into different RL environments and then they’ve used that to try and iterate the model in different ways. So you know I do think that’s a useful skill to have. The fact that they have a seven fifty B model that’s as good as it is is interesting to me. The fact that, you know I think this is known. I mean that the next big boy model is coming later in the year, you know, call it October, maybe a little bit earlier, maybe a little bit later. But at which point you start having probably the best seven fifty B class model, or certainly it to me it is at the moment. and if you have something that’s competitive in a much bigger model size, then you actually occupy two parts of the of the Pareto Frontier front potentially, which is Interesting strategically. so I but I think most of all it’s just that, you there are these three labs I think are frontier. one of them is a little bit captured in terms of, you know, having to answer to the government to some degree. Kimi I like as well. It’s you know, I think Kimi’s doing some really interesting things on a number of fronts.Grace Shao (39:50)Yeah, I was just gonna say actually, like Kimi obviously you don’t cover officially now given that they’re not public yet, but you know, they kind of reset expectations around CI. I think when five point two came out, they felt like the world felt like CI was the leading l lab coming out of China. Kimi K three kind of put themselves on the global stage again. In fact, I think they did a you know, really, really big marketing splash globally, and captured a lot of attention. And then given that they don’t have the kind of CAI ent like Was it entity list complication. They actuallyRobin (40:23)Yeah.Grace Shao (40:24)Have it easier with international expansion, but it just kind of looking at, you know, Kimi K3, how do you view Moonshot and theirs their positioning right now?Robin (40:35)Yeah, I mean the fact is that they have the biggest Chinese pre-train, right? And the Kimi K3 is a very capable model. it’s significantly more expensive, but at the same time, you know, if you’re among c corporate customers, I think there is the argument to say that you just you know, it’s cheaper than the US labs anyway and you just pay for the best capabilities on some level. But yeah, look, I think, I think they will be up there in the fullness of time. They will hopefully get listed before too long. You know, the last time I asked them the answer was soon. so we’ll see. But yeah, I I think they’re you know, what happened with GLM-5.2 and K three was was kind of interesting because there was a point before the summer where we could have launched coverage on these on these AI labs and almost Around the same time basically GLM-5.2 happened. It was great from the perspective of somebody that used these models, but then I think Z.ai’s share price went up like fifty five percent in the week or that week or something. And it like, All right, maybe we’ll take a break and see how see what see how it goes. And then it got to the point where I think, you know, Z.ai’s share price was pricing in being a winner-take-all type winner, at which point, you know, when Kimi K3 came out then there was an unwind of that expectation. I would argue that there shouldn’t have been that expectation, but you know, go figure. So I think now, you know, I still think that these are the two to watch. DeepSeek obviously will always be up there, but I personally find the Kimi and GLM models way easier to use.Grace Shao (42:18)And Why was it that, you know, when Z.ai and MiniMax went public that it felt like MiniMax was more of the market darling, or at least investors in Hong Kong were buzzier around them.Robin (42:29)Yeah, I think there were two reasons. I think one was MiniMax was considered to be a lot more international. or it was it was much more international. And then the other thing was given the Entity List listing that Z.ai had had, that it was kind of thought that they would find it more difficult to expand internationally. And the other the other problem that investors had with Z.ai was the on prem Segment, which, you know, was kind of this it reminds people of the bad old days of China Enterprise Software, right? Where, you know, you had these companies kind of toil for years and years without really getting anywhere. and so that was, I think, the initial impressions. we put out something in quite early on, I think after CNY, where you know we took a deep look at these companies. I think I was always of the view that You know, model reasoning capabilities are more important and the state of these companies today versus six months ago versus six months in the future is gonna be so wildly different that it’s yeah, you need to evaluate the machine that makes the machine more than kinda where they are at any given moment, which I think was you know, people were guilty of in January.Grace Shao (43:41)And you make the somewhat sacrilegious sell-side argument that traditional financial analysis of these labs can be almost irrelevant essentially. Like are we effectively valuing these companies right now? Like what do you think we should be actually looking at when we are putting valuations on these companies right now?Robin (43:56)Yeah, I think the market really struggles to value these companies because, you know, everybody agrees that AI is a big deal and you can have these debates on how many trillion dollars of TAM AIs going to be in the fullness of time. I’ve largely given up having that conversation. It’s just it’s going to be big. and you know, investors do value these stocks on the basis of multi-year ARR trajectories and you know, revenues multiple years out. But yeah, like, you know, these companies are about to report first-half earnings. we’re about to I mean, there are things that you can watch out for, like inference margins and you know, obviously the revenue growth and How AR converts to revenue and so on. But you know, f for example in the case of Z.ai, like you’re you’re basically staring at a bunch of numbers that reflect GLM-5, GLM-5.1, which is ancient history in AI terms. So it’s kind of useful, but not really at the same time. yeah. So to me it’s much more important to just kind of look at the iteration, look at the architecture tricks that they are coming up with and the ability to kind of, you know, scale these new innovations to much bigger models. And you know, if you can do X then you should be able to do Y, and then what does that unlock in terms of capabilities? so yeah, I do think it’s you know, I do think that Stuff is at least as important as kind of scrutinizing the numbers. Even if you know it’s kind of my job to multiply two numbers together at the end of the day.Grace Shao (45:31)It’s more important to be looking forward than kind of looking back. but like then I have a question that’s like, you know, StepFun has alreadyRobin (45:37)Mm-hmm.Grace Shao (45:37)Filed for the IPO. we just said Kimi is likely going to go public in Hong Kong too somewhat, sometime this year, next year, whenever.Robin (45:44)So yeah.Grace Shao (45:46)We’re looking at like four leading labs already, just the Hong Kong Stock Exchange. Then we have obviouslyRobin (45:51)Mm-hmm.Grace Shao (45:51)The BATs, which I want to talk about later as well. Like, how do we understand? Is this not a pretty crowded space? Like there’s a lot of labs going public in China.Robin (45:59)Okay.Grace Shao (45:59)Does that make sense? How do we understand that? How do we pick the winner? Robin, no one How do you pick the right stock?Robin (46:07)How do we push the right? I’ll push back and say that this is the least crowded new-tech cycle in China that we’ve seen so far. you look at you know in the past, you know, EVs and batteries and you know, or what’s going on with humanoid robotics at the moment. you know, the AI labs piece has probably been one of the more concentrated fields that we’ve seen. And within the names that you’ve Mentioned, I do think that there are kind of there’s a clear hierarchy of, you know, which ones are closer to the frontier. I think DeepSeek has decided it wants to embrace the national champion role and potentially list in the A-share market, which is fine. But yeah, I think, you know, I’ve I’ve said for a while I think Z.ai and Kimi are, you know, my picks for the frontier. I think, you know, based on what they’ve done, based on the, you know, I the architectural innovations, based on the adoption of their innovations by other labs is kind of one thing that I watch for as is being quite telling. yeah, I you know, I guess there will be more than just two players in the Hong Kong market. But yeah, I think my view is, you know, like in the US where you’ve seen fewer players at the frontier over time, I think that will show through here as well.Grace Shao (47:37)All right, let’s move up the stack. while you’re covering BAT, you’ve been covering them, well, Tencent and AlibabaRobin (47:42)Mm.Grace Shao (47:43)For quite a while, just given that byte dents is not public. you know, what is your mental model thinking through these big techs in China right now? Clearly they have a bit of FOMO, they don’t wanna be left behind, they don’t want to just be known as their internet as the internet phase. They’re all in IAI. They have the benefitsRobin (47:59)Mm-hmm.Grace Shao (47:59)Of talent, they’re the benefits of money, but somehow the like just like the US, they’re not the ones actually pushingRobin (48:04)Yeah.Grace Shao (48:06)The frontier. How do you think we should think about that?Robin (48:10)Yeah, I mean psychologically, they are quite different businesses. Like, when you talk to Tencent, the focus is a lot more around the application layer and how do you apply AI and agentic functionality within ecosystems like WeChat, within, you know, WorkBuddy is potentially a new platform for them, you know, on the AI front. you’ve got the games and ads businesses, which are I actually think are good platforms on which to apply AI and generate kind of benefits. But you know, it’s much more focused on the application layer. And there was a comment in the latest slides from the Q2 earnings that said, If all else fails, then we’ll just rent out the compute to whoever else. so that’s that’s considered to beGrace Shao (48:53)I’m dead. I love how candid they are.Robin (48:57)So that’s kind of their psychology around AI. Alibaba obviously has you know you’ve got the e-commerce business, which is kind of stuck in this retail growth environment that’s not really growing. And the cloud business is you know has — well, you know, two years ago it was growing like plus eight, now it’s growing plus fifty, in the upcoming quarter, give or take. and so Yeah, it’s become the new thing, right? Where, you know, the hope is that they have Qwen, the hyperscaler layer, and T-Head, which is one of China’s better ASIC programs, which is worth something. And so, you know, to try and integrate that as a stack. But yeah, you know, if you look at the kind of growth algorithm of the business itself, it’s probably the compute layer that’s driving a lot of it. Yeah, just in terms of renting out capacity. So yeah, these are these are quite different businesses.Grace Shao (49:56)Yeah, and But the thing is they’re still going ahead, like you said, there’s Qwen, there’s Hunyuan, there’s Seed. should they still be in this model game, in this in this extremely competitive game, or do you think they should beRobin (50:07)It’sGrace Shao (50:08)Focusing on, like you said, like just plug in all the other models, focus on growing their existing business, right? Like how do you make up it? How shouldRobin (50:18)Yeah. I mean IGrace Shao (50:20)They balance that?Robin (50:21)Mean cloud and AIs probably Alibaba’s core business at this point. Like the e-commerce is kind of the cash cow that funds everything. but this is clearly the future and you know they’ve guided explicitly for cloud growth to be more than forty percent for the next bunch of years. so to Alibaba this is the core. in Tencent’s case I think there have been There have been multiple debates, you know, that I’ve had with investors around, you know, do they need a super frontier model? Do they need the best model in the market? Or do they you know can they just be the orchestrator layer? Can they just be WorkBuddy? Can they just, you know, use games or ads as a way to kind of monetize AI? I think right now the approach seems to be let’s do everything and see what sticks. or you know, like hopefully everything sticks, but you know, that’s That’s still the strategy and the you know the I guess one benefit that Tencent has is that they do generate a lot more operating cash flow through the core business that then funds a much bigger bonfire of capex over the next whatever number of years that allows gives them some level of optionality.Grace Shao (51:30)So obviously a lot of that money is also going to building out compute right now, right? So do you think China’s compute build out could be a double-edged sword at one point? It might erode some of that scarcity on pricing power and inference. Or, you know, some people are writing about overcapacity on compute. Like, is that a thing?Robin (51:53)Yeah. I mean, look in the in the in the infinite long run, and if you just kinda take that Tencent comment of if all else will rent out the compute, like if everybody builds compute with that as the fallback option, then the reasonable terminal outcome is that you get a big overbuild of compute, right? Just logically. but so that is a concern. And in the long run, you know, I guess you can make the argument that every new tech Cycle in China has ended up in some kind of overbuilding in the end. to me that’s most likely probably to happen in the compute layer because the level of specialization and tech and you know differentiation that’s required in semis is reasonably high. In AI labs, if you really wanted to be frontier, then there’s a level of math and science capability that you need, whereas standing up boxes with servers in them feels less Complicated. you and I probably couldn’t do it, but with enough money and help you know it should be doable for large corporations. so yeah, I do worry about that. I mean, like it’ll probably take a long time because you know even growing 100% a year, it’ll take a while for the domestic semis industry to catch up with demand. But you know, for now, I think compute tightness and You know, cost pressure in the supply chain is probably you know, it’s almost a good thing in the sense that it reduces the risk of everything kind of collapsing on itself and pricing you know, price wars and things of that nature happening in AI in China.Grace Shao (53:34)But compute abundance will be good for consumers, right? Well, at least for end users. Which is not a bad thing.Robin (53:39)Well yeah, so yeah. Well, I think there will be the, I guess obviously initially when there’s more compute, yes, you know, everybody has more capability to serve up more inference demand. And so the industry grows. I think and then obviously, you know, the hope is that there is a balance between demand and supply. In practice that almost never happens. and when you end up with an environment where there’s a you know, there’s a number of good enough models, there’s a lot of compute. Then it’s probably more likely that you know on one hand, you know, the cost of compute then goes down, but yeah, there’s more likely to be price competition in AI inference at that point than today.Grace Shao (54:19)And we start another round of juan. There’s never-ending juan so you talked a lot about you know how much the harness around a model can change the actual output. And I think it’s quite topical around the big tech right now. do you think we’re putting too much focus on how smart the base model is? do you think eventually really like the focus should be on memory, tools, routing, verification?Robin (54:42)Yes.Grace Shao (54:44)And then therefore like these big tech companies actually have A lot more experience in building these products, understanding consumer user behavior,Robin (54:53)I think both layers matter. I think it’s you know, if you’re gonna reduce the question to extremes, then if you just have the orchestration layer and you don’t have AI, you know, the reasoning capabilities of the model, then that doesn’t work. And I think the vice versa also doesn’t work in the sense that, you know, then you kinda kneecap the model’s ability to complete tasks and so on. So I think you’ll see the labs and The big internet companies all try and compete on both layers. one you know, back to your earlier question of, you know, why should all of these companies be developing AI models? one of the fears that I always have around these big Companies is that there’s always there’s always going to be big-company bureaucracy in politics and who takes credit for what and you know that sort of thing. And then I was in the when I was in the US over the summer, I had conversations with a couple of people who described working, you know, we were talking about Google and DeepMind at the time because it was during the week when everybody seemed to leave. And one of the ways it was described to me was you know, if you want if you think you’re going to change the world and you want to make AGI happen and so on so forth, then the most convex place where you can go and do that is at the frontier labs. And you know, doing the same thing at a big internet company feels like you’re designing a better toaster. which I mean it’s it’s not I don’t know if it’s a completely fair comparison, but yeah, and financially at the individual level, if you’ve done a super difficult PhD in something, you come out and you want to monetize that, then You know, today the most convex way to monetize that is probably to join Kimi ahead of their IPO, right? So, you know, there are those types of incentives at play as well. So, yeah, you know, so you know I do ultimately think the labs will be up there in terms of their ability to deliver these reasoning capabilities. and then the first and third party harness question ends up being kind of something that iterates in real time.Grace Shao (56:58)Right, right. And I think it’s kinda like going back to your earlier comment, like I think it’s what happened with Tencent when DeepSeek came out, they’re like, you know what, our Hunyuan is kind of meh. So maybe we’ll just focus on building products that are like a harness around it, like, but it just didn’t work because their own models weren’t good enough and then you can’t always rely on other people’s models, right? So these big tech are still going ahead with their own models now.Robin (57:19)Yeah. Well, they now own twenty percent of DeepSeek. So, you know, I think Tencent has the deep pockets and has the kind of strategic patience to be able to go down multiple routes where it, you know, you are doing your own model, you know, Hunyuan 3 was good and now Hunyuan 4 is coming soon. and meanwhile you’re still serving DeepSeek within your Yuanbao and yourGrace Shao (57:41)Mm.Robin (57:42)WorkBuddy apps and so on, and over time And they use, for example, GLM in their ima app. and so yeah, they’ve always kind of done both. and you know, I guess it is reasonable that WorkBuddy allows them to see the reasoning traces of different models and that then somehow feeds back into their own model development, which is so yeah, I think Tencent’s in a slightly different position than a lot of the other guys in this conversation.Grace Shao (58:11)But wouldn’t Alibaba and ByteDance have the same kind of deep pockets?Robin (58:16)They would. But do they have the do they have the kind of social infrastructure andGrace Shao (58:22)Hm.Robin (58:23)The ability to pull everybody into a you know, an open third party harness? Whereas you know, if you look at Qwen Work, if you look at TRAE, these tend to be a lot more first-party-heavy. I don’t know if they will be as open in the fullness of time as Tencent. So yeah, they they y you’ve got these companies. And then, you know, each of these companies will then have their own internal kind of puts and takes in terms of who gets what. And so Yeah. Tencent historicallyGrace Shao (58:49)Yeah. They’re definitely all trying to follow theRobin (58:51)Had a bigger had a better track record of being kind of open and being theGrace Shao (58:56)Yeah.Robin (58:57)Somebody called them the benevolent gatekeeper of China Internet, which is yeah, it’s not a bad way to describe them, I guess.Grace Shao (59:04)Yeah, I think the other two are trying to follow the WorkBuddy route as well. They’re They’re all revamping DingTalk and Lark right now. So I think it’s like putting it into Qwen Work or something, and then TRAE—Robin (59:13)Yeah yeah yeah.Grace Shao (59:14)—TRAE and Coze were put into Doubao, and now it’s called Doubao Work, with Lark kind of grouped into it. Anyway, I wanna ask a question on recursive self-improvement. So I’m quite curious about what you think about the gaps between a lot of the labs. I don’t even want to position China versus the US, but if you have to put it that way, you know. A lot of times people are saying, you know,Robin (59:35)Yeah.Grace Shao (59:36)The Chinese advantage — sorry, the US advantage right now is that the labs are putting a lot more money into R&D and into figuring out how to go forward, right? And then the Chinese models some some ways are basically following their footprints and figuring doing theRobin (59:48)YeahGrace Shao (59:51)Knowing the answer key to the homework. It’s that kind of analogy people are saying. But if there really is RSI, then would that change things? Then would these labs — sorry, the models themselves — just start figuring out How to improve themselves quicker and quicker and that gap would just shrink and compound with time or how do you see that?Robin (1:00:10)Yeah, I mean the I guess it depends on how you define RSI, but I guess the way I think about it is that there will be a gradient of different levels of how of you know automation and how to what extent AI can train AI and you know you can have them write kernels or whatever. But you know, going forward, can you ingest data and going back to the RL kind of example that I mentioned earlier, can you have AI write The verifiers and the gates and the success fail kind of conditions and you know have AI set up RL environments rather than somebody with a PhD doing it. so I think that will happen relatively quickly and then you know you start automating that process more and more. But then you still ultimately I think I’m very big on this. Like I still think you have taste and judgment be very important in terms of what kinds of verifiers you get the AI to to develop, and how you know there’s still ways to you know to do it better than the next guy rather than just have AI brute force everything. there will be some elements of that. But yeah I think you know and more and more it’ll be kind of you know the human supervising the AI doing more and more of it and but still kind of leaning on it and offering a bit of a steer in terms of where it where you want it to go and the types of behavior you want it you want to reward and so on. So yeah, I think I think there will be a gradient of how much AI versus human involvement you have in some of the model training. On the compute gap, then yes, if you have if, say, tomorrow OpenAI figures out RSI at a pretty high level and they’re able to iterate quickly, then that probably means that the gap between US and China widens again to some degree. You know, We seem to be in this Quite circular debate about whether it’s three or six or nine months and you know, I think the reality is that there’s it kind of oscillates. The US comes up with something and then China closes the gap again quite quickly. but yeah I think given how quickly information is kind of you know the flow of information between different parts of AI has been so rapid I think it’s, you know, over time the gap then, you know, maybe doesn’t widen infinitely.Grace Shao (1:02:35)And do you think this whole three-, six-, nine-month thing, end of the day, when we take a step back, surely it’s just so minor, no? Like how do we understand that?Robin (1:02:44)I think it matters to a bunch of people above our pay grade. You know,Grace Shao (1:02:49)Ha ha.Robin (1:02:49)A lot of it is being kind of hijacked in the media, in kind of geopolitical discussions and, you know, things of that nature. and if you are a frontier researcher, if you’re doing, you know, I think Anthropic has started to talk about medicine or drug discovery as a as one field that they want to be good at and If you’re trying to discover new drugs or trying to cure cancer, you know, which maybe we are now starting to do, then you do want, you know, the super frontier latest three months of model capability because and y you’ve seen enough c you know, like early-stage biotech whether these things either go up or down a hundred percent or w you know, whatever. But Because either a drug works or it doesn’t. And so for those types of use cases, yes, you do want the absolute frontier. If I’m just having a conversation with my Hermes bot, like if my model is three months out of date, does it really matter? I like to think it does. In reality it probably doesn’t.Grace Shao (1:03:55)No, it’s true. do you think there’s anything else that we need to talk about today, just for our audience to better understand the China AI landscape right now, where it’s at, or how to evaluate it?Robin (1:04:07)Yeah. Yeah, I’ll talk about something that’s a little bit of a tangent. But you know, one of these hills I’ve decided to die on is gaming. You know, I cover a lot of gaming both in China and Japan. There was this kind of moment where Google came out with Project Genie. I mean this was in this was in earlier on in the year where you know US software seemed to go down 5% a day every day. And Gaming got thrown in with that. And I think since then I think folks have come back a little bit and you know, back to the judgment and taste kind of thing. yeah, I continue to be of the view that gaming is actually remarkably difficult to disrupt and actually probably benefits from AI evolution over time. that’s probably you know and recently, you know, Google actually Was showing off one of my companies, Capcom, to demonstrate, look at how Capcom’s using AI and therefore, you know, AIs useful in the real world. And so yeah, I think I think the logic has kind of been turned on its head a little bit, but that’s that yeah, that continues to be the hill that I’ll die on when it comes to AI.Grace Shao (1:05:21)You think like gaming, filmmaking, these are all spaces where production cost is a lot lower, but you know, consumption will still be there. Is that kind of the thinking behind that?Robin (1:05:32)I think yeah, I mean production costs should come down as you automate more and more of these things. you know, you are starting to see AI video start to take over, you know depending on how complicated like you’re you’re not gonna have Nolan-level movies with generative AI anytime soon, but you are seeing short form video platforms essentially become AI-centric. so yeah, I think I think, you know, That will continue to grow, albeit the issue I’ve always had with multimodal models or video models is that, you know, the ceiling for commoditization, or the ceiling for at least you know, me not being able to tell the difference between one and the other is quite low. And so, you know, how do you differentiate video models from each other when that happens is kind of the thing that I’ve not been able to fully resolve. But yeah, if you’re just a maker of visual content, then this is great for you, right? You’re able to kind of Make stuff much more quickly. gaming in my mind is a lot more complicated because it’s not just about, you know, rendering of 3D environments or rendering of characters or whatnot. You have to have a storyline, you have to have, you know, action, music, fe you know, combat and so on and so forth that makes it more difficult. But yeah, or you know AI solves for production, you get a lot more output. I think the question in media and games and movies is like do people then care? Right? but then we’ve also just seen Niu Lai go viral. So maybe, you know, you need a more nuanced version of what people care about.Grace Shao (1:07:10)I like how we’re ending this conversation full circle. We started the conversation with having you look at the Ox Alpha thing and then the meme going around is the Niu Lai picture.Robin (1:07:19)Yeah.Grace Shao (1:07:20)So let’s see where that goes. I was like, is this a representation of niuma (牛马)? ‘Cause the end of the day we’re just all like worker bees and in Chinese we’re all horses and cows. But he said probably not. That’s not where the meme comes from.Robin (1:07:35)I’m gonna leave that alone. I’m gonna leave that alone.Grace Shao (1:07:43)How do you use AI in your own research, but I want to ask you like there’s just a lot of AI tracking tools out there. There’s a lot of scattered data. AI itself is obviously very broad to even kind of you know, it’s just to say what are you tracking? So actually the question for you is like, what are you using for your own evaluations or what do you track? Like, are you looking at OpenRouter, ModelScope, GitHub? Like what, what do you use to understand? Where demand is going, how compute is used, which models are good, etcetera.Robin (1:08:15)Yeah, I mean we track all the obvious things. I mean I’m not sure that’s differentiable necessarily, but you know, OpenRouter, Hugging Face, GitHub, ModelScope. We’ve set up kind of bot routines to try and scrape these things every so often. And, you know, we have these gigantic Excel files of, you know, daily data, weekly data. And then that at a high level paints a picture of you know who’s actually engaging with some of these things, who’s actually, you know creating repos, who’s then downloading the models, who’s doing this, that, and the other. one of my pet peeves with OpenRouter is that, you know, it’s a really, really small chunk of the market. a lot of people, especially on in the investment world, are kind of overindex on it as a as an indicator of and you see the media do it and say things like, You know, Chinese models are now two-thirds of consumption or something. It’s not, it’s two-thirds of consumption on a relatively small part of the market. but fine. You know, we do track it and we do look at kind of, you know, which apps are being used, be it kind of Z Code or some of the other stuff. So we do all that. I guess one thing that we do, which I don’t know if anyone or many other people do, is we do run our own evals. So we When a new model comes out, one thing that we do is we run it through or my bot runs it through these benchmarking tests and you make them solve tasks and it gives me I mean there’s a there’s a variety of reasons to do this, but effectively gives you a more first-hand view on whether you think something is good and then I tend to try and use them day to day because that gives you then potentially a more varied kind of read on Model feel and usefulness beyond just making it crack hardcore software engineering tasks. which, you knowGrace Shao (1:10:13)It’s not for everyone.Robin (1:10:14)Well, that, but also it’s you know, these labs presumably then train on something like Terminal-Bench or SWE-bench. No one’s gonna train on my own day to day nonsense. So yeah.Grace Shao (1:10:26)Do you have any other tips for how us like research-driven people or our jobs are just here typing away on our computer how we can better use AI in our research Process?Robin (1:10:40)I will say it’s an iteration process. Like I think even you know, you have to kind of use it and be hands on, figure out, you know, what is useful for you the most and and you know find ways to kind of make it multiplicative, right? And just not just kind of have it summarize the news, but also, you know, we’ve built different Constructs on top of my data extraction funnel to try and you know have it actually analyze what’s going on, give it, you know, send me kind of reads on different things that I care about at any given point. And then I use it to iterate some of the stuff that I do day to day.Grace Shao (1:11:24)It can be as smart as how you make it to like how smart your inputs are, really. Yeah.Robin (1:11:29)I think so. And you know, what I found is you go around in circles, like you give it some stuff, you kinda work out whether it actually can do that or not. And sometimes you know, the answer’s not always yes. and yeah, and you know, try and kind of extend or you know, or try and give it you know more and more things to do until you so you know, just organically there will be use cases that kinda pop out from every so often based on my experience.Grace Shao (1:11:59)I don’t condone the fact that you weren’t spending time with your daughter full on one on one and actually on your voice AI tool. But my husband’s gotten to a point where he’s wearing his Apple Vision Pro and he has like seven of his agents in a line and he’s just sitting there like controlling them. I’m just like it’s gone too far. Like I think if there’s gonna be like a pushback by all the kids and wives at this point, not to overgeneralize. Now, look, what has really changed for you or your view on over the last six months? you know, has something fundamentally shifted in your research or something your understanding of AI and the technology.Robin (1:12:39)My understanding of AI seems to change every two weeks. So, you know, that the whole process of being in January, staring at these two IPOs, and then you know, fully going down the rabbit hole of trying to understand them and keeping you know, trying to keep track of everything, build a network around, yeah, I mean it’s it’s been it’s been wild, how much everything has changed. I’d love to be able to distill it down to one thing, but I think the reality is just, you know, everything has changed and the thing that I’ve discovered actually that’s been interesting is you know, I run a small team and there are people who normally work for me and in the old world I used to kind of staff them on a small handful of things a day. And then they will go away and do it and they come back and I would have to make, I don’t know, three or five consequential decisions a day or, you know Have kind of deep, deep conversations with myself about how to do something. Now, increasingly, with AI — I mean they’re still doing that, but at the same time I’m knocking around ideas in my head and I’m sometimes using AI bots to help me kind of process what I think. and they come back so quickly that I find myself having to make five consequential actions an hour. And then at the end of the day I’m like, you know, my workload has increased as a result, which is I don’t know if that was the desired outcome, talking about AI making people’s lives better. But there you go.Grace Shao (1:14:15)I think it’s just because you’re too much of a doer and too competitive. I think for people who are high-agency people, they are doing more and they’re experiencing AI fatigue. For people who wanna just clock in, clock out and just do the three things they were told to do, their lives are made easier. You know, it’s like you know, writing three emails used to take maybe like a day, now it takes twenty minutes or less.Robin (1:14:35)Then I wouldn’t be on this pod, so there are upsides.Grace Shao (1:14:39)Look, one last question for you. I asked everyone, what is one differentiated view you hold or you know, something a bit non-consensus you think?Robin (1:14:49)Yeah, I think the gaming thing is probably the most controversial. you know, or the maybe the broader idea of I think if you want I think it’s it’s the whole idea of of judgment and taste where, you know, there’s still this belief and it’s almost a religious belief at this point, which is because I don’t know how to disprove it either. which is I think on some level it’s you know That will always be valuable, the idea that I’m still of the view that it’s quite difficult to get AI models to spit out something that’s outside of its own distribution and training data. And so, you know, on some level you still have human input being the arbiter of something that’s ninety percent of the way there and something that’s truly kind of differentiated. So yeah, I vote at the end for humankind.Grace Shao (1:15:47)But then I have a follow-up question on that. It’s just like I think it’s easy for people who’ve built up taste or, you know, like yourself, you’ve been in the industry for long enough to build up your own taste, your own judgment. How do people without that kind of experience build up human taste still when they’re joining the workforce or they’re growing up like our children are growing up at an age where, you know, AI is natively embedded in everything they do? So, how do you still differentiate that taste? Because You know, now like we’re seeing these like parallel structures of sentences everywhere you go. It’s driving me crazy. Even Instagram ads are like, you know, they have like these very obvious parallel structures and it’s like driving me crazy. I’m like, dude, like this marketing associate did not write this, but I think what you’reRobin (1:16:29)Okay.Grace Shao (1:16:29)Seeing is people without that taste judgment and now just copy and pasting whatever AI spits out.Robin (1:16:36)Yeah. I mean we’re gonna end this conversation like every good Asian parent and talking about parenting at the end of it. no, look, I mean it is a conversation I have with myself. Like how do you educate somebody or how you know, whether it’s your own kid or whether it’s somebody that you work with or whatever, then, you know, how do you develop taste from first principles and Yeah, I don’t know. I don’t have a great answer, but I do think exposing yourself to original content and doing things the hard way, at least in the beginning, is still important.Grace Shao (1:17:16)Well, thank you so much for your time. Very generous with your time today, Robin. I really appreciated your insights and everything.Robin (1:17:21)Appreciate our conversation.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  From 3D Design Software to Spatial Intelligence: Manycore’s Next Chapter 26.08.2026 52min

  In this episode, I speak with Bei Shen, CFO of Manycore Tech, the Hangzhou-based company behind Kujiale in China and Coohom overseas. Manycore started as a cloud-based 3D design software company serving designers, furniture brands, retailers and property developers. Today, the company is expanding into spatial intelligence, using its 3D data, simulation capabilities and software to explore applications beyond design.We talk about Manycore’s evolution from startup to public company, including its IPO earlier this year and how the company’s story has changed since going public. While its core SaaS business still accounts for the majority of revenue, Manycore is increasingly positioning its proprietary 3D data and technology as a foundation for spatial intelligence and new applications.We also dive into SpatialVerse, AholoWorld and Manycore’s work in robotics and embodied AI. Bei explains how the company thinks about spatial intelligence—not simply as a data business, but in terms of the systems and simulation environments needed to help AI understand physical space. We discuss potential applications in robotics, game design and filmmaking, as well as the question of how much intelligence different types of robots actually need.Finally, we discuss Manycore’s global expansion, partnerships and long-term strategy. We explore whether spatial intelligence will become a market dominated by a few global platforms or remain fragmented across industries and geographies, and what Manycore sees as its role as more robotics companies begin building their own physical AI systems.The AI Proem Podcast is under the AI Proem newsletter, which has over 12k followers globally. To learn more about China AI, the business of AI, and how AI is impacting businesses, please check out the newsletter here and more insightful conversations here.Chapters01:18 Leaving Investment Banking for a Startup in Hangzhou05:29 From Silicon Valley to Going Public in Hong Kong06:19 First of the Six Tigers to IPO07:28 3D Shift Toward Spatial Intelligence14:20 Data, Simulation or System: What Is the Product?18:14 Learning More of SpatialVerse and AholoWorld31:59 The Role of Spatial Intelligence in Robotics34:04 How Manycore Fits Into the Robotics Stack38:11 Global Expansion and Strategic Partnerships42:31 Will Spatial Intelligence Consolidate or Fragment?46:20 Manycore’s Focus and Strategic PillarsAI-generated transcript (for reference only)Grace Shao: Hi, Bei. Thank you so much for joining us today.Bei: Hi, Grace. Good to be here.Grace Shao: Yeah. So, you guys are one of the hottest AI companies that listed in Hong Kong this year, and a lot of people have a lot of questions. But to start with, tell us about yourself. When I was learning about your background, I thought it was quite fascinating. You’re almost like a Joe Tsai story. You had a very successful finance background and a successful career in Hong Kong, and then you decided to jump over to Hangzhou to join what was still a relatively unknown startup. Tell us what made you want to make that jump and leave your cushy banking role. What was the spark about this company for you? And tell us a little bit about where the company is right now.Bei: Sure. My name is Bei Shen. I’m CFO of ManyCore. I joined the company in 2019. Before that, I was an investment banker for 14 years. I worked for Citigroup in New York, then moved to JPMorgan in Hong Kong. The last nine years of my banking career were at Goldman Sachs.Around 2018 or 2019, I started thinking about what I wanted to do with the rest of my career. Traditionally, I focused a lot on clients in more traditional industries. I covered companies in the power, mining and energy spaces. It’s an interesting job, but the sectors are relatively traditional.So I started asking myself how I could get more exposure to technology and internet companies. For me, it was very difficult to switch industries within the bank, so I started looking around for opportunities. Luckily, ManyCore was looking for a CFO. After talking with the founders and the team, I found the company very exciting, so I joined in 2019. I can’t believe it, but it’s been almost seven years now.Grace Shao: Yeah. And I know you recently took the company public, but before we get into all that, tell us about your three co-founders, because they have quite interesting backgrounds. They’re quite young. They came back from Silicon Valley, bright-eyed and wanting to start something in China. Tell us about the vision they had in the early days and where it has led you now, roughly 15 years later.Bei: Yes. The company was founded around 2012. The three founders were classmates at UIUC, which has a very strong computer science program in the U.S. Our chairman, Victor, and our CEO, Chen, actually went to the same undergraduate university, Zhejiang University, which is also where our company is based. We still recruit a lot of people from Zhejiang University, which is a great school. And our CTO went to Tsinghua.All three of them studied at UIUC in fields related to computer vision and high-performance parallel computing. After graduation, they all went off to cut their teeth in Silicon Valley. Victor worked for NVIDIA for a couple of years, Chen worked for Microsoft, and our CTO worked for Amazon.They were all working in Silicon Valley, but they wanted to come back to China and participate in this exciting market. Back then, Victor had this idea of putting GPUs on the cloud to serve more clients. He was working at NVIDIA on the CUDA team, so he was involved in the early days of figuring out how to put compute on the cloud and serve more customers. Obviously, this was before AI became what it is today.They built a demo and came back to China. Luckily, the Hangzhou government was welcoming overseas graduates and helped them start the company.The original idea was very simple: they wanted to put GPUs and compute in the cloud and make that compute available to more people. In the beginning, it was very difficult because AI hadn’t taken off yet, autonomous driving wasn’t in full swing, and crypto wasn’t either.Luckily, they found a very interesting application in interior decoration. In the old days, if you used on-premise software, it could take a very long time to render a photorealistic picture. With their technology, you could put that computation on the cloud and use multiple GPUs to accelerate the rendering process. That enabled users to create photorealistic renderings in minutes. Now it’s seconds.That really changed the industry in a big way. So that’s how they started. It was fundamentally a technology company trying to find applications for its technology.Grace Shao: How would you describe the company today? How would you position ManyCore in two or three sentences? Clearly, it’s no longer just about Kujiale and 3D interior design.Bei: Obviously. The company has had 14 or 15 years of history. Before 2023, we were basically the largest 3D design software provider for interior design. But since 2023, the company has increasingly focused on spatial intelligence.To put it very simply, we’re trying to help AI perceive, create and eventually act in a three-dimensional world, so that AI can eventually move from the digital world into the physical world. That’s where the company is focusing right now.Grace Shao: Perfect. I think you were definitely one of the hot IPOs earlier this year. You went public in April and were one of the first of the Hangzhou “Six Little Dragons” to list. It felt like a point of pride for Hangzhou and for this new wave of Chinese AI companies. What did going public mean for you and for the company?Bei: Obviously, it’s a big milestone for the company. We raised fresh capital to fund our future growth, especially in spatial intelligence. We need more compute and we need to hire more talent.But from a business perspective, it also put us on the international radar. We already have many international clients, but it can still be difficult for a Chinese technology company to sell products to overseas customers. Being a public company, with your company story and financials becoming more transparent, definitely helps a great deal in promoting ourselves and selling our products in markets outside China.Grace Shao: I want to double-click on something you said earlier about how the company evolved. When you filed the prospectus, I went through it, and it was still mostly focused on your 3D interior design technology. Now you’re clearly pushing a new narrative around spatial intelligence, which frankly wasn’t emphasized nearly as much even a year ago when you filed the prospectus. Things are moving so fast.Tell us about how that shifted and why you had this moment of pivot. Was there an epiphany during the process of going public, or after you went public? Did something hit you where you realized there was this gold mine you were sitting on? Tell us the story behind that.Bei: Sure. That’s an interesting question. Just to go back a little bit in terms of our IPO history, we really started preparing for a Hong Kong IPO in the third quarter of 2024. Then we filed our prospectus on February 14, 2025.The IPO process is relatively lengthy for Chinese companies because every company going public needs approval from Chinese regulators. For us, it took a bit longer because of our structure. We finished the IPO in April this year. So looking back, the process took almost a year and a half.Obviously, both the company and the industry changed enormously between the day we started the IPO process and the day we actually listed.Our thinking was that it would be unreasonable, or even impractical, to keep updating the prospectus every time the company changed because this industry moves so quickly. So we made a decision to keep the discussion of the new business and products relatively minimal.That’s why, when you read our prospectus, you see a lot of disclosure about our older, existing business, which is obviously still important. But the new businesses were changing so much that we didn’t go into as much detail.After the IPO, we started talking to more analysts and investors and trying to give them a more updated picture of where we stand in spatial intelligence.Grace Shao: For sure. So what are the top-of-mind questions or areas of interest you’re getting from investors right now about the business?Bei: This is a very frontier area. Large language models have obviously received a lot of attention over the last couple of years since ChatGPT came into existence. There have been many advances in model capabilities, coding, image generation and video generation.But we’re focusing on a relatively different type of AI. We sometimes call it physical AI. As I said, we’re trying to help AI understand the 3D world, which is very different from reading text and giving you an answer, or generating a picture or a video.One of the challenges in our space is that we don’t have nearly as much data as large language models do. They can access internet text and enormous amounts of video. In our space, the amount of data is several orders of magnitude lower and much less dense compared with text, pictures or video.That’s why it’s difficult. It’s very hard. People also need to spend some time understanding what we’re doing because I believe we’re working at the frontier of what could be the next wave of breakthroughs in AI.Grace Shao: My understanding, according to your public filings, is that roughly 90% of your revenue is still coming from the traditional business, particularly Kujiale, and that’s really funding the new initiatives.As you move into spatial intelligence and physical AI, you touched on data as the bottleneck. But data is also part of your moat, right? You’ve had more than a decade of experience working with 3D data. Tell us more about the connection between your traditional business and this new business.Bei: Sure. I wouldn’t say data is the only reason we chose spatial intelligence, although it’s obviously a very important aspect of the business.There are many connections between our traditional Kujiale business, or Coohom internationally, and what we’re focusing on today.Even 10 or 12 years ago, we adopted a very integrated technology architecture. We bought our own GPUs and did rendering using our own GPUs in order to provide the service to customers worldwide.That’s actually quite similar to what large language models or 3D models are doing today. You’re utilizing compute to provide products and services to people around the world over the internet.So that’s one connection in terms of the technology lineage. We did a lot of hardware-software optimization to make sure rendering could be provided at the lowest possible cost. Similarly, if you want to do inference today, even if you have a very good model, you still need to keep inference costs low in order to remain competitive. That’s something we’re obviously very good at.Data is also very important. We’ve accumulated a large amount of data. In hindsight, it’s fortunate that, compared with the on-premise software that came before us, we had all the data on our cloud platform. That wasn’t necessarily by design at the beginning.But in today’s world, 3D data is extremely valuable and very difficult to obtain. Because of our cloud-based architecture, we’ve been able to accumulate a large amount of data, especially structured 3D data, which is critical for training 3D understanding and 3D models.So between our technology lineage and our data library, I think we’re in a very unique position to explore spatial intelligence.Grace Shao: So you have a lot of 3D data, but this is spatial data. It’s not necessarily the movement or motion data people talk about needing for robotics training.But when I spoke to your team while visiting Hangzhou, it sounded like a lot of your clients may actually be robotics companies. What are you providing to them today? Is it a 3D intelligence system? Is it data that helps robots operate better in physical space? Or is it a bit of both?Bei: This has an interesting history. I think it was back in 2022 or 2023, during the pandemic, when nobody could really go anywhere. We were all stuck in offices or at home and couldn’t travel abroad.We received an email from Silicon Valley from one of the large technology companies. They came knocking on the door and said, “I heard you guys have some interior-setting data.”We said yes.They said, “We’d like to buy some.”At the beginning, we thought it was spam or some kind of trick. But it turned out to be a real client doing research.We didn’t think too much about it. We struck a deal and helped put together some synthetic data they required. We made some money, not much.Then the next year, another technology company came and asked for something similar. This time, we took notice. We thought, okay, there must be something valuable in our data.So we started asking these U.S. clients, “What are you actually doing with our data?” We’d had it for over a decade and hadn’t really thought too much about it. Luckily, we hadn’t deleted it just to save storage costs.Only then did we find out that these were large technology companies in the U.S. training robots. They needed synthetic settings in which they could test and train their robotics policies.That’s when we realized we were sitting on something interesting and valuable. We started thinking about how we could better commercialize the data we had.Obviously, we’re not satisfied with simply providing raw synthetic data. Right now, we’re speaking with customers both in China and the U.S. and trying to help them train and evaluate their policies more effectively.Eventually, we also hope to train our own model.We believe that if you want a world in which robots, or physical agents more broadly, can become fully autonomous, they need their own brain. The ability to perceive and understand physical surroundings, reason about them and act within them may require spatial intelligence. That’s something we’ve also started working on ourselves.So right now, we’re still providing synthetic data to customers. We’re also trying to train our own model, which we eventually hope can be put into intelligent robots or embodiments of different forms so that they can really act in the physical world.Grace Shao: That’s really interesting. There’s a bit of serendipity there. Things just happened and you were there at the right time with the right data.I was reading through your materials. There’s something called SpatialVerse, and you’re also releasing HoloWorld. What are these things for? Tell us more about these products.Bei: Sure. These names keep popping up. Sometimes I get confused as well because things change so fast.As I said, in 2022 or 2023, we started selling synthetic data solutions to robotics companies. Later, AR and VR companies also came to us asking for something similar because they need data to train goggles or glasses.We put this type of business together under the name SpatialVerse. That’s one line of activity and business we’re developing.As I said, eventually we’d like to train our own models and put them into robots.Another branch of research we’re working on is helping agents create synthetic worlds. This is somewhat similar to what Dr. Fei-Fei Li’s company, World Labs, is doing.Basically, using a simple prompt, text or pictures, you can quickly create a virtual space where you have geometric information as well as information about the objects within that 3D space.People talk about “world models” a lot these days, and sometimes the term is misused or misinterpreted. But the way we understand it, in order to have this capability, you really need a model that can generate a 3D world.It’s not just a continuation of pictures. There are very good models today that can give you 20 or 30 seconds of short video. But what we’re after is the ability to use computing technology to generate a 3D world where the objects within that world have certain physical properties, and where you also understand the geometric relationships between those objects.That’s critical for robots eventually being trained inside that world.Grace Shao: So rather than a video-generation model, which is where a lot of multimodality efforts are going right now, you’re really trying to create 3D spaces. It almost feels like creating a little Sims world.What are the use cases then? Off the top of my head, there might be game design, filmmaking and, of course, robotics training. What do you think people are missing when they think about what this tool or product could eventually be used for?Bei: That’s a great question. Again, this is relatively new.Historically, 3D design has been a relatively niche market. Before us, you had all these on-premise 3D design software products, but they’re difficult to master and use.Compared with picture editing, 3D design has historically been quite niche. It was mostly used in architecture, industrial product design and VFX.What we did was make the 3D world easier for ordinary people to generate.One area where we’re already helping customers is media. In China, one-minute and two-minute micro-dramas have become very popular. You see many of these productions on Douyin, and a lot of the content is already being generated by AI.We’re helping some of these creators produce spatially consistent 3D worlds. If you rely purely on video generation today, you can get hallucinations after a certain amount of time. Objects start floating around. You leave a room, come back, and suddenly the objects are missing.Using our product, these producers or content creators can maintain a spatially consistent 3D world and produce something higher quality. You don’t have the same spatial hallucinations you get from video models.The other application is robotics training and evaluation, which we’ve already discussed.It’s inconceivable that you can train robots in every possible physical environment. Thinking through all the corner cases would be too expensive and too time-consuming.So in order to train and evaluate these policies, I believe it’s critical to have virtual worlds where you can put robots through testing virtually.Those are two areas where we’re already putting our technology to use. Eventually, I think there will be many more applications.Grace Shao: That’s really interesting. I want to double-click on the micro-drama example.To help me understand, are people using your technology in parallel with something more traditional like Seedance? One is more for aesthetics and one is for spatial control? Is it layered?Or could your technology eventually compete with and replace a more general-purpose video model like Seedance?Bei: That’s a great question. Right now, the way we serve customers is really a layered approach.We already have a product out that you can try called LuxReal. We rolled it out about a month or two ago.Basically, you can upload a script, and it’s an agent that helps you produce a 30-second, one-minute or two-minute micro-drama based on that script.First, you use our product to create the 3D world. For example, if you want to shoot a micro-drama set inside an ancient Chinese palace, after reading your script, we’ll generate that palace for you. Let’s say you want two rooms inside Beijing’s Forbidden City. We can create those rooms, and then you can move your camera around within that space to shoot the scenes.Our customers don’t only use our modeling capability. They also use Seedance because we don’t actually do the video-generation part right now.We help you create the spatially consistent 3D setting, and then you put Seedance on top of that. You can very quickly produce a one- or two-minute micro-drama.If you only use Seedance, you may have to do a lot of editing afterwards because certain things don’t look right. You have to spend manpower editing out hallucinations.With our product, the room is always there and the table is always there. The table isn’t going to change when your camera changes.So it’s basically a very efficient tool for these micro-drama producers.Grace Shao: That’s very interesting. So essentially, you’re producing one asset layer in the broader workflow for these creators.It’s funny because when I was talking to people at Kling and Kuaishou, they said people don’t necessarily care about inconsistencies yet. Sometimes the cat turns out white, sometimes the cat turns out black. There’s definitely an understanding that AI content, as of now, isn’t that sophisticated.I want to shift the conversation to models and robots.I’m going to put you on the spot here. You mentioned Dr. Fei-Fei Li. Yann LeCun and Fei-Fei Li are both world-renowned scientists working on something around this realm. They’re both trying to push forward ideas around world models. Obviously, this is still in a very nascent stage.How do you see the field? And frankly, how do you see your company’s position among all these global competitors, which have a lot of technological influence and obviously a lot of capital behind them as well?Bei: Great question. I wouldn’t say we’re directly competing yet. As I said, this is a fast-evolving and changing industry, and everyone is still doing a lot of exploratory work.Dr. LeCun’s approach, to be honest, I don’t understand in great technical detail because I’m not computer-science trained. I’ve read about it, and one apparent benefit of his approach is that you may not need as much compute to come up with these highly realistic representations.We haven’t paid too much attention to his work yet, but obviously we’ll be very interested to see what he develops.Dr. Fei-Fei Li’s approach is more similar to ours. Both companies are trying to figure out an efficient model for creating a 3D world where you not only see the world as we see it, with the correct textures, sizing, depth and perception, which is the rendering side and something we’re very good at, but where you also embed more information.We’re trying to add physical properties such as friction coefficients and how wind behaves. As customer requirements evolve, we’ll try to put more and more information into that model.Eventually, it will be interesting to see what applications emerge outside media and robotics training when people have these kinds of worlds available.Grace Shao: I listened to one of your CEO’s interviews, and he said that what you’re trying to do is create spatial intelligence that can help translate physical space so LLMs can better understand it.He also discussed how world models shouldn’t necessarily be produced by every individual robotics company, or at least shouldn’t be viewed as interchangeable, because each use case can be so different.So I guess my question is: how should we think about a robot being used to remove blood clots, where the work is incredibly meticulous, versus a robot designed to lift heavy objects? What kind of 3D data and 3D model does each need?It seems like “world intelligence” or “world models” is too broad a category to serve every single demand in the space right now.Bei: I’m not 100% sure which episode or interview you’re referring to, but I think what he was probably talking about is the robotics industry today.Obviously, the level of optimism varies depending on who you talk to. But based on our conversations with the industry, a general-purpose humanoid robot is still pretty far away.I wouldn’t say it’s next year. Maybe it’s five years, maybe it’s 10. But just imagine a humanoid being able to do 100 chores in your household. I think that’s still quite a few years away.There are so many challenges. One of them is exactly what we’re working on: can you teach a robot to understand different settings?The minute it walks into a room, can it immediately understand, “Okay, this is a table, this is a desk, and these are the relationships between the objects”?We’re still working on that.Building a general-purpose, fully autonomous humanoid robot is hard.But if you narrow the problem down to more vertical or specific-purpose robots, I think that’s more doable. The level of complexity and intelligence required is much easier to achieve at this stage.I think the industry is more likely to evolve through more and more specific-purpose robots. One robot might pick up boxes. Another might help with laundry. I’m just giving examples.That seems like a more likely roadmap than trying to immediately build one general-purpose robot with omnipresent capabilities.As you build more and more of these scenario-specific robots, maybe eventually you arrive at a stage where a more general-purpose robot becomes possible.That’s how we see the world and how we’re tailoring our R&D efforts. We’re not trying to go after one extremely general spatial-intelligence model right now. We’re trying to crack these silos one by one.Grace Shao: So which verticals are you focused on today, out of all the different kinds of robots you’re serving?Bei: To give you a few examples, we think household robots are probably difficult, at least in China, because Chinese households tend to live in relatively small spaces. The margin for error is extremely small.So right now, we’re focusing on helping robots in more industrial settings.For example, in warehouses, we can teach robots to quickly understand the warehouse because the level of complexity is relatively lower than in a family setting.We’re also helping some robotic dogs patrol power stations. They need to walk around, identify anomalies, record them and report them.We believe these are some of the low-hanging-fruit applications today, where we can teach robots or robotic dogs to perceive and understand the 3D world.Grace Shao: Do the economics make sense right now? Frankly, if you’re trying to replace relatively simple tasks or labor, especially in China or elsewhere in Asia where labor costs are relatively low, does it make economic sense?Bei: That’s a great question. The economic equation is definitely important as we put more effort into this.Some of the key areas we’re trying to explore are places where it’s dangerous or costly for human beings to operate.For example, around high-voltage power stations or transmission lines, it’s definitely safer to have robots patrol instead of human beings.Or in remote areas and underground mines, if you have water leakage or some geological situation, it’s safer to send robots and perhaps drones to inspect first rather than sending a human rescue team directly.Grace Shao: That makes sense.But my understanding is that you’re purely on the software side right now. Does it make sense for these humanoid robotics companies to pay for your service and technology, or does it make more sense for them to train and build their own models internally?How do you view that? There are obviously different camps. Some people say OEMs can build different types of hardware while companies like yours provide the intelligence underneath. How do you see that trend?Bei: Right now, we’re only focusing on software, as you correctly pointed out.We’re trying to be model-agnostic, and we’re also trying to be embodiment-agnostic.Basically, we’re trying to develop technology that different robotics companies can use to train and evaluate their policies.Obviously, we’re not there yet, but that’s our goal.We’re not trying to build robots or robotic dogs ourselves. We’re trying to help these companies find a very cost-effective way to evaluate their policies. That’s our approach right now.Grace Shao: What do you think people are getting wrong or misunderstanding about the industry today?In spatial intelligence and robotics, there’s obviously a lot of buzz and a lot of hype. Unitree has been getting a lot of attention as well.Do you think there’s too much hype right now and that we should be more cautious because progress is still going to be slower than the public expects?Or do you think the misunderstanding goes the other way, and people are underestimating how quickly this technology could proliferate in niche use cases and eventually reach consumers?It’s a big, open-ended question.Bei: Sure. From my perspective, I obviously believe this technology has very broad applications in the future. But the capabilities have to get there first, and the cost has to be low enough for the technology to proliferate.I believe spatial intelligence is a critical part of human intelligence. It’s almost innate to us. Dr. Fei-Fei Li has made a very good argument around that.After thousands of years of evolution, human beings can see things and quickly understand their geometric and 3D relationships.That’s something large language models don’t really have today, but it’s critical if AI is going to operate in a physical context.Right now, it’s great that AI can solve math problems or write poems. But can you actually ask a robot to do your laundry? Can you trust it to do all these tasks?Right now, we’re not there yet. But I believe we’re on the way.I wouldn’t necessarily call it a misunderstanding. I think the difference in opinion is really about how long it’s going to take.As I said, one of the biggest challenges facing our industry is data. We need to find smart and cost-efficient ways to obtain more data because models are a product of that data.Large language models are really the product of compute multiplied by data. Our industry is no different.That’s why we’re thinking about different ways of capturing more 3D data. We’re working with different hardware companies. Robotics companies are one category. Scanning companies are another.We hope to have more hardware companies work with us so that more users can use our technology to capture or generate 3D data.In the long run, we need a flywheel where applications, data and models all improve in tandem, level by level.But right now, the flywheel isn’t flying yet. We’re working very hard to push it forward.Grace Shao: That makes a lot of sense. More users mean more use cases and more scenarios, which give you better data. Better data improves the models, which then lets you serve clients better.On partners and clients, you mentioned earlier that you already have quite a global footprint. I think that’s fairly unique among Chinese companies that are trying to go global today.When you think about international expansion and distribution, what’s most important? What kinds of partnerships are you looking for?You mentioned that some large Silicon Valley technology companies are already clients. How should we understand those relationships?And more importantly, do you face localization as a bottleneck, or is that less of an issue in your particular sector?Bei: Great question.Putting it in the context of Kujiale or Coohom, localization is actually very important.For example, if you want to sell an interior-design product in the U.S., the industry is very different. China and the U.S. both consume furniture and decoration services, but the industry relationships and dynamics are very different.Localization is therefore critical.Just to give you a very simple example, even the measurement systems are different. China uses meters, while the U.S. uses feet and inches.That’s a tiny example, but there are many localization changes you need to make in order to fully satisfy the local market.What’s interesting is that this has changed quite a bit in the AI context.ChatGPT basically became global overnight. I think one reason is that the model itself became so powerful.You can almost think of the model itself as the product. You don’t necessarily need to build many layers of user interface on top of it.We’re beginning to see that in our space as well.If you have a very powerful world model, for example, you can generate 3D objects or scenes relatively easily. There may still be differences in language, but in terms of usability and application, it becomes much easier to promote globally.That’s a big opportunity for us.Eventually, we hope to offer a product that has a global appeal similar to ChatGPT, where you have users all around the world.Obviously, that’s not easy. First of all, you need to come up with an extremely strong model that can actually serve clients and users worldwide.Grace Shao: So what I’m hearing is that on the consumer-facing side, something like Kujiale obviously requires more localization.But if you increasingly position yourself as a B2B support technology or an infrastructure layer underneath consumer-facing products, you may need less localization. Is that a fair understanding?Bei: That’s a fair summary.Coohom, by the way, is the international version of Kujiale. It’s not just a language translation. We’ve adapted Coohom depending on which market we’re entering, so we made a lot of localization changes.But for a new product like LuxReal, the micro-drama product, we didn’t have to do much localization besides language.That gives you an idea of how, in this era, products can become international much more easily because the underlying layer becomes extremely powerful and important, while the application layer on top can be relatively simple.Some clients can even develop their own applications based on our technology.That’s how we envisage the future.We’d like to develop very powerful models and offer them through APIs or SDKs. People can then do their own development and build secondary or tertiary applications on top of the model.That’s a change in paradigm compared with the past. As a software provider, you used to have to build many of these applications yourself.Now users and customers can use things like vibe coding to build many applications themselves. We don’t necessarily have to do all of that anymore.Grace Shao: That makes a lot of sense.So just one last question on this part: should we understand the future of spatial intelligence as being more fragmented and vertical by sector and use case, rather than by geography, compared with how software evolved during the internet era?Bei: I would tend to agree with that assessment.As I said, because the data is so difficult to obtain, I think the industries where we can establish these small data flywheels will develop more quickly than others.So I believe it’s going to be a more fragmented landscape compared with large language models, where eventually you may have fewer than half a dozen truly global companies. There are obviously more today, but I think LLMs will ultimately become quite concentrated.In large language models, the data is relatively open to everyone because everyone has access to the internet.A lot of the competitive landscape is therefore determined by who has more compute or who has the best talent and algorithms. Large companies have a huge advantage in that environment.Our space is different.There isn’t a universal 3D data library where everyone can simply start working on the same dataset.First of all, obtaining the data itself is a challenge.We obviously have an advantage because of the work we’ve accumulated over the years, but eventually we still need to find more and more methods of getting additional data.So I think the game is somewhat different from large language models.Grace Shao: I want to take a step back.You guys are based in Hangzhou. Like you mentioned earlier, there was an effort in Hangzhou to attract people to come back because of how strong the ecosystem is.Obviously Alibaba is there, Ant is there, there are a lot of e-commerce players, and many of the startups that came out of Hangzhou over the last decade have somehow been related to e-commerce.It’s interesting that you didn’t get sucked into that orbit.When I was reading about your story and looking at the earlier days, I thought it was funny because you could very naturally have gone into e-commerce staging and 3D content creation. That could have been a logical path for serving domestic clients, especially given that you were based in Hangzhou.What was the thinking behind not going into that vertical?Bei: We’re no exception. We tried e-commerce. It didn’t work out.Grace Shao: I love the candidness.Bei: We’re no exception.Going back to Kujiale’s early days, we came up with this interesting software for designers. The natural next step was: can we sell furniture?We tried. It didn’t work out.I think that’s probably largely due to the genetics of the founders. They weren’t from that industry. They’re not e-commerce experts.It’s also simply the nature of furniture and home decoration. It’s very difficult to commoditize. It requires a lot of service. It’s not like selling a book or laptop through e-commerce.Even Alibaba tried, and I don’t think the results were very satisfactory.So we dabbled in it. We burned some investors’ money, but not too much.Then we realized, okay, it’s not for us.We came back and said, we’re going to focus on software.And lo and behold, we found this opportunity in spatial intelligence.Grace Shao: Definitely. That makes a lot of sense. Furniture isn’t an easy thing to sell. It’s tailor-made, personal, huge and bulky, and logistics aren’t easy. I can imagine it’s not an easy business.Looking forward, as CFO, I’m sure you’re thinking about how to invest the newly raised money and looking at the next three- to five-year horizon.Where should we be looking? What is the company’s focus? What are the strategic pillars for you?Bei: Great question. We think about this every day.Going back to the basic AI paradigm, it’s always formed around compute, algorithms and data.We’re really going to focus on those three things as we try to push the company to the next level.Data is probably the hardest part because it’s not just about money. You need to think about smart ways of obtaining that data.Talent retention and talent recruitment are also obviously the number-one priority for management.You definitely know how expensive data scientists and algorithm scientists have become these days.Grace Shao: How much are they making these days in China? Give us a range.Bei: Not as much as their U.S. counterparts, I think. But even for college graduates fresh out of school, if you have the right experience and pedigree, you can make at least five to 10 times what a traditional software engineer might make.It’s a very highly sought-after pool of talent.Grace Shao: Okay, so are we talking about RMB 2 million to RMB 3 million? I’m trying to force you to give us a range. Five to 10 times is a big figure.Bei: No, no. It’s a big figure, but software engineers don’t make as much as they used to anymore.Grace Shao: The irony in all of this.Bei: Exactly.So talent is obviously a huge priority for us.We’re also looking at interesting opportunities because we don’t know where the next technology is going to come from.These days, acquisitions are really about people and talent.If we see interesting algorithms or ideas coming out of labs, we’ll consider making our own moves.As you know, we work very closely with Zhejiang University. We have a postdoctoral lab with Zhejiang University where we put a lot of effort into computer-vision research together.Hopefully, we’ll identify talent and interesting early-stage products along the way.Grace Shao: Would you go into hardware? Would you build your own robots?Bei: Not right now. It’s already a pretty busy space.But we’re definitely looking at hardware, probably not robots directly.As I said, we’re already working with hardware companies to collect data.We work with some scanner companies and LiDAR companies in China to collect 3D data.To collect 3D data, you don’t only need cameras. You also need LiDAR, which gives you geometric information.For example, we’re working with Hesai, which is a very good LiDAR company, to come up with solutions.So we’re starting to dabble in hardware. We’re not purely a software company anymore.Going forward, I think the two are going to be coupled together.Especially in our world, if you want to have state-of-the-art 3D models, you will definitely need help from hardware companies, and we’ll probably do some of it ourselves.Grace Shao: That makes a lot of sense.You guys are well positioned given the amount of interest in you right now, and given that you’re in Zhejiang and close to Zhejiang University, where there’s a lot of talent coming out.I think over the last year, the West has really opened its eyes to Zhejiang University, but in China everyone already knows it’s an absolute top-tier school.You have people like Liang Wenfeng, and there’s just a lot of talent coming from that region.Anyway, I really appreciate your time.I want to ask you one last question, which is something I ask everyone who comes on the show: what is one differentiated view you hold? Something you think is non-consensus?Bei: I think the TAM, the market for spatial intelligence, is actually going to be bigger than the market for large language models.It’s still a little early, but if you look at human intelligence, language is only one part of our intelligence. Spatial understanding is also critical from an evolutionary perspective.Eventually, if we can help AI crack that capability, the applications will be extremely broad.I think there will be many things that robots or agents can eventually do that people probably haven’t even thought about yet.It’s still early. It obviously requires a lot of exploration and effort, and there will be many pitfalls along the way.But we believe this is a very, very large opportunity, and we’re fully committed to it.That’s one view I think may still be a little bit off-consensus today.Grace Shao: Thank you so much. I think we still have a long journey ahead.Bei: Thank you, Grace.Grace Shao: Thank you for your time, and congratulations again on the IPO.Bei: Thank you very much, Grace. Nice talking to you.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  China’s pragmatism, state-market hybrid, and how that shapes AI and capital with Baiguan’s Robert Wu 19.08.2026 59min

  In this episode, I speak with Robert Wu, the founder and CEO of Baiguan . Our conversation focuses on two questions that increasingly overlap: how AI is reshaping the business of information, and how China’s distinctive mix of pragmatism, markets and state involvement shapes the way new technologies get adopted and financed.We start with the professional data industry. As AI agents become a new orchestration layer above terminals, APIs, and research products, Robert argues that the biggest disruption may come not to the production of proprietary data itself, but to its distribution. For niche data providers like BigOne Lab, the opportunity is to make differentiated real-time data accessible at inference time. The unresolved problem is economics: licensing, access control and who ultimately captures the value when an AI agent becomes the interface.From there, we widen the conversation to culture and political economy. Robert explains why debates about AI in China tend to focus less on existential or metaphysical questions and more on what the technology can actually do. We discuss whether that pragmatism comes from China’s history of technological catch-up, whether similar attitudes extend across East Asia, and the potential trade-off between being exceptionally good at applying technology and creating the conditions for more fundamental scientific discovery.We then turn to the role of the state. Robert rejects the simple idea that China’s technology industries are created through top-down planning. Instead, he describes a hybrid system in which entrepreneurs often discover the opportunity first, while the state later supplies policy support, capital and the resources needed to scale. We use EVs and DeepSeek to explore that model, before moving into state subsidies, local-government incentives, private capital, Beijing’s evolving approach to public markets and why so many young AI and technology companies are choosing Hong Kong for their IPOs.We close with two of Robert’s more differentiated views: that China could be entering a multi-decade equity bull market, and that outsiders often misunderstand China by assuming it has the same impulse to export its own political or cultural model. And for a slightly lighter ending, Robert explains one of the Chinese stock market’s most vivid metaphors: why generations of retail investors are compared with chives that get cut, grow back, and get cut again.btw sorry for the weird glitch in the video around 12-13 min of the recording.The AI Proem Podcast is under the AI Proem newsletter which has over 12k followers globally. To learn more about China AI, the business of AI, and how AI is impacting businesses, please check out the newsletter here and more insightful conversations here.Chapters00:00 Robert Wu, BigOne Lab and Baiguan03:09 From alternative data to the AI era06:02 Why AI disrupts data distribution12:11 Inference-time data, licensing and economics15:17 Why China feels more pragmatic about AI21:47 East Asia, belief systems and scientific discovery30:32 China’s hybrid model of state and private innovation45:24 Funding AI: state capital, private capital and IPOs50:30 Beijing’s market-stabilization playbook and policy risk01:07:29 Robert’s non-consensus views and the meaning of “cutting chives”Transcript (AI-generated, for reference only)Grace Shao (00:00)Hey Robert. Good morning. So good to have you join us today.Robert (00:05)Good morning, Grace. Hello, everyone.Grace Shao (00:08)Robert doesn’t need much of an introduction. If you spend as much time in the Substack world as I do, you’ll know he’s a prolific writer covering everything from capital markets and property to technology and culture. My favorite niche is when he calls out Noah Smith’s articles for being wrong. Those are pure entertainment for me.For today, though, Robert is a student of history, politics and business, and I think it will be interesting to have him walk us through some of the bigger questions people have about China. I’m also curious, because he runs a data company, about how he sees the future of data providers as AI changes that relationship.So I’m handing the mic over to you, Robert. Tell us about yourself, your journey with BigOne Lab and Baiguan, and how you’re seeing the business evolve.Robert (01:12)Yeah, hi. So this is Robert. As Grace mentioned, we run a newsletter. But that newsletter is really our kind of side business. The actual BigOne Lab is a team of over forty people, which exclusively most of us work on data products and research products for institutional investors and corporates. Both in China and outside of China. But we have we’ve been very China focused. All of our data and research are about China, Chinese companies, Chinese industries, businesses. The Baiguan to me was partly accidental, but partly also kind of fateful. Actually in the very beginning during college I actually wanted to be a journalist. But I didn’t find a way. So I kind of dabbled in capital markets in investing, corporate finance for a few years. But eventually it kind of hit me that, there’in this new world there’actually other ways to do journalism. Data tracking, data analysis is actually could be a new form of that. And even with data you can do more powerful storytelling and that was the genesis of our newsletters as well. Right. So here we are. We are backed by S&P Global as well, which is I would say one of the most ris backed respectable, respected companies in our industry. And yeah, so it’a brief intro about ourselves.Grace Shao (03:09)Yeah, so tell us like what is unique about your data then in that sense.Robert (03:14)Right. So we started as a so-called alternative data company. Alternative is alternative to the traditional financial data, macro data, market trading data. It’no longer alternative now. All alternative data is mainstream data now. But it was first happening, it was because the explosion of data and information in the internet and especially the mobile internet age. There are just so many data being tracked. There’payment data, there’online com commerce and social media data, so vast number of data and multiplying exponentially every year. And some investment firms they realize that by harnessing all these data and aggregate them together and put them in the right context, you could actually generate a lot of alpha that is previously not available. Right? So that’how we started the business. It was a very investment firm hedge fund driven business. So that you know kick us to look at a lot of the industry verticals, a lot of the different kind of industries where there’data and we try to find the most granular, the most high frequency data we can find. Perhaps the you know we can have massive amount of data about mobile transactions in China, for example, every day, even every minute, all the transactions that we can have access to and analyze on. So that’different from many of these you know mainstream data providers, which we try to be very granular. We try to be very frequent. Yes.Grace Shao (05:18)Yeah, so that’really interesting. I think p one thing that really stood out to me and relating it back to AI is that when we were having our catch-up conversation, we were saying, okay, data plays obviously a huge role in AI. But what you distinctly said, there is the people that are involved in the pre-training data bit, there’like the Mercores of the world. There is the people who are more pivoting towards kind of the post-training data provider, which is what you guys are doing. Just tell us about that relationship and how you think the whole data vendor ecosystem is adopting to AI and or evolving with AI, especially and how like AI is now affecting, say, Bloomberg, Factiva, those mega data platforms that we traditionally know of.Robert (06:02)Yeah. So the term data company is really problematic for us. It’really a kind of a name that covers very different kind of businesses serving different needs, entirely different kind of businesses. So you mentioned that there are data companies that are serving the large language model training right now, the Mercor, the Surge AI. So they are they are they are good at massively labeling data, connecting the you know different type of data and help the help build up the data sets that are used for the training. Well for us, we are more on the on the on the more on the real time data end. And it’so for the industry that we operate in, we have Also, we have not a consensus on the name for our industry, to be honest. I call it professional data industry. Some people call it market data, some people call it market intelligence data. But at the end it’it’about tracking and understanding of the real world on a real time basis, if we have to define it. So it’much more about what is happening rather than the logical connections between different pieces of information, which I think is what the pre-training data i is mostly about. And so in our industry, AI is placing is playing a huge kind of disruptive role for our industry. So in the professional market data industry, there are main several main stages, maybe three. There is the production the original production of the data, there’a distribution, and there is the what we call activation. I won’t maybe go to details of each one of them, but if you understand production, production is really where the data is originated, right? For example, if you are Nasdaq, all the trading data on your Nasdaq platform is originated at Nasdaq. That’called production. The second is distribution. Is how you combine all this data into products, right? Companies like SP’marketing intelligence, like Bloomberg, like you know FacSet are in the distribution part. They don’t generate data on their own or mostly don’t not on their own, but they provide the interface for users to interact. Right. And activation is really how data is used. I won’t go to detail for that part. But right now the one the stage that is facing the biggest disruption is not the production side, right? You still need to generate data. You still have to have some kind of source of data. AI won’t help that. But on the distribution side, there’a there’a huge, I would say, change that is undergoing. Imagine if you are an analyst twenty years ago. It’required for you to have either a Bloomberg terminal or you know a FactSet terminal, or if you’trying to a wind terminal, right? It’it’a terminal kind of portal driven business. A go-to portal or source for information. AI is fundamentally going to change that by adding a new what we call orchestration layer above all the data types. You’not going to go to any terminal in the traditional software sense, but you’going to have this advisor to you that is going to massively, quickly, rapidly going through all the data, find the data you need, give you the conclusions, do the comparisons of and all that. Right. So there’a big tension right now between this trend of increasingly more people is rel relying on their AI agents to do research and the existing incumbents. Of the of the market which and then if you look at the incumbents the different companies are adapting differently so you have companies like SP and Faxet they are embracing AI and they signed big contracts with large language models they allow you know clause users or open AI users to access their data through these their AI products and they are they are embracing it. But then you also have company like Bloomberg, which is really at the core, at the at the at the apex of traditional financial and market data industry. I think they’still trying to figure out what to do with this. And I think their natural tendency is to build their you know in-house AI. They still want people to, go into their universe. And to make to like people still go to their universe to check the data. Right. So there is some debates right now and it’going to be interesting, who is going to prosper, who is going to stay. Yeah.Grace Shao (11:39)Yeah, Bloomberg definitely still wants you within their terminal. Everything is within their terminal. And once you exit terminal, that’majority of their revenue. They don’t want you to jeopardize that. But for someone like you, it’quite interesting. I said something I made a mistake earlier. What you told me was that you guys are a data provider on the inference end now. How do we understand that? And how do we understand how you are going to work with whether it’the model companies directly? Or how you will provide your institutional clients your data differently.Robert (12:11)Right. So at this moment we are still observing. We are actually niche provider of some really high value data, but not needed by most other people. So we’not like the mainstream data sets, but we are observing and we believe that in the end we’ll have no choice but to kind of open us up to the large language models. To us, this will be a new form of access to use our data. Apart from, right now we provide our data to our clients through API, through Excel spreadsheets, through even research reports, this is our current method. But in the end, I think as more and more clients rely on AI to pull data, we will we will we will kind of open us up. And that’the That’in the inference part. That’when people actually are using data to do analysis, to do research. And so we are firmly in that part. And I think it’just inevitable that we will be connected to these AI at some point. It’just the problem right now is about the economics. How do the economics work? How do we kind of get us exposed to it, but also make sure that there’strong enough licensing and you know strong enough gate that we can put on our more exclusive, more differentiated data sets. That’a question that there also hasn’t been a consensus yet in the industry. Yeah. So that’why we are taking this kind of stacking back and observing kind of view of it.Grace Shao (13:55)I see, very interesting. Okay. So enough about the dry stuff. The most interesting stuff I read from you are actually your takes, because I think your takes are very nuanced. They’you’a deep thinker. You bring together cultural sentiment, history, political reality, and then the business together. To start with, I think one of the questions I get the most from people right now is just that this general attitude around why Chinese people feel more normal about AI. I wouldn’t even use optimistic. I mean as a as a society as a whole, it does feel more optimistic. But it just seems like whether it’how the government and the regulators are looking at how to regulate this new technology or how people are adopting it, like a trial error kind of feel, there’less of a philosophical push up a pushback towards AI, but more maybe, obvious concerns of what disruption or change might may mean for job displacement, whatnot. But in general, quite optimistic, quite normal. How do you view this right now? If I just kind of bringing together all the different aspects and it because I can’t, I don’t believe people just saying, just because people are more pragmatic. That’that I mean, I made that argument slightly, but I even think it should be deeper, more nuanced than that.Robert (15:17)Right. Yes, I mean this is a good question. Without if you don’t ask me that, I wouldn’t even realise it’a question. Because sitting in China, it’true that it is well not you know most people don’t talk about that. Some people do, but definitely not a mainstream discussion on the kind of existential kind of risk of crisis that AI is posing to the humanity. That type of question is not that asked is not you know asked that often in China. For the good and bad, right? I personally I don’t know w which part which approach is better, the more pragmatic one or the more philosophical one. But that’the phenomenon. It’true. Most people don’t think i in that way. The exactly why, you know Probably I would if you are looking for a more subtle, more nuanced answer, probably you won’t be able to find here. Because I was I was also thinking that it’it’really because of the pragmatic and down to earth nature of most things in China. People tend to ask more, what can this be used for? Other than why we have to do this or w what’the bigger contact what’the bigger picture? People tend to focus on the productivity side of things. I mean that’just prevalent in all industries or new industries. And especially China attached a premium to new industries. I think that’the kind of cultural reflex of the last few hundred years, after China kind of fell behind the West. In terms of technology and suffered all the consequences from that. So there was now a kind of reflex to emulate the world, to catch up to the world in all kinds of new technologies out there. Every time Silicon Valley coined some new term, some new idea, there would be some at least some kind of reflection and discussion about that. If you remember a few years ago there was this concept called metaverse. Right. Now nobody talk about that anymore. But back then it was also a very hot topic in China because you know people might think this is maybe the future because the Silicon Valley, the US chose that, and maybe we should think about whether that’the future. When crypto came out first, China was at the very beginning also embracing it, right? My very first Bitcoin was bought in China with RMB, while when there was like many RMB exchanges there. Well, then it hit some problems, it got banned and all that, but that’what happened later. But China ha always had this at this contemporary China had the tendency to learn the new things and to try to, to grow their our own knowledge and strength along these new verticals. So that’the I would say the big context, the big framework that people use, kind of equipped themselves with when they look at these new things. And less so about the philosoph philosophical and maybe not a philosophical but metaphysical, right? The ones that are hard to prove or disprove at this point and just kind of descend into discussion about contact concepts, on the abstract side. That kind of discussion, that kind of discourses really doesn’t have a big market in China. Small circles, yes, but most people Just don’t like to engage in that kind of discussions. Platonic, Aristotle level discussions. Yeah.Grace Shao (19:23)Is it just because it’kinda I don’t know, it’just like what’there to gain from that for the average Lao Bai Xing the average Joe? When they think about it, itGrace Shao (19:32)Seems like it’kinda Okay, if I embrace it, I win. I don’t embrace it, I lose. It’a bit of FOMO. Especially for the next generation, when I talk to parents, there’less of a concern about what this technology might mean in terms of safety for the kids, but there’more about how do I embrace this technology and teach my kids so my t kids can go basically go ahead of everyone else and come on top? I don’t know. Is that kind of whatRobert (20:01)Yes and I think big part of that the these better philosophos philosophical questions don’t tend to produce results. Right? It’not a question that w if we debate and discuss we’ll have some kind of agreement. They just tend to stay philosophical. But most people I would say that most people I know here don’t tend to keep going. Keep debating on this type of questions. And maybe that’right, maybe that’wrong. I don’t know. But that’just the phenomenon that we are seeing here. Yeah.Grace Shao (20:39)Just how it is. Less of a chatter class, if you must put it, or at least less prominent in.Robert (20:44)Yeah, good way to put it. Yeah. Yeah.Grace Shao (20:49)I think another thing we kind of touched on briefly when we were catching up for this recording was that we said, look, this Chinese pragmatic approach to technology and like how to even day to day life is not really just limited to China, right? Like it feels like it’a phenomenon across maybe even East Asia. Other markets like South Korea, Singapore, a lot of, studies, whether it’by Stanford or by local communities, have shown that people are also embracing, Singapore’own ministers are coming out talking about how they’claud coding or vibe coding out there. Why do you think I know that you write sometimes with a bit of a this versus that or like a bit of a historical and a eth ethnic and history kind of tied to your analyses? Why do you think that maybe East Asia feels more pragmatic towards AI or contemporary East Asia, like you put it just now?Robert (21:47)It’a it’a very deep question. So I think if I have to attribute, and I’m just throwing out ideas right now, religion is definitely huge part. The whole tradition, the tr of the history of thoughts, the history of religion, the history of philosophical discourses, has to play plays a huge role. So we don’t have a tradition of seeing something abstract, either as a natural law or a god in this part of the world, historically, right? So people the w the reason why the people are more pragmatic about things is just like all the things that happen in your life are pragmatic. There’a there’a flood and then we have to fix it. We have to you know do some work on around that to fix it. All the laws, I mean all the folklores, all the all the lessons of history surround about how to deal with these you know disasters, wars in human life with a human way. Right? So it’it’more there’a concrete problem, there’concrete solution. And very seldom you you see like people in China turns to God, for example. For a solution. MaybeGrace Shao (23:19)That’really interesting, but I will push back. They are like the Buddhas and the temples that still exist where people like pray for money, which is hilarious. Again, it’a very pramatic result. Or you like you pray for a child. You literally, you’reRobert (23:31)Exactly. Right.Grace Shao (23:33)Not you’just give me a child. Like you pray for fertility, give me money. You pray for money. But there’praying in that, but it’not omnipresent. It’like each god has a or like each Buddha has a very clear ask and reward almost. I don’t know, likeRobert (23:51)Yeah, exactly, exactly. So I mean there are like symbols of belief or faith or whatever, but exactly as you said, people use these very pragmatically, practically. They’the you know when Buddha said that you know w we w you know Buddha doesn’t want it the original Buddha doesn’t want him to be worshipped as a god. Right. It’really a teaching about how to position how to think of oneself and how to position yourself to the universe, to the world. Right. That’the whole teach but then it lost that favor in China. It became this, inf fused with all these other very down to earth beliefs and to become this, now you assign this Buddha. To ask for kids, that Buddha to ask for money. It’definitely not what Buddha originally taught. So it’just it’just it has been like that for not just decades, centuries, even millennia. So I mean that’also that’a very big part about China, which I’m yet to write about, but I kind of touch on it at several points. Is that you know a big part of China is China really I mean Chinese people and maybe East Asian in general, because we don’t have such a strong kind of belief in some abstract things, we also tend not to want to convert other people into our kind of belief, right? So we tend to focus on the practical. That’why we have a lot of business people, for example, that are focused on making deals, right? Trading, benefit you, benefit me, and so it’all very down to earth, but very practical things. And that does definitely have limitations. I would argue that in terms of fundamental pure science discoveries, that kind of mindset creates a disadvantage. You really have to, when you do groundbreaking scientific discoveries, you really have to forget about all these worldly stuff. You really have to forget about things well what’this mathematical formula have to do with my life? You have to forget about that. You have to just focus on this the purity of sciences, of mathematics to have to have some great discovery. Then that’why you know like the people were debating recently you had these metal this mathematicalist, Chinese ethnic ones, but getting their awards not in China, not while they are in China, but because they have further studies in the West. Right now in China there’a big debate about you know whether Chinese college graduates can do you know achieve that kind of level of achievement in sciences if they stay in China. I think right now I’m not that optimistic because overall people are still very focused on the use cases, the pragmatic use cases. But most of the time when some big scientific truth is discovered, they don’t have a direct use cases. And that’definitely not how they start a discovery exploration. Right. So that’well, the thing is, the reason I want to sometimes compare the China and the West is not I want to say which one is better. I actually my main point is we are many societies can be different. But in our world, different societies, different economies, different kind of people can play different roles. You so you need thinkers, you need the people who think about the abstract, but then you also need the people who actually can put things into use and create productivity and make people’lives better. And it’it’it’great that you know you have different kind of people serving their different kind of purposes. And I and I think I love I actually think that you know China being different and the West being different in their own ways, a net benefit for the whole world. And that’you know my actual overarching key point in writing about all of these.Grace Shao (28:39)That’really interesting. I think, like offline I wanna dig more into the religious aspect. It was just really interesting. I never thought about it that way. And we might get some heat and pushback on this because obviously South Korea nowadays is like a very Christian country and you know it kinda goes against what you earlier said. But I do think what you meant by East Asian worshipping in general is not so much omnipresent, but it’I don’t want to say it’opportunities, but people often go to these Like Buddhas when they need something, when that thing happens, or when something bad happens. But it’not like you’not taught to be thinking about it day in, day out. So that godlike attitude is very, very different. And I think it does translate to how people are perceiving AI these days, because in the West, right now, AI is seen as like a kind of or a lot of cult like figures are coming forward. And, a positioning AI like a new magic or something that will fundamentally change society as we know it. Anyway, so we can talk more about that maybe offline, but I want to bring it back to then things that you write about a lot, which is how should we understand then China’unique state planning and how it drives economy? Because I think it all relates to what you just said. China itself, in a way, you can say a lot of people are taught to be very, very strong execution and execution people doers, but theyRobert (30:01)Mm-hmm.Grace Shao (30:01)Are maybe less of these creative, wild thinkers. Obviously that’not. All true, but in a general sense, yes. So then when it comes to then how the state interacts with the private sector in terms of innovation and state planning, we see that again, there’a top-down vision or priority. And then companies or sectors as a whole will start executing and create abundance. How does that all work and how do you view that kind of relationship?Robert (30:32)Yeah, I think, when we talk about relationship between state and business and innovation, again, people frequently fall into the traps of big concepts, right? People the classic question is China socialist or is China capitalist? And these are also tend to be kind of the Western preference in terms of discussing things. While again in China, People tend to be not so focused on the on these conceptuals, on these, black or white. So when Deng Xiaoping said, black w black cat, white cat, whoever catches the mice is a good cat, it’not just his opinion. He’only a manifestation of the average most of the people in China. Whatever works, whatever can solve the problem of the day. We will use them. Right. So that’the bigger contact context here. And when we look at specifically industrial policy, innovation and all that, I think people, both the government and business people, tend also adopt this view. Whatever works. So if we look at the EVs, for example. When EV became a thing in China, it’a confluence of forces. It’not just like the state said, we want to develop an EV industry, and then it happens. If you look at say BYD, the Wang Chuanfu, when he started to have the idea that we should start an EV business, it was actually earlier than Tesla. And Wang Cheng Fu when he did that. It’not because he think that the state should do it or the state tells him to do it, right? He did it on his own. He has his own vision, his own dream. And it’just how so happens that the priorities, the goals of these business people and the state converged. And really for say something as massive, as important as the EV industry to happen, you have to have all these factors line up. You have to have entrepreneurs who are really willing to take the risk. BYD at the time took enormous amount of risks. But then you also have to have government that have the policies that are friendly to EVs. You know the consumer rebase for EVs, for the infrastructure build out and all that. All these forces are important. Fast forward to today, AI, for example, DeepSeek is a very great example. Of this dynamic. When Liang Wen Feng started the DeepSeek venture, he never thought about Beijing. I mean, Beijing even didn’t realize that a quant fund could you know incubate such a you know important AI company. You know back in 2023, 2024, there was even a crackdown on quant funds, causing some kind of market crash. Back then. We call it the quant crash. That was only two years ago. You knowGrace Shao (33:56)Why were they being cracked out? Why were they being kind of scrutinized?Robert (34:02)So two years ago there was this moment where the market was like sliding down and the quant was like kind of magnifying that sliding down. And the reflexes of regulator was really to kind of hold it, hold them back. There was one episode where the regulator kind of stopped a quant fund to from trading, basically plucked out the cables. So they were, because the mechanics the mechanism of quant trading is usually to kind of magnifying could help the market trend to get even you know more pronounced than it is so there’always some kind of controversy about our industry. So it’hard to imagine that Beijing actually found a quant fund and say, we are going to place a huge amount of money or huge amount of resources and ping our hope on you. Right? It just didn’t happen like that. Now had no state backing. He had his own dream for AI and he had money, he doesn’t have to rely on anyone else. And but after he became successful, after DeepSeek became a an international sensation, then you know a few ye a few days after last year, DeepSeek’moment, he was received by Premier Li Qiang. And then you know they become kind of a national priority. And in the this year’fundraise. There’also very top level state fund from Beijing that invested in DeepSeek alongside with Tencent and all these other companies. Right. So I think that’dynamic is interesting. It’at the same time there is a strong hand from Beijing, but also there’at the same time a huge tolerance for the natural growth of you know companies, industries, people on their own. And Beijing is less a planner, but more a kind of a picking picker of the winner. Right? So they set the long term goal. They say that we want to develop new productive, I mean new quality productive forces, but they never define specifically what are they. They kind of leave that open for the for the markets to explore. To for our own talents to explore. And once there is some clear winner, they come in and back them up with the more resources and help them scale. So that’I would say that’a hybrid. That’really a hybrid model. No single side of it can define this whole model. And this hybrid nature rests on the fact that people again we are flexible We don’t we don’t stick to any single type of ideology or ways of doing things. Whatever it works, right? Some industry needs creativity, then it cannot be top down. It has to rely on these spontaneous ventures and people. But also some industry if they want to scale, they need to have massive allocation of capital to them. And in China, if you want to really get massive amount of capital, you have to have the backing from the state. And that’how it happens. And so I think it’just natural. And it’also it’it’a hybrid model that is proving to be working and maybe for the new other industries it will also prove to be working as well. Yeah.Grace Shao (37:48)It’really interesting the way you put it. It’almost like they’a parent. So you get enabled and you get resources when they like something that you’doing, but you get beaten downRobert (37:54)Yeah. Yeah.Grace Shao (37:57)Or you get scolded and grounded if you’doing something they don’t like you’doing. And that brings me to the next point, which I want to ask you about. And you kinda alluded to this already, you touched on it. It’like the relationship between the state and the prime, it’something I think a lot of people find hard to understand. State as SOEs, state owned enterprises. State subsidies into industries, and then like obviously favorable policy making. It’very interesting because from your point of view, you’saying this is natural. Like you said, it is what it is. You need that kind of parental help or you need that parental guardrail, whatever, or safekeeping in one hand. On the other hand, from obviously a very American perspective or a Western perspective, is why are you involved? Is there for state subsidy than unfair, which I find kind of interesting of an argument. But there’obviously accusations from the West saying these Chinese AI companies are state subsidized, therefore they’not really competitive. I’m but they’still competitive from an innovative perspective. But anyway, and then you obviously have a lot of these AI companies now worried about taking state capital because if they want to go global or even go l like go get listed publicly somewhere non-mainland China. Then there’also concerns about shareholder setup if there is like clear state backing. Anyway, this is a again a bit of a big open question, broad commentary, but I’m gonna throw it back at you. How do you view all these different agents or different stakeholders and their relationship? And how do you view whether it is fair for certain companies to get state subsidy or not? And how to view their then independent competition and innovation.Robert (39:47)Right. So this whole kind of debates or controversy about subsidies in China, there’just so many I mean so many ways that I don’t I don’t feel okay with. I mean, like for example the in the West, it’not as if the Western government don’t have subsidies and don’t even have huge subsidies, right? I mean in EU many industries are being subsidized. In the US, if you look at say Tesla in the early days, I mean SpaceX even, all these companies rely a lot on policy support. So I mean maybe the difference between the US and China or EU and China is the I would say the role of the local governments. There is a huge tendency for local governments to go out of their way to support new businesses, which is really part of their own incentive arrangement. It actually helps them to grow the local GDP and help them promote it. So and it also creates some kind of over competition between the local governments. But it’not by design almost. It’just naturally happen that all these government sector support they just come in and out of their own interest They support these businesses. However, I would always argue that all these controversy or debates about subsidy tend to make people believe that it’because of the subsidies that Chinese companies become competitive. I think any basic student of economics would understand this cannot be true. I mean no businesses can be subsidized to be competitive. It’just It doesn’t work like that. Not in China, not in the US, not in EU, in not in Latin America, not in any history, in any human history. No competitive businesses become competitive because they have state subsidies. And usually it’the opposite. Subsidies only create uncompetitive businesses. Because whatever you do, if you are profitable or not profitable, you still have the state backing and which will make you artificially profitable. Who will do that? Who will be competitive? It just doesn’t make sense. And the reason that subsidies or state support or whatever support policy work in China is because every actor in this industry are working towards the same goal. Businesses, owners, the state, central government, local governments, all other stakeholders. It’really about everyone pushing, everyone going, and all the talents engineers in these companies. Everyone agree on something and push for walk forward to it. So it’definitely not just the subsidies. It’it’a whole spectrum of this converted uniform action of every party that make Chinese businesses competitive. And if the West just comes in and says, it’a subsidy that’responsible for that, i it’just not a very effective criticism. I mean and then reflex will be the West will have more subsidies to support their businesses, which, in fact, the wrong kind of prognosis will lead to a wrong prescription, which will be interesting as well. Yeah, I mean I’m pretty kind of I would say it’it’kind of kind of emotionally bit charged topic for me, but I really wantGrace Shao (43:37)You’passionate about this topic.Robert (43:38)Yeah. So I really want to speak it out about this, yeah.Grace Shao (43:44)Yeah, so it’interesting then, how do you view this generation of AI companies and kind of the I guess how they’overlapping these space? Because like you said, and we know here at AI Prome where a lot of these labs actually even struggled to get capital in the beginning, before the GPT moment, like your point, Silicon Valley can set the tone. Once ChatGPT took off, Chinese labs. Were able to kind of rally up and garner attention and interest domestically. They got their first kind of pot of gold, set the labs up a bit further, more like bit more sophisticated ways. Clearly they’still struggling to, or not struggling, I would say they still need a capital. So then two of them rushed to go public. Now more thinking about that. All of this indicates, first of all, obviously training models is extremely expensive. But they’still not really getting the funding they need. And some of them are choosing to not get the state backing or state kind of related capital they, that’out there. I guess this question is a bit long windy, but I guess just how do you see the relationship of the AI companies right now with all the different stakeholders and capital players in China? Because the state has the money, some of them don’t want take it. The state clearly is have favoring AI right now and rolling out a lot of strong policies and helping them with compute and energy and whatnot. How are they interacting with SOEs? In fact, how are they interacting with the big tech? How are these different stakeholders now I guess involved with each other?Robert (45:24)I think a key variable that was not on the table a few years ago was the role of the capital market. So we have we cannot leave that out when we talk about funding for these new companies. So I think the Beijing is very proactively pushing and helping many of these AI or even right now robotics companies to go list it. Either to Hong Kong or prefer preferably even in domestic A share market. The speed of making these companies public even just a few years after they were founded, it was actually unprecedented by Chinese standard. The y the capital market used to be closed to most of the new economy companies. So that’why when Alibaba went listed they the default was go to Nasdaq. Right. So that default was no longer applicable. No company by default want to go to the US for listing. While at the same time, China Chinese regulators did make it easier for companies to go listed in at least greater China, right? Hong Kong and Shanghai, Shenzhen. And I think that’a yeah.Grace Shao (46:46)Jump in really quickly. I think people also don’t understand sometimes and miss the point on a lot of these new economy companies from China are not going to go list in the US is not actually like actually help us explain. Is it a China’regulation reason or is a US regulatory reason?Robert (47:06)So it’actually a combination, but I would say the most of the issue is on the US side. Maybe sixty percent US responsible, forty percent China responsible. But anyway, there’a pull and push that make companies think about. So at the same time it’get just getting harder to get listed in the US. There’always a risk to be delisted, for example. And while to apply to US listing now you have to go to Chinese regulator as well, which there was no such approval process before, right? So it’hard. But then at the same time, it’getting easier to list in A share and also in H-share. And also liquidity in Hong Kong is way better than before. So there’both push and the pull. There are still some companies get listed in the US, very few. Recently this year there’this company called Taso Chuo that was just got listed in US. I think they have their own reasons for that. But most companies would prefer to just stay put in this part of the world. Right. So that’a key variable. And I think that’the key leverage that Beijing is using to help these companies raise funding. Like to be honest, I think Beijing is very I would say sometimes like a very strict you mentioned parent, right? Beijing is a very stingy parrot. Actually Beijing doesn’t want to spend too much money on, all the projects. But they are ambassadors at leveraging other people’money to achieve their own goal. Right? So like if you look at deep seeks fundraise, Beijing invested only a small part of that. Most of the money is contributed by you know Tencent or other private investors. For them, it already achieves a goal. It helps the company that Beijing wants to grow raise funds while at the minimum amount of money that Beijing can actually need to chip in. That’pretty smart, you know. It’it’not like it’not like i it is smart to keep resources at your hands and try to leverage other resources other people’resources to support your goal. And capital market is exactly like that. It’not just capital from big companies and big funds, but a capital from all over the market. Everyone, every even retail investor, get to participate. The that only that way you can ensure a everlasting strong stream of support in the in the future. So that’I think a very different that’actually very different, say compared with a few years ago, where you don’t have such a as strong a capital market as we have now. And now Beijing also have a vested interest in support the market. And they have also developed their own techniques and their own muscle memories in supporting the market, which is what we don’t have even five years ago. Right.Grace Shao (50:22)Right. But some still argue that the Chinese government could support the stock market more. I don’t know. That’just things I hear. Well, how do you view that?Robert (50:30)Yeah. Actually they are now sophisticated enough to understand that you need to be balanced. So what I mean is there are actually two episodes that could remain as lessons for them. One is the twenty fifteen, twenty sixteen market crash. Second is the recent market crash in South Korea. In both episodes, there was a bull market, even a crazy bull market. And in both episodes, the governments initially played a very strong role to boost the market. Back then, in 2020 I mean 20 fif fifteen, there was a People’Daily article saying directly that the market should go above, I forgot it’five thousand or or four thousand points. Which was cited as a kind of a rally call for many people to go into the market because the Beijing says we should, buy, buy, buy. So Beijing actively kind of contributes to the building up of a big, big bubble. And then after Beijing felt it was too crazy, it cracks down on leverage. And a lot crackdown on leverage burst the bubble and it has become a really bad market for the next two years. Same thing as South Korea, right? Like they prime min president of South Korea said, I’m also buying the stocks. Every policy going to support the market. But then the government was too concerned about a leverage. So crackdown on leverage. And then boom, the market dropped. And so I think you know Beijing of today is Pretty sophisticated with that. They want to have a bull market for sure, but they also don’t want it to, turn into a crazy boo. And exactly how they do that, because this is some not something that you can say, I want this, I that so I can achieve that, right? Because it’a market. There’a lot of players. When the sentiment builds up, even Beijing cannot stop people from buying or selling. So exactly how, interestingly, they all have also developed. Dev develop their own technique, which is this so-called stabilization mechanism. So for the first time in history, in the last two years, Beijing was actively employing and deploying capital to act as a stabilization factor for the Chinese capital market. By stabilization I do not mean just a buying mechanism. It’a stabilization mechanism. Which means when the valuation was really depressed and Beijing wants it to go up, they actually now come into the market with real cash to boost the market to help reset the valuation. This is different from before. In the past, I think there’never been an episode where Beijing used real cash to support the market. There was messaging, there was this policy, that policy, this tax policy, that tax policy. But never before was Beijing deploying so much capital directly into the market. But then after the market become more hot, or hotter than what they want, they actually sold what they have. Right. So it’stabilization. It’almost like also recently in the oil market, the moment that Hormuz was closed, Beijing stopped buying oil, waiting out the episodes. Which was a contributing factor, decide a determining factor for right now the oil prices didn’t went through the roof. And same thing was you know the same thing was when in the ancient China. There was a big role of government was to be a stabilization factor in the grains. Right. So when there is a lack of there’more grains than there’needed and the prices are low, the government actually comes out and purchases the grains and store in the storage. And when there is a famine, it’government’role is to release these grains, selling them at maybe a higher price, but eventually serving a social purpose. This is just it’just Chinese regulator is now using his ancient technology to apply it to modern statecraft. And it’working. It’working. Last year the market was just about to be crazy. Last December, last November. And soon Beijing started to sell off their holdings in the ETFs. Which tempered the sentiment, right? Beijing is very smart. They actually made huge profits about after this buying and selling in their own game. And now they have more cash than before and so if the market goes down from some level they are ready to come in again. So this is actually very nuanced and I think I think it’it’it’great that there is not only a desire for market to go up, but also a desire to for the market to grow up in within a safe zone. A zone that’that’that’will not be crazy, that will not cause a lot of sentiment crash, especially for the all of the retail investors. Right. So yeah.Grace Shao (56:20)Yeah, I think that’really interesting to hear. I’ve obviously not heard of that like in detail. But then, the question I get a lot is then how do you view the flip side of the government had in the market? Obviously, we’ve seen, kind of internet crackdown, education, property, whatnot. Like you can name a few industries in the last few years, it’been hit pretty hard in valuation can get wiped out overnight. So, How do we view that kind of government hand in the public market? And then I do want to tie it back to then how do we then find confidence in investing in AI and a lot of these publicly listed companies right now coming out of China, like these AI wave companies beyond the model companies that we talked about? Like you mentioned, there are the robot ones, there’infra layer ones, there’even now spatial intelligence companies getting listed. But yeah, just tie it all together.Robert (57:17)Hm. Yeah. So my mental model, my personal mental model to understand policy risk in China, is that I think Beijing, the regulators there, are learning. They actually didn’t have as much experience about capital market say even five years ago. So you mentioned the education industry. That was a very important episode in policy making, in expectation management, a very important lesson for Beijing regulators. So when Beijing cracked down on that education industry, actually I don’t think they have realized what kind of you know problems that would cause for the wider you know sectors, especially capital markets. They are narrowly focused on the industry itself. But they actually learn from that. They actually learn that you have to think about all these other factors because all these things are interconnected. There are signs of that, there are evidence of that. Maybe I wouldn’t have time to go into detail, but maybe can go check my newsletter about my years of observations of Beijing’scale. At expectation management and also at thinking this as part of a bigger whole, not just like single policy. Right. So that’my key mental model, which is to treat it as an evolution, to treat all these necessary lessons as part of a bigger learning curve. So here in 2026, I would say today’Beijing. Has way more lessons and way more skills and way more sophisticated than Beijing five years ago. And it’it keenly understand the importance of capital market and also in understand the importance of expectation in the capital market. So they are now very they were they are they are they are way more holistic than before. And I would not think that the double reduction education episode in twenty one would repeat because they have learned. It’a lesson for them. Right. So in that in that policy risk, actually it weakened the risk weakened, lessened considerably than before. And well in terms of investments though, if you just look at these AI and robotic company as you know a pure investment from the pure investment angle. I’d say that it’really not for everyone. The valuation judging by traditional standards is really, really high. But then if you’a believer in AI, displacing ten to twenty percent of global GDP, then all this valuation doesn’t seem high at all, right? So it’really up to the taste and the style of different investors and the risk appetites. In general, I would think the Chinese market will be more and more mature, the capital market will be more and more mature. And the stronger state’hand compared with say the Western market is also I would say understandable given that China’market, especially A-share market, is a is a highly retail driven market. Seventy percent, eighty percent of the money is retail. And retail tend to fall into the traps of herding. Which means like everyone going to one direction. So someone has to come out and be the shepherd. So it’a it’a it’a shepherd to herd model that is different from the West, where people most of the market participants are more mature and more sophisticated, analyzing, researching, which is different from China. So it’just natural for Beijing to play a role, to play a balanced role. Not a like a not a like a very strong role, but a silent, invisible role. Give you one example. So this whole stabilization mechanism I mentioned, actually it’only my name for it. There the Beijing doesn’t even have a name for it. Beijing doesn’t even disclose what exactly are the mechanisms. When they purchase stocks, is they don’t purchase directly. They purchased a list of ETFs and those ETFs purchase the stocks. So they are also very Conscious of their presence and they want to lessen their co their presence. They want to be the kind of the secret shadowy force that is making it making the market stable. But they don’t want to say, we want it stable and this is our message, this is our view. It’not crude, it’actually very nuanced. Yeah. So they are learning. They are really learning really fast.Grace Shao (1:02:35)That’very interesting. It’like it just makes me think of like high school teenager parenting again when you influence them, but you don’t directly tell them what to do. You have to influence them in likeRobert (1:02:43)Exactly. Yeah. Yeah.Grace Shao (1:02:46)But one yeah, just like I guess I want to wrap up soon, even though I feel like I can keep on asking you questions. I have another hour of questions for you, but for the sake of today,Robert (1:02:56)Thank you. Yeah.Grace Shao (1:02:57)How do we understand then, we are seeing a Crazy wave of IPOs right now in Hong Kong. Like we said, a lot of them are directly AI labs, obviously. The others are AI adjacent, or some are pegging to AI. SoRobert (1:03:15)Mm-hmm.Grace Shao (1:03:17)How do we understand these companies? Like or how or why do they want to go to Hong Kong first and not maybe A Shares first?Robert (1:03:26)Yeah. It’definitely easier to go it relatively easier to go to Hong Kong for listing rather than A-share. A-share is stricter and mostly because A-share in A-share there are a lot of retail you know mom and pop’investors in the A share. And as you as you frequently alluded to and I agree with is that Chinese political system or regulators, I don’t think the authoritarian is a is a good word, but I do think paternalistic is a good word. They do see themselves as parents. And people do see themselves them as parents, right? So as parents, they tend to be kind of over caring for their kids, which are the people and r retail investors. So the threshold, the bar for listed in A-share is actually very high. And even if you get listed, the pricing that you can place on yourself is also I would say much lower than it should. Like if you look at CXMT, for example, when it first got listed, the IPO price was about one fifth of what it was, t ended up trading at on the first day of trading, right? So why is that? Because they artificially kind of compressed evaluation to make sure that every mom and pop who joined the IPO earn money, make profits, had a good experience. So there’a very clear kind of kind of emphasis on retail investor protection in A-share. Well in the A-share though, not many mainland retail investors can trade in Hong Kong. Some can, but most of the retail investors are not qualified to trade in Hong Kong. Right. So it’very institutionalized. So it’really so for Beijing it’really like a pressure valve for the IPOs. So they actually encourage you to go to Hong Kong. And the Hong Kong exchange, stock exchange, they also encourage you to go listed there. So there’a confluence of interest there. And also at the same time, if you go listed in Hong Kong, you raise US dollars, and which are as which are great for, China based company because there’still capital control in China. Right. So there’a there’def defin just a confluence of interest of all stakeholders to now go to Hong Kong to list first. Unless you are CXMT,Grace Shao (1:06:07)Makes sense.Robert (1:06:09)They are really good and you qualify for A share. But then you also suffer a bit because of the valuation for the for the kind of money that you are you can raise, but you cannot. Yeah. SoGrace Shao (1:06:23)It’it’interesting. It’like a balance between over caring and overbearing, it seems like. And yeah.Robert (1:06:28)Yes. Yes.Grace Shao (1:06:30)All right. Well, look, I wanna ask you one question that I ask every single guest, which is what is one differentiative view you hold or something you think is non consensus? It could be about anything. It could be about China, it could be about the stock market. And I know we touched about touched on quite a few different topics today. I always appreciate again your nuanced view on a lot of these things. I don’t frankly agree with everything you do say, but I do think, what I appreciate is at least you try to really string together different parts of how the world works instead of just over-generalizing China as this one unit. And I think sometimes China observers unfortunately just over-generalize China or oversimplify China. Anyway, I wanna throw this question to you. What is one differentRobert (1:07:20)Okay.Grace Shao (1:07:20)Of you hold? Or maybe you think something that the world still misunderstands about this part of the world, especially when it relates to technology and capital market and everything.Robert (1:07:29)Right. So they’actually a lot. I’m just trying to pick through my mind which one is relevant for today’discussion, and maybe this one. I think ChiGrace Shao (1:07:38)Give us two then. Give us two.Robert (1:07:41)Yeah. Okay. So there’a the there’a small one and a big one, right? The small one is about the capital market. I think China is entering a multi decade bull market. The U A share. There is just so much kind of tailwinds that are supporting it. I’ve already mentioned some of them, like a very sophisticated Beijing. But also RB is trending up. I mean, there it’been the joke of the day that despite the tenfold, twentyfold of growth of Chinese GDP, Chinese stock market is going nowhere. I don’t think it’going to be true in for the next at least one or two decades. It’a new paradigm. So that’you know definitely a big part that all the investors should pay attention to. And I don’t think that’appreciated enough. And a bigger question that I always love to share about China, like if you ask some American or some you know Westerner what’the single most important thing. If just one thing you have to remember about China, nothing else, just one thing. I will always say that Chinese people or China are not interested in changing other people. We are not in this preaching or you know proselytizing mindset. We mind our own businesses. We don’t want to change other people’lives. So much as some Westerners will want to change other people’lives. We don’t. And the reason I want to emphasize this point is that I realize that when you have both sides who want to change the other party, that’a recipe for conflicts and wars. But if you realize that actually one big party of that is not interested in the other party, then I mean in changing other parties. Right. Then you realise maybe there’a chance for peace and prosperity. So I want to I cannot stress this point strongly enough, but I want to maybe use your platform to voice that again. Thank you.Grace Shao (1:10:09)No, I really appreciate ending on such a positive and somber note on that. And then I think another thing I wanna ask, which is a bit for fun, is can you explain to us what is good jot hai? Why do people go around talking about like cutting ChineseRobert (1:10:26)Yeah.Grace Shao (1:10:27)Chives? What does that mean in the capital market space?Robert (1:10:31)Yeah. Chives a very interesting vegetable. It tastes a little bit strange, definitely not for everyone. But the key things about chives if you are in the farming business is that chives grows really fast. So when you cut, one chive, a few days or a few weeks later it grows up again. And you cut them down and they grow up again. This is what retail investors are, right? They get cut down all the time, but they grow back all the time. This current generation of chives when they were cut down, they will leave the market. But then you also have a new generation of investors who have no experience, no knowledge of that and they want to try out themselves and they got cut down again. So again and again and again. So that’why they become a term. Each generation have their own kind of symbol for that. Like the last generation, for example, for many of them, they got cut down on Xiaomi, for example. They got a huge IPO, but then it kind of crashed for a few years. Maybe that next this generation for this generation is all these AI names. I don’t know. But every generation, I mean it’a generational thing and it’kind of it is built into the system Right? Because you will have new people coming in. And new people, by definition, don’t have knowledge of the old. So they just kept it’very I would say very figurative, very apt kind of explanation of the mechanism, yeah.Grace Shao (1:12:14)I love how technically you got into like people know agriculture and how farmers no, I just thoughtGrace Shao (1:12:19)It was like it’one of those Chinese internet slangs again that are just so hilariously random if you don’t understand the context. But like you said, if you actually understand the thinking behind it, it makes a lot of sense. And so it’actually a very popular internet slang people use to describe retail investors that get kind of hurt and then the joke is institutional investors will just wait for the chives to get cut.Robert (1:12:42)Yeah. Yeah.Grace Shao (1:12:42)Or chives get cut one around and after another. Anyway, thank you again, Robert, for your time. Really, really appreciate it. I also appreciate that you let me kind of take you in all kinds of directions with this conversation. Please come back again.Robert (1:12:57)Thank you for all the tough questions. Okay, yeah, see ya.Grace Shao (1:12:59)Yeah, thank you.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  Qoder joins again to talk about China's workplace agent war. Redirected focus on EMEA & APAC 11.08.2026 37min

  Show NotesHi all,Christian Hu from Alibaba’s Qoder joins the podcast again to talk about the fierce domestic workplace agent competition. For context, Anthropic and OpenAI have decided not to allow access to their models and products in Greater China; thus, the competition for models and agents is largely driven by domestic players. For workplace agents — coworker-style products — the most popular ones at the moment are Tencent’s WorkBuddy, Alibaba’s Qoder, and ByteDance’s Trae. The other agents we see coming from the labs are mostly coding agents. With that, I’ll hand over the floor to Christian, who graciously found ~40 minutes while on a business trip to talk to us about the landscape and Qoder’s own strategic shifts.This episode was recorded on the day Qwen3.8 Max launched.For more, check out the podcast lineup here and explore the episodes!Chapters* 00:00 — Southeast Asia Expansion and Market Dynamics* 02:43 — The Shift Towards Business Logic in Coding Agents* 05:29 — Industry-Specific Applications and Vertical Agents* 11:22 — Global Strategy: Lessons from Market Differences* 14:00 — Partnerships as a Key Element in Market Strategy* 16:41 — The Future of Coding Agents and Market Trends* 19:22 — Pricing Models and Inference Economics* 22:08 — Open Source Trends in AI and Market Competition* 27:34 — Building vs. Buying AI Models* 29:38 — Future Directions for Coding Agents* 32:14 — Final Thoughts on Market OpportunitiesTranscript(AI-generated, for reference only)Grace Shao (00:00)Hi Christian, friend of the pod, you’re back on. Really excited to have you here. Where are you these days?Christian (00:07)Yes. I just landed in Singapore last night.Grace Shao (00:12)Okay, perfect. Cause we’re gonna talk about your Southeast Asia expansion. But since we last spoke, competition among coding agents has intensified quite a lot in China, particularly — the market has changed a little bit. Where are you seeing the whole market going, and how do you think Qoder fits into all of it?Christian (00:32)Yes, it is a war, you know, between every major tech company in China. Because it’s a war, nobody wants to lose. I think the logic behind this coding agent war is that most companies believe that a coding agent shall be the fundamental path to AGI — artificial general intelligence. So nobody wants to be behind in this race.But the most interesting thing behind the war, or this race, is that something is changing. When we look back to the last twelve months, everyone is talking about the models, everyone talking about what kind of model will be the most competitive advantage for your coding agent. But now, for most leading agents, they are trying to be more focused on the business logic and workflows. Just like what Qoder is doing — we want to be more into the business logic of our customers. And even for some individual users, they are trying to do something for business. They have one-person companies or one-person workflows. So they need the coding agent to do more about the business workflow, not just code generation.So that’s the shift behind the war. For at least most of the companies, they are trying to raise not just for a coding assistant, but want to be dominant as a desktop assistant for employees or maybe some individual users. And maybe in the future, they want to be the digital employees for the industry. I think that’s maybe the ultimate race for the coding agent.Grace Shao (02:28)And you’ve got players like WorkBuddy from Tencent that’s been doing really well, right? Exactly to your point—Christian (02:33)Yes.Grace Shao (02:33)I think they’ve been plugging in, kind of operating as an assistant on desktop, very intuitive and user-friendly. You’ve got ByteDance with Trae pushing in a similar direction. So how do you feel about that? Where is Qoder’s differentiating point or offering here?Christian (02:51)I have a few things to share with you. I think for the past year, Qoder has accomplished very remarkable business performance. In terms of ARR or revenue, we are the leading one — we are number one. And maybe we are more than the combination of number two to number four. That’s a huge achievement for Qoder in business growth.And as I just said, Qoder from day one is more focused on building an agentic platform for developers and AI builders. It’s not just a coding tool or coding assistant — we want to be a platform for the next generation of agentic power. So the skill marketplace, the plugins, the connectors, the MCP connectors — all of these features are now shining. All of these features are bringing advantage to our business. That’s the truth of what’s happening in China and even in overseas markets.Grace Shao (04:04)That’s really interesting. And so when we caught up in Hangzhou recently, you were saying that Qoder appears to be broadening from just coding into industry-specific agents. That was something quite fascinating, because from the start of the conversation, you say coding capability is the fundamental foundation for all of these agents, but people are moving towards these vertical agent use cases. So tell us a bit more about that. What kind of industries are you guys targeting? What is your thinking behind this new strategy?Christian (04:34)Okay, I can share an example. We got a very important customer case with Xiaopeng. Xiaopeng is one of the leading electric vehicle producers in China, maybe one of the best. For Xiaopeng, for the company now, Qoder is not just a coding assistant or code generation tool. Qoder has become an agentic driver for the restructuring of their workflows and business logic. They’re trying to educate their developers and their engineers, maybe even the HR department, to use Qoder to renew their business logic and their workflows.For example, for the legal department, they’re trying to use Qoder to reduce the legal review cycle from six days to one day, or even less than one day. And it’s not just Xiaopeng — I think there are a lot of similar cases. So for Qoder, it’s not a shift to pivoting to vertical applications or specific industries. From day one, Qoder wanted to be a platform. The platform means we want to be a tool — we don’t want to be just a coding assistant competing with other coding tools. We want to be more embeddable and compatible with the customers’ business logic and workflows. So it should be more vertical. But it’s not for us to do the vertical things — it should be let the developers, the customers, and even some individual power users develop domain-specific agents on top of Qoder. That’s the philosophy for Qoder.Grace Shao (06:51)Okay. So the thinking is you guys are offering the tool, but really it’s still on the user to build out the tailor-made agent for themselves. Not that you’re specifically pushing—Christian (07:01)Yes, that’s the truth. Actually, we got some organic creation of skills by the power users. They are creating some skills, some creating some plugins on our marketplace. We are going to open the marketplace to all the business users so that the users can deploy different kinds of skills or plugins from the marketplace. That will be the market for Qoder. Maybe in the near future, we could be a marketplace for agents, a marketplace for skills.Grace Shao (07:49)Interesting. Okay. Well, this leads to something else you talked about. You said that you guys were building an ecosystem around Qoder. So tell us a bit more about what that means, and how should we understand or expect how Qoder might change as a product in the next few months.Christian (08:05)Okay. I think we can change the view of Qoder — from a coding assistant to an agentic platform. I can give an example: we are doing something together with Microsoft. That may be an example. We want to collaborate with Microsoft to make Office more usable and more accessible on an agentic system just like Qoder.You know, in the past, Microsoft Office is Microsoft Office, ChatGPT is ChatGPT — they’re quite different products. For the users, they have quite different experiences on two different kinds of products. And now we want to be one. For Qoder, for the agents, they need the agent to know how to use Office, how to deploy Office, how to use the best features from the Office suite — the toolkit — to help the users do presentations, do documents, process data. So that’s maybe the very interesting thing for the users in the near future.This kind of collaboration is happening everywhere. Qoder is trying to partner with partners from collaboration software, finance software, even legal software, HR SaaS providers and vendors. We are doing things like that to be more compatible and more useful in the near future.Grace Shao (09:55)Very cool. Let’s take a step back. I want to talk about your business expansion, because I think when we talked about a year ago, you guys were really gung-ho about going to the US, going to Japan. I know you’ve been spending a lot of time in Japan yourself. But recently it seems like you are pivoting, or at least putting more priority and focus on Southeast Asia, and now you’re in Singapore yourself. Tell us a bit about the thinking behind your global strategy, and which markets you’re currently focusing on, given that you’re the head of GTM on the international expansion side.Christian (10:26)Yeah, you know, I actually got a lot of lessons from the last maybe twelve months — about ten months for my global journey with Qoder. Everything is different in different markets. I just came back from Paris. I think Europe is quite different from Japan. Most people think Japan and Europe share something in common — they are pretty slow in AI adoption, they care more about compliance, they care more about trust. But things are also — you can still find something different between Japan and Europe.In Japan, the buying cycle is pretty long. Maybe six months or maybe even twelve months is very common. But in Europe, the cycle maybe is not too long — maybe one month or maybe one week. Because they are trying to catch up with the AI wave in Europe. But there’s still some obstacles in Europe. They care more about the law, the compliance, the GDPR, the AI Act. That’s the truth.So for every AI marketer, or anyone doing good marketing in Europe, you need to care very much about the legal — the law, the act, the things changing and happening in Europe.But for the US — I spent almost the first quarter this year in the United States. I met a lot of AI developers, AI startups, AI founders. The story is quite different from China, from Japan, from Europe. In the United States, speed is the most important thing. Speed means you are innovating. Speed means you are upgrading your product. Speed means you are accountable. You’re telling the users that you are accountable because you are innovating, because you are upgrading your product day by day, conversation by conversation. You need to upgrade your product.Now I come back to Southeast Asia. Singapore is the first stop for Qoder to be a global product. And now I came back to Singapore, I will go to Vietnam and Thailand in the near future. For me, Southeast Asia may be the mixture for my go-to-market strategy, because in this place, in this region, you’ll find you have competition against some American AI vendors or AI producers. You will also find some Chinese competitors. That’s the mixture. This is a very competitive market, but it also has very high potential in this region.Grace Shao (14:00)Really fascinating, because you’ve done a world tour and given the high-level vibes of each area. And I can totally see that it’s also quite fascinating you say, like, ending up in Singapore. In some ways, it’s almost the most competitive for a sales role, because you know you have all the options in the world and nothing is actually off limits, and it’s really a price war as well. It’s quite fascinating compared to maybe other areas of the world that will be leaning towards certain companies or certain countries’ technology, given maybe geopolitical concerns or compliance reasons, regulatory reasons, whatnot. And other areas might be purely driven by price sensitivity. So anyway, fascinating. Thank you so much for that. But why then? Why are you guys now doubling down on Southeast Asia after your big global world tour?Christian (14:51)Yeah. Okay. I guess I forgot one of the most important things in our last conversation. Partnership is the key element and the key part of our go-to-market strategy. Because we believe that local partners, a local ecosystem, is the best way to get a connection with local community and local industries. Around the world, we have different kinds of partners. For the past twelve months, the most important thing I did was to find partners as many as possible. That’s the strategy for our global markets.Okay, let’s go to Southeast Asia. Actually, I don’t think Southeast Asia is the most important one. Maybe I think for now it’s too soon to nominate which region will be the most important. But Southeast Asia should be a very important part, because Southeast Asia is a fast-growing market. It’s not too much affected by geopolitical concerns, and it’s fast-growing, so for every major AI company, there are huge opportunities, huge market potentials.And for Qoder, we are growing very fast. We are evolving every day, every conversation. People in Southeast Asia — the developers really find that it’s very interesting and they can get much from Qoder’s evolution. Because for most users in Southeast Asia, to use closed-source agents from the United States or some other place is maybe too expensive. Maybe it costs too much for most users or most developers in Southeast Asia. But if they want to try Qoder, they maybe have the best position and best time to catch up with the evolution of AI and coding platforms. That’s a good starting point for most developers in Southeast Asia. And Qoder is evolving, so they will be evolving every day. I think that’s a good point for both Qoder and the developers and industry in this region.Grace Shao (17:30)Interesting. Let’s take a step back and look at just the overall industry at this point and the trends that are taking off. So the underlying coding models are improving quickly, and we’re seeing that it’s increasingly becoming commoditized, or at least the price is coming down, right? As all of that is happening in the background, how does that actually affect the tools that are built on top of these models, such as your product Qoder?Christian (17:56)Yeah. I believe the token, or maybe some of the large language models — I think the cost of token may be zero in the near future. Most of them. Maybe in twelve months.Grace Shao (18:13)Wait, how would it become zero though? What’s the thinking behind that?Christian (18:17)It should be. It should be. Maybe it’s science — it’s about science and engineering, but it should be zero cost. Looking back to the last twelve months, if you want to buy one million tokens, it may cost you about twenty dollars. But now it’s about half a dollar, just in twelve months. Maybe in the next twelve months, the cost may be quite near zero.But I think in the future, there’ll still be some very expensive and exclusive models — some frontier models. There may still be expensive and exclusive for some users, for some industries. But most models will be very inexpensive, and even cost zero for most developers. And I think maybe ninety percent of the daily tasks can be accomplished by these kinds of models — the public models or maybe some cost-effective models. I think that’s the future.Grace Shao (19:22)Okay.Christian (19:23)So on this kind of zero cost, the agent layer — I mean the context layer, the agent layer, and the business layer — will be the most important one for the builders, for the users. So that’s actually what Qoder wants to do in the future.Grace Shao (19:43)I see what you mean. But coding agents can be very token-costly, right? So how are you then thinking about the inference economics, usage limits, or even your own pricing models when you are selling your products to users?Christian (19:57)Yes. I just said we want to charge our users — we won’t charge by token consumption. We will charge [differently].Grace Shao (20:03)Mm.Christian (20:04)We won’t charge our users by token consumption. We will charge.Grace Shao (20:10)Okay. So it’s not directly token consumption. If token costs come down, your cost of your product also comes down with it. Okay.Christian (20:17)Yes, actually, the shift is happening. We want most of the profit of Qoder to come from the agency layer — the workflow layer, the agentic layer, the context layer, the memory layer. That’s close to your daily work and operations, not just token consumption.Grace Shao (20:42)How do you view the subscription model? We still see that dominate coding agents today.Christian (20:47)I think for today, if you are just a coding assistant, you will be dominated by the large language model. That’s the truth. If you’re just a coding assistant, you should be dominated by the large language model. For now, the best model determines the best coding tool, because you’re too close to the large language model. If a lab has a very powerful logic model, the large language model can easily change to be a coding assistant. You just need an interface — you just need to type your language, you can be a coding assistant for every user. So if you are just a coding assistant or just a coding chatbot, you will be dominated by large language models.But if you are an agent — an agentic workmate, an agentic layer that empowers users to regenerate their workflows, to connect with their collaboration layers, connect with their ecosystems — you will not be dominated by large language models.Grace Shao (22:08)I see. Okay. I want to pivot and kind of look at the model layer right now. I know you don’t work in the model layer, but you’re very familiar with the ecosystem. So help me understand. Chinese labs right now have actually used open source very effectively, right? And I think they’ve built up a global reputation and taken over a lot of the market share. And obviously, there’s a lot of discussion on whether they will continue to open source some of their best models, or maybe become more and more similar to US frontier labs, what we’re seeing with what they’re doing. What do you think of this trend? Will they remain open, or do you think they’ll gradually start closing up some of their best models?Christian (22:51)I think I can give you two angles. For the angle of the regulators — for the government — they want the open source strategy. They want the AI wave to be a new engine of growth for the national economy. That’s actually the national strategy for the regulators. So open source should be the trend, should be the future.And from the angle of the market side — the market players. Actually, for most players in this industry, in the AI industry, open source is maybe the only strategy, or maybe the only path, for Chinese players to surpass their counterparts in the United States. I think maybe the only path.Grace Shao (23:53)Why is it the only path?Christian (23:54)For now, we can’t see the advantage from chips. We have no advantage about chips. We have no advantage about human resources, even some capital. We have no advantage. So maybe open source is the only path for Chinese companies to surpass United States companies, to be dominant in the industry — in the applications. We have some figures to prove that — Xiaomi’s language model and Zhipu’s language model are the most used around the world, right? They are the most used models, not a Claude, not an Anthropic model, not a Google model, in terms of usage. So actually, open source is maybe the only path, or maybe the only way, to compete against the counterparts in the United States.Grace Shao (24:57)Question on Qwen then, just because you guys just released another very large model — it’s not getting as much attention. I feel like the headlines are being taken all by K3 right now. But Qwen3.8 Max is claiming to be just as good as Claude. What’s your view on that? And it’s available on Qoder right now, right? So how do you view the model?Christian (25:22)I’m not a model expert. I can’t give you an expert view about which one is better, which one is not the best. For developers, for users, you can try Qwen3.8 Max — the preview — on Qoder. I think maybe positive. It should be positive. And we believe that, as I just mentioned, the model gap is closing. In the near future, the model gap is closing. So for Qwen3—Grace Shao (25:53)Actually, why is that? Why do you think model gaps are closing, closer and closer?Christian (25:58)That’s my belief. I’ll give you an analogy about this. I think the best analogy is education. You know, for education — university education — we’ve still got some top universities, right? The Ivy League, the best in the world. And maybe some of the best two or three universities in China, they are at the top. But most universities and most education in university, I think they’re universal. I don’t see too much difference in the education in most universities around the world. People can get educated, people can learn the skills, people can learn the knowledge in most — I mean ninety percent of universities around the world. That’s not the biggest difference.There should be some frontier models, there should be some ecosystem models in the future. But mostly — more than ninety percent — will be the same. Almost the same.Grace Shao (27:05)So you’re saying basically it comes down to talent. Talent is strong. So therefore, more and more talent going into the industry, thus it’s catching up. But why wasn’t it catching up a couple months ago? Why was the gap, say, six to nine months prior to that — it was claiming to be nine to twelve months. All of a sudden now people are saying maybe it’s only one to two months. Where does this number come from? I’m always fascinated by these claims.Christian (27:28)I’m not a scientist, I’m not an engineer. I just have the philosophy, I have the belief.Grace Shao (27:34)Yeah. Okay. Well, let me ask you another question that’s kind of taking over the broader industry right now. The broader debate right now is about whether enterprises should be fine-tuning privately and deploying their own models, or even — you’re seeing more and more so-called tier two, tier three smaller internet companies in China leading the way in already building up their own models. Obviously, some people are saying they have the edge of having a very strong open source ecosystem in China, so the barrier to entry is easier. Anyway, from your point of view, do you believe this trend where companies are actually going to start building their own models or hosting their own models and fine-tuning on top of it? Does that make sense? Or should companies continue to do what they were doing, which is, you know, paying for the most frontier models from the frontier labs?Christian (28:20)Okay. I think for most companies, even some large enterprises, it doesn’t make sense for them to fine-tune their private models. I can’t see the sense, I can’t see the business logic behind this. Just like the analogy of education — I don’t think you need a private school for your own children. I don’t think most people need a private school for your own children, just for your two or three children.Maybe ten percent of enterprises have very complicated, very sensitive applications, sensitive data. They need to deploy their own private models, fine-tune their own models. But I think most companies don’t need to do that. Instead, I think they should invest more in the context layer, just as I mentioned. They must invest more in the business logic, in the business, in the workflow, in employee management. That’s where they should invest. Because that will benefit their business growth, benefit their management, benefit their governance. I don’t think it’s necessary to invest in fine-tuning their own large language models.Grace Shao (29:54)Makes sense. Not every company should be working on the R&D of this, but really should be putting more energy into managing their own data, context, memory, expertise. Okay, well, I have a last question for you, which is, stepping back and looking at the whole industry right now, where do you expect the coding agent market to evolve over the next year? Because you alluded to a little bit throughout our conversation, but where do we see this whole industry going? Are we going to continue to see standard products that are going to be the main driver in coding agents, or like you said, model labs will swallow everyone’s lunch, and then it’s really all just putting up an interface on top of them? Are we going to see more enterprise-grade, specific use cases and systems built? How do you see this direction?Christian (30:48)Yes. I don’t think there will be too many coding agents in the near future. I think some of the coding agents, even some existing coding agents, will be swallowed by large language models. I believe that. Because language models can easily build a coding agent. So there should be some — maybe two, maybe three, or some number of coding agents still in the market. But most of them will be swallowed by large language models. And there should be some new agents — business agents, workflow agents, or some domain-specific agents in the future. We’ll have more domain-specific agents in the near future. Legal agents, human resource agents, even some agents for you — just like podcasters or bloggers, we’ll have some new agents for you.Actually, agents will be just like digital employees. That’s the future. You will have more digital employees. You can ask your digital employees, just like you can ask your teammates, you can ask your friends — but it’s digital friends — to help you do anything you want them to do. That’s not just a coding agent, that’s your working agent.Grace Shao (32:14)Very interesting. All right, Christian, I have one last question actually for you, which is a question I ask everyone that comes on the podcast. You’re not unfamiliar with this. What is one non-consensus view you hold now? Or has anything changed since we last spoke?Christian (32:28)You mean from last time? What’s changed?Grace Shao (32:30)No, just any non-consensus view. Even what you said earlier was against what a lot of the industry is saying already, but do you have any view you think is very non-consensus or against consensus right now?Christian (32:48)Okay. I have something, maybe something shocking for many AI builders. I don’t see too much potential in the United States market. I mean for Chinese AI—Grace Shao (33:06)You mean for Chinese companies going global, whose first stop is often the US? Okay, very interesting. Go on.Christian (33:09)Yes. Actually, I want them to quit. You can invest in the US market, but I don’t think they can benefit, or get real margin and profit from the US market. But it’s a different story between consumer AI and enterprise AI. For consumer AI, there may be some possibilities for Chinese companies, because we still have some advantage — we have cheaper manufacturing, cheaper human resources. That’s the advantage for consumer AI companies to do business in the United States. But for enterprise AI, I think it’s quite difficult. Quite tricky and quite — it’s just not a friendly strategy to do business in the United States.Grace Shao (34:11)Where should they be going? Southeast Asia right now, Singapore, Japan, like you guys?Christian (34:16)Southeast Asia is much easier for them to do business, compared to the United States.Grace Shao (34:24)But the thinking from a lot of these founders is often that Southeast Asia, frankly, doesn’t have as much purchasing power either, or willingness to pay, especially on software.Christian (34:32)But they are growing. They are growing. We must invest in the future. And one thing I want to note — there’s much more potential in Europe.Grace Shao (34:41)Interesting. Okay. I think Europe has been a bit overlooked. People have kind of — some people have written it off a little bit, frankly, just given the recent years of slower innovation. How do you view the European market?Christian (34:54)I think Europeans are catching up. They are awake, they are working to catch up.Grace Shao (34:59)Mindfully.Christian (35:01)Yes, they’re working to catch up. That’s what I got from the Paris Summit, very intensively. And I believe — I think from the economy side, you must look at the market from the economy side, I mean the macro side. The economy in Europe — they are struggling. That’s my personal opinion. The economy in Europe, for many countries in Europe, they are struggling. So they need a new power, they need a new engine to restart the economy. I think AI should be the best engine for new growth in Europe.Grace Shao (35:40)Very interesting.Christian (35:41)So what kind of technology? Whose technology do they want? I don’t think they have the time to catch up just building their own AI labs, their own AI infrastructure.Grace Shao (35:54)Their whole stack, yeah.Christian (35:55)Yeah. They need help from China, even some other places. So I think that’s a huge potential for Chinese companies to do business in Europe.Grace Shao (36:05)Interesting. Okay. Very differentiated. Very cool. Okay. Thank you, Christian. Thank you for your time.Christian (36:11)Thank you.Grace Shao (36:12)Enjoy your trip and travels.Christian (36:15)Thank you so much. I enjoy it. Bye bye.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  From Sourcing Engine to Agentic Commerce with Alibaba's Accio Agent 04.08.2026 51min

  In this episode, I speak with Ziwei Chen, product marketing lead for Accio Work at Alibaba.com, about Alibaba’s effort to turn decades of sourcing data and commerce know-how into an agentic business platform for small and medium-sized businesses. Accio began as an AI sourcing engine, but Accio Work is designed to support a broader workflow, from product strategy and supplier selection to store operations, marketing and growth.The most interesting distinction is between AI that tells a business owner what to do and AI that actually does the work. Ziwei explains how agents can research products, compare and vet suppliers, draft inquiries, follow up on missing answers, update Shopify listings, generate structured data and coordinate campaigns across existing tools. Alibaba’s advantage is not only its supplier network, but the category knowledge, transaction context and direct communication layer built around it.We also discuss where human judgment remains essential. Accio Work can narrow a supplier list, negotiate across variables such as MOQ, lead time and materials, and prepare an order, but the user still approves purchases and typically takes over the final supplier relationship. That balance matters because commerce is not only a workflow problem: branding, product taste, trust and long-term supplier relationships remain difficult to automate.Finally, we explore why Alibaba built Accio as a separate, more open product; how it works with third-party platforms rather than replacing them; its subscription and usage-based model; and the future of agentic commerce across B2B and B2C. Ziwei’s non-consensus view is a useful one: not every problem needs AI, and domain expertise becomes more valuable, not less, when powerful tools are widely available.For more, check out the podcast lineup here and explore the episodes!Chapters00:00 Introducing Accio Work01:01 From Alibaba.com to agentic commerce05:18 What an “agentic business team” does06:27 The commerce workflow and human control11:11 “Do it for me” versus “tell me what to do”18:32 Connecting fragmented commerce tools22:36 Alibaba’s sourcing-data advantage26:03 Supplier quality, matching and verification31:58 Accio versus general-purpose AI tools33:36 How agents communicate with suppliers37:09 What Accio offers factories and suppliers39:29 Designing AI for non-technical SMEs42:42 Business model and monetization43:54 The future of agentic commerce48:57 Why not every problem needs AIAI-generated transcript for reference onlyGrace Shao (00:00)Hi, Ziwei. Thank you so much for joining us today.Ziwei Chen (00:02)Hi Grace, so good to see you.Grace Shao (00:07)I’m excited to talk about Accio So I just came back from Hangzhou like a month ago and I met some with some of your colleagues on the ground. Was very impressed by the product and thought was just really intuitive. So let’s get started. Tell us a bit about yourself and your role at Accio and what Accio is all about.Ziwei Chen (00:24)Well, hi everyone. my name is Ziwei Chen. I work at Alibaba.com as the product marketing lead for Accio Work. prior to Alibaba.com, I actually spent a few years in the world of developer marketing where I was kind of driving good market plans for software tools that are meant for developers who are building AI products. So now it’s kind of completing the whole picture for me to now move on to the other side about actually growing the products. built by those developers to the end users. So that’s kind of my kind of AI journey coming from the development side now to the end user side, driving the growth for Accio Work among our small and medium-sized businesses around the world.Grace Shao (01:01)And tell us, what does Accio do actually? So I think a lot of people are not familiar with it and just how it fits into the whole bigger, I guess, Alibaba ecosystem as well.Ziwei Chen (01:09)Yes, absolutely. So maybe I’ll take a quick pause and take a step back just to talk about the overall broader picture. So Alibaba grew as a large enterprise. we were founded in 1999, and our kind of main North Star was to make make it easy to do business everywhere. So that has always been kind of our focus of focusing on B2B and focusing on small and medium-sized businesses who typically don’t have that resources that large enterprises have. So throughout the years since 1999, we have been really just focusing on what we can do, either there’s a new product. And new services to make it easier for them to start a business, to launch a business, and to grow a business, maybe something to exit a business. So along that line, Alibaba.com is kind of the bread and butter where we first started, focusing on B2B sourcing. So actually, I will kind of almost break down our development or our growth journey into three eras and then show you where Accio fit. I will call it the digitization era, the AI co-pilot era, and then the agentic team era. So the first digitization is when we are first founded with that platform of Alibaba.com where on this digital platform we are connecting the sellers or what we call the suppliers, the factories, with the buyers, our buyers, maybe sellers on the other side, into one platform. So now they’re not limited by time zone, they’re not limited by the location. And then in that process, we what we have learned over the years is that things can get overwhelming. There just so many suppliers, so many products, right? So then when all of these foundational AI capabilities came about, we were like, okay, this is the moment. This is the moment where we can introduce AI capabilities to make it easier for our buyers to find the right products and the right suppliers. So the name Accio actually comes from Latin, which means summon. So the idea is we help you summon the right products, summon the right suppliers, summon the right information. So that’s when we first launched Accio as a sourcing engine back in November of 2024. And that went really well because a lot of our users now, even without any experience in sourcing, without any experience in physical products, and find it really quickly with confidence. But then what we had learned over time is that First, sourcing is only one small part of a broader business journey, right, for a lot of our users in the US, Europe, and around the world. So now we’re thinking, okay, what else can we do to expand on that? So that led to kind of the last phase for Accio Work. So when I compare Accio Work with Accio, a few things that stood out. The first is the focus from sourcing only to sourcing plus. The other thing is about the agentic part. So earlier when I talked about the three phases of digitization as a platform, right? The website. And then the second era is called the AI co-pilot. So that means is that you are still on the driver’s seat, AI is in the passenger seat. It’s giving you advice, it’s doing some little stuff for you, but you are still making all the decisions. You’re still wearing all the hats. And that’s what Accio did back then. For example, it can find suppliers, it can recommend messages for you, but that’s kind of it. So now we’re moving into what we call the agentic team era. where actually we’re gonna get things done for you and get more types of work done for you. so that’s kind of where we are really sort of moving into this phase, where truly kind of it’s like the spirit of the agentic commerce world, where you’re not only using AI as a passenger seat, but you’re actually arranging a team of agents to get things done and maybe let the teams work among themselves, what we call A2A. So that’s kind of the overall context of where we have been starting from Alibaba.com. To Accio the sourcing engine and now to Accio Work as the Agentic business team.Grace Shao (04:45)That’s very comprehensive over you. I appreciate that. So just to help listeners understand, 1688 and Alibaba.com are the sourcing kind of platform within Alibaba. And then obviously everyone knows Alibaba for the Taobao and commerce side of things, but that’s actually a merchant-facing product versus the Taobao and T-mall that are consumer-facing. with that kind of context, okay, so if I had to ask you to describe it in a very short sentence or like say two sentences. How would you actually describe the product today? It’s just like, who is it for? What does it do?Ziwei Chen (05:18)Absolutely. I think I would Start with three words that summarize what it is we will call it the agentic business team. and I’ll break it down each but one of them that eventually lead to the who and the how. So the agentic part means these are agents that get things done for you. It doesn’t just give you the recommendations, but it can write emails for you, send messages for you, publish things for you, right? And then the second part is business, right? So we are dedicated to the world business, specifically e-commerce as a really strong emphasis. an all all Aspects of business starting from the front development side to the growth side to the operations, everything. And then team part kind of aligning up to that as well is that you can build multiple agents to work among each other. So tip from a format perspective, we have a desktop app that you can download, also a web app as well. You can access the same thing on your mobile devices, on your web as well. So all these things are connected where you are creating agents on this platform. telling the agents what to do and then you can go to sleep, you can go to work, you can go to your events, right? All of these things, go to your shop, right? Do all these things while the agents are in the background running all these tasks assigned by you.Grace Shao (06:27)So exactly your point, like you can do a lot of things, and that’s what I found the most fascinating thing. So, you know, we know a lot of products on the market these days that can do a lot of the, I guess, step three, step four in selling. So what I mean by that is they can help you manage this SEO, the content management, the marketing, and the like, you know, plugging into Mailchimp and sending out your emails. But your edge really isn’t just the agentic AI part, what I thought is really your supply chain network, right? so of course Across the entire workflow right now. Like walk us through like the different steps. Cause I remember there was like a slide your colleague showed me. It was like there’s four different phases of commerce building a business. you guys can go across it and how much of it is actually truly a genetically run right now and how much of it is still, I guess, needing direction from a human to really verify and process in the pro yeah.Ziwei Chen (07:17)Break it down to the two parts. I’ll talk a little bit about the overall flow first. I think it can kind of follow a typical business like life cycle where you’re starting from the strategy and the planning part. Let’s say what product, what’s the portfolio, what’s your branding, right? What’s your competitor, what’s your positioning or pricing, all of that. And then into the actual product development and sourcing part of that. And then you’re going into your store management, especially your e-commerce and different marketplaces. And then eventually the last one will be for marketing and growth. it can be for your B2C. Your traffic, right? Your conversion. Also, some of our users are not only doing B2C, but also B2B to wholesale. So, how do you deal with that B2B, like management or relationship, all of that? And then, so that’s across the different core stages of run launching and running a business. And then Accio Work supports all of that, but not individually, but it connects to that all together. So that entire ecosystem play, right? It’s really where it stands out, where we call the sourcing plus. So it’s not just about SEO, right? It’s not just about how to improve, let’s say, the images on your store, but how do you do that starting from the moment you have an idea, bring it to life, bring it to your store, and then drive it to more users, and then maybe iterate over time all the way back to what other products you can do, or how can you make your current product better. And along this way, I think when it comes to which areas that are truly agentic, or kind of where does that relationship between human and agent do? I think that there are a few ways to go about it. So I think for example, all of the critical decisions or area still remains a human to be to approve. For example, you can develop or create an order. The agent can create an order on behalf of you. So you can just click and then go into the checkout page, but it will not check out for you. So you have to go in and confirm that or from a security perspective. That’s one example. The other example is about your own vision as a founder or as a brand owner. So let’s say it’s the position of your brand. Some of them can be subjective. Maybe it’s your personal story, your personal style of the brand. It can also be kind of that. More objective or critical thinking that after learning about different perspectives, this is where we decide to do. I think running a business is it is a lot about the science part, but it’s also the personal, the entrepreneurship, right? So a lot of these kind of personal decisions still plays a role. So for example, maybe the agent can develop all different kinds of images and videos on behalf of you, but how do you d determine what’s Good, it can be through A/B testing, right? But it can also just be this is kind of the aesthetic you are going for that you think what matters the most to your audience. So that’s a second example. I think the last one, taking another pause, I know that when we talk about human, it’s not just about the users, it can also be about the developers, right? It could be the people in the Accio Work team. And then so where we are seeing is that we put we will love for Accio Work, we are specifically targeting small and medium-sized business operators who are not. The strongest in AI tools. They’re not developers by training, right? That’s not where they want to dedicate their time to. So what our product team and engineering team and algorithm team have been really focusing on is what are the things that we can do it for you. So take the burden off of you. For example, in this broader ecosystem of everything that Accio Work can do, it does require a lot of connect connections to different channels and platforms right can be about marketplaces having different tools i think that ecosystem is where it really plays a really great job but then who is creating those connection points you know you can have your account but then from our perspective where our team spend a lot of our time is to build those connections so you can do a one-click activation to connect axial word to your email to your CRM to your Shopify store so that is one of the strategic areas where we decided to take more of a burden onto ourselves so our audience can just click and then log in and then start to connect the dots for themselves.Grace Shao (11:11)Very cool. I want to double click on the partnership and like how you guys built the plugins definitely a bit later. I do want to ask about the use cases because so here my brand. So when I went to visit you guys, there was like really it was really interesting. So on one hand, it was very obvious on how SMEs would use this, like you said, like you know, if you already have a very strong intent on how to what you want to purchase, what you want to sell, so then that whole process is very clear. But one Example one of your colleagues showed me was so fascinating. Basically, like they were running an event, someone was running an event using Accio. They went on the Accio like webpage and then basically uploaded a picture of like the banner, the physical banner like size of like an outdoor space and said, I don’t know how large this space is actually. I don’t know what looks the best with this background, but I need a banner to run this event. And then Accio became like a thought partner. And it was really interesting because it was not like just a general like AI like thought partner. It was actually someone that had a lot of know-how and industry know-how from creating these things, from manufacturing. So they would literally say, it seems like blah, blah, blah, the weather is like this and that. You might need this material because that kind of texture is more durable in the sun or durable in the rain. And I just thought that was so fascinating. and I just want to hear from you what use cases are most common and then beyond that, what are some like I guess not so mainstream use cases that you’ve seen that are quite fun and interesting.Ziwei Chen (12:34)I think when it comes to use cases, you know, we can break we can slice the pie in a lot of different ways. One way is about the specific type of like actions for a business owner to do, but I’ll take it a different approach to talk about I think just in general when it comes to agentic tools in general, I think there are two types of use cases where air comes into play. The first is like do it for me. And then the other is tell me what to do. So the do-it for me part is that I know exactly what to do. I know the specific steps. Either I’m just busy or I’m just one person, there’s not enough hands. So this is where AI does a really great job as a follower, but it can really scale for you, right? It can be, for example, When it comes to assessing different suppliers. If I’m just up one person, I can only s assess so many, but now the tools can do it for you. So what we are seeing in similar cases, it can be using Accio to find, evaluate, vet, and compare suppliers or products. It can be you up creating and updating listings, especially for those sellers. with categories that have multiple SKUs, multiple kind of products where I want to make an update to all of my marketplaces, all different colors, all different sizes, right? It’s really doing taking that repetitive work off their shoulder, but making sure things are consistent across different channels. can be creating content, right, on either social media, it can be about blogs, all of that. That’s one area. The other area that kind of really aligns well with what you’re saying is that tell me more. Like I don’t know what I don’t know. Sometimes I don’t even know what’s the right question to ask, or maybe I do know the right question, but maybe the LM itself did not have that training to start with. And this is where we are seeing a really strong aha moment from our users. And when I say users, it’s not only just people who are new to e-commerce or new to physicals or like products. That’s definitely the area where we have seen the most appreciation. But it can also be for even successful sellers or operators entering into a new category. And it happens a lot, right? Maybe extending your product line, maybe you successful exit once one brand entering into a new one. They all they they truly understand. the cost or the costly mistakes and how important that could be. So in both cases, what we are seeing is open-ended questions on this is kind of what I’m thinking, this is what I need, or just this is my store. Tell me what’s wrong, tell me what’s missing. And then lear first learning from actual work based on what we have seen in the space about key areas, key specs, key steps or things to flag. And also, I’ll tell you what’s wrong, or I’ll tell you what you should pay attention to, and then let me fix that for you. And I’ll show you a few examples. The first example about banner for sure makes a lot of sense. we had one, a similar case, but with a different flavor to that. We had a almost like a con like a product consultant. So he would take out new projects all the time. Very common, even as someone who’s experienced in the field, to enter into new categories. And he was helping a client with the bronze. plaque outside the building where you know there’s a building name or the history and all of that. And then so he it was a retro case where he’s already spent the time and the cost to pay for a designer to kind of put his client’s need into this kind of sizing and whatever. so he took the time, spent the money, sent it to the factory, and then realized that actually the font size did not work for readability purposes. So while he actually replicated that need into axial work. One of the first things that actually were told him is that depending on the technique for the for the font, is it going in or out from that plaque? here are his recommended size or the height of each each of these tags to make sure that you are following the best practices. And then he was like, Wow, I wish I had that when he was working on that project. So he can save not only the cost, because he had to redo the thing, repay the designer to fix it, but also lost weeks in between. So there’s lots of examples around that. They’re small, but they can be costly, right? Especially you’re busy, it’s your own money, you’re bootstrapping into your business. So that’s one example. The other one, it can be we recently had a had a user who was just who has a Shopify store and was just trying to see like what what can I do? What can I do with Accio Award? So he’s not someone who knows exactly this is a tool I’m gonna use for X, Y, and Z. He just pasted his Shopify link. To Accio Work and tell me what’s the low-hanging fruit. And Accio Work actually dig out a bunch of like very specific areas for improvement, especially when it comes to SEOs and GEO. For example, for all his images on his store, he was lacking the image alt tags, which is a really important thing for SEO purposes. There were thousands of images. And he was like, he knew that was kind of important, but he never really paid attention. But Axe Work was like, if with with this, now you can improve your ranking. And then in about a few hours, Accio Work did all of these tagging for him in the back end, on Shopify. And then the other thing was that his product was baby Like baby beddings or a baby clothes. So there’s a size guide. But then what actually work called out is that his size guide was just an image. It does not have structured data. So LLMs cannot read it, which means that he’s not getting any traffic when people, when new parents are searching about sizing for their babies, right? If it were baby clothes. So then he actually told me that he he started this task right before a drive, and it was an hour drive on the road, and then actually worked basically using that and One hour, recreated his size guide to make sure there’s structured data behind it. And then so far, he’s already seeing like new traffic from like LLMs or like AI, like what we call now the AEO, right? for the first time. So these are all really great examples of what I call like teach me what to do, or teach me kind of what I’m missing out on, where we are seeing lots of like aha moment from our users as well.Grace Shao (18:32)That’s incredible. I think I’m like absolutely in shock because I think a couple of years ago during COVID, I was like playing around with the idea of drop shipping things. I wanted to build this like pet shop store. But just like not having that kind of guidance is actually really daunting to start something new when you’re not in this space. And if you just had that kind of thought partner, that would be incredibly helpful. Actually, I want to ask you about the partnership thing you mentioned earlier. You talk it did kind of touch on, you know, there’s these plugins that are built in. at SEO will help the vendors actually distribute across different distribution channels. So walk us through that. Does that not kind of cannibalize your own business? Or are you is that not like, you know, you guys are not in the same category, you don’t see that? Like, how does that relationship work?Ziwei Chen (19:16)A few thoughts here. I think first thing first just putting Putting down our Alibaba.com hat and just think from the user’s perspective. I think one of the biggest learnings that we have had by looking at our e-commerce sellers in this space is fragmentation. It’s truly, and when I say fragmentation, it’s not just about multi-marketplace. Cause you know, some people have like a Shopify store or Etsy store, that’s one type, but also think about you have your storefront, you have your B2B sourcing side, you’re speaking with manufacturers and different messaging channels, and then maybe you have your own like CRM. System, you have your own, let’s say, analytics system, right? And the all of each of these might have its own very professional SaaS solutions already, or maybe they’re hiring a consultant for that. So I think a lot of their time is really spread thin by just connecting these dots. So I do think that’s even from a North Star perspective, connecting a dot, putting them into an ecosystem is ultimately what’s gonna drive benefits or impact to our users, which is what we want, right? So I think that has always been we feel confident in that direction. And on the other hand, I think just like how there are so many different startups or even statute companies focus on each one of these, just like how Alibaba.com, our bread and butter started from sourcing. So I think yes, we can choose to do everything on our own or which is gonna spread thin. Odd, right? Or we can choose to collaborate with them as well. Especially a lot of times maybe our users are already so familiar with an existing tool. So making them give up on and then transfer is one option, or maybe in the transitioning phase, right? Or maybe just as a collaboration to say, bring your tools into Accio Work with just one click and then you’re login, and now you can connect all the dots together. We are definitely seeing that, for example, we have a user who has their own kind of email, like like solution system where he was struggling partially because of setting up campaigns and take lot of time but also you know he’s using Accio Work for research for messaging and now he has to translate all of these learnings right into a different tool. So he just integrated that into Accio Work and use Accio War as a central hub of say I have a new product now establish a new email campaign establish a new SMS campaign and then this is my key message you go To those sites on behalf of me. So I think that consistency of I have a decision, I want to apply it across different platforms, is where it’s gonna bring the benefit to our users, while without making them transfer or give them everything in their ecosystem that they have built out over time. And I think in that process, what we are also benefiting or learning through those partnerships with those channels are also just like learning opportunities on what’s what’s the future of AI when it comes to different kinds of areas. I think a lot of those traditional SaaS solutions still have are also experimenting. So I think that also creates a really win-win collaboration like experimentational type of like collaboration of let’s see how what kind of new agentic behavior that maybe actual work introduced through our our plugin to the new platforms and now they are learning what else they can improve or enhance or ex or or in integrate into their own ecosystem as well. But ultimately, because the our sellers or users are using those tools back and forth, it’s a shared type of traffic that has that synergy in general.Grace Shao (22:34)Very fascinating. So so it’s.Grace Shao (22:36)It’s really the user experience first, but like Alibaba second in that sense. So let’s talk about Alibaba then. so it’s no secret ATT sits on top of like Alibaba supply network, you know, guys got transaction history, sourcing know-how, all the data in the world from e-commerce over the last two, three decades. how do we understand that? Like is that like huge edge for you guys? do you think you guys have leveraged Alibaba’s ecosystem to build your things out? How do you see that relationship?Ziwei Chen (23:02)I think in general, I would say that having our own like our own supply chain and our own learnings and the industry know-house is definitely like the biggest part about our remote. or the starting point of our remote. I don’t think it’s the only thing, but I think that sourcing plus storyline is something that we definitely want to keep in mind to differentiate from the rest. obviously at the same time, we also understand that Alibaba.com is only one of the marketplaces that our users might be sourcing from. So we’re also trying to make it more open in the sense that it actually where you can also search for suppliers and like products even outside of Alibaba.com’s ecosystem as well. but I think how that looks like into how that translates into kind of our own product experiences. So far we have briefly touched upon that industry know-how because we know all of these Category best practices. So no matter what kind of categories essentially we have experience in, you’re gonna get that intro that recommendation or that guidance along the way. That can also include negotiation and supplier communication. So for example, because right now through Accio Work, we have our AI Auto Chat function, meaning that now you can train the agent to conduct or multiple agents to conduct that sourcing process. Task on behalf of you. So instead of you having to stay up late, depending on time zone, asking or answering questions, you have 20 agents, right? Each speaking with the supplier, negotiating. And then when I say negotiation, it’s not just about the bulk order price. It can be about the sample cost. It can be about the lead time. It can be about your MOQ. It can be about different material flexibility. So all of these kind of How do you approach negotiation? How do you approach establishing credibility as someone new in the space? How do you nurture that relationship with the supplier? All of these longer term things. So it’s not just like you find a supplier, you place an order and you’re done. So that the overall growth journey is something that we see continues as our users’ business grow or kind of evolve over time. That’s one thing. I think the other thing again is kind of that. synergy or consistency across platforms where you made a change to your product because it happens quite often where you are updating your products, maybe because of your learning and the competitive space, sometimes learning from Accio Work, or it can be your suppliers are recommending the latest techniques to the production, like you know, capabilities that you’re incorporating that. So how do you make sure your communication on the production end is reflected to your marketplace? is reflected to your social media, is reflected to your community, to your newsletter. So I think that sourcing plus, kind of that type of like experience or that consistency is also where we see we take pride in as kind of that holistic experience or evolution for our users as well. and then so that’s something that we’re definitely taking pride in, keeping keeping it as our mode while expanding on How do we translate that know-how into every aspect of our users or our operators business lifecycle?Grace Shao (26:03)Yeah, actually I’m gonna double click on that. So Alibaba itself has cited one point five million verified suppliers, you know, more than something like four hundred million SKUs behind Accio. these are wild numbers. this is not even including other suppliers, right? Like you just mentioned. How do you maintain that data quality and actually how do you ensure there’s fairness in terms of like how you actually provide your users the right supplier network or the supplier contact because I don’t understand how the back end ends. you know, when I’m looking for say a hair clip, what comes up? How do I understand what goes behind the interface?Ziwei Chen (26:40)I think I’ll answer it in two different layers. So I’ll separate Alibaba.com where our supply chain network as one layer and then talk about how where actual work fits in. but starting from the Alibaba.com as our own network, it has we have established our own very comprehensive system for that verification. for example, for all of our verified suppliers, that verified batch means it’s being verified by a third party where they have submitted their like certificates, all of the requirements and the registries and all of that. That’s one example. And the second example is that for example for all of those like Trade Assurance or all of these things that we’re constantly evolving to make sure all of these things are available for you to make sure you know you can choose and also including a lot of so that’s kind of on our own network side that we have our own system. And then where Accio Word comes in because it’s a it’s a different name, right? It’s not called Alibaba.com So it offers a few different flavors. The first thing first is that in Accio Work, there isn’t any sponsorship or ads in place. Well in Alibaba.com, yes, there are those different placements, but Accio Work purposely decided not to do that to make sure you are getting recommendations based on the best matches of your requirements. So let’s say depending on where you are, sometimes you might be in an exploration phase. Let’s say, I’m interested in in building what was the other thing I looked for the other day? It was like Wooden bits for like board games. And I was like, I don’t even know like where to start. And then Accio can tell you. So here are the five key areas you should consider. Let me walk you through how to make each of these decisions first. And then let me match it with the right supplier. So don’t rush, let’s take it slow. Or it might be, I don’t know exactly what’s the model, what’s my location, what’s my MOQ, what’s my price training, etc., and then match it for me. So you’re what you’re gonna see in Accio Work is let’s say you have a list of five things, and then for each of these suppliers, are they three out of five, are they four out of five? And what else might be a recommendation? And this can be hard requirements. Let’s say it must be this model. it must be they must have expert experience in certain places. It can also be about preferences. Let’s say maybe you already have lots of factories in a certain region, and then you just prefer that this new factory for this new component is in the same region. So it’s easier for consolidation for shipping. So you can be a preferences. And those are areas where a regular label, right, doesn’t or tag doesn’t cover as well compared to an AI for LLM to process that for you. So that’s one that’s one area. And then the other area is that so that’s the first thing that actually baked in is that no ads just based on what fits the best, based on your matches. The other thing obviously is kind of having that third triangulation of data. So we do have this ability for you to vet a supplier on Accio Work, which means that what it’s gonna do, it’s it’s not only gonna search for in the back end for alibawa.com, like what kind of certificates they have, but let me go on a custom public records. Let me go in, let me go on social media, double check, do they have a presence? do they have they attended trade shows? So all of these additional data you don’t get from the platform, actually was pulling in. Compare notes to make sure you are making a confident decision on that end. so that’s the second thing. And the third layer again is that even with all of this fixed data, or sometimes could be outdated, or you just want to verify whether triple or triple check it, and that’s where that automatic communication comes in. Where now let let the agent directly speak to the suppliers on behalf of you, ask for photos of certificates, right? As for images of the factory if they don’t have any, triple check on all of these areas. So all of these three layers are how Accio Work is building a different experience to make sure you are making a more confident decision.Grace Shao (30:28)Okay, so why then was it so necessary to build a separate agent on top of Alibaba.com and 1688 instead of you know just adding a chat interface or conversation interface on top of I believe a lot of the Alibaba products that right now all have an AI chat bot it embedded in their product interface, but why was it so necessary to build a separate Accio?Ziwei Chen (30:50)Yeah, good question. I think A few things. the first is that maybe back to our North Star when it comes to that open ecosystem. I think having a separate brand or even of an identity is a must-have to make sure our supply chain is not limited to Alibaba.com and also our use cases are not just limited to sourcing. So that explains everything that Accio Work is doing by incorporating new supply chain options, partnerships, incorporating new connectors or services or like solutions together. I think that’s kind of the biggest thing itself. And I think the other side of the things is that when it comes to branding, to some extent, although we do have lots of existing Alibaba.com users adopting Accio Work for that new value add as well. But we are, Accio Work is really reaching a brand new group of audiences around the world. A lot of them are like net new, aspiring entrepreneurs. Who now realize that opening your business on your own is possible. So I think there’s a brand new type of like audiences that we’re reaching, with its own kind of the AI flavor of like a branding as well. So we’re building a community that is also beyond Alibaba.com.Grace Shao (31:58)That’s interesting. I have a bit of a a spicy question for you. Then what’s the difference really if I were a solo entrepreneur using ATSIO versus maybe I just get Claude then? I get Cowork.Ziwei Chen (32:08)That’s a great idea. I think a few thoughts here. The first thing first, the simple answer is that Accio Work is the only agentic platform now that has the official connection to Alibaba.com. So all the other AI tools, maybe they can search for some of these suppliers by using the browser extension and all of that, but they’re not gonna get all the back-end data about those suppliers and not gonna be able to directly communicate with the agents, cannot chat directly with them and also cannot connect the dots all across the board. I think that’s a the simple, the simple way out. But I think also because all of our team’s energy is super focused on this field. And I think in this world where everything is nothing, right? So by us putting all of our resources and energy into just supporting e-commerce, small and medium-sized businesses, we are making more progress, more in-depth progresses on just that ecosystem specifically. And I think in that phase, the connectors with the plugins are just one of the areas. The other one is the just the general industry best practices. And what I’m referring to is not just kind of what the platform has learned, but also the people in the ecosystem. we’re building a community, we’re sharing skills, very like or even sometimes building different agents and now you can learn from each other as well. So I think it’s not just about The data is about a community, it’s about a best practices as well. So people are learning from each other. all of those solopreneurs are aspiring entrepreneurs of how to start something and then grow from there.Grace Shao (33:36)So then I wanna ask you about something you mentioned, I think, in passing a couple of times, which is like the agents can help you speak to the suppliers. It was quite fascinating. how does that work in the back end? Like our are at this point are just like agents talking to agents and then making deals happen and then you know, you have a product that’s purchased for you and the next thing you know it’s already out there on a website. What does the human need to do still? So tell us about that.Ziwei Chen (34:01)Yeah, so maybe let’s we can walk through a pretty classic source and experience. Let’s assume that you kind of already have a pretty good idea about what you’re looking for. So you’re gonna go into actually we’re gonna talk about let’s say maybe this is a category, this is my specs, the color, the material, etc. give me recommendations and then you can it will give you some top recommendations you can select and then actually were the agent will develop a pretty professional inquiry for you to make sure it’s speaking. The industry language to set you up for success as someone who’s an experienced series buyer. And then you can say, select these five, what’s like top five, and send this inquiry directly to them. So then the back end, it’s connected through the Alibaba.com. So the agents are sending those information. That’s the first thing. But what else that’s gonna turn on is what we call the AI Auto Chat, meaning for each chat conversation, the agent is actively engaging in conversation during that time. And then what we have seen As the most common workflow, which actually aligns very well even in the pre-AI era, is that people start broad, they shortlist, and when it comes to the final one or two, they actually go in for the full-on investigation, right? So that’s what we’re seeing for Accio Work for our AI Auto Chat as well, is that the agents does the best, brings the most value in that early short listing phase. So let’s say for example, I have a really highly customized product. It’s really hard for me to tell or some really unique needs. It’s not enough for me just to go on a website to evaluate that. And I have a list of five questions for every single supplier. In the past, I have to ask each of these five questions, maybe two five suppliers max, right? But then not all of them are gonna answer the question in the right order, or maybe some are missing something. So you are going you’re the you’re the Excel sheet, right? You go into this chat and say, okay, question number one, number Number three, number four is done, but not number two. The other one is only as another whatever, right? So the agent, what it’s gonna do is say, okay, you gave me a list of five questions. I will make sure every single supplier responds to each one of them. If they’re missing one, I’m gonna chase them and then I’m gonna save those answers into a table so you can make apples-to-apples comparison. And usually halfway, this supplier says they cannot accept this, that supplier says they cannot do that, or they’re out of order for something. You end up with one or two, and then Typically now I can jump in to say, okay, now I feel good about it. auto-chat. You can take a pause, let me take over the communication just to make sure the final collaborations are on are I feel good about that. and then I can place an order, etc. And in that process, I do want to highlight that obviously AI can automate a lot of things for you, but in many cases, just like in other industries, it’s a lot about relationship, right? Unless You’re paying for what we call like ready-to-ship, like standardized product. Let’s say I’m just buying some balloons for a party or buying some bracelets for an event. Otherwise, for highly customized product, you’re building a relationship. So at the end of the day, it is recommended for you to have some interaction with the suppliers because you might be working with them on your next iteration and new product as well.Grace Shao (37:09)Very interesting. So it’s really just helping improve the lead quality and then shortening the negotiation time in many sense. And then but the ultimate actually decision making is still like in the hands of the human. that makes a lot of sense. So I want to turn the conversation around. We talked a lot about how it’s helping businesses, right? Helping the vendor, the seller. but the other end of it is the suppliers. And I believe that at a lot of suppliers are actually on at SEO and also turning on auto chat and whatnot. So what basically support do you provide suppliers and help them in selling their products to the vendor in the between or like the brand owner.Ziwei Chen (37:41)Yeah, absolutely. so we do have a version or specific plugins that are meant for the factories and the suppliers who are who can also choose to be on Accio Work for all of their for their side of the sales as well. and I think even pre-Axial World, a lot of the more AI tech savvy factories are already developing their own chatbot for services as well. So I don’t think that’s new to some extent. But I what I think a few of the really key areas that we are seeing success, the first One is actually a little bit more about background research on potential buyers because a lot of them maybe they’re overseas, they don’t have all the all the awareness or the ability to search across the world about learning about who is this buyer, are they serious, what’s the scale, right? All of these things. So we’re seeing a lot of our factories or the sellers, our sellers on the platform using Accio Work to To understand their potential buyers. Because a lot of times, again, maybe in the maybe there are buyers who are not as experienced but are very serious. That wouldn’t come across in their messaging, especially maybe English and other first language either, right? We have users in Europe, in in Latin America as well, so and also in the US. So I think by giving more data to the suppliers to get a better picture about the potential buyers, it also helps them. capture opportunities or avoid losing or missing out on opportunity. I think that’s a really critical part for sure. And I think the other part is about that general selling optimization as well. It can be most of the times it will be on the Alibaba.com platform. How do you stand out as a seller by learning about the data, learning about the user behavior as well? So I think these are a few areas that we are seeing a lot of usage from the supplier side.Grace Shao (39:27)I see, I see. It makes sense.Ziwei Chen (39:28)Excuse me.Grace Shao (39:29)Something we talked about offline before recording this is, you know, that a lot of the users, whether they’re the s your sellers or, you know, the actual brand owners, is that AI is still relatively new, right? They’re not the people in Silicon Valley. they’re not working for big tech. And that’s quite different from a lot of the other genetic tools being sold or marketed to these more so called sophisticated AI users. Now, how are you helping them, I guess, whether it’s upskill or understand or not be so intimidated by AI?Ziwei Chen (39:57)Yeah, great question. I think a few thoughts here. maybe the different a few different perspectives when it comes to the product development side, on the go-to-market side, on the educational side, et cetera. I think to start from the product side, I think what we have been really emphasizing or de-emphasizing is the is a is a focus. It’s too much focus on the features themselves. So what we have learned is that when we’re packaging them, we’re not like It’s almost like sometimes our users they don’t need a toolbox, they just need like an almost built up tool that can run itself. So what our product team has been really focusing on is not just to build the building blocks, but build the framework of those building blocks. So we’re not making our users to learn to become a Lego expertise like expert right away. so I think from a product end, it’s more about how can we take off the burden of the configuration as as much as possible. That has always been an emphasis, but even more importantly, as we’re getting more newer users in the space. I think that’s one thing. The other thing when it comes to just overall marketing side, what we are also experimenting, exploring now is a lot more when it comes to live events, either it’s in person or online, where we’re taking things slow. And then in this process, what we are trying to emphasize is more the workflow, the use cases as opposed to the feature. Like let me show you how this thing works from what’s the input and what’s the output. And this is all you need to do is to copy and paste and whatever. And then I can explain to you what happens on the back end. Because ultimately what we call the job to be done, right? They’re not adopting a nail, right? They’re adopting a hole like on the wall, things like that. I think that kind of stays consistent. I think the very last thing in this process From an educational perspective, that it’s also learning from my perspective, is that a lot of times the users are not only learning about AI, but they’re learning about just how to launch and run a business. That’s almost even more important than a tool. The tool is an enabler to help you achieve the goals in business as well. So what we are trying to do is to resurface the business know-how. It can be sourcing, it can be about product design, it can be about how do you do SEO or like how do you do like B2B, like pipeline or lead gen? And then let me bake in those automation in the back end. But what you’re taking away is how to do this thing, even in the pre-AI era, but now just making it easier to do. So I think that has been more of an emphasis from a go-to market or like a community building perspective as well, to make sure we’re not overwhelming our audiences. because maybe they’re already overwhelmed by all the AI tools out there in the market as well. So how can we shorten that and let them get to the aha moment faster and easier?Grace Shao (42:42)So how do you plan to monetize that SEO then? Are you guys charging subscription? Are you guys gonna start plugging in advertisement?Ziwei Chen (42:49)Yeah, so Accio Work is on currently on a subscription plan model, both for personal subscription plan and also for business plan as well. and then all of these typically by a monthly plan or an annual plan. It’s primarily based on token usage. So depending on how much how much work. How many types of things was the frequency of the task that you anticipate on the platform and then you’re upgrading yourself based on the amount of work that token consumes. So typically that depends again on the volume, on the complexity, and also on different models you choose to activate for each of these tasks. And that kind of r it aligns with our vision since the beginning, at least starting from Accio. the sourcing engine to now this the agented platform as well. So it’s mostly focused on usage. we do not currently have a plan again because of that kind of fairness or the quality of results, we’re not dealing with like advertisement from suppliers. most other things is more about how do we empower our our like sellers, like or buyers on our platform, the sellers to the consumers to get their business running using the agentic capability.Grace Shao (43:54)Now throwing it forward, where do you see agentic commerce going? Because it’s really interesting. I just interviewed agentic payment solution company last week as well. And then now obviously talking to you guys about agentic supplying, supply network support. where does agentic commerce kind of take us? Like in the future, are we just gonna be like telling the agents to do this whole transaction, this whole activity loop? Or Do you think that human is still needed for a lot of the verification and and and quality control in between?Ziwei Chen (44:22)Yeah, I think a few thoughts here. I do think that human in the loop is critical at different stages of a development. So for example, there are when we talk about agentic or agentic automation, right? We’re we’re automating an existing workflow. Yes, there are a lot of best practices, but it really varies, right? So even having your you’re co-developing an automation workflow with the AI tools in the beginning, and that will vary all the time. The more you The more you invest in that co-creation with the agents in the beginning, the better the automation or the workflow will work well for you. And then along the way, what we’re also seeing from our uses is that they’re learning while doing. You’re building a plane while you’re flying it. So there’s always gonna be learnings along the way. Just like in coding, there are ways for you to build the agents to do that automation by themselves. But I think the more complex the considerations are, the better. Better impact a human can bring to that loop as well. So that’s in the middle part, right? And also in the end, I think there’s still we’re still building that trust along the way, like between the human and the agent or the agentic loop as well. So I do think that having the option to or for an agency, for the human to have that agency, is always critical. That I think it’s it needs to stay. but then on the other hand, what I also want to add is that I think the word of agentic commerce. Can vary quite a bit between B2B and B2C. for example, in the B2C world, there’s always that shopping experience. It’s not gonna get taken away, right? Just sh going to it’s browsing that emotional experience is not gonna be replaced by AI. While the B2B world is a little bit more calculated, is more, right? But still it has that you have that relationship part. So I think in both end, I think there’s still human touch. In addition to human decision, needs to go in. But where I’m the most excited about is that because our target audience or our target user sits literally in between. They’re in this loop for B2B agentic commerce, and also they’re in this loop of B2C agentic commerce. So what I’m excited to see is how these two worlds are merging, or our our core audiences sit in this overlap in between. And then I’m I’m excited to see more of that. synergy of what works in the B2C authentic commerce world can get better integrated with the B2B world as well. Because again, like our sellers or our buyers are sitting between the factories, right? And them as a brand owner and then the consumers as well. So I think this is where I think the next iteration of innovation or maybe redefined like r the new definition of workflows of tools that might happen as well.Grace Shao (47:04)That’s super fascinating. Cause I think when I’ve been speaking.Ziwei Chen (47:05)Yeah, but.Grace Shao (47:06)To people who are working in the agentic commerce space, it’s often so very compartmentized, like what you said, like you there’s a B2C world but there’s B2B world, and these two worlds don’t really, you know, co like collide at all because whoever’s talking to a factory is not really telling what’s happening to the consumer and there’s no really feedback. Factories don’t even know where the product’s going and how it’s being branded. But now there’s that kind of ecosystem, I g like communication or the world colliding between on Accio. It’s very interesting. what do you think the world’s still getting wrong about agentic commerce? Because I think there’s still some people who are very reluctant or resistant towards agentic commerce or the idea of it. What do you think is people are having misunderstanding about it?Ziwei Chen (47:47)I think one thing that we briefly already touched upon is whether a gender commerce is taking away the agency of human. so I think that’s the part that causes the most concern or hesitance is like is the agent gonna replace me? But I think all a lot of these judgment calls, even that branding, right? that personal story about you being as an operator, you having that guidance on the branding on the story and all of that, it’s not gonna go away. But also it’s not gonna go away, but it’s also critical for you to inform where the agent should go. I think that’s one thing. But the other thing that I feel like maybe where I think it’s also like A of times when just as you said, when people think about commerce, they’re th thinking it still tend to go more siloed or go into just on specific steps. So I think it’s still that orchestration layer when things start to get connected and st start to flow together. So it’s not just about how well each of a step is done well, but how well all of these steps are connected. And then that’s also where that human in the loop or your agency is bringing that flow together. So I think that’s an overall area where I think it’s it’s People might be evaluating the wrong thing as opposed to their making the wrong judgment call about a one specific thing.Grace Shao (48:57)Thank you. And then my last question for you is a question I ask every single guest that comes on, as the podcast name is called Differing Understanding. what is one differentiated view you hold or something you think that’s very non-consensus?Ziwei Chen (49:08)Yeah, so I think my the first thing that really stood out to me is that not every problem for not not every problem needs AI to be solved, or AI is not a solution for everything. and I think that has implications both on the development side and also on the user side. So on development side, what we mean is that there are solutions or there are things that we can build that is more efficient or more accurate even without AI. So we should not shy away from that. And that should that will impact how people build products. But on the other hand, even for the users, that also means that skills in AI is important, but that’s not enough, or that’s not the only thing that I will focus on in this era AI era phase. Instead, I will still spend time in building your domain knowledge as well, because without that. You’re not gonna be able to learn how to use AI the best because maybe sometimes AI cannot solve all the problems as well. So I think be having a balanced view about where AI sits in the product or in your tool stack is something that will allow you to get the best out of all the AI tools available and drive results for yourself.Grace Shao (50:21)I love that it’s like keeping the human side of things in check. But I think it’s only non-consensus of where you sit because sit in the Bay Area. Only people in the Bay Area believe AI is taking over the world right now.Ziwei Chen (50:31)Okay.Grace Shao (50:32)But thank you so much for your time, Switzue. really appreciated your insights, and you’re very you know, deep understanding of the agentic commerce world.Ziwei Chen (50:39)Yeah, thank you. I had fun. Appreciate it.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  The Future of Agentic Payments with Clink Founder Patrick Wu 28.07.2026 49min

  In this episode, I spoke with Patrick Wu, Founder and CEO of Clink, about what payments need to look like in an agentic commerce world. Patrick previously worked on payments infrastructure at Amazon and AWS Payments, and later led global payments at Temu as the company expanded market by market. His view is that payments today are largely built for humans at checkout, but not yet for agents acting under delegated authority.The conversation started with a basic but important question: if Stripe, PayPal, Airwallex, Visa and Mastercard already exist, why do we need another payments company? Patrick’s answer is that the problem is not just processing a payment. For agentic commerce to work, the system needs to understand who the agent represents, what permission it has, whether the purchase fits the user’s original intent, and whether that authorization can still be verified later. Clink is positioning itself less as a replacement for payment processors and more as a connector across agents, merchants, payment providers, and card networks.We also spent a lot of time on why payments remain so fragmented globally. Payment habits are local: cards, wallets, bank transfers, convenience-store payments in Japan, and different payment methods across emerging markets. Patrick argues that no single provider is best in every market, which is why AI builders and global merchants may need orchestration across multiple PSPs, local payment methods, and eventually multiple agent platforms.The most interesting part of the conversation was around trust and liability. If an agent buys the wrong thing, who is responsible? Patrick’s view is that fully autonomous shopping is possible eventually, but the trust layer is not there yet. In the near term, agentic payments will likely work through delegated mandates: user-defined instructions, spending limits, merchant controls, passkeys, audit trails, and chargeback mechanisms. His differentiated view is that the industry may be too focused on creating brand new crypto or blockchain rails, when the more immediate challenge is making existing fiat rails work safely for agents — and getting the ecosystem to align around clearer standards.Get in touch with Patrick via LinkedIn. And company website here.Chapters00:00 Introduction00:13 What Clink does and why payments need to evolve for AI agents01:18 Why today’s payment stack is not ready for agentic commerce03:07 Why payments remain fragmented across geographies, regulations and user habits05:48 Clink’s role as a connector across agents, merchants, processors and networks07:06 Why AI builders may need more than Stripe, Airwallex or PayPal09:34 Visa partnership and what it means to be an “agent enabler”15:23 What happens when agents spend a user’s money21:02 Trust, liability, chargebacks and why China’s wallet model is different23:26 What agentic commerce actually means today43:54 Patrick’s differentiated view on fiat rails, stablecoins and protocol fragmentationTranscript (AI-generated, for reference only)Grace Shao (00:00)Hey Patrick, so good to have you on today on AI Proem. Thanks so much for joining.Patrick (00:05)Hey Grace, thank you for having me here.Grace Shao (00:06)Yeah. To start with, give us the high level. Who are you and what is Clink in a few sentences?Patrick (00:13)Sure. so at simplest level, I’ll introduce Clink first. Clink is building the the payment and the bill infrastructure for AI builders and for the agent that they will serve. So payments today we see at design around human being on a checkout page and but we’re building the layer that let authorized agents complete the same transaction safely while helping merchants accept the global payments through the system they already use.And for myself, I started my payment career path at Amazon and AWS. I learned how to build a payment infrastructure that has to perform at a very high level of reliability and the security. And at Temu, I led a team to solve the problem with payments and the global market expansion. So we expand country by country, market by market, and solving the payment issue locally.And Clink bring those lessons to a new problem that when we see agent becoming the new commercial actor, but we see the payment stack is not fully ready for that, so we won’t solve that problem.Grace Shao (01:18)So what do you mean by the payment stack is not ready for that?what do you mean by the world’s not ready for agentic payments? And where do you see the gap from existing offerings? Because obviously, when we talked offline, I did challenge you on this. I said, you know, there’s a lot of major fintech players already and payment solutions, Stripe Airwallex PayPal,right? But where does Clink come in and how does Clink solve this issue,Patrick (01:44)Yeah, I think payments are largely solved for human who is present at checkout. so the industry had made it much easier for developer to accept payments like except from card, from our wallet and from like so many payment that local customers prefer. and also the developer can easily launch a product to launch a subscription, send a payment link, right? Those those are like a real progress already made the word.So but what is not solved on the payment side we see is agents acting under the delegated authority. Right. Processing the charges only one part of that transaction, but the system also needs to know who the agent represents and the what permission that it has and whether the purchase is within the policy, within the intent, like matches the original mandate that the user has given. And then the how the evidence being being persist, right? And if we come back in six months later.is that transaction still like able to be verified at the point that the agent made the decision. So I think like global merchants still face another unsolved problems like a single provider isn’t no single provider that best at the every market and solve all that agent issue. And then we know worldwide that the agent can can buy product from anywhere. So we see that’s a big gap that the agent e commerce need tohave a both the authentication layer authorization layer and also a practical way to across with all the providers in the market today.Grace Shao (03:07)Yeah, actually on that, you did say, you know, payments are very fragmented, right? Like, and but is that fragmentation mostly based on geo geography, regulatory reasons? Is it about payment habits? Like how do we understand that? Why is it so fragmented and it’s not a one solution fits all?Patrick (03:24)Yeah, I think that’s a great question and a great observation, right? And to be honest, I think it’s all about like all about that because payment really reflects a local financial system, right? Like you spend twenty dollars in Singapore that you may use a card, you may use your wallet, your bank transfer. But if you know in Japan that many e-commerce orders actually being paid over konbini payment. Konbini is the Japanese word for convention store. If you would like to place an order online andgetting to a company store and the pay when they check out some some goodies. So it’s just a habits and also the regulation and also how the payments being developed in that particular marketplace. So we see that it’s like all different across the world. That that’s a lesson we learned from from building Tammu payment stack that we actually adopted 70 payment method when in the first year. So every country has their own top three payment method.And every time you add a new pay method, you got some new customers. So it’s kind of very interesting. And then but then the fragmentation is not simply like I I guess you can solve that by building an API and building more like a convenient experience, but we see that as a reflection of local, right? Like habits and migration, all that just as we said. And the the things that we cannot force the users, right? Use got their own votes for what they want to pay.And that will always exist because that’s how the world works here.Grace Shao (04:48)And so thus like where do you fit in then? Like do you have a certain geo gro geography you’re targeting or a certain kind of I guess use case that you’re targeting?Patrick (04:58)you mean the agent e commerce or like in general? I think Clink today we try to solve two problems. Yeah. Yeah, we’re trying to solve two problems. Yeah. So one one problem is for today, like the global monetization for the digital servers and AI builders, right? They they’re selling their product, their great application to worldwide, and then the users from worldwide that like to pay them with the local payment.Grace Shao (05:02)Just in general because Mm. Yeah, yeah. Sorry, go on.Patrick (05:23)So that’s the monetization issue we we try to solve. And also also we’re trying to solve a forward looking issue that when we see the trend that the the actor, the consumer is moving from human to agents, and how agents can leverage the user’s assets, users’ local pay methods, right, and to make that payment, make that purchase, and to make all the information flow and the funds flow fat like try to flow fluently as it is today.Grace Shao (05:48)I see, I see. Let’s double click on the business. Help me understand your business a bit more. So one thing that really stood out to me from our earlier conversations that Clink acts more like a connector. how do you describe your role in the whole like payment stat?Patrick (06:01)sure, yeah. So I would describe Clink as a connector, as you said, right? And a controller or an aux trader. So we do not want to replace the bank, the car network, a processor, all the commerce platforms, the merchants or other agents, right? So we translate the agent’s intent into a transaction, or it could be the human’s intent and delegate to the agent, right, into a transaction. And the merchant existing system can accept.apply the rules that the user has set earlier and route the payment as the user preferred payment and also preserve the record for the future validation or whatever verification that comes from. So that will let merchant connect once rather than building a customer payment pass that for every agent, for every processor. So that’s the important part that we won’t be that connector anywhere alive.And it carries contacts, permissions, all that and solving the issue to connect all different kind of agents, different kinda agent platforms, and also the different kind of mer merchants and all the PSPs and different kind of networks as well.Grace Shao (07:06)I see. So I’m I’m gonna challenge you again on this way. I brought it up already earlier, but why would clients then choose Clink instead of an established platform, given that it’s so important for trust, reliability, proven processes in in payment, right? Why would they choose you over another major platform?Patrick (07:24)Yeah, we we mentioned some names earlier like Stripe, Airwx, PayPal, right? And there’s so many of them. Actually, I think probably thousands of processors all over the world. So I think sometimes when when you just want to sell one product in a particular market, then a single PSP might satisfy what you want. but I think the trend here today is all the AI builders, we are all selling products. what do I?So that means like if you do want to of course trip and water can cover majority of the market, but we see in emerging markets and all different places, like different processes have different advantages, right? And if you as a merchant want to maximize that, then you may have to connect with multiple PSPs. Also pricing is another concern that if you want to lower your cost on payment acceptance, then you may have to have multiple PSPs to lower your cost.So Clink will be like helpful as a connector that we saw the problem cross bond trees that you have multiple markets by default and you have all different kind of local payment methods, especially for example, we have some solutions in India, in Latin America. So like we are beyond one processor, right? So that’s on the payment acceptance side. But also we see that the AI application today requires, well, not that complicated, but does require some sort of bidding.Right, top ups and subscriptions. And if you want to build from scratch, that’s something extra. And if you stay with existing PSPs, that’s a solution. But the expansion may not be that flexible. So we see that clinks serve as a connector layer, but that’s for the human business, the co like the product side of monetization. But when agents come in, that the problem becomes bigger, right? Because the you you don’t know where the agent platforms from.It could be Kodas, could be cloud, it could be Gemini or any vertical agent platform that you never heard of. It could be own, like Ermers or Open Cloud. Right. And on the other side, the merchant may, as we we just said, the merchant may have only a single processor, but they may have many. But for that particular issue, then if there are many, who’s doing the routing? And who is connecting the agent to a particular merchant and their processor?Right, and then we see that a single processor may not be capable of solving all that problem at once.Grace Shao (09:34)I see, I see. so tell us like how you’re working with existing platforms too, because this is another interesting point when we talked offline, you were telling me you have actually a lot of partnerships with traditional payment platforms like such as Visa, whatnot. how do you fit into this stack here?Patrick (09:50)you mentioned Visa, right? So I wanna bring an interesting fact that Visa described Clink’s role as an agent enabler. So I think that captures very well. So like there’s so many agents out there today, right? The big names we just mentioned, and also lots of self deployed or vertical agent platforms, right? And then we see that all those agents have the need the the requirement or the demand to make payments.to do transactional for the user that trusts them. So but however that agents may not be able to integrate with our like Visa system. Right? Visa requires you to have a PCI because agent may not be able to touch the credential information. And if for self-deployed then like we don’t know like what is being uploaded and all that, all the security reasons and privacy concerns are there. So as the agent enablerWe process all that credentials, all that sensitive information. So we connect with the visa for all the like pass key verification, the mandate creation, intent verification, all that. So we perform that on behalf on the agents. And also so that that means we turn on or we enable the existing merchants to be able to connect with visa’s network securely or in a certified way. So that’s how we play with it.existing I mean like schemes like a visa building a network also similar for MASCAR. And you mentioned some other big commerce platforms like Shopify and so I think Shopify is highly complementary for to to to zero, right, for sure. And there it’s already giving the merchants a catalog, a card, a great system. And they’re like a great pro it provides great solution for the merchant to easilyset up your own site and start selling goods. And we’re not going to rebuild all that. It’s it’s just simply not feasible and we see that the ecosystem is super strong. And we want to be add on to the ecosystem. Right? We see that added to like being a plugin for example, then we will turn on Shopify’s merchant to be able to like agent ready. Right. We build that agent readiness into the merchants. doesn’t matter it’s Shopify or WooCommerce or Mikado or so so many like open sourcebuilding platforms and commerce systems. We see all of them are being like like have their own adoption in different marketplaces and then the role of Clink is not going to replace all like any of them. And we want to be like add on to them. We solve the problem that the merchants at this point may not be aging ready and we want to make them aging ready. So that comes back to the earlier question that the role of Clink as a connectorGrace Shao (12:23)Really interesting because basically, actually, you’re not trying to take the payment companies pie, you’re not trying to take the merchants business either. Then who are you charging in between how are you making money? Are you taking a cut from say visa and the transaction fee, or are you taking are are you making the merchants pay you for your service?Patrick (12:42)in in general the goal will be having the merchant pay us. But we are seeing different like from our own business models, right? Different monetization paths that if the merchant in our network that we have the influence on the consumer agent side, that we have the potential of charging them the billing management. So the the billing is not just to human billing but also to agent billing. That agent billing includes likehelp the agent to check out and process that information, process the aging side of credentials that to allow the merchant to have the order and we capture all the credentials and the intent of all that. So that’s a potential of charging the merchants on that. And also we see the card schemes having some solutions to treat the agent and the everybody on the path being a likethe the the search changing or GEO side of thing that you have the potential of charging a fee for for the influencing capability. So I think in general today we want to build the ecosystem and having more and more users, agents and merchants on board and the monetization path will be clear and across all the actors over the ecosystem.Grace Shao (13:46)I see. Okay. Well, there’s often a disconnect between the merchant brand people and how they see, you know, payment versus what the infrastructure is behind the payment. ex explain to us like why there’s that mis disconnect, I guess.Patrick (13:59)Well, the the customer sees the merchant becomes the merchant they own the product, right? The price, a promise, and for e commerce is a fulfillment as well. And underneath that the connect the process actually connects the payment and the network and the process the transaction. So and there’s the issuer that decides whether to approve it, to decline it, and there might be fraud, credentials tax and settlement, all that. So those roles matter and when something fails.So who made the promise, who improved the payment, right? Who processed the pre the payment, who protects the credentials, right? Who should handle the fraud, who’s responsible for that, who owns the liability. So I think the agent e commerce adds another actor and the chain becomes even more important. I think the the biggest difference in agent e commerce is when we look at from physical store to e commerce, right? like the ultimate decision makers do human.But with Gene Commerce, with the autonomy that the agent is performing, does a potential the decision making is from the agent. Then is that decision making legitimate? So those questions are still remains and not being solved clearly. So all that we see that the clinic’s role is to try to carry that authority and the transaction context and all that across the trend. So again, come back to the definition of connector.So we connect all the parties and try to pass through the information, the authority, across the chain, across the rails.Grace Shao (15:23)I see. We can double click on the safety and liability side of things later. I do find that quite fascinating. But it one thing you just brought up that was quite fascinating is that it agents will have autonomy. However, even though they have autonomy, the money, the feat of money is not theirs, right? So how do we understand that relationship if your agent is out there spending your money? do you always have to give them consent like before they go out? OrPotentially what we’re gonna see in the future is agents literally just like going out there buying things without you even knowing they’re purchasing things.Patrick (15:55)Well that that’s a good question, all right. And I think the step by step would would just describe that agent in the future that like shopping fully autonomously it’s possible, right? But I I just think it’s not there because the trust is not there yet. So all the we we see all the demand is being delegated. So that means you as a human that you have some requirement, you have some demand and you want to make some purchases that essentially the demand is from human.And the agents help you discover, help you to make some decisions, but maybe not for for the final decision. Right. So we see that as like involving pass. So but but as you just said, after all, the agents spend your own money, right? Spend your asset, see, spend your fiat. So we see that for today, in some case by case, like just one-time purchases.That the user or the human will set up intent, give enough contact to the to the agent and the sign off on that. The sign off could be like a passkey, like we with a signature. Or it could be like A P two kind of a protocols, right? So there’s many actors in the industry try to solve that issue. But after that I seeLike the agent later on will do the the sourcing and do the patriotization as long as the falling fall under that mandate or instruction that was given, then we approve it. But essentially if we come back, see like where the orange and the context from, that’s from the user. So that’s for like a one-time instruction. But for sometimes if the merchant is trusted, like a digital service you always buy or digital you always top up frequently, then you can set up a recurring mandate.Saying, okay, for this particular product or this particular merchant. as long as like over two hundred dollars a week or like every top up is a five dollar, that that’s fine. Then the agent does not have to receive or like get your approval every single time. But rather it’s just like a one time setup in the very early. But the setup or the mandate or instruction had to be super clear saying, okay, this is a trusted merchant. And we like it’s it’s fine for the agent to keep top up on that.Grace Shao (17:53)It’s like auto renewal but the without like resubscription.Patrick (17:57)Yeah. WellI think the this is a little bit different, right? People are saying like if from merchant side if you give the merchant’s car and then merchant tell the merchant to do auto reload, yeah that that sometimes will achieve the same outcome. But I think the role is different. So on that side is merchant W and on this way it’s like the agent top up, like the actor is different. Right. And I think in general it’s like you trust the agent more or a tr tr or trust the merchant more.Grace Shao (18:19)Mm-hmm.Patrick (18:24)Right, that we see a lot of issues like disputes or chargebacks from the merchants actually deduct money, debit money from the user without that without the the the approval. And then with the agent being the actor that and also clean in the middle, then like we we actually protect the user. Right when the when the agent yeah, gotGrace Shao (18:28)Mm mm.I see what you mean. The ballthe ball’s back in the buyer’s court, kind of like the actual intent comes from that. It’s very interesting while we’re talking. I was thinking about a conversation I had with Ali Baba and Tencent recently. Both of them obviously are exploring agentic commerce. And then for them, you know, the biggest hurdle is how to manage this liability issue with who approves the payment. And obviously they both have payment systems embedded. So on the Baba side with Quinn.Patrick (18:46)Exactly. Right.Grace Shao (19:09)It’s really interesting. They’re saying that even though they’re doing agentic commerce, actually every time the money is gonna go out of your account, they’re still gonna push out a human verification kind of notification. You still have to like manually take that responsibility and press that button. On the Tencent side, I think they’re exploring the idea of creating a separate ReChat payment account, like kind of like two separate accountings, like two separate accounts.And then one account will just potentially have very limited amount of money for the agent to play with. So you kind of just gave the agent like you know access to that one account, but not your full account. I don’t know. It’s just very interesting. I I’m kind of going on a rant, but it is funny because, like, on the other hand, it could potentially become like a scenario where essentially you give a credit card to your like unhinged teenage daughter and they would just go rogue and buy anything. yes. but let’s talk about agentic commerce.Patrick (20:07)No, no, I was just saying that which just is right, that just like giving a teenager your your child a card, right? And then you in in you don’t know what they’re gonna s spend and but after all you know them, right? But for Asian, I think the issue is like they may not have a trust yet, like fully trusted. So that’s what you mentioned, the Tencent has a weChat card and also the the Q and they they want the user to prove that every single time. And we see that that’s because thethe the different level of or different like I mean the path of payment being developed in in the country is different. So China has moved to the e-wallet and has very few dispute and chargebacks. So that means when comes to this particular situation, I think it’s just we we skip the credit card time. Like we skip the credit card UA and then move into the bank based or U wallet based directly. But if you look at the theGrace Shao (20:49)Why is that? What whatSorry, my question is more like, why are there less dispute because it’s a U wallet? Wouldn’t like cat merchants still charge you if they want to charge you? Like if you’d like subscribe to certain things?Patrick (21:02)the thebecause yeah.yeah, so there’s two side of like if the the the payment is one side from the buyer to the seller, right, and then that’s real time transfer. And then we all know that today you have to scan your face in China, right? Or use a fingerprint to authorize the payment. And in those cases like the the dispute or charge back it become nearly impossible or or not necessary. And owning with a card card yeah, yeah, it’s so hard.Grace Shao (21:23)I see.Like fraud is harder.Patrick (21:37)And then for for the auto debit thing then indeed and Alipay and WeChat has a compliance check and they revoke a lot of merchants auto debit. And now if you go want to receive the auto debit, it has to be super critical and then describe your business and the models clearly to the payment team and they will approve that. And it’s merchant by merchant of approval today. Unlike previously, you may either two turn that on. So but come back like when wewe see that issue particularly in card system, right? And then because like we we all know the disputes and chargebacks are all fraud, right? And like it’s just like so popular. Well not popular, it’s a common to see, right, in in the United States and in Europe as well. And that’s that’s why Visa has the entire chargeback management system to protect the the rights of the consumers. Right. so when they come to a genetic word that that create the the convenience thatbecause it is card based or the credential based that the user may not necessarily to approve every single transaction. So that brings convenience. But again that brings the risk, right? But however, thanks to the the chargeback system that has been developed over decades, the the risk can be like manipulated or to be like managed properly. so that’s why we we need the mandate system, we need the instruction toto validate afterwards where when something really is bad then we come back to see if whether the transaction was being authorized properly by the human. And if it is, then well, probably the chargeback or dispute will not go through. Right. And so that mech mechanism does not e exist in Chinese payment industry today. So that that’s why that’s what I’m saying thinking is like may maybe the reason that they have to use a small amount of like a sub account, subcar.Or a ask you as a human to approve every single time. And but honestly I think that actually yeah. So I actually Yeah.Grace Shao (23:26)I see, okay. That’s so nuance. Yeah, yeah, interesting.okay, so but let’s talk about agentic commerce. So I think so many people have confusions around what really agentic commerce means. it’s you know, is it just that like basically like you said, you go out there, your agents are out there like going rogue and buying you things, or agentic commerce more understood? Like there is a process right now being built out. Like, help us understand what gender commerce means. What is the use case for agentic commerce right now, and are we actually seeingit being proliferated in the real like real life right now.Patrick (23:59)yeah, yeah. So I think agent cameras is a very big word, right? And then it has many aspects and the dedicated execution. like I think that’s the most important happening right now ‘cause the the as I said earlier, the the the demand is still from the human and then the the product could be recommended by the chatbot or the agent. But after all that the the final decision may still come in fromthe the human at most of time at today, right? But moving forward it could be the agent, with your authorization or understand you better, like has more context of you from the lifetime conversations, right? And then make a decision for you as long as the decision actually sits fitting the policy that you have set for the agent, right? So that’s another sort of like a next level of agent e commerce. so I think todayWe see more and more like agent being an actor, but the actor could be on the sourcing side, it could be on the queuing side. And it really depends on how you as a user trust the agent. So in general I think agent commerce is very big but in general, like e commerce is a long chain, like doing all the actions from from the very beginning where the intent started and where to to the final the others play placed or the fulfilled. Right. Soin that long pass, any part that agent take kicks in, right, I think it’s agent commerce. But most of the time right now the agent is in the consumer side to provide help sourcing because the efficiency that they search products, right? And they may discover some very rare product that you may never be able to find. Right. There was a joke back at the time that probably as a human you read only first page of the Google search results, right? Super clear.Right. The best place to hide their body is a second page of Google Search Results. But agent can easily search through twenty pages and get all different products or they they just have more context of you. They know your taste, for example, moving forward. And then they may be able to find like from a very real merchant, right? You you never know. Right. So that that’s a great potential that we see agent can unlock. And I think today that’sSome of the aha moments I’ve talked to many people on Gene Commerce is the product search or recommendation site that the the the agent gave them some such suggestion or recommendation they never thought of as themselves.Grace Shao (26:14)That’s interesting. But I also think wouldn’t it make more sense for enterprise use case given the nature of like you said, sometimes it’s repeat purchases, it’s not always evolving. would would would that make more sense? Like if you’re a construction company, you’re buying like cement every year or whatever for this project, you know exactly the quality, the the the price you want to pay. your lumber company, whatever. You know what I mean? Like, wouldn’t that be much easier for agentic commerce to be inPatrick (26:40)Yeah.Grace Shao (26:42)integrated, implemented.Patrick (26:43)I think that’s a great point. but I I I do want to call that those cases are easier because they’re repeat repeatable, right? They’re bonded and you need to verify. it doesn’t matter the the buyer is enterprise or a pioneer of consumers, right? So it’s rather the behavior or the product itself define that the that use cases is more trustworthy or like you are more comfortable because to r like rebuy laundry stuff for example, right? And thenGrace Shao (27:00)I see, yeah.Patrick (27:09)You don’t like that you always use a single brand and you you probably most of the time don’t want to try something new then then just a repeatable ask agent to to keep doing that. I think that makes it total total sense, right? To have the agent do that delegation, right? And like to to help you execute. And but like if it’s enterprise or like the pioneers of consumers, I don’t I’m not sure. It’s always like a some group, a small group of people.They’re they’re interested in trying everything new, right? I like all the new stuff they they on board. I think those people are really push the word forward and help us to do the early adoptions and explore all the issues and helping improve the systems. It could be some pioneer of the enterprise, we don’t know, right? Small teams like startup teams, they are always willing to try stuff new. It’s all possible, but we don’t we don’t limit there. But I think you’re right that we use thea small amount, repeatable and you need to verify purchases, to build the trust between the consumer and the agent. But the consumer could be human, it could be enterprise.Grace Shao (28:11)walk us through that case study ‘cause you were telling about it beforehand called Hello Minds. That was quite fascinating.Patrick (28:16)yeah, so it was a it was a short story. Actually, it was quick. when we were at Supreme in Singapore, they came to our booth saying, okay, they they see a clear need from their customer, they want to do transactional stuff. But as an agent platform they are not able to do that because they talk to Visa and then it’s just simply difficult for for them to to receive a PCI and all that in a short time. Right. Sothen visa recommended us because we as aging enabler we’re designed to to turn on the capability of the aging platforms to do transactional. So and then we we just work with them super easily. There they have our skill pre-installed for their platform and then the the user be able to tell the HelloMind I want to buy this and that and then the system will actually get the user into our aging portal.and add the card and we will go through the entire visa process for the verification, for device registration, and then it just happened automatically. So I think that’s a great example seeing like out of like all those vertical agents, they see the clear customer requirement, but for the role that they are today, they see a longer path to achieve that by themselves. And they see the they are seeking for for the help and for the experience partnership as Clink.from the ecosystem to help them. So and then I think it it’s super clear for for Clink as well. We like to help them, right? And help other agents because our goal is being a connector. We’re now building our own consumer side agent. Of course we can do it, but we see it’s just like a payment method, right? Different people, different location, different markets have different preferences. Hello Minds is a great Asian platform over Hong Kong and and APAC area. So that’s kind oflike a possibly a capability that Clink provides actually help all their agent platforms to build their own and to satisfy or serve their customer better.Grace Shao (30:12)I see. Okay. Yeah. Cause you did say you’re agent agnostic. So that makes sense what we’re saying here now. but if the feature is, you know, many different agents acting on behalf of users, what does the payment layer need to do especially well then? Is is it in the identity, authorization, routing, settlement, security? Like how how I guess for you guys in the middle layer, what is the core, core offering that you have for everyone?Patrick (30:36)yeah, I think you’re right that like a one model may not fit all, right? Like a one base model or one agent may not fit all and the one payment solution may not fit all, right? Payment methods and the acquires, carnet was so many things. So all you mentioned, like identity, association, routing settlement, security, all of them actually matters. Right. So for us like weWe’re not going to like just build a single rear or like being a connector, we want to optimize every single layer, right? To be a connector that brought like try to bring every parties all together and closer and then try to solve the friction along the path for the agent to perform transactions. So in general that’s Klink’s goal at this point. We want to create a smooth word for the agent.from talking to users and to the placing order on the merchant side and also have moving help moving the the fiat funds from from consumer to the merchant as it is today to have ‘cause we we see that as most smooth because after decades of development, the merchant solution, their commerce systems, their payment stack are being mature.And there’s no reason to rebuild all of that just for the agent, right? And the return on that investment could be super low. So that’s why we see that as an evolving pass but rather a revolution pass.Grace Shao (31:59)look, I think let’s talk about the the most sensitive bit now. I want to talk about the trust and safety bit. So, you know, you do emphasize, you know, you’re building reliability, you’re building safety into the product and everything. But as the consumer, it still seems like really, really unsafe or scary to try out a new payment system. You know, as the average consumer, we will still default to big names like Visa and MasterCard, even if we know we’re paying them much more, right?so help us understand that. Like what what is what is a trust barrier here and how do you build that up? and I guess who bears that liability? So hypothetically, I’m using Clink to buy something on whatever, let’s say Amazon. Okay. If something goes wrong, am I supposed to go to Amazon? I’m or am I supposed to go to you? Or is Amazon Amazon gonna come chase you down? What what is the relationship here?Patrick (32:51)Yeah, yeah, good question. I think so we are being agent enabled there or the connector here. So that means we are not the merchant of the record. So you still own your order on Amazon, right? So that’s our relationship with the merchant as well. We are not going to replace a merchant, repla take over their customer relationship. That’s not what we’re going to do. So what we’re doing is here, like it’s being a connector. That means we pass along the information.like the payment credentials and all that, then we also capture your intent instruction that the the user talk to the agent and then send that over to Visa. So after all you’re still seeing your Visa card paid Amazon. I’m just using Apple as example. You after all you still see that you have an order with Amazon. And then you still pay with your regular car, right? Just the the in the middle the two actors, one is the agent. So agent performs the excursion.as you authorized. And the clin perform as another set of actors and take over the agent’s direct execution, but help you and your agent to place the order on Amazon because we have been certified by Visa. And we have certified by the payment industry because we are PCI compliant. PCI but by the way, PCI means that we can securely manage all the payment credentials, right? Being authorized by the certified agents that we canpersist and the process user’s kind numbers, right? And given that we have capability to pass that over to Amazon. But after all, still you place an order. We’re just solving the problem that agent may not be able to directly touch the credentials or you don’t trust the agent too, right? And I mean at this point. So that’s why the we as a connector in the middle, we persist your critical information and then pass along to the merchants. And also weHelp you validate the policy that you set for the agents and the the initial instruction. For example, you authorize the agent to buy a shoe, right, and under $100. And somehow the agent got mad and bought you a t-shirt for $200. Then clearly it does not match the initial instruction. And we will block you before we send that over to the particular merchant that the agent wanted. Right. So that provides additional layer of protection. And also because like all that is being also certified by the visa.Like because we are the first agent enabler and the agent side of partnership with Visa in APAC in in the Visa intelligent commerce program. So all that is being like even though it’s still pilot, we see the potential that we can add additional layer of protection and also additional layer of privacy.Grace Shao (35:23)Really interesting because I actually have two questions. That means number one, I mean, this is not unique to you guys, like, but you essentially have a database everyone’s credit card. There’s definitely a risk there, right? And then the second part of the question is that, like you said, when you don’t trust your agents to touch your credit cards now. So, say one day the education has been done, the market has been educated, agents have been normalized. Five years down the line, everyone is using agents to do shopping.Does that just completely skip over Clink then if we’re all just gonna give our agents our credit card numbers, if we even still have credit card numbers, whatever payment look like back in the future? Would that just actually frankly o omit the the the role of Clink then?Patrick (36:03)that that’s a good great question, but I don’t think that will happen because for like even though the agent development all that then the the the concern to like you still need additional layers of protection because it’s like we are the neutral layer and give you the additional protection, right? Even though you trust the agent much but you still want the third party. It’s just like at the very beginningof the e-commerce in China, you have Taobao, you have the consumer, like you have the buyer and the seller. But you still need to about just LP in the middle, right? To prov provide you that layer of security and you you want another layer. Yeah, so a a neutral standpoint to to to also right to to monitor, to audit the agent behavior. And also from the car scheme or the merchant side, they want some additional layer of protection as well. Right. No one can simply trust a single agent can perform all of that.Grace Shao (36:38)See what I mean?Patrick (36:54)Right. It’s just like additional yeah.Grace Shao (36:55)I see what mean. So there’s guardrailsbuilt into you as like the guardian almost of all this.Patrick (37:01)Yeah. Yeah, exactly. the an an a layer of protection and I think Clink by the time it will build the trust across the consumer side and also build the trust across the merchant side. Because we see that the potential of having more a merchants being aging readiness and into the aging ready merchant network will bring them the advantage across the five years development that you just mentioned.Grace Shao (37:24)So I have a question. I I have two three questions to wrap this up. first one is what is your vision of the future of agentic commerce then? Like where do you see this as going? You can start with this, and then I want to throw you another one first just for you to think about in the back of your mind, What is the smartest question you’ve heard someone else ask you about the agentic economy that I have not not asked? SoPatrick (37:44)Sure.I think yeah, I again I guess we can get started with agent commerce, how that will go and aging payment. I see that has a long way to go. That as you said, like you have a child and like if you have a child that they grow up, they become adults, they become mature. And actually at this point we see that agents are growing fast, but are they close to the AGI?Right, are they close to a particular point that that they’re being smart enough enough to do all that? So I’m not sure about that. And on the scare side, if they are really that smart and as another adult, do you really trust them to manage all your assets? Right. So those are like I I see those questions are tough to understand, are tough to answer, but at this point we see that agents are being super good or super useful as a tool, right, as a helper.to help you do the excursion to do all the repeatable work or like all that you want to skip. And the sum of part of the commerce is being part of that. But I also see that shopping is a great journey, right? I think you as a lady understand what I’m saying. So I I mean as I mean I I typically don’t do enjoy that process, but rather just get what I want, right? But my wife and they do enjoy like the journey of exploring products. II think those are entertainments, not just commerce or shopping. So those will never be replaced by agent. But it could be like you I I know there’s some services in in China I’ve heard of that. like you you got a guy or like a girl that accompany you or walk through the the big malls and do shopping all together, like a company, right? It could be like in the future those accompanying role replaced by agent. It could be. But still provides a a great journey.help you sourcing and find a great product that fits you. But I think that also would be a very pleasant experience. But I I think in general like for now we see that it’s a delegation for e-commerce for most of that valuable part. it’s not a new demand. But we also see for the digital service side and also for the like aMostly on digital and the model requirements or all the model f model side of features, those are new requirements. But it it it replacing a lot of the previous like data sourcing work or like investing work investigation work that you’ve done as a worker. But this may be replaced by agents as well, and along that path will generate a different requirement or different demand for genetic payment. So those are new, I think.And in general along the past, hopefully we see that agents and humans in a word that can both like pay and get paid. Right. So that like today the the we’re we’re talking about AI applications, but in the future the the the product may be served by agent. But you never know. So it’s like a human to human, human to agent, agent to human. We’ve seen an interesting case that Waymo plants order online.get a human to close the door. Right. If s a passenger got off the the the ride and forgot to close the door, and then you you place an order and get a human to do that. Right. So it’s like aging to human, human to aging. We never know. It’s that’s what you said, agen agent economy, right? Just not not commerce. So I I think like that word will eventually come and along the way that we have the base models to develop being smarter and also a lot ofIssues like around identity, around trust in general to solve. And then that’ll that’ll be the case. And then to a second question, like the smarter question, I think is like really come to that when when intelligence or labor or execution becomes abundant, right? what remains valuable? Right, or die, right? And do we still need transactions?Because whatever you want, like the goodies or the services is being so abandoned and probably at no cost at all. So where is the transaction? Where is the value? Right? I think then that I think that’s a smarter question or tough question could always come to me saying, like really like if we as a society or the entire world develop who develop to that level of then what what’s what what are you p people paying for? We never know.Right. And maybe that that’s a time that come back to okay, the the taste and the the people that creating the value that I I don’t know, I just don’t know. I think that’s a very broad question, open question. And we may not have an answer when really that comes. Yeah. What becomes valuable and what are people paying for?Grace Shao (42:07)First, I wanted to say it’s really scary to think that we potentially have machine overlords telling us what to do and working for them. second of all, I think the second half is something that’s really interesting. It’s it’s not just in the whole abundance of a gentic era. It’s just like you’re seeing it play out already. Like even people are saying, you know, luxury is a structural short because why who is still buying luxury? When you have more money, are we really spending money on puttingPatrick (42:15)Well, that’s a bit scary.Grace Shao (42:35)you know, brands and logos on our bodies anymore or is society moving towards like paying for experiences, praying for health, paying for, you know, things that actually cannot be so easily replicated because, you know, logos can be replicated easily these days. It it’s kinda interesting. So it it will be interesting once intelligence is abundant, what will happen. But then I do think that question is also overgeneralizing humanity because we do forget that, you know, not everyoneIs working in a career that actually requires intelligence. I’m not saying people are not intelligent. I’m just saying intelligence as a currency to for, you know, salary or, you know, whatever or or capital gain is actually not for everyone. And if anything, then do we say the value is more in craftsmanship, laborist work, you know, you know.Patrick (43:08)Yeah.Grace Shao (43:28)experience and different things. It’s just very interesting, but for sure it’s already moving away from objects, like tangible objects, like where people really want to pay, right? Patrick (43:36)Yeah. Right. And you th you mentioned labor go go ahead, no, that’s a sign. No.No, I was just saying like a labor as well. Like the the labor may even the work may not require intelligence, but that work and like the labor side of work may still be replaced by robotics. And with a small robotic, that that’s even more scary, right? Like as a move yeah. Yeah, yeah, yeah. So but but hopefully we we humans still aren’t in in control when that comes.Grace Shao (43:54)I s I see where you stand in all of this, Patrick. I see where you stand.I don’t know, Patrick, the way you’ve pro you painted the futures, we have machine overlords telling us to close the door. Why are we even leaving the house then? okay, jokes aside, I have my last question for you, which is the question I ask every single guest that comes on. What is one differentiative you you hold, something that you think is maybe against consensus or people still you think get wrong or underestimate?Patrick (44:12)Yeah.I I think the two two sides I want to answer that. One is that I think we’re we’re spending too much attention or like on the itching payment side require a new brand new rail like broad chain a crypto rail. So I’m not saying that’s wrong and we see the potential of the stablecoin or the capability of blockchain that for cross border settlement, machine too much micropayments, all that, but like a a reel thatLike the the real exist because we move value, right? And most of the time today the buyers and the merchant that they use VAT and we see we may not spend enough attention to solving that side of the rails issue. Right. So but I I see that on the long run, of course, the blockchain aspect stable coin will have a great value and being super useful. But at this point if we want the agent to be capable of handling payment.instruction doing all that payments and we probably want to solve more issues around the the fiat rail and to to make that smooth. So that’s one thing. A second thing is I I think like even you’re not a payment in the payment industry you may hear of a lot of protocol names, right? A P two, UCP, ACP, SPT, and it’s just so many of them, right? And I’m not saying likelike every single protocol as the has their values and standards and so try to solve a particular issue or a like a vertical scenario, right? Or some of them being try trying to be generic and some of them well like we’ll want to solve the issue of product discovery and all that. And but I feel just too many of them. Like just way too many of them. And that creates a barrier or creates a a like confusion for the merchant.And for other actors in the ecosystems, like which one should I adopt? Which one should I integrate? And the more and more are coming out, right? Every single player has their own standard. And as a merchant, I may feel like so confused, like which one I should adopt. I think really that the entire ecosystem will want to align or I mean to get some agreement.Well, I mean not necessarily a single one or two protocols, but to have some fundamental rules in place that people understand okay, that’s how we want move and that’s the direction we go and people start investing on that. And eventually some generic or universal protocol that’s been agreed by everybody, like by the majority of the actor in the market, they’ll come out. And that’s a time where I see thatagents or the merchants will be more comfortable. Otherwise it’ll be too many of them for for the ecosystem to advance to next step to to next stage that agents and merchants are like feel so like worry free to onboard. Otherwise they feel like okay if I bought I onboard A C P last year, right? And they’re really like like not all agents are supporting them. Right. And then if we are on board U C P this year, we see other agents not support them. So like those kind of issues are beinglike a making just like a too too fragmented. And as a generic like a pass forward, I I I really want that we we somehow get aligned in one particular vision, the entire ecosystem and try to push that new outcome.Grace Shao (47:38)There needs to be some standardization consistency to help agentic commerce go forward. All right, Patrick. Thank you so much for your time today. Really appreciate all your insights. I’ve learned a ton. if anyone’s interested, feel free to reach out to Patrick or I’ll put the link in the podcast show notes as well as on Substack notes. thanks again, Patrick. Speak soon again.Patrick (47:56)thank you. Thank you, Grace, for having me here.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  Pony.ai’s Founder and CEO James Peng on What It Takes to Scale Robotaxis 21.07.2026 55min

  When James Peng founded Pony.ai in 2016, many in Silicon Valley believed autonomous driving was only three to five years away. But he expected it would take at least a decade, because the challenge was never just teaching a car to drive. Commercialization also required regulatory approval, public trust, reliable fleet operations, and a cost structure that could support large-scale deployment. Ten years later, his vision is becoming reality.In this conversation, we start with his founding journey, the milestones and how Pony.ai became a leader in the autonomous driving space. We also discuss the gap between assisted driving, Level 4 autonomy, and the longer-term goal of Level 5, as well as how Pony.ai uses simulation, real-world driving data, and increasingly capable AI models to improve safety. James explains that the hardest problems are often not the obvious ones but interpreting unpredictable human behavior and handling rare edge cases consistently.The conversation also explores China’s cost advantage in robotaxis. A mature automotive and electronics supply chain, close collaboration with automakers, and faster iteration can materially lower vehicle and system costs. But moving into new markets still requires Pony.ai to adapt to different road conditions, regulations and driving cultures, from trams and roundabouts to local pickup behavior.James’s broader point is that the industry has focused too heavily on the initial technological breakthrough. Getting a car to drive itself is only the beginning. The next phase is about deployment density, utilization, maintenance, charging, remote support, and economics. At the end of the conversation, I asked what he believes is underrated. James, an experienced operator, replied - scaling. Pony.ai may have crossed the zero-to-one threshold, but the harder task is scaling from one to ten, and eventually from ten to one hundred. Check out this insightful conversation.For more interesting conversations with people who are charting the way of the future of AI, check out the podcast tab or follow us on Spotify!Chapters02:36 Why James Peng founded Pony.ai06:36 The milestone that proved robotaxis could work09:27 How passengers learned to trust driverless cars11:41 Level 2, Level 4 and Level 5 autonomy18:59 How AI and simulation improve self-driving24:13 Teaching cars to understand human behavior29:11 China’s cost advantage and global competition35:03 Expanding robotaxis into international markets41:13 Why Pony.ai is also building autonomous trucks48:40 Adapting to new cities, roads and driving cultures53:45 Why scaling is often harder than reaching zero to oneTranscriptGrace Shao: Hi everyone, welcome back to another episode of AI Proem Differentiated Understanding. This is your host, Grace Shao. Look where I am, the back seat of a car. Doesn’t look that exciting, does it? Let me flip this around. Look at that. There is no driver. I’m in the back seat of a Pony.ai robotaxi. Today joining me is James Peng, co-founder and CEO of the leading robotaxi company. It’s expanded its footprint across the globe, in Asia, in Europe, in the Middle East. But obviously, today we’re in its leading home market, China and Shenzhen, where it has a fleet of a couple hundred vehicles deployed on the streets already. Hi James, thank you so much for sitting down with me. I’m really excited to be having this conversation with you. So you left Baidu in 2016 to found Pony.ai when many in Silicon Valley were saying self-driving cars are only three years away. But obviously that wasn’t the case.Grace Shao: So, what did you believe then that this consensus was getting wrong? Tell us about your journey from 2016 until now.James Peng: Yeah, sure. We were founded in 2016, about 10 years ago. But even at that time, I didn’t believe that autonomous driving can be solved in three to five years. Just from a technical point of view, because even back then, 10 years ago, even a demo for autonomous driving was already very hard. Later on, there’s complexity involved in the autonomous driving industry that involves regulation, user acceptance, the readiness of the ecosystem. So because of the sheer complexity, even then, my prediction was it’s going to take at least a decade for this to be a real application. It turned out to be that my prediction was about right. Now, 10 years down the road, we actually have fully driverless commercial applications in many cities. Of course, it’s just the beginning of the long journey for autonomous driving. But at least now we have real commercial applications.James Peng: So I think people, like any new industry, people were super optimistic for the short term, but they were underestimating the potential for the long term. So I think autonomous driving is definitely one of those industries.Grace Shao: What really drove you to want to actually work on this, work on this technology and the future mobility?James Peng: I think the motivation was twofold. One is that the potential, both commercially and also societal benefits for the autonomous driving is so huge. Think about like everyone needs to have some sort of mobility. Autonomous driving is much safer than a human driver. So it has huge societal benefit of saving people’s lives. So essentially, it’s just such a great industry to work on. Although back then, 10 years ago, it was very unclear when this can be done. The other reason is, of course, because the sheer technical challenge of autonomous driving involves because I was actually in my previous jobs. I worked on different areas, software, hardware, large scale distributing systems, AI and whatnot. But none of the things I worked on is as complex as autonomous driving, which is a field that involves hardware, software, hardware and software integration and Many other things. There’s AI, there’s real time system, there’s also large scale AI training and all that.James Peng: So just from a sheer technical point of view, it’s such an amazing and challenging thing to work on. So I think those two reasons propelled me to start the company.Grace Shao: There’s definitely a lot to unpack there. I think later on we can definitely double click on the hardware, software integration, as well as the safety concern there. You say that autonomous driving is much safer than humans. For sure, it’s safer than me driving. I know that. But some may argue otherwise. So let’s talk about that later. But first, I want to ask you about something that was quite interesting. During 2020-23, there was a bit of a public reckoning, I think, within the industry. A lot of peers folded during that time. People decided to pull out of this sector. Some people worried that autonomous driving would really become a reality. But you guys charged ahead and you really believed in your vision. Tell us about that period and how maybe that changed your vision or your growth mentality.James Peng: I think 2020-23 was a period of time where the autonomous driving industry has evolved for roughly 10 years. I think that was the time of reckoning. That’s the time where the haves and have-nots have really diverged. So I think that’s actually exactly the time. As a company, we have seen tremendous progress. At the end of 2022, beginning of 2023, that was the time we actually finally had the first fully driverless commercial applications operations on the road. So because we made such progress, both from a technical and also from a regulatory point of view, that, of course, we made the progress. We finally see the glimpse of hope. Then, of course, we charge ahead. I think a lot of the other companies who weren’t able to, either from a technical point of view, or from a pure capital-raising point of view, or from a regulatory approval point of view, that weren’t able to have fully driverless applications. Then they were faded away.James Peng: So it’s sort of like, well, everyone is in school. There’s no big difference. But after graduation, then there’s haves and have-nots. So I think that was the time of division.Grace Shao: Yeah. So speaking of milestones, I want to kind of go back into history a little bit. So in 2021, Pony.ai had the third highest number of miles driven behind Waymo, Cruise. In 2022, Pony.ai became the first autonomous driving company to get a taxi license in China. In 2023, Pony.ai was licensed to operate robotaxis in Guangzhou, etc. And expansion continued. So kind of following what you just said, there was good momentum behind you guys. Now, today marks Pony.ai’s 10th year officially. You kind of talked about how you guys have grown. But what was one or two of the biggest milestones that you’re really proud of looking back now and that you think have really set the tone for your company Now as you are really expanding globally?James Peng: Yeah, I think in my view, the biggest milestone, actually, I have already mentioned, is the end of 2022, beginning of 2023, where we were granted the fully driverless commercial license in both Beijing and Guangzhou. We start to have the operation to the general public. Actually, it was in mid-January in 2023 that I was the first road in our commercial robotaxis operations in Beijing. Surprisingly, it was exactly on that day, it was snowing in Beijing, and I was in the vehicle by myself. That was the moment where I actually saw our vehicles were able to drive by itself. Anyone besides me in the vehicle. Because of the snowing, it was also a very challenging scenario. We were actually not being suspended for operation. We continued to operate, and I was in there. That was the moment. Finally, it felt like a dream come true, right? Finally, it’s not just because our technology is ready.James Peng: Also, because we actually got the approval from the government to have the license to operate. So it’s like all the seven plus years of efforts finally pays off. To me, that was felt like, as Lyndon Johnson said, the small steps for a person, but a giant leap for the human race. Although I wouldn’t call it as big as the Apollo, but to me, it felt like it’s finally from zero to one. So I think that was a deciding moment or defining moment for Pony.ai.Grace Shao: That’s a personal Apollo moment. I love how you visualize it because I could just imagine how chaotic the roads were in Beijing. Also to be quite romantic when Beijing is snowing because it’s such a beautiful city. Okay, but let’s talk about what is a robotaxi and how the public actually even felt about it when it first rolled out. Before we started recording, Ivy was even telling me, I was like, hey, look, I get a bit scared when I see Waymos on the roads or Pony.ai vehicles when there’s no one Driving behind the wheel. Now, that’s because I’m not used to it. You said, oh, yeah, it’s okay. People get used to it eventually, right? But let’s look back at 2022 when it first was deployed to the public. What’s the public’s reaction?James Peng: I think because it was a gradual process in the operational domains, in the operational zone that we had. We used to have a safety operator behind the wheel, although the driver actually didn’t touch the wheel or push the pedal. But people gradually get used to it. Actually, at the very beginning, when we were just deployed in Guangzhou, in those days, if you look at the picture of our first and second generation of Autonomous driving vehicles, you still see those spinning LIDARs on the top, and they were very much visible. People were curious. But gradually, people are just getting, this is like business as usual. As a rider’s point of view, the experience of a robot taxi is exactly like a typical taxi. The only difference is there’s no driver inside the vehicle, right? So the way you get the vehicle, the way you get in and get out is exactly the same.James Peng: Also the other traffic participants, like the pedestrians and cyclists, they get used to it. So I think it just takes time. It’s just like the first cell phone comes out, the first real smartphone comes out. People were very curious. Now it’s just, nobody cares about it. So I think it just takes time.Grace Shao: It normalizes eventually, right? Absolutely. I think we’ve really had a few years of consumer education done by quite a few of the players, including yourselves. Okay, well, let’s talk about the technical side of things. For outsiders, people might not understand the nuances between L2 and L4. Increasingly, we’re getting closer to L5 supposedly, are we? So help us understand your thinking on there. How do you structure your own teams, your products, working on different technology? Who gets held accountable for the actions in an L2 vehicle versus an L4 vehicle? Then finally, are we getting a glimpse into the future of L5? Are we going to be able to complete the road anywhere we want with autonomous vehicles? It’s a big, broad question, but I’ll throw it to you.James Peng: Yeah, sure. So the definition of the level of automation for vehicles was actually defined about 20 years ago. So, of course, the industry evolved quite a bit. I don’t think that the levels from L0 to L5 might be the right way of defining what the level of automation is. So in my opinion, actually, there are two different products. One is what we call the driver assist systems, ADAS. The other is fully driverless. So in a broad sense, I think there are two categories. There are definitely two different products. The biggest difference is not on the technical side, but rather, as you mentioned, probably on the regulatory side, is who is first in line for the responsibility if there is ever an accident. I think for any ADAS system, any driver assistance system, it’s always the driver behind the wheel that’s responsible. Regardless if he or she is looking at the road or has their hands on the wheel.James Peng: Whereas for the fully driverless systems, it’s the system that’s first in line. Because of that requirement, right? Think about if there’s a driver behind the wheel, it sort of serves as a safety net. So the system does not need to be bulletproof. It’s probably, well, as long as it can handle 99%, the case is probably good enough. Whereas for fully driverless, it has to be dealing with all the edge cases, all the extreme cases, and have a fallback system. We can get into those details later. But essentially, in my opinion, there are two different products. Of course, for the driver assistance systems, there are different levels, right? You can be, say, only highway or there’s only keeping in lane. Or they will actually even be able to handle some of the automations in the urban environment. For the fully driverless, of course, as you mentioned, there’s L4, L5 in a traditional definition. L4 means in certain areas. It can be fully driverless. L5 is everywhere.James Peng: But I think it’s never a clear division. Essentially, you can think of it as how we drive, right? We start with the area, then we gradually improve. Eventually, it will be everywhere. So I think that will be a gradual process instead of a clear division.Grace Shao: So actually, I want to double click on what you just said. So then help me understand, what is the gap between L4 and L5? Right now, Pony.ai is at L4, right? They’re robotaxis. Is that correct? How am I understanding this?James Peng: No, I wouldn’t call them a gap. I think it’s a different product definition. Because they serve different purposes. I think most people view this as a process of evolution, right? From L0 to L2, L3, L4. But it’s actually a wrong way of looking at it. As I already mentioned, because the clear difference is that who’s first in line with responsibility. That’s decided by regulatory, actually. By product definition. By regulatory as well. So because of that, it’s essentially two different products. As the product is getting more and more mature, getting more powerful, in my personal view, the division of two different products is getting wider instead of narrower.Grace Shao: Okay, then I’ll push on this. Then what is the real bottleneck right now for companies like you to deploy at a faster scale? Or to go into more cities quicker?James Peng: I think that’s the reason that I wouldn’t say it’s one single blocker or one single bottleneck that prevented us to grow faster. I think it’s because the sheer complexity of the autonomous driving and what entails to ensure safety. There’s regulatory, there’s technical things. We also, because this is such a brand new system, that we need a manufacturing capacity. We need deployment. We need to get all the operational things ready, like all the garage space and whatnot. Also user acceptance, user education, as we already mentioned. I think all those take time.Grace Shao: I believe also we have different partnerships with different managers of your local fleets. That kind of know-how also takes time for them to understand, to transfer over, right? For them to manage robotaxi fleets versus human fleets.James Peng: Absolutely, absolutely. All those takes time.Grace Shao: So I want to bring it back to technical. You have said publicly that you use the least compute footprint to reach L4. I thought that was quite interesting. Help us understand how you achieve that. How the model on the vehicle versus the large model you train in the labs actually work together.James Peng: I think all the AI systems more or less take the same approach, is that you have data on the backend, on the data center side. You train a large model where you essentially try to get all the cases to be learned. In our case, we use the word model, where you can think of it as a simulated city, where we train the virtual driver and let us drive on all different kinds of roads and learn the driving ability. So that’s what’s condensed as a model from all the learning that we deploy on the vehicle. In the traditional AI sense, that’s called edge computing. You put it on the edge, put it on the devices, and put it on the car, where it’s a much smaller model. In a human sense, it’s like we learn everything. Then when we go to a test, we don’t need everything. We just need to be able to have the ability to handle the test.James Peng: So that’s typically the training, where the backend system needs a lot of computing, but on the actual usage side, you don’t need that much computing power. So when the car is running on its own, it’s actually only using the model on edge, essentially.Grace Shao: Absolutely. I see. Okay, so let’s talk about AI systems, because AI systems for language, images, code have improved dramatically. There’s also obviously a lot of hype right now around world models, but what you’ve been describing actually has been something that’s not been coined world models for a decade, over a decade. What has generative AI done for you guys? How has it changed how you view your own AI system? Do you think, I guess, the word world models do your system justice in that sense?James Peng: Yes, it’s a little bit different, and they’re also related. Again, use human as an analogy. It’s actually quite similar to how we think, right? Because think about the large language model, how we process image, how we process knowledge. It’s sort of related to our memory and our logical areas of the brain. Whereas when we drive, it’s not just the memory and our knowledge. It’s also how we react, how we action, and all that. So the example is, one is related to how we learn a new skill. That’s the language model. Whereas for driving, it’s like how we learn to ride a bike. It’s actually different types of brain, different types of skill sets. That’s why it’s different. It’s not the same AI, because that’s how humans deal with different skills for knowledge.Grace Shao: So Gen-AI has not affected you, but how do you view the idea of now calling, I guess, the physical AI world, world models? Because you guys have been doing this for more than a decade. That’s kind of my question, I guess.James Peng: Yeah, that’s why I’m trying to get to it. For language model, it’s related with knowledge, with language, with logic. That means you need to be a very large model. Because think about it, if you don’t know a historical event, there’s no way you know it. So you have to have all the knowledge of a human ever created in your model for it to be powerful. So that’s why a large language model requires a lot of computing power and memory and everything. Whereas for driving, that’s how we learn riding a bike. We don’t need to have a PhD degree to learn how to ride a bike. But rather, it requires a lot of practice and training. That’s what world model is related to, or is assembled like. It essentially is a model where the virtual driver can start learning by itself, to learn how to interact with other cars, cyclists, pedestrians, and then learn the driving skill out of that.James Peng: So it’s a bit related with the large language model, but it’s quite different. Because it’s related with action, related with manipulation, related with collision avoidance. So that’s the key for the world model.Grace Shao: So tell us about how you simulate these systems. How do you leverage simulation systems for these edge cases?James Peng: So essentially, that’s how we learn how to drive. There are several key factors for the world model. One is it needs to be very real. So we call the fidelity. It needs to have high fidelity, means it resembles the real world. Second is everything that moves in the world model, meaning cars, pedestrians, needs to be smart. Meaning that because the thing that’s driving is the interactive process. Our action, because we constantly make decisions in there, our action will affect everybody else around us. So they need to react accordingly. So it’s interactive. It’s more like interactive gaming, where we react with everything else. So that’s the second challenge is all the interaction needs to be smart, needs to be intelligent. The third challenge is how do we evaluate what is a good driving? You can interact and everything. You avoid collision. Is that a great driving? No. Not enough, right? Because there’s a passenger inside. Comfort is important. Efficiency is important.James Peng: From A to B, we want to use the minimum amount of time. So essentially, it’s a multi-metric evaluation system in there to see what is a good driving. So there are three key challenges for the world model. We certainly, all our effort developing the world model related with that three areas.Grace Shao: But human drivers can be so emotional, right? Either you can be scared or you can be rage driving. Or we can be communicating sometimes without obvious signs, right? We’re looking at each other. We communicate with eye contact, hand gestures. How do you train your fleets to understand human behavior right now? Because obviously, human drivers are still the majority of drivers on the road today. In your case, I actually think you’re right. At one point, maybe removing all the human drivers will make it even safer, right? Especially removing drivers like myself, if I say it again. But yeah, how do you actually help these cars understand all these non-obvious signals? Not someone quite directly clashing onto you. Someone forgetting to turn on the turn sign. Someone stop sign looking at you, waving to go.James Peng: Like all the nuances. Absolutely. See, that’s why the first thing is how we become a better driver. Essentially, it’s a continuous learning process. The first thing, that’s how we learn how to drive, right? The first thing is you avoid collision. You were a cautious driver. Then gradually, you start learning a bit of everything else. All the signs, all the nonverbal cues, and the hand gestures. So that’s exactly the case for us as well. The earlier model of our system is just driving and try to avoid collision. Then gradually, we put a lot of new things, new recognitions, new perception models into our system where we start recognizing, for example, the hand gestures, especially all the policemen, all the typical police gestures, stop, Go, and all those things. Then we start recognizing, for example, potholes on the road, small obstacles on the road. So it’s sort of how we learn. We start getting all the big pictures first.James Peng: Then we start learning all the nitty-gritty details down the road and put them to enhance our system. Regarding the second point, you’ll see, when all the cars are autonomous driving by themselves, it will be easier to drive. Yes or no? Because the thing that the big, especially in China, in the roads in China, the biggest challenge is not the other vehicles. In a lot of cases, it’s cyclists and pedestrians. While we can’t make them to be autonomous driving, so I think having the ability to recognize pedestrians, recognize the intention, their sign, and give You a specific example on a crosswalk, the way pedestrians look at and how they pay attention. For example, if they want to directly cross, they typically look straight. But if they were looking back, that means they will more likely not to cross the crosswalk. So we actually take those cues to decide whether we let them cross or proceed straight ahead.James Peng: So a lot of those details need to be put into the system to make it safer and at the same time efficient.Grace Shao: Is the judgment made on the spot using the cameras?James Peng: Yes, using all the sensors. Cameras and LiDAR provide the sensor input.Grace Shao: We take it all and then we make the comprehensive decision based on the input. Definitely China has more complex and less predictable driving conditions, especially given the number of pedestrians, cyclists, motorcycles we just talked about. So if you can drive safely there, I bet you can drive safely anywhere. But jokes aside, it’s really interesting because, we talk about as your fleet grows, you accumulate more and more world data, real-world data. Is that kind of data eventually becoming an advantage and a serious edge for incumbent fleets and a structural barrier that makes it very hard for new entrants to compete then?James Peng: Data is important, but data is not everything. So how we understand that is this. Probably give you an example. Think about how we learn. Let’s say we learn math, right? You can think of the data is like the practice sets that we have. Of course, you need to do enough of practice to be a good knowledge about the subject. But doesn’t mean that you have the problem sets of the whole world that you become math experts. So that’s exactly the same case. We need enough of data sets in order to know what the real world driving condition looks like. But we don’t need everything because once we know enough, we can always generate enough knowledge about the driving. So in a way, I think the driving data is important, but it’s not everything. So that’s exactly how you view this.Grace Shao: So as we speak of this, how do you view the whole landscape right now? Who would you say are your biggest competitors globally? How do you view the different markets playing out?James Peng: Yeah, that’s a very complex problem. I think a question to answer because I think that I think the first and foremost, I think maybe I got some premises on this. First, the whole mobility industry, especially related with autonomous driving, is very large. They certainly have enough room for several players. Second, it’s still at a fairly early stage for the fully driverless. I don’t think the landscape is already divided in the set. So giving that two promises, I think currently, when I look at the players, I have to judge their current deployment. Although everybody can say, oh, they will have, they will, they will have thousands, hundreds of thousands of vehicles on the road. Actually, giving the current situation, I use the metric as having fully driverless commercial operation as a baseline. Giving that as a factor, I think in the US, Waymo is definitely leading the way. Because Waymo already have 4,000 or 5,000 vehicles on the road, 4,000 plus.James Peng: Then, of course, there are some other players trying to play a catch up. Zoox, Cruise, maybe Tesla as well. So there’s, of course, some. So I would say in the US, Waymo is leading the way. There’s three to five players trying to play a catch up. In a global sense, I think from a technical point of view, China’s player is certainly on par with the US players. But from the total cost or the economical sense of a vehicle, for example, our vehicle is four or five times cheaper than Waymo’s vehicle. So in the global markets, such as Europe, such as Middle East, I think we will play a huge edge compared with the US players. Certainly, the whole landscape is still evolving.Grace Shao: But especially in the global markets, you’ll see we will definitely not play a catch up, but taking a leading position. You’ve been an advocate for hardware optimization, software optimization, battery solution optimization. Is that the strategy behind being able to have a vehicle that’s four to five times cheaper than Waymo’s? Or where’s the edge? Or how are you building these comparable vehicles at a relatively cheaper cost?James Peng: Yeah, I think as you mentioned, you definitely mentioned the most important factor to have the much cheaper price on the vehicles, which is optimization on Hardware, software, and everything else. I think another reason, of course, is because the whole ecosystem related with autonomous driving in China is relatively mature, and the scale is larger. So that price is cheaper. For example, the vehicle itself, the sensors, they’re relatively cheaper in China than anywhere else. Because of the ecosystem, because of the scale. So that plays an important role as well. That touches on something. A lot of physical AI, a lot of robotics companies are also now leaning into the Chinese supply chain. A lot of your peers, actually, autonomous driving, or even the EV players are now looking to expand into physical AI, whether that’s robots, humanoids, Quadrupeds, whatnot.Grace Shao: So you’ve stayed really focused. You’ve not launched any robots out there or anything. What’s your thinking behind this?James Peng: Yeah, absolutely true. I think autonomous driving definitely is probably the first large application of physical AI. All the others, humanoids, robots, and everything else, probably will have real applications down the road. For us, we view the autonomous driving as our brand and partner. Of course, as I mentioned, this is still early stage. I think we still have a lot of mileage to go. For all the other physical AI applications, we don’t have any specific plans yet. But I think they’re definitely interrelated. We may enter them down the road, depending on whether we need it or not. Because my judgment is that for the physical AI, it probably will follow the similar trend as autonomous driving. It might take another decade for it to mature. I think for us, it’s more like whether we have real applications for it. Give you a specific example.James Peng: Even for our fleet, once we go to hundreds of thousands, millions of vehicles, how we maintain those vehicles, how we do the charging, cleaning, servicing, They may use robotic applications. So I guess my view is that we will not probably do robotic actions just for the sake of doing it. But we may do the related applications when we see the real applications.Grace Shao: So it’s fair to say you’re cautiously optimistic that there is a potential use case further down. But it’s nowhere close to where it’s been hyped in the three to five years kind of use case.James Peng: Yeah, I think it’s the same thing as autonomous driving 10 years ago.Grace Shao: All right, well, let’s talk about your international footprint. You mentioned earlier, you have a global strategy. You’re in Europe and Luxembourg was your first launch, right? You’re in Southeast Asia, parts of East Asia, you’re in the Middle East growing really fast over there. Tell us about how you think of your next steps in your global expansion.James Peng: Yeah, I think the mobility demand everywhere is the same, right? There’s a strong demand across the globe. But we have to focus on the most important markets first. I think eventually we’ll go everywhere, because that’s our motto is we have autonomous mobility everywhere. That’s our ambition when we started 10 years ago. But our first launch, we have several criteria. One is related with regulatory, right? It needs to have relatively accommodating regulatory environment. Second is it needs to be a relatively mature mobility market. In a more obvious sense is that the local taxi fares needs to be relatively high, because I think that our pricing anchor point is always a human driving Taxi. So that price needs to be relatively okay. The third criteria are that we need a good local player to partner with, because a lot of other things like regulatory, like the back end services needs to be Handled by the local partners.James Peng: So judging from that three categories, I think Europe, Middle East, Southeast Asia, Japan, South Korea, Australia maybe. Those will be probably the potential markets for the initial launch. Of course, those are already big enough of number of countries. So we’ll pick and choose some to start with.Grace Shao: How do I understand your partnership models? Because I believe you’ve quite a few different kind of models depending on the location, the regulatory environment, potential partnerships, know-how, etc. Tell us about that.James Peng: Maybe I’ll take one step back first. Think about what is a robotaxi industry. The type of players, I’ll divide them into four categories. One category is for the user acquisition. Those are ride-hailing applications. Those are the Ubers and the Lyfts and the DDs alike. The second is a vehicle. You need a car, how you manufacture a car. The third is a driver. The fourth is all the back end services, cleaning, charging, servicing, insurance, and everything else. For us, our main job is creating a virtual driver, is making a really safe, efficient driver. So that’s definitely what we do. All the three other categories, we might have partners, we might do ourselves. So that, depending on the market, depending on what’s the strong local players, we might pick and choose players who’s handling one or two or three of the Other things. For example, we work with the ride-hailing platforms for the user acquisition.James Peng: We work with some of the back end services who’s providing the parking space, who’s cleaning, charging our cars. We also have OEM partners that work on the cars. So that’s how we view the partnerships landscape.Grace Shao: So after you deploy, say you send these out to Australia, what happens walk us through that. Because once these cars actually get off the boat and ships and they land in Australia, are they your responsibility or your partner’s responsibility? Do you send an engineer? Do you send your own management? Or do you transfer that know-how and maintenance know-how to the local partners to handle?James Peng: Great question. That really depends on the different partnerships and different regulatory environment. In some markets, it’s the local player who’s first in line with managing the fleet. That means in those cases, we manufacture the cars with OEM. But then once we ship the vehicles to the local country, Australia, giving you an example, or Singapore, let’s say, then we actually, in those cases, we sell The vehicle to the local partner. It’s like selling hardware. It’s like selling hardware. But we will, of course, have engineers handling the driving because we are in charge of the driving. So all the driving related work will be done by us. But then the user acquisition, the cleaning, servicing, charging will be done by the local partner. So those are one case. But in some markets, we actually ship the vehicle and we apply licenses by ourselves. The vehicle is still on our own book. But those are rare cases.James Peng: We actually, our preferred model is to have the local partner that handles most of the logistics and we will be the tech providers. We’ll essentially have the virtual drivers handling the driving and everything else is done by the local partner.Grace Shao: I see. Would you ever view OEMs as competitors in any way? Because right now you’re partnering with them. You’re giving them the software enablement, right? Would they produce their own robotaxis?James Peng: I think in most cases, in my view, that they probably will be partners instead of competitors. It’s very different because they were mostly on the hardware business. Very few of them will be in the robotaxis business because they’re quite different.Grace Shao: I see. I see. Something a bit niche is, I know you run robotaxis as well as trucks. Walk us through how you think about that. Why do you guys also have a truck business? What kind of scenarios are they already being deployed in? I believe they’re the heavy trucks and then the light trucks. How do I understand this?James Peng: Yes. As I already mentioned, think about our business is that all our technology is that we are creating a safe virtual driver. Virtual driver is our core. As a driver, you should be able to drive all different types of vehicles. The two biggest applications for driver is one is for the transportation of human beings and the other is for goods. That’s related with robotaxis and then for all the trucks. Within the logistic industry, there are actually three categories. One is for the long haul, which is typically done by the heavy trucks, the 18 wheelers and whatnot. Then there’s also in-city network, which is the light duty truck. Then there were also the last mile, typically is handled by much smaller vehicles. Our main focus, of course, is on the long haul and the intra-city transportation. On the last mile, we are the providers of the domain controllers, but that’s not the areas we’re working on. So think about we’re creating driver.James Peng: Driver should be drive different types of vehicles. That’s how we view the trucks versus the robotaxis.Grace Shao: Usually, I would assume these are like ports, airports, maybe?James Peng: They will eventually be everywhere as well. We start with ports. We start with some dedicated routes, for example, like a minefield to the local distribution center, those 30-50 mile routes. The reason we start with those applications is because typically it’s mostly because of regulatory reasons. Because the ports and the dedicated routes and those are typically a semi -private road. It’s much easier to get the regulatory approval. Of course, we are working on long haul trucks as well. We already actually have a fleet of heavy-duty trucks doing the real goods transfers on highways, but still with a safety driver, of course. Eventually, we’ll be fully driverless as well.Grace Shao: You’ve said you have a target of running fleets commercially across more than 20 cities by the end of this year. What do you know today that you could not have learned without actually operating at scale already on the streets? What makes you have the confidence to do that now, I think, compared to maybe a few years ago?James Peng: Again, I think for robotaxis commercial business to be a reality, there are three important factors. One is technology. Second is regulatory approval. The third is user acceptance. I think within the last three to four years, we have gained a lot of experience on all three categories. The reason we were confident to deploy in 20 cities is because clear vision on the regulatory approval. There’s a lot of cities globally, both in China and in some global cities, they actually start coming out with regulations for supporting fully driverless commercial applications. Also we have planners. Planners want them. So I think all the important factors are falling into place. That gives us confidence.Grace Shao: I’m going to play devil’s advocate a little bit here. With the rise of AI right now, there’s a bit of a fear of replacement of people’s jobs. The rise of autonomous driving obviously lead to job loss in people who are currently drivers. How do you view that? Because just now we talked about robotaxi drivers. We talked about people driving heavy-duty trucks that could potentially be replaced. Frankly, I’m in a camp that people could be maybe freed up to do more things that they can do otherwise. People will find alternative careers. But are regulators becoming more cautious. How do you feel about the current public pushback a little bit on AI, autonomous driving, autonomous everything at the moment?James Peng: Yeah. Actually, driving is a hard job. Driving is a lot of cases in a stop vehicle for 10, 12 hours a day. It’s a really tough job. The thing that because autonomous driving itself is a highly regulated industry, the pace of our roll up is determined by the number of licenses. The thing about also a lot of the drivers were not young. The young generation, younger generations actually don’t want to be drivers. So I think, especially a lot of the global markets, we actually come in to fill the gap for the labor shortage for the driver. We’ll not change the human driving vehicles overnight. It will be a gradual process. So that’s sort of the development of the cities and the human society. It takes time. It becomes gradually a norm. Then, as you just mentioned, then the drivers can find other jobs.James Peng: Even we actually absorb a lot of jobs, for example, for the remote assistance, maintenance, which are much safer and much less strenuous job conditions. So I think society as a whole has always a way to absorb jobs. To adopt, adapt, and then evolve.Grace Shao: The current pay for a lot of times for these heavy truckload drivers are like 200 to 300k USD. They’re considered very high-earning jobs. But at the same time, people forget they’re extremely dangerous. There’s life lost constantly on the roads. So I can see that could be very valuable if people can actually replace those routes with robo-drivers.James Peng: It’s not just replacing. Look at the truckers. Their average age is 45 plus. In North America right now? In North America. In China, they’re 40 plus as well. So a lot of younger generations, they don’t want that type of jobs. We’re coming not only to replace, but actually to fill the void for that job shortage.Grace Shao: All right. So I think I want to wrap up our conversation soon about this. Is there anything I’m really missing, you think, about robotaxis and your business at this point?James Peng: I think we’ve probably covered a lot of topics.Grace Shao: Oh, I had one question. Another one about your business before we go into your personal thing. You mentioned Croatia just now when we were talking offline. I thought that was so fascinating. In my mind, I thought these robotaxis were being deployed mostly in futuristic cities like Silicon Valley and SF, out here in Shenzhen where we’re here today. But Croatia, help us understand the need for robotaxis in these countries where a lot of the roads are aged, are not really made for cars to start with, Are not easy to drive in, actually, even for humans. Then how does that make sense even for your economics, actually?James Peng: Of course, there were some challenges. From a technical point of view, two challenges initially. One is there’s a lot of roundabouts. Actually, there were not many roundabouts in China. So although a lot of other very complex situations like heavy storms and whatnot, we were able to handle them really well. But roundabouts, we had some, but we haven’t trained that much. So we actually have to retrain a bit on the roundabouts. The second is the trams. There were just a lot of trams in the Zagreb. Their behavior of the trams is different from cars. So we need a little bit more training to get used to it. But it’s like how we drive. When we go to a new city, we might not drive as a perfect driver initially. But then we learn and adapt. Once we have a good learning system set up, then we can quickly learn. That’s exactly our experience in Zagreb, Croatia. Two things that we actually have to learn in Croatia.James Peng: One is the roundabouts. The other is trams. Because those are not something that typically you will see on the roads in China. So for those new situations, it’s like how we learn. How we learn driving. When we go to a new city, we probably know 95%, 98% of the situation. Some of the scenarios probably we didn’t encounter previously. Then we learn. We adapt. So that’s exactly the case for us in Croatia. After three to four months of learning and training and retraining, we actually were able to handle those cases like roundabouts and trams really well. Because there’s a lot of roundabouts in other cities in Europe. They actually have different rules for roundabouts. Some of the roundabouts, I think the cars outside roundabouts have right-of -way. Some of the vehicles inside the roundabouts have right-of-way. But we can adapt once we have the system set up.James Peng: So as I mentioned, the most important characteristic of our system is not how powerful it is, it’s how adaptive and how easy to learn on our system so that We were able to adapt.Grace Shao: Brilliant. So a lot of localization as well for your vehicles. I have two last questions. One is, what is something you think people still get wrong often about your sector, in this case, autonomous vehicles, autonomous mobility? The second question is a bit of a curveball. I’ll throw it to you first, you can think about it. What is one differentiated view you hold? Something that’s a bit against consensus, maybe.James Peng: Autonomous driving industry, I think people put too much focus on technology and probably underestimated the complexity of robotaxi as a business. Essentially, of course, technical is the most important. If you can’t drive safely, you’ll not have a business. But once you even have the most safest driving, you still have to, as a business, there’s a lot of other things involved. For example, how you deploy a fleet, how you make the pickup and drop off easy for the user, how you handle all the edge cases of the complaints of the Passengers, how you make the charging, servicing, cleaning efficient. For example, especially give you a specific example, the electricity fares during the day fluctuates. If you have the charging at the low fare, you can save a lot of cost. Then how you manage your fleet? Although you have the low fare for electricity, but the demand of the passengers is really high. How do you make a decision?James Peng: So essentially, it’s a lot more optimization involved than just the driving itself. I think a lot of people underestimate the complexity with the management of a fleet of autonomous driving vehicles. We actually, as a company, have put a lot of emphasis and take a lot of efforts in optimizing everything. So that’s why I think those will be a very strong competitive edge down the road.Grace Shao: Once you guys scale further, especially.James Peng: Exactly, absolutely. Very interesting.Grace Shao: The second one, I’ll put you on the spot again. What is one differentiative you hold?James Peng: I think I’ll take the one related to the answer of my first question. Is that, again, people always put too much emphasis or give too much credit on zero to one and think about less for one to ten. Give a lot of examples, right? People always think an invention is so hard, but putting an invention to be a scaled application is equally hard or a lot harder. Because the scale involves cost optimization, involves user education, involves a regulatory approval, it involves making the things a lot easier to use. So many examples like this, right?Grace Shao: Definitely. Say a rocket is put in the sky. Oh, it’s so hard. But having the rockets to always be able to safely take off and recycle, that’s extremely hard.James Peng: So I think related with autonomous driving is we certainly crossed zero to one. I think we crossed one to five, maybe. But from five to ten, ten to a hundred, I think there will be still a lot of challenges ahead.Grace Shao: That’s very insightful. I agree with you. When we look at the internet era and a lot of players that still stand today versus who are the actual ones that created a lot of the internet use cases we know of today. Thank you so much, James. It was a pleasure and an honor to learn more about your business, yourself, the man behind the company that is changing the future of autonomous mobility. Thank you again.James Peng: Thank you for having me.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  From Beauty Apps to AI Agents: Meitu’s CFO Gary Ngan on the Future of Visual AI 14.07.2026 50min

  In this episode, I spoke with Gary Ngan, CFO of Meitu, about how the company is evolving from its roots in consumer photo editing into a broader AI-native visual creation platform across photo, video, design, and agents. For many investors, Meitu is still associated with beauty editing and selfie apps, but Gary frames the company today as an AI application company serving both leisure use cases and productivity workflows.We spent a lot of time on Meitu’s business edge: why visual AI is not just a foundation-model race, and why aesthetic judgment, controllability, and vertical context matter. Gary argues that visual creation is highly subjective. The same prompt can mean very different things across countries, cultures, product categories, and commercial goals. That is why Meitu is building verticalized products such as Picchi, DesignKit, Kaipai, Vmake, and RoboNeo, instead of relying only on one general-purpose AI model.We also discussed the business model. Consumer subscriptions have become Meitu’s main revenue engine, while advertising is no longer the strategic growth driver it once was. Gary explained the shift in Chinese consumer willingness to pay for apps, the higher ARPU potential in overseas markets, and how new AI-native products like Picchi could introduce additional monetization through personalized models and AI credits. He also addressed AI compute cost, why more than 90% of Meitu’s AI outputs come from its own models, and why the company sees AI as a TAM-expanding opportunity rather than simply a margin risk.Finally, we covered competition and globalization. Gary explained how Meitu thinks about competing with ByteDance, Kuaishou, Canva, Adobe, Shopify, Alibaba, and other AI-native visual tools, and why Meitu’s approach is more vertical-driven than general design-platform driven. Lastly, we touched localization, from different beauty preferences across markets to why true globalization requires understanding culture at a much deeper level than translation or marketing campaigns. CHECK OUT THIS CONVERSATION. Gary’s so cool.To find the previous episodes of Differentiated Understanding, see here.Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.Season two will host a series of guests from analysts, VC investors, builders, researchers, founders, and product managers. For more information on the podcast series, see here.Chapters:00:00 What is Meitu today? Mapping Meitu’s product portfolio04:14 Why vertical focus still matters in the age of AI agents06:36 Aesthetic standards, subjective prompts, and visual AI nuance11:31 How AI changes art and creative expression15:02 MeituHub and MiracleVision as visual AI infrastructure17:01 Why Meitu needs its own models20:55 How Meitu chooses models and the role of designers25:09 Meitu’s AI legacy and generative AI strategy28:27 AI compute cost, ROI, and gross margin38:22 Subscription growth and advertising dependence40:47 Partnerships with consumer chatbots and platforms43:27 Competition with ByteDance, Kuaishou, Canva, Adobe, and others49:49 Deepfakes, misuse, and AI safety safeguards52:42 Globalization, localization, and cultural differences59:16 The biggest investor misconception about MeituTranscript (AI-generated, for reference only)Grace Shao:Gary, thank you so much for joining us today.Gary Ngan:Hi Grace. Good to be here.Grace Shao:I’m really excited to have this conversation. To start, tell us what Meitu is up to these days. For a lot of investors and users, when they think of Meitu, they still think of the selfie and beauty-editing app. How would you define Meitu today? Is it still simply a consumer AI company, or is it much more than that now?Gary Ngan:Meitu is no longer just a selfie or beauty-editing company. I would define Meitu today as an AI application company specializing in photo, video, and design.We focus on very high-value verticals where we can leverage AI to deliver high-quality results to users. We often refer to these users as prosumers: people who have strong design needs, but no prior formal design training.So that is how I would define Meitu today.Grace Shao:That makes sense. Tell us about the products, because you have quite an array of them. Some are more consumer-facing, some are more prosumer-facing, and some may even be a bit more enterprise-facing. There is Meitu, BeautyCam, Wink, Picchi, DesignKit, Kaipai, Vmake, RoboNeo. Help us map out the ecosystem.Gary Ngan:We think about Meitu’s product portfolio in two main categories: applications for leisure and applications for productivity.Applications for leisure include the Meitu app, BeautyCam, Wink, and Picchi. They serve use cases such as photo-taking, photo editing, and video editing, usually for sharing on social media.The Meitu app and BeautyCam are our core consumer applications. Wink extends our capability from photo to video editing. Picchi is our latest portrait-retouching agent, focused on personalizing editing styles.The second bucket is applications for productivity, which includes DesignKit, Kaipai, Vmake, and RoboNeo. These products serve professional and commercial content creation needs.DesignKit focuses on e-commerce product-listing design. It helps merchants and creators produce product images, model images, and marketing materials much more efficiently.Kaipai and Vmake focus on talking-video and marketing-video creation. Kaipai is more focused on the domestic Chinese market in verticals such as insurance and real estate, while Vmake is seeing strong traction in the U.S. fitness and wellness market.To give you a sense, as of May this year, Kaipai had about three million monthly active creators, and cumulative content creation exceeded 400 million pieces. For Vmake, ARR in the first quarter of 2026 was about US$4 million.Then there is RoboNeo, our AI-native agent product launched in July 2025. It is currently targeting the AI short-drama vertical. Its agent workflows can support scriptwriting, characters, storyboards, visual generation, and asset management.So that gives you a rough idea of the different vertical products. But the ecosystem logic is very important, because many new products come from user insights we observe in existing products.For example, DesignKit came from the poster-design function within the Meitu app. Kaipai came from the AI teleprompter feature in BeautyCam. Picchi came from new user behaviors we observed in the Meitu app.So our portfolio is not a random collection of apps. It is a structured expansion from consumer imaging into AI-native workflows across photo, video, and design.Grace Shao:That makes a lot of sense. But in the age of AI agents, would it make sense for Meitu to consolidate a lot of these apps? Or do you still think it is better to keep them separate for different types of users and workflows?Gary Ngan:In the age of agents, we still believe we should focus on high-value verticals, because different verticals have many differences.First of all, aesthetic standards are very different. I’ll give you an example. The phrase “handsome guy” would be interpreted very differently in an application serving the U.S. market versus an Asian market.Even within the Asian market, if you are addressing e-commerce merchants selling gym products versus formal apparel, the word “handsome” will also be interpreted very differently across those verticals.So being able to separate these different verticals gives you a very good head start in focusing on the aesthetic standards that each vertical needs.Also, users in different verticals have very different behaviors and workflows. It is very important to build those workflows and that know-how into each vertical in order to create the right products.With agents, you can cover a slightly bigger boundary. But I still think you want to focus on different verticals to maximize the output for the user, and also make it more efficient and easier to market within each vertical.Grace Shao:That is really interesting. You touched on something that a lot of people discuss when they think about visual AI, which is how to ensure consistency and accuracy when translating language into visuals, especially when text can be in different languages and words can be subjective.As you said, if you say “handsome” and I say “handsome,” that could mean very different things in our heads. How do you ensure that identity, description, and nuance are not lost? You mentioned vertical focus, but what is the technical side of that?Gary Ngan:Instead of calling it fragmentation, I would say vertical focus is very important. That sets the tone.Behind that, we also have a large team of designers who control different points in the model fine-tuning process. They help set the right direction for the aesthetic standards within each vertical. That is something differentiated in our product offerings.Then, if you move one step forward, the data flywheel is also very important. Users within a vertical give us data through their behavior: which photos they use, which photos they edit, which ones they throw away. That is very important for us to improve image creation.We are also in the camp that believes controllability in visual applications should not just come from AI. You still need manual touch-ups at the end for users to make last-mile improvements, because aesthetic judgment is very subjective.Even if an AI model works with you every day, you will always have subjective comments and small edits you want to make.One other interesting point is that when we talk about aesthetic standards, in the case of leisure products, the face is usually yours. So you have a strong say and a strong sense of what is good for you.The way we learn that is by studying the trend in your geographic location, giving recommendations, letting you try them, and then as you use the application, you tell us what is most suitable for you.Picchi is a newly launched app where you can upload three to five sets of original photos and edited photos that you have done yourself. We are then able to learn that pattern and create a specialized model for you. The next time you want to edit a photo, you can call up the model that you trained yourself and apply your own aesthetic standard to your photos.On the productivity side, however, aesthetic is not the ultimate holy grail of an image. It is very important, but whether that photo or video is effective in driving conversion, likes, or comments is also very important.When we deliver images and videos to users, we take into account key data from that particular vertical and the metrics that matter for results. It is not just whether someone is subjectively handsome. It is whether this person, image, or video can help sell your product.So the two camps are quite different.Grace Shao:That is really interesting. From a pure consumer point of view, you pointed out an important nuance. If I upload my own face to a Meitu product, it might give me very smooth, pale skin and a more angular jawline or chin. But if I use an American fine-tuned product, it might give me more contouring. It is a very different aesthetic.But to your point, if you are a prosumer, a content creator, influencer, or e-commerce seller, then it is not only about whether the image looks good. It is about whether it drives sales.I want to ask something slightly more philosophical before getting into the businesses. Technology often changes art. Photography changed painting. Software like Final Cut Pro and Photoshop changed photography and video. How is AI now changing how visual artists approach their vision and craft?I have also spoken to companies like Kuaishou, where they have Kling and are partnering with AI-native film studios. These people are creatives, but they do not view AI as disrupting their work. They use AI as a tool to create their work. What do you think about this at a high level?Gary Ngan:If you look at our core value proposition, our mission statement is uniting art and technology.One step down from that, we are trying to democratize design, art, and creative expression.What AI changes is that it enables people who have creative ideas, but not the actual training or skills, to express those ideas.A lot of the time, we have creative ideas that we want to express, but our motor skills are not refined, or we do not know how to put colors together. AI can help us deliver those ideas.That is fundamentally changing the artistic landscape to a certain extent.Other companies may say that existing professional filmmakers and designers can use AI to make things more efficient or create things in a different way. That is great. But I think the bigger impact on the world is enabling many people who previously could not create anything. They had ideas, but could not express them. Now they are able to express them.That is what is fundamentally changing the industry.Grace Shao:We have talked about how you have many different products, and you explained that they are targeted at different verticals. How should we understand MeituHub and MiracleVision? Are they the operating system underneath everything?Gary Ngan:MiracleVision and MeituHub are the visual, image, and video infrastructure that we have. Our applications are built on top of these things, so they go hand in hand.We are still fundamentally an AI application company, but we also need visual infrastructure.To give you an example, over 90% of our AI outputs come from our own models. There are many situations where we think the models we fine-tune ourselves perform better than third-party models. There are things that other people do not necessarily focus on, so we have to invest in R&D and create that infrastructure ourselves.MeituHub is also a way for us to export that technology. People can use our APIs and skills to build their own applications or integrate them into their own systems. That also reinforces our vision of democratizing design.Grace Shao:That is a perfect segue to my next question. I understand your team fine-tunes your own models, but you also build on various open-source models. Why does Meitu need its own model?Traditional application companies often did not need to own the foundation layer. So why does Meitu need that? And more broadly, why are so many Chinese consumer internet companies pushing out models? You see even companies in food delivery, ride-hailing, and other consumer internet sectors releasing models. Is this a cultural push, or something else?First, how does Meitu think about it at the company level? And if you can comment, how do you view this competition across the China ecosystem?Gary Ngan:It is harder to comment on the overall market, because what we do, visual image and video models, is quite different from language models. So I will focus on why we do our own models.Our belief is that one general model will have difficulty performing well across all verticals, because context is so important.If we do not have our own models, then aesthetic standards will be set by third parties. When a third party creates a model, they have their own idea of what aesthetic standards should be. They have their own idea of what should be generally good given a certain prompt word.But that may not be applicable to the verticals we are working on. That is why we need our own models to serve those purposes.At the same time, we integrate third-party models because even within a vertical, there are corner cases or edge cases that our core model may not be optimized for. In those situations, we call on third-party APIs to serve users.As an AI application company, the only point of optimization is user satisfaction. We use a combination of our own models and third-party models to serve that purpose.Sometimes we see more and more users calling third-party APIs for similar prompts or similar creation scenarios. Then we will augment our models to cover those scenarios as well.Our model is continuously growing, but we make it very vertical-driven. We have told the market that we are not in the business of creating a general-purpose model. We are creating vertical models. But that does not mean we are giving up model training altogether.Grace Shao:So there is a lot of industry know-how in each vertical that you have.When it comes to which foundation model you choose for each task, how do you make that decision? I spoke to one of your colleagues at SuperAI, Rocky, your VP of R&D. We discussed the fact that you use a series of open-source models and also work with different model providers. What is the main factor in choosing which model to build on for which vertical? How do you delegate tasks across models?Gary Ngan:At a high level, there are two main forces behind that.One is user behavior. If a user uses Model A to create a certain task, and many users do not press save or do not continue working on it, then we probably need to serve that task with a different model. It is a data flywheel type of operation.The other factor is our large team of designers, who are very involved in training these models. Designers help set the standard for what the right model should be for a particular task.This is a very important differentiation for our company versus most technology companies.I am not sure if you are aware, but our founder and CEO was an art student by training. In his day, he was the top student in the Tsinghua Arts Academy entrance exam for oil painting.In his mind, aesthetic standards are always very important. Because of that, designers in our company have a very strong say in every product and every feature we launch.Over more than a decade of designer training, the rest of the company has also developed stronger aesthetic standards. Product managers and R&D engineers also have quite high aesthetic standards now.Our company is organized toward delivering the best aesthetic standards for users. That is very differentiated from most tech companies.Most model companies may think: We solved this problem, the photo is done, the video is generated, the main character is stable throughout three minutes, so the mission is accomplished.But for our designers, apart from the stability of the main character, they also look at whether the lighting is realistic, whether the color fits that vertical, and whether anything feels wrong from an artistic point of view.Those are the things we really focus on when fine-tuning. That is something we are very proud of, and I think it is a major differentiator.Grace Shao:Even as a consumer user, I can say your products have that extra last-mile touch-up tool that others often do not offer. It is meticulous and accurate. You can zoom into pores or details in the background. It is interesting to hear about your founder’s background and that artistic legacy, because that culture really shines through the products.Speaking of legacy, I want to understand Meitu’s AI legacy and strategy. You have been working in image and video for over a decade, so you obviously have a vast database and deep know-how in visuals. How does that industry expertise translate in the age of generative AI and in the future agentic world?Gary Ngan:Generative AI has changed the speed, scope, and value of what we can deliver.First, speed. New AI capabilities can now be translated into user-facing features much faster, helping us launch popular effects globally and drive overseas growth.Second, user experience. Generative AI enables effects that traditional computer vision technology could not fully achieve.For example, facial and body retouching is no longer just manual adjustment. AI can reconstruct details, lighting, and texture in a much more natural way.Third, target addressable market expansion. AI helps us broaden into productivity workflows like DesignKit and Kaipai, which were things we traditionally could not do.Overall, AI is very empowering in helping us get to where we want to go.Before AI, all we could deliver was better tools. But in order to use those tools, you still needed pretty good aesthetic standards or some understanding of the basics. Otherwise, giving you those tools did not really help much.With AI, you still need maybe 10% or 20% of that understanding, but the requirement is reduced massively. AI can give you many choices to choose from, and then you can start building from there.That helps us move from leisure applications to productivity applications. That is really what the strategy is about today.Grace Shao:AI can act like a guide or mentor if you are new to a certain craft or sector.Let me ask the spicy question. AI compute cost is obviously extremely high. Image and visual generation are expensive. How does the economics work right now? Does AI compute affect your gross margins, or are you seeing ROI already?Gary Ngan:As I said, currently over 90% of our generative outputs come from our own models. As long as we are using our own models, the cost is very manageable. Our gross margin is still over 70%.Also, when you are editing your own face or editing a product photo, these things are not purely AI-generated. You may want AI to edit a little bit, remove someone from the background, or create a new background for a product, but the entire photo is not purely AI-generated.It is true that AI inference has a cost, but it is not as if every photo now incurs a lot of cost. We need to make that distinction first.As we move into new verticals, like music videos and AI short dramas with RoboNeo, those are more experimental. We are using more third-party models, so margins on those new applications will be much lower than something like Meitu Xiuxiu.But as we continue to progress, we will develop our own models to replace some of the third-party costs. Over the longer term, we also believe API costs will come down.So we do not see this as a threat. In fact, the integration of AI has expanded the addressable market so much that it is a much bigger opportunity than threat.Grace Shao:I appreciate that nuance. You are explaining that the first type of usage does not use as much AI or token cost as people might expect from the headlines. The second part may be more expensive, but we are still in very early stages.Let’s take a step back. For some of our American or Western audience, they may not be as familiar with Meitu. How do you fundamentally make money?In your public disclosures, consumer paid subscribers grew more than 30% year-on-year. What is driving that growth? Is it that the AI features are much better now? Is it global expansion? Help us understand the business model and what is driving growth.Gary Ngan:Our main revenue source is subscriptions, mostly on the leisure side.That is our second growth curve. The first one was advertising, but that business has matured.The second growth curve, which is still growing quickly, is subscription on the leisure side. The main driver has several parts.The first is China user behavior. Paying for apps really started after COVID. Before COVID, virtually all applications were free. They competed through free usage, advertising, or redirecting traffic to other applications to generate money.After COVID, many user-facing applications realized advertising was under pressure, and they wanted new revenue sources. Without colluding, many of them started charging users. That kickstarted the user subscription process.What is less understood is that users then began realizing that applications have to be paid for. As time goes by, the behavior of paying for applications grew on them.Now there is much less of the issue of, “This app has to be paid, so I am not using it.” That was a real mindset before. Now it is more like, “This app costs 15 RMB a month. Is it worth it?”That is what I would describe as the beta factor, meaning the overall market. Users are becoming more and more used to paying for mobile products.That is one reason we are confident that paying subscribers and the paying subscription rate of our leisure applications can continue to grow.To give you a sense, we have done surveys. The global paying percentage for photo and video applications is about 20%. If you benchmark music and video apps globally versus Chinese equivalents, the Chinese equivalent is usually around half. For example, if Spotify is around 40%, the Chinese equivalent might be around 20%.So if global photo and video applications are at about 20%, China should at least achieve about 10%. Right now, we are around 5% to 6%. So there is still another 80% to 100% growth headroom there.The second growth potential is international expansion. In high-ARPU areas like the U.S., Europe, and East Asia, including Japan and Korea, the base ARPU is already much higher than China, anywhere from 100% to 200% higher. The paying percentage can also be much higher.To give you a sense, one of our applications called AirBrush has over 50% paying percentage in the U.S.As we launch stronger operations in these high-ARPU countries, we expect our blended paying percentage to grow further.One final point about monetization is that we are integrating more generative AI capabilities into these applications. For example, Picchi is an application for leisure, but it uses an agent for editing, and that has a completely new business model.On top of regular subscription, if you want to create your own model to apply your own editing skills, you have to pay for that model separately. That is another monetization test we are currently working on.Grace Shao:When I was reading your earnings reports, I was a bit surprised that your highest revenue generator is consumer subscription, because the default mindset is that people have very little willingness to pay.But as you said, whether it is the change in behavior in China, or people having more appetite for premium add-ons or AI-plus features, willingness to pay is changing.There is also the fact that advertising can be annoying to sit through. You do have a lot of advertising, I have to say. Spotify does too, and I think that drives people to pay to get rid of advertising.On that note, do you think you will gradually reduce your dependency on advertising? It is still your second-largest revenue model.Gary Ngan:We have not relied on advertising since 2022. At the corporate level, we made the point that we are no longer strategically trying to drive advertising.You have seen our advertising business grow at low single digits over the past few years. Advertising is not what we are fundamentally trying to drive.However, we are experimenting with advertisers on fun and engaging AI-infused campaigns.It is hard to describe with words, but you can imagine users generating viral photos with a brand advertiser’s branding that fits the brand image. That gives the uploader a lot of likes and gives the advertiser a lot of exposure.So we continue to experiment with those things. But in any case, we are not relying on advertising for business growth.Grace Shao:On partnerships, I had this idea and I do not know if you are doing anything like this. Would you partner with some consumer-facing chatbots in China to help them with video and visual capabilities?For example, could someone go into a consumer chatbot and call up Meitu’s capabilities? There may also be competition there. How do you view your relationship with these players?Gary Ngan:We are open. In fact, we are already an official partner with WeChat, not on the Xiaochang side, but in another area. I do not remember the exact English name, but basically when you are using the chatbot, you can call up Meitu.Right now, it is still a lighter relationship, almost like traffic redirection. But our goal is to democratize design. Being able to work with more people and enable more people to access that power to express themselves is something we are open to.Grace Shao:That makes sense. It feels like they may not want to put as many resources into this specific use case, and you have the know-how in doing the best video and image editing.Gary Ngan:I would not say they do not have the edge. I think they may just not want to focus on that.Creating these applications requires a lot of focus. It requires the right organizational structure and a laser-sharp focus on trial and error, and on creating the best aesthetic output for users.These may not be the things that larger companies want to invest in. It is important relative to our size, but to them it may be something they do not want to focus on. If they wanted to do it, I think they could.Grace Shao:Let me challenge you a little bit on big tech. In China, ByteDance and Kuaishou clearly have a lot of edge and moat from massive pools of image, visual, and video data. In the West, we have Canva, Adobe, and other global applications. Even Shopify and Alibaba are creating e-commerce staging and design tools.In this big world of competition, or peers if we put it more nicely, how do you see Meitu’s strength? Who are the most relevant competitors that are similar to what you do? And who may look similar on the surface but are not actually doing the same thing?Gary Ngan:We have to separate it into two categories.On the leisure editing side, with the exception of one business unit within ByteDance, there are not many large companies doing that globally. I do not think there is any real large company doing that in the U.S.There are smaller companies, but they are much smaller compared to us.On that side, our edge is really continuing to follow and set the trend for the latest aesthetic standards and what helps users stand out on social media. These are the things we have been doing for more than a decade, and we will continue to excel in them.On the productivity side, there are many companies doing similar things, but taking a much more general approach.For example, Canva and Adobe use one product to satisfy different verticals. Adobe is organized around media: photos, vector diagrams, video, effects, and so on. Canva is one editor trying to fit many situations. Figma is also one app serving many applications.They are design-driven. We, on the other hand, are much more vertical-driven.We are not restricting ourselves to a specific media type. We are saying, within e-commerce, what do you need?You need product photos. You need very short product videos. You need the ability to generate batches and batches of photos. You need to know the latest trend on the e-commerce platform you are selling on. For that particular product, you need to know the selling points. You also need to know the rules of Amazon or Temu and what you need to abide by when selling those products with pictures.All these things are baked into DesignKit.If you are using Canva, I highly doubt it will have a red flag saying, “You should not be using minors in this product photo.” Canva may not even know that you are creating a product photo in the first place.So these are the different focuses we have.In terms of competitors, it is hard to say who is a direct competitor, because at the end of the day, you can use Photoshop, Canva, or our products to create an e-commerce photo. They are all peers, but we take different approaches.If we take a step back, generative AI is still very early. It is 2026 now, but generative AI really only started in earnest late last year for visual use cases. Before then, a lot of generative AI photos still looked AI-generated.Grace Shao:They were quite bad. There might be six toes, or the face was disproportionate.Gary Ngan:Even if there was nothing obviously wrong, you would look at the photo and know it was AI-generated. It did not feel real.Now we are just starting to see things that are harder to distinguish between human-made and AI-made. This is how we can empower the industry and increase efficiency.We are still very early in this market. That is why we are very optimistic and see a lot of opportunities.Grace Shao:A little side note: in 2019, when I was still with CNBC, I covered deepfakes. At the time, there were a lot of deepfake videos of Obama or Zuckerberg. A startup even made a deepfake of me. It was literally just plugging someone else’s face onto my head and body, and nothing really worked.But now, fake images and videos are getting very hard to distinguish with human eyes. How do you view the ethical side? How do you stop misuse of the technology?Gary Ngan:First, we have put in safeguards.For example, on Picchi, if you generate a model of yourself using your own photos, that model cannot be applied to anything other than your face. If we detect that it is not your face, we will not allow you to apply that model to another face.In some of our generation applications, we have also put in safeguards around certain words, such as violence or pornographic images. You cannot generate those using our applications.So there are safeguards that we put in place. Obviously, we can only do so much.One thing that makes it slightly easier for us is that we organize our applications into different verticals. Users come into our applications with a very strong intent. They know they are creating e-commerce photos, for example.Instead of giving them a general chatbot where any random person can come up with a random idea like putting their face onto the President of the United States, it is harder to imagine someone using DesignKit to run a prompt like that.Organizing into different verticals also helps us mitigate the risk a little bit.Grace Shao:The last area I want to talk about is globalization and your global strategy. Meitu is globally available. It is interesting because, as you said, you focus on each vertical, and you have not done a big splashy general marketing push. It also feels like that is true geographically. You are in Southeast Asia, Japan, Korea, Europe, the U.S., and so on.Help us understand global scaling. What have been the challenges? How have you done it successfully? And how do international users from different regions behave differently from users in China?Gary Ngan:I will answer the second part first. Users in different regions all behave very differently.Grace Shao:Give me all the stereotypes.Gary Ngan:Not stereotypes, but I will give you one example.We were doing a user focus group in the UK and spoke to a male influencer. He said, “Your app can edit my jawline? That is incredible. I would totally pay for it. But I do not think it is a good idea to smooth out my skin.”Grace Shao:That is interesting. So it is not okay to pretend you have better skin, but it is totally okay to have a chiseled jawline?Gary Ngan:He did not mention whether it was ethical or not. That was just his feedback, word for word.The challenge, or the interesting thing, is that we have to really listen to what users want in those markets. Different geographic locations need the right mix of features and marketing campaigns.I will give you another simple example. A few years ago, we were looking at Lunar New Year. Koreans also celebrate Lunar New Year, and in China Lunar New Year is a festival where we get a lot of usage.We had launched features in China that were very popular that year, but in Korea there was no uptick. Later, when we had local Korean colleagues helping us run marketing campaigns there, they told us that Koreans generally celebrate Lunar New Year with white clothing and a white theme, while Chinese people celebrate with red.Our Chinese marketing team was surprised, because in China, white is usually associated with funerals. It did not register.That example tells us there are many things we need to immerse ourselves in culturally to understand how people behave, what they care about, and what the standards are.We cannot stereotype anything. Every place and every person behaves very differently.That is the biggest challenge, but also the biggest opportunity.Now we are setting up offices in different parts of the world. We are sending product managers overseas regularly to do more focus groups and, more importantly, to experience the lives of the users they are trying to serve.In the past, we relied too much on consultants, reports, or reading online. That is not enough anymore. We are fixing that, and I think we are making progress in some countries.Fingers crossed, we will continue to grow bigger in Western markets.One other tailwind that has helped us is TikTok and K-pop. Back in the day, editing a photo seemed socially unacceptable to a certain extent. But with TikTok, people are more relaxed about filters being applied and playing around with your face. It is no longer as taboo in many Western countries.The rise of K-pop is also influencing cosmetic styles, and that becomes a segue for us to try different things in Western markets.There are very interesting things happening. But the most important thing is for us to really understand, live, and breathe those cultures so we can create things users want.Grace Shao:That is meaningful, and it feels important for a new generation of Chinese companies going global. Localization cannot just be reading headlines or high-level reports. You have to understand the culture, because culture influences the business.To wrap up, I really appreciate your time. My last two questions: first, what is the biggest misconception investors currently have about Meitu’s business?Gary Ngan:One of the biggest misconceptions is that general models are going to destroy everything, and that there is no place for AI applications.We think that is quite unlikely on the visual side. I am not sure about the language side, but on the visual side, aesthetic standards are very subjective and personalized, and a lot of controllability is needed.Different verticals have different interpretations of the same words. So the way models are trained and organized, even with agents, makes it unlikely that a one-size-fits-all general model can satisfy all verticals.Every vertical has its own workflow and standards. AI application companies are very important in making those adjustments and optimizing workflows for users.That is the biggest misconception.Grace Shao:I agree with that. We are seeing more of that realization in the market now. You have strong vertical use-case AI-native companies coming through, like Harvey. I have also met companies where former investors are building equity analyst research tools.You can say generic GPT can be used for research very easily. But to your point, these teams know the niche use case. They know the process, the standard, and the workflow better than anyone else. Even if the TAM is small, it can be big enough for their business.The last question I ask everyone on the podcast is: what is one differentiated view you hold? Something that is a bit against consensus.Gary Ngan:Is it related to the company or the industry?Grace Shao:It could be anything. Usually people answer about their topic, but it can be anything.Gary Ngan:I think life expectancy will be a lot longer than we think today for our generation.Grace Shao:So we are going to live to 150, thanks to Bryan Johnson’s experiments?Gary Ngan:Possibly. Then there will be more time.There is a lot of advancement in AI. It speeds up many pharmaceutical processes. You can run different trials much faster and understand the underlying issues more efficiently than before.And with more time, there is more time for us to create more art.Grace Shao:And live a healthier life. Although right now, anyone working in AI knows AI never sleeps, and I think we are all working more than ever.But thank you so much. That is definitely a differentiated view. I really appreciate your insights and your sharing today. Thanks again, Gary.Gary Ngan:Thank you so much, Grace, for this opportunity. Really nice talking to you.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  Paul Triolo on Chinese labs makings chips, SMIC, Huawei and the importance of AI governance 13.07.2026 1t 25min

  Hi all, we’re back with the podcast. This is the perfect time for Paul Triolo to join us as he gives us a preview of the upcoming WAIC and walks us through some of the top-of-mind questions we have about China’s AI space right now. I do want to apologize for a bit of the echoing in the background; lesson learned to always use headphones going forward.In this episode, I speak with Paul Triolo, partner at DGA-Albright Stonebridge Group, about how to think clearly about China’s semiconductor ecosystem, Huawei’s role in the domestic AI stack, and whether U.S. export controls are actually working as intended.We start with the latest debate around restricting foreign access to advanced Chinese AI models, before moving into the semiconductor stack itself: SMIC’s role, capacity bottlenecks, domestic GPU startups, hyperscaler chip efforts, Huawei’s vertical integration, and why software ecosystems like CUDA, CANN, and MindSpore matter just as much as hardware.Paul argues that the usual framing- whether China can “catch up” to Nvidia or TSMC is simply too narrow. The more important story is that export controls have pushed China toward a broader systems-engineering response across chips, tools, packaging, memory, software, and cloud deployment. We also discuss HBM, rare earths, remote-access loopholes, the logic behind Huawei’s roadmap, and why the collateral effects of controls may be larger than policymakers expected.We close on the bigger strategic question: whether the U.S. and China are drifting into an AI race dynamic that raises risks for everyone, and why more direct dialogue — not just more restrictions — may matter most from here. [Paul co-authored a piece here discussing how to navigate the complexities of the U.S.-China AI safety dialog] This is an extremely insight-dense episode, and I hope you enjoy it as much as I did. Thanks again Paul Triolo. Btw, coming up next are a few episodes featuring founders and execs from hot-listed AI, autonomous driving, and spatial intelligence companies.To find the previous episodes of Differentiated Understanding, see here.Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.Season two will host a series of guests from analysts, VC investors, builders, researchers, founders, and product managers. For more information on the podcast series, see here.Chapters 00:00 Beijing, Chinese AI models, and why regulators are paying attention04:43 Why AI labs are moving into chip design08:12 SMIC’s role and the fight for domestic chip capacity17:56 Huawei’s capabilities and why it became the center of the conversation26:53 CUDA, CANN, and whether export controls really worked38:24 Can Huawei’s software stack win developer mindshare?51:10 Why China can still progress despite compute constraints57:07 The AI race narrative and why Paul is skeptical of it1:07:46 Why governments still lack the technical capacity to respond1:08:42 Will frontier AI labs eventually be nationalized?1:13:59 The risks of zero-sum U.S.-China AI policy1:20:46 Why more direct U.S.-China dialogue mattersTranscript (AI-generated for reference only)Grace Shao (00:00)Hi, Paul. Thank you so much for joining. Really excited to have you on. And I feel like you’re the perfect person for a few of the questions I have prepared in the beginning of the podcast before we get into the actual topic. There are so many things happening, and I can’t keep up with like Twitter these days. So no, so supposedly Reuters reporting saying Beijing is restricting foreign users in accessing Chinese models. Like, what is that all about? What’s your view on that?Paul Triolo (00:15)It’s hard to keep up. Yeah, I think in the wake of the fable mythos fiasco, if you will, in the US and the capabilities of these advanced models getting really good and getting into areas like cybersecurity and biosecurity, I think it’s not surprising that the Chinese government is at least considering what to do about open-weight more properly models that are that are that are reaching sort of frontier level capabilities, particularly DeepSeek and Zhipu and then even more recently, you know, Meituan and others. I think my sense is this is just preliminary discussions with the labs about this issue because, you know, even in the US, there’s lots of confusion about what the government’s role should be here in determining when models are released and under what circumstances and how do you measure capabilities. even though the US has been thinking about this for a while, and I’m sure that’s to some degree that’s happening in China, there’s no general agreement on how do you do this. And these companies in China, as you know, are all commercial companies that just like in the US are under a lot of pressure to continue to put out models. and so I think I would not read too much into that. I think that’s it’s clear that the Chinese government is Trying to figure out what to do with about this, but I don’t think they’ve reached any conclusion about which models to control and how to control them. they’re they’re learning from the companies. They’re probably going out and saying, you know, how do companies themselves evaluate these models internally, in terms of capabilities that could be of concern? and what should the Chinese government eventually do? I mean, they’ll they’ll they’ll do something eventually, but I think we’re in the early stages still. as a result of theGrace Shao (01:57)Yeah, for sure. I think you know, Zhipu, Minimax, these companies are public listed, like they actually face, you know, just shareholder pressure as well. So it but the one thing, the nuance is Chinese companies usually are a bit more prepared or aware of potential regulatory, I guess, involvement. so yeah, let’s let’s wait and see. Because I read this and I was like, this seems bit counterintuitive, frankly, to the model’s going forward. I think like the deadline was a little out in front here. I think it’sPaul Triolo (02:49)Right. I think that the headline was a little out in front here. I think it’s clear that there’s concern as these models become more about what to do about tiered releasing. but this is a more general discussion I think that’s happening. it doesn’t surpr it to people who’ve been following this sector for a long time, the idea that we would be here at this moment, you know, was not surprising. The problem government gov the ability of governments to keep up with the pace of development of the technology is just clearly here, it’s it’s woefully inadequate to the moment because you know within these large AI labs, and I’m now calling DeepSeek and Zhipu, along with Anthropic and OpenAI, you know, the top four global frontier AI labs. you know, th researchers n understand these issues and they’re they’re really concerned about this because P particularly things like recursive self improvement, which is which means models are basically training the not training themselves, but they’re they’re optimizing some of the orchestration or the harnessing that the sort of platforms that these things operate on. You know, that’s been a that’s a growing concern because they’re you know, the models themselves now are able to improve the overall ecosystem without human intervention. Right. and that people miss that, I think, in the in the US with all the fable mythos kerfluffle. The Anthropic released a blog that talked about this, that recursive self-improvement is now sort of part of the landscape. And so that’s also I think part of the concern including in within the Chinese government about okay, you know, Chinese labs are getting pretty good. what should the government how should the government think aboutGrace Shao (04:43)Yeah, definitely. And I think, you know, in China, usually the regulators and the industry actually work pretty close together. so hopefully, you know, people can kind of regulators can keep up, will hopefully catch up on understanding technology a bit faster and better. Okay, so another quick commentary on what is happening in the news. Supposedly DeepSeek and Kai AI are going out and making their own chips. What is happening? What is your high level view on this?Paul Triolo (05:08)Everybody’s doing it. Now, you know, in it this is not surprising. US, of course, some of the hyperscalers and more of the hyperscalers like Google and AWS have long d determined that it would be useful have specially designed ASICs, application-specific integrated circuits that are optimized for running certain workloads in their cloud. and you know and optimi na and now optimized for models specif specifically for you know large advanced models for which general purpose GPUs, which is what NVIDIA and AMD produce, may be, you know, may be suboptimal. but this is a complicated issue because to do semiconductor design, you know, this is a whole nother thing than building models. And so for both Zhipu and DeepSeek, you know, this requires building a team of design so some semiconductor design engineers, right? Who now they’re not they’re not a lot of these guys laying around that are not gainfully employed, particularly for designing really sophisticated chips here. So I think it’s not surprising that they want to do this, but I would be again sort of a little bit skeptical that they’re gonna do be able to do this in the you within the next year even. You have to build a team. It’s expensive. use you have to get advanced semiconductor design tools, electronic design automation tools. and then you have to begin figuring out where you’re gonna manufacture these, right? And in China, of course, as we know, because of export controls, these companies are likely gonna have to use SMIC, the domestic foundry. So they’ll be competing with all the other players, all the other GP GPU designers, the general purpose GPU designers like Biren and More Threads, a of these companies that as you know have gone public recently, they’re all vying for this limited capacity at SMIC to manufacture these advanced designs at say let’s say seven nanometers, which is a feature size of these chips. so I think it’s really interesting that they’re gonna do this, but it’s gonna be a challenge on two fronts. One is you know, assembling a team Sustaining that team over time, you know, this is expensive. It’s very expensive. Lenovo tried to do this, for example, at one point. They were considering getting into semiconductor design, but they determined it was it was too expensive and it was going to be a long term drain potentially if you know, depending on the success of these teams. and so, you know, I think if anybody can do it, I mean DeepSeek seems to already have a lot of expertise on hardware and understand the hardware really well. But it’s it but semiconductor design is another whole nother discipline, if you will. And then in China it comes with the added constraint that you have to you’re sort of stuck with SMIC and you know, maybe Huahong down the road will have sub some kind of seven nanometer process. but it’s you know it’s it’s a tricky thing. But again, the trend in the industry is to do this. so in the US you have the open AI, I think just recently Anthropic are both also considering you know designing their own semiGrace Shao (08:12)Yeah, they have partnerships as well with other vendors. So help me understand. I think twofolds. One is what is SMIC’s role in China and what is their biggest bottleneck? For them to actually churn out better chips. One thing you said is capacity, other thing you said is access to certain technology, you know, instruments, machinery. The other part of the question, if you can fold into it, is who are the actual players? So, you know, DeepSeek and Zhipu wants to build their own create their own chips, but Baba’s in this, you know, Baidu’s in this. There’s a lot of big tech trying to create their own chips as well. How do we understand all their relationships in the ecosystem?Paul Triolo (09:06)Wow, okay, you got a lot a lot in there to so let’s just let’s just look at the demand side, then we can look at the supply side. So the demand side, as I started to allude to there, is pretty heavy, right? So you have Huawei, right? They’re designing the ascends, and those are all at the at least the most advanced node, which is this horrible technical term for the process that is used to manufacture these at SMIC. For example, here. And so Huawei has a lot of demand. And past, Huawei was given most of the capacity at SMIC, just because a year ago or two years ago there weren’t as many other players. Now there are. Now it’s more complicated. But certainly the Chinese government probably also heavily weighed in to have SMIC prioritize Huawei production, both for their smartphone, their Kirin smartphone, and for the Ascend processors for AI. But now in the last year, we have all we have a we have two s two additional sets of companies vying for this capacity at SMIC. One is the G the sort of GPU makers in China, the startups. And this is Biren, Moore Threads, SoftGo, Inflame, Iluvatar. You know, there’s at least there’s a couple more too, but those five are sort of the top companies. And some of those companies were started by engineers from Nvidia and AMD. And so they their designs are really advanced and they are they’re more compatible with the NVIDIA ecosystem, et cetera, et cetera. So they’re all now producing GPUs at SMIC, and there’s allocation issues. I when I was in China recently, I heard that one of those companies had been promised certain number of wafers at SMIC, but then one of the big hyperscalers would come in and offered more money. And so SMIC had said, Okay, well, we’re gonna reduce your allocation, right? So there’s a lot of fighting for that. Th and then the other group that you’ve just mentioned here is are the companies like Alibaba, the hyperscalers, and then now the model developers like DeepSeek and Zhipu that are also doing their own designs. And I think again, as you noted, Baidu and Tencent and Alibaba are more are farther along on this. They have semiconductor design teams which are already producing, in the case of Alibaba, and in the case of Tencent, they have dedicated ASICs. They’ve been doing this for a while, sort of under the radar. Tencent’s a very capable company and has been they really good at this, right? It turns out, but they don’t they don’t they’re very low key on this. and then Baidu of course with Kunlunxin, which is also gonna go public on the Hong Kong market. So the other part of this also is that all these companies of course need capital To function and to do all these designs and to hire these engineers and to and to do all this stuff. So all the in the last year we’ve seen these companies tap into capital markets, particularly, you know, the GPU makers, the new GPU startups have gone public in Hong Kong and in Shanghai. And then you know, we’ve seen DeepSeek, of course, raise seven billion in funding from a variety of sources, and part of that will go to presumably the building up a design team and doing Designing their own chips. and then of course Zhipu has gone went public and its stock is crazy high on the if you look at the valuation on Hong Kong. So it’s a new a new game where Chinese companies are playing this game of d you know, designing their own chips. and then of course they’re all competing for this capacity at SMIC. So now we can turn SMIC, right? So SMIC is this crazy company, right, that for a long time was you know was sort of under radar. but they are under heavy US export controls. So that started in around twenty eighteen, twenty nineteen, when they were they had actually ordered a very advanced lithography machine from ASML, which produces all of the advanced lithography machines, extreme ultraviolet lithography, UV lithography. So they were denied that. They actually ordered it. And then they and then the Dutch government, under pressure from the US government and Wassenaar, which is this interagency or intercountry group, this multilateral group, pulled that license from them. So for the last whatever, six years, SMIC has been using its deep ultraviolet lithography, which is the second best, but pretty good. They’ve been pushing their suite of DUV machines to the utmost limits to try to get to these lower and more advanced nodes. And seven nanometers is sort of the limit, seven and maybe five It’s complicated. Some layers of these semiconductors can be but they don’t all have to be at the most advanced levels. but they’ve been doing something that nobody else in the world has been has done. Now in Taiwan, they did use some for example, that SMIC is using, but they that when they had a access to the more advanced lithography, they went to that because it’s it’s it the throughput is faster. And the yields are better. So SMIC is trying to do something that you know that nobody, no but no other company would have to do because of US export controls. And that means pushing these lithography machines to their limits. they’re having obviously there’s a they’re being successful, but there’s a limit to sort of the yield too that they can do. So for example, the AI semiconductors are much more complicated than for a smartphone handset. So for the smartphone handset, they yield. you know, say ninety over ninety percent. But for the GPUs or the NPUs as actually as Huawei is using, the yields are much lower because these are very complicated and dense chips, right? And so using some of these advanced techniques just it’s just hard to do, right? And it’s and you end up with not as many use useful chips at the end because the yields you know, you’re you can’t do it because you’re you’re you’re sort of pushing the machines to their to their the limit of capabilities. So but this neck but over the next six months it looks like they’re gonna bring online SMIC is gonna bring online more capacity at these advanced nodes, maybe double capacity, because obviously they there’s so much demand for this for this these chips in China that SMIC is responding to this demand and is putting in place more production lines in Shanghai, by the way, at SMIC South to produce to meet the demand for all of these. AI primarily not just AI but mostly AI optimized hardware because of these because of those three batches of companies. You know Huawei is sort of a batch in its in its own right. But then they the GPU makers, the startups and then the ASIC makers. So the challenge then is who decides who gets the capacity, right? and as I said, there I’ve heard a lot of anecdotal you know, chatter in China about that. It’s very complicated. The government obviously weighs in to favor certain companies, but then you know, there’s a tremendous amount of competition. And then finally, the other piece of this not just the logic, if you will, the sort process or die. It’s also memory, right? So for more for the for the AI hardware, high bandwidth memory is a really important part of that because These advanced GPUs are packaged with lots of memory on the on the actual package, in within the actual package, co-packaged, if you will, with the logic. and there, of course, US export controls again have affected CXMT in particular, this case ChangXin Memory, is in Hafei and other places in Beijing. I just saw a big fab in Beijing when was there. and so there but again, it turns out is it to of the export control restrictions on memory is not quite as hard as it is for logic, although it’s not easy. and so CXMT is also ramping up its production of high bandwidth memory, which would then be packaged with those with those dyes. Huawei in particular stockpiled a lot of HBM in twenty four before the US controls were put in place from what Samsung and SK Heino, some of the South Korean producers which are leading In the production of high-bandwidth memory. So anyway so it’s a combination of several bottlenecks that the Chinese domestic semiconductor industry is trying to overcome to meet this demand for these for these AI this AI optimized hardware, whether it’s the Ascend series from Huawei or these other GPUs or these other ASICs that are that are now being designed by the hypo hyperscalers and the model developers like Zhipu andGrace Shao (17:56)I appreciate that context. I think for me, like I’ve been reading about SMIC for a long time, but that just really clarifies exactly their role in the ecosystem. And it’s interesting to kind of see how they’re prioritizing certain companies over others. but anyway, I want to double click on Huawei. I think it gets the most heat, you know? It obviously is not And it’s a very interesting company because it’s not just such like it’s not only just like you know pushing out their own models, but they’re a foundry, they’re they’re a chipmaker, they’re a designer, but they’re a bit of everything, they their own hardware. And then they also have obviously then CANN and then MindSpore that trying to compete on the ecosystem side with NVIDIA. So walk us through just Huawei’s capabilities, Huawei’s competitive edge. And then why Huawei was put on the entity list and got so much heat over the last couple of years.Paul Triolo (18:44)My God, great question. Great question. You’re really asking good questions here. But you know, there’s some, and I’ve written so much on this. So look, Huawei, if you remember, was primarily a telecommunications equipment company. the in the 2000, 20, that, you know, up until say 2018, 2019. And then Huawei got into the handset business too, right? They built they started building smartphones, they were ramping up. you know, to they were competing and out competing in some sense Samsung and Apple. And then the US put Huawei on the entity list in twenty nineteen, May of twenty nineteen. I still remember where I was when they were when I heard they were put on the entity list. It’s like when I when I remember where I was when shot. and then they were and then they were the even more critical in twenty, they the US added this so called foreign direct product rule, which meant that not manufacture its designs at TSMC basically. And I remember being in at Huawei in 2019 when I toured the their headquarters in Shenzhen and they showed very proudly all of the semiconductor designs they were doing at the most very advanced notes for all of their product lines. But at the time those were you know there was more on telecommunications equipment and also on cloud and on you know servers for clouds, the Kunpeng series of chips, for example. So anyway, so Huawei and the secret there that still important is that Huawei had spent a lot of effort to develop their that design team for those semiconductors, right? So I mentioned earlier how hard it is to do that. Huawei had over the about a ten year period had developed this arm of the company which was designing by the by the twenty time frame, was designing cutting edge chips, you know, on par with like Qualcomm and Nvidia even and some of the other areas. And it was getting better and better before the US basically you know w by putting Huawei on the entity list, HiSilicon was also there. So then High Silicon could not use TSMC. But in the process, Hi Silicon of doing some generations of chips at TSMC, Hi Silicon gained a lot of knowledge about you know both semiconductor design and how to use those complicated tools, those EDA tools, and how to do manufacturing. Because when you work with when you’re doing design and you’re working with a fab like TSMC, you’ll learn a lot about how that happens. So high silicon is sort of the secret weapon and if you will of Huawei. And so after the US controls, the Huawei kept high silicon designing. The designers kept designing Even though they didn’t have a place to manufacture yet, right? And eventually SMIC, working with Huawei figured out how to do all these optimizations of their existing equipment, this DUV equipment, to allow Huawei to manufacture SMIC, even though they weren’t the most advanced process, they were still pretty good, right? And Huawei made all these innovations in terms of overcoming some of the that not being not having access to the latest and greatest tools meant, which was, you know, for things like power consumption and other things. They did they designed around this, right? So that’s the other sort of this is a theme that you’ll that I’m sure you’re familiar with is, you know, the force Chinese companies to do different things and optimize things. This is what happened with DeepSeek, right? And other Chinese companies that don’t have access to all the latest and greatest Nvidia chips. So same thing with Huawei, they figured out how to design around this. Now the other thing they did, of course, which I don’t think mentioned was software, right? Because the US controls, for example, restricted access to Google Mobile Services for their handsets, Huawei invent had to invent Harmony, the HarmonyOS, right? So Huawei had to become a software company, right? Which they didn’t really want to do. Arguably, I talked to the senior Huawei officials and they were like, wow, you know, if we could use Android, why would we go to all the trouble inventing having to invest in and build A whole new operating system for which it doesn’t generate any revenue, right? It’s it’s just it’s sort of a cost center for Huawei. But they had to do it because they were under pressure. And so as part of that process, it’s important to understand that because now we get to the AI stack that you mentioned, the AI era. So Huawei has as a result of US export controls and having to develop Harmony, they have now known sort of the process of how to develop a software ecosystem. and how to get developers to use it, right? Although again with AI it’s it’s harder, right? So you mentioned CANN, the Compute Architecture for Neural Networks, which is Huawei’s equivalent of CUDA, which is the sort of developer and software developer environment that’s critical for NVIDIA. So yes, Huawei is trying to now develop as it did with HarmonyOS. And that was a long process by the way, a long and hard process. It wasn’t easy to do. They had to they and still, right, still China s smartphone companies still use Android, right? They don’t all use Harmony. But Harmony is sort of a cross device thing that you know goes for automobiles. You can use it on your car for the Huawei the Huawei invested EVs. So it’s so it’s a it’s a pretty good system. I’ve seen it when you go with your phone to your car, it’s all seamless, et cetera, et cetera. So anyway, so Huawei knows how to do software development in a complex hardware environment now. And so for AI, which is harder, they’re doing yes, they’re doing can is something that they’re working with. And then also MindSpore is sort of the equivalent of like PyTorch and it’s an it’s a development environment that AI developers use. And so they’ve gone a come a long way on that. In 2020, 2022, 2023, you know, because Chinese AI companies could still use Nvidia, they you know nobody was using Can and Huawei. But now DeepSeek and Zhipu and even Meituan, which looks like they have their million perimeter trillion parameter model on 50,000 ascends. This is something that Meituan has complained. And then now we have Minimax saying they’re going to do a 2.7 trillion parameter model. They’re all working with Huawei to optimize some of the aspects of that software development environment. Now it’s complicated because there’s training and there’s inference. And so there’s different needs for each of those in terms of development. But the again, the US export controls have forced the Chinese model developers to work very closely with Huawei because Huawei is the main alternative, right? And the Chinese government, of course, has been encouraging this. And so, you know, now we’re in the situation where the development environment around the Huawei and the SENS and CAN and MindSpore is better, arguably, than it was even a year ago. When I was at the World AI conference. last year in Shanghai, I talked to a lot of hosting, you know, various capabilities using Huawei Sense and they said that the Huawei system was hard to work with. They would tell me, they wouldn’t tell me this, you know, they I didn’t want to be quoted on this, but they said, you know, they were being told to use Huawei hardware, but it was hard for them to offer the kinds of services they were offering with Huawei hardware the same the same caliber of as with NVIDIA. But now I think that gap is closing. It’s not like Everybody in China’s all the AI developers are rushing to Huawei. but there’s still a complicated mix of both NVIDIA hardware and as we’ll see now, they’ve they’re the Chinese government is allowing I think 10 companies to buy these H200 GPUs, which we should talk about. anyway, so it’s a very complicated and heterogeneous compute environment in China. But one thing we can say with some certainty is the Huawei because of the export controls and because they’re closely with. DeepSeek which is very good at programming the hardware, for example, the that environment is at a stage where probably it wouldn’t have been without the export controls. And so we’re in you know, it’s it’s it’s getting better and better. and because companies are gonna have to use it at some point, they’re sort of d deciding, like DeepSeek is deciding, well, we better put a lot of effort into helping optimize that development.Grace Shao (26:53)I think I agree with you and what I’ve been hearing on the ground as well. A lot of the developers saying like if they had a choice, they wouldn’t really leave CUDA just so much better. But if they don’t have a choice, it’s kind of like damn it, I’ll have to just try to learn how to use this. And even if it’s not as good, like we’ll try it’s also like a chicken egg thing, the more developers on it. The better the s the software and the system. But I want to play devil’s advocate here. Like obviously, we all heard the Dario and Jensen like interview. Right. So like we don’t have to get into the details of that. But part of the argument. But part of, you know, what you just talked about was like, you know, high silicon kind of got shafted. They couldn’t get access to certain machinery. You know, obviously, right now we can say that. Objectively, factually, Chinese chips are probably not as good as the leading chips globally. So thus some may argue exp export control worked, right? Like so what’s your view on that?Paul Triolo (28:02)Wow. Okay, that’s a that’s a rather large topic. So it sort of depends on what you mean by work. so look the original goal of the export controls as sort of articulated in the you know federal register notice in October twenty two was originally related to s you know to sort of military other sort of nefarious end uses of right? but the real driving force, if you will, was really the this idea slowing Chinese companies’ ability to develop frontier models down so that the US would get to some advanced level of AI first, right? And so if you just look at that and you look at say Zhipu releasing GLM five point two, that’s not as quite as good as fable or mythos, but it’s pretty good, right? And it’s the gap between le the le the leading models from anthropic and openai and the leading models from Zhipu and DeepSeek and other Chinese companies and Alibaba in particular and now you know Meituan and even Xiaomi and Minimax, you know, that gap is still there, but it’s not really is it months, is it a couple of months? So if you’re going to argue that the export controls worked, then you know, is the does a two or three month gap even matter now, right? so that’s that’s one way to look at it, right? Now, if you look at it in terms it make did it reduce the ability of Chinese companies in the semiconductor industry to manufacture advanced GPUs, for example, on par with NVIDIA, of course it worked, right? Nobody would argue. that it did that didn’t happen because if you’re gonna restrict exports of GPUs and you’re gonna exp r you know restrict exports of critical tools that are used to manufacture those GPUs, of course you’re gonna you’re gonna slow them down. But then they then you have to say well how has Chinese how has Chinese industry responded to that, right? And what are and what are the costs of that for US companies, right, for example. And then you have to look at what is the retaliation from China to those export controls. So you have to at least look at it, look at the picture more broadly than just, you know, did the US slow down Chinese model development? Arguably there, the jury’s still out on that, right? Because I would argue that they haven’t the slowdown hasn’t really been that significant. If a company like Zhipu, like who had heard of Zhipu like even a year ago, right? if they can release a model like GLM 5.2, okay, wow, that’s a that’s a frontier model, right? It’s it’s matching fable in some benchmarks. Okay, so it’s clearly somewhere near the frontier. How close we can argue about and a lot of that is complicated, depends on the benchmarks you’re using. But in the in the semiconductor industry, then the you have to look at that in a little more depth. One thing that I’ve written quite a bit on of course it’s forced Chinese toolmakers to work With the with SMIC and Huahung and some of the other manufacturers, CXMT and YMTC. And so the overall level of capability, for example, of Chinese, the Chinese semiconductor industry to do stuff domestically has gone way up. So those toolmakers, for example, NARA and AMEC and Piotech, they’re now competing outside China with US companies in a way that was inconceivable in 2022. And so what happened, of course, is As a result of the controls, the US companies had to pull all their people out of those fabs in China. guess what? US competitors, US company competitors from Japan in particular, and also Chinese domestic companies had got access to that equipment. And they learned things from that equipment that they wouldn’t have learned if the US companies had been in control of that equipment. And so that’s one just one of many examples of sort of the way you have to look at this if you’re gonna say, did they work? Because as a result of the controls, the US now US companies now have competitors globally for the in the tool making sector. So for example, NARA and other companies in China have been qualified for TSMC to provide tools to TSMC and to Intel and Micron, right? and so now US companies face bigger competition. And the ability of China’s semiconductor industry to pr to eventually produce more advanced chips has gone way up, right? So because the that semiconductor part is complicated. You know, the idea that the US, example, could use controls to forever keep Chinese companies from developing n capabilities sort of, you know, it’s unrealistic, right? Because th this is a this is an applied science. And so the ar the US argument was this is a choke point that we can stop China from doing, but no, China’s designing around that because there’s many ways to do things, right? There’s there’s more than one way to do to develop a tool, for example. And The industry has pursued many different paths and over the years, some have more commercially viable. Those have been the ones that dominated, but now China is pursuing other ways to do things. And so you know, that’s that so like hu like Huawei, just a quick example. So Huawei just you probably saw a couple those last month, I think. They came out with this Tao scaling idea. And so is the re reduction of feature size is to increase the speed and the and reduce the power consumption of these chips. And so that’s Moore’s law has held for a long time. But now we’re run, you know, the industry is running up against just the limits of physics in that in that regard. We’re down to you know one nanometer, you know, really small feature sizes. And so Huawei is saying, okay, well there’s maybe another way to do that. We can we can use a sort of three dimensional structure here to also to move the components closer together and to reduce the time, the latency between signals going to those components. And so that’s not new in industry. This approach has been used before or you know, people have been looking at this. But Huawei is now putting a lot of effort into the actual tools and the technologies to actually do that at some kind of scale. Now the jury’s still out on when that will happen. They’re saying by 2030, for example, they’ll have a s a feature size that will be a system that will be of like a 1.5 nanometer system. And again, the other thing to remember here is that it’s not now just about feature sizes, it’s about sort of the entire package of the system, right? It’s about the memory, it’s about the interconnections, the optical interconnections between the GPUs, where Huawei, for example, has a lot of knowledge of optical interconnectivity. And so you can’t just you can no longer just look at the individual sort of feature size die to die and then and then determine that you know China is ahead of the US or US is ahead of China. So it’s a more much more complicated calculus. And I’ve written about this quite a bit. But you know, I wrote the I think two years ago I noted that you know that now we were in a different ballgame. It was really systems engineering at a at a higher level that’s going to determine you know the capabilities. And here again, Huawei has some significant advantages. so anyway, so the long worded answer to whether the export controls worked is well, yes, of course they worked at some degree to stop and slow down Chinese industry. But at the same time, they’ve accelerated key parts of that industry. And then finally, I would argue the rare earth issue, which by the way I live every day because we’re trying to help companies overcome some of the issues around many licensing and other things, you know, that has been a huge thing because that was directly responsible in response Gallium, graphite. And then of course in April last year, the controls on heavy rare earths and magnets, right? And so those are still, you know, with us. Just today, you may have seen and yesterday, and Nikkei had a story about Japan, Japanese companies who which have been cut off from rare earths in Jan starting in January. They’re filing with the Tokyo stock exchanges are indicating they’re because they’re running out of these critical materials. And all of that result of the of the US export control regime and China’s response, which is to put in place this very strict licensing regime around rare earths, the way, are key inputs for the semiconductor industry too, right? So yttrium, for example, is used to line etching chambers. and most of all those machines I mentioned, DUV, UV, they all use lots of rare magnets for various purposes. every ch semiconductor produced in the world, virtually every one, is touched by a plasma, which is this gaseous, you know, material that’s controlled by Chinese rare earth magnets, and the chambers where that plasma is contained are lined with Chinese rare earths materials. So in other words, the export the US ha put in place have resulted in this very serious response China. That we still are in the middle of. We don’t know how it’s going to come out, but it’s already had a huge impact on the entire supply chain for the semiconductor industry. And not just semiconductors, but of course and power tools and any industry that uses these materials. So anyway, so the disruptions that caused by that are huge. And so when you’re looking at the cost, so we’re looking at the costs and benefits. Did the US slow China’s AI development? Yes, degree, but Jury’s still out on how much. And then if you look at all the collateral damage that those controls cost, you know, those are stacking up and there’s no end in sight right now. So that’s but my view is always, you know, you can you can I agree that the controls work to some degree, but then the question is, you know, what was China’s response both from an from an industrial point of view in terms of working around the controls, and then what was the collateral damage created by the controls and that is that is considerable, I can tell you.Grace Shao (38:24)Paul, I love interviewing guests like you because I was gonna follow up with like HBM in the whole picture, how that affects it. You already answered. I was gonna ask you about inferencing versus training on Huawei chips. You answered it. I love you give the full picture. but I wanna ask, what is it like you talked about collateral damage and how these like industries kind of came out because of export controls? Now, how do we understand actually potentially CUDA? I sorry, not CUDA can. Taking some market share, I wouldn’t say lead at all, but some market share away from CUDA and potentially courting more developers globally, maybe beyond just China. How does that new ecosystem and operating system meet work? Because I would challenge and say harmony at this point is still nowhere close to being a dominant operating system, right? So despite you making the point that they obviously had to go around it and create harmony and it does exist and suffice for their own hardware ecosystem. It’s not a leader. How do I understand that?Paul Triolo (39:23)Yeah, yeah, that’s a great question. That’s a great, great question. So, you know, this is a this is an older question remember, you know, the China didn’t the Chin there was no Chinese operating system, you know, for j like for PCs back in the day, remember? so we had things like Red Hat Linux, you know, or Red Flag Linux, right? Which was the which was a sort of source Chinese version of Linux that was touted as gonna you know, that was gonna be sort of the Chinese version of Windows because of course China has been dependent on Windows for a long time and still is to some degree, right? And so this big the big this issue of sort does how does how does China how does China develop alternatives to existing dominant software ecosystems like Windows or like Android CUDA. You know, is a is a is a really good question. And it’s and it’s sort of it’s a complicated issue because each of those has a different a different dynamic there. And it’s and it turns out to be really hard, right? Because developers and I know this from installing CUDA software environment on my home computer where I have a I run an RTX 4090 NVIDIA GPU, which is export controlled to China, but I wanted Seek. a deep seek model on my home my home system and I had to install all of this development environment which is very complicated which included you know PyTorch and CUDA and all these things, right? And so when you’re a developer and you’ve been working with all of these things for many years, the idea and somebody tells you, you’re gonna have to now switch over to this other system, which you don’t know, and you don’t know the limitations and the and the strengths and the weaknesses of that be like, Like really? Do I have to do that? You’re not gonna want to do that. You’re gonna resist, right? and so this and same with Nope when Chinese companies were using Windows and somebody said, Hey, here’s red flag Linux, which of course wasn’t very good. and you by the way, you can’t run all your Windows applications under Red Flag Linux. so you’re gonna have to run, you know, weird open source versions of all of all your favorite programs. Again, you know, I did that for a while. I actually Linux exclusively for a while, but then I ended up coming back to because you know, there was certain things I couldn’t do. so same thing here and same thing with Android and Harmony. So it’s a it’s a but as I said, when like when Huawei first started on Harmony, you know, they had a hard time convincing developers in China to use Harmony. But now you know I think that process is pretty far along and other I re just recently you know other companies are starting to use Harmony and so Event it depend and again it depends on their business model. If you’re s a Xiaomi and you want to sell handsets outside of China, you’re probably gonna go with Android because you can still use Google Mobile Services, right? I mean it was really a d a devilishly clever thing for the for the administr for the for the Trump administration to control access to Google Mobile Services because that really killed Huawei’s business China. And that was a key source of revenue, by the way. So that was not an accident, right? Like wh at one level it was like why should they do that, right? It’s not military technology. It’s it’s it’s you know, YouTube and Gmail, right? But the reason was they really wanted to kill Huawei’s handset business. And so that’s why they targeted that. But other Chinese companies can still use Google Mobile Services. So Huawei in that case is operating in a in a in an environment where Android is still out there, they haven’t Android is still available in China. So they have a they’re competing against they’re still competing against Android. Now CAN, it’s tricky here because in addition to Can, as I mentioned, those other GPU companies like Biren and others, their develop they have their own development environments. And those development environments are more compatible with And then in addition to that, Huawei is trying to make CAN and the whole environment more compatible. with CUDA. So the idea is that you know the difference between the two. If it was here three years ago, now the difference you know, is less. And so it that willingness of the developers to move to environment easier as you sort of reduce the differences. And so and it’s hard to gauge exactly where that is, right? Because each and each company is different. So DeepSeek, example, I is different in the sense that those guys were programming the hardware directly, right? So if you don’t if you if you are really good and not that many have engineers that can do this, you don’t need CUDA. You can program the hardware directly, right? You CUDA is sort of this intermediate layer that makes it easier. It’s a bunch of libraries and it makes it easier developers to train you know use the training environment. But if you know how to program the hardware directly you don’t need CUDA. So anyway, DeepSeek is sort of unique in that they were really good at the hardware. and so that’s why it’s important that they’re working with Huawei because they understand you know the sort low-level way that these systems all work together so they can help Huawei to improve the ability of the capability of Huawei’s hardware development environment to more to be with CUDA. And so I think we’re in the process of having that happen What’s probably gonna happen in China is gonna there’s gonna be a sort of s system where Huawei will be and CANN will be used more for inference on the inference side to inference and some and CUDA and NVIDIA will still be used to some degree on the training side. Because remember, it’s complicated. Right now, Chinese companies can still, for example, use remote access to services like in Japan and Southeast other places. to train their models. And so they can continue to use the NVIDIA development environment for that, right? And then and then when you get to inference, they can then use Ascend and they can run that on there and they can they can optimize using CAN to run on the on those on the on the for on the inference side. So we’re in this sort of a weird world where you know there’s the developers haven’t all switched over to Huawei and they still don’t really want to. But more and are there’s more effort And ease that transition CANN with CUDA. And so where we exactly we are in that is hard is hard on any given day is hard to say. But clearly, as you noted earlier and I and I tried to stress, the problem is that for the long term, the Chinese government and these companies don’t know what the US policy is here. So we just saw that hundreds, you know, maybe two hundred thousand H200s will probably be approved by government. For companies like ByteDance and Alibaba and Tencent and others to buy, right? Okay, so they buy those. They can use those. Those are really good for inference. They can just, know, they can they have a lot of demand for their for their services. They can they can throw those in and they can be used for inference. They can also be used for training if you know what you’re doing, right? You can tie a lot of those together. but what next, right? So what is the US government’s policy? basically, under the influence of Jensen Huang and agreed to stop. Forcing NVIDIA to downgrade their chips for a set sale to China. And so Trump said, okay, you can we’ll allow them to sell, you know, not the cutting edge, but something a couple generations behind the cutting edge. So hence the H200 class GPUs. But what’s next? So if you’re if you’re a Chinese company, you can’t count. Any other in the world doing AI design can say, okay, I’m gonna, I’m gonna, I’m gonna upgrade my cluster from H200s to Blackwell, and then I’m gonna upgrade to Vera Rubin, which is the next one, and then I’m gonna upgrade to Feynman, right? So there’s a roadmap of updating your hardware cluster. China, you know, what’s what comes after the H200s? So therefore the pressure is to and this is why Huawei eventually issued a roadmap, right? Huawei had never done a roadmap for any of this, but now Huawei, because dynamic, had to come up with a roadmap. And so that’s why they have the Ascend 950 you know, the nine the nine twenty and nine fifty. So now they have a roadmap out to twenty thirty or tw twenty thirty one that’s their roadmap for upgrading their the domestic processors. So if you’re a Chinese company, like ByteDance or like you know Alibaba or Tencent, all the leading players, you have to figure out a very complicated equation which is how do I keep my core developers who are using CUDA happy, using some hardware, either in China and then how do I gradually transition to using domestic hardware for some workloads, right? And again, these companies can they can run different workloads on different systems depending on what the need is. and so they’re and then at the same time, you know, maybe they can get some GPUs from REN or you know Sofco or some of the other smaller players and run those are primarily inference workloads. And so but they can but those are those are really good, you know, those are very, very capable. GPUs. So they can run some stuff on those and experiment with those. And those are gonna be easier because those are more compatible with the Nvidia ecosystem. So anyway, so have a very complicated hardware environment to navigate compared to Western companies. You know, like OpenAI can just keep its clusters depending on you know how many GPUs it can Nvidia and AMD. so it’s it’s it’s a very interesting and heterogeneous situation here. Where it’s different than Harmony because Harmony is, you know, it’s still developing the developers develop apps to run on Harmony, right? And so you have you have that piece. it’s like a it’s there’s inference and there’s training and there’s a lot of different things going on here. there’s runtime stuff that you’re doing, there’s harnesses, which are the ecosystem around these models that make them capable. so AI development environment is much more complicated than har than for a mo just a mobile operating system. Will. But again, the export controls and the uncertainty of policy really they’re there, right? And you know nobody has said that eventually the US will allow black wells to be exported to China, for But we’re still in this weird Chinese companies can and access those restricted semiconductors outside of China. They can they can run training workloads in Japan, right? And so that loophole may be may or may not be closed over the next year or so. but in the meantime, you know, Chinese companies have options and each company’s different, right? Because DeepSeek, for example, wants to have its hands on the hardware. So they don’t they’re they’re probably not gonna use anything overseas. They wanna have the actual hardware because that’s what that’s what they do. But Alibaba and ByteDance and companies, the hyperscalers that have data centers China. You know, they’re probably gonna they’re they have more options, right? And some of those H200s I think will probably go into could go into data centers outside China too. and then NVIDIA is selling the CPUs now are also really important for some of this. And so there’s no controls. It’s a weird loophole, but the Vera CPU, which is used with the Vera Rubin GPU architecture, can now be sold to China NVIDIA just this in the last couple of weeks is marketing that to China. That’s a very capable CPU, which could paired with other accelerators and used for AI training and other things, right? So that so the compute environment in China is really complicated by the because the are there, but they don’t cover everything. They don’t cover remote access. They don’t cover CPUs. and so Chinese companies now have some options here, but they’re, you know, but again, it’s like what is Here, right? It’s much more complicated for a Chinese AI developer than it is for OpenAI or Anthropic, and that’s that’s sort of theGrace Shao (51:10)Absolutely right. I’m really glad you brought up the H200s because I was gonna ask you about that. And it’s very interesting to learn that the CANN like system is trying to become more like CUDA and it makes sense if you’re trying to entice people to move over. Okay, I have a question, I don’t know how to ask it because I’ve heard it asked in both ways. Some people are saying, therefore, why is China still lagging behind if they’re capable of still getting access to certain ships and they’re so talented, right? Or the question could be asked in different kind of framing, which is why like wait, I just asked why are they so behind, right? Others are saying, why are they be able to catch up, play catch up if they’re so limited to, you know, generations ago. So, like, you know, the same question is basically being asked with different framing. At a high level, how do you view this right now? Because frankly, going back to your commentary even on open AI being able to just keep spending and keep purchasing. The most frontier GPUs, then the question is, is that price even justified if you can get almost frontier near frontier with, you know, four generations ago GPUs, then why do you need to spend so much on the latest, right? Like there’s a lot of discussion around that. Is the CapEx kind of justified? I guess this is a big question. See how you want to answer it. It’s complicated. Yeah noPaul Triolo (52:25)We should probably do a whole show just on that, because it’s complicated. Yeah, no, that’s a great question. And you’re you’re as you’re you’re really good at asking, you know, the really tough questions here. So I mean the and I think the d the difference then is that in the US, this idea of scaling, right? The scaling laws still hold. So the more GPUs you throw at training, the better the models will be. You know, that’s still sort of the view in the US. And it turns of that may be true to some degree, but there’s a lot of factors, for example, besides scaling that make models capable. There are these, there’s the harness thing, right? Which is the which is the ecosystem, the orchestration around the model. That’s really important in terms of the performance of the model. What tools can the model call, right? There’s a whole huge effort, you know, to standardize the calling of MCP protocol. which is used to connect the model to other applications. I just hooked up, for Claude. I gave Claude access to one of my brokerage accounts. And it can go in there and pull all the data on all my investments and analyze it, right? and so it does and it’s really good. And it learns, you know, more about you know certain other topics. A little bit well I have I had to sign away I had to tell the brokerage made me like you know sign away all the rights to any you know that happened.Grace Shao (53:38)That sounds so risky, Paul, and you’re so brave.Paul Triolo (53:50)So anyway, but the point is that the raw model and the scaling and the GPUs, it turns out that there’s more the scaling does still hold, right? I mean, Dario from MADA, where of course the CEO of Enthropic, you know, he was like discoverer of the scaling laws. And so you the major US labs like OpenAI and Enthropic and Google, and you know, there’s still the sense that the that the more GPUs throw at it and the more training you’re doing, you’re gonna get better models. But it turns out that like DeepSeek and others, there’s you know, through optimizations, because they don’t have access to unlimited compute, they figured out ways to optimize the and to enable them to run more cheaply. and that and that’s that’s affected the diffusion of the models. So if you’re when you talk about you know who’s ahead, there’s sort of raw model capability is one thing. And then there’s like who’s using the models, and everybody, it turns out that everybody doesn’t need the most advanced models. To run to run really useful applications, right? And so that’s where the Mabel fable and mythos thing, it turns out that you know people are now worried that the US government will, for example, cut off access to these models. And so why wouldn’t you use an open s a really capable open source model from China like GLM or Moonshot? Kimmy is very popular in the US. And you see in these recent reports that you know Coinbase and Microsoft and all these US companies are considering or using Chinese open source models in production, right? And so the question there is, you know, those models are exactly as good as the US models, but they’re pretty good, right? They’re good enough. And so it’s is the question of who’s winning the sort race, you know, is sort of less material in some sense because of these other factors, right? And so it turns out that the that both the model capability, once it’s near frontier, it’s good enough to run most things, right, that need. Some companies will still want to have the most cutting edge model. And also they’ll wanna have the issue of like their where their data is, right? They’ll wanna trust the company that’s that’s running their data whether it’s through an API. they’ll wanna trust that company their data. and maybe they don’t wanna they don’t wanna do that you Chinese model cloud, but they might be willing to run it on premises, a Chinese open source model and build on top of that. And that’s that’s that’s also what Fable and is sort of forced issue. Now companies are thinking, why do I want to give all my data to Anthropic or OpenAI when I can run a very good Chinese open source model on my own infrastructure and I can control the data and the and the security of that of that of my you know my business model. you may have seen Alex Carp’s sort of rant, people some people called it a ramp a couple days ago where he was talking about that issue where he was basically saying that, you know, don’t want to give all their data over to these model developers because then those model developers will compete with them for certain things. Which what’s happened like whatGrace Shao (56:41)Yeah, they will eat their lunch instead. And people also have a misconception that like when you use a Chinese model, it’s not like you’re giving the data to an open source Chinese model because you actually can self host that model I in your home countries. Anyway, I’m gonna start wrapping the conversation. I want to go big picture. Last okay. Last question on this. The dominant narrative, DC, is that, you know, AI policy circles, you know, circles often talk about, you know, whoever achieves AGI first, whatever that means these days. Will gain a decisive strategic advantage and ability to reshape global power. So it’s very, very scary. You know, how do you view this? Because through our conversation, what I’m hearing is that both sides are, you know, cautious, both sides are healthy and skeptical, but both are putting regulatory pressure, whether domestically on the companies or, you know, on protecting them from, I guess, foreign actors. Is this actually conducive for the future? Like how should we kind of view this? Because It also feels like from our conversation, a lot of these export controls, protective measures are not actually working or actually good for the industries domestically. So just a high level, like how do we understand this? Yeah.Paul Triolo (57:47)Yeah, great question. I think level, my concern, and I’ve written quite a about that, you know, if we race argument that the US is indeed to prolong the gap between US models and Chinese models so that when we reach something and I think AGI, I think we’re already at We’re already the models already passed the Turing test, right? But we’re talking about like artificial superintelligence where models are you know self they’re self-improving and they’re they’re coming up with really amazing new designs or weapons designs. If you look at the AI 2027 scenario, that’s sort of that’s sort of how people are thinking. Now I’m skeptical of that scenario because I think you know we’re still away from these models being to do you know the design super weapons and take over everything and so that one side who gets there first wins And then can kind of lord it over the other side. You know, this is essentially what Dario saying in his essays, like the machines of grace, and the adolescence of technology. He has said basically like AI, d democratic AI needs to win so that then that can be used to sort of force regime change in China, right? Or force authoritarians to sort of, you know, kawtow, if you will, to Western the Western governments. But I think that’s a That’s a I don’t like that framing because I think you know we’re not gonna wake up one morning and have that capability. It’s gonna be a gradual thing. and then the real issue is how governments deal with this? Like this whole issue of fable and mythos has forced the issue to the fore of how do governments deal with even just capabilities? This isn’t super intelligence, but this is like really good capability to detect vulnerabilities and software that exploited. And we don’t even have a framework for that, let alone for something more advanced intelligence. How would the government and industry work together on that, right? So there’s a couple things. One is, and I’ve written I just wrote a piece in Cairo Review about when does the government think about nationalizing the AI labs, right? Because people are using these analogies like these are nuclear weapons, right? Even though AI is not nuclear weapons. And I think it’s a very dangerous analogy. But people are saying, you know, this is a technology developed in the private sector, previously, weapon systems that were very capable were developed by government. And here we have a private sector ca capacity that’s that’s starting to edge towards weapons systems with cyber capabilities, for example. What is the gu how does the government do fit think about that? And the mythos thing, frankly, showed how unprepared the US government was for this, right? If you’re in the industry, you know that you knew that this was coming. I we talked about this last year at the Paris AI conference, right? You knew that this capability was coming, but nobody in the US government, the Trump administration was just saying innovation, innovation. China’s in the same way, right? How do you balance regulation and innovation? They want the companies to compete. So there’s no regulation. now Mythos and Fable have forced the issue of like, my God, well now we need to have some government role in determining, you know, how to test the models for certain capabilities and how to determine what’s a covered model and what should the conditions be around w how that model is released. And at least with Fable, we saw company, in this case Anthropic, have to, you know, put guardrails around the cyber capabilities of that model. And now they’ve finally the government has allowed them to release Fable. But that’s not there’s still a lot of questions around that, right? So the problem is if we’re in this, if we accept this race idea, then we’re never going to get collaboration between the US and China here, which I think is really dangerous because then, you know, malicious non state actors are gonna get access to this capability. and then, you know, the implications of that are really, really serious. And so the problem with the race idea is that it forces everything is it that then becomes distrust. The US distrusts China, China distrusts the US, you know, the US is gonna ban open could ban open source Chinese models. China could you know r restrict the release of open source models because they don’t want to contribute to The US developing capabilities. So we’re gonna get if we get into this race, then it’s a bad thing for everybody, I think. So we have the US China AI dialogue, which is coming up hopefully after the World AI conference in Shanghai, which I’ll be attending next week. and you know that’s gonna I think that’s the last chance. It’s the last chance for the US and Chinese governments to say, okay, we understand we don’t trust each other, but this is a threat, the threat of you know uncontrolled access to these models. It’s a exactly. It’s so it’s the last chance for the for governance to recognize that. And I think we I think that’s the mythos fable thing. The good news is that really drove I think this agreement in Beijing and Mart and May to between the t the two presidents to start talking about this. But it’s a complicated issue because ha you know, you got what are what is the goal here? How are you gonna agree on both sides to some limitations on this, right? And then how do you how does this translate into eventually a global agreement on putting guardrails around frontier AI models. It’s it’s it’s the problem is the technology is developing so fast that the ability of governments to keep up with this and come up with, you know, credible and viable structures to put some controls around this, you know, it’s really tough because there’s just not enough expertise in government. It’s going to probably have to be an independent, private sector led effort to do this. And this is This these are the kind things that are going to be discussed in week. I’m on a couple of panels, including some closed-door panels, that where these issues will be discussed. Now nothing’s gonna be decided next week in Shanghai, but I think the level of the discussion will be much higher because of fable and mythos and because the Chinese government is kind of freaked out about this. and you know, and the good news is that at least at some point the US and China will eventually sit down and try to begin this. Scott Bessent is gonna head up the US side and Vice Premier He I just did a piece with Alvin Graylin that you’ve probably seen in on the ASPI website, which tries to look at who’s gonna participate in this from both sides because there’s lots of lots of equities and we saw the that whole issue become complicated just in terms of deciding what to do about Fable. it was good in the sense that the governments to have a serious discussion of what are we going to do about this, right? So that’s the good news. But when I was at the way the finally, when I was at the World AI conference last year, I think it was Stuart Russell, my good friend Stuart Russell, who said, you know, he had talked leading CEO of a of a US lab, and he had said the best thing we can hope for in the next two years is a Chernobyl style event, right? Now think of what that means, right? This is the head of a lab. admitting that you know AI could lead to a very bad outcome here, right? and so this is where we are here in the summer of twenty six, where US and China both have leading labs. Governments don’t seem to know how to get a handle on what to do about that. But the US and China have to talk about it. Because if we get into this race, if we if we if we if we basically give up sort of say, okay, it’s gonna be a race, you know, and then th what will happen is something bad will happen and then and then people will say, my God, now we this. and you know, some people think that Fable Mythos thing is good because there were there was no bad event, you know, no loss of life or no nothing. But it did sort of force people to realize, okay, now we need to do something, you know, that’s a good thing. But still the political pressures and all these things we’ve been talking about, the export controls and everything, you know, there’s no there’s just no trust on either side. we’ve dug a deep hole in terms of and so Digging out of that, as you say, you know, to for the benefit of humanity is gonna be r a real challenge now. but you know, hopefully, as I say, in the next couple months, we’ll know better where that dialogue is gonna go and where China’s gonna be, for example, coming out of the World AI conference. They might announce the World AI Cooperation Organization, for example. I suspect they will announce that. Xi Jinping is coming, just to show you how important this issue is. Xi Jinping is coming to Shanghai. So the security arrangements around this conference are nuts. I’ve just been trying to figure out where I’m gonna be on different days. so that shows you how important it is. If Xi Jinping is coming, it’s important. and if Xi Jinping has agreed with President Trump to discuss this at some level, that’s that’s good. That’s good news. But as I say, I think this is like humanity’s last chance to get a handle on this because you know it’s the US and China have to agree. Everybody else matters, you know, there’s a big safety community, there’s other capable model developers in other places, but really the US and China are where ninety percent of the action is. and so if there is no agreement between the US and China, beginnings of an agreement around this, then you know, then all bets are off. And so I think this is a really the next couple months are really critical in this in this arena. And you know, the and that’s the technology continues advance and recursive self-improvement kick in. And as, you know, these things, you know, that’s not gonna stop. There have been all these efforts to say, hey, let’s let’s stop until we figure out what to do, right? Let’s pause, right? Who’s gonna pause at this point, right? I mean, those a year a year and a half ago that all these scientists, including it from China, signed on, like, we gotta pause, six month pause. The hard part is if you pause, you know, how do you decide when to start up again? Right? it’s it would be you know impossible. So nobody’s gonna agree to a pause. So therefore we need to agree on A minimally viable framework around governing these advanced models. And that’s what the goal is going to be in the next couple months. But you know, it’s it’s it’s really going to be hard because of the government the lag in capability in government you know so the mythos thing highlighted both need to do something but also wow like it’s the people who really understand these issues are still limited in numberGrace Shao (1:07:46)Yeah, we need more technical people in the government. Like actually every government. That’s the thing. Because this technology is not for the laymen to understand, frankly. Like you need someone who’s technical to understand it. But I think, okay, on that, like I’m feeling serious FOMO. I was planning on not going, but maybe I’ll go up. It seems like everyone is going. I was speaking to Alvin Graylin this morning actually. We’re working on a piece together. So it’s very interesting. I’m glad he’s he’s potentially going, you know, Ray Ma’s going, a bunch of people in the circles going. So You know what, like I think you’re right. Like I really do hope that something positive comes out of this. It’s just seems like it’s really hard to regulate something when regulation takes so much time. There’s so much bureaucracy that comes with it. And then on the other hand, like exactly to your point, AI doesn’t sleep. I was joking with my husband, I was like, I just want to summer. And he’s like, AI doesn’t summer, you can’t summer. And then he like being a tiger husband there. But you know, the reality is no one’s gonna stop right now, right? And likePaul Triolo (1:08:28)Right, right. I want three months off from all this, but catch up.Grace Shao (1:08:42)There’s a commercial interest and there’s also the com competition competitor like I guess even spirit in these researchers at this point. So it is gonna be incredibly hard. Yeah. So are these things gonna be nationalized? Do you think these like I mean, the irony and all this is like a look at my Cairo Review article? Yeah came out last month when I tried to layPaul Triolo (1:08:50)Right, and we also have massive IPOs coming up, right? We have anthropic that’s the other complicated Well, take a look at my Cairo Review article that just came out last month, and I tried to lay out how both the US and China view this. And I think y arguably already, you know, there is some there isn’t national you know, is gonna happen in different ways, but some level of nationalization is gonna have to happen, right? We’re already talking about open AI g you know, pr that the government taking a taking a investment or taking a share. OpenAI. So that’s kind of a there, right? so I you know it’ll be it’ll be different than nationalizing other industries, right? But yes, I think at some point it’s it’s hard to see the government leaving this capability in the hands of the private sector fully, right? because a important capability. And as we get closer and closer to more advanced you know AI and The idea of like loss of control, what happens, what happens if we lose control of the AI? all these things are out there. And so I think, but again, we can’t even figure out basic government role in, you know, how do we how do we determine what is a covered model and who is who is equipped to test that model, right? there are very there’s some efforts going on that I’m aware of to try to figure that out. Right. And it’s gonna but it’s gonna it’s not gonna be just the government. It’s gonna have to complicated, you know, body outside the government that’s that’s that’s that’s plugged into the government, kinda like the IEA, right, for n for nuclear for nuclear technology. But it’s right, there needs to be standards and there needs to be there needs to be a sort of neutral international body. But you know, we’re still quite a ways from that too. So we first have to get US and China to at least agree, Then building on that There could be the some new body. Now, again, I my the cynical view in the AI safety community I is that there has to be first a Chernobyl style event, right? That hopefully won’t be too serious before people get concentrated. Yeah, yeah, it does. It does. It’s ac absolutely Right. Right, right. But that’s sort of this that’s the worst case sort of cynical view within the AI safety community. But upcoming US China dialogue, it’s gonna be really important to see who’s participating, how serious it is, and you know, how quickly something can happen, right? Because the safety community has been arguing, we’re getting closer, we’re getting closer to artificial and it’s gonna take time. and so we need to start the serious discussion now. And that You know, that it started happening under the they were very serious about this, very thoughtful people. But then when Trump came in, it was basically let’s let her rip, right? Like US innovation is gonna dominate AI, and there was really a downplaying of governance. And then, you know, mythos sort of punctured that optimism in some sense and was like, okay, now we have to do But, you know, having not thought about that for a long time. You know, there were thoughtful people like Dean Ball and others who contributed to the AI Action Plan. And who’s now jumped up and out? Dean’s a great Dean’s great. I love Dean. He’s a great thinker on all these issues. So there are people out there who’ve been thinking about these, but it still turns out to be really, to, for example, set up a new organization. Late in the Biden administration, there was a discussion that Frontier AI is so different, right? It’s a different technology. You need a different regulatory structure around this. But it’s really hard to do that, to set up a whole new body and fund it and find but now I think people realize no, we this as another technology that we can just fit into our existing regulatory structure. It’s a different problem, it needs different capabilities, and so we need to rethink how to do this. I think that could happen too on both sides, both in China and the US, is okay, we need a we need to figure out a new structure here, an organization with the right authorities and the right capabilities and the right technical expertise to actually manage this problem, right? and I have I have a paper coming out with Alban’s not part of this paper, but I have a paper coming out with ASPE that’s looking at you know how one potential structure that could down this road having and both between both the US and China, right? A structure that includes the key players on both sides that would allow this to happen. But again, very tough you know, we’re it requires a sort of trust and concessions on both sides to figure out how to do this right. and, you know, the bilateral tensions that you see every day, right? are still a real impediment to this, right? Because AI in Washington, as you know, has become such a charged issue. You know, I mean we I mean people are talking about, you know,Grace Shao (1:13:59)But some of it’s talking point and some of it’s reality. I feel like at least in the business world, right? Sh maybe the policy world should have a little bit of that too. You know, what happens on the surface, what happens under understanding the reality and the realistic consequences that these talking points may lead to.Paul Triolo (1:14:01)Chinese. Right. Right, right. But Right. Well Right, no, that’s a great point. My the is that the during the later Trump first administration and the administration, this constituency developed around the AI issue, right? This sort of this weird sort of consensus that AI China, you know, we had to slow China down, we have to restrict these things. And that there’s a you know, that was in the in government, in the in the media, in think tanks. So there’s this huge sort of constituency of people. Who are wedded to the idea that we have to win over China at all costs on AI, right? and then there’s a group, the AI safety groups and others, and then people like me who were saying, well, no, that’s not the way that’s the sort of that zero sum thinking is gonna lead to disaster, right? and that we need to fig figure out a bet a better way. Like that the better way is how can we collaborate with China in these areas where we do respect national security concerns, but we don’t over index on them to the point where we can’t collaborate with China and then, you know, it’s a free for all and you know, bad things happen. and so that’s sort of where we are now. And I think the good thing is the Trump administration isn’t wedded necessarily totally to the previous administration’s approach to this, but it’s still it y you need smart people in D C like David Sachs and others who understand the industry and where the industry’s going and the technology who can kind of who are outsiders. outside the beltway who can actually look at this more holistically and say, okay, wow, we can we can work with China here, we can compete with them there, we can we can, you know, control certain things, but we need to figure out a way to skin this cat. We need to figure out a way to get to some basic level of agreement here. Otherwise, you know, we’re all in for a world of hurt, as that CEO of the of the lab admitted in a private setting I mean Chernobyl style events sounds pretty serious, right? And avoiding that needs to be something that focuses people, in DC and Beijing on, you know, how to how to howGrace Shao (1:16:23)Paul, I agree with you. You are full of knowledge and insights and you’re full of differentiated views, but there is one question I ask every single guest as we wrap up the conversation. what is one differentiated view you hold? You think that’s something just very against consensus?Paul Triolo (1:16:41)Well, I just think that technology controls and the idea of choke points really bad idea because we’re in a world as an interconnected world, right? And in fact, like when I’m working with clients across the AI stack every day. And when I look at US China, you know, y the degree of inner of interdependence and interconnectivity here is much deeper than people think, right? People are like, decoupling here and there. No. I mean if you really if you really look at the at what’s happening on a day-to-day basis and the complexity of supply chains, for example, the idea that we can simply decouple in AI or elsewhere is just, I mean, yes, we could do that, but the cost of that to the to the to the and to the companies and to global supply chains is just, you know, really fully accounted for that. So my I of like always coming back to the reality of okay, what is what is the what’s what’s what’s happening with businesses on the ground on a day to day basis and how are they being affected by this, right? And when you look at that level, you the sort of, you know, the thinking and comments that I hear that are just are very divorced from sort of that day-to-day reality of how interconnected the US and China have become over the last thirty years. And, you know, if we’re gonna indiscriminately, you know, pursue policies that where that collateral damage and the sort of the full cost benefit analysis done, you know, then it’s like, what’s you know, what we’re what are we doing here, so I’m always just I’m always just sort of arguing for a thinking about policy that’s based on a sort of a really deep understanding of the reality on the ground and you know how innovation happens and companies de-risk supply chains and how there are certain dependencies. For example, like rare earths turns out to be a real choke point, right? In the way that semiconductor technology is not, right? There’s just way around China’s Chinese company’s dominance of say samarium cobalt magnet production, right? That’s a real choke point. that will take ten years to you know to unravel. whereas other choke points that have been used on the US side are not really choke points. So I think they’re just my differentiation is the need to step back and look at this and think about like what is the point particular policy? Is that does it make sense? And is it is it having you know is the cost benefit sort Clearly on the side of, you know, too much cost and not enough benefit. and so that’s what I keep coming back to. And then we didn’t even talk about Taiwan. My also my sort of nobody few other people talk to is the impact of all this potentially on Taiwan and the risks around Taiwan. We work with companies every day and we do exercises, for example, about around a risk around Taiwan to their supply chains. You know, something short of a military exchange, which then there’s no de-risking. but you know, it turns out that, you know, Taiwan and supply chains and Asia in general are so intertwined that when you start pushing on some of these buttons, the worry I have the worry that you’re gonna, you know, you’re increase the potential for disaster there, you know, unintended or intended or whatever. and I think not enough people are thinking about that. they’re only thinking about you know, deterrence and arming Taiwan, think is frankly a sort of a mistaken way to problem. so anyway, so I think that out-of-the-box thinking and sort of getting a getting a getting away from the standard view which has developed over the last 30 years, you know, is necessary. And I just don’t see enough of that in Washington or Beijing. how do we rethink some of these things in the age of AI? So what we need a we need China policy for the age of AI and we need a technology policy for the age of AI, And I thinkGrace Shao (1:20:46)We need more dialogues. The thing is like I hear from so many people, whether they’re working on policy side or the actual researchers and developers, they’re like people aren’t talking officially because of all the geopolitical headwinds and noise. And then but actually, you know, obviously people talk, you know, behind the scenes, but we need more official dialogues to get to more fruitful results. I think more Yeah. So Paul, I’m glad you’re going to WAIC. You’re gonna be leading these dialogues.Paul Triolo (1:20:47)That’s where governments and we need more dialogue. Yeah. Mm-hmm. I’m with you, Grace. you. And I really appreciate your perspective. Well, I’m gonna try to contribute a little bit here and there. I mean, I love the people that I’ve met with a lot of the Chinese AI safety people, for example. They’re very thoughtful. they’re very good. and you know, in some areas ch China’s China’s Chinese you know, organizations and individuals are leading. But also there’ll be a lot of really good people from the broader AI safety community right? From the Future of Life Institute and from Concordia AI and s you know, really good Players who are really have smart people that are thinking about these problems also. So it’ll be a really good effort. Unfortunately, because it’s in China, you know, some of the leading US AI labs and some people and the US government, you know, will not be participating in this. It’s it’s seen as a sort of Chinese thing. but there will be the a the APEC meeting is happening just after this. And so I think there may be some there’ll be a US presence at the APEC meeting. And then as I said, you know, eventually. Probably shortly after this, I imagine that the US and China will kick off this AI dialogue. And so you’re right, dialogue is really critical here. And this is such a complicated issue that, you know, the sooner this dialogue gets kicked off and the sooner that they can they can, you know, feel each other both sides can feel each other out and get to the real issues. Yeah, exactly. Exactly.Grace Shao (1:22:36)Paul, I’ve taken up so much of your time today. I really appreciate it. I’ve learned so much. Can we please do this again sometime? I have more questions for you. You have you’re so knowledgeable, but thank you so much today. Thank you for your time.Paul Triolo (1:22:36)Yep. And thank you, Grace. I really appreciate your thoughtfulness and the and the thoughtfulness of your questions on these complicated issues. You really bring a lot to the conversation.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  Future of mobility, a deep dive into the forces driving the Chinese EV revolution with Tu Le 17.06.2026 1t 22min

  Hi all, I’m really scared to even share this episode because the last time I recorded an episode with Kyle Chan and mentioned cars, I got ripped online. So I just want to emphasize again that for car enthusiasts, I AM NOT A CAR person. I am here to learn. Haha, ok now that I’ve made that disclaimer…Joining me today is the ever-so-knowledgeable Tu Le. He is the founder and managing director of Sino Auto Insights, author of the SAI Weekly Substack, and co-host of the China EVs and More & At The Wheel podcasts.He has worked across Detroit, Silicon Valley, and China, so he views the industry from the inside, through the traditional auto industry, the tech industry, and the Chinese market.I wanted to do this episode almost as an educational primer, not just for you all but for myself as well. Most people now understand that Chinese EVs are competitive. But very few people understand why and how that is translating into the Physical AI space.We talked through the Chinese EV landscape, why traditional OEMs struggled to make good EVs, how autonomous driving fits in, how these carmakers are integrating AI, and why home appliance and smartphone companies like Huawei, Xiaomi, and Dreame are suddenly making cars.Follow Sino Auto Insights here: https://x.com/SinoAutoInsightFor consulting inquiries, go DM Tu Le on LinkedIn!Website: https://www.sinoautoinsights.com/Btw, I’m rebranding Differentiated Understanding to AI Proem Podcast.To find the previous episodes of Differentiated Understanding, see here.Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.Season two will host a series of guests from early-stage investing, as well as builders, researchers, founders, and product managers. For more information on the podcast series, see here.Chapters00:00 Introduction to Tu Le and Sino Auto Insights04:28 Mapping the Chinese EV Industry09:19 The Rise of Xiaomi in the EV Market14:17 Understanding BYD’s Market Position17:54 Challenges for Traditional OEMs in EV Production31:29 The Role of Government Subsidies and Policies36:12 AI Integration in EVs and the Future of Mobility45:13 The Evolution of Brand Experience in EVs46:53 The Future of Manufacturing and Market Dynamics50:46 Safety Concerns in Rapid Development52:51 Current Landscape of Autonomous Driving in China57:51 Challenges in Deploying Autonomous Vehicles01:05:00 The Future of Mobility and Urban PlanningAI-generated transcript (for reference only)Grace Shao (00:00)Hi Tu. Thank you so much for joining us today. I’m really excited to have you on.Tu Le - Sino Auto Insights (00:04)Thanks for having me on, Grace.Grace Shao (00:06)Yeah, to start, why don’t you tell us a bit about yourself? We were just having this conversation right before recording. I find your background really fascinating.You know, you can talk to a very diverse group of kind of people. You run a successful consulting gig, a consulting company. Tell us about everything that you do.Tu Le - Sino Auto Insights (00:24)So name is Tu Le I’m the managing director at Sino Auto Insights. I also create content. I run or I co-host two podcasts, China EVs and more, and at the wheel with my co-hosts that are very, very good at what they do as well. And then I write a weekly newsletter, almost weekly anyways, called Sino Auto Insights Weekly that just kind of goes over my thoughts on what’s happening in the industry now globally every week. And I’m actually not Chinese. I’m Vietnamese. And I was born in Vietnam and moved to the United States when I was a year old and grew up right outside of Detroit. My whole family, youngest of eight, whole family’s automotive. So grew up car kid and did that for a few years before going back to grad school and moving to Silicon Valley to work for seven years. So that’s where the knowledge of the tech comes in, especially the hard tech, where hardware software integration is such an important part of creating a great user experience. And then I met a girl and in San Francisco. my girlfriend, who’s now my wife, was transferred by her company over to Beijing, where she was born. And I decided to pull the ripcord and and follow her over. And what we thought was going to be a three- or four-year assignment ended up being thirteen. And during this time I worked in automotive; I worked at a few Chinese EV e-commerce startups. And so that’s when I learned and experienced nine nine six myself for about two years. And yeah, it’s not fun, super intense, but again I wouldn’t trade those experiences for the world because it gives me the perspective that I have now. about eight years ago I saw this huge disconnect because EVs were becoming a thing because of Tesla. Companies like NIO and XPeng had just been founded. And you know, in Beijing, as you know, Grace, there’s a lot of the German OEMs, and so there was a bit of arrogance about how hard or how simple they thought software was and really, really being consumer focused as opposed to product focused. So I saw this opportunity, and I started this consultancy, Sino Auto Insights, and you know we’ve been growing since we’ve done traditional work. We’ve worked with the UK government, US government on things. And then also when I moved back four years ago from Beijing, I left during COVID. So August of 2022 and then November, December timeframe, China opens its border and says, What COVID? Come on in. So we didn’t know that was going to be the case. so we decided to move back. And we opened an office here in just outside of Detroit. And we’ve been helping more on the investment side, looking for investment opportunities, what’s around the corner, but also giving our clients a better understanding of the Chinese EV players and the battery players and what they’re doing outside of China. So it’s a very, very interesting time. The mobility space, as you know, Grace, involves now AI, silicon, data centers, data privacy, data security, batteries. So it’s just, just a tremendously unique sector that I get to be a part of.Grace Shao (03:55)Thank you so much for sharing your life story. First of all, kudos to your mother. Eight kids. Like, I don’t know how she did that. Like I have two and I’m already dying. And also, I love your personal touch, you know, why you moved to Beijing and just learning about your background. I think it’s super fascinating. You pointed one thing out. Like when I was living in Beijing in Shanghai as well, I met a lot of German OEM like employees and people kind of low key don’t know this, that there’s a huge German community i it in the huge like and and French as well. A lot of Europeans are actually working in China for these, especially like luxury vehicle companies. and a lot of them did relocate out of China during COVID times. And a lot of them I’ve even heard anecdotally from two friends who say they’re dying to get back because they were born in Munich or Frankfurt. just because they’re so bored.Tu Le - Sino Auto Insights (04:23)Huge. We hear those stories a lot, don’t we, Grace? We hear those stories a lot.Grace Shao (04:48)it’s just because it’s just the fast-paced energy in China. However, okay, COVID was crazy. China’s fast paced. Let’s get to that actual topic today. I wanna talk about EVs. I wanna learn everything from you. so before we get started, when we think of Chinese EVs, most people outside of China think of BYD. think of maybe like the few other ones you mentioned, like NIO X Peng. Now Xiaomi Dreame, which is crazy; essentially, these home appliance companies are going into the space as well. they are there are state-owned companies, there are old independent automakers, there are startups that we just talked about. And then some of them also produce batteries; some of them are, like I said, home appliance and phone companies. Basically, all of these different moving parts, they’re all coming into the same arena. Help us map out the industry first. Like to start with, who are the main players? What are the buckets? what does each group bring to the table or what’s their differentiating kind of offering? I know this is a very big question, but start with a big picture.Tu Le - Sino Auto Insights (05:45)So well, let me press rewind and kind of frame it and create more context as opposed to just we’ll we’ll zoom out and then we’ll zoom into the China market. So last year, twenty twenty-five, Toyota was the number one global automaker, eleven million units, around eleven, just over eleven million units. Volkswagen was number two at eight million. To give you a sense of scale, Tesla was one point six. million units and BYD was about 4.6, which makes them a top 10 automaker. The other top 10 automaker for the Chinese was Geely. Geely and everybody else outside of BYD and Tesla build ICEs and EVs. Or in China, they call them NEVs, new energy vehicles, which means that they’re battery electric vehicles plus plug-in hybrids, E Revs, and then fuel cells. So fuel cells, for our intents and purposes, are rounding error. So when we talk NEVs, we’re talking battery electric and plug-in hybrids and extended range electric vehicles. So Toyota’s been number one for a long, long time. And you know, the China market has been the number one passenger vehicle market since 2009, overtaking the United States. And now the China market is almost twice as big as the US market. If we add the European market, which is around 12 and a half, 13 million units, and the US market, which is around 15 and a half, 16 million units, it’s almost the same as China. And so the scale of the China market is enormous. And so to talk about EV specifically, I would create different sets of buckets. And I would look at BYD, Chery, Geely, Great Wall as separate companies, SAIC because they produce in the millions of units. Okay. And then this lower tier, I won’t say lower, but this other tier of EV makers, the NIO, the XPengs, the Li Autos, the Zekers, these are companies specifically that Western investors pay attention to because they’re traded publicly in the US. They’re in the hundreds of thousands of units. Go ahead. Yep.Grace Shao (07:58)I want to comment on this. So you’re actually separating them by the number of units versus their technology, because it seems like, just not like SAIC, they’re actually a traditional OEM company, but they’re also producing EVs, whatnot. So you’re actually categorized by number of sales versus, I guess, I don’t know, EV native like NIO and XPeng. Just help us understand what why that industry does that.Tu Le - Sino Auto Insights (08:18) so the automotive industry is very capital intensive. And so scale creates cost efficiencies. Okay, so if I buy 10 of something versus one of something, I’m gonna get a better price, generally speaking. And that’s why it’s so important that BYD has this enormous scale of 4.6 million units. And that’s through the traditional lens. Now It’s multi-layered as you’d mentioned. You know, they’re they’re EV only companies that we can talk about. But from the standpoint of scale and global reach, that’s where I’m really creating these separations because scale also creates flexibility because it’s gonna be harder for a company that only sells 300,000 units of anything to go global as opposed to someone that sells four point six million units of something. Because these companies, the BYDs, the Geelys, the Leap Motors, they all already ship and build and manufacture outside of China. Big, big steps. And so those are really kind of the uniqueness to some of those top-level guys that have the sales volume. They have the ability to go abroad. Because think of it just from a number standpoint, Grace, if we have capacity of half a million units. You and I run a car company. Building a hundred and fifty thousand unit factory is a huge consideration for us because we have to find demand somewhere for that hundred and fifty thousand units. Whereas if you have millions of units, 150,000 units isn’t that huge in a grand s in the grand scheme of things of sales and distribution. And so that’s where it’s a little bit easier for these larger companies to really command, you know, the pricing scale that they can negotiate over some of those smaller players. But you know, back to kind of how I would look at this. You know, the NIOs, the XPeng, the Leottos, they’re publicly traded in the US. That’s why there’s a lot of attention paid to them. But they’re still puppies. And you know, all of them shipped less than half a million units last year. Now Xiaomi is one of the newer players, but it is making a huge, huge impact because their automotive division, and you and I, I think, were there when Xiaomi first was founded in 2010 in Beijing. But their automotive division is less than six years old. And this year they’ll ship over half a million units. Contrast that with the NIO and XPeng, who’ve been around since 2014, 2015. They are barely shipping 500,000 units. So that tells you kind of the impact Xiaomi has had. Xiaomi only has two products. NIO and XPeng have four, five, six products. And so, now with Huawei, I would pull them into a very unique budget because they don’t build any cars. What they do is partner with other OEMs, whether they’re state-owned or non-state-owned, and offer the hardware and software stack. Okay. They call it HEMA, which is the Harmony in Mobility Alliance. And so there are companies like when you hear about Ito, Micstro, Stelato, Luxe; I won’t talk about who their partners are, but these are all using Huawei technology. And more and more, there are foreign companies that are using Huawei technology. So, Audi, I think Mercedes is using some of the Huawei technology in the China market. So we get into a lot of crossover when it comes to Huawei, because in order for Huawei to really, really legitimize their tech stack, they probably need more foreign automakers to sign up to it. And they’re really, really, really aggressive on trying to scale that part of the business because, you know, once they lost some of that handset business from North America. They try they’re trying to quickly find revenues to replace that. And I think focusing on the automotive sector was one of the ways they were thinking of doing that.Grace Shao (12:33) That’s super, super helpful just to get an understanding of the different buckets. so why is Xiaomi doing so well then actually? Because, you know, I saw a Xiaomi car, I think, maybe last year when I visited Beijing. It’s very sleek. It feels very nice, but just in comparison to Li Auto, XPeng, and NIO, which, as you said, they’ve been around for like more than a decade. They’re very, very they’re actually beautiful cars. I was quite like shocked when I first saw some of them during COVID time. Yeah, why is it that they’re just outbeating them? Is it because of the ecosystem? Is it because of what people are talking about with the control, the operating system? Because Xiaomi’s operating system is just significantly better because their software is better? Or what is it that’s driving consumers by them over others?Tu Le - Sino Auto Insights (13:20) So there’s a few things going on with Xiaomi. So the Su 7, which is their Sedan, if you squint, it kind of looks like a Porsche Cayenne. And then the U7, which is the yeah, so that helps, I think. And then the U7, which if you squint, looks a little bit like a Ferrari Purosangue, which is their Ferrari UV or FUV. So theyTu Le - Sino Auto Insights (13:46) Have borrowed design language from these two amazing automotive brands, and that’s helped them. But they have offered these vehicles at less than $40,000, starting at $40,000, with features that are very, very technology-forward. And one of the big reasons they’re successful is I bet. Grace, if I looked around your apartment, I would probably see a Xiaomi product, one or two at least. And in China, I didn’t know anyone who did not have some sort of Xiaomi air purifier, rice cooker, TV, computer, mobile phone. So the brand is ubiquitous in China. And that really helped Xiaomi when the vehicles launched, create this automatic instant demand because the brand is there: complete awareness of the brand, and there’s a lot of trust amongst Chinese consumers. And one of the important things about the China market versus the rest of the world is that anyone born after 1990 in China is a digital native. So they grew up with WeChat, Didi, Meituan, and Alibaba, you know, and Xiaomi. So, and these are all Chinese brands. So, they trust Chinese brands, not like their parents who only bought foreign brands. And I think that’s the larger shift in the Chinese Chinese market across sectors, consumers goods, you know, technology, high, you know, consumers products, now automotive. But also Xiaomi is very well connected across different, you know, product segments. They have an app that controls everything; it extends into the vehicle, and you would call that a consumer-focused product company. I think that resonates with a lot of Chinese consumers. So the impact that they’ve made is enormous. I haven’t even talked about how cool the cars are or what they can do. They’re breaking records at one of the most historic racetracks in Germany, the Nürburg Ring, and they’re beating the pants off of Porsche. So if I am Porsche and I know that Xiaomi is entering the European markets, Germany in 2027, I’m pretty worried. Because Porsche’s not doing well in the China market. And I think that’s the canary in the coal mine for a lot of companies in Europe, that Xiaomi’s gonna be a major player as long as they can continue to build these cool cars.Grace Shao (16:17) Super interesting. I would wanna look I wanna talk about Chinese vehicles, Chinese EV companies going global and going to Europe later. But to start, I wanna talk about the hype that BYD gets as well. Some of them cost less than even a hundred thousand RMB. You know, what is it about BYD, and how to understand them from a business perspective as well? Like they create their own batteries. They are getting into semis. They have their, you know, their whole whole integrated industrial platform. Like help us understand BYD.Tu Le - Sino Auto Insights (16:51) BYD’s story is amazing. In 2009, my first day in Beijing, there were BYDs. I got into a I I want to say a BYD cab, and it wasn’t great. It was not good. You could hear the exterior, the outside world, pretty, pretty clearly. And I I wasn’t feeling that safe in the vehicle, to be honest with you. But fast-forward to 2026, and Let’s just look at the last seven or eight years. Before COVID, BYD was shipping less than a million units. They’re at four point six in twenty twenty five. We’ll likely get to five million. They’re gonna be exporting about a million of those, a little over a million of those. So they’re currently in over 100 markets. So they are uber aggressive. And to your point, they’re very vertically integrated. BYD started out as a technology company that supplied batteries and other components to companies like Apple, and I want to say like Intel, but they got their experience and their scar tissue from working with some of the toughest technology companies. And that’s really kind of created s this resilience and ability to grow and scale and stay aggressive. Now they created a monster because companies like Geely and companies like Leap Motor are right on their heels, and they’re actually not doing that well. Their growth is flattening out, and the competition in China is super, super intense, hence the importance of them to export. Wang Chuang Fu is the founder CEO, and Stella Lee is the head of international. They’ve made it clear that they’re targeting Toyota to become the largest automaker in the world. And three years ago, they were going to be number one with a bullet, but now we’re seeing, as they scale and that denominator gets much bigger, double-digit growth is much harder to come by. Competition is catching up. They created the competition. They really, really were catalysts for all these other companies to build these sub $100,000 or sub-100 RB cars that are. Pretty amazing. Now, you make a great point because in China, BYD is the mass-market value car. Okay. But it creates an entry point for Chinese consumers that wouldn’t otherwise be able to purchase a vehicle, especially in big cities like Beijing and and Shanghai, where it’s, you know, getting a license plate is a challenge. And then finding parking and being able to pay for that is also very challenging, especially if you live right in the city center. And BYD has really, I think, in 10 years, 15 years, we’ll point to BYD as a democratizer of many, many, many things, not just mass market clean energy vehicles. And their focus on going international really, really put them behind a little bit on the technology curve. And Companies like XPeng are leaning into the technology. And Wang Chuang Fu has acknowledged that they’re a little bit behind on some of the features that other Chinese automakers are providing in the China market. But he’s determined, with his over a hundred thousand engineers, to really push that envelope to catch up and surpass some of their domestic competitors. Examples, recent examples of that. They’re launching a megawatt charger, or they’ve launched a megawatt charger in the China market. And that’s basically charging as fast as gas. You can charge fully within six, seven, eight minutes. And then also recently, just last week, I want to say, their intelligent driving system is called God’s Eye. And they are now providing a year’s worth of insurance. If there are any accidents while you’re using God’s Eye in China. So they’re putting their money where their mouth is. You don’t see Tesla doing that. And so they are willing to take that next step that everyone else begrudgingly has to follow them on. And, you know, one of the important things in, you know, I’d mentioned ten years ago that in 10 years we’ll say they’ve democratized things. The other thing is they’re offering God’s Eye as standard on many of their vehicles. And so think of it from the standpoint of not only in China, but to your point, now in Hong Kong, in Thailand, in Latin America, these people that have a 10,000, 12,000, 15,000 US dollar car, they might be able to drive themselves or at least have portions of the road where the vehicle has intelligent driving capabilities. And if BYD doesn’t do that. Intelligent driving doesn’t happen in those emerging markets for 10, 15 years at least. So they’re really, really pulling, I think begrudgingly, a lot of their competitors forward on that technology curve. And I applaud them for that. Now, at 4.6 million to get to 11 million, I think Wang Chuang Fu and Stella Lee appreciate more the level of management capability and the operational efficiency needed, like by a Toyota, to get to eleven million units. And so, but they’ve doubled down, and it sounds like they’re still determined to be a top two, top three player in the next five to seven years.Grace Shao (22:32) That’s really interesting that you pointed something out, which I didn’t notice at all. It’s the fact that they’re democratizing the technology, so it’ll be very interesting that they will actually introduce the kind of next-generation technology to these markets. So I guess bringing down their price right now is a long term strategy because once you capture that. you know, mind share, then they could always increase their prices later on.Tu Le - Sino Auto Insights (22:53)One of the most important things, Grace, is that there are two emotional buys in a person’s life generally, and that’s a house and a car. And in China, I think there’s around 60 hours, I wanna say, of research being done online, at the retailer, test driving, before you actually pull the trigger and buy something. And soGrace Shao (23:02) Yeah.Tu Le - Sino Auto Insights (23:17) If you and I- I don’t know if you have an iPhone or an Android phone, but if you lost your iPhone tomorrow, you’d be upset, but you’d walk into an Apple store and buy another one right away. But, you know, a car, people take consideration because it’s a reflection of who you are, who you want to be, you know, and really outside of your home and office, you spend most of your time in the car, especially if you live in Asia. And so That’s why it’s so important for them to be in these markets as a first mover. They create that awareness first, they build that trust first, and it helps them elbow out other players that might have equally impressive products, but because they came two or three years later, BYD already is in the mindset of a lot of these international consumers, especially in the emerging markets where a lot of times they’ll enter and six or seven months later, they’re the number one brand. In this segment, in that segment, or these segments that they enter.Grace Shao (24:19) Definitely. So let’s bring it back to traditional OEMs. You worked with some of them or you worked at some of them. Why did traditional automakers struggle to build compelling EVs now? Especially, you know, in China. The factories were there, the people were there. As you said in the beginning, you said some of them became a little bit complacent about the idea that, you know, Chinese consumers maybe just really liked luxury cars from Europe; you know, the default was buying Japanese cars for families. Why is it that almost every traditional OEM has produced a kind of crappy EV version?Tu Le - Sino Auto Insights (24:55) A lot of it has to do with, so let me first qualify this by saying if you ever talk to someone that tells you they predicted that the market was gonna move quickly over to clean energy vehicles in China, do not believe them because no one could have predicted how fast the market moved over in twenty twenty. We were in 2019, we’re at like one point two million units of NEVs relative to a twenty-two million unit base. Okay. And then in twenty twenty we were like one point three. Then we got to three point five, we got to six point five, we got to nine million, and last year we were close to eleven and a half, twelve, thirteen million units. And so we are currently over one of the inflection points, over 50 percent. So every one of every two cars is an NEV sold in China. Okay. And no one could have predicted that. So everyone was caught flat-footed if you’re a foreign automaker. Now let’s add in the fact that they’re analog companies, right? They’re not software companies. They’re not technology companies. And I bet today if you and I were to go into a boardroom or any meeting room in Detroit or Dearborn or Auburn Hills or Stuttgart, They’re still talking about the product. Okay. If we look at NIO, XPeng, Li Auto, their founders come from tech. Okay. They iterate. You know, they don’t, they, they ship product that’s good enough, and then they figure out what the bugs are, and then they create over there updates to fix those bugs. And whereas traditional automakers, they try to wring out as much profit as they can over a five-year period. And that’s because traditional product development cycles are a five-year period or a four-year period. The best company on the legacy side is Toyota, and they’re at around 30 months. Most good Chinese EV companies, like BYD or Zeekr, can go from clean sheet to job one. And job one means the first sellable vehicle off the production line. They can do that in about 15 months, which is absolutely insane. Now they don’t have the blinders on that say we can only do it a certain way. They’ve challenged everything all the way through. Simple things like where you might be you know a product engineer or component engineer and you you throw it over to me as a manufacturing engineer. Everything’s in serial. That’s why it takes so long sometimes. The Chinese EV makers, they do a lot of things in parallel; they simulate. A lot of safety tests, you know, validation to engineering validation tests, you know, manufacturing validation tests. They simulate a lot of that stuff, and it shrinks the timelines. And they treat manufacturing like a technology as opposed to, you know, an analog product. And if I’m being frank and honest, and I know that you want me to be that, the Volkswagens, the GMs, the Mercedes, the BM, they’re busy back in their home markets counting their money for a long time. You know, these narratives that they they steal IP. You know, is there IP theft? I think you and I would agree yes there is IP theft in China. But let’s qualify that: if there was IP theft in this instance, the Chinese automakers would make great ICE engines, right? Like gas engines, you would think. Exactly. So in this particular case, it it wasn’t because they stole this IP and it wasn’t because of subsidies. Because there have been subsidies since 2009, and it has been a substantial dollar figure, right? Tens of billions of dollars, if not hundreds. But if we look at Tesla, Tesla has not sold a vehicle.Tu Le - Sino Auto Insights (28:59) Globally without some form of subsidy. Okay. Full stop. They got their factory in Fremont for next to nothing. Shanghai Giga, tons of incentives by the local government. Li Chiang put that deal together. Guess what? He’s now the vice premier of China. So that was probably one of the reasons he got that promotion. So the idea that Chinese subsidies are bad, US subsidies are good, European subsidies are good. That needs to just stop. And at the end of the day, if you have those subsidies, it doesn’t automatically mean that you’re going to win. Because you probably remember this, Grace. X Peng Li Auto NIO in 2016, 2017, 2018, they were struggling. They’re almost bankrupt. And it wasn’t until December of 2019 that the first Model 3 rolled off the line in Shanghai Giga. That you really saw that hockey stick inflection point. Okay. So despite all the subsidies, despite all these promising EV startups, it took Tesla to really bring excitement to that market. And so you can credit Tesla for being the catalyst for EVs globally, not only in the United States, but China. I think we should acknowledge that they’re a huge part of why EVs became a thing in China. Now, because of the competition, all these competitors came in because of the subsidies, because of the the the RB that was being given out by local governments, you had a ton of players come in. And last year it got so competitive that the Chinese government was like, no mass, no mass, no involution. And so we have to acknowledge that the China market acr across a number of sectors, not just automotive, is likely the most brutal automotive market in the world. And if you’re able to survive out of this mess, you’re gonna be a very, very formidable competitor outside of the China market.Grace Shao (31:15) Definitely there’s the Musk effect, I think, on you know, Tesla bringing out EVs, and now we’re seeing that with humanoids again. Cause with Elon Musk obsessing with humanoids, we’re seeing the world obsessing with humanoids. We’ll talk a bit about that later. I appreciate you giving kind of the backdrop of the history of China’s EV space. Tell us about the industrial policy push though, because you mentioned subsidies as a very vague term. But what kind of subsidies or industrial push do you think China actually gave this industry to bolster it?Tu Le - Sino Auto Insights (31:48) The Chinese government has the ability to long-term plan. That doesn’t happen in the United States. Unfortunately, and it’s really costing us in a lot of areas and sectors. And we’re seeing these sectors that would normally be pretty strong be a pretty competitive struggle against the Chinese. But l let’s say in 2009, the Chinese government looked at manufacturing as a pillar industry. That supported jobs. And within the manufacturing sector, they wanted to become number one in batteries, number one in silicon. And silicon fabrication. Not only silicon design, but silicon fabrication, automotive EV manufacturing, and then, you know, and that all supports this notion that they’re the world’s factory. Okay. And they doubled down on that. Now The types of subsidies, whether it’s tax abatements for land, whether it’s discounts on building factories, whether it’s purchase subsidies or you know, tax abatements for the consumer who don’t have to pay taxes on buying EVs. To your point, in Hong Kong, same thing, right? Similar things going on. I I wanna I wanna stress that anywhere in the world, emerging technologies, in order for them to become really ubiquitous and blossom, governments need to put their thumbs on the scale. Okay. So it’s not just a Chinese thing. The United States was ready to give consumers $7,500 for every car, every EV that they bought. Okay. So it’s not just a China thing. Norway, which has a ton of oil money, used that oil money to subsidize EVs. And now the take rate in Norway is over 90%. Now their market is tiny, 400, 500,000, 600,000 units a year, but nonetheless, it’s another example of the government putting the thumb on the scale. In Germany and other parts of Europe, there are also subsidies for EV purchases. And that’s also when you saw growth rates a little bit higher than they are now because they’ve taken away a lot of those subsidies. So again, emerging technologies need help. And normally the governments in any one of these countries need to step in. The exception is kind of the UK, which has done pretty well on EV adoption, the growth of EV adoption, despite not having a ton of subsidies on the consumer side. But that being said, it these subsidies, this focus, this diligence, this perseverance, this investment over time. It didn’t look like, let’s say, 2010, 2012, 2014; it didn’t look like it was going to really, really work out that well. And then all of a sudden COVID happens, and you get this huge spike in demand for electric vehicles or clean energy vehicles or new energy vehicles. Now, my quick story. You know, it’s it’s aGrace Shao (34:51) Why is that?It’s like we’re locked in our homes. So why do we need EVs?Tu Le - Sino Auto Insights (34:58) It’s a weird phenomenon and and I haven’t read anything or talked to anyone that can I you know, I th we can try to theorize. For me, it’s like, yes, we’re locked in. I wouldn’t say we’re locked in our homes, but we’re locked in the country, and we don’t travel, so maybe, you know, we spend money on something that we think might make us happy or something like that, right? Like, I can’t tell you why outside of, okay, Tesla starts building in 2020. And and and a quick story about my COVID experience. My family went back to Michigan. So I was sending my kids to a local school. So they had basically the month of January off in 2020. We were going to go back to the United States for three weeks to visit family. And the return flight was canceled. After two weeks in the US because China was starting to have COVID. And we bought two two one-way plane tickets that were canceled. And then a third one where we finally got to fly into Narita, stay there a night, fly to Shanghai, and then to Beijing, and then quarantine for two weeks. And we didn’t leave China for two and a half years at that point. The day we got back, the day after we got back, the Chinese government closed the border. And during that time, Grace, you saw more and more and more of these green plates. And I I was just amazed because think of all of the things that need to happen for demand and supply to work together. Because it’s not just, okay, we can produce these things, no problem. There needs to be charging infrastructure; batteries need to be scaled up to support, you know, the EVs. And then creating this awareness, creating this excitement. The NIOs and XPengs and Li Autos weren’t able to do it on their own. The BYDs weren’t able to do it on their own. But on the backs of the the the Chinese consumers really, really trusting the Tesla brand. And we’ve seen that the Tesla brand in the China market is extremely resilient because they’re still selling quite a few, despite not upgrading their vehicle or updating their vehicle in a number of years. And so it is a strange phenomenon that I can’t definitively tell you why. I just know I saw more and more green plates when I was in Beijing and Shanghai over twenty-twenty through twenty-twenty-four or twenty-twenty-two, so.Grace Shao (37:41) Yeah, it’s an interesting phenomenon. And like every single tech founder or AI founder I’ve ever met basically says they drive a Tesla. So contrary to what you were saying earlier, you’re like, a lot of Chinese younger generation actually prefer the Chinese consumer brands. There’s something about Elon Musk and his brand in China. People like love it, worship it, wanna become him, whatever. So all of all the founders and tech people still drive Tesla. I I wanna bring it to the next point, which is on AI and tech, actually. So we talked about EVs, and I know I can ask you like 3,000 more questions on this, but I wanna understand the AI element to all of this now, because essentially EVs are not like old cars; they are tech-first cars, right? Like you said. But because they're tech-first cars, they seem to have integrated. AI or hardware plus software much more seamlessly. We’re seeing like Li Xiang push out like even wearables like glasses that can control their cars. we all know all the mentioned brands just now have voice control. They all have some sort of AI already embedded in them. Now it’s just like giving them a more formalized name. Autonomous driving obviously is a form of AI. We can talk about that as well, but help us understand: for people, for these cars, are these kind of like software-hardware integrated really that seamlessly, or are they struggling as well? And then on top of that, you’ve said something I think in one of the podcasts before is that you push back on the phrase software-defined vehicle. Tu Le - Sino Auto Insights (39:22) So the terms mobile phone on wheels and software-defined vehicles, to me, and I would love your opinion on this, Grace. That tells me that these people don’t know technology or understand technology or have worked in the technology space because software doesn’t define anything. Software, AI, you know, silicon, these are all tools that create a compelling user experience. Now, you put them together, you design them well, you combine them with the right hardware and the software that instructs the hardware what to do, when to do it, how fast to do it, that creates the user experience. And I’m an Apple alum, so I learned that early on. It was really, really drilled in my head that the user experience, the stickiness, that creates the brand, that creates the brand loyalty. Okay. And what the Chinese automakers are doing right now is like throwing spaghetti on the wall to see what sticks. And because of the enormous pressure from competition, they just try to be first. Okay. What they likely need to do, and I don’t know if they’re going to be able to do this in the next 18, 24 months, just because I don’t see competition really, really slowing down in the China market. Is to take a step back, you know, I had a conversation with Sam Livingstone and Matt Mechelvoigue talking about the Ferrari Luce and some of the missteps. And part of that is, what does this mean to the NIO brand? What does this mean to the XPeng brand? Because that is what creates the awareness, the trust, and the loyalty; like everything just kind of makes sense. Simple design is really, really, really hard. Johnny I’ve has said that many, many times. In I’m sure. I would again love your opinion because I’m a Westerner, so I’m not used to Chinese apps that have a million things popping up at me or Chinese websites that have all these windows and all these lights blinking and stuff like that. It’s it’s a little intimidating to me. And I feel like front consoles of Chinese EVs are still a little bit overwhelming. Now, if we look at Xiaomi, I think they do it pretty well. They could They can improve, but they have years and years of experience among their teams to build out consumer experiences. Okay. And to me, like the reason I don’t like mobile phone on wheels is because a mobile phone can’t run you over and kill you. Okay. So anything automotive grade is a completely next level thing. So a big thing on the battery side is energy storage systems. Okay. A battery for an energy storage system is not automotive grade. It needs to be bulletproof if it’s automotive grade. So the level of engineering and manufacturing quality needs to be much, much higher. That’s why when we oversimplify it like that, I don’t think people appreciate the amount of effort and level of detail that needs to be had for putting something on automotive grade. And then software-defined again, it just tells me that these guys. think software is the end-all, be-all. Software, if software, AI, and hardware is running great, you don’t notice it. You just experience, you just have a great experience. If I have to say this hardware is not working or that hardware is not hard, then the automotive designers and engineers need to go back to the drawing board because something’s wrong. and and that’s what I want to emphasize being an Apple alum, that that hardware and software integration is is is what is going to create and differentiate you in the market long term. And one thing I will point out about Apple is that because they have a closed system and they don’t have to Frankenstein a bunch of disparate, you know, firmware together because they control the design of the hardware and the software and integrating, well. They might integrate third-party AI now because they’re super behind. But that’s kind of the only third-party thing that they’re doing. but eventually I’m sure they’re gonna look to to create a native AI support system for their ecosystem. But I I think that’s the huge differentiator. Now, is it realistic for an automaker to have a closed system and control so much? If you’re Tesla, maybe, but you started. on day one as a closed system. Okay. It’s gonna be extremely difficult, extremely, extremely difficult for most other automakers to do this. Now, you know, one of the areas I think you wanted to talk about was partnerships. And this is where the Chinese automakers are getting a lot of credit through the announcements of all these partnerships with their Western counterparts. And the important thing is that there’ve always been partnerships in the China market. You know, it’s been a requirement of the Chinese government historically, you know, with the exception of the the the Tesla factory in Shanghai recently. But now these partnerships are bleeding into Europe, they’re bleeding into North America, and we’ll continue to see that as long as the Chinese are pushing the envelope on innovation. But again, Grace, can the legacy automakers use somebody else’s tools? Again, they’re tools. Can they use somebody else’s tools? To create a Volkswagen experience, a Volkswagen brand experience that you know historically is this way or that way. Okay, like you trust Volvo, right? You don’t like their new cars, but can if Volvo is using Geely software, Geely AI, Geely hardware, or you know, like Geely qualified hardware, is it still gonna feel like a Volvo to you? And I think those are kind of the important things moving forward. that’s gonna differentiate some of the legacy automakers and some of the better Chinese EV makers from the rest of the field.Grace Shao (45:28) I feel like listening to you explain this actually makes me feel like there’s gonna be two fragments of the market where the hardware people, like the people who still want the best hardware experience, will still go with a traditional OEM because you will still get the best craftsmanship, get the best kind of hardware experience, right? But if you are gonna go for an AI-native experience as we go forward with this, you know, you want the best voice control, you want the best. Whatever interaction with your AI agent within your car, then it would make, as you said, it would be extremely hard for a Volvo to use someone else’s software. So wouldn’t a company with their own software actually have the advantage of building that? So like a Xiaomi or Tesla, right? Like your point, you have your closed ecosystem, you have your existing s software, you have everything you need to make the experience better. But that said, the car might not be as sleek as an Audi, Mercedes, whatnot, right? I don’t. What do you think?Tu Le - Sino Auto Insights (46:32) Well, I think that when you get to clean energy vehicles, so especially battery electric vehicles, manufacturing is is simplified by orders of magnitude. So the GMs, the Volkswagens, the Porsche’s, they all Mercedes, they all have entire powertrain divisions that only work on the engine. Imagine those entire departments effectively going away. Okay. Electric motors I’m oversimplifying this, but they’re fairly commoditized. Okay. They’re super fast, super efficient, generally speaking. Automotive grade is something different than everything else again. But that is really going to be the differentiator moving forward. Grace, if I told you that in 15 to 17 years, maybe less than that, building a car is going to be commoditized. So there’s no value in that aspect or part of it. Now, with the world being as bifurcated as it is, especially in North America, that doesn’t want Chinese battery cells in North American vehicles for now. Maybe it’s a longer timeline, but effectively China is really, really creating or forcing other companies to rethink how they manufacture things, how they develop things in the spaghetti on the wall. There will be some spaghetti that sticks for the Chinese automakers. And you better believe that that copycatting is gonna be reversed now. The Europeans and the North American car companies are really, really going to create their own versions of X, Y, and Z. Now, can we say that it was innovated and perfected in the China market? Probably moving forward in the next three, five, seven years, but we also need to look at the demo. Right? Because you had mentioned some people want performance, some people want the digital experience. And I think a lot of that is gonna be is gonna correlate to what the demographic is, because a BMW or Mercedes owner in China is around twenty, twenty-five years old, younger than in Europe and North America. As a you know, as an old man, I have different needs than you do, as a as a young woman. So What I like in a car is going to be different than what you like, than what your husband likes, than what my wife likes. And I think that’s where the Chinese are gonna have to play the global game. Okay. Now they need a solid foundation of extremely high sales in their domestic market to create the flexibility to sell abroad and to sell at a premium abroad. But are all these digital features And technological advancements going to resonate with a 60 year old year old European man in Germany who is used to driving a BMW? Probably not. Okay. But one of the other big advantages is that the Chinese have is, and I’ll give you a quick example. Friend Nick Carey, who writes for Reuters, last year he wrote an article about Chery taking six weeks to change the suspension and steering system in an Omata 5 because they were shipping the China-spec version of the Omata 5 to Europe and the Europeans were like, yeah, this steering is way too mushy and the feel isn’t there. The Europeans will not like this. And so over six weeks they qualified new parts, they updated the software through OTAs and firmware and then shipped the new product And that would take a year in in at least a year at most legacy automakers because of the layers of bureaucracy, the approvals needed. But this was done in six weeks. Now that’s also a reflection of the nine-six in China.Grace Shao (50:24) Does that not frighten you a little though? Because like how fast, like you mentioned, how fast they ship, w I wanna ask something slightly sensitive. Then what about the safety of these cars, right?Tu Le - Sino Auto Insights (50:36) Well, if you asked the Chinese automakers, they will assure you that they’re not cutting corners. Okay. And I have driven many of these Chinese cars. Now I d I haven’t owned one for 10 years. So long-term efficiency and safety, I don’t know. Most people don’t, because a lot of these cars have been on the road for less than five years. But you know, when you talk to the automakers, and again, a lot of these people come from the automotive space. A lot of the leadership of some of these Chinese companies, they come from the Mercedes, the Volkswagen groups. And so they do have some visibility into how things are being done differently. But I would also counter what you just said with yes, there’s a little bit of risk with cutting so quickly the product development and so severely the product development cycle, but Also, how things traditionally work at large conglomerates and in even governments is there’s something new that’s happening. We’ll add a layer. There’s something new happening; we’ll add a layer. Technology changes, we’ll add a layer. So no one ever takes a step back and says, These 15 layers, does this still make sense? Because I mean, that’s kind of the definition of bureaucracy, right? So time will tell. I do I feel not safe in these cars? No. you know, and I’ve driven dozens of them, soGrace Shao (52:01) No, I was playing devil’s advocate. LikeI’ve been in so many of these in China, and you know, especially across Asia. But I just think the it’s just people tend to ask questions like, it’s so short then. If you’re shipping them out within a year, what kind of corners are you cutting? And then thus the question is easy to say. The next question is, is it safe? Right. But I think I I I kind of feel you on the point. If it’s completely new, we treat it like a startup; it’s innovative. There’s a lack of bureaucracy, there’s a lack of this is how we do things. Then you actually can just get things done much faster. I’m mindful of time. I want to ask you some questions beyond the traditional car makers and whatnot. Help us understand where China is with autonomous driving right now. Who are the main players and just roughly understand, you know, who are the ones kind of competing with Waymo, who’s Pony AI, right? Like who’s We Ride? I know again, it’s a super big question, but let me just throw this to you like open-ended.Tu Le - Sino Auto Insights (52:57) Let me kind of close out that last topic that we were talking about with food for thought in something that I know you know as well, but maybe your audience isn’t quite aware of. If any Chinese company is found to be cutting corners in the China market, the Chinese government would not look kindly on that. And there would be severe consequences, right? So I think there’s this healthy fear of If we are cutting corners and we’re found out, we’re gonna be in a lot of trouble. There’s not gonna be years of litigation, there’s gonna be severe penalties right away. And I think that healthy fear motivates many of these Chinese automaker leaders to stay on the right path. Right now, again, time will tell, but to pivot towards your question about autonomous vehicles, so Waymo is the global standard. I think most people would acknowledge that. They’re in many, many markets. They’re entering foreign markets. But there to your point, there is WeRide, there’s Pony, and then there’s Baidu. These are the three largest players in China currently. But then in Apollo Go, yep. And and and unfortunately, when I was in Beijing last month.Grace Shao (54:13) I do is call Apollo, right? Or yeah.Tu Le - Sino Auto Insights (54:22) Or two months ago, Apollo Go was not running because in Wuhan about a thousand of them or a hundred of them were on the roads and they just turned into bricks, right? On the roads. And so they had stopped the pilot programs. I don’t know if they’re running again, but I normally when I’m back in China will try out all these systems for the latest software to make sure to just kind of see, feel, understand, what’s going on and and what’s unique about China is that the feel is a little bit different in each of these cities just because how people drive is so different in in different cities. Grace Shao (55:02) This is something I feel like no one understand if you don’t live if you haven’t lived in China. Like people in Beijing are just aggressively wild. Like people don’t realize this. Tu Le - Sino Auto Insights (55:11) What? Aggressive? man. So I’ve driven in like Changsha, I’ve driven in these tier two cities, and you’re like,Grace Shao (55:17) Okay, I haven’t driven into your two cities. I just haha I usually just get a car there. Like, I I I already think Beijing is so terrifying. Like, I I start driving 16 years old in, and I refuse to drive in Beijing, and because you stop the car for someone to pass. Next thing you know, like 10 cars have passed, like 20 bikes passed you, 30 pedestrians, and you’re still there, and then there’s like 20 people behind you honking you, and you’re just like, yeah.Tu Le - Sino Auto Insights (55:33) my goodness. Yeah, so a fun, quick funny story. My wife used to be very worried because I would get super fired up when I’m driving in China. For some reason, I learned to compartmentalize it because I would get super upset. And then when I got out of the car, I would just not be upset. I don’t know how I did it, but because my wife didn’t want me to take yeah, I it was just.Grace Shao (56:04) Like you have to. Because you’re like constantly road raging. Anyway.Tu Le - Sino Auto Insights (56:10) And so what you’re talking about is called cutting in. And if you have a meter of space between you and the car ahead of you, someone will cut in. Someone will cut in for sure. And if you’re not used to that, someone will cut in, and then there will be a San Luncha delivery vehicle turning the opposite way. And your head needs to be on a swivel. And it is quite an experience. It’s similar, and I won’t say similar, but it has a similar feel as Southeast Asia because it’s so crazy. And it just kind of works in Southeast Asia. And it doesn’t work super well in China because there’s traffic jams all over the place. But Pony and WeRide are trying to help some of that stuff. Let me segue to that.Grace Shao (56:57) Yeah, so how does it work though? Like that’s my point. Like how do these autonomous driving cars work if, you know, people are so unpredictable? The whole idea is they’re supposed to predict what the car is gonna do, but you know, they just like zigzag and people just pop out of nowhere. Like i is it safe? Like what’s really a holding up, like what’s a bottleneck of deploying these at scale right now? Is it regulation, is technology, or is it just the craziness of the roads?Tu Le - Sino Auto Insights (57:28) Again, let’s do a 30,000-foot level. We ride, pony. They’re both publicly traded in the West. And so I think there are Western investors that know who they are. They’re not as large as Baidu Apollo Go, which has, I want to say, well over a few thousand cars on the road in China pilot programs in a dozen cities. But Pony and WeRide are also moving aggressively outside of China, partnering with Uber, partnering with other companies, ride-hailing companies, and there’s pressure because they’re publicly traded to really scale and create some profitability. Whereas Waymo is still owned by Alphabet. So I think they have pr internal pressure, but not pressure from external markets. in the difference between China and the US, because at the end of the day, these are really the only two players that have multiple horses in this race. Now in the UK, there’s a company called Wave. And I think there would be other European players that would argue that, hey, we’re also a major player, but let’s, for the intents and purposes, oversimplify this by saying there’s the US and the Chinese players. In China, there is this first wave of A V companies: the Werides, the Pony AIs, the Baidus, and then there’s this other wave, and we’re only talking about robotaxis. Because on the commercial trucking side, on the slow-moving delivery vehicles, we also have autonomous vehicle players. But for robotaxis, there’s this second wave. And they’re more of an asset-light company, autonomous vehicle startup. So like DeepRoute, Momenta, QCraft, you know, they’re what they’re doing is partnering with traditional OEMs in China to get their stacks onto these vehicles. DeepRoute, for instance, is working directly with Great Wall to integrate their hardware and software stack into the design of the vehicle. So even before, because what we normally see right now, Grace, is a car with lidar, sonar, radar bolted on as, you know, an afterthought. But once these companies are working with the traditional OEMs, they can design them, and it looks like part of the form of the vehicle as opposed to like this bolt-on after the fact. And the convergence between RoboTaxi Company and traditional OEM is blurring. And I’d mentioned earlier that Huawei also has a significant stack. So they’re a player as well. But Huawei, as far as I know, is not getting into the RoboTaxi space. But they’re going to move into level three intelligent driving, and they’re hoping to be in millions of vehicles in China within the next few years. And that’s where it’s very different in China because there’s not a lot of convergence going on in the US market. Now we know about Neuro, we know about Zoox, we know about Waymo, and with the exception of Neuro, who’s working with Lucid to put their stack on the Gravity for a premium experience. Most of these companies are not working with OEMs. And then the OEMs have their own systems as well. And that’s the big difference between the China market and the US market. And what we’ll likely see is a bifurcation of the US market being primarily North American autonomous vehicle providers working with the Ubers and the Lyfts to create that larger install base to try to reach a broader audience. And That’s one of the big reasons why these companies are working with the ride-hailing companies, because it’s gonna be hard for Pony to attract 100 million users. Whereas if I partner with Uber, my install base is 160 million global users. And so that creates an opportunity. And I think long term, Uber sees Robotaxis and Evital as their profit drivers. And you know, the delivery services and all these ancillary mobility services as a way to increase their install base but not make a ton of money. And and soGrace Shao (1:01:45) Wouldn’t that cannibalize our own business, existing business a little bit?Tu Le - Sino Auto Insights (1:01:51) Yeah, and you know that’s that’s the that’s that’s the million-dollar question because Uber is now also buying its own autonomous vehicles. So not only is it a ride hailing platform, but it’s a fleet manager now. And that changes the economics of their yeah, exactly. So that really changes the economics on their balance sheet. Okay. So I I don’tGrace Shao (1:02:06) hedging.Tu Le - Sino Auto Insights (1:02:17) To your point, I do think they are kind of hedging their bets a little bit. Because if to answer your question, we should separate autonomous vehicles into those that use lidar and those do not use lidar. Tesla does not use lidar. Wave, which is the UK company based out of London, does not use LIDAR now. We can have, and I’m sure you’re well- I think you’re talking to some expert in a couple of weeks, so maybe you can ask them to use lidar or not to use lidar. they are using lidar, so I’m sure he’ll he’ll say that lidar is necessary, but it’s more philosophical now, right? It’s more philosophical because Tesla can’t all of a sudden put lidar because it changes their whole system. Okay. But to me, LIDAR.Tu Le - Sino Auto Insights (1:03:06) Prices have gone down so significantly that creating another redundancy and, you know, kind of creating that sensor fusion with multiple sensors and lidar creates a safer environment, I would think. but again, it it it it’s an interesting thing that I don’t think a lot of people definitively can answer. I’m sorry?Grace Shao (1:03:29)It’s a philosophical choice. Or is it actually a design choice because the vehicle already can- like, you cannot put a LIDAR on top of it because Teslas cannot use LiDAR. Like, is there a reason?Tu Le - Sino Auto Insights (1:03:42) So, to me, it was an engineering choice at first, but now it’s more philosophical because now you’d need to change your system if you all of a sudden incorporate lidar into it. Right. And for Elon, I think it’s also a mienza thing because he’s been so strong against LIDAR that if he turns it around, now don’t get me wrong, he says things sometimes thatGrace Shao (1:04:03) Yeah.Tu Le - Sino Auto Insights (1:04:09) Never come true or haven’t come true yet. So that’s kind of the crazy thing. But I think LiDAR, that’s one of those things where I think he’s willing to die on that that doesn’t need lidar. But anyways, I don’t want to get into this this this discussion about lidar, but but but but to the bifurcation thing. The reason I say bifurcation is becauseGrace Shao (1:04:24) Okay. It’s gets getting too technical. All right.Tu Le - Sino Auto Insights (1:04:34) We know that Europe has a strong data security, data sovereignty policy, security, privacy, and sovereignty. So will Europe allow the Chinese autonomous vehicle makers to ship European data back to China to the servers to train the models? And/or so the United States, because we have a ton of allies, China has a ton of allies, that’s why. It’s likely that the Chinese allies would use the Chinese systems first, and then the US allies would incorporate the Waymos and the Zoox. That’s kind of, and I’m oversimplifying this, but because the Trump administration has kind of poked the eye of a lot of our traditional allies. So maybe they wouldn’t want our autonomous vehicles on their roads. But I’ve ridden in all of these systems.Grace Shao (1:05:08) I see.Yeah, like Canada might be saying no these days. Sorry, I’m just going. It’s like really late for me and I’m just thinking about yes, but like now Canada’s gonna have eight Chinese EVs. Now American cars aren’t gonna sell there now; American Waymo’s not gonna be in Canada.Tu Le - Sino Auto Insights (1:05:28) Yeah, yeah. No, you’re well, we could do an entire episode about all of that stuff just between North America, right? So I think that would be an interesting conversation. But you know, that’s exactly my point, right? Like, traditionally, prior to Trump, I think Waymo was going to rely on our allies to launch its services. And if we look at the Middle East and India, theyTu Le - Sino Auto Insights (1:06:06) kind of want to be Switzerland a little bit. So because they they they they don’t want to take one side over another. India’s the same thing. And these are potential markets where both of them will compete. Famously, in London by the end of this year, early next year, Waymo and Do will all be testing and rolling out pilots. So next time you’re in London, if it’s early next year, you might be able to try all three systems in the same place on the same streets, which I think is gonna be a very, very, very unique experience. because I don’t think there’s gonna be many cities that you’re gonna be able to do that in over the next three, four, five years. And, you know, at the end of the day, is it a data thing? Is itGrace Shao (1:06:47) That’s pretty crazy.Tu Le - Sino Auto Insights (1:07:02)Who has the most data? Who has the most robust edge case data? Is that ultimately who wins? Or does AI really change the game? Because if it’s about data, if it’s about kind of real-world miles, then you would think the Toyotas and the Volkswagens would have a distinct advantage because guess what? They put 11 million cars a year on the road. Okay. So a company like a deep route, a company like a wave would love to work with these companies that high have high sales volume. But what is the great equalizer? If you talk to Elon, it’s his system because again, everything is closed, everything is native to the Tesla system. FSD is the best intelligent driving system. Is it better than Waymo? And will there ultimately be a convergence between level three and then level four? You know, can a Waymo system compete directly versus a Tesla because and and I don’t have answers to that. And that’s what makes the industry so interesting because the politics of things change, the technology changes, and then the commercialization opportunities change as well. And what I do know is that Waymo is backed by one of the most valuable companies in the world, which means that they haveGrace Shao (1:08:09) Mm-hmm.Tu Le - Sino Auto Insights (1:08:28)A huge check to help them get to scaling. And one th other differentiator with Waymo and the rest of the players is that they’re really moving into a lot of four-season cities, like Detroit. So next year, Waymo is going to be launching a service. And what we’ve seen so far is that a lot of these autonomous vehicle companies are launching in Arizona in the Middle East, where guess what?Grace Shao (1:08:29) Resources.Tu Le - Sino Auto Insights (1:08:55) Weather is super predictable, and it’s pretty one-note. And where the edge cases are, you can look at it like an inverse normal distribution curve, and I’m oversimplifying this, Grace, but the edge cases- so they happen few and far between- but they’re likely where the most severe accidents happen. Okay. So so it’s like there’s the least amount of data available. Exactly.Tu Le - Sino Auto Insights (1:09:21) Right. Severe storms, blizzards, snow, whiteouts. And so what we’ll likely see the final frontier being Robotaxis, because we’ll see commercial trucking from companies like Kodak. Remember that company like Too Simple? Those competitors. We’ll see those Aurora. We’ll see commercial trucking happen sooner. And likely highway to highway. I I divide commercial trucking into like three segments, and that’sTu Le - Sino Auto Insights (1:09:49) You know, intra-city, city to highway, and then highway to highway as three separate use cases. Yeah. And you know, robotaxis, I think it’ll be phased; it’ll be geofenced for a long period of time. There’ll be certain use cases that it makes a ton of sense for robotaxis, you know, but ultimately, in order for this service to become ubiquitous, there probably needs to beGrace Shao (1:09:54) It’s just much more predictable. Yeah.Tu Le - Sino Auto Insights (1:10:16) More services that have multiple people than one person in a car. And so that’s where it’s interesting because Waymo just launched the OHI, which is the Zeker contract manufactured vehicle, which has multiple seats. And I think that’s how Waymo looks towards profitability to get more than one person in an autonomous vehicle and almost looking at it like a bus, you know, a smaller bus to getGrace Shao (1:10:41) I was just gonna ask actually, like, are we gonna see a redesign of what robotaxis should look like? Because you, you, I tried out Waymo in San Fran, and it’s kind of creepy because you still have the driver’s seat, but like no one’s sitting there, so you’re constantly freaked out, like, what, you know, if you’re not used to it. So, like, will we see more like these little boxes or something that will signal to other drivers as well? More obviously, this is a robotaxi versus like a normal car.Tu Le - Sino Auto Insights (1:11:10) For sure, for sure. Right now there are policies in place that say you have to have a steering wheel, you have to have brakes. But as the autonomous vehicle landscape evolves, we’ll probably start to see, and I have a theory, Grace, that more and more cities will limit private passenger vehicles coming into the city center. Okay. If we look at Paris, they’re investing 300 million euros to make all the boulevards that lead into the Champs-Élysées bike-friendly, and they’re gonna limit private passenger vehicles. And so especially in Asia, I could see that being, you know, maybe you park, or you take the train into the fourth ring road, and then you take an autonomous vehicle into the city center. And then to get to your office, you take a scooter. Right. So there are these scenarios where I think more and more cities will try to take back some of the streets, some of the roads that bleed into the city center in order to lighten up traffic and take back some of the land. Because if we think about, and we’re getting getting off topic here, but I think these are important kind of secondary and tertiary effects of autonomous vehicles. Look at these parking structures and like these parking lots. We use them from like 7 a.m. to 5 p.m. And then they’re not used for the entire rest of the day. It’s kind of a waste, to be honest with you. And then and I’ll right. And so if we can take that back, you know, ideally make more housing affordable, make more office buildings, or whatever, right? Like round out, make more green space as opposed to having so much so many parking structures. Hong Kong could use more green space because all they can do is build up. And so there’s a lot of opportunity.Grace Shao (1:12:36) Hong Kong has, like, only I think like less than ten percent of the population even have a car because the public transit is so good. To your point, like there’s minibus, there’s a double-decker bus, there’s like the T MTR. Everything is walkable. you it’s and it’s c really, really, really well planned. And I think a lot of Asian megacities are like that. Actually, like think of Singapore, think about like Shenzhen, obviously obvious Shenzhen where you have likeTu Le - Sino Auto Insights (1:13:05) All right. Yes.Grace Shao (1:13:26) EV buses to EV cars to EV scooters, all for rent, all for access for the like average person on the street, right? So yeah, that does make sense.Tu Le - Sino Auto Insights (1:13:34) I’m gonna drill down on that.I’m gonna drill down on that because when I was living in Beijing, I was a mobility practitioner. I walked, I rode share bikes, I rode subways, I rode high-speed rail.Tu Le - Sino Auto Insights (1:13:53) Yeah.Yeah, well, I mean, rings around a road is a little weird, but now that I live in the United States, I’m just an advocate ’cause all I do is get in my car and drive everywhere I go. And on the weekends yeah, yeah, wellGrace Shao (1:14:07) But it’s also because they’re in Detroit. It it makesa difference, right? If you’re in suburbia versus like the middle of like ring three Beijing.Tu Le - Sino Auto Insights (1:14:14) Yeah, and I think that’s a big difference between like North America and Asia. And I would lump North America and Europe a little bit into that because a lot of these cities aren’t very big. So to invest in subways and things like that would probably be dis a disproportionate expense for the city’s budget. Where when you’re in China, there are dozens of cities with over a million people, you know, hundreds of cities over a million people.Grace Shao (1:14:41) Over like twenty million people, like a couple cities are over, yeah.Tu Le - Sino Auto Insights (1:14:43) That’s why I think, and what’s important about China or distinct about China as well, is that the automotive sector didn’t build out this transportation system because there’s a balance of high-speed rail, to your point. There’s a balance of subways, intracity transportation. But I’m off topic.Tu Le - Sino Auto Insights (1:15:07) The autonomous vehicle space isgonna be very interesting because our is the US government going to restrict silicon? you know, does because right now NVIDIA basically supplies every automaker with a a high-end transport or intelligent driving feature. Okay. But you and I know that the Chinese government is really pushing for companies like Horizon and and andTu Le - Sino Auto Insights (1:15:35) Black Sesame and XPeng, NIO, theirHuawei, they’re all silicon design companies now, too. And eventually NVIDIA is gonna get pushed out. And that’s also another bifurcation point as well. So if the Chinese can’t catch up to NVIDIA and Qualcomm and some of these other silicon design, Western, more established Western. Silicon design companies, does that mean their AI is not as good? Does that mean it’s not as robust? I think these are really open ended questions that you probably have conversations with your other guests on. So, and and I listen to you because that’s important to me of about understanding other perspectives on that stuff, because although I understand the chip sector, not to the level where I’m not an AI expert. And so I, like I said, I use it. as a tool as opposed to the end-all be-all. But but yeah, soGrace Shao (1:16:32) No, appreciate your insights. Really, really appreciate your time. I’ve definitely taken up more than, you know, I asked for. So, to end, I wanna ask a question. what is one differentiative view or you think a misunderstanding the the world might have of on the topic of China EVs, mobility?Tu Le - Sino Auto Insights (1:16:51) I think in twenty, twenty-five years, we’re gonna look back at this time as a renaissance in mobility because of everything that’s happening so quickly and being driven by the competitiveness of the China market. And we’re gonna see BYD is definitely gonna be a player. And you know, the other thing that I think is really, really important is that. I don’t believe traditional automotive folks can think outside of their normal way of seeing how the world works through transportation. And the top 10 mobility providers, to me, in 15 years, there might be a handful of traditional automakers, but I see an Uber maybe being a top 10 player. I see a Baidu or a Waymo being a top 10 player, and they don’t. build cars, but we’re the the importance of building vehicles is not going to be it is going to be reduced over time very quickly because of China. And so if you’re not providing a value added service in the mobility space, which I think which I think China is going to be able to do at at a much more affordable price point, especially in the emerging markets. I think that’s where the important thing is because in the Western markets, the BYDs and the Geely’s still have challenges and customer acquisition costs are much higher in in those emerging or those established markets. But in the emerging markets, the Chinese are going to try to roll out not only passenger vehicle buy-sell and their brand, but they’ll probably try to sell a lot of services once they have that sale. And I think that’s really going to be that opportunity for the Chinese to really make a name for themselves. Because you know this, Grace. One of the coolest things about the United States for me as an American is that anywhere I go, and you can not like the food, you can not like the coffee, but it’s consistent. If I go to a Starbucks in Munich or Vancouver or Toronto, and that’s soft power. That’s American soft power, right? The Chinese would love to have four Chinese brands. Creating soft power for them, creating aspirational desires to have their products. And and so that is gonna be the priority for a lot of these entrepreneurs that you and I speak with because, you know, they’re as ambitious as Elon, you know, maybe maybe they don’t get covered as much by Western media because their English might not be fluent or whatever, but they shouldn’t be underestimated just because they’re in China and they’re not in the rest of the world yet. And and I think that we’re gonna look back at this time and point to a few Chinese people at the level of being close to Elon. SoGrace Shao (1:19:47) Very interesting. Yeah, I think I think to your point, a lot of the entrepreneurs I speak to these days, they set their eyes on the global market in the first day and they want to set the industry standard. Like that is their goal. And it’s no longer about shipping out something cheaper, shipping out something just to make that quick buck anymore. So there’s definitely like a a sentiment shift and that confidence is different as well. to it Yeah. Well yeah. Thank you so much for your time today.Tu Le - Sino Auto Insights (1:20:09)It’s off the charts. Confidence is off the charts.Grace Shao (1:20:15) Really appreciate your time. I feel like I need to invite you back for another conversation because, you know, for what I prepared, we can go on for another two hours, I feel like. But it is late tonight, for me. So I’m gonna call it a day. Thank you so much.Tu Le - Sino Auto Insights (1:20:30) Thanks for having me, Grace.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  Where does Europe fit in the so-called China-US AI race? 08.06.2026 1t 9min

  Joining me today is Alex Lu, who offers a unique perspective. Alex works at the intersection of three very different AI worlds: China, Europe, and enterprise transformation. Having spent more than a decade in France and now advising European companies on AI adoption (often Chinese models), he offers a perspective that is often missing from the broader AI conversation, which is typically framed as a competition between the United States and China.In this conversation, we explore how European companies are actually approaching AI implementation. Rather than racing to deploy the latest models, many are focused on organizational design, employee adoption, process changes, and measurable returns on investment. Alex explains why European firms tend to be more cautious than their Chinese counterparts, how concerns around AI sovereignty shape technology decisions, and why companies increasingly find themselves balancing U.S. frontier models, Chinese cost-efficient models, and European alternatives such as Mistral AI.We also discuss the economics of AI adoption, including the emerging concept of “tokenmaxxing” or rather if that is even the wise path forward, whether AI is truly replacing jobs, how companies should think about ROI when AI introduces variable costs, and why the future may involve token budgets becoming as commonplace as mobile data plans. Finally, we explore Europe’s position in robotics, industrial AI, and regulation, and whether Europe’s strength may ultimately lie not in building the largest and best-performing models, but in defining how AI is deployed responsibly at scale.To find the previous episodes of Differentiated Understanding, see here.Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.Season two will host a series of guests from early-stage investing, as well as builders, researchers, founders, early adopters, and product managers. For more information on the podcast series, see here.AI-generated transcript (for reference only)Grace Shao (00:01)Hi Sheng Yun. Thank you so much for joining us today. Really excited to have you.Alex Lu (00:05)Yeah, thanks very thanks for inviting me. I’m also very excited to have this conversation with you.Grace Shao (00:11)Yeah, awesome. So tell us about your journey. I think you’re in a pretty unique position. You know, like I said in the intro, you know, a lot of the conversation about AI right now is often positioned between China versus US, But you actually work predominantly with European companies in adopting AI and their digital transformation. So tell us about your your background and how you got into this.Alex Lu (00:31)Yeah. so thanks a lot. So actually, I went to France. I spent more than 10 years in France. I went to France in 2004 and I studied in a school called Ecole Polytechnique. and then when I graduated from the school, I started my work in in Europe, mainly for automotive industry and afterwards for the consulting industry. And still when I was in the consulting industry, I worked mainly for for the auto sector. SoI have a very traditional background of automotive. That’s why some of the work I’m doing currently in the in the AI, we can come back on that, is in the automotive manufacturing sector and mainly for European companies. Because I started my career in Europe, so I know I don’t I know them pretty better, pretty good. And the the the other thing point I want to mention is the school I started actually the Ecole Polytechnique wasLet’s say it it was a famous school in France or in Europe, but it it’s not so famous in in the world. actually this is in France they have a different educational system. but still with the with the rising of of AI in Europe, especially the French large language model called Mistral AI, the school becomes famous because the founder of the of of of Mistral AI comes from the the same school. So basically it’s also a a little bit likeTsinghua university in China is like the the Tsinghua in in France, having the best talents for for the AI. So nowadays, when I continue my work in the AI transformation for companies or AI implementation for the companies, I work a lot with European companies. Firstly, I know that my I as I said before, and secondly, is when we look into the global competition between China, US, and Europe.In the AI landscape, it’s pretty clear. It’s like China and and US or US China being the tier one or first ranked models. And Europe is kind of lagged behind. So most of the European European companies, they have this kind of attitude of being a little bit complex, I would say. on one hand, they are kind of seeking for, of course, for the best technology in the in the world to enhance their company’s competitiveness.There comes the question, how I can define my AI strategy for next year’s between Chinese and US tech stack in AI. And the second question they raised often is while we are European companies, we want to keep keep our AI sovereignty, which is a very important topic in AI. again, we can come back on that. So their question is: okay, between this US and China tech race.Is there any place for European companies regarding the foundation model companies or application companies or even corporate clients? What could be the playground for European companies? So these are major two questions are often received from European companies and you will you can see the thinking angle is they European companies want to at the same time keep it keep the AI sovereignty and at the same time keeping their competitiveness. That makes the question a little bit complex. Yeah.Grace Shao (03:49)Actually why don’t we just double click on the unpack that a little bit? What’s your view on it? Like what what do you advise your clients to do then if if they are kind of cut caught in a pickle or unsure how to build out the next stage of their infrastructure kind of being caught in between China and the US?Alex Lu (04:08)Yeah. So the the first thing I I always shared is in in in this tag race actually China and US we are not I want to twist twist a little bit the angle saying this is a competition between China and US. Actually, if we look into details, actually China and US are taking different directions in terms of the AI development, if I can say, because let’s say if we look into the US,AI ecosystem or the AI development. I think a lot of efforts are put on the foundation model or kind of foundational research regarding how AI can be become AGI can be bring beneficial benefits to the humanity, or how we can guide Rails AI so that okay, one day we will not go into the direction of science fiction movies. So this is a little bit the the push from the US AI companies. While in China, actuallythe ecosystem or from the national perspective, China’s AI is more about applications and more about how we can have the s beneficial from the whole society from the AI and how I can combine AI with my traditional technologies or traditional business to to to to to grab more values. So if we think in this angle, actually it will give us two different pictures. One is we cannot say that it’s kind of fromfront to front front competition, because these two nations are just take different angles. The second thing is if we look into details based on these assumptions, we will say one nation is pursuing having the most advanced AI technology and one nation is pursuing most kind of most beneficial AI for the society regarding cost effectiveness, et cetera, et cetera. So then it comes to the question that you raised for European companies isWe always brainstorm and conclude on the simple question is what kind of AI are we looking for for European companies? Are we looking for, let’s make it simple, I take some an analogy. Are we looking for kind of you need all the employees to be the PhD employees having the most intelligence in the world? Then that will be the US foundation models. Or if we want to say we have the most cost efficient and best performing employees, virtual employees in your company.Then we might consider Chinese models, foundation models. Then this is the trade-off. I think the companies should figure out. And the answer will not be so simple like that, saying, tomorrow I will switch to all US tech stack or Chinese tech stack. I would say the two ecosystem, as in the past in the digital area, will still continue for European companies, meaning that they need to juggle with Chinese tech stack in certain markets.maybe in Chinese market for sure, but for other markets, developing markets where the Chinese foundation model are taking influence and as well as with US models. So this is the thing. And I think the other angle answer to to to their question is I I usually take the statement from Jensen Jensen Huang saying the AI is kind of five layer cake.So what we are talking about is only one layer, which is the foundation model. And if we go deeper, then we will have infrastructure like data centers, like powers, chips, and electricities. And if we go upper, we will have the applications. So I would tell European companies or I told European companies often is I think the use cases in Europe makes a lot of sense because the cost is there and the employee was prettymuch expensive than Chinese employees. So if we deploy the same model, let’s say, and it of cost the ROI return on investment, you make the business case very easily in Europe than in China because the labor cost is kind of lower. And the the advantage of Europe, one I would say one of the advantages is about power and electricity. I study in France and in France y you would see they have the most advanced nuclear nuclear power technology in the world, at least in the past. AndI think the French government is also think about how we can build more power plants in the in the country to support Mistro’s AI development. And I listened to the founder of Mistral AI, Arthur Mensch. he explained to European Commissions how we can keep the AI development in the Europe is just he make a very simple analogy, meaning that intelligence equals to token. So we all know that, and he said token equals to electricity.So if we want to make our society more intelligent in Europe, then we need to build infrastructure and more efficiently and more sustainably. And and my last point is I looked into the report released by Stanford, the HAI index. And very interesting because US is far away beh in advance compared to other countries in terms of the number of data centers, it’s around2000 or more than 2000. I I didn’t remember the exact numbers. And the second and third, it’s not China in terms of the number of data centers. Based on the data, it’s United Kingdom and Germany. So Europe, Europe has the capability to build power plants, but I I I tend to believe these power plants are not currently used to train US models, in my opinion. So again,This is the European competitiveness if we want to talk about AI. So if we enlarge a little bit picture, we say that okay, it’s five or five layers cake, then again, China and maybe is better performing better in terms of electricity, maybe a little bit less performing regarding the chips. Same situation for Europe. So it’s not only about the most performing models, right?Grace Shao (10:13)That’s an a really interesting take. So help me understand like what do you actually advise on these companies for? So you gave me a big high level picture, right? Well give me some examples on the kind of work you’re working on. It’s because I think on this podcast, we often invite people who are builders, founders, investors, and they give us a lot of high level views, which is great, right? But but I want to hear from you, how are you actually helping companies go through this AI transition? AndWhat are the bottlenecks? Maybe further down we can talk about that. What are the challenges? What are the exciting areas? But just help us understand what are the day to day tasks that you’re working on.Alex Lu (10:49)Yeah, thanks. again, I I might share two different perspectives from my experience when it’s again with the European companies. It’s very interesting example because actually I build a product doing this kind of market intelligence, market research for European companies and the value proposition at the time it at that time is we can save time for your employees and they a and and we make your organization more efficient.And basically we find when we when we s when we sell this kind of value proposition to different companies, I see very interesting different answers. One is on the European side, he would say, this is very interesting, but before implement implementing, we need to think about a kind of tomorrow’s process process, meaning that if we put your AI product into our organization, how our employees will work with the product together, what’s the process look like?And how many new skills would my employees need to perform or to better use your products? So I think the European company’s mindset is they will need some time to conceptualize the AI products or AI use cases. And then they will need to conceptualize and project, especially once the product is in place, what my company will look like. So they will spendA little bit more time than Chinese companies to figure out the the regarding the talents, regarding the organization, regarding the process. And in my opinion, it might be a right right approach, in my opinion, meaning that they put human before the techno technology. And this is what I observed when I implement AI for companies saying that, okay, I bring you the best technology, but often we might have improved efficiency by 10 times or five times.In one single process, but actually there will be some bottlenecks in other organizations, in the in the rest of the organization, then you cannot you cannot increase the efficiency of the whole workflow, let’s say. So that’s why a lot of people in US they talk about AI native organization to kind of remove the bottlenecks in the in the organization. And I worked another example, I worked for a European company, and it’s very interesting. He said, I receivehigh level management. He said, I received so many reports from my employees, I I don’t have enough time to review and to approve them. That’s the case because we increase efficiency of the working level of the people, then the bottleneck becomes suddenly the a the leadership. And then we need maybe an empowered leadership by AI in the future to make the whole organization more efficient.Or we need to think about a new organization where we include AI agents and human beings together because the two natures are producing things on a different scale. So this is mo most of the time, this is the European companies. And for the Chinese companies, the mindset is totally different. if we implement the AI solution, the same product to a to a Chinese client.The the the answer would be, that’s very interesting. You save 20 or 30% of my employee’s time, but you know I cannot let’s say lay off the employee and to make some savings. So just tell me for the time we saved where he can work to produce more. So it’s always in the mindset, okay, we have some some time saving, but you I cannot pay 80% of the salary to the same guy. SoIn in order to so I I need to pay him hundred percent salary, so I y your business case doesn’t work for me. So where where we can grab more values. Yeah, so you see a diGrace Shao (14:37)That’s really interesting. That’s a really interesting approach.Yeah, because it’s like the company is reflecting actually a broader, I think, social, even cultural and perspective on how they’re perceiving AI. And in the China and US, often the conversation is so fixated on improving efficiency and people who are utilizing AI are actually more burnt out because they are like 10Xing themselves or whatever these days.Alex Lu (14:49)Exactly.Grace Shao (15:03)But you know, in Europe, that conversation is so different. And you can say maybe much more humane. However, like you said, the bottleneck right now is then how do these companies become the next generation? Like still relevant in the future, once this becomes normalized. So it’s interesting. I wanna go back to that a little bit later as well. I I wanna touch on something before we get further into I guess the comparison of you know, the adoption and everything isAlex Lu (15:17)Yes.Grace Shao (15:30)You and I met each other essentially online because I found out about your work that you helped a lot of European companies adopt Chinese model. I found that was very, very fascinating, right? you advised them on how to basically integrate, say, the Minimax and C AI of the world. Now, a lot of these model companies, when I speak to them, they say their priority right now is to basically sell globally. And of course, the Western markets are some of the most lucrative markets. the US.headwinds are mostly in geopolitics, compliance, Europe. How do you view that as a market for them or opportunity for them? Like, is it equally challenging for these companies to sell to enterprises in Europe or do you think there are more opportunities for them right now and they’re they’re kind of taking off a bit more?Alex Lu (16:16)Yeah. I I I I if I think the conclusion if I I can state at the very beginning is kind of in Europe definitely there are more opportunities than for Chinese large language model companies than in in Europe, than in US, sorry. I have two proofs for that. Firstly is I discussed with a a CTO who is also a schoolmate from my school Polytechnique.And actually he was very interested also by my newsletters on LinkedIn and then when they he asked me the question so apart apart from Deep Seek, what are dip other models and Chinese models that we we that we can use to improve our efficiency? Because when we meet all of he said he told me when we meet most of the IT implementation companies, they came with the solution like Anthropic or ChatGPT or OpenAI or or Google Gemini.So we don’t see so many options. He mentioned the word options of Chinese models. And we know that Chinese models are more cost efficient. And then we can talk about token mapping. I think it’s kind of related topic. So this is one thing. I think in Europe, actually for the companies from the business perspective, they are also looking for different variety of different models so that they can bring what I said before, a best cost performance ratio models in the in the organization.So this is one thing. And then I I told him that most of the Chinese large link model companies, firstly they started their business in China and then they tried to inf have the global influence. Like the most advanced one is zero dot zero one dot AI, but the other ones they are trying to catch up, like that AI you mentioned also Minimax. so I I think the thing is what I see today is the ecosystem of Chinese models.Are not currently penetrating into the European markets. But definitely there’s a a room for Chinese players. The second thing is I always take the comparison with the other industries like EV industries, like car industries. because you you will see Europe put a lot of tariffs on Chinese vehicles. because okay, you you see a lot of Chinese vehicles because ofEurope wants to protect their own industries, et cetera, et cetera. But at the end of the day, they are not putting hundred percent tariffs. They are putting somehow reasonable tariffs on the Chinese vehicles. So the bottom line I want to mention is I lived in Europe before and I know the mindset of Europe European people. The mainstream, of course, we have different views. I think the mainstream for European people, most open ones.Are saying okay, we need fair competition. The EV cars is just because okay, the European commissions are claiming that okay, you produce in China, but we in Europe we produce in a more sustainable way, so our cost is higher, blah blah blah. So if we take this comparison, I think definitely there will be some places for Chinese companies in condition that we play fairly in the European market.And then we might come back to the third point I mentioned before, of course, there’s a a point of AI sovereignty. the biggest, the biggest player of European AI ecosystem is still Mistrol, so it’s the biggest player in the foundation model. And of course, Mistrol should be one of the choices options when we suggest to European clients as the large language models.So I would see that if tomorrow the Chinese model enter these European markets, they will face a fierce competition with Mistro because Mistro basically they have a government back, let’s say, from France, and they have a very good positioning in the ecosystem. you would see in in two or three weeks you’ll there will be a VivaTech in France and Mistrol for sure they will be on the stage and for sure they will beFrench or German presidents, French president and German chan chancellors. And with their unique positioning, I think most of the European companies they were firstly considered Mistrol, but still if Chinese companies can bring something on the table, business wise, the European companies will not only limit to only one model, there will be some balance between different models. And today, the balance I see is Mistral versus other US models.Grace Shao (21:11)No, I just think it’s really interesting ‘cause I think it also totally makes sense when I to talk to people who are in the Korean market or, you know, covering the Middle Eastern markets. sovereign AI is just such a top of mind like conversation for companies, whether it’s for compliance reasons, or regulatory reasons, whatnot. So it makes a lot of sense that Mistral’s position very well in Europe. However, are there any other players that maybe we’re overlooking outside because we’re not that familiar with the European market? Any otherfoundational model labs that we should know of coming out of Europe.Alex Lu (21:45)Yeah, there will be apart from Mistro there’s another large language model whose name is H, but it’s less famous. And then you’ll have it’s not if we can say it is kind of word model by Yen Laquen, the ex researcher in Metafair, and he just came back to France and lab raised raised a large amount of money for the for for his model. It’s called AMI, yeah, AMI.Grace Shao (22:01)Mm.Interesting. Okay, so let’s talk about the token maxing thing you touched on just now. So offline we talked about this a little bit recently. There’s been getting some buzz. It’s quite funny, you know, whether I’m it’s like big tech in the US or big tech in China. When I talk to them, people are saying, Okay, our managers are pushing us to token max. If we don’t basically use AI in our job and figure out ways to essentially replace ourselves, we get replaced, which is the irony in all of this. It’s it’s all kind of sci fi. butGrace Shao (22:41)Then the joke’s kind of been played now on the companies because you know there was just headlines coming out saying, this one guy basically spent like more than half a million dollars on tokens in a month, and that’s obviously more than his salary. And then companies are realizing, wait, this token maxing strategy is not cost efficient at all. So from an operational standpoint, I know you are someone who work a lot with companies to implement AI and findAlex Lu (22:54)Yeah.Grace Shao (23:10)the most cost efficient way for their for their operations, right? And not just costs, like you mentioned, it’s like a balance of costs, you know, and and operational sustainability as well as obviously company morale and everything. So how do we view this trend? Where is this going? Is this sustainable? Like just just give us some high level views on this.Alex Lu (23:32)Yeah, there there’s a a lot to talk about this, because the token is becoming really a trendy topic for individuals and as well for companies. so to answer your first firstly to answer your questions, I don’t think that’s sustainable. My view is the token mapping is kind of marketing for infrastructure companies. and of course, as you say, there’s a lot of people burn a lot of tokens and more than their salaries.Then the question would be if I pay your salary or if if I pay your tokens. We’ll come back to this point afterwards. I discussed with some some Chinese companies. Very cost cons cautious. I think the the the thing is today when we actually for for the tech companies in China, there’s also some ranking of token consumed. but it’s kind of indicator of how people are use AI. ButIt’s not is it the right indicator? I don’t think so. basically I think in the in the in current status we didn’t we didn’t find a very good metric to measure the performance of a human being empowered by AI. that’s the thing. So we take a kind of proxy indicator, which is the token for and of course there’s a lot of waste of token in in in in in the usage and I’m I’m not sure that every single employeeswould be the master of AI if we don’t provide the sufficient upscaling in terms of the AI. Because from individual perspective, sometimes we use by coding, but if we don’t master the basics of coding, then we might waste some time and as well as some money and tokens in the by coding. So this is my view. So the token maxing is kind of marketing stuff and and the the day when we find outAgain, for the AI organization or for the organization, how we can measure the performance of individuals with AI, then we might have a clear picture and no longer token max. And the other interesting thing you you mentioned already, but I read also is Microsoft they are kind of switched to their copilot because ever s if everyone used used the entropy cloud model then become too expensive for the whole organization. It’s just not just not cost efficient.And brings me to my point is when discussed with some Chinese companies. So, you know, Chinese companies are very cost conscious. And they are thinking is I think that it was a joking, but this is right angle of thinking is show we in the salary of our employees to allocate a part of the tokens monthly for our employees. meaning that okay, if theIf in the in the in the past situations hundred percent of the salary tomorrow might be eighty-five percent of yesterday’s salary plus fifty percent by tokens. And the tokens you can you can use and if you don’t use tokens efficiently then it’s the savings for the company. So this is it’sGrace Shao (26:43)That’s really crazy. But I kind of see what you mean.Like so essentially it helps you with your job. So that’s why it’s on you. But then what if you just don’t w but what if you don’t want to use AI? What if you just like I can do my job perfectly fine the way I did it before and I don’t want to token max and I want to keep my hundred percent?Alex Lu (26:50)Exactly. That’s the question that the Chinese company needs to answer, but you reflect on your point mentioned that the token consumption is sometimes much more expensive than the salary. So it causes Chinese company companies to think that okay, I spent salary, I spend tokens for the intelligence, I spent two times to hire employee. So why not combine them together and doing kind of tomorrow’s package is your basic salary plus tokens?Grace Shao (27:32)So actually on on that,how should companies think about it then? Because, you know, it’s really easy to say, okay, this is an AI native company. There’s 20 people in this company. Everyone’s token maxing because it does bring the 20 people’s efficiency to say like 400 people, whatever it is, right? However, what about the traditional companies, especially the ones that you work with? Like a lot of them are OEMs, manufacturers, you know.It it doesn’t make that much sense for them to really jumping on this AI bandwagon as well then. Or how do you advise them then? Or how do you think how should they think about it?Alex Lu (28:06)Yeah. I I think for the for the traditional companies or European companies, it doesn’t make sense for everyone to give the token maxim because as I said, I’m pretty aligned with the European approach saying that okay, in order to release or unlash the value of AI, we need at least to upskill a little bit our employees. We cannot expect employees like with thirty ex years experience in the industry and tomorrow he switched to a kind of AI expert in thein the in the in his company. So I I just want to combine our question with my previous comment saying that today if you look into the Chinese market today there are some big big traditional telecommunication companies like China Mobile they are proposing the token plan for individuals it’s like your smartphone monthly monthly plan yeahGrace Shao (29:02)Wow. Like data plan.Alex Lu (29:05)It’s a kind of data plan, exactly. So the token is becoming kind of infrastructure like electricity, like water, or like your smartphone, monthly subscription. So this might be the way the companies might pursue, saying that, okay, yesterday I might give you a kind of monthly plan for your telephone. So I can reach out to you and you can read the emails and you can use the telephone to walk with emails, work teams or with Zoom, etc. etc. And tomorrow it might be a com kind of monthly subscription.For different employees, then you have a monthly token plan you can use for your personal, not for professional work in AI. I guess that might be the way that the China might be moving for individuals and for companies. And again, for European companies, they are not there yet, but when I discuss this vision and this kind of trend, and they are pretty interested, they might be moving in the same direction.And for the companies, at least not at the national level, but at the company level, to provide kind of a monthly subscription to a limited number of people who master AI, and the first wave of people adopting AI is their coding team, their IT team, their digital team. So they will be the first employees to use this kind of concept of monthly subs subscription to tokens.And of course, for manufacturing companies, there’s a lot of people working in the factories, in the plants, or or in the on the production lines, and they are not be impacted, they will not be impacted by this kind of AI wave. But still, I think the things are are moving slowly and it’s it’s changing so quickly. but this is currently my discussion with European companies.Grace Shao (30:48)That’s actually very interesting. I it makes a lot of sense actually to build it in in as like a infrastructure like 5G data. And then it’s really, it’s really like there’s a cap on how much the company will pay for, but then how you utilize it should be, and you’re more mindful of how you’re utilizing this, right? And not wasting the tokens and and buy the that thus you know, wasting your energy, compute everything. SoGrace Shao (31:13)I want to bring it back to the Chinese pricing models really quickly. I know you work with a lot of European companies, they are the buyers essentially. You also help them connecting with the Chinese vendors, essentially, which are like the Chinese LLM labs, Minimax, Drupal, Moonshot, etc. Now, how should we understand the pricing model of these companies right now? Because it’s obvious that they are pricing themselves much cheaper to US peers.Some might you know, obviously argue that their performance might not be as on par like on par or as at the frontier. however, even when they do play catch up, you know, the reflection of it is it just s seems like a complete different cost structure. Help us ex understand that, like they’re thinking, why they’re pricing it much lower and how that plays out in the long run.Alex Lu (31:46)Mm-hmm.Yeah. Actually, there are two perspectives on that. maybe I will firstly talk from the client’s perspective and then I I might conclude with the recent price decrease by Deep Seek. maybe you you have already read about it. so f from the European companies as I said before, the thinking is w that the that that’s that’s the statement for the companies I met. we do not need entropic models for all the time.That’s for sure. Because this is very expensive even for a company. So for sure they will need a kind of different options from different models, like the best U US models and the cost-performing, the best cost-performing models from Chinese models and the AI sovereignty models like Mistro. So basically there will be three combinations and then there will be engineering of technical issue that meaning that how to manage these models toPerform the right tasks. So, meaning use cloud to perform the most complex tasks and use Chinese models to perform kind of less complex tasks. And I think European companies they understand this. And they, of course, they are looking for Chinese models for the cost effectiveness. And I would say this is also one of the bottleneck of US models because they are veryIn a relative way, very expensive. Therefore, it it’s the bottleneck of the massive adoptions. Only the European big, big companies can afford like continuous use of US models. well there are a lot of SMEs in Europe. So this is this is the thing. And for the Chinese model suppliers, I think the the way I I see the the the price issue is if you ask meCan Chinese companies increase their token prices? I would say surely, because if you look into the financial report of ZAI or Minimax, actually they are not they invest a lot in the research to develop these models. And the expectation from the industry AI industry is if you want to train or pre-train a next model, you will cut it will be more costly than the previous pre-trainings. so for sure.Chinese companies can increase their token prices. And w that that’s what they are doing actually after the open cloak, if you read into the news. And the thing is, compared to the US model, still the Chinese model are very cheap. I think there’s one very strategic thinking thinking angle is if you think about the Chinese models, most of them they are open source models. And the the the the thinking angle is, I think, for the Chinese model players isWe want we open source these models because we want people to use these models. Because they can deploy it on their own infrastructure, they will have more freedom, or they can use our open source model to train their own models. and maybe they they will use our our tokens by or they will they will understand or know our models better by open sourcing. So if if we combine this thinking angle, I would say.The Chinese model strategy might be to increase the influence in the world, maybe in the developing markets, where people are more cost conscious, and to help people to use this AI to adopt AI in a cheaper way. And then in the long term, in the future, that’s very Chinese, maybe again to increase the prices once we take the market positioning.It’s like the price competition for the last decade regarding this digital sharing economy or digital era. Nothing has changed. So a very aggressive c pricing strategy to at least to to have the market share and then once we have the market share then we can establish our our our our position in the market and ca kinda do a lot of monetization stuff.That’s the one thing regarding the increased influence globally and taking the lead in the AI industry for the developing countries, in my opinion. Of course, go going to Europe is is part of the their strategy. So this is from the Chinese model’s perspective perspective, and it’s a very special case, of course. It did this is Deep Seek. DeepSeek released just the before and right after the release, during one month, I think for the developers we enjoy the75% of discount regarding the token price. So it’s very deep discount. And recently, I think one or two weeks ago, DeepSeague announced that they will keep this 75% discount for for for forever. So it’s kind of they they just discount their token prices by such huge amount of discount. I’m pretty surprised. andAgain it di it it launched a price war in the market and you see recently Xiang Mi decrease also their token prices and I don’t know if other players will will follow in Chinese market at least. But if we think about Deep Seek cases, it’s a very special case because Deep Seat this year it doesn’t create a lot of buzz in the AI community in the US. I think so. I I’m not living in US but I read some newses. news, sorry. I think thenowadays DeepSeek, I’m not saying that we have the best performing model. and and and in terms of of the tok coding performance, DeepSeak is is not at the top top level compared to other models. But the interesting thing is DeepSeag this time is trained on the Huawei ASEAN chips. so again, I think the price decrease of DeepSeag combining with their recent news of raising money and hiring some harness engineeringAcross the world, I would suspect that DeepSeek by decreasing their prices, they just want to break through the ecosystem established by NVIDIA. This is my thinking, and and that’s why after the President Trump visit to Beijing, there are 10 Chinese companies are not authorized to buy Nvidia chips, but up to now you see few others.Grace Shao (38:09)That’s interesting.Alex Lu (38:23)I think there’s a thinking from the national wise from from the nation thing that okay with Dipsy can we break through the Nvidia chips plus CUDA? And if because that’s so cheap, so most of people they might use Deepsi in the future and they might be used Huawei as ASN chips because Deepsi got trained on these chips and it’s best support DeepSeak’s performance. So this is another angle. Yeah, so you would seeGrace Shao (38:23)Mm-hmm.So the open source strategy. Sorry, go on. It’s basically a strategyto get people in to get the developer into its ecosystem, its own community first, which is what Jensen’s been saying the whole time. Yeah. no, I I agree with you on that. I actually I I wanna and steer away from the chips today because I I am quite fascinated. So you work with companies, adopt AI, but how does that actually what are companies really using AI for? Like we hear about stories.Alex Lu (38:54)Exactly.Grace Shao (39:16)you know, companies are token maxing, whatnot. And obvious the obvious one, like you mentioned, is in coding capacity in IT, but no again, not every company is in tech, you know, not every company needs coping co coding capacity. sorry, let me just say that. Not every company needs coding capacity. So like what are we seeing actually on the ground, especially for maybe more brick and mortar stores or old school traditional industries? Why would people all want to adopt AI right now?Alex Lu (39:47)the the the adoption rate actually for European companies is pretty low, to be honest. most of companies, if we say at a large scale, they don’t adopt sufficiently AI and they just are afraid of missing out something. So this is a FOMO. they are just feared of missing out some opportunities, and if they don’t use AI today, they might be less competitive in the future. So the the f the most common use cases I see incompanies for coding and for it and sometimes it’s easier to measure the effective effective sorry effectiveness of ai that’s in the most most of the time in the sales marketing department so meaning that if you use ai you can produce produce more contents and with more contents you have more impressions with more impressions you might have more conversion rate you might have more conversions and you might have more sales revenues soThis chain is actually well formed. So by using AI, you can track the individual metrics on the chain, and then you can kind of monitor the results by using AI. And most of the time, I get a very simple question of European companies, and very difficult question actually to answer is: what’s the ROI of implementing AI? What’s my return? then it’s a very difficult question because in thedigital, 10 years ago in the digital era, I can tell the ROI, I can estimate why, because the incremental cost of using digital products is kind of almost zero. You just need your digital products and then it makes more efficient, it makes more automate. Well, in AI, that’s very difficult because if you think about it, if you use more AI, you will consume, as you say, more tokens. So, meaning thatAn employees, you need to pay the salary. If he is a heavy AI user to produce more content, then you will need to pay his tokens bill. And then the ROI might not be so immediate. Or there might not be ROI actually for the individual use cases. Then we come back to the question: is okay, by using this AI, how we can make the whole organization more efficient and how we can generate more revenues for the whole organization.While for the individual users, maybe there’s no business case. So I think again, the the the the the difference compared to 10 years ago is the people who use AI and who use heavily AI, then he will have a bill to to pay. That’s a variable cost. That’s very important. And secondly, is the variable cost will be reallyThe beneficial of the variable cost will really depend on the skills of each individual. You may pay $100 for employee A or employee B. If B master better AI, then you will have 10 times more results, financial results, compared to the first case. So again, I think you asked the right question. the ROI question is definitely a very good question. and most European companies they seek about ROI before investing. So they are very cautious.While again, if we compare to the Chinese companies, we are more pragmatic. So let’s implement a POC. it costs a little bit, but let’s implement it. If it doesn’t work, never mind. We waste some money, but we we we continue, we iterate or we continue with another use cases.Grace Shao (43:17)So you think the Europeans are taking a more cautious approach, but actually more cautious on what the potential ROI is. Then I bring it to the question that is a bit more philosophical and like a societal, not so businessy, is then isn’t the headline or the mainstream discussion on AI is replacing our jobs completely overblown then? If companies are not even investing in the like, you know, buying tokens, I don’t think they’re replacing people and comp just replacing roles with. Like AI, are are they? How do I understand this?Alex Lu (43:50)For the tech companies, I think your statement or the statement is true for the tech companies because they’re traditionally there are a lot of coders, there are a lot of programmers, and and actually I see a lot of developers, individual developers in the market because they work for tech companies and now with the with AI. That that would be very challenging. And again, currently for European companies, if I would sayThey’re still at very, very early stage compared to to China. the cost is one thing, and we can take at the other angle, causes equals to conservative. So they are a little bit conservative and they care a little bit more about their employees. So actually I I I will not see in European market AI replace a lot of human workers. It’s not happening today. Will will that happen tomorrow? I think so.Grace Shao (44:46)Mm-hmm.Alex Lu (44:49)but again we need to find another society structure or we need to find other job opportunities for the human beings when AI comes to the companies and replaces some of them. It we’re not like very aggressive like at the tech companies like Meta or other tech companies. it will happen slowly, but of course AI has impact on the on the employment on employment, even for European companies. andGrace Shao (45:13)Mm. The economy itself will evolve and and jobs will look different.Alex Lu (45:21)Exactly. it that that’s exactly what I I was in Europe ten years ago. It’s exactly the discussion around industry four point zero if people remember. We say that okay tomorrow we’ll have some automated machines in the plant. So it’s kept it’s not it’s happening currently in China. We call it a dark light factory. So it’s very automated. you can run the factory without turning the light on.so basically at that time in Europe we had a very big debate on where the employees employ employers should go once industry four point zero is in place. And the answer was there were sorry, the answer was there will be some upscaling and new job opp opportunities created with industry four point zero, and we need more skilled people to master these machines. And that’s that’s the same thing for the AI.Tomorrow we will need people who can orchestra, who can manage the agents, AI agents, instead of doing the same job as a simple agent.Grace Shao (46:23)Yeah, I see. So so on that, I wanna ask, you know, given Europe’s strength in industrial, like industrial strength manufacturing, where do we see opportunities for companies to really couple that with the development evolution of AI right now?Alex Lu (46:41)You mean the the use cases, right, for the companies?Grace Shao (46:44)Use cases, new opportunities, new potential businesses. where could we see p like, you know, new businesses come out or, you know, new business revenues for current industrial companies?Alex Lu (46:55)Yeah. for European companies currently the use cases we’re discussing is more around kind of efficiency use cases. So for example, they want AI to help them to do some root cost analysis because if you run a a plant and if the machine is kind of done, the production line is kind of stopped, and then you you you lose basically a lot of money because you missed up.opportunity of producing X unit units of of your products of your cars. So basically people care a lot a lot about how I can analyze the root causes of of a machine being done. And this traditionally was a very heavy task. We need we need a lot of experts to be involved and because there’s a whole system of different machines in the same plant. And the machine is kind of the product production line is kind ofmade in a industrial sequential. So every parameter on different machines might have an impact on the chain. So we need to involve a lot of experts and by using AI actually we can we can understand better. We can do some causality analysis and do some root cause analysis and find the root causes more easily and in the future to do some predictive predictive maintenance and to improve the efficiency of the companies. So this is currently happening.People are asking for that. And some companies are also asking for these kind of knowledge management platforms. Like we we need knowledge management for new enrollment of employees, for HR policies, for reimbursement policies, for new employees onboarding, etc. etc. So a lot of around that. And if we look into the vision and into the future, I think European companies are start to think about it.I’m talking a lot a lot about European companies, but that’s the same thing for for the companies in China, it’s just kind of more advanced. So sorry, I I’ll come back. So if we take into the vision of European companies, actually they are also thinking about the future, which is how I can use AI to increase my revenues and to make the pie a little bit bigger. And then it comes to the discussion of agentic economy.Meaning that can I use my agent to kind of sourcing, to kind of sourcing for my company? Can I use my agent to do some business development, to write emails, to do some code calls, to reach out to potential clients? So these are the things that people will come to think in the next wave, saying that okay, if we have a very good engineering of our agents, guidelines of our agents, what an agent can say, what he cannot say.what he should say in which context. So once this is done, again it’s very European, they need to use everything kind of under control. Then I think we are ready to to go for the athentic economy so meaning that agent can do business in in the place of the companies.Grace Shao (50:04)I see. And if I were to say I’m the founder of AI native company, how would you advise me other w because it would be very different from what you’ve been saying about advising more traditional industries?Alex Lu (50:10)Yeah, it it i if you are a AI native founder, I think I’m I’m doing the currently the same position. there are a lot of things to consider. For example, in terms of the technology, the foundation model is evolving very pretty quickly. So how I make sure that my AI agent idea or concept or business model will not be revolutionized or disrupted by thisFoundation models. This is something we need to think about. The second thing is I always tell myself and also people in the say same AI community is we we don’t start to build our products from scratch without discussing with the clients. So why not in in a more safer way, why not discuss with the clients, build products for certain clients, and then kind ofConceptualize the products and build more standardized products that we can sell, we can say, we can sell to market and we can scale in the future. It means the build of the product comes always from a specific demand of the clients. And once if there’s a demand, then we can do something, we can build things. Why this? Because, in my opinion, all the AI native funders, I think we are pretty aligned is produce.something or build a product in the future will be much easier in the past. And if we compete with AI in terms of the intelligence, there’s no way a human being can catch up with AI. And we should place our time where the AI cannot compete and where we still need a human being. I I I make very simple analogy to some friends of mine saying emotional intelligence, meaning that how we can establish relationships with the people, how we can build a trust.So still I think if I’m a founder or if AI native founder, he should go out to meet clients, discuss with clients, build a trust and have some demands from the clients because building the process will be pretty easy and the cost of failing is pretty low. So build fast, fail fast, scale fast and it works even more in the in the future.Grace Shao (52:36)And then my question on that is how do we actually understand how to build guardrails and safety around this? Because you talked about how Chinese companies you work with are often a bit more like gung ho, let’s go, we’ll t we’ll fix it if after it’s broken, kind of mentality. Whereas the European companies maybe are seen as a bit of a slow adopter in many ways, you can say more cautious, more humane, and protecting their concurrent employees. But, right, likeEnd of day, if this is the future evolution of our economy, how do we go forward with this? And then how do we actually build more intentionally?Alex Lu (53:13)Yeah. technic technically, actually there are a lot of skills, there are a lot of technical stuff in the area to build the guardrails for the agents, like Anthropic, I they are doing doing a very great job, and also some Chinese foundation model companies and also agentic companies. So all of all of that they call that the harness engineering. So they put every concept into the harness saying that okay, we need to build a harness and to make the guardrails.So this is the technical perspective. But still, this technical perspective is very from the developers or programmers. And if we bring the case into a real company case, then it really depends on each use cases on each company. I would say for any new human employees which is who is a new hire in the company, at least when I join European companies, there’s always a code of conduct.You see, it’s it’s simply a a document that we need to learn. We need to we need to we need to be compliant in the future in in our work or professional work within the company. So I would say for the AI agents that the same thing. they are very important in the future, a kind of infrastructure to evaluate the performance of the AI agents, meaning that if the AI agents is delivering the performance as we wished before, so there’s a kind of benchmark evaluation.And also the evaluation should include also is the AI agent performing correctly as we wished in terms of the code of conduct. And the code of conduct should in my opinion, be written by human human human beings. It’s like an extra bic team, they have they have written a a hundred-page of constitutional constitution for for for for cloud. And then each company should write their code of conduct for.every agent in every department. And a lot of Chinese founders then they are entrepreneurs, they are also joking at okay, we develop an AI agent today for companies but the next question will come shortly is when should we retire our AI agent it if it doesn’t perform correctly or why when we should replace them. So you see the evaluation or benchmark of of the AI agents wouldshortly become a a a pro a p a problem in the market when we adopt massively the agents.Grace Shao (55:45)So then each organization will have to institutionalize this, essentially you’re saying, and have their own standards of code of conduct, whatnot. That makes a lot of sense. Yeah. And right just like how companies right now regulate data usage, even company devices, whatnot, right? Like this will all just be part of the compliance that employees will have to learn. I want to ask you one last question, which is what’s one differentiative view you hold?Alex Lu (55:54)I think so. In terms of the AI?Grace Shao (56:16)In terms of everything, it’s a question I like to just kinda throw throw it at people when they come to the podcast. It’s a it’s a wild card.Alex Lu (56:24)Okay. I think one of the points I always mention, it comes back to my background, is today the AI race is between US and China. So we say that European is kind of lagged behind. but do not forget that actually technology is one thing and the usage of technology is another thing. And again, if we come back to ourmy my statement saying that implementing AI is not about technology. It’s not it’s about process culture and organization and human being. So I think the placard of the Europe is they’re pretty good at regulations. And if you think about they issued GDPR before the Chinese PIPO, which is protection of personal data. And they have this kind of European AI Act. And then if I think about how anthropicThey penetrated these enterprise solutions versus ChatGPT and generate today more revenues than open AI in terms of AR, because of the simple concept of responsible AI. Then I would say tomorrow, if the AI comes to the enterprise level, enterprise implementation, and if everyone should be responsible in the company with their own agent or with their own developed AI, maybe Europe has a part to play in that.in the in the AI in the in the world of AI, because their initial statement is kind of we want AI to be regulated, we want AI to be responsible. So this is my point of view.Grace Shao (58:06)Thank you so much. You know, today you’ve been really generous just explaining to me and and the audience just how AI is really being implemented into these big companies and the more European perspective. is there anything else you think we’re missing or any misconceptions we might have about the relationship between European companies and Chinese companies or how Europe is perceiving AI? Is there anything you think we’re missing or do you think we covered it all mostly?Alex Lu (58:36)Yeah, I I I think we covered most of them, but I just want to mention one thing is even though we say that okay, there’s two different nations in the world, US and China, competing AI, or in we we we take different directions of AI. And still I received a lot of recently questions from European companies, and they are really, really interested by Chinese tech companies. So you would seethey are pretty open and they come frequently nowadays to China and they have the mindset of learning what Chinese companies are doing, what Chinese foundation models are doing, and especially seeking their use cases. so one thing I would say is when I receive them, we show some very advanced Chinese use cases. They would say, you are in a different environment because we have different laws, we have different regulations compared to you Europe.but they are quite interested about what’s happening in Hong Kong because the regulations in Hong Kong is pretty closer to European markets. So still, I I see we might have a lot of potential collaborations between China and Europe in terms of the AI, in terms of the physical OI. We didn’t mention the robotics, and definitely it’s an area where European can have more playground, not onlyAbout the humanoid robots, they want also to have their places in the hardware value chain for the robot robots. Like a lot ofGrace Shao (1:00:10)I’m sorry, it’s I know we’ve hit our time, but what what is your view on that? Because you know, European companies traditionally been the leaders in robotics, right? Industrial robotics, like machinery. where do they stand now in the world? You know, are are the Germans and the Japanese still leading the space or or how how are they gonna be kind of presenting themselves or positioning themselves on the supply chain right now?Alex Lu (1:00:15)No worries. Yeah. So for the very traditional industry robot robots that let’s say it’s like KUKA, you have a lot of robotic arms. So they are still kind of leading the world, so you have a lot of robotic solutions implemented in the in different car makers’ plans. but for the humanoid robots, actually Europe Europe is lagged behind again because it’s not all only about the valueAbout the not only about the supply chain of the robots itself, it’s also about again the software and the large language models behind the robots. So the mindset of European companies today is: okay, we understand China again has the most advanced humanoid robotic companies in the world. US has maybe advanced in software in large language models or word models. China is pretty good at the supply chain.So again, the same question they ask themselves. But the the recent demands I receive from European companies are are two. The first one is as a traditional European companies, we know that they know that the value chain of making a car is quite similar. Let’s say it’s not hundred percent the same thing, but there’s sixty or fifty percent are common of making a car and making humanoid robots.So their thinking is okay, can we participate in the wave of these kind of robots with the development of China? So like motors, like electric motors, like actuators. Yeah, German, German guys are pretty good at at this apply. So that’s the first thing. The second thing is demand is a lot of European companies saying that okay, we have the real use cases in Europe because we are lack of workforces in our plants.It could be an aging population, it could be some strike of labor unions. So they in order to keep the plant working, as we said before, about the predictive maintenance, they are very welcome, the Chinese robotics in the European markets. Again, the robots need to be compliant with European regulations, conditions, and they are very welcome. So the most common demand I receive is hey, hey, I I want to do a kind of analysis abouthow I can be part of the supply chain in China and how I can leverage Chinese supply chain to be more competitive. The second one is okay, I have a use cases, then we need to think about how I can implement the humanoid robots in the European markets. And then we we can discuss about the business model of the robotic companies like Unitree of AJ Boss, because it’s not only about putting their robots in the factory, it’s about calibrating the robots, it’s about capturing the data, it’s about think about a closed loop of robust training. It’s aboutthe again, the guardrails how make sure robots will not harm a human being if they cross each other in the plant. So yeah, this is quite common nowadays for physical AI for European companies also, yeah.Grace Shao (1:03:35)Mm-hmm.But in fact, actually you mentioned CUKA and it was bought out by Matee, right, a couple of years ago. So you’re also seeing a lot of Chinese companies like in the embodied AI, physical AI space actually actively buying out traditional brands in in Europe. How is that received actually locally?Alex Lu (1:03:47)Yes. actually for the for the embedded robots humanoid robots, there are not so many MA of Chinese players acquiring European companies. So basically I think for the humanoid robots, let’s say the robots like AJ Bot or like Uni3, China is much more advanced. And there was one robotic company in France, but they are kind of in financial difficulty. And another robotic companyThey were in they are invested by Renault in France, but still their technology if you look into that is not as advanced as Unitree or AJ Rob A Gi bots, for example.Grace Shao (1:04:42)I see. one last question is just do you think it’s fair that we’re overgeneralizing all the European companies into just one EU right now? Or do you think actually a lot of different com countries have different goals, ambitions, or even, you know, future tracks for them laid out?Alex Lu (1:05:03)very good question. So I can only when I see European companies, sorry, actually I’m thinking about French and German companies. So actually I cannot represent all the European countries and for different countries like Spa Spain, Italy. I’m I’m not familiar familiar with the country. I didn’t live there. I I didn’t receive enough clients from from these countries. So actually you are right.when I talk European companies, I’m more thinking about French and German companies. And of course, they are pr pretty different.Grace Shao (1:05:35)Okay. Well, thank you so much. Yeah, thank you. I just think it’s such a unique perspective because, you know, it it’s it’s more it’s easy for me to find someone who tells me the pure European perspective. It’s easy for me to find someone in the China US, but it’s harder for someone to for me to find someone f you know, who straddle between Europe and the Chinese market. You know, it’s obviously not as mainstream. So I’m really appreciative of your time and your insights and your sharing. Thank you so much, Alex.Alex Lu (1:06:04)Thanks,Grace. Yeah, thanks a lot again for i inviting me and accepting me for the podcast. And thanks a lot for your audience. And yeah, let’s keep in touch if any chance happens. we can have another talk if needed.Grace Shao (1:06:17)Definitely.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  China’s internet ecosystem, manufacturing base, batteries, EVs, robotics, and semiconductor becoming an AI-enabled industrial system 01.06.2026 50min

  In this episode of Differentiated Understanding, I spoke with THE TP Huang, an independent China tech analyst known for his work on fintech, EVs, batteries, AI, semiconductors, and the broader China industrial ecosystem.The conversation traces China’s technology evolution from the early internet era to the present. TP argues that China’s internet ecosystem was shaped by a combination of censorship, protectionism, local engineering talent, and intense competition. That created powerful domestic champions such as Tencent, Alibaba, Huawei, Baidu, and ByteDance, which later became the foundation for super apps, payments, e-commerce, cloud infrastructure, and AI.The discussion then moves into China’s shift from software and internet platforms into hard tech: EVs, batteries, robotics, drones, semiconductor supply chains, and AI-enabled industrial systems. TP emphasizes that China’s technology companies are unusually willing to enter each other’s markets. Xiaomi moved from phones to chips and EVs; Huawei moved from telecom to semiconductors, AI chips, and autos; BYD moved from batteries to cars, solar, transit, chips, and potentially robotics.A major theme of the episode is that China’s AI story is not only about large language models. It is also about the physical stack around AI: batteries, sensors, motors, chips, power systems, critical minerals, factories, and real-world deployment. TP argues that this manufacturing and supply-chain density may become a major advantage in embodied AI and robotics, especially as real-world robot data becomes more valuable.Follow TP Huang here on X or Substack here To find the previous episodes of Differentiated Understanding, see here.Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.Season two will host a series of guests from early-stage investing, as well as builders, researchers, founders, and product managers. For more information on the podcast series, see here.Chapters00:00 The Evolution of China’s Tech Landscape05:58 China’s Internet and Tech Sovereignty09:01 Investment Trends in China’s Tech Sector11:04 The Role of Government in AI Development20:00 The Intersection of EVs and Robotics26:07 China’s Competitive Edge in EVs and Robotics36:18 Global Strategies of Chinese EV Companies42:31 Advancements in AI and Robotics in China48:31 China’s Digital Infrastructure and AI Adoption57:38 Underappreciated Developments in China’s Tech Landscape01:00:00 Non-Consensus Views on China’s Economic HealthAI Generated Transcript (for reference only)Grace Shao (00:00)Hello everyone, welcome back to another episode of Differentiated Understanding. I am your host, Grace Shao. As many of you know, I also write the newsletter AI Proem, which is AI PROEM on Substack, so do give that a follow.Today we’re doing something special. We’re doing an audio-only version. I’m joined by TP Huang, an independent China tech analyst who writes about the intersection of fintech, EVs, batteries, AI, and broader China industrial policy. He has built a large following on X and Substack by combining data, supply-chain detail, and geopolitics to explain where China tech is actually heading.In this conversation, I want to use TP’s lens to understand the bigger China tech landscape: how China moved from internet platforms and payments into EVs, batteries, robotics, and now AI-enabled industrial systems. And since he quite literally said, “I can talk about anything China tech,” when I reached out, this conversation may follow the themes that I prepared, or really just go anywhere it naturally takes us. Very excited to have him on. Welcome, TP.Grace Shao (00:02)Hi, TP. Thank you so much for joining us today. I just did your intro before talking to you. And I told everyone that when I emailed you and reached out, I said, here are some topics I want to talk about. Is that okay? And you quite literally said, “We can talk about anything China tech.” So the conversation today could cover quite a lot of bases. I’m so excited to hear from you and have you kind of dissect a lot of your knowledge for us. And, you know, I’ve been a big fan of following your Twitter, your X, for a long time. Anyhow, thank you so much for joining us today.TP (00:33)I’m just really glad to be here, Grace.Grace Shao (00:37)Yeah. So you’re a mysterious man. Give us some color on your background and why you are so knowledgeable about China’s tech ecosystem, because you’ve really been covering everything from robotics to LLMs to the internet era. You cover them all, including hardware and chips and everything.TP (00:56)Yeah, so it’s kind of interesting that my actual background is not very technical in that area because I’ve been working mostly in the finance sector, or fintech sector slash crypto, for most of my working life. And I did spend a year recently working in an AI firm, so that was something different. But now I’m back to doing more crypto kind of stuff. So my background, I guess now, is a lot more AI-related.But a lot of the interest I had back in the day was in the renewable space and climate change and things like that. So that really got me started following solar panels, wind turbines, and then EVs. I first read about BYD back in 2008, like a lot of other people. And then as EVs were really taking off in China, that’s when I thought, okay, I really need to understand the full tech stack behind it. So that kind of got me into the entire battery supply chain, a lot of the upstream stuff, and then chips.The chips part became such a big deal because of AI. So then we had the October surprise back in 2022. That’s when I decided, okay, I’m really going to try to understand how the semiconductor manufacturing part of it works also. And thankfully, I was able to be connected to a lot of people. That allowed me to really understand a lot more.So I don’t profess to be an industry insider or anything like that. I’m just talking to other people who are working in the industry for some knowledge and writing about it. And then with AI, I actually worked on my own, no, not on my own. I worked with an AI startup, and one of the projects we did was actually for an AI toy. So I had experience running what I would consider to be AI robotics efforts. So I have a lot of real-time experience with embodied AI and also just using large language models. That’s kind of how I got into all this stuff in the first place.Grace Shao (03:26)It’s really cool because you have experience across the whole array. One personal question is: what drives you to really continue writing? Because you do write prolifically on Twitter. You have these hot takes, you put things together, and I think you’re quite widely followed by anyone who covers China tech. So what makes you want to share things publicly?TP (03:49)Yeah, I guess it’s more like a personality kind of thing, where I really just enjoy writing. And I think there’s something missing in the information space about what is going on in China.Last summer I was in China for a month, and I plan to be in China again for a month this summer, and I just saw a lot of really cool stuff. I think it’s good for the world as a whole to understand what’s going on in China, for Americans and for all Westerners to understand what’s going on in China, so that we are better informed in understanding how people can work with China and what kind of things people who want to compete against China need to know. But as a whole, I think it’s better to get proper information out there.And because China is a different language, and most people in China post in their own internet ecosystem on Weibo or WeChat, people don’t really read this stuff. So they get their sources from very bad sources on the English internet. A lot of them are just missing the nuance of what’s actually going on inside China. So because there is this vacuum, I just felt I’m obligated to actually do something about it, to help everyone understand better.Grace Shao (05:31)That’s awesome. It’s part of why I write AI Proem too. Well, okay, let’s get into the real stuff today. You’ve been following China’s tech for a while, like you said. Help us understand, just with the sentiment shift, how you view the early internet era to today’s success in hard tech and AI. What really has propelled China’s success in the tech sector in the last 10 to 20 years?TP (05:58)Yeah, so I think if we look back on things, China made a pretty big bet on developing its tech sovereignty back in the early 2000s and 2010s. It put a lot of policy in there under censorship reasons. It said, we’re blocking, we don’t want Google or whoever wants to enter China to actually censor the search results so that it fits our local law. And then what actually ended up happening was it became more of a protectionism kind of thing. So China was protecting the local tech champions at the same time that it was pouring a lot of money into these firms.So it allowed firms like Tencent and obviously Huawei and Alibaba to grow up. Later on, China also developed ByteDance. And if you look at how things are around the world, most countries, most leading Western countries that could have possibly developed their own tech ecosystem, like European countries or Japan, didn’t do it. The only other country that has a pretty robust local tech ecosystem or tech champion is Korea with Naver.And if you go to Korea, you notice that if you’re using Google Maps, it’s almost unusable. You kind of have to use Naver. So I think there’s a clear correlation between blocking US tech and some level of protectionism to having a local tech ecosystem being developed. And obviously it requires good local engineers also, so that they can take advantage of that. But China had all the ingredients for it.So even though it started maybe a decade after the US in developing this ecosystem, it was able to develop it because it didn’t have to face this immense competition from the US right away.And I think also there’s a lot of, you know, we talk about involution in China. I think there were stories of how when Uber tried to enter the Chinese market, because they had to face all these local Chinese companies that were working under 996-type hours, they were eventually pushed out of the market. So I think those are really the interesting parts of how the China tech scene developed in the 2010s.Grace Shao (09:01)Does that kind of feed into what we’re seeing now? Because right now it’s a completely different set of technology, yet in many ways it is building off the digital infrastructure that we just talked about, that got built out in the last 10 years or so.TP (09:17)Yeah. I think as a whole, if you go to China, even the internet ecosystem works entirely differently from America. In America, for the longest time, we had a search-oriented internet. You use Google, you use a lot of Google products, or you use social media. Whereas in China, because Baidu was never that great, people kind of advanced right away toward these mega apps like WeChat and Alipay.And as part of the movement on these fronts, you have these giant ecosystems developing where they not only have their own super apps, they also have their own e-commerce networks, their own payment systems, and they all got enough resources to eventually build their own cloud infrastructure and now develop into the AI world. So some of the biggest players in China when it comes to AI are the usual tech giants like Alibaba and ByteDance.Grace Shao (10:35)Yeah. So okay, let’s move on from that, from that big holistic overview of China’s internet space and tech sector. So much of the investor focus right now is still through the old internet platforms, like we mentioned, because of the natural progression of how they also become the major players in AI.But what kind of breakthroughs and capital moved from apps and payments into EVs, batteries, robotics, AI, and hardware? Are we seeing that these hyperscalers or big tech companies are also the major players in these other technologies that we’re talking about? Are they the main investors and backers, or is that a completely different ecosystem?TP (11:17)Yeah, China is kind of interesting to me in that a lot of the players are so uber-competitive that they are willing to get into other people’s spaces. So we saw Xiaomi move from the phone into developing their own pretty advanced AI team. They have their own chip design, and most notably they have their own EV division, which is doing really well.We saw Huawei start off in telecom and then move into the entire semiconductor ecosystem, and also their AI chips, and also into the auto division. We saw BYD start off as this battery company, and then it got into all these areas. It got into cars, it got into solar panels, it got into public transit, it got into the chipmaking side of things, and now it’s also looking to get into robotics with humanoid robots.Whereas you don’t really see that as much in America, where it’s mostly a typical thing I used to listen to on Wall Street, this entire idea of capacity discipline. Which is basically: how do we reduce competition so that we can get a higher margin? Whereas the Chinese marketplace seems to be one where everyone’s trying to squeeze in at the same time and just fight it out until whoever has the best cost controls ends up winning.From that point of view, I think this is why for some time people saw that the Chinese stock market hadn’t been growing as much as the US stock market, because there’s just so much competition inside China. So a lot of the funding for these efforts inside China actually had to be backed by the government, these big funds and things like that. And also these things, they are willing to put money into areas of lower initial returns.A lot of the car factories, maybe they’re not the best investment if you’re looking for a 100% return. Maybe it’s not the best for that. But because it provides local jobs and things like that, the government is willing to put some money into it. And we saw that right now with semiconductors also, and also the data center build-outs. So that is how, over time, the entire Chinese manufacturing ecosystem kind of got built out.America is trying to do a little bit of that right now with AI data centers and trying to do that with the tariff wars. But fundamentally, the market in the US is about squeezing out competition and lowering capacity in order to charge more. Whereas the Chinese system is about how to scale up production and lower the cost of production in order to have higher margins. So it kind of works differently.Grace Shao (14:43)Yeah. So on top of government help and actually putting money into sectors that often have lower initial returns, sectors that are not so sexy in the beginning, let’s talk about DeepSeek.I think it’s been interesting because we know DeepSeek and many of the other Chinese labs weren’t getting a lot of capital until maybe 2022 or 2023. However, now they’re obviously being pushed front and center as the main economic drivers. Not only are they being looked at as very sexy investments from the private side, but the government funds are also looking to put cash behind this.How do you view the relationship between government policy, government mandate, and the AI labs in China? That’s part one of the question. Part two is, if DeepSeek and a lot of these Chinese labs permanently price their models at, say, one-thirtieth of the American labs’ prices, what’s the thinking on that? And what’s the sustainable business model for them looking forward?TP (15:44)Yeah, so I think it took a while for China to really catch on to this entire large language model thing, because a lot of the Chinese AI, when I looked at it back in the early 2020s, was aimed at embodied AI. So in terms of smart manufacturing, how to improve the grids, drones, robotics, and also EVs, things like that.Whereas a lot of the US funding for AI was, I guess, kind of abstract. You want to develop the best models, and then we will find the use cases for them. But once it took off, I think there was kind of a light-bulb switch inside the Chinese sector that we can’t just let this go, we have to catch up. The way Chinese people think about things is like, we have to get in on these opportunities.So in the beginning, with Chinese large language model development, I think it was mostly the big tech companies like Baidu that were kind of leading the efforts. But over time, more recently, I think you find that it’s the startups that have done some unique research that have done the best, like DeepSeek, obviously Kimi, and Z.AI.And obviously some of the big tech companies are still quite successful, like ByteDance. They have a very good AI product. And Alibaba, with the Qwen stuff, is also very well developed. But you do see that the Chinese government, ever since the DeepSeek moment, has been investing more in funding to make sure that the domestic AI startups are able to get the funding they need to compete.In the most recent example, DeepSeek, they actually got paired up with Huawei, or maybe they came together somehow. But you can see just in the V4 release recently that there was a lot of integration work between Huawei and DeepSeek. The DeepSeek models are deeply integrated, so that you can use the Ascend chips from Huawei to better train and run the models. And this is part of China’s overall strategy of being self-sufficient in both the hardware and software side of things for AI.So even though it’s probably easier to just buy NVIDIA chips, the risk of getting cut off by the US government is pretty high. So it’s in China’s long-term interest to have its own ecosystem across the board.No other country has that. China has not only the chips and the software, but also the entire AI data center build-out ecosystem. There has been a lot of investment or money put into AI build-out-related stocks recently, like optical modules, optical transceiver suppliers, fiber cable suppliers, PCBs, power chips, and things like that.So there is a lot of investment across China, not just in the software part of it, but also in the hardware integration part of it. And at the end of it, it’s all supported by the Chinese government in some way because they want to make sure that they have a domestic supply chain, so they can’t just get cut off at any point.Grace Shao (20:42)So you’re basically in the camp of what Jensen was saying: export controls are not working. In effect, they are cutting American suppliers or vendors out of China, and in that case, actually pushing China to become more and more self-sufficient.TP (20:57)Yeah. I mean, for a long time there, Jensen and the good people behind SMCI were trying to get as many NVIDIA chips to China through backdoors, or through Asian and Southeast Asian data centers, as they could, right? So that the Chinese AI suppliers remain hooked onto the NVIDIA ecosystem. But you can see that by sometime late last year, the Chinese government was actively blocking these things from happening because they really wanted the domestic AI players to use the local ecosystem.Grace Shao (21:39)But is it actually being replaced right now? Or do you think in the short term, medium term, long term kind of thing? The long-term strategy is self-sufficiency. Short term, it doesn’t seem like it’s realistic yet, right?TP (21:51)Yeah, so this is the interesting part. For much of 2023 and 2024, what the Chinese players were doing was that a lot of them were importing the permitted versions, like H800 and H20s from NVIDIA, through official channels.And then there was a lot of smuggling of chips into China at the same time, and the Chinese government was allowing this. So whenever they were building AI data centers, they would have the data centers that use domestic chips and ones that don’t use domestic chips.So what would happen is, let’s say Alibaba was looking to access NVIDIA compute and it doesn’t want to get sanctioned by the US government. So what it would do is, it buys some NVIDIA H20s, puts them in a data center, and also leases compute that runs on NVIDIA from one of the state-built or local government-built data centers that smuggled in chips, because it didn’t want to get in trouble by buying them if it’s not allowed to.Another thing that these firms started doing that’s entirely illegal, again, is actually just setting up companies offshore that would buy these NVIDIA chips and then build data centers in the rest of Asia, places like Japan, Thailand, and Malaysia. And then they would lease the compute for these NVIDIA chips from these data centers.And that’s still going on right now. The Chinese government is allowing that because domestic firms like ByteDance would just say to the Chinese government, we need this ability to use American chips in order to not be left behind. Because if you talk to the AI developers in China, they don’t enjoy using Ascend libraries for training. They don’t mind using them to run inference, but for training, they still prefer to use NVIDIA chips.So there is an effort right now to also get the training part of it up to par. And that’s kind of what the DeepSeek work with the Huawei team in recent months has been about. It’s kind of interesting to see how much better the integration has made the Ascend chips run training and inference on the DeepSeek models.There has also recently been a Qwen model called 3.7 that came out. And they also released their own AI chip called Chengwu MA90.TP (25:10)And part of the interesting thing about that is not only did Alibaba have the self-designed chip, because it was designed internally, it used its own internal AI models to write the kernels for the chip. And it had some really good results. I think going forward, a lot more of these domestic chips will actually be able to at least do part of the training also.Grace Shao (25:39)That’s really interesting. And it actually echoes some of the stuff I’ve heard on the ground as well. So like I said in the beginning of our conversation, I don’t want today’s conversation to only focus on China’s LLM and model space. I want to double-click on something you mentioned at the very beginning of this answer. You said China actually started with its capital focus and technological focus on EVs and embodied AI.What’s interesting is that that side of things didn’t really pick up in the US or in the West, per se, until more recently. So did the EVs come first, or did robotics come first? Or did they kind of converge and come at the same time, and there’s synergy there?TP (26:23)Yeah, so when it comes to the EV and robotics story, I tend to think of it as something that started because China was doing all the manufacturing of consumer electronics. And that’s how it was able to then develop these OEMs in the smartphone space, like Xiaomi, Huawei, Vivo, Oppo, and Honor. Basically, they developed this entire workforce inside China that was very good at dealing with supply chains and also integrating things together and doing manufacturing.I personally had an experience with this about a year ago, where we were trying to make this AI toy, and I got on a call with a Chinese factory. I won’t say which one. But basically, about five minutes into it, I realized America was in trouble because we had all these American engineers who are decently smart people. And the sales lady at the Chinese factory just knew way more about how hardware works and should work than any of us did.It was a very humbling experience just to see how there’s a lot of process knowledge involved in this. There’s a lot of experience involved in this stuff, right? And my cousin actually works in Shenzhen.Grace Shao (27:46)It was like learning from experience instead of PhDs, right?TP (28:03)They developed their own automated device that tests blood samples to see what kind of disease you might have, something like that. And what I realized talking to him was that this entire supply chain in China around Shenzhen or around Hangzhou is very deep.Because of that, a lot of the modern tech that we see with embodied AI comes from this basic understanding of supply chain, software-hardware integration, and also electrical platforms. What are the commonalities between drones, robotics, cars, and EVs, right?First, you need to have this battery underneath. You need to have electrical platforms. You need to have PCBs. You need to have cooling systems involved. You need to have control chips. You need to have power management chips. You need to have main control chips for the actual device. You need to have AI chips.All this stuff, in the beginning, Chinese suppliers were sourcing from abroad. Over time, due to export controls, they started doing this domestic substitution. They’re still the biggest importer of chips globally, but a lot of that stuff is coming in-house now.So if you do a teardown of a DJI drone, you’ll probably find memory chips from CXMT and YMTC. You’ll probably find CMOS chips for the camera modules from maybe OmniVision or something like that. And the battery is obviously going to be domestic. And all the stuff that we saw with drones and with EVs, we’re now seeing with humanoid robots and other kinds of robots, because at the end of it, a lot of the basic concept is very similar.You need to have some kind of a brain for the embodied AI machinery. And then it needs to have some kind of battery source to actually do the functionalities. And then it needs to move using some kind of motors, and then it needs to be able to absorb information from its surroundings with these sensors.That is why China has such a large ecosystem, because it has a good upstream supplier network and a lot of people working on this stuff. Whereas if you come to America, there’s just not a lot of that talent around.So if you want to develop an AI robot, you have to do everything in-house and figure it out. Because if you can imagine, if you don’t develop in-house and you contact a supplier in China, you can’t really iterate things quickly because you’re working with someone over there who doesn’t speak English and also doesn’t work the same hours you do. So the turnaround time is just much slower.Whereas if you have an idea in China for an AI robot that you want to build and sell to the market, you can get it produced in a month. That would be crazy for any kind of AI startup in America to do.Grace Shao (31:59)Yeah. In fact, I think there are a lot of robotics companies right now with founders who are literally tweeting about this thing: we must move to Shenzhen. Or I know of companies that actually get their hardware completely end-to-end, basically buying from OEMs from Shenzhen and slapping on a tag elsewhere.But I want to ask, why did China ultimately come out on top in EVs? Because from what you just mentioned, technically wouldn’t countries like South Korea have an edge? They have car manufacturers, they have chips, they have memory chips, especially when you just talked about brains. It’s not like the brains that we’re talking about right now are AI brains yet.So what made China actually come out on top with EVs and robots? Was it again this narrative around government push, because the country needs clean air? Was it because of innovation? Was it because of renewables and everything coming together? How do we understand this?TP (33:01)Well, I think Korea itself is actually a country with a lot of industrial policy also. So I wouldn’t necessarily say that the Koreans were less aggressive about government support than the Chinese were.I would say that if you look at just the human capital side of things, we’re looking at a magnitude difference in the number of engineers coming out of South Korea and China. So that’s something not easily made up.If you have 10,000 battery engineers from China every year, and let’s say you have 1,000 from Korea, the 10,000 are going to crush the 1,000 over time. And you can kind of see that. Back in the late 2010s, the Koreans were ahead of China in battery technology. But because Chinese industries were moving so fast and the supply chain was moving so fast, China has been ahead of Korean battery makers for several years now. And the gap is only expanding as we move toward more advanced solid-state batteries, or lower-cost sodium-ion batteries.Batteries are such an important part of the modern electrical transition that it’s kind of mind-boggling that China controls so much of the entire ecosystem. People keep talking about TSMC, or Taiwan having some percentage of manufacturing for chips, which by the way is not true. But Taiwan only has a small part of the entire ecosystem. Korea only has a small part of the semiconductor ecosystem, right? America has a huge percentage of the semiconductor ecosystem.But if you look at things like rare earths, critical minerals, and batteries, China actually probably controls 80% to 90% of these ecosystems. So even the Korean battery makers rely on the Chinese supply chain for a lot of their inputs now. And there’s just no way to get around it because the Chinese process knowledge, cost advantage, and engineering advantage are very hard for a smaller country like Korea to overcome.Grace Shao (35:47)Interesting. Yeah. So how should we understand these companies’ international strategies? Because I think you’ve written about it before. Like you said, they are major exporters. How do the battery companies and EV companies position themselves globally? Are they quite aggressive? Are they suppliers along the supply chain? Are they building up consumer brands? How do we understand that?TP (36:19)Well, it’s different with different people. I think because the domestic market is so aggressive and so competitive, companies like BYD had to go abroad to get higher margins on their products. That’s kind of forced a strategy where they’ve aggressively expanded. Things especially picked up in the past few months because of the Iran war, where there’s also a lot of demand for these EV products abroad.And as a result of that, it helps what I call China Inc. As you see more of these high-tech EVs abroad, as you see more of these DJI drones and Chinese AI models abroad, there is a generally higher view of Chinese products now from much of the Global South. And as a result of that, Chinese firms are also having greater success selling their products.I think one of the interesting things recently is just to see how much the Chinese automakers’ market share in Europe has already surpassed the Koreans and is catching up to the Japanese. Just looking at that, it gives me the impression that the Chinese automakers, and just China Inc. as a whole, have gained a reputation for quality in a very short period of time. And you can only do that if the automakers themselves are making a real effort to build their brands and promote their products in these markets.And I think they’re getting paid off because my guess is that BYD’s automotive sales have much higher margins on stuff sold outside China than inside China.Grace Shao (38:43)I see. So it’s still like a pricing strategy, or winning on pricing, you’re saying.TP (38:50)I think in China it’s more of a pricing strategy, but abroad you see them actually marking things pretty high. So maybe there is a pricing part of it, but if you listen to Stella Li, Executive Vice President of BYD and President of BYD Americas, talk about the new models that they launched in Europe, they’re very much trying to frame it as a luxury brand, with the Denza model brands.She would say that this is technology that does not have any competitor or equal in Europe. We’re just way ahead of the Europeans here. We’re going to build the fastest charging network that you’ve ever seen. You can charge your car in five minutes, for example.It’s kind of interesting because BYD can sell its cars at a much higher price outside China than inside China. Inside China, it might have to sell its cars at a discount to Tesla cars. Outside China, it might sell them at the same price as a Tesla car. So yeah, I find that interesting.Grace Shao (40:04)That’s very interesting. And I’m kind of playing devil’s advocate purposely. Anecdotally, I’ve obviously been in a lot of BYD cars when traveling in China. They are actually really, really sleekly designed. And like you said, in China, for some reason, they’re positioned more as not a luxury car at all.But even in Hong Kong, I’m seeing more and more Zeekr cars and BYD cars taking the roads, and they’re definitely replacing previous Audi and Volvo owners. It’s very interesting that that’s the trend. Outside of mainland China, the reputation of these Chinese EVs is almost more premium than they are in China.TP (40:49)Yeah. And one of the reasons BYD wanted to do well in Japan and Germany was that it thought that once it started selling well in Japan and Germany and got approved by those automotive nations, people inside China, especially suburbanites in Shanghai, would then accept BYD as quality products. It is kind of interesting that a lot of times the Chinese can’t really accept that we have quality products unless it’s also being accepted abroad. It is kind of interesting how that works.Grace Shao (41:21)Psychology, I guess.TP (41:31)Yeah.Grace Shao (41:37)I guess it’s a little bit of a psychological play on this as well. I do like your framing on China Inc. And I think recently we’ve seen that even in the consumer space. It was so interesting that Luckin Coffee bought Blue Bottle coffee, and you’re getting more and more of these kinds of purchases, like SHEIN buying out Everlane, etc.But I want to bring it back. I want to bring it back to AI.You said earlier that China’s mastery of hardware manufacturing has given it an edge in scaling humanoid and service robots. But how do we understand where we are with world models and the actual next stage of embodied AI and physical AI right now? Because like what we just discussed, China’s manufacturers are very experienced in building out the robots, drones, and various forms of robot mechanics. But where are we with actually injecting that with AI?TP (43:02)Yeah, so I’ve been in touch with the guys behind the China Research Collective, and they are actually inside China, so I’ve had some discussions with them about this. They’re telling me that because China has this hyper-competitive local market for jobs, a lot of young people are having trouble getting the jobs they wanted. So they’re willing to help these AI companies collect data on doing things to help these world models.It’s kind of interesting because you need a certain amount of data so that the robots can simulate human movement and then do the tasks. But at a certain point, if you have a child, you know that it takes them a long time to be able to walk around and then run, because they need to first feel and touch everything and learn everything over a year or so. During this time, their muscles develop and their muscle memory develops so that at a certain point they no longer need to think about how they walk. They can just walk. They no longer need to think about what they can or cannot eat, because they already put that stuff in their mouths to test it out.Longer term, I think once you have enough robots in China, they will just be able to improve exponentially in their capabilities because they will be able to fast-track all this, what I call reinforcement learning in the real world. If you try grabbing an object a million times, eventually you’ll figure out the best way to grab it. And once a robot learns how to grab it, that gets shared amongst all the robots of that family.So I think as you see the Chinese robotics rollout speed up, this is when you see this decisive edge in the world models. We already saw this with drones, right? The Chinese drones are just so much better at moving around and doing stuff because they had so much more data than anyone else.We’re seeing it now in EVs, where the Chinese self-driving cars are really good because they’ve had a lot of data out there, where people are just using autonomous features to do all the work. And you’re seeing that BYD today is having this entire unveiling where it’s talking about its path toward L3 and L4 autonomous driving.The more data it has, the better it’s going to get. That data becomes an advantage going forward. In the future, whoever has the most robots out there in the real world, and has all that data, can then train their robots faster. That’s why it’s kind of a big deal right now that BYD says it’s going to have 20,000 robots in its factories this year, because then it has all this data on using robots in a factory setting. That’s going to improve the performance of the world models by leaps and bounds.Grace Shao (46:48)Mm-hmm. Because the biggest bottleneck right now is just not having enough 3D data. And collecting that kind of 3D data is extremely challenging without, like you said, real, actual physical deployment. That’s fascinating.TP (47:10)Yeah. I also want to point out one other big difference between the Chinese players and the foreign players outside China, which is that China has this entire critical mineral supply chain. That is foundational to the rare earth magnets, for example, needed for the different robots and EVs, and for the motors, and also the materials needed to build the humanoid robots themselves, like magnesium. It produces about 80% of the world’s magnesium, and magnesium alloy is considered to be the main material that you want to use for humanoid robots.Grace Shao (47:57)I want to tie it back to what we also talked about earlier. Does the very strong digital infrastructure layer, just from fintech, IoT, and 5G, now contribute to China’s very quick adoption and diffusion of AI in the real economy? And how do you view this kind of positive cycle versus in other economies, where sometimes the digital infrastructure maybe just isn’t there yet and seems to need time to build up as well?TP (48:31)Yeah, I actually think this is one area where America might have a leg up on China, because the American big tech companies tend to also be the biggest cloud service providers. The Chinese ones are a little smaller. So right now, you only see the competition between the US and China because they’re the only two countries that have this data center and AI infrastructure advantage over the rest of the world.The biggest players in China, like ByteDance with their entire AI cloud infrastructure and their entire AI app ecosystem, are also the ones that are able to deploy their apps globally the fastest. In America, ChatGPT/OpenAI has this commanding position not because it has an ecosystem, but just because it was the first to do it. It had a first-mover advantage.But if you look at the players outside of ChatGPT, it’s Google slash Gemini that probably has the largest market share, because it has this big data center hardware, this AI infrastructure advantage over other players. And also it has this app system that people can use the AI features in.In China right now, personally, I don’t get to use the AI apps in China all that much, but I do have a Chinese phone, and I use ByteDance’s Doubao app, and it’s really good. So that has allowed ByteDance to have the best video generation model out there, called Seedance 2.0.Grace Shao (50:29)Mm-hmm. And they really leverage and lean into their data advantage as well. Obviously, if you own TikTok and Douyin, you have the most amount of video data in the world.TP (50:43)And not just that, they also have CapCut.Grace Shao (50:58)They do, which is the editing tool. I actually use it to edit our videos here on AI Proem. It’s great. I kind of want to wrap it up soon.I want to ask you a forward-looking question. If we connect the dots from your fintech days covering the digital economy to what we just touched on, EVs, robotics, hardware, everything, where do you think China’s digital economy goes over the next five to 10 years? What are the biggest bottlenecks? Will that look very different from the rest of the world? Or do you think the evolution of technology will be organic and go in the same direction, no matter your geographical location or your domestic strengths or weaknesses?TP (51:30)Yeah, so I will first talk about where I think they can possibly see the most improvement, and that will be the semiconductor part of it. I do think they will have a fully domestic semiconductor supply chain pretty soon. And that, along with government support in terms of putting money into these high-capex, maybe lower-rate-of-return investments, will allow them to more aggressively build out the domestic semiconductor infrastructure.Once you have that infrastructure, then you can produce all the AI chips, all the phone chips, and all the analog chips that you need for your various embodied AI products and EVs and all these other leading sectors. And once you have that, that means you’re no longer constrained. You’re no longer constrained by compute. You’re no longer constrained by possible Western tech export controls on you.So then the AI players in China are equal in terms of AI infrastructure. And that allows them to compete a little bit better with their American counterparts. Now, they do have some obvious advantages over their American counterparts. We’ll have to see how this plays out, because China does have this entire grid build-out that is just unrivaled. And as we move to a more electrified global economy, being able to build not only data centers but the entire grid is actually a huge competitive advantage over the rest of the world.I don’t really want to say who wins the AI race, because I feel like you can only lose the AI race by not participating and investing in it. But if you invest and put a lot of money into it, like both the US and China have, both of these countries will have a huge share of the global economy going forward.TP (54:15)I just don’t see how you can put this much effort into AI in America and not get something out of it.Grace Shao (54:24)I just feel like it’s not a zero-sum game.TP (54:28)It’s only bad if you don’t try to build your own AI industry, right? If you don’t invest, that’s a problem. But if you invest, something good will happen, I think.Grace Shao (54:42)What about the smaller countries where they don’t have that capital, and maybe they don’t have that much capital to deploy into this, or even frankly the talent to build their whole AI stack? Where do they fit into all this?TP (54:53)Yeah, so I think that’s one of the factors that might help the Chinese ecosystem over time, because a lot of the open-source stuff is coming out of China right now. So if you’re from one of the smaller countries, let’s say Singapore, and you want to develop your AI sector, you are more likely to use an existing open-source model and do reinforcement learning training on top of that, and then develop your AI product on top of that, than use something you don’t have any control over, like Claude, for example.Grace Shao (55:41)Interesting that you use Singapore, because I was just there last week and literally OpenAI just announced their satellite office. I think they said they would employ 200 people. Singapore is an interesting story because, if anything, they’re super gung-ho on AI, from top-level diplomats and ministers to companies. So it will be interesting to see how they play out this strategy.My question for Singapore is: they can attract a lot of talent globally to go over. They can attract a lot of new companies to go over, which is what they did with the internet era too. ByteDance, Tencent, Facebook, everyone’s there. But then what is the value they propose for the locals? Or how do they plan to diffuse AI into the economy? I don’t know how they make themselves that relevant globally beyond being a hub for these companies.TP (56:35)That’s a very hard thing to say because I don’t see Singapore, just on its own population, actually developing anything unique. The people who would work in Singapore’s AI industry could work in any other country also. So I think Singapore has always put itself out there by being a country that attracts talent from all over Asia, right? And they attract a lot of capital also from the rest of Asia.There have been a lot of issues in recent years where they say all this money coming in hasn’t really helped the local-born population in Singapore. So that is something interesting to watch out for.Grace Shao (57:25)Yeah. I don’t want to go on a tangent on Singapore too much. So, last two questions. One is: what is one underappreciated hard-tech development you think people are missing?TP (57:38)Yeah. Last year, I wrote a thread about a list of what I call sanction-breaking tech that was happening in China. A lot of these are not things you see in the media as much, because they are the zero-to-one steps in the upstream supply chain that need to be achieved in order for an end product to be built three or four years later.So things like high-speed analog-to-digital converters and digital-to-analog converters, advanced diamond substrate for heat sinks and other purposes, high-end gallium chip designs, and a lot of the lower-level material science-related stuff that people don’t really see.But once China develops these things, that’s when you see this really fast iteration afterward. Because everything in China is kind of built upon the idea of having the upstream supply chain and the process knowledge. And then it can iterate through the end product a lot faster.So as fast as China has moved in the past 20 years, I don’t think the West is really prepared for what is to come out of China in the next 10 years. I really don’t.Grace Shao (59:36)Interesting. Okay. Well, I think that’s a topic that no one really has an answer to. No one really knows the future, right? But I appreciate your thoughtful answer.My last question for you is a question I ask everyone who comes on the show. What is one differentiated view you hold that you think is non-consensus?TP (1:00:00)Interesting. Well, one thing that I’ve talked a lot about with people recently is that if you listen to mainstream media, when they talk about China, they always talk about the economy not doing well and that China has this housing bubble that’s apparently a real problem, right? And that China has this demographic problem going forward, and that’s why China might have problems going forward.I’ve actually always held the opposite belief, in that I’m always under the impression that China grew overly rapidly for many years because it built up this real estate bubble, and all that money went to real estate instead of the tech sectors. And at a certain point, it decided that it could no longer blow up this real estate bubble because young people weren’t getting married and having kids because they couldn’t afford homes. So it deliberately deflated the real estate bubble in order to solve this problem.And then it still claims to have grown at around 5% a year for the past few years. If you can deflate a bubble and grow at 5% a year, that is quite the accomplishment, actually. So I would say the Chinese economy is quite healthy.You would rather have an economy that can grow strongly in the middle of an asset bubble deflation versus an economy that is growing just a little bit in the middle of a historically large asset bubble, like you have in the equity market in the US.Grace Shao (1:02:05)That’s a very interesting take, actually. I’ve never heard someone say that. But yeah, I kind of see where you’re coming from.TP (1:02:14)Yeah, that is my take.Grace Shao (1:02:17)I love it. TP, look, I’ve taken up an hour of your time. I really appreciate your insights. And you entertained my brain going in all directions as well. We’ve really talked about a lot of different topics today.Is there anything else you think we didn’t cover that you would like to share with everyone? Or do you think we can always pick this up again another time?TP (1:02:40)The only thing I would say to everyone out there is, if you enjoy AI, try one of the cheap Chinese models and see how it works for you. I’ve tried it myself. It’s great for my work purposes. And I highly recommend everyone use Kimi.Grace Shao (1:02:58)There’s a plug. No, I’m kidding. They are good, actually. I think I use different models for different things, but ultimately I find that if you’re really using them for more basic writing and everything, the Western ones are better. But if you’re really hosting your own models and running your own agents, then a lot of the Chinese ones are a lot more cost-efficient.So thanks again, TP. Thank you so much for your time.TP (1:03:28)I’m glad to be here. I’m glad to be on your show. And you can all follow me on X at TP Huang. I’m really glad to be on this show.Grace Shao (1:03:38)Definitely. And TP is on Substack too.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  China's open-source ecosytem and the future of AI bootstrapping with ex-Hugging Face APAC head 25.05.2026 1t 3min

  Joining me today is Tiezhen Wang (Tom), formerly of Hugging Face, where he worked with researchers in China, Australia, South Korea, Japan and across APAC, to help make open-source models more discoverable, usable, and visible to the global developer community. In this conversation, Tiezhen explains why Hugging Face became the GitHub for models and why open source is not just a distribution mechanism but a different way of coordinating research. We discuss why Chinese AI labs have leaned so aggressively into open models, how DeepSeek changed the commercial logic of open source, and why Qwen, Kimi, GLM, MiniMax, and others are using openness as a way to win attention, recruit talent, and accelerate the whole ecosystem.His core argument is that China’s open-source AI push has three layers. At the researcher level, open source preserves attribution and career mobility. At the company level, open models can become benchmark-led marketing, developer distribution, and a recruiting advantage. At the ecosystem level, government and university incentives are beginning to cultivate open-source culture among younger engineers.We also discuss why US frontier labs have pulled back from openness as research and business have become more tightly coupled, why distillation is much murkier than the public debate suggests, and how DeepSeek’s releases increasingly function as shared R&D for the broader AI ecosystem. The conversation then turns to monetization: why open-weight labs can still make money through API tokens, base-model access, post-training services, and inference optimization.Finally, he lays out his current thinking on AI bootstrapping: the idea that agents may eventually help improve their own harnesses, generate training data, and even improve the models they rely on. We close on a more philosophical question: if a handful of closed labs control access to frontier capability, open source becomes more than a technical preference. It becomes a check on the concentration of power.Tiezhen/ Tom is based in Sydney, Australia. Feel free to reach out to him on X to chat.To find the previous episodes of Differentiated Understanding, see here.Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently. Season two will host a series of guests from early-stage investing, as well as builders, researchers, founders, and product managers. For more information on the podcast series, see here.Chapters04:07 The Philosophy of Open Source at Hugging Face12:51 Challenges and Opportunities in Open Source17:12 The Role of Collaboration in Research21:50 The Future of Open Source and AI33:58 What Constitutes Distillation in AI37:18 Navigating Copyright and AI Distillation37:43 The APAC AI Landscape: Insights Beyond China43:08 Understanding the Ecosystem: Labs vs. Hyperscalers46:21 Monetizing Open Source AI Models52:02 The Future of AI: Bootstrapping and Self-EvolutionTranscript (AI- generated for reference only)Grace Shao (00:00)Tie Zhen thank you so much for joining us today. I’m really excited to have you on. We’ve been trying to make this happen for a while and just so glad the timing’s finally worked out. To start, can you tell us a bit about yourself, your journey, and where you’re at right now in your career and how you see the whole ecosystem? And also, just help us understand Hugging Face a little bit as well.Tiezhen Wang (00:19)Yeah, thanks, Grace, for inviting me. I know, sorry for the long delay. It has been a while, but I’m recently in transition because I just left Hugging Face. So to give you a quick information about very high-level overview, you can think of Hugging Face as the GitHub for AI. If you are not familiar with GitHub, you can think of Hugging Face as Amazon, where you can find all kinds of models in one store.And we are helping, so my job is to help researchers to get their models, which is the open source models on Hugging Face. And they can use the best, like all the tools, all the services on Hugging Face to make their models more discoverable and available to everyone. We also offer all kinds of technologies. For example, we allow them to create demos so that developers do not need to download the whole models.and they were able to try it out and see how it goes. And we also offer services so you can create your own agent using open source models. We do all kinds of scaffolding on top of open source models. another part of work that we do is to help them get more traction. We use LinkedIn.I use Twitter mostly to help them getting well known by the public. And we write analysis on their models and letting people know what are the new inventions from the model, et cetera. we work with researchers across the world. Like myself, it’s focused on APAC, especially Chinese researchers. Yeah, that’s pretty much the goal.quick overview of what I do. If you have any questions, just let me know.Grace Shao (02:03)And how did you get to this role? Because I understand you were with Google for quite a while as well.Tiezhen Wang (02:07)Yes, I was with Google as an engineer. work on ML frameworks. But then we had a bunch of reorg. And I was assigned to a project which is not open-source. But I really like talking to people in the open source world. It’s kind of very different. So when you are paid to work something versus you want to work on something yourself,Like you have very different mentality and very different feelings. So when I was working on the open source machine learning framework, I talked to people outside Google. And I can see the stars in their eyes. They do want to work on something they want. And even though they may not get paid, et cetera, I really like this feeling. So after I was assigned to the non-open-source project, I want to try something likenew but also in open source and I was like talking to people in Hugging Face and I really liked them. At that time, like Hugging Face was not like part of the mainstream. It was like a niche product for researchers where researchers can upload models. But I do see there’s a huge potential for Hugging Face to grow up because first I believe in open source and the second like Hugging Face is going to be the entry point where like all people will come in and search for open source models. But the most important of all is that I feel that Hugging Face is a company who understands how open source works. Open source is a huge leverage. If you use it well, it’s going to be very powerful. And Hugging Face is like 200 people, like very small companies compared to other companies growing up from the same area. But they are able to use open source as a leverage.and called for collaborations across the world and do very impactful things. a lot of people, a lot of big companies are doing open source, but they just don’t understand this age. That’s the essence of open source. And I do feel that Hugging Face is doing really well there. That’s one of the reasons why I want to join Hugging Face.Grace Shao (04:06)Yeah, I think that’s amazing. I think that’s something we definitely will double click on later, especially when we talk about why China’s labs seem to have been embracing open source. Just kind of one last question on just the whole ecosystem and how hugging face fit into it. What was the philosophy really held by the whole company? Because I actually listened to one of the founders interviews, Clem’s interview recently. And during the interview, he talked about how Chinese scientists have always been long term contributors to open source technology. And then he said it was really like kind of a pivotal moment around 2022 where American open source contributors kind of took a step back and then there was a sentimental shift in the ecosystem. Why is that and how does Hugging Face kind of view the whole ecosystem?Tiezhen Wang (04:47)Yeah, there are several questions. Let me try to address them one by one. The first one is the philosophy behind Hugging Face. I think it’s really the mindset. so anything that we see where we can have a collaboration, like Hugging Face will just reach out and see if we can collaborate. So if you go to see a lot of work released by researchers, they will have paper on arXiv.and also their project on GitHub. And you’ll see me on all of these issue number one, which is the first issue after the repository has been released. And we just write something saying, offer blah, blah, blah. Do you want to collaborate on something? So for anything that we can collaborate on, we will just call for collaboration. And some we’ll go through, some we’ll not. But this collaborative mindset is very, very different from.like a business point of view. From a business point of view, you will first think, what is my edge and how I win the market, how I compete with others, and what are the end areas. After the competition, what’s the end game, how it will go. So that’s the way of how you can justify the investment and everything. In open source world, it’s totally different. It’s like, I want to do something.I just say it and I do it and there are developers who want to join in and we do it together and we grow the pie gradually. we do not have like, let me put it the other way. So if you see an open source model coming from one of the Chinese lab, for example, GLM 5.1 is released and you may think like Kimi or Minimax like other open source model provider.in China would compete with them. But actually not. Like you will see they are commenting on the Twitter saying, congratulations, et cetera. This is a collaborative mindset where everyone is stepping up on each other. we can do a lot of, as a group, can continue to push the frontier forward. So I think this is very, very different.Yeah, and talking about your second question, the Chinese, well, I wouldn’t say labs. Chinese researchers, labs, companies, et cetera, they all want open source. I think there are three different folds. The first one is on the researcher side. A researcher would always prefer if their work is open source. That’s coming from their academia background, because when youLike on the CS world, when you write a paper, you have to show that it’s actually working. You have to show that all the numbers are real. Other people should be able to verify that. And you can only do that by releasing your code, releasing your models to the community so that other people can evaluate. So a researcher, after they graduate and they go to a company, they will bring this mindset forward. And by default, they are open source people.And another perspective is for their self, for the career development of themselves. So as an engineer in big companies, it’s very often that you are working on some project and nobody knows that you are working on that project until you say that out on the game or on your resume. But open source is very different. We know precisely who has contributed to DeepSeek before.And that’s very attractive for for researchers, because if I have done great work, I want the whole world to know that I’m doing excellent work. This will help me have better branding, help me to do more collaboration, help me in the future step in the career. So a researcher would always love open source, by default. So that’s the first part from a researcher’s level. The second one is from business level.So well for individual is quite easy to embrace open source from manager level from the executive, they need to justify the investment on open source. I have to spend tens of millions in training a model and you want me to give it for free. That’s crazy, right? That’s how people think before DeepSeek. Although we have lot of open source models before DeepSeek, but the trend is completely changed.Before DeepSeek, people were thinking, oh, maybe the model is not that good. Maybe I’ll just open source it. But if the model is good enough, maybe I’ll keep it for private. And that’s one of the reasons why you see a lot of people were saying open source is not that good, especially from Robyn. And lot of people do not understand how the open source works.works. But then people do realize that if they do not open source, they do not even have a chance to stand on the market. Because their model first is not really good. If they just compete on the marketing level, on the business level, they do not stand a chance, not even a chance. So you spend tens of millions and you get nothing. But if you open source, at least you have some sharing and people will remember. And also you can have the market from.for the researchers. I think Qwen team was one of the first team who understand it from a business level and start like open sourcing work. And as the result, it’s very, very good. Like they almost taken the ecosystem from Llama and now they are becoming the default for researchers to do research, which is like a huge branding for Alibaba. And like, I guess like if Alibaba wants to do any kind of business, like it’s quite easy for them.to approach to researchers saying, we are not nobody, right? We are the provider of Qwen and everyone wants to talk with them. And another side for the business is that they find it really hard to attract top talent if they do not do open source, because all these talents want their name on papers, et cetera. if they can pay a lot of money.but they still do not have the best talent. But on the other side, if they do open source and the researchers know that they come to this group and they can have their name marked on history, it’s going to be very attractive. So like this company, even not releasing the best models, they try to release something to make researchers happy. It’s kind of like their...company perk. So that’s another route. But after DeepSeek, everything changed. People know that if I do open source, I can have huge branding for my company. DeepSeek is not doing any kind of commercial stuff, like alteration to cusTiezhen Wangers. Yet they still have a huge evaluation of, I think the most recent number is [unclear: “14 million HKD” in transcript; confirm figure].That’s a lot of money. So by doing open source alone, they can make money. And that changed the mindset for lot of people. so after DeepSeek, Kimi, GLM, Minimax, and StepFun, they all come into this open source world. actually, they have made a lot of success stories, like GLM and Kimi, by doing open source, lot more people understand them. And they kind of open up.the global market, not just the market in China. for them, I feel that it’s not like losing a lot of money because they doing advertisement in a different way. Kimi was spending tens of millions RMB per year on advertisement. And the result is very short retention. People know them, come to their side, and they do not feel any different. And they just move away. Now, the researcher team, the manager, the executive means, knows that the best score on open source benchmark is the best advertisement. So they can concentrate all their power, not wasting them on advertisement, but concentrating all their money and resources on training the best model. But this best self, it’s the best marketing, and they can create great models and start earning money.So I feel that on the business level, everything starts to make sense. But now there is a new challenge, which is how you can stop people from taking the free ride. It’s a longstanding problem for open source. I did something, for example, I made a database. I spent a ton of engineering hours. I open sourced it. But I’m not making any money, because the cloud provider is taking that for free and start making money and monetizing it.it’s happening for open-source world as well. I open-source the model and all these inference providers and chipmakers and BDA-AMD are making money, but not the researcher who created the initial model. That’s why you see some licensing change and discussion on that. Kimi did the first non-commercial license, and then MiniMax made a more restrictive version. Tiezhen Wang (13:40)made a more restrictive version. But I don’t think that’s the final version. People are still trying different things. And I believe maybe in one or two years, we will have a more standard way of balancing open source and commercialization, et cetera. So that’s the second level. The third level is the third level. So the Chinese government is really encouraging people to do open source.If you do open source, you have extra credits on your bachelor education, et cetera. And Shenzhen recently announced a very interesting policy. So you can have housing points if you do open source on GitHub. basically, they are categorizing.Grace Shao (14:21)So the incentive, yeah, go straight to the students, like even in academia, while they’re still in university.Tiezhen Wang (14:27)Yeah, so it’s kind of cultivating this open source culture when other researchers and developers are still in universities, which is really good. So I do feel that the culture of open source is, if they are winning the young students, we are going to see more open source projects. And to be honest, I do feel that that’s the right approach.Because if you’re not thinking about open source, you are thinking like traditional way of collaborating with people, which is company or corporation. And I feel that the essence of why we had cooperation or company is not keeping peace with how we evolve now. I think about, you set up a company in Hong Kong 200 years ago. Why? Because you have a group of people. You want this group of people.That’s why it’s called company. You have a group of people and you want them to work together. And how you can make sure that everyone had their benefits. Everyone is doing a lot of work. Obviously, they want to have a return. And you do that by setting up the shares and also the voting system. that’s how a group of people is working together. But now the word company has changed. It’s more like amulti-international company where the worker in the company has no work in deciding how the company runs. Whereas open source work is more likely the original version of a company. You have GitHub, you know who has contributed what. Everyone knows your contribution, and you can have your name listed. the group of people coming from all around the world, can.collaborate on something. They do not need to be part of a big company going through all the interview process. They can just collaborate. So I think that’s very, very interesting. And now with Zoom, Tencent meetings, and all the Google Docs, it’s much easier to collaborate internationally. I don’t need to know who is contributing to the PR, but I know someone is interested in my project, and we can work together. And I feel that.That’s probably the future way of how people can collaborate. that’s to end the last point on society level. I think the society is advocating for open source. also open source is probably the way how the society will evolve.Grace Shao (16:48)Thank you. is like so insightful pack that I have to digest that. But you you mentioned quite a few different topics, which I can definitely take this straight, conversing different directions to start. have two questions and they’re actually unrelated. So one at a time. Number one is you really make a point about China being really, you know, strong advocate on open sourcing the LLMs. However, I thinkCould you tell us the history of open source in China in general? Was there a tradition to want open source technology even pre-LMDs? That’s number one, first half of that question. Second half of that is you say there’s a lot of incentive for researchers to actually want to open source everything, right? And then therefore they can claim their contribution. Well, in the recent interview between Zhang Xiaojun and...deep minds, Yao Shui Yu, I think maybe you’ve also listened to it. You know, one thing that really stood out to me was how he was saying people need to be like responsible. And like for someone who’s not technical, I actually really struggled to understand what he meant at first until like actually Jiang Xiaoxuan actually asked him to clarify as well. His whole point is that in academia, people are so used to only claiming a certain section of what they contribute. So for example, for a big piece of paper or research,that you would take credit for what you contributed, right? And you want to make sure that it’s best optimized, known, heard, seen, whatever, right? Recognized. However, in terms of how LLM can work properly in terms of the long run, whether it’s like, you know, further in post-training and further, you know, know, usage, whatnot, it’s important that people don’t claim so much credit to their own part of the work. It’s more important that people work collaboratively. But kind of to your point on open source that, you know, they can work collaboratively and make sure that each piece works together better instead of each piece working best on their own. So it kind of contradicts your comment on why people want open source, because in that sense, wouldn’t it make sense for people to not want open source? I don’t know. That’s another question. And the third part of this is really if open source makes so much sense for tech companies and makes so much sense for academics.then why are the American labs so anti open source right now? Like what is driving that? Is it purely because commercial reasons or philosophical reasons? This is very big, but you did throw a lot at me. So I’m going to throw these questions back at you.Tiezhen Wang (19:07)Yes, sorry for my very long answer. I think it’s probably by itself worth writing a blog post with enough content, and I can elaborate more. But great questions for the story. Can you remind me? I guess we can go through them one by one. Can you do mine? Yeah.Grace Shao (19:25)Just like in general, source China, China open source. What’s the sense on that? Beyond LLM, right? Like why did Chinese companies always contribute to open source technology? Clem talked about this in his interview, but he didn’t go into that about it, right? So number two was just about, yeah, number two was just about like, why do these academics want to claim their names, right? Is it better for the company in the end or is it just best for them, like the selfish reasons?Tiezhen Wang (19:37)Yeah, okay. Let’s try it. Yes. Mm-hmm. Yep.Grace Shao (19:52)And number three is why are American labs kind of anti open source right now?Tiezhen Wang (19:56)Yeah, so let’s try to address the first one. I think it’s a great question. And I do see the shift. So I feel that AI is probably one of the very few areas where Chinese open source contributors dominate. If you look back to, for example, I would say the initial days of modern open source comes from like anLinux or Apache or database and everything. And where you do see a lot of individual contributors from China, but you are not seeing enough Chinese company creating a project. And then the project gets adopted globally. You are seeing that gradually when we move to the area of cloud-native, like when the Kubernetes comes out.And a lot of Chinese cloud providers are trying to really pay attention to this whole open source world. And you will see that this grows. But now it’s like this. So it grows exponentially. So I think it comes from two folds. The first one is the Chinese participation in the global market. It needs time to warm up.Like for example, lot of Chinese contributors, they can only contribute two projects in Chinese because of the language barrier. So that kind of limits how much they can actually do. And now with larger language models, with better education in the new generation of developers, the language barrier is not that strong. That’s why.That’s how the Chinese open source contributors can make a better impact. And another one is, so in the traditional way of a company’s, like how a company’s structure itself, if you do open source project, it’s kind of hard to justify your credits because the open source by itself is not the core business of a company. There are very, very few companies whohad their core business made on open source. Like PingCAP could be one of them. PingCAP start with open source and then find monetization plan. But that’s so small. So few of them. And in the new areas, a lot of companies, their core business is open source and plus monetization. Even for IPO companies, for public list of companies, Minimax is basically one such example. They have their best models, open source.and then trying to make money. So this is very different. If your core business is open source, of course you will put more resource on open source. And it’s more likely for your project to gain a lot of developers. And I feel that the third one is the international collaboration has never been easier before, apart from language barrier. So after the pandemic, I feel that all of a sudden everyone is used tolike Zoom and Hangout and collaborating with someone who you don’t see face to face. And this is a great chance for open source project to ramp up. Because before that, you have to meet face to face, and the bandwidth and the people you can meet is kind of limited. And now you have a huge, like, as long as your project is great, like you have a huge pool of potential developers.And the last one is probably AI by itself, like coding agent itself. Although it does make code review much harder because there are probably a of AI scope. But it really lowers barrier of who can contribute to an open source project. Before, you want to contribute to a project. They are probably developing language you do not know. And also, the code base is pretty strong. A developer might not be able to.contribute to the project until he has a very thorough understanding. And that’s probably like months of work. Now you can just ask AI how this part works. And I only need this feature. And what are the code I need to modify? And I can just give it a test locally, and it works. I contributed to some Rust project without being a Rust expert. So that’s how AI makes everything better.So I think it has all these reasons. There are probably more, but I think someone at the end of the day, in two or three years, maybe starting to write some history about how every single aspect of technology, moment and everything, people’s mindset shift, how to cultivate the open source spirit. But I think, yeah, that’s the...Top ones coming out of my mind.Grace Shao (24:26)And then the second question was just that would researchers focused on their own name and frankly ego in this sense actually be the best way to help cultivate the best LLM or whatever whatever product that’s the end product that’s to be shipped. Does that make sense? Because it kind of contradicts what Yao Shunyi was saying on Zhang Xiaoxuan’s podcast, right? He was saying in that sense a lot of researcherswill try so hard to only own what they are working on, but they have less of a sense of responsibility for the bigger project.Tiezhen Wang (25:00)Yeah, I haven’t really read the broadcast entirely. But talking to your point, feel that open source or not, or having researchers name these data on papers or not, it’s actually a game changer. If you are a researcher and you might want to stay in academia, why? Because all the papers you publish is very important to your career. Everyone sees.Like you have published which paper with like who and the paper well like also mentioned that you contributed which part of work are you Look like my corresponding author. Are you the main person or you are just like contributing a part of it? And you there’s h-index to measure the impact of a researcher So like you have all that infrastructure working for you if you stay in academia But if all of a sudden you want to start workingfor company, you lose off that because the company might be able to say, we have a policy where all your open source and all your paper publications, even writing a blog is controlled by the PR team and they have to decide if you can do certain level of things. So your exposure is reduced. You might be very happy because you earn much more, like 10x the salary compared to be an assistant professor.Like after five years, if you ever want to go back to academia, that’s impossible because you lose all of your track record. And with open sourcing, things are very different. The top nailing app wants to hire the best talent from academia. And the people from academia wants to work for the app. But at the end of the day, the two systemIt’s the same system because all the record is public. So it’s very easy for people to come in and come out, come in and come out. It’s kind of different from people coming from academia and then lose track in and get lost in the company world. So I do feel that having your name listed on the work you publish is very important. It is kind of the concept of [Chinese phrase unclear]. So you.your branding grow with whatever you have done. So your reputation is based on whatever you contribute. So you need to pay extra attention on that.Grace Shao (27:16)I see what you mean. And the last bit of just now what we’re talking about was just why is that if you believe open source makes so much sense to these researchers, that so many researchers in the US or at least some of the companies, the entities right now are not willing to go open source.Tiezhen Wang (27:31)Yeah, there are lot of companies who change their position. Google used to be the company which impressed open source the most, like Google open sourced and like TensorFlow Kubernetes and bunch of other important open source projects. The open sourced transformer, which is the cornerstone of our modern AI system. So Google was really impressing open source. But I feel thatAt some point in time, Google stopped doing all the open source work because they kind of lighting OpenAI and other companies taking the free ride. Google did a lot of fundamental work and then it’s kind of taken by other companies for free. And also, OpenAI and Anthropic, I feel that they are still contributing to open source, but that’s not their main project.Their main project are hidden secrets that they do not want to share so that other people can catch up. And the research and business is getting so coupled. So for example, a researcher in OpenAI found a way to improve the intelligence level by 10%. Let’s take o1, for example. They managed to find out how to make model think. And by this chain-of-thought and thinking process, the model islike way much smaller, way much smarter. But they do not want to share the gist.As long as they do, other people will catch them up. So it’s kind of a very restricted environment. Although researchers may still want to open source some of their work and have their name listed there and sharing very detailed observation. But the business doesn’t just allow them to because you are trying to. Yeah. also, researchers can also talk to like in some conference. I know how open source, sorry, I know how o1 was roughly made by reading bunch of YouTube videos from the internet made by OpenAI researchers. But after that, you see less and less very detailed research sharing, even the videos or recordings by them. I guess they kind of learned the lesson.But like, yeah, yeah, that could be part of the rhythm. On the open source world, like, it’s kind of different. Like, so before DeepSecR1 was released, lot of people were speculating how OpenAI was doing o1, and they’re trying things in different way. And after DeepSecR1 is released with all the recipes and all the data they shared, like, the open source world seems to be converged on the path. Although that path mightGrace Shao (29:46)There can’t be compliance reasons.Tiezhen Wang (30:12)be different from o1 because we never know how o1 was made. But then because of DeepSeek’s contribution and sharing, everyone knows how to make thinking chain. And the whole ecosystem is evolving really, really fast. That’s one of the real value of open source because everyone can just collaborate. No one is holding secrets. Well, there are still a lot of secrets on how you can run as efficient as DeepSeek, but that’sLike too technical, like that’s not too much on the research side. yeah, like I...Grace Shao (30:44)So on that note, yeah, I want to follow up on that. I think I recently wrote about something which is, speaking to our researchers, it got me a sense that DeepSeek in a way is now becoming essentially like a foundation for everyone because, you know, a lot of the labs in China are looking to DeepSeek to see if there’s any like, you know, engineering breakthrough, like your point, and they build on top of each other. Help us understand like each of the labs, because you said, they’re cost constraint, they’re compute constraint.Tiezhen Wang (31:06)Thank you.Grace Shao (31:12)They’re teleconstrained, right? Their resources are constrained and every single asset you can think of compared to the American peers. Now, why does it make sense that they all open source and how are they all optimizing for their own goals at thisTiezhen Wang (31:24)Yeah. So open source by itself, as we just talked about, is an accelerator of the whole ecosystem. So DeepSeek shared all their like, knowings and discoveries and what things work, what things doesn’t work. This by itself is accelerating the whole industry, not just Chinese open source, but also like US open source and US like closed source. Like they just don’t say how much they learn from DeepSeek, but I believe everyone is learning from DeepSeek.Not just that, DeepSeek also contributed to GRPO, which has become the most used algorithm, reinforcement learning algorithm in the industry. So they did a lot of contributions. if you check recent model architecture evolution, what’s proposed by DeepSeek is becoming the standard and getting adopted by many people. For example, Kimi 2.5 was using a model architecture very similar to DeepSeqs. And GLM 5.1 was adopting a lot of components from DeepSeek architecture as well. So it’s kind of sharing and learning and co-evolvement is one of the, I would say, secret of how China is able to. catch up with the US in certain area, although having restricted compute and restricted capital, I would say. If US open source is working again, like the whole ecosystem, like everyone was trying to open source, I would say the human race would be evolving much faster than what we are doing now.Grace Shao (33:01)So on that, how do we understand the accusations of what is being distilled? What is technically shared? What is, how do I understand the gray area of that? Like the accusations from a lot of American labs, Chinese labs right now, like you just said, a lot of American labs are learning from Chinese labs. Frankly, within the researcher community, it’s not even Chinese versus US, it’s really just labs with each other and against each other if they have to, right? Intellectually competing. So then how do we understand what the, industry agreement is on a distillation, why is it so contentious rightTiezhen Wang (33:32)On distillation, yeah, that’s a great question. I can only give you my perspective. first, distillation is a very broad word. We are distilling from each other as well. I learn from you, you’re learning from me, and we are all learning from books and papers and all this public information. So I would say,distillation is a very common practice, like basically how you learn from others. Like you might have a model which summarizes the books and like doing bunch of explorations. And the way for the model itself to move forward and evolve is to distill from its historical data and historical experiments. And like that works for like another model trying to learn like your model as well.And on the research field, distillation is very common. DeepSeek R1 was released with MIT license. Specifically, so I actually asked the team about it. They choose MIT license because they want their model to be distilled by others. Because that was the only model that works really well with the thinking chain. And they want all the open source model to be able to have that.Like they have shared all the recipes, but others do not have data. So DeepSeek design like they’re like small models so that and also the recipes so that other people can easily distill DeepSeek, getting the thinking chain and use that on their own models. like this distillation is happening like everywhere. And I think like US companies are distilling from each other as well. Like I’ve seen like the recent discussion on Twitter in public.where Elon Musk and Sam Altman were kind of battle on that. yeah. And if you think about it the other way, so if you do not allow a model to distill, I mean, the output of a model to be able to train a model which is from a competitor, it’s kind of a very interesting point. Like if we say, I’m reading a book.I’m telling you the story. So you, after reading the output from me, which I think of me as a model, you’re reading my summary and you are not allowed to share the summary to others. You have to read the book, the initial book, not using my summary because of the license, et cetera. That’s kind of ridiculous. That’s not how human transfer knowledge in the past a few thousand years.Like I have a very bold argument. I think that anything like generated by AI should not be copyrightable. So like it should be in public domain, like anything generated by AI, because like anything generated by AI is a distillation of like human entire history and everything that human has created. And if you just take that for free and asking other people do not use that.Like it’s kind of a waste and it’s kind of like blocking people from evolving forward. Because like human content do not have this restriction and why you are putting this restriction on something not copyrightable and generated by machine. So that’s something I do not really understand. So I do see there are terms and conditions saying that my model output cannot be used to improve other models. But I don’t think that’s kind of valid.I’m not sure if someone eventually will file something on the court and we can have a case on that. currently, think there are a lot of things to discuss, but it’s not about if we can distill a model or not, but about something bigger. Should the model creator even have this right to restrict others from distilling from their models?Grace Shao (37:18)That’s really interesting. I think that a lot of the discussions in the public space is really about whether you can use copyright work of human output. And then the argument is always like, just you cannot distill because the company said there’s no distillation allowed. But like to your point, there is no actual clear black and white rule of regulation around this right now. And in fact, it’s it’s bit murky. Yeah. Yeah, yeah, that’s interesting.Tiezhen Wang (37:37)I’m not a lawyer, but I can find a clear answer on that.Grace Shao (37:43)Okay, I want to kind of go to China. Like we’ve kind of talked a bit about the big picture. Well, a lot about the big picture. But let’s look at just the China labs. mean, I know that you represent APAC back then with Hugging Face and you worked around APAC, you lived in Australia. But for the sake of this, know, Chinese labs right now probably are the most relevant out of APAC. Do you think I’m missing anything actually on the APAC conversation? Like, do you think anyone else in the region is relevant in this space that we can talk about?Tiezhen Wang (38:08)Korea is doing really, well. Yeah, Korea is really well. Well, the most, one of the best model is probably Upstage. they, initially they create a Korean model leaderboard, like open source version of like model leaderboard. Well, no, no, the leaderboard was not funded by government. The, the, the,Grace Shao (38:10)Yeah, yeah, give us some picture on that. That’s funded by their government, right? That’s their government funded.Tiezhen Wang (38:26)So Korean, it’s actually a very impactful country, but as the other days, there aren’t enough Korean data. Even for ChatGPT, I think until ChatGPT 4, the model doesn’t speak good Korean. So the model was able to speak very good Chinese from day one, like from ChatGPT 3.5, but because of the data volume, et cetera, speaking Korean was always a challenge until ChatGPT 4.At the time, like now, the open source model is able to speak like Korean. So Upstage create a leaderboard. So the way they solve problem is very interesting. They’re not solving problem by solving problem. They’re solving problem by helping others to solve the problem. So instead of creating a model right away, they create.Grace Shao (39:09)I heard about Upstage from VC in Korea as well, but I don’t know the detail about it. Tell us more about who they are, what they’re doing.Tiezhen Wang (39:15)Well, I don’t know too much about who they are, but I only see their open source contribution. I think the founder is a professor in Guangzhou, but he’s Korean and moved to US. Correct me if I’m wrong. I’m sorry. I’m not really up to date with that information. But I just want to call out because I think that’s a very interesting paradigm. For example, if you are a company, you have your own problem you want to solve.Tiezhen Wang (39:42)Like, how do you want to solve it? Like, you are going to hire some people and define a problem and try to use your own people to solve it, right? So that’s the old way. What’s the open source way? Is you publicly define the problem. You have a leaderboard. Like, you might do a private eval or public eval. It all depends on you. the problem is you have to list your problem.publicly and you have to tell everyone that you can contribute to this problem by submitting a model to a URL and we will do evaluation and see how each model is evolving on this area. So they basically have a leaderboard. And you will like a lot of researchers would be very interested because now they have a problem to solve before they do not even know Korean what’s the problem. So now they have a problem to solve and you will see that the curve goes like.it goes up because there are more and more researchers coming in and all their work are open sourced. So a new researcher wants to jump in the field. They will first have a look on the leaderboard to see how far away from a really usable benchmark. And then he can investigate all the previous attempts and find his own way of kind of just changing something a tiny bit.and apply that to the past people’s work and submit to the leaderboard. And now we are seeing people making progress on the leaderboard. So that’s a very, very clever way because it’s not one company solving the problem. It’s like we are opening the door for everyone to come into this playground and try to solve the problem together. I think within a few months, they were able to get thousands of submissions.which is really massive because just imagine you hire 10 % people, you won’t get that. And now it’s by this new way of doing things like building public, evolving public, you’re having a lot more submissions and you are educating people, et cetera. So they have this very impactful and inspiring leaderboard and then they release a model called Upstage for something. I can’t remember it has been a while.And the Korean dataset and the Korean models are accelerating very fast on high-netics. I think it is now the fourth largest models, speaking Korean. Yeah.Grace Shao (42:04)Very interesting. Yeah, I’m going to shamelessly self-plug in. People can listen to the episode I recorded with one of the leading Korean VCs as well that was published last week. He gave a good AI ecosystem breakdown of stuff.Tiezhen Wang (42:12)okay. Yeah, could you help me like do some like DD first and like just make sure that are correct. Yeah, you can.Grace Shao (42:22)Yeah. No, no, no, he did talk about Upstage as well. It’s very interesting. Yeah, I want to... sorry, go on.Tiezhen Wang (42:29)Yeah. And also, so you asked for APAC. So in Singapore, there are a lot of great researchers, like lot of Chinese researchers will go to Singapore as well, like Cancun too. Yeah.Grace Shao (42:43)Yeah, I think the ecosystem is a bit overlooked by I think Western markets, but definitely there’s a lot happening in around Asia. Like APAC has been including Australia as well as Southeast Asia, East Asia, and Northeast Asia. Okay, I want to bring it back to China. We’ve been kind of talking about China kind of more on the high level sense. Now looking at the companies themselves or the labs, we want to break it down. Just give us a sense like, how do we understand moonshot?Mini, Max, Deep Seek, Zhipu, if you have to put it in one bracket, versus the hyperscalers, Tencent, Alibaba, and ByteDance, in terms of their strategy, in terms of the capabilities. Like how should we understand this ecosystem right now? Are there other relevant players that you think I’ve missed, maybe like Xiaomi or anyone else?Tiezhen Wang (43:25)You mean like how the model creator, model lab, are collaborating with hyperscaler? Is that your question?Grace Shao (43:32)No, no, I just think it’s like the people, the people who are creating LLMs, like are researching on how to deploy LLMs. These are the main players, right? Now, how do they defer? How are they similar? What are we seeing like on the ground? Are some of them becoming more irrelevant? Are some of them becoming maybe say, we just talked about DeepSeek becoming almost infrastructure provider for the whole ecosystem.Tiezhen Wang (43:39)Yep.Grace Shao (43:58)You know, are that mini-max is very, focused on multimodality. Zhipu is very focused on coding capabilities. know, Alibaba really trying to push out commercialization by their existing applications. How successful that is, that’s a different question. Just like an overview of these players.Tiezhen Wang (44:14)Yeah, I do think they’re kind of converging. Yeah, because everyone knows that coding is, the whole market for coding is booming. And if you have a good coding model, you can sell it for profit, for large profit. And I do feel that everyone is rushing for coding. There are people exploring different things, like,For example, Tencent is putting a lot of efforts on Hunyuan and doing OCR stuff. And lot of other companies are doing video generation. But at the end of the day, think from a strategy level, I don’t feel that there are a lot of difference. It’s more likely a case where, you have data? For example, it makes a lot of sense for ByteDance and Kuaishou to work on video generation models because they have a ton of data. And also, do you have?like a large enough scale. Like for example, Kimi is not very active in making all the apps. Like Tencent is making models. They are making like great apps. Like for example, Yuanbao, like a QA app, like based on all the Tencent data, it’s very popular. Like they make QClaw. Like Tencent is able to do that because Tencent has a huge talent pool. Like Tencent is a huge company, Whereas like if you look at the Kimi, Kimi is very conservative in...doing all that because Kimi is still a very small company. So I think from a very high level, everyone was on the same page about the strategy. It’s just more, how much resource do you have? What are the advantage of you? Do you have data? Do you have distribution channel? Do you have product design, success story, et cetera? So yeah, I’m not sure if I answer your questions.Grace Shao (45:57)No, no, that’s good. So we kind of talked about why researchers want to open source. We talked about these companies are somewhat doing the same thing. So then this leads me to the question. We know that open source, open weight does not actually mean they don’t make money. However, obviously means that it’s harder to commercialize as we like alluded to with the US labs, why they make those decisions. Then how do these companies find ways to monetize and sustain their businesses then?Tiezhen Wang (46:21)Well, in the US, are also labs dedicated in making open source models and still making money from other donations or from other parts, like selling apps, et cetera. It’s basically the same way in China, too. For example, DeepSeek is run by, I would say, donations from the people who play the stock market.like there are labs run by VCs and lot of labs are already profitable by like selling tokens like GLM has recently raised the token price because like they see a huge number of demand and they’re like running short on compute. yeah, like open source can make money. Like there are a ton of ways for open source model provider to make money. I have a lot of ideas. if in case you are interested in like making yournot profitable, can contact me. But honestly, there are lot of ways. The simplest way is to sell token. If you have the best model, you can sell a token for profit and people will actually buy your token. so it’s very interesting because when we combine science and technology, always consider it’s the same thing.Grace Shao (47:15)Yes, everybody find Tiezhen Wang.Tiezhen Wang (47:37)For model, it’s the same. When we think about models, we just think of a model that generates tokens, et cetera. But actually, there are two different parts. The first one is training, where you have the model. And after you get training, open source the weight you trained. Another part is the inference. So you need to run a lot of optimized CUDA kernels in order to make your token cheap and fast.Either bracket can make a lot of money. For example, you can open source the fine-tuned model, not the base model. So if a company want to use open source model for fine-tuning on their own data, they cannot be building on a fine-tuned model. cannot build. They have to find the base model. And if the base model is not open sourced, you can sell that for profit.And also different clients might have different requirements on the model. The NeoLab can collaborate with the client directly and provide some kind of training and post-training support. So that’s a way of making a lot of money, actually, because training is very expensive. It involves very expensive researchers and data and compute. On the inference side, too.Grace Shao (48:45)Yeah.Tiezhen Wang (48:52)Because the inference is tightly coupled with the data center you own. So your optimization strategy does not, there’s no guarantee that your optimization will work on a different cluster. So a lot of people just do not open source the inference recipe because it’s not that useful. And also it’s kind of a moat. So the model provider who creates the model, they know how to optimize the model best.when the model is released because they have seen the model for four months and they have done a lot of optimization on the model inference. And when the model is out, like everyone else, it’s just starting to know the model and doing some optimizations. So of course, the model provider will sell token in a much efficient way compared to all other competitors. Three months later, when the outside inference provider gets toknow all the secrets and do very optimized kernels, there’s a new model coming up. So the model maker, the people who know the model from day zero, always have an advantage on selling the tokens. So that’s one of the very important ways how they can make money.Grace Shao (49:59)I see what you mean. Mm-hmm. Yeah. And does DeepSeek v4 coming out have an impact on how the GLMs of the world or Kimi make money? Like essentially their strategy with the fact that you just said they just raise prices on their tokens.Tiezhen Wang (50:16)Yeah, so GLM and Kimi doesn’t sell DeepSeek or Qwen. So they are not competing with each other directly. I would say the capabilities are on par with each other. So it’s more like a user test. Which one is better? There is no clearly winning between all three models. So we’ll see like Zhipu’s stock price was getting down because people were so worried about DeepSeek. But then they realized that like theZhipu token selling is not quite impacted, so the stock price bounced back. But at the end of the day, I would say it’s actually a good thing for them. So GLM 5.1 is adopting a lot of core design in DeepSeek with 3.2, I think, model architecture. And they were able to cut down the cost.by adopting all these exploration from DeepSeek. And now V4 Pro is out. I don’t know the details, but a very simple guess is that Zhipu is able to cut down the cost because they can adopt new things from DeepSeek architecture. So Zhipu on one side, because of the demand is so high, so they can increase the token price.and they can learn from DeepSeek and cut down the cost. So Zhipu is going, yeah, exactly, exactly.Grace Shao (51:34)you have a higher immersion. Yeah, this is something I think they’ve talked about as well, like really being able to learn from the engineering breakthroughs that DeepSeek puts out every time. Okay, I have mindful time. I just kind of want to have a few questions on the future outlook. You posted on X recently saying that you’ve been thinking a lot about how do we make AI bootstrap itself? And you you’re going through this transition yourself, you’re thinking about the future of AI. What does it mean for the open source future as well?Tell us a bit about where you stand right now and how you think of this bigger picture.Tiezhen Wang (52:06)Yeah, I’m still doing some exploration on my side. I think this whole AI bootstrapping logic has already been implemented by a lot of big lab internally. The idea is very simple. In compiler world, you can design a programming language and write a compiler probably in well-known languages like C. And then you will first implement this language using the C code.In the next iteration or after a few iterations, you are able to implement this language using your own language. So it’s called bootstrapping. You are basically evolving on your own. You are not relying on something which is not from your language. So it’s like putting it another way. If you see how normal living creature, how they replicate itself and how they evolve.I don’t need to have a screwdriver somewhere to engineer my kid, right? My kid’s just born. All by itself. But how far are we from AI to do similar things? Now we have a coding agent very powerful. We have our AI training pipeline recipe kind of stabilized, at least for small sized models. So are we really far fromlike AI able to get one of my idea, like I give him the direction, and he’s able to like first bootstrap a very simple version and gradually evolve towards that goal. Like I think it’s like highly possible. So at the end of the day, we might be able to like just tell him what I’m going to do without like giving him all the harness and all the like detailed guidance and.I’m not talking to him 100 times, and he’s able to first lay out what he needs to do and have a plan, and then probably design a DSL or agent all by himself. And probably he will create a model ways to help him to get adapted to this goal. And then he can just keep evolving. All I need to do is to give him more fuel.which is compute, and he’s able to do some evolution and all by himself. It’s kind of like if you have recently read Andrej Karpathy’s Twitter, there’s a concept called auto-research. But auto-research is just evolving on the model weight. It’s not evolving on the agent and harness. I think on the agent level and harness level, there are also a lot of things to do too.So I’m quite new on this journey. What I was able to do is to bootstrap a very simple agent and I can use that agent to optimize the agent. But I think eventually we will get the weights involved too. When the model realized, okay, I’m not just needing an agent, I can create a bunch of data and improve my weights. He’s able to evolve from that too.Grace Shao (55:05)So in the future, how important is the capability of the models versus the harness and then the industry expertise then? Because right now, so much of conversation is still about, you know, the models are very strong. We are seeing what you’re saying already, the agent’s starting to build out things auTiezhen Wangatically. But we still need the taste. We still need the industry expertise to guide them. I find it hard to imagine that, you know, you can plug in something just say, want this to be done. And the agent just starts doing it exactly to your taste and your...imagination? Do you really think that’s happening?Tiezhen Wang (55:34)Yeah, I do feel that it’s happening. Like we are using agent to like especially coding agent to code something that we are completely unfamiliar with. And I’m quite confident that it will actually work. The reason is that like I have defined a set of goals and as long as I see that is moving towards that direction, like I’m good. I do not need to understand the code line by line. Like it’s just the box. But like the difference is I’m using the coding agent.Grace Shao (55:58)Mm-hmm.Tiezhen Wang (56:02)to do something else. What I can do is to use the coding agent to improve coding agent itself. And using the coding agent to generate the data and train the model that coding agent is using. And I would call that a bootstrap, not like just using, like, I think I’m already quite happy with coding agent to do something else. But just like, yeah, yeah.Grace Shao (56:23)Interesting. I want to end on a more philosophical note. So do you view the argument that AI is going to replace humans then? Or do you think AI is going to be in the role to support humans if we could keep on going down this path?Tiezhen Wang (56:35)Well, it’s actually a very, interesting question. And I feel that people do have different feelings. But from a pure technology point of view, I do feel that it’s one condition of like, so technology is not the only thing that will decide everything. You mentioned that if it’s going to help humans, well, it’s not really technology by itself to decide.Like it can be used in different ways, in different social structure, in different like tradition and such. It’s like giving you a gun and you can do things in good. Yeah, it’s not. Yeah, yeah. But like just imagine that you’re going back to history with all your knowledge of modern society. Are you going to help theGrace Shao (57:12)It’s not a good analogy. But yeah.Tiezhen Wang (57:26)like the society, like the history you go back to? Or are you able to help? Like I think it’s basically the same. If you have AI that knows everything, you can just think of it as human being in like 2000 years in the future. And now you have it. And what it is going to help on the society. Like it really...Grace Shao (57:31)Yeah. It’s like that saying, your own capability of using it is the cap of itself. Also, I think there’s a lot of argument and discussion around the fact that even the society as we know it today, the knowledge work that we all have, that we normalize, are not even created until the recent 100 years. And if AI is to disrupt that and replace human in that sense.Why is it so bad? Because it alleviates us to do other things that human multifaceted beings that we are can do. Is that kind of part of the argument as well where like, even if it replace us or helps us, it’s only helping us actually alleviate some of the things, if you take a step back, the things that we don’t want to do, right? Where we can maybe go touch grass. I don’t know, maybe this is very optimistic view of it, but there has been people saying like,The cap on AI capability is a cap of your own intellectual, your own cap of your own ability to navigate or use AI. So the more you can use AI, the more it can help you. The less you can use it actually, the more it will replace you.Tiezhen Wang (58:45)Well, I think it’s very interesting to define what is you. Are you defining you as everyone, or are you defining you as people who have compute? Well, no, it’s not. It’s actually a very, very, very interesting question happening right now. You know, Anthropic coding agent is able to do lot of things. But people are of imagining that we areGrace Shao (58:53)We’re getting really philosophical now.Tiezhen Wang (59:09)Everyone is getting a lot more powerful with models. But what if one day Anthropic just say, you cannot use your coding agent to do certain things? It already happened. Anthropic said, you cannot use your agent to do auTiezhen Wangated tasks. The other thing, there could be other limitations. I have a very bold argument is that the reason why we are able to use AIso cheap that even us, like we do not own a data center, right? Even us can use that. It’s because our data is still valuable. You know, if you use subscriptions, your data is going to be distilled by Anthropic to further improve the model. And like they are able to give us a discount because they still need our data.Grace Shao (59:52)So your point is that one day when they capture enough data, they will not even give us this kind of access for free or for cheap price.Tiezhen Wang (59:59)It depends on how they define you. You ask them, like, you or something. How they define their user. How they define who could be part of the game. Like, if one day, like...Grace Shao (1:00:09)So then the question is, no, but then my question is, then there’s another argument where they’re saying too much power is in the hands of a few companies right now, right? Or a few founders, what not. We need open source, that’s your point, right? No, that’s really interesting. And that was actually gonna be the last question I was gonna ask you. What is one differentiated we hold? And I think you’ve already answered that in that sense, right? Yeah, I think it’s for us to really think about it. But then as the average user, my question is, how do you actually boycott?Tiezhen Wang (1:00:18)That’s why we need open source.Grace Shao (1:00:36)these companies or if not boycotting, how do you actually make an impact? Because if I’m not the developer creating an open source model for the average person to use, me as an average user, what do I do?Tiezhen Wang (1:00:47)Well, just use the model to do the thing you want to do. Try to embrace the model and be more patient for open source models because obviously the open source model is not as good as top tier closed source models. you kind of like, well, I mean, with open source models, you keep all your secret to yourself. So you can have.like better security and you have better control. Open source model will never betray you if you just write on your local laptop. So although the model is not performing as well because he’s not distilling you, right? So still you can trust on your open source models and give it a more task to do.Grace Shao (1:01:20)You host yourself.Tiezhen Wang (1:01:34)I do feel that a lot of open source model is actually capable of doing things. But the expectation might be, think of it as six months, like cloud version of, sorry. Let me put it another way. So think of it as old closed source models and be patient with that. And you can grow up with the open source model together.Grace Shao (1:01:54)That’s very interesting. Thank you so much for your time, Tiezhen Wang.Tiezhen Wang (1:01:56)And thank you, Grace.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  Nathan Lambert Reflects on China’s AI Labs: DeepSeek, Open Models, and the 'Race' with the U.S. 19.05.2026 1t 3min

  Joining me today is Nathan Lambert, author of Interconnects AI and a post-training lead at the Allen Institute for AI. Nathan recently returned from a major tour of China’s leading AI labs, where he met with researchers and teams building some of the most impressive open models in the world.In this conversation, we discuss what Nathan saw on the ground: how Chinese AI labs differ from their U.S. counterparts, why open models have become such an important part of China’s AI strategy, and how labs like DeepSeek, Alibaba, ByteDance, Kimi, Z.ai, MiniMax, and others are navigating compute constraints, data access, and commercialization.We also dig into some of the most debated questions in AI today: Are Chinese labs really 6-9 months behind U.S. frontier labs? How meaningful are distillation accusations? Can domestic chips like Huawei’s make up for restricted access to Nvidia GPUs? And is China’s AI ecosystem actually government-directed, or is the reality more fragmented and commercially driven?Ultimately, this episode is a more nuanced look at China’s AI ecosystem that looks beyond simplistic narratives about subsidies, copying, or geopolitics, and instead examines the technical, cultural, and economic forces shaping the future of open models.Check out his two recent articles here:* Notes from inside China’s AI labs* How open model ecosystems compoundTo find the previous episodes of Differentiated Understanding, see here.Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently. Season two will host a series of guests from early-stage investing, as well as builders, researchers, founders, and product managers. For more information on the podcast series, see here.Chapters00:00 Insights from the China Trip11:51 Cultural Differences in AI Research18:15 The Role of DeepSeek in China’s AI Ecosystem25:26 Overview of Major Chinese AI Labs30:56 The Future of Open Source in AI37:50 Market Dynamics and Consolidation in AI42:28 Distillation and Model Convergence Controversies51:58 The Gap in AI Performance: US vs China61:09 Monetization Strategies in AI: A Comparative Analysis62:32 Government Influence and Misconceptions in AITranscript (AI-generated for reference only)Grace Shao (00:00)Nathan, thank you so much for joining us today. Yeah, really, really excited to finally hear your thoughts on your big China trip, on what’s happening between the Chinese AI labs and the U.S. AI labs, what you think the potential compute constraints might mean for these labs and their performance in the future, and obviously the open-source ecosystem. So before we get into all of that, could you...Nathan Lambert (00:02)Yeah, thanks for having me.Grace Shao (00:23)Briefly tell us about how you ended up actually working on post-training and open language models. Just a bit about yourself.Nathan Lambert (00:29)Yeah. So I actually started my PhD at Berkeley in 2017, not working on AI things. I was an electrical engineer by training in undergrad, which is funny looking back, because that’s the same year that the Transformer paper came out. And I was like, I think I should do this AI thing, and tried to get the famous advisors to mentor me. And they’re like, we can’t take you. So I had my PhD as this wandering path to become an AI researcher. And then I ended up at Hugging Face after that, which was, realistically, the only industry research job that I had, but also a very hot startup and very fun to learn kind of at the intersection of these tools that people use a lot for AI and research, which is what I was doing.And then when ChatGPT hit, the kind of RLHF thing blew up as the hot word on the technical side of things. My PhD had ended up being in reinforcement learning, which is just the first half of reinforcement learning from human feedback. So it was kind of a natural pivot to be like, well, I might just do that. And Hugging Face was a good place for doing that, because the whole company is kind of all for that, which is like: figure out how to support the community on the hot thing and build platforms there. So they were very happy about that. And I helped build a team at Hugging Face.And then I was kind of burnt out on the remote-work time-zone thing and found out that the Allen Institute was doing such similar stuff. And I was like, wow, I have people that could be in-person friends and do similar things. I was like, quality of life — I need to do this. And a few years later, I ended up building a bunch of models. And I think being at a nonprofit opened me to this ecosystem vacuum of information, where there aren’t many people who can talk about what they’re doing. So then, with some luck and committing to write every week, I just feel like my influence filled the vacuum of nobody saying reasonable things.And it is this nice synergy between what I write about and what I work on in my day job, and it just kind of got bigger and bigger in a very fun way. I think that, generally, at the highest level, I’m motivated by wanting AI to go well on this trajectory. And I worry about a lot of near-term things, whether it’s social unrest in the U.S. and just kind of the massive hatred for AI — I think is a very big near-term problem — and then, medium term, concentration of power, because I think AI will be super powerful in ways that people don’t expect. So generally, open models are a nice way to curb both of them by being a bit more transparent to people, and it naturally is a hedge against concentration of power. There have been different reasons throughout that, but that’s kind of a recurring theme in my life in the last few years.Grace Shao (02:50)Definitely. I love your work because I think you help non-technical people like myself really understand what’s behind what’s happening in these labs a lot better. And then I actually just spoke to your former colleague, Tiejin Wang, and he was with APAC Hugging Face just last week. He was saying the same thing. Open source, in many ways, is kind of the best way to go forward as we know that this technology will not stop evolving, but it’s the best way to kind of put up guardrails and checks and balances for the monopolies.Okay, I don’t want to take up too much time on that side of things today because our focus really is about your China trip. Before we get into the weeds of all that, I want to hear about the trip itself. Most people who are writing about Chinese AI are getting their information secondhand. You really went there, you spent time with the researchers, you met with people who are building the models. Tell us about what you meant when you said you came back with great humility, right? Your eyes are a bit more open, whether it’s the good or the bad. Tell us about your trip.Nathan Lambert (03:50)I feel like I kind of went in — I mean, I had this horrible English phrase in my writing, which was like, “I knew I knew nothing about China,” which kind of tried to indicate that I knew going into the trip that I knew nothing. And it was still the fact in my current writing. This is a horribly written sentence that I had in there. And I only talk about it because somebody called me out on it. It’s like, what is this? And it’s like, leaving, which is knowing that it’s such a big country, there are just such vast amounts of talent working on these problems, and how unpredictable it is as a human to model people with very different worldviews and upbringings and training systems. Realistically, the way that people are trained in China is very different.And I just think that even being there, you can’t fully grasp: what are the pockets of three to six researchers doing that is actually a bit different than in the West, even if they’re working on the same goal? I think you could get down to that level of granularity and a sociological study and actually see differences in what they’re working on, and that’ll always change the output. I didn’t get to that level of granularity, but it’s just to start having real experiences and understanding how people explain how they work on these problems.And for me, realistically, a lot of it is coalition building, which is just like: I want there to not be vitriol at the level of the technical companies doing things in international bodies. So just meeting all the labs on both sides is really nice, because you need to do that for them to talk to you about more sensitive issues in the future. I got some criticism on the piece, which is like, this is how you shouldn’t visit China. And it’s like, well, what are you going to do if you’re going on an official visit to a bunch of companies? How do you expect to get in the door without being nice? You have to start somewhere, and I think it’s important to be respectful.Grace Shao (05:31)I think the piece was, frankly — I don’t think the criticism was fair, to be honest, because I think you were really transparent with the fact that you’re not a China person, right? It’s not like you’re going there and exoticizing everything. And if anything, a lot of people, even with China backgrounds, like to use certain dragons and tigers to describe things. I feel like you actually were really humble going and being like, I’m just a technical dude meeting with these labs, talking about their technical research, right? And then because you were physically there, you had observations of the culture and the people. So yeah, I actually thought your piece was quite good. And yeah, sorry.Nathan Lambert (06:05)I agree. I was willing to let that sail past, but I think it’s important for people who listen to realize how actively these companies are trying to court Western audiences, which is why we could get in the door. I mean, we had some prominent people on this trip, but that’s why we got all of them in the days that we wanted them, except for DeepSeek. So essentially some, like Catherine Rintel, who works with me at Interconnects, and some other creators...Grace Shao (06:23)How did you get everyone? Yeah, how did you get everyone?Nathan Lambert (06:29)He used to live in China and has connections in China. So he kind of orchestrated the mix of his connections and leveraging my connections to labs. We had some bigger names on the trip as well. Just stringing all of these together to get all the various labs in place is a few months of networking to make sure the trip lines up with people with established networks and contacts with the various labs. But these people want to look good to Western audiences, so they’re only going to say yes to the right researchers.And the researchers know that there are two to four comms/ops people in the room, hanging out, making sure that it goes well. Especially the bigger the company, the more comms people. You go to Alibaba and there are three to five various people, from the head of comms to some special offices. You’re not going to get these people in the office, or at all, without accepting the cost of these types of handlers. It’s the same thing in the U.S. You’re not going to just plop a senior executive into a chair.So it’s also good because now I have the WeChats of a bunch of researchers from China that I could just text about things. It’s like, hey, congrats on the new model release. It’s like Lay Lee works at Xiaomi, Xiaomi MiMo. It’s like, talk to this guy for an hour at a mall — I don’t remember the name of the tea store — but it’s like...Grace Shao (07:27)No, of course. No, of course.Nathan Lambert (07:49)Now we have these relationships, which is very useful, and that helps information spread across the ecosystem to these trusted parties, which doesn’t really exist. There are not that many, I think. And the opposite direction of the trip is very hard because Chinese researchers can’t really enter the U.S.; the visa purgatory is too complicated. A lot of us on the trip were either Canadian or entered on a transit-without-visa entry, which makes it very easy for American technical talent to go to China right now, which is why I think there are so many trips. I think there’ll be more of them.We’ve got a lot of inbound from VCs and open-source labs in the U.S. that want to establish collaborations with these various labs because they’re the best open-weight models, and they want to build a stack for companies in the U.S. building open-weight models. So I think there are going to be more prominent, but not gigantic, U.S. startups going to try to build these relationships, which I think is a really interesting technological development because we’ve never seen this type of professional work trip in China from U.S. tech companies. Most tech companies have a “bring a device to China, it auto-bricks itself, and you have to hand it into IT.” So to actually proactively send people in a professional capacity is a really big change. There are a lot of angles you could take this, and I think it’s cool to see how it unfolds. This isn’t even really about the trip. This is the follow-on that we’re hearing from people that are like, hey, how’d you do this? We want to do this trip.Grace Shao (09:06)Yeah, definitely. Actually, from my end, I hear about VCs or investors always being quite active going to China because previously American funds were very, very active during the internet era. People were kind of always trying to find a way to either get into these good deals or potentially keep their pulse on it. But I think it’s really, really positive for the whole AI ecosystem to have this kind of fair, transparent exchange in some capacity. But to your point, there’s no way that star researchers can come out and talk to you off the record without any compliance, because that doesn’t happen in the U.S. either. That’s just companies protecting themselves.I just think your trip was quite meaningful, and I want to bring it back to your observations. You talked a lot about the cultural aspects of it. You talked about how you felt like in China there was less of this star-researcher celebrity status around people. People were more humble, or there was more humility. It was very focused on execution. You argue that Chinese labs are particularly well suited to the current LM-building game because they’re very focused on meticulous stack-level work. And there’s less ego sometimes to work on the dirty work, or the non-sexy work. So kind of unpack that for us. Why do you think that is? You kind of touched on it — you said they were brought up differently, they were taught differently — but what’s so different?Nathan Lambert (10:27)So essentially, an interesting part that synergizes on this trip is that we stopped by some academic institutions. I think it was like AIR and Tsinghua and stuff. And you hear all of these academic leaders talk about how they’re pushing hard to try to change it. So yes, they know China is producing more papers than anyone else, but they still think that it’s not as transformative of research. And they think that they’re trying to cultivate the academic domestic ecosystem to change just the type of work it works on, and the distribution, and take more risk.And then you would talk to some industry leaders off the record behind closed doors, and you would hear things like, it’s never going to change because the education system is so structured. There are so many layers of the funnel that reward things like memorization and stuff that they’re just like, this research culture is not going to emerge. And then the follow-on with the AI labs is that these labs are doing fast-following. They kind of have a proof of concept, and they know what it needs to look like. Therefore, in that domain, you’re not trying to invent the new paradigm. You’re not trying to make the model that is o1 or o3, or the first model to work in Claude Code. You’re like, I see it, and I’m going to try to do that and make it the best thing. And I’m going to try to make it cheaper and just maximize that goal.A lot of companies don’t need to invent the new paradigm. OpenAI has done this so many times. That’s their bread and butter: never doubt OpenAI’s ability to release a blog post and a plot that changes how people think about AI. I still think it’s going to happen a few times in this massive boom over the next four years. OpenAI just kind of has that sense of what is the thing that you can push on a bit earlier and just transform things. But I don’t expect — and other people wouldn’t expect — the Chinese companies to do that as much, because it’s just such a culture of, I guess, building. I don’t know how to describe the positive version of this. Maybe it’s slightly more practical-minded, in terms of: it’s your job to build this thing.A lot of the researchers, maybe because they knew their managers — some of them had managers in the room — see their role in the company as being to make the models excellent. And especially for students, I work with students and that’s what they say. I work at the Allen Institute and we have students that will co-lead our language models. It’s not that surprising, because if you do an industry research job in the U.S., a lot of mentors will tell you that you’re kind of free of the burden of bureaucracy and politics. So the naivety of students, and the simplifying, is actually so good at just getting a lot of technical work done.There’s also the life-stage side. If you’re younger, you don’t have as much family, and you normally haven’t built up as many habits and other things you do with your life. Language models are so complex, and the amount of context that you need to absorb to understand what the bottleneck is — there’s so much information, and you have to be able to pick what the bottleneck is and break it. If you just don’t have the mental space to absorb all the context, you kind of end up doing things that are cute but don’t make breakthroughs on the model.So that’s kind of a difference that I’ve seen in people who were both very successful academically before language models. Some of them are able to pivot to this practical mind, which is: what is the state of the system? How do I improve it? And then some try to make kind of these abstract frames of what’s happening and approach it like an academic, and it normally doesn’t improve the model as much. So I just kind of see, if the academic system is a bit more practical-minded, a bit more structured, and the work you’re doing is structured in the language model — make this kernel implementation faster, make this idea work — then maybe it can be...I think it’s an oversimplification. I push on that a bit in the piece just to really contrast what you could think a U.S. lab would look like. And I have a few anecdotes. I’ve heard a U.S. lab paying off a researcher to be quiet about their thing not being in the model. All of these one-off things are more storytelling devices than anything, because most one-off things don’t matter at all. But also Llama 4 imploded, and that was because it was described as a Game-of-Thrones political-style environment, with all the VPs vying for influence and showing that their thing made the benchmarks go up. It kind of fell. Many, many people will tell you that. And we’ve had the Qwen turnover, but it doesn’t seem like it was quite the same type of thing as Llama 4 or xAI. xAI barely exists now. There have been some dramatic things in the U.S. with how these companies have kind of come and gone out of the fold.Grace Shao (14:55)Yeah, I kind of agree with you, but also I would push back on that. I think there’s obviously a more rigid and competitive academic system, which by default in East Asia results in a culture of students following the bureaucracy and authority a bit more. So I agree with you in the sense that they’re very pragmatic. They focus on the task that is given to them. However, I wonder if things will change with how AI will disrupt education. That’s number one. But also, a lot of the young researchers that you’re working with today seem quite different. At least a lot of the entrepreneurs I meet today are born in the ‘80s and ‘90s, some even younger and born in the 2000s. And I think there’s a kind of aura or confidence coming from them. If anything, you want to say they’re a bit more individualistic-minded. You went to Shanghai, right? They are dressed very, very uniquely. They have these outrageous outfits on the streets. People are seeking individual ways to showcase their personality. So I wonder if that will shift.But for sure, for the academic institutions like the Tsinghua and the Beida of the world, they are still very old-school. But I would say that is the same maybe in some academic institutions in the West still. Okay, I think on this topic we can go off on a tangent on academics, but let’s go back to China’s ecosystem.When DeepSeek V4 came out, we talked about it offline, the two of us, quickly about a piece I wrote saying how DeepSeek is starting to look a bit more like a base layer for China. And if anything, some of the labs kind of admitted to that. They’re like, we have very limited resources. And to your point earlier...Nathan Lambert (16:11)Yeah, you could take that in so many tangents.Grace Shao (16:34)Limited people — these labs are tiny. They’re run by 100 to 200 people max. Limited capital, obviously limited compute. They have constraints all around. And in that sense, in a way, the ecosystem’s looking less like a zero-sum game and more like different players optimizing their own strengths. So correct me if I’m wrong, but DeepSeek is providing a base layer where a lot of labs will quickly follow and basically adopt a lot of their engineering breakthroughs. And then Zhipu, Z.ai, will focus on the coding; MiniMax focusing on the multimodality, et cetera. There are a lot of these different players. ByteDance, obviously, very, very focused on their video models. And Qwen, like you mentioned, had the whole open-source saga break apart with Lin Junyang leaving. But in general, they’re still kind of the leader in hyperscalers on that front. So everyone’s doing their own thing almost, instead of really...Nathan Lambert (17:27)I agree with the people specializing, which I think is normal business evolution. You figure out a bit where you’re good at. And there’s so much opportunity that they are like, okay, I’ll follow this because they see that they’re good at it. I just am more skeptical of DeepSeek as a base because I have no idea what DeepSeek is doing. And some of the labs when we were there, because DeepSeek V4 had just come out, were like, yeah, we look at the things they’re doing, but they seem more intricate than needed. And if you read the paper, there’s just so much going on in this model. As a researcher, I’m like, some of it seems a little fake or a little dependent on their setup and not necessarily going to work in every model.Grace Shao (18:04)What does that mean? Break it down for me.Nathan Lambert (18:18)Essentially, I will say that building an LLM is dependent on where you have your GPUs, your pre-training dataset, your intended deployment setup, and stuff like this. So you make decisions based on your constraints, and you build the model. DeepSeek has these constraints and they end up with their model, but Moonshot and Zhipu have different constraints, maybe more flexibility, and they ended up building a different model. They will test the DeepSeek innovations. So they’ll say things like, X innovation doesn’t improve our model. These two organizations are on different development paths that have core similarities, like these large mixture-of-experts models and the general methods are similar, but a lot of the parts end up being a bit different.That’s why I’m like, I don’t know exactly. If DeepSeek was a base, you would see the Chinese labs just do post-training. We just take the base model that’s out there and we adapt it to our domain of specialty. And we have users that do that, which is something that I think about a lot. I’m thinking about starting a post-training lab and how to format post-training research better. So I think about this a lot. I think about what a shared base actually would be. They go through — some of these labs put an extreme cost on creating their base model. And if they didn’t need to do that, they wouldn’t.One of the labs told us how long their pre-training run was, and my jaw dropped. I was like, that’s way too long. Any U.S. advisor would be like, you’re taking way too much risk on this pre-training run. If they didn’t land that pre-training run from one of these past big MoEs at a Chinese lab, I don’t know if the company’s dead, but that’s a huge amount of time. Most U.S. companies now know that you don’t want your big pre-training run to be more than a few months because it’s just so much risk and time to put all your eggs in that basket.That’s a sign that, in that case, they don’t have as big of a peak-size cluster. Essentially, pre-training time can come down a lot when you have a bigger overall cluster; you can just get more throughput on it. But if your biggest cluster is smaller, it’s harder to get a certain amount of throughput, so you use that one for longer. That’s a compute constraint. To loop it back, I think the specialization is real, but I’m more like, I have no idea what DeepSeek is doing. I know they’re raising money now. I don’t know what the plan is there. They seem the most without a specialty in the Chinese ecosystem.Grace Shao (19:59)Dependency on. Yeah.Mm-hmm.No one knows, though. No one knows. They’re secretive.But that’s my point, right? I feel like they’ve been kind of nationalized, whether willingly or not, because they’re taking the Chinese government’s money. They’ve kind of gone secretive. And it’s not like there’s a secret that they prefer Chinese-educated researchers. They’re keeping a very domestic stack, from talent to capital to the whole stack. So to me, it seems like they’re being Huawei’d, in some ways, because they did well and they got their name globally, and then by default they’re becoming the next Huawei, willingly or not.Nathan Lambert (21:01)I don’t think nationalization makes you a base for the other companies, at least not at this stage. There could be something, but it’s hard to force.Grace Shao (21:06)But then you have some incentive, right? But then it is some incentive. You’re like, well, if you can propel one of the teams and propel the whole industry as a whole, it could be in your KPI or some kind of unspoken expectation.Nathan Lambert (21:17)The coordination problem is so hard. Essentially, both in the U.S. and China, even the open labs, what they do is they fork open-source code and match it to their internals, and every company does this. Therefore, all the improvements that could potentially be going to the open code and forming this base that is far more efficient — they’re not completing the feedback loop. I think China could be closer to it. If people really lean into DeepSeek as a standard architecture and DeepSeek shared their training code and all the specifics and how to do this, from a Chinese economic perspective, that would be a huge win because you’re just saving compute. But I think it’s too decentralized and too competitive to have that happen. It wouldn’t happen in the U.S. either.Grace Shao (22:04)It’s so cutthroat. Yeah.Nathan Lambert (22:08)Even though I think for open models to be closer to the frontier, it would be better. I talk about open models in the U.S. needing a consortium. But there’s definitely enough money to make a consortium in the U.S.; then you fail because the model won’t be good because you’re feeding too many asks into the model. That’s the only way to create a shared base.Grace Shao (22:25)Interesting. So it’s not really just commercial. Yeah. It’s not the commercial reason.Okay. So if you had to give a high-level commentary on each of the major labs, what would it be? If you look at ByteDance, Alibaba, Tencent Hunyuan, if they’re relevant, DeepSeek, Moonshot, Zhipu, MiniMax, Meituan, Xiaomi now being part of the ecosystem too.Nathan Lambert (22:46)You might have to prompt it or say more, but I could just kind of ramble through them, which is kind of fun. Alibaba: cloud-focused, understands that open models can enable more usage of platform. So I would say Alibaba is very, very cloud-focused. ByteDance: mostly characterized by everybody else being intimidated by them, and very user-focused, including multimodal. Kimi: vibes of the office were great. It would be one of the best startup vibes that you would visit among U.S. or China. Zhipu: very AGI-pilled, surprisingly cautiously excited about being entity-listed, even though they have no idea why they are, because they’re like, it stamps them as a big deal. And then there’s some...Grace Shao (23:27)I think they previously worked with SOEs. That’s the main reason. Or they still do, but that was one of their main sources of income. And unfortunately, because a lot of these labs spun out of Tsinghua, and Tsinghua is, for people’s context, in Beijing. It’s really close to the government, obviously. But the thing is, when it’s close to the government, it could mean there are three layers of agency underneath the actual government apparatus. But then people like to link it to the fact that it’s taking government money, so therefore they are suspicious. It’s very unfortunate, I think. A lot of companies get thrown into that category. Even companies like Lenovo and a few other Chinese companies have previously been called out by U.S. senators saying, they’re taking Chinese government money, but really it’s that their scientists or their research labs spun out of a certain government-affiliated or government-funded academic institution. That’s what it is. Anyway, yes, go on.Nathan Lambert (24:23)Yeah. Some more would be: Xiaomi — surprisingly great research vibes for a new team at a random company. They seem to be crushing it.Grace Shao (24:31)What do you think of Luo Fuli? The star researcher.Nathan Lambert (24:31)I didn’t get to meet her. I think she’s as close as they have to a star researcher right now. There’s the tier of star CEO, which there are obviously others — Dario and Sam, the analogies are there — but the star researchers, like the Sholtos of the world in the U.S., obviously you can come up with many more. She’s the closest you have to this. I need to watch more interviews. We’ll see. But she wasn’t in our meeting.But they just seem to be doing the right thing. They’re making general models. They don’t really have specialization yet. Florian, the person who helps me write about open models on Interconnects, and I took a detour to go see Meituan because we’re like, why is Meituan building these models? And they’re very practical about it. It was a less glamorous visit at a normal tech office. It wasn’t an official visit for them. They were like, yeah, we’re a major online platform. We obviously are going to use LLMs everywhere once we need to build our own LLM and specialize it to our products, which, surprise, is very practical-minded. I’m guessing there are many more companies in China like this.Grace Shao (25:39)That’s what Tencent’s saying too. It’s because they want to serve their existing consumers and optimize their LLMs for their own distribution and their own basic interface or activity loop.Nathan Lambert (25:52)Yeah. After I left, some people in the group went to Xiaohongshu, like RedNote, and they’re there. They’ve released some language models that are multimodal. They’re like multimodal data-processing things. So a lot of them are not that surprising. The startups just have different cultures. I have met some MiniMax people before, so I left the trip early before MiniMax on this one. But MiniMax was quirky. They have a ton of women in their company, which was very fun. And they have products. They’re maybe slightly more product-focused, but I feel like the quirkiness of the company kind of matches maybe Western confusion over what their products are doing and what they’re trying to do. But it kind of matches their language models that are a bit more efficient.Grace Shao (26:35)Well, they came out with a lot of very consumer-focused applications, right? They had Hailuo and Talkie, all these character companion-bot products before.Nathan Lambert (26:45)Yeah. And then the last one I went to was Ant Ling, which is also very corporate, but in a less intense way, because I think they see it as serving their own products, whereas Alibaba Cloud is like, this is the gold mine we have to win. It’s a much bigger deal for them than Ant Group. But a lot of these things, when you list them — I don’t know, eight to 10 companies — they’re all pretty reasonable with respect to the age of the company and what the company does best. There’s not as much confusion.Grace Shao (27:14)Yeah. And Ant is low-key best at medical chatbots right now, which I guess makes sense because everyone has access to Alipay. And then for seniors, apart from WeChat, it might be the only application they’re using on a regular basis. So it became the default medical consultation app, which is really random, but it’s their niche now. Yeah, I think you’re pretty spot-on. It’s pretty cool that you got those takeaways, even just meeting with them for a couple hours.Nathan Lambert (27:41)I have been reading about them for so long, so a lot of these priors are easy to confirm when they kind of fit with things you have seen. The Chinese showroom culture is so interesting, and also one of the most surprising things to have at software companies. It’s so funny. They’re definitely appealing to Western audiences. Z.ai had poorly translated merch. What was it? Something so — it would be borderline inappropriate translation in the U.S. It was like “ship big, go hard,” or something. Just some really weird translations. And they have live API statistics in their showroom. So Z.ai was like, we’re serving 5.5 trillion tokens a day. All the U.S. companies are so closely watched for when they announce token statistics.I know at least one of these numbers is wrong. It’s something like Fireworks does either 30 or 300 trillion tokens a day — or I meant Together for that one — and then one of Fireworks or Together, and the other one, are like 100 trillion tokens a day. Don’t take these as sourced; go look them up. There were some public announcements recently, but those were the first updates that anyone has on major infra companies in the U.S. Inference is a huge market. You don’t hear anything from Fireworks because they’re just struggling to demand and they’re making bank, because inference is a much better thing to sell than bare metal.Essentially, inference is selling the software implementation to serve tokens more efficiently, and you can just get more margin when you improve the stack for a fixed model. So a model comes out and you host it, and then you can make your stack more and more efficient on that model. You just get more margin and hopefully growing usage. That’s way different than GPUs, where the best case is that you lock in a huge commitment for a long term.Just being able to walk into an office and learn about their API is interesting because they also had geographic distribution, which was like: China was, I don’t know, two-thirds; U.S.A., 20%; and then the last percent was Singapore, Korea, Japan on the Z.ai API. So that’s cool. This is s**t that I always want to know about the companies, and I have no idea. One of the things I always want to know is: how are open models being used outside of the U.S. and China, and has this decades-long process of technological diffusion started to kick in in a way that any company can measure? I don’t think anyone has good data on it yet, but I think it’s obvious that at some point, open models that are cheap to run are going to have some interesting playbook across the globe for the long tail of countries. Maybe I’ll just walk into the front door of a Chinese open-weight company and get my answer.Grace Shao (30:30)But actually, I think the culture of these labs — a lot of them, because they’re run by really young, passionate people — you would feel like they’re a lot less commercialized or less corporate, or at least less sleek. They’re not sophisticated with, you can say, the capital-market side of things, but you can also say that they’re just really naive and open-minded and passionate about the product they’re working on, with less of a corporate guardrail built around them.Nathan Lambert (30:56)Yeah.Grace Shao (30:57)Okay, I want to talk about...Nathan Lambert (30:57)Yeah, go ahead. It’s like one of the people at Z.ai who’s known on X — I don’t know, 9,000 followers — it’s like Lu. She came up and was like, hi, I’m a student, I’m 20. I’m Lu from X. And I was like, that’s hilarious. There was a lot of s**t like that. It was like, oh, okay. I don’t want to call her a kid, but it’s like...Grace Shao (31:06)Yeah, yeah, yeah. And I think the one that runs Moonshot’s developer ecosystem or something is literally a girl fresh out of school, right? And she just posts hilarious memes all day long. There’s no filter on her social media. It’s funny.Okay, we go on these tangents, Nathan. We need to come back on track. Open source, open weight. Why? Why do you think Chinese labs are adopting it or embracing it, however you want to put it, especially after visiting them? Is it because they simply have to, because of what we talked about — they are leaning on each other because of all the constraints they have? Or do you think the philosophical drive is actually bigger in that ecosystem? Or is this a bigger strategic thinking for diffusion in the long run?Nathan Lambert (31:54)I actually don’t feel like it’s that special ideologically. I think it’s easy to say the ideological line when you are doing it. Now you can look at Zuckerberg: he said the ideological line when he was doing it, and then he stopped. I think it’s mostly just that, for one, distributing within the U.S. ecosystem, especially to enterprises, is the highest-value market, and they can’t sign many enterprise deals. And the closest best thing is things like Cursor adopting Kimi’s model. Even if Kimi doesn’t get paid for that, they’re happy. That’s the biggest sign of credibility for them, and they can figure it out in selling tokens or whatever in the future.Practically speaking, one, the only way to influence the U.S. market is by releasing these models. And two, it seems like they don’t feel like they’re losing as much if they release and share things. If the model was closed, they just think they would get less influence, they would be seen less, fewer people would use the model, their actual paid offerings would be adopted less. It just seems almost overwhelmingly obvious, because there are all these benefits and not as obvious of a drawback. There will always be better models, and just keep going. But I think every scientist loves...Grace Shao (33:08)Then why are so many U.S. labs against it, or not willing to?Nathan Lambert (33:12)Because they can make as much money without it. Anthropic and OpenAI make more money by not releasing them. They can just make so much money, so why bother thinking about an open model that doesn’t make money? There are different scales of influence. Same with Google. Google’s making so much money. I think Meta will make a lot of money by having good AI models in their products, if they get their act together. Even Google could release more models. They have so many surfaces other than Gemini that need AI to be commoditized and used, like the cloud and all of this. Meta could release the models. It’s just not worth the effort for some of them. They’re like, we need to do this high revenue target; it’s too much of a pain to go through legal and make it ready to release. Why bother?I don’t know, maybe it’s a little bit of a cynical take, but I think Microsoft and Meta could release their best models openly because they benefit if it’s a commodity layer. But I don’t expect them to, because it’s just kind of like the benefits of focus are so high, and they just kind of see it as something they don’t have to do.Grace Shao (33:56)And it’ll be good for them. But then eventually, we will see some consolidation in the market as well, assuming — because you can’t really have 10 labs in each dominant country right now all exist.Nathan Lambert (34:26)I do expect consolidation. I think this is potentially a subtle cultural point, which is that the U.S. labs are more likely to buy into “we’re special, we need to go fast, keep it closed,” and the Chinese labs are not. There could be something there. That’s also who the decisions funnel up to. I don’t know. I talked to the Alibaba people that make these decisions. I can’t say all the things that they say about them. Some of these were two-on-one and off the record, so I can’t say all these things. But at all the other labs, there is a person that makes the call, I’m guessing. I think those are senior leadership that we’re not talking to. So it’s kind of hard to know exactly what they really think.I definitely expect consolidation. My thing is that I expected it in China faster because the capital markets aren’t as strong as in the U.S., but I don’t have a model for that. I think you can model it, which is: what do you think the revenue growth would be? What do they need to do to raise to keep training bigger models? What is the compute cost? Then you look at the potential raises and think about which country would not be able to do that race first. But also, it’s this wild thing with OpenAI raising $120 billion. Are you kidding me? What is that?Grace Shao (35:47)Yeah, the valuations in the U.S. are not really understandable by anyone else right now. I think in China — so on your point on that, I’ve been writing about this and I think it would make sense for Tencent just to buy out one of the labs. They have the money, they need the capabilities, and frankly, they’ve really been struggling to compete with their LLMs, with all the labs talked about just now. So my...Nathan Lambert (36:05)Their licenses are so bad. They release all these models that have horrible licenses. They’re not that good, and the licenses are just horrible.Grace Shao (36:13)So I feel like it financially makes sense for a company like that to optimize and just buy out a lab. Then the labs can also lean on their distribution, because at the end of the day, how are they going to win consumer mindshare or distribution in China right now when it’s really just dominated by Alibaba, ByteDance, and Tencent? That’s my spiel. But when I spoke to some of the researchers...Nathan Lambert (36:33)I think big companies have a lot of inertia, and the senior leadership has the call, and they can have inertia. I still think Apple just ends up buying some lab for $25 to $50 billion. It’s not the worst thing. Just golden-handcuff the researchers. Some will still quit.Grace Shao (36:43)Yeah. But I think right now they don’t want to. The labs still have a dream. Some of the researchers still have a dream. So when I spoke to a lot of them, they’re like, no, we don’t want to do that. We want to commit to our own frontier research. If I wanted to join one of the big tech companies, I could have. So why would I want to sell? That’s what the researchers think. But to your point, we don’t know what actually the one person or two people at the very top think, especially if they continue to have hurdles with compute access and capital access, which brings me to the question.Nathan Lambert (37:14)It also depends on your view of inference. You can ask your next question. I don’t need to cut you off. It depends on your view of inference. If these agents are just so much inference, I do think it’s going to be an oligopoly-style market, not a monopoly-style market. And what’s the difference financially between two and four or five big companies with great models? Is that actually not sustainable if there’s so much demand? There are a lot of cases where we have two or three, like the cloud, but what’s stopping that from being four?Grace Shao (37:40)I think they will be the infrastructure providers. Yeah, yeah. And they would kind of lean into each of their existing ecosystems or distribution, whatever you want to call it, and serve certain specific models for specific uses. So enterprises can choose what matches their needs the best as well.I do want to bring the conversation to a more contentious topic, which is on distillation and model convergence. You raise the question of whether Chinese models are structurally different. Often we are hearing claims saying a lot of these labs are about three to six months or six to nine months behind U.S. labs. There’s obviously a lot of noise or allegations and accusations from certain U.S. labs saying Chinese labs are distilling them. How do you actually see that accusation or that kind of dynamic?Nathan Lambert (38:36)The biggest unknown that I don’t have an answer to, which actually has a lot of sway, is how much of the Chinese companies are actively trying to hack APIs versus just showing up as a customer and paying. If you’re trying to hack the APIs, normally you get reasoning traces out so that you can create a reasoning foundation that would be similar to the model that you’re trying to do this from. That’s very different than the API standard form, which is just the output of the model, which is a less direct process for learning from.I don’t know the magnitudes. If it’s more just like, I walk up to an Anthropic API and I use it as intended, but I’m making a competitive model, I’m not very sympathetic to Anthropic. They could ban it if they want to. And I think the impacts are kind of a standard practice. You can do it with many different models and so on. The evidence Anthropic provided is not large enough scale where I’m like, this is industry IP theft at mass scale going on 24/7/365. So there’s definitely some gray area to what is actually happening in distillation.That’s why, on the policy side, I try to push people to not call all of it the same thing. Essentially, using any API endpoint to make synthetic data to train your model is some form of distillation, but it’s very different if you’re trying to break this model so that it gives us a different behavior that is hyper-useful for training and not get caught. Those are pretty different actions, and they’re all looped into this common phrase of “distillation” right now. That’s my biggest problem, which is that academic researchers and small companies use distillation extensively as the core of their business and the core of research methods. So if the U.S. government nukes that as a thing that could be done in the AI ecosystem, it’s mostly bad for small players, bad for U.S.-China tensions, and bad for academics. That’s my primary concern.And then trying to get the labs to actually say more. There’s a distillation side and then performance is the other side, which on benchmarks, it does seem like the Chinese labs tend to be six to nine months behind. When it comes to general use, I’ve always found the closed models to be better in ways that are hard to measure. So I go very back and forth on whether the closed models are better. I think we will especially see Anthropic and OpenAI pull ahead on knowledge-work tasks like legal, healthcare, financial services, because I just don’t see the Chinese labs paying for that data. All that data is going to be people that charge hundreds of dollars an hour to annotate and create these environments. So it’s a whole new capital build-out that goes on there right now. It’s going to be billions of dollars if you’re going to buy a billion dollars of data and a billion dollars of compute and a billion dollars of talent to train your model.Grace Shao (41:30)They don’t have the money.Nathan Lambert (41:30)I don’t think they have that. Mercor has some of these evals, and I think there is a bigger gap there. So it’s very interesting. Florian, the guy that helps me, and I disagree on it. It’s this fine line between, yes, the evals — coding and lots of these things, and even random evals that surely the Chinese labs aren’t training on — the open models really are genuinely crazy impressive scores. So I think there’s also a tester’s bias, where I don’t use the open models as much. Maybe it’s hard to ground in my head what I was doing with AI six to nine months ago. I wasn’t even using Claude Code as extensively.I guess the question is, at the end of this year, can I use an open model in something like Claude Code and feel like it works at all? That’s the test on the performance gap, starting in June, June to August, and whether or not that hits. I don’t think the open models have hit that yet. I think it would be way more of a narrative if all the companies spending billions of dollars on Claude are like, oh, we can spend 1% and just use DeepSeek. These CIOs and all the big companies — some companies spend more on tokens for their employees than on headcount. These are normally startups. But they would happily reduce that token cost to 1% expenditure if it really was that similar, because then you could just use 10x the tokens. I don’t expect that to happen. And I expect things like the latest Claude and GPT-5.5. I expect more of these things through the year, and we’ll see if I end up being right. Both are right at the middle of us, as a world, getting more clarity on them. They’re like 18-month-long stories unfolding, and I feel like we’re just in the middle of performance gap and distillation and learning more.Grace Shao (43:25)Yeah, it’s interesting. You mentioned — it helped me recall a conversation I had with other people as well. The point on distillation is that I just had a conversation with your former colleague at Hugging Face, who leads APAC, called Tiejin Wang. He was just saying, look, the distillation accusations don’t really make sense because we’re all distilling off of each other as we speak. I’m learning from you; you learn from me. We’re distilling. It’s so vague of a terminology to just use that to accuse all these various behaviors.So to your point, I think people in the technical world who understand what’s happening actually want more clarity on what is the gray area, what is actually black and white, and what is not appropriate or unethical. That needs, I think, the industry to come together to really put guardrails and rules around.Now, number two on the compute side and the data side. Something anecdotally will be interesting to you is that when I spoke to one of the lab researchers in Beijing, I think in February around Chinese New Year, they were saying, look, they want to get better data, but they can’t because usually a lot of American labs would pay tens of millions, if not even more, like a hundred million dollars, for a set of very obscure or niche datasets, but they would have an exclusivity contract. What the Chinese labs will do is that they will literally wait out the exclusivity contract and then, say two or three months later, pay for it at one-tenth or one-twentieth of the price for that same dataset. So then once they start post-training on that dataset, that’s where the three to six months or six to nine months come in as well. Yeah. On that note, I want to...Nathan Lambert (45:00)Yeah. I think the data industry in the U.S. has two things. One, the lab asks the data vendor, we need this specific type of data. And the data vendor is a network that connects the people to the lab. The other thing is the data vendors know evals that are important, so they try to create good data for hill-climbing on specific evals. That data could be sold to multiple people, but is less expensive because they make it once and expect to eat margin or take margin on it. There could be a pipeline where once OpenAI is at the cutting edge, creates this new thing, they create deep research, then the data industry is like, let’s make things that are a little bit cheaper to sell. So there is time lag in these various things.But I heard the same thing on the ground, where they have a negative view of the data industry. It’s like, quality is bad, we don’t really have access, we do some in-house. That’s a very big difference from today, which is that you have the data companies in the U.S., which is insane.Grace Shao (45:53)Yeah, the American data companies are so mature. It’s its own sophisticated ecosystem.Before we get into data, I actually want to ask you this question. I think recently a lot of the narrative is now saying, look, Anthropic and OpenAI have kind of proven that pre-training scaling laws continue to hold, especially with the recent models. There’s an obvious compute constraint on the China side that we talked about. And then it will likely be even more amplified with the absence of Blackwells in the coming months.So as we move forward in this race, per se, if you have to put it in China versus U.S. in that sense, will we see a wider gap between the performance and benchmarks between the Chinese labs and U.S. labs? As in, will we see the gap going to 12 months, 24 months, as Chinese labs are very, very constrained on compute for pre-training breakthroughs?Nathan Lambert (46:40)I think it’s more of pre-training as a thing that you could actually finish. How big can you pre-train a model that you can finish and serve? The Chinese labs could train models that look like GPT-4.5, which is this giant model, but you can’t serve it. They end up training a model that is 2.5 trillion parameters and they release it, and no one can use it. They could barely serve it on their API because they don’t have Blackwell NVL72 racks or something — these racks that are definitely what are serving these large MoE models. They just don’t have the quantity of these.So there’s a difference between models that you can build and models that are actually useful. I think some of the Chinese labs are definitely like, we don’t need to release the gigantic models because nobody is going to use them in open weight. The biggest models end up getting served via API. So there might be some segmentation in that market. But I do think the inference and amount of economic resources that you have to serve your customers is becoming a thing that dictates what models are built. That’s why I think the gap will continue to rise. All signs point to GPT-5.5 being a bigger model, and I don’t expect that to stop.And then the economics of it is just the basics of: you need a certain volume to have the margin to support the research, because you can’t keep raising these ridiculous rounds forever. I think OpenAI, Anthropic, and Google are the only people with that AI usage volume to keep marching down the scaling laws to another 10x of training compute, which is mind-boggling amounts of investment in a model. That’s why, when the economic markets slow for fundraising, the model gap between these big three will just show a lot more. That’s the distilled way to say my prediction of when things will look different. It’s like these labs can’t fundraise, they go public, they can’t generate revenue more on their paid services, and then it’s just: look at how much training compute can be allocated or can’t be allocated.Grace Shao (48:41)Yeah. Basically, we’ll see a bigger gap, I think, in the coming months. Then what can make up for that? Domestic chips, or, like you said, better data. And why is it that sometimes people assume China has a very strong data ecosystem or data products, but actually the data vendor ecosystem is very weak in China?Nathan Lambert (48:41)So generally, I think I agree with what you said. I don’t know on the data side, but the way domestic chips could help is that if Huawei chips are fine for inference, and if they have sufficient volume to support the inference economics, which then trickles back into revenue, my read is that they just don’t have the volume of the chips, especially spread out across the amount of companies that they have. Essentially, the total FLOPs of Huawei, all the things produced, and it’s going to all these different places — it’s just not big enough.It could be something like ByteDance and Alibaba, with offshore data centers, can keep up a lot longer because they have access to Nvidia compute and have for a long time through this kind of offshoring. Maybe that stabilizes the ecosystem, and we’ll see what the AI startup, the younger startups like Kimi and Z.ai, end up doing. No one wants to do this, but if they pool resources, they last an extra year. You get another order of magnitude if they all pool together, but I don’t see them doing it.Grace Shao (50:00)But that’s the thing we were just talking about, right? MiniMax and Zhipu, how can they possibly compete with the hyperscalers at this point if you need offshore data centers? And the fact that Zhipu is on the Entity List doesn’t help, right? It’s not going to be easy for them to access these data centers either.Nathan Lambert (50:12)Yeah, I think they can’t. I think they won’t. Human nature will make it so they won’t collaborate. They’ll just do something smaller. They’ll just have successful businesses that are different.Grace Shao (50:22)They just have smaller ambitions, want a smaller piece of the pie. Yeah.Okay, so you wrote something like, nothing’s a secret, but everyone wants Nvidia chips. They want it, they don’t know how to get it, they’re fighting over it.Nathan Lambert (50:34)Yeah. They’re the only thing that works for training. All the models are trained on Nvidia. I don’t believe the DeepSeek propaganda that it’s trained on Huawei. The only models that are trained on Huawei are tiny. Inference on Huawei works. Every lab is like, inference on Huawei works. The labs that don’t have meaningful inference are like, we are told to get Huawei, so we buy them, but we don’t use them. Earlier research labs are like, we don’t have any inference and we don’t have a need for Huawei. Any company that has meaningful use of their models has figured out how to run them on Huawei for inference, which, to Jensen’s credit, is like — it’s happening when he said it was going to happen, but it’s not that surprising.Grace Shao (51:11)Yeah, the Dwarkesh interview. I don’t actually understand why he got so much hate for it because even without your political stance, what he said actually made sense logically by saying, if you don’t sell them the crappier versions of what we have, they will have an equally quite crappy version to serve themselves, or they would just want...Nathan Lambert (51:28)I think they would buy both. Buying both is actually true. The amount of Nvidia chips that you would have to sell to China for them to stop buying Huawei — because Huawei is almost surely way cheaper because Nvidia margins are insane — when would they actually stop buying both?Grace Shao (51:43)But then you have to go on CANN. You have to reroute everything back on CANN. The developer ecosystem is not there. That’s Jensen’s point, right? Or the habits are not there. So I think that’s what, when I talked to a research lab...Nathan Lambert (51:50)Yeah. But I’m saying they would also use Huawei. I think they are so supply-limited, they would use both. Anthropic uses everything. A lot of companies in the U.S. will use multi-platform. Meta is a huge buyer of AMD. Demand is so high that any chip that is potentially viable on the models within a few generations is very valuable. And the fact that you can run some reasonably large model on any Huawei chip is a big line crossed for Huawei.I don’t know if they can produce the volume of chips and scale that quickly, especially as they try to move to lower nodes. That’s the standard semi debate. But the question is: can Huawei scale production? That’s the only question. And if Huawei can manage to scale production, Jensen will just look really right. If Huawei can’t scale production, Jensen will look a little bit like a lunatic, but it will be outside of his hands.Grace Shao (52:43)And we don’t really know what happened during this trip. It seemed like nothing really substantial happened after this big Trump delegation. It was more like a high-profile tourism trip versus an actual deal trip.Okay, I want to ask you something you wrote about that’s a bit niche, not something you usually write about. It’s on the SaaS side of things. You said that there’s a common argument that China struggled to monetize AI because they’re unwilling to pay for enterprise software. We looked at how China tries to monetize on consumer AI, but clearly that’s not really been proven yet. In your piece, you push back on the claim and say that there’s a distinction between SaaS spend and cloud or inference spend.Tell us about what you think about that ecosystem and how Chinese AI labs are trying to make money maybe a bit differently from American AI labs.Nathan Lambert (53:32)I don’t know if it’s necessarily different, but I ask a lot of researchers about this. They say that everybody is trying the new AI tools when they come out. If they don’t like them, they stop using them. If they like them, they keep using them on the consumer side. So something like Claude Code would be an example: tons of people tried it. I’m guessing lots of them churn in China, just like in the U.S., but consumers are very quick to adopt and try new things, but won’t stick if it’s not actually serving them.And then the enterprise is like: there’s definitely cloud that exists. Digital services are gigantic. They essentially think that there’s more runway for making money on AI models that falls into that. And they all use coding agents; they all use Claude. It’s a hilarious thing. They’re all very Claude-pilled. There’s almost no mention of Codex, where in the Western media, Claude versus Codex is this whole thing. They all use Claude. And that is obviously a paid service. So I think there are cracks in the argument, and I expect AI models to be seen as a bit of cloud, but potentially it is the thing that changes some of the expectations, where it’s just so transformative because they’re so competitive, and it could be seen as a bit of a phase shift.Grace Shao (54:41)Yeah, and I think it’s a generational shift, a phase shift. Also, actually, recently Doubao raised their prices on Seedance usage and whatnot, and it’s a shift into trying to capture the prosumer market. You can say the average uncle and auntie on the streets still don’t want to pay for a consumer app, but I think there’s more prosumer market share that could be captured in China, maybe not fully enterprise either.I want to ask you about government roles and geopolitics. I know there is a common narrative that usually people assume Chinese AI labs are heavily subsidized. Actually, when I was in San Fran in March, I was at a dinner with a couple of investors, mostly public investors, and one guy asked me, “Hey, are all labs just basically subsidized by the government?” I was like, definitely not. The majority of them are not. If not, they frankly don’t want to take money from the government.It was really hard for him to understand that, because I think the misconception is all Chinese labs or Chinese tech are just funded by the government. Kind of to our point earlier, where any affiliation to any government agency, just by default, is assumed to be therefore backed. First of all, the government, I don’t even know if they have that much money to give out. Number two, I don’t think that’s how competition works, right? So what’s your thought on all of this?Nathan Lambert (55:55)It seemed more like a provincial government trying to help the companies do stuff, which is like get offices, get talent. I don’t know what the provincial government can do. In Beijing, there’s Beijing Academy for AI or whatever, which is a real research institute that’s just funded by a certain neighborhood in Beijing. It was like, okay, the U.S. could do that. But much less of the Ant Group-style thing, which is government takes major ownership stake in an investment round and goes on. Maybe Kimi’s latest round, there were mentions of government-backed VCs, and I don’t know how that kind of intermediary works. So I still think it’s very indirect. And because the government system is so competitive across the different layers, each of those layers are competing to help the companies, but they don’t have piles of cash sitting around to buy GPUs.Grace Shao (56:44)No, they don’t. And they frankly don’t know what they’re doing half the time. This is an argument like what you said: Haidian District or Chaoyang District of Beijing will be funding an academy, and the academy will be in the effort to help AI go toward AGI. But the reality is they’re trying to follow this high-level KPI of being like, let’s make AI happen. All they want to do is write in their report and say, we funded something about AI, so we’ve hit our quota. I don’t think it’s as hands-on as people assume.Nathan Lambert (57:09)Yeah. If you read Breakneck — most U.S. tech people haven’t read Apple in China and Breakneck — and all you need to do is read these books and learn a little bit about the interface between tech and China and understand that they are also hyped about AI, and then you’ll understand that it’s a messy trickle-down process in the Chinese government. It would be very obvious if they were nationalizing a lab. It would be as obvious as if it was in the U.S. It has not happened.Grace Shao (57:18)Both great books.Yeah. So to close, what is the biggest disconnect between how the U.S. AI ecosystem right now thinks of China, Chinese AI, and what you saw on the ground? And what is something you think we didn’t touch on today that you want to share?Nathan Lambert (57:52)This is the thing that everybody asked me. They normally asked me the first thing when I got off the plane: what’s the big thing? And it’s like, I don’t think there’s anything that shocking. I think that many people just haven’t read basic books about how tech is interfaced with the government, and know these things, or hear narratives that are very geopolitical, which is targeting the top end of the government system and how that in the U.S. engages. And there’s a lot of shrapnel from that. Anthropic pushes very aggressive China narratives, and Anthropic is a very followed company in tech.Most people don’t spend the time on this in the U.S. ecosystem and just don’t go deep on it. I don’t have anything shocking. It’s good to encourage people to do some of that because these dynamics impact things like: the Chinese open models are really influential, and now Silicon Valley is building AI. So it matters to a lot of people, but they don’t study the causes of why they might do this. They just are like, it’s here, I don’t need to think about China.Grace Shao (59:00)Yeah, and I don’t know what it is. You and Bill Gurley were saying this in a couple of his public appearances. It seems like Chinese researchers, tech people, CEOs, whoever, are a lot more aware of or following more closely U.S. leaders and thought leaders, tech leaders, business leaders, than vice versa. There’s something about that. I don’t know if it’s just easier to dismiss it or easier to not have to learn something new. But the goal of AI...Nathan Lambert (1:00:27)I think it’s American culture. American culture is very obsessed with its own weird world. Yeah, it’s hilarious. American culture is ridiculous. It’s so ridiculous.Grace Shao (1:00:27)As a Canadian, I can’t say things like that. You said it. I’ve lived in the States, but I can’t say this. But I think, look, shameless self-plug here: as a Chinese Canadian, my life goal here with AI Pro is really just to bridge that gap. I think to your point, there’s going to be geopolitical narratives and rhetoric at the very top, but for the average person, or even for builders, tech people, whatnot, it’s probably in everyone’s benefit to understand what’s happening on the other side and stop alienating it or stop making it as if it’s so different.I think throughout this conversation, it’s really just to say, look, so much of it is so similar, but so much of it is slightly different. The difference is not really a government mandate versus maybe a cultural difference or resource-constraint difference, especially in building technology. But that’s kind of my view.One last question for you, which is a question I ask everyone on the show. What is one differentiated view you hold? Throw me something crazy.Nathan Lambert (1:01:28)I know, I’ve always kind of been open-models doomer, even though I build on them. It’s just that it’s so unsustainable, and there’s so much money to be made with building closed software, that I’m constantly doomy about the prospects of open models. I’m always a skeptic.Grace Shao (1:01:40)It is a bit sad, isn’t it? How does a company like Hugging Face actually make money?Nathan Lambert (1:01:45)I don’t know. You can look into how much money they actually make. It’s not very much, unfortunately.Grace Shao (1:01:48)Yeah, I think that’s the unfortunate reality of the capitalist world we live in. As much as it incentivizes the competition and breakthroughs, that doesn’t help with what we just talked about earlier. Yeah.All right, Nathan. Thank you so much for your time. Really appreciate your insights and your sharing.Nathan Lambert (1:02:04)Yeah, thanks for having me. 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  AI x education, a contentious but unavoidable future. Designing tech for children with Dex's Reni Cao 18.05.2026 1t

  I spoke with Reni Cao, the CEO and co-founder of Dex. Dex Camera is a language-learning camera for kids. Reni is a dad, a former product lead at YouTube, and on a mission to build technology that does good for kids and gives digital autonomy back to parents. We dive into his personal story from his high school days that drives his passion for AI, and why he believes the current education system is a “cookie-cutter” that fails curious kids.We get really into the nitty-gritty of what makes “good” tech versus “bad” tech for kids and why the category of ‘children-first tech’ is very overlooked. Reni explains why most children’s apps are built on an “attention economy” model that forces them to compete with addictive content, and why his team needed to build physical hardware to break that cycle.We tackle the hard questions, including the pushback from parents who believe in “no tech” childhoods. And he shared his most non-consensus view: that the era of standardized, industrial education is over. He believes we are entering a golden age of “scaled homeschooling” where AI meets kids where they are. Whether you’re a tech investor or an anxious parent, this conversation about nature versus nurture, “nei juan” (involution), and raising resilient humans in an AI world is a must-listen.Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently. Season two will host a series of guests from early-stage investing, as well as builders, founders, and product managers.For more information on the podcast series, see here.To find the previous episodes of Differentiated Understanding, see here.Chapters00:00 Reni’s Journey to Dex Camera03:48 Designing for Children: Principles and Insights08:05 Technology’s Impact on Child Development12:09 Bridging the Gap: Business and Product Design15:36 The Role of Parents in Tech Development25:20 Leveraging AI and Language Models29:48 Value-Driven Pricing Strategy32:05 Defining the Product Category34:33 Subscription Models and Content Delivery37:58 AI and Parenting: Balancing Technology and Safety43:29 Unexpected Use Cases and Impact47:29 Personalized Education and Parenting PhilosophyAI-generated TranscriptGrace Shao (00:00)Reni let’s start with your personal story. Who are you and who are your team members? Because when I met you in SF, I was so enamored by the product and I thought your story was so interesting. So please share that.Reni Cao (00:11)Hi everyone, my name is Renny, CEO and co-founder of Dex. We’re a technology company in San Francisco, almost all parent company, which is pretty special in a startup setting. We’re a bunch of parents that having trouble with the same kind of like a reality where like our education system is a sort of like cookie cutter and our entertainment is also cookie cutter for children.So we’re like, can we harness technology, especially the latest development of the AI, in different way for families that really gives children a chance to become the best version of themselves and ⁓ give the digital autonomy back to parents themselves rather than accepting the fact that they have to struggle between technology versus no technology. So yeah, we’re the parents, of like a bunch of missionaries in this journey together to explore how can we make the best use.of the AI and our first product is called Dexta Language Learning Camera where kids can take pictures and turn the whole world into language immersions. And it’s a product targeting young children three to eight. And we’ve sold 10,000 pieces so far and ⁓ ratings has been high and we’re pretty excited about this. But yeah, this is pretty much about us.Grace Shao (01:24)But Reni, tell us a bit about what you did before Dex actually. What kind of led you to this path? I know becoming a parent really did inspire you. You have a young daughter, I think similar age to mine, around three years old. But before that, what really led you to this path? Were you always passionate about children’s tech or education?Reni Cao (01:41)I actually have been a product management guy for the last decade in Silicon Valley, some big companies like YouTube and LinkedIn, some smaller s***, ZFS, Wish. But I have been a builder since the beginning. I would actually say that my passion for decks actually originated much earlier than I started my career. It actually started right when I was at school, but happy to say more if you’re interested.Grace Shao (02:07)Yeah, no, do tell us a personal story there.Reni Cao (02:09)So I was always this random kid with tons of questions back in high school. And very unfortunately, I think the education system, especially in East Asian countries, is not designed for meet kids where they are. So every time when I come up with a random question, my teachers are usually a little bit impatient and will be like, can you just go back and finish your quiz, et cetera, et cetera.So the moment I saw when GPT-4 comes out, I was thrilled and I posted a long like blurb on LinkedIn. Basically saying like, you know, if I had, have this as a kid, I would have grown into a more complete human. So this kind of like, I feel like this like generative AI’s capability to meet kids where they are, especially meets your needs for curiosity. It’s game changing. So.I feel like I’m building this product first and foremost for a younger me that could have benefited so much from this. That’s pretty much the story about me. yeah, I know we see it and of course our parents right now we see there is a tectonic shift in terms of the skill landscape and what the future of workforce is going to be and even the existential challenge of what does human mean in a future society.So we do want to build something that’s centered around children, centered around the family to help them find what they love and build agencies around it at the end of the day. So yeah, that’s the two main driving force of me coming to Dex. But I would be honest about it. It’s like very random. When I want to start a company, a lot of my colleagues are very surprised, being like, oh my god, Renny, you’re getting into this field. But yeah, I guess I finally find the work of my life.Grace Shao (03:48)I love it. think you need to understand the passion and the personal reason behind the businesses to really understand why the design was frankly so intuitive and why you’re so passionate about building this and leaving such a comfy, know, like cushy corporate role. I think that’s the one thing that stuck out to me. The product itself is actually so natural to how children behave to your point, like my three year old.from morning to night, know, morning she wakes up, it’s like, mommy, what’s this? What’s this? What’s this? What’s this? How do say this? Why do you know that? Sometimes she gets angry at me. If I don’t know something, she’d be like, but you’re an adult, you should know everything. But the reality, especially with languages, it’s really difficult. So for example, yesterday she was coming back from her Mandarin class and she said, liu shu, she was pointing at random tree. And I was like, that’s not liu shu. All I know is not liu shu, but I actually don’t know what liu shu is in English because I think it’s only really common in mainland.I’ve never seen that kind of tree. Well, I guess it’s a willow tree. You don’t see it very commonly elsewhere. And then she kept on pointing at trees, but in Hong Kong, you clearly don’t have liu shu because Hong Kong is like tropical. And then she got really, really mad at me. And that moment I was like, wow, if we had a Dex camera, that would have been perfect. But I was literally trying to take a picture of it while we’re moving car and try to upload it to GBTB, like what tree is this? What’s the name of it? So anyway, I think it’s really great product design. And I want to kind of get into that a little bit.When you were designing it, what was the thinking? Like, what does it mean to be children first?Reni Cao (05:10)I think there are three layers of children first as a principle. The first layer we already touched upon that. So young children, their hand anxiety is very different from adults.they tend to use one hand to operate a device and another hand they want to use for sensory explorations, like they want to touch. Sometimes they want to just move things around. So this requires a different form factor that one handed use, very tactile, very intuitive for young children such that they can explore a world while harnessing the power of AI in this case. So this is kind of like the user, the special things about the user.And it’s a different design. think that’s layer number one. I think the layer number two is also that the device itself is a metaphor for the market as well. And in the market, we want to build something that’s drastically from the so-called adult-centric smart devices, namely the phones and tablets, to send the market a message that there could be a different option. There could be a good technology. There could be a family-centric technology. And we’ve picked this form factorutilizing the metaphor of magnifying glass. It is something you use to see some hidden wonders, otherwise you cannot see. I do think that’s the ⁓ second layer of the things, which is like metaphor and category creation. And at the end of the day, I do think we intentionally make the device kind of worth finding this fine balance between engagement andlearning or kind of like a healthy aspect of the technology, meaning like we add a assistive screen, but we make it really kind of like limited and not the center of the whole kind of like a user journey. And we want to kind of like find a new way to put all the components in our consumer electronics world in a way that it strikes a more delicate balance and ⁓ let the device itself to be kind of like, you know, retentive.for children without getting them to be addicted. So it’s kind of like we intentionally make it a little less stimulating, actually much less stimulating than a lot of a thought-centric ⁓ product. So that’s the main three kind of like principles around the product design. There’s a lot of conflicting constraints here, as you can see, but we do our best trying to find what is the answer. And here we go. Like what you see right now is our first, you know,⁓ answer we have thought through and I think the market validated the answer quite well so far.Grace Shao (07:39)Yeah,definitely. think exactly to your point, know, like a lot of times, I think when we as young parents looking at introducing technology to children, really worried about the big screens, addictive nature, or even the parental, even though a lot of them allow parental control, it’s the unlimited access to a wild, wild internet out there. Like all of these things are basically concerns and or reasons why we hold back technology from our kids.So actually on that note, do think you kind of mentioned it, right? Like technology over the years, especially big tech frankly, has garnered a bit of a bad reputation. And I think that was really tied to the rise of social media and all of this mental illness that came with it. And obviously like you mentioned the addictive nature. So what do you think is actually harmful to the children’s development when we are looking at tech? What are areas actually we can really embrace technology?I think you kind of touched on it lightly, maybe explain it to us in an even deeper, more technical way.Reni Cao (08:37)Yeah, our thesis is that why a lot of parents think technology is negative for a good reason. And the reason is that all the main status quo technology for children are built on top of the attention economy, as we call it. Everything revolves around time spent and how much attention, how much engagement in terms of like a minute, seconds, sessions you can get.That, is the reality because, think about it, you build an app on an iPad, immediately you’re entering a competition with Roblox, with YouTube Kids, with all the videos, all sorts of things out there. You could do well. You can try to do good for the society, for the families, but you’re effectively competing against more like...addictive kind of like a form factor of information and it’s a losing battle and as we call it is a rat race. So no matter what type of like educational apps or content you’re trying to deliver at the end of day you have to deliver them in more and more engaging way more and more gamified and more and more animation used etc etc. That’s I think that’s why it’s another reason why we need hardware at the end of day. I think the first stephow we can create alternative reality is that we need to create a new world, a new kingdom where the business is built upon outcome rather than attention. Meaning like it’s not the time spent logic anymore. It’s like, can you use this device? For example, for Dex, you can use the device and you see the child speaks better after two months or your kid starts to have a like a love to speak Mandarin and not preserve the rest of their childhood.I do think there is a business model there like that, but I believe that business model warrant a totally kind of like a different design of the experience from ground up, from the device layer to the software, to the content, all the way to like user interaction. So I do think like that’s why the current technology is considered bad because it raised towards attention. And I think ultimately, inside Dex,I believe the final answer to create that alternates like a reality is can we deliver something that’s purpose built for children before we build a general sort of like, you know, like time spent logic, like a product in, in, in the case of the decks, is something that, you know, purpose built around the languages. cannot do a lot of things. It cannot, it’s not a chatbot. It cannot, it cannot play videos.But I do think even do one thing super well with the Frontier technology already delivers so much value to the families such that you can build a viable business model on top of that while creating values for families. I think being courageous enough to limit our scope to something to begin with, like really hold onto our principle, deliver a promise, create values there.is another internally operating principle to get there in terms of how to harness the technology. And I want to say that it’s very interesting. What we noticed that is a lot of people are trying to use the AI in quite an all-in-one way. So you can see a little device with tons of features in there. can generate pictures. You can talk to celebrities at chatbot. You can talk to Elon Musk on that device. And we think, actually, that would be a very slippery slope.⁓ in terms of harnessing the technology at the end of the day. yeah, purpose-built is another very critical principle we’re holding on to, to create a good technology.Grace Shao (12:09)No, I love that. But I mean, from a business perspective, sometimes people might not have purpose built businesses, right? Unfortunately, some are not. Then thus, how do we basically help the industry align the business incentive to the product design incentive? Because, know, like what you’re saying right now, it makes a lot of sense. And I think once I saw Dex Camera, I was like, wow, why is there not something like this on the market?but it does feel like there’s a huge gap where, like you said, there is a big devices and the big tech. There’s this tiny niche little products, whether software product or hardware for children’s ⁓ use, but it doesn’t feel like people are taking it seriously, even though we all know parents are willing to spend on children if it’s for their good. It’s not like the economics doesn’t make sense. So why is there’s that gap right now?Reni Cao (12:56)I think you’re hitting on one of our most recent realization that the parenting needs and the children’s needs are quite long tail or as we call it, very like a versatile, right? Different parents have different parenting needs. Even when you look at the language as example, there are tons of different languages and even more dialects you wanna learn. Like let’s say you wanna learn Mandarin, you still got so many like a dialects there. There hasn’t been a real...kind of like technology that can enable a venture scale business that attracts talent, that attracts a good backing in terms of like a capital to build something that’s like a generational. But I do think this is the moment AI is strong. We finally have to make sure we can build one system.that can consolidate all those long tailed needs. Even for Dex, very specifically, you can learn a lot of languages and even more dialects with just like a nine person team building the hardware plus software. I think it’s the catalyst that’s much bigger than Dex itself. And I’m really excited about that. But I think another very interesting angle is like, despite the technologies there, you have another question. It’s like why there is notmore company like Dex. I have a personal opinion here. When new technology comes out, people will tend to use it in the most sloppiest way possible. They were trying to just like, OK, you can chat with the AI, so why don’t we just shovel AI into a little box and put it into a Talking Fluffy and call it an AI toy. And that’s it. That’s my business. I do think it is like a gravity that’s pulling people away.from deeply think how to harness technology and pulling them towards something that’s so trivial and it’s just almost like a shortcut. I think that’s kind of like also, I would call that a trap on the entrepreneur side, that the technology is changing so fast and everyone’s a full mowing, everyone just wanna use it in some way. But I think in this sense, we as Dex, the company, we believe in that.we need to think very deep about how should we use this technology to meet users where they are and deploy like AI in certain ways so Shell can deliver the value. So that’s why we start small, but we’re going to expand from there.Grace Shao (15:09)Yeah.No, it makes a lot of sense, but I think I wonder if you guys all being parents like you just said have made a huge difference. I hate to overgeneralize, but like, you I’ve been in the tech space for 10 years, but usually either I meet men who are like 20 years older than me or they’re very young men who have not, you know, settled into a family yet. And I’m just saying when I tell people my mom, it scares people. They’re like, I don’t know what to say. I’m like, OK, like.I’m not trying to scare you off by telling my mother, but the reality is most of us one day will all have families. And when we do, we start thinking about the things around us very differently, our perspectives shift. And I think to your point when you guys had a lot of purpose designing this product, I wonder if it has a lot of, you know, reason because you guys are parents. Whereas if someone is an entrepreneur for the sake of being a business person, they might not have the nuanced understanding of what a kid needs and what they even think is good for a kid.So to your point, they create little stuffed animals with an L-I unplugged into it, which is horrendously scary. I would never introduce that to my kid, right? I’m getting very, very agitated about this. But you know, another one that we talked about kind of offline was like, I should be ambassador and be paid by Tony Box at this point, because I probably gifted at least like 20 of them out to friends with kids. I think they’re just like, on the surface, you think about it, they’re like, ⁓ a little box that plays music. You’re like, this is so easy. I can just use my iPhone.Reni Cao (16:13)Me neither.Grace Shao (16:32)to exactly to your point. It gives the kids agency, allows the kids to start navigating the world themselves and have preferences. For context for people who don’t have Tony boxes or kids at this point is you put these little miniature IPs, essentially they’re Disney or whatnot, and you can put them on the little box as a magnet. And then the box starts singing and has like seven or eight pre-programmed music or ⁓ stories. And then you can control with your little hands. And basically like you press theReni Cao (16:54)stories.Grace Shao (16:58)big ear, the ear just like the volume goes up, small ear, the volume goes down. It’s like really, really great. So basically introduce technology to kids where they’re like, oh mom, I can control what I want to listen to today. But I don’t need to nag you about it to control the iPhone. I don’t get exposed to a screen. And I can sit there and be entertained for like half an hour myself. So I think Dext really falls into that category for me. Like, you know, we kind of skip the part where we explain how your technology work really and in a very day to day way.It’s basically like you hold a camera, you point at things, you click the button, you can say, what is this? And you default choose languages, right? You actually explain better than me, please.Reni Cao (17:35)So there are actually four questions here. So I want to actually react to all of them one by one. I think this is a lot of good insights here. I think Tony Box and Dex share one thing in common, which is they are children-led, or they are child-led in this case. Think in the POV of a child. The world is kind of like a scary place that you’re told to do this or that.you are brought to here or there, there’s not much quote unquote autonomy you could have. But now there’s a device that your parents actually are willing to let you operate and you can decide what type of content media or interactions you can get. That is just a huge reward to children’s like unlimited curiosity and their like a strong needs to be considered sort of like, you know, a big kid or aeven grown up in a way. I think that’s the intricate magic that if you were not a parent, you haven’t interacted with children a lot, you will miss. So instead of saying like a parent’s made us a better product builder, I think at the end of the day, it goes back to the product 101 that you really need to know your user. You really need to know who are using your product. We spent such a long time with our kids every day.And early days, which is very funny, like ⁓ the first group of users using DAX is just our own children. And that gives us a huge edge there. Right. And I do think you mentioned that a lot of like startup founders in this category, sometimes they’re doing something with raised eyebrows of the parents. I do think they’re a little bit distant from the kids is one reason. And another reason is I do think there is a misconception that children are less.at the end of the day, lot of founders think, you know, those are toys or some gimmicky stuff. Kids, you know, you just give them something that can flash, they can make some sound, and children would love to use them. But I reject that answer. I think that assumption is completely wrong. Children are actually smarter than adults in certain ways. They just cannot verbalize it. But as I said, they already got their little taste.as the famous word, popular words, they got their taste and they sometimes can tell what’s a soulful piece of story versus it’s a very sloppy kind of story. So children actually knows that and they want quality experience, they want quality product, they can actually absorb something that’s really built well for them. I think that just gives us kind of like this endless.sort of motivation to polish our product as if we’re building this for the most critical sets of adult users because we think actually children are more and they deserve more. Now, coming back to how Dex works at the end of the day, I think the core loop of Dex is quite simple. You just take the little camera. I’m happy to actually send a video to be the bureau here. You just take a picture.⁓ And they would just literally just tell you, let me actually take a selfie here. Hi. Let’s see what I can learn about this. Look at that big smile. It’s like spreading happiness everywhere. Can you say a smile?Just smile.smile.Yeah. This is like you get unlimited, like smile comes with some laughing too. It’s when you make happy sounds like, ha. Can you say laughing? Laughing.like the ones we use to listen to music. Do you like music too? Can you say headphones?This is actually English immersive mode. So you can, you can improve your vocabulary there.Grace Shao (21:06)how many languages you have now.Reni Cao (21:08)We have 16 languages and more than 30 dialects and it’s still expanding. And interesting observation here is like the smaller, the more niche the languages is, the stronger the demand is there, which we find is super interesting.Grace Shao (21:21)probably just harder to find offline solutions otherwise, right? Or like harder with the communities, assuming you’re an SF, finding a Mandarin community is not that difficult. You know, if you’re in England, finding a French community, probably not as difficult. if you go, you were saying like maybe like Arabic languages like that are not as mainstream, maybe in San Fran, you have people in San Fran wanting to do that, right? Or like people in Dallas last time you said, trying to learn Mandarin, which again, you don’t have a huge community. Very interesting.I’m sorry, I got very passionate about the topic. So I want to of swerve back to our conversation here about raising children with technology. I’m sure you get pushback. think people right now, there’s the other side of argument where everything should be organic. Everything should be very simple.Reni Cao (21:53)Yeah, of course.Grace Shao (22:08)And I myself, I’m a big fan of a lot of the Montessori toys. You know, they’re not buttons or not even power charged. They’re just little wooden blocks, but they’re designed very well for them to, you know, develop motor skills. So how do you kind of explain to parents today who are saying technology should be rejected in the childhood. Kids should just be reading physical books. should learn the way that we learned or even like previous generation learned. We should go back to touching grass only. SoLike, yeah, what’s your argument there?Reni Cao (22:37)First of all, you are completely right. Every once in a while, we got a comment on our social media that, why don’t you talk to your own daughter to teach that language? Why do you need a device to do that? So your assumption is completely right. And my response to that is, first of all, actually, I respect that parent a lot. I believe in the most ideal world, organic human-to-human interaction and free play in the real world is great. There’s a lot of tech, like researchers actuallyProve that right, right? However, I do think the parent miss out constraints here. Number one, you may want to talk to your daughter, but you don’t know Cantonese, for example. So there’s no way for you to teach some subjects or some skills that you want them to learn or you want to immerse them with. And second, all of us know that the contemporary society is more and more fast paced. Not all the parents enjoy this privilege.of saying, let’s slow down, set up a dedicated time for children to go out to places. All sorts of this ideal family style back in the 80s and 90s changed a lot, I would say. So we are, believe, rather than just blaming the parents, not spending enough organic time with their children, I do believe that technology should be introduced more as an option, as kind of like a gap stop.as one of the extra tools on the table. That’s why when we design decks, we don’t introduce chatbots, but we spend so much time on sharing the insights that what your children are interested in. What did they take a picture of? What do they want to geek on? What did they learn today towards the parent app? And just give them this little window to see the world through their children’s eyes. Give them good downtime topic.giving them a way to reconnect even as asynchronous. So I do think the concern is real and the overall kind of like, you know, judgment is very well reasoned. But I think what that’s the approach here is much more nuanced than saying like, let’s use technology to replace human. It’s not, it’s actually using technology to connect the humans, connect the parents and kids better. That’s the nuance I have to take a bit.Grace Shao (24:42)I see what youYeah. No, no, I love it because actually I’ve seen some parents even give kids like little Kodak cameras these days and these little toddlers go around the world, take pictures of how they see the world and they’re so cute. My own daughter sometimes takes my phone and takes pictures around the home and I come back with a lot of selfies and pictures of her sister’s foot or it’s just very cute because you see the world through their eyes, right? And it gives like, it’s like technology doesn’t take all connection away.on technology. wanted to ask you about the technology. How do we understand that? Like how are you actually leveraging LLMs? How do you route through different LLMs or different languages? Is this something we talked about briefly? But I wanted to understand that bit more.Reni Cao (25:20)to share details. Where should we start?Grace Shao (25:22)Like how does it work? right now? So basically for the little Dex camera, can’t ask it, like he’s to your point, you didn’t build a chatbot. So I can’t ask a question. I can’t have a conversation. It’s not a companion, but I can ask it what’s this? How does all that work in terms of the back end technology and the guardrails you built up?Reni Cao (25:38)Yeah, I thinkin a 30K feed view, Dex are utilizing basically all the multimodal LM capabilities to understand what the children are looking at. And on top of that, we build sort of like a profile, interest profile for the children and the parenting need profile for the parents to help contextualize, you what responses should we give in that case? To give an example, if you’re a three year old,just starting to learn Cantonese and you are sort of like interested in a bunch of like a museum topics or you love like dinosaur skeletons and stuff like that, we will render you more challenges around kind of like hey let’s bring Dex to a museum and learn about different terms there and it will be English the primary languages teaching entry-level Cantonese things there. So basically like the visual understanding you certainly use like a multimodal LLMThe response definitely use kind of a conversation API of a lot of like an ALM. And I think building out this context layer or this memory layer of like a children’s interest and parenting needs, that actually is more complex. That takes kind of like a full agent system to try to understand what matters, like condensing or distill insights into a profile and gradually kind of injecting that into our responses. I think that’s on a very high level. That’s it.We do use a wide range of LLM, mostly with Gemini and OpenAI. yeah, that’s kind of like the high levels.Grace Shao (27:08)I’m going ask a question you might not like, but I’m going to put you on the spot. When we talked last time, said specifically on Cantonese and Mandarin, you do use different LMS, but the accents can be quite funny. Like they’re a bit off. They’re not native sounding. Why is that? And how do you overcome something like that? Or other maybe non-English languages. Yeah.Reni Cao (27:12)No, ask me.First of all, you need to try again because we have a solution already. But definitely, hit. We are already squeezing. I’m so hard that we’re hitting the boundary of a lot of like, in this case, it’s a TTS of the leading providers. Because I think about it, I’m pretty sure you’re using English plus Cantonese. It’s basically using English to learn Cantonese. Is that the case?Grace Shao (27:50)Yes.Reni Cao (27:51)is a mixture of languages cases. The challenge there is that without fine tuning, there is very limited sample of someone that speaks very good English and very good Cantonese, and they mix them in like one sentences. So the data, the training data to begin with is a little flawed. Either you have accent English or Cantonese as the more common cases. That’s the fundamental root causes of this. And we’re having kind of like heavy lifting tasks to kind of like solve that.And with the foundational model getting better and better, think one day we’ll get there. And we can see that to be fully fleshed out in the next six months. You definitely hold us accountable. And I think this is right observation for mixed languages. It’s really hard. Yeah.Grace Shao (28:32)Yeah, I bet. how does it actually work right now? Like in terms of economics, like people pay you about $249, right? That’s the price of the product pre-tax. That’s not cheap. Like it’s much more expensive than a toy, but obviously bit cheaper than an iPad. How do I understand the pricing decision there and price? And then how does that relate to, I guess, how you pay for your token usage right now? Does that cover it?Reni Cao (28:57)Yeah.Yeah. Oh, big time. We actually have a pretty healthy margin and the tokens are getting incredibly cheap. Much cheaper than where we started. I’m talking about like in 96, 97. It were a fraction of the token cost of where compared to when we just getting started, which is back in 2024 February. At that time we don’t even have GBD4, we have GBD3.5. that’s the kind of like, that’s the kind of like, actually that time we have GBD4 butis we don’t have GPT-4.0. So it’s very expensive at that time. So now pricing. Actually, I have a let’s talk about the user-centric view and a business-centric view. On the user side, we’re actually adopting this value-based pricing model, which is like any enough day, language is a high value skill to acquire. I sent my daughter to a language immersion in the US. I’m very embarrassed to mention how much I spent on that school.Grace Shao (29:33)Okay.Reni Cao (29:49)And if DAX can offer 1 % lift or enhancement on top of that school, the price is fully adjusted and much more than that. So this is what I mean by like, and very funny that you mentioned toy, right? Toy is something that you get it, you play it for a couple of days, then you don’t see it, you don’t worry about it. And this is not what we’re trying to do. What we’re trying to do is we want to use a relatively high price to keep ourself honest aboutthe value we’re delivering to the parent. Do we really teach a language or do we really get the kids to fall in love speaking that language? If we do so, that price is well-justed. If not, we’re going to give you 90 days of free return period. No question asked, just return it to us. I do want to use this pricing model to push us to deliver more value for the user. So that’s one aspect of it. And on the business side, very funny, you mentioned, I hate when people box us.into toy category. I don’t blame them. Natural reaction, but I want to send a signal to the market that if a team of talented people, hardworking parents, put their heart and soul in building a purpose-built device that harnesses AI and delivers concrete results, we could get out from the typical, stereotypical, like a toy average order value band and go much higher. Above that, it’s less about, I to keep myis more kind of like, want to send a signal to prove that the market, we have enough parents waiting anxiously for something similar to this and want to pay a perceptually higher price for it, a premium for it. But yeah, that’s kind of like we landed on that price. And it’s so funny that so many people in the early days tell us, you’re going to do $1.99, because anything that started with a oneGrace Shao (31:22)Premium, yes.Reni Cao (31:36)is night and day different than like two, that it started with two. But I actually, I’m like launching a suicidal mission. was like, let’s actually make it start with two, but let’s deliver more value there because it’s never like, it’s not a retail business at the end of the day. We’re trying to create a new paradigm of digital parenthood and childhood. We need to hold a high bar for ourselves. And the price is very telling, like in that case.Grace Shao (31:59)No, I actually agreeand I think would you categorize yourself in the same box as Tony box vertical? Would you?Reni Cao (32:06)Not really. ⁓ Tony Box is a, I would say they are a content business. they are, same thing with Yoto. Actually, their founders have deep backgrounds in labels, music labels specifically, and IPs. So they are effectively a distribution business that they are creating a new channel to distributing those IPs from Disney, from Spin Master, and et cetera, et cetera. And the other side, you can see that at Dex, we’re notGrace Shao (32:19)I see.Reni Cao (32:31)I think like IP partnership or putting characters on our device. And we actually optimize for value and outcomes, like I promised to you in one of our principle. So I would put ourselves in, I don’t know, the de facto smart device for families. Just very honestly, the family device, the family technology, maybe like this is where we’re trying to go to, but it’s a completely like non-existent category before we’re still exploring.Grace Shao (32:33)Yeah.Yeah, yeah.Okay, like family tech device.Reni Cao (32:59)and it may change how I call it.Grace Shao (33:00)think there’s some moresimilar things maybe in East Asia because the audio learning like you know even when I was very young like I remember my grandma had a 步步高步伏机 I don’t know if you know what that is it’s like those like tiny little yeah yeah basically what it is it’s like people learn English with it and I think it’s very very like mainstream in China for a while but like you know these things been around I think in East Asia because everyone is using it to literally learn EnglishReni Cao (33:11)The steps are fine.Grace Shao (33:24)But it’s very one dimensional. It’s like one language to one language. They basically embed a dictionary, make the dictionary into a digital one. And you can ask search questions. You can ask what this word is. might, more advanced one might be even like with images, but I think, I don’t know, in the 90s, I didn’t see any images. But yeah, it does remind me of that technology and that vertical. haven’t seen something like that too mainstream in the West growing up, you know?I think if I was when I was learning French and German growing up, that would have been so helpful to your point. But yeah, so I want to bring it back to sorry, I just want to bring it back to the the business. On the Tony box comment, I do believe their business actually could be really high margin because their product is only say like 199 or something like that, right? Like they’re the box. But each character is not a 20 bucks or 30 bucks.⁓ My daughter is drying me up here because every two months she asks for a new figure. But my point is, it’s a great business, right? Like that thing just keeps selling. It’s like Spotify and a physical thing. So would you guys have add-on any services, software, hardware, anything?Reni Cao (34:32)We do.That’s a lot of investor has been pushing us regarding this razor razor blade business model. I think for us though, what we are ultimately delivering is a business more like an app store.It’s like where you can get personalized content and software for your parenting needs and for your children’s growth needs at the end of the day.We’re launching, not we’re launching, we launched two tiers of subscription so far to validate that. One tier, $10 per month, you got unlimited LTE, plus you actually got a curriculum packed in like a content library. Every day we give you one topic and in the topic you can explore a lot of new vocabulary, expression, know, new languages and it’s good kind of like content to consume. And I think what’s most interesting is our future vision is actually a $20 per month tier.In that tier, you can actually create activities for your children, personalize. Grace, can be like, I run this podcast. I’m a podcast host. How do I explain that to my kid and make it a little bit fun, exciting, and even adventurous as if the recording a podcast is a little journey? And by the way,my kid likes this way of storytelling. You could give a lot of like a prompt there. They’re actually based on the profile, the context layer, we’re gonna build sort of like interactive, like a content that involves taking pictures, speaking, and just like looking at the device for explaining what does podcasting mean. And this tier actually got really good like attraction. And when we look at their subscription retention,is above like 90 % in three months that shows early signs of product market fit. But this is what I mean by like our business setting of day. We are a channel to deliver like harnessed intelligence to parents such that they can build whatever content and software that adapt to their needs rather than just a purely search, then filter or control kind of like a timer. I really want the digital world to revolve around them, running out of way around. So in this case,Put it in a simple way, we give them a tool to build whatever they want, and we charge on the usage of the tool, pretty much.Grace Shao (36:41)No, I actually really see that. I love it. Because I think my husband was trying to use chat GPT for a while to create stories with my daughter. Like, add a pig, add a dog, add a whatever in this. And obviously, it’s not made naturally for this. So the stories don’t come out as, I guess, natively understandable for children. So I see where this can go. And the funny thing, you use my profession as an example.Reni Cao (36:49)Exactly.Grace Shao (37:05)example, like my daughter just thinks I talk all day, that’s my job, and she thinks that her dad sits at a computer and press buttons all day. So between the two of us, none of us are doing it much, just talking and pressing buttons. So it’d be really great if, you know, I can, I guess, lean on technology to find a better way to explain to children modern day careers, you know, that may be not as easy to explain as, know, mommy’s a doctor, and doctors go help people and save lives, which is like what my family has.you know, explained to us when we were growing up, you it was very clear. I want to kind of go on a little bit more about AI and parenting. I think there’s a huge discourse right now in the US, especially, I think from my point of view, where I sit in Hong Kong, in Asia, even yesterday, I was speaking to someone from South Korea, venture capitalist, they’re saying that parents and society seems to be a lot more open to bring technology into their day to day lives.They’re much more open to the idea of leaning into technology for personal use and less worried about privacy and you know these kind of issues I guess. So at a high level, what do you think, should we be concerned when we introduce technology to children? will they, you know, for example, taking pictures themselves that automatically goes into one of the LLMs. Is that something that...he should be mindful of or are there guardrails that can be built in?Reni Cao (38:26)We should be definitely mindful. That’s why we enforce ZDR, zero data retention across our stack for images. So even let’s say your kid take a picture of themselves, you cannot retrieve that picture even you want. You can ping me through my personal email. You cannot find that picture anymore. And OpenAI and Google signed a contract with us to burn a picture immediately, like zero data retention on all the usages. But overall, I do thinkGrace Shao (38:48)See.Reni Cao (38:51)It’s the company’s responsibility to introduce technologies to family and the family should hold a high bar there for sure. Because like the AI is so early and it’s way too powerful in certain way. And it’s like a kind of like a black box in certain way in a lot of different ways. that I definitely, I’m not a, I’m not that one of the technologies that wanted to like, you know, glorify AI and it is the future and stuff like that. comes with a lot of risk, especially like unproven.aspect how it impacts the children’s cognitive development and something like that. That’s also a reason why we work with researchers and professors ⁓ closely like in Mount Eucalon from UCSF and Harvard professors doing education and doing research using text. I do think there is a substantial risk here such that theAnd we as the entrepreneurs and we as the parents, we need to hold a high bar for ourselves and roll out things one by one. So I guess that’s why you will hear more about like, oh, that’s like, you could have done this. You could have made it more engaging. You will hear this much more often than you’d be like, oh, there is like an incident because, you know, we always prioritize, you know, safety first. We’d rather the device to be boring in certain way rather than introducing consequences that we don’t understand.So I think there’s a very interesting dynamic between the Western and Eastern in terms of their views about technology. And I don’t think it’s a family parenting only. It’s also even a whole society, general perceptions. Happy to chat about that, but maybe it’s a little bit off topic here. Yeah.Grace Shao (40:12)comes from the mindful design as well.⁓ No,we can definitely talk about that a little bit, but I kind of just follow up on what you just said. So how should a parent evaluate an AI device or tech device when they are purchasing for children, right? ⁓ I’m sure there are different devices out there, maybe not exactly doing the same thing as what you’re doing, but other devices are tech native ⁓ or AI enabled for children. How should parents kind of go about this?Reni Cao (40:52)I’m not a parenting coach. I will share my views. Number one, do think we should bias, we should start from our needs first. Maybe let me put it this way. Don’t get carried away with all the possibilities of the AI. Ask yourself, what is the unresolved parenting needs you have and find solution there. Rather than, this AIX, then that’s just to buy that AI device and give it a try. That’s number one, I would adopt that.Number two, I do think it’s important to see what a company’s method is. They definitely put their methodology somewhere, their belief somewhere, their principles somewhere, they’re kind of like, like how you ask me about how we use ALM. I believe that the parents should definitely hold the company accountable to explain those details and ask, verify, and that’s crucial step. That’s the due diligence on them, right? And I do think thatFinally, for any sort of AI product, I actually even think the parents doesn’t have to be getting into this searching and validating mental model. They could literally build their own in some sense. Given all the agent codings rising up and reducing the piece cost of software so low, I do think for lot of stuff, they should try.to accommodate their own parenting needs in certain ways. Like I saw tons of the parents go into cloud code generating like a, know, almost like a story writer for their daughter. That’s actually my previous colleague at Wish. And it was awesome. It’s just different blanks to fill in. It’s kind of mad lips type of like a story. I do think the parents can change also their way that they are in the autonomy right now to build whatever they want to build.Having said, it’s still a little bit of kind of like a Silicon Valley bubble type of answer, because honestly, in the world, the adoption of a cloud code is probably less than 2 % or 1%, I’m pretty sure. But I do think I would encourage parents to use AI themselves and explore a boundary, it can do, what it can does well, what it doesn’t. So then, the kind of I make a decision from there.Grace Shao (42:45)Yeah, no, I I appreciate that. It’s a very like thoughtful answer because it’s not just like A or B. of the day, think it’s parenting itself is so personal. It’s on how your family dynamics work, how you prioritize your time, how you want to parent. So when you want to buy technology for your children or incorporate that into their lives, it’s also a personal decision. I wanted to ask, actually, do you have any good case studies to share with us just a little bit?Reni Cao (43:11)We have quite a lot. What aspect, what, what type of case does he want?Grace Shao (43:14)Just like, I don’t know, like things that unexpected people use. For me, I mean, by default, just assume, yeah, people use it in urban areas, right? But then I think when I met you, you said, actually a lot of people use them, you know, in unexpected places, like orders come through all over.Reni Cao (43:19)Alright, I’ll give you one.One of, I immediately think about one thing, one, almost like ⁓ close to 5 % of our users, they bought Dex to help with speech delay. That’s something we never anticipated, but those parents are very frustrated with all the, as we call it, sometimes autism tech or the speech therapy tech there. It’s not meeting their bar and they saw Dex, they’d be like, I would try everything right now for my kid. And surprisingly Dex helped them.and it makes them real happy. And you can find actually all those real reviews in our review sections. Quite a few family mentioned that their kids refuse to speak certain languages or just even English, but that’s kind of necessitate the language as a fun activities. And all of a sudden, the kids start to open up and speak more, and the parents are really happy about it. This same exact story happened with my co-founder, who is really worried about his, at that time, two-year-old young son having speech delay.But I want to disclose the name, but the song he actually first time spoken like coherent like ⁓ Chinese phrases using Dex and he caught it on a video. That was one of the most wholesome moment of our kind of like a user feedback in our channel. And right now we’re actually ⁓ volunteering to develop this special need mode. That’s kind of like, you know, customizing to special needs children.especially like April is the world kind of like autism awareness month. And yeah, we just want to do it. And we want to donate to Dextre researchers and speech therapists to help us do it. This is a totally kind of like a side quest, but it just like give us it gives us so much kind of like energy. You’re thinking about technology can be used in a way that’s like immensely helpful.Grace Shao (45:00)That’s amazing.Yeah, and something unexpected, right? Okay, I think I want to wrap up our conversation because I don’t take up too much of your time, but I do want to ask you one big macro question. With you working on whether you like to call it physical AI or not, essentially like a physical product hardware time software, how do we understand that trend going forward? Do you think AI will be essentially integrated, plugged in to more more hardware devices? What’s your view on that?Reni Cao (45:33)I do think there is a consensus that every wave of software technology revolution, there will be kind of like a device revolution following that. We are at the tipping point there. That’s like people starts to reimagine, where is this? this cloud? Is this the recording card? Maybe it should be a separate, like an ⁓ AI. Or this is a sort of like a little pendant that can kind of like ultimately listen to your life, help you organizing. I do think we’re at the ⁓the dawn of a next wave of hardware. But it’s less about we’re doing the hardware because of the, I do think this is a software or technology driven type of hardware revolution out there. I do anticipate that. I do at least what I’m 100 % sure is like smartphones are not designed for children. Tablets are not designed for children. Families deserve something built with their interest.their needs in the center of the spotlight. And I see that happening. And that’s why we started this company. And I bet there’s going to be tons more use cases there.Grace Shao (46:33)No, amazing. Thank you. I think ⁓ one last thing. Is there anything I missed or anything you would like to share with us?Reni Cao (46:39)By the way, I time. If you want to turn through all the questions, I’m happy to be here. I don’t have anything else after this meeting.Grace Shao (46:44)no, don’t worry. think it’s a lot of times like I use them as prompts. But you know, when we’re chatting, like we actually covered most of it, you know. ⁓ Yeah, is there anything you think we missed? But from my end, like I feel like I covered most of it. You know, we did technology, we talked about children, AI philosophy, talk a bit about your business model.Reni Cao (46:50)Yeah. Yeah.Yeah.I do think you would want to talk about. Yeah, go ahead. Go with one last one, and I have one for you. Yes, go ahead. Ask yours first.Grace Shao (47:04)I think one last one. You go.So I wantto ask you one last question, which is a question I ask every guest that comes on the show. What is one differentiated view you hold? I feel like your whole thesis around devices right now on the market are not made for children is already a differentiated view. But is there anything else you think that you hold that’s non-consensus?Reni Cao (47:29)Yes, with this view, I got beaten up so many times, but I still got to say it, right? I believe that education should not be cookie cutter. It should be highly personalized. So is entertainment. So is the parenting software. And we’re about to enter the golden age. Finally, this is becoming the reality. And let me say it this way. You look at a school in the US, how you tell the school is good or not, you look at one ratio. It’s called a teacher-student ratio.One teacher taking care of less, but why? Because then the teacher can accommodate, individualize the needs. I actually have a very radical view in terms of our education system is definitely lagging, significantly lagging against how our society evolves, how the technology evolves. It’s still a one size fit all and industrial way.to handle education, handle like, you know, testing, standard testing. It hasn’t really changed in the past couple of decades, but the world is a different place now. And I guess my view is like, it shouldn’t be that. The default shouldn’t be that. The default is like every kid should almost have their personalized tutor and the playmate that deeply understand them. Unfortunately, that’s impossible before, resources-wise. But I guess we need to strive to get there.as a race, as a humanity. Because each kids just come up, come with their own spark.that will miss out the window to make that spark their lifelong journey. But I’m not trying to attack on educators or school systems, something like that. I just feel like there needs to be more forces from the society, especially from the tech side, to help together build this alternative, enhanced of like a system that really delivers individualized education.Sometimes I use the word scaled homeschooling. And you cannot imagine how much people hate that. people are like, homeschooling, you’re taking away the social aspect of it. People are very constrained on the vocabulary of how they describe things. But I guess when I say homeschooling, it’s not about keeping the kids at school and hiring a teacher. And that specific process right now, I’m talking about really meet children where they are in terms of their growth, in terms of their needs, in terms of the skills they’re going to develop.I call that a differentiator, but maybe actually lot of people will share the same views. I’ll be happy to know who shared the same view and please join us in the journey. Follow us along.Grace Shao (49:49)I definitelythink that view is definitely, feel anecdotally a lot more prevalent in SF when I visit. I’ve met other people like yourself, other people in the tech space or, you know, investors who are embracing this idea of modern homeschooling. And they say the same thing. They’re like, we don’t like to use the word homeschooling because, it sounds like a bit more cultish, but it really isn’t right. Like it’s really focusing on individual ⁓ growth.I think it’s amazing because I also think it’s because Silicon Valley itself kind of harbors this kind of growth and mentality and that the fact that people can succeed without degrees, people can succeed by building different things, people can succeed in just being different and being themselves, but the best version of themselves have always been, I think, what drives a lot of people who want to go to Silicon Valley because it’s like in many ways, as a mayor, talk to see like the best version of my talk to see, right? I thinkin East Asia, even as I put my kid in school right now, I find people definitely a lot less like that minded. ⁓ I don’t know if it’s a cultural thing because like, know, for you, you know, I grew up in Canada for me, I always felt like, you know, having that freedom to learn, explore when you’re young, which is more Western kind of way of, I guess, education was good. But I think a lot of peers here actually believe that, you know,for the first like say eight to 10 years, that foundational education should be drilled in. know, ⁓ grit should be taught, discipline should be taught. But it’s very interesting because it does kind of, I guess, manufacture different kinds of stereotypes. And I think it’s fascinating. And I think one more comment on that, I know this conversation has been more personal than we thought it would be, but I love it, you know.I don’t really get to talk about motherhood that much in my podcast. It’s usually about tech and bros and tech bros and about, ⁓ and about finance. but I think even, you know, when you have kids, people talk so much about nature versus nurture. And what I realized is I was shocked to see the nature come through, as young as like six to eight months in a child.Reni Cao (51:36)YouGrace Shao (51:53)their personality starts coming through and by the time they’re one to one and a half, they start kind of babbling, start demanding things. I realize 80 % of it is all nature. It’s like their preferences for how they socialize, their preferences of even noise, even you can realize like your point, your taste. You’re going to find a six months old who just wants to sit in a corner in a play group who just wants to flip through books literally and just undisturbed. You’re going to find someone who’s screaming in the middle of the whole group.you’re gonna find my daughter who’s rolling over everyone and just like trying to knock everyone out. And I don’t know why. You know, you’re gonna realize all of it is nature. And even I believe agency, autonomy, grit, and desire to actually succeed, that itself is nature. And I don’t think you’d be taught. And I think this is a bit controversial. But definitely I think my husband and I have been thinking a lot about this. We’re like, we can just provide them what we can. But there is no...point of even pushing them when they don’t want certain things. the best is to push them in a direction that they want to be pushed and they will tell you. I think this is like kind of the difference in our generation of parents. yeah. Reni, thank you so much. ⁓ Yeah, go on.Reni Cao (52:49)Exactly.Yeah, but can I, I know thisis over time, but can I add one last comment towards what you say? But I think what you said, especially growing up in East Asian, like, you know, education system, it has been industrial for a good reason, right? At a time where stuff like AI doesn’t exist, the most effective way,Grace Shao (53:03)No, of course, of course.Reni Cao (53:19)to develop fundamental knowledge workers, plus finishing the job of dividing the children into different segments and give them different levels of education. That education system works perfectly. I entrance exam, as I’m talking about, taking standard tests and stuff like that. But all we know that is AI is sweeping through all the knowledge works.and specialized in knowledge works, honestly, Asian parents like favorite jobs, like being a doctor, especially radiologist, you know, and or being a lawyer, you’ve got to start somewhere as associate. Now it’s getting kind of like his hardest. The world has already changed. The tsunami already hits. But I don’t think people actually understand the level of the s**t. A lot of like everyday people in the world, they haven’t felt.this like a tsunami, right? So when you say you want to kind of like, you know, like find define your children’s nature and push them towards kind of like what they are intrinsically motivated about and give them resources to set them up for success, building grades are on the way. I do believe that I think I will 100 % agree with you that it will become the most fundamental aspect or element of education in the next like five years or even sooner to be fair.That’s why I don’t send my... to put it in a simple term. I don’t send my daughter to Kumon. I don’t want my daughter to do Russian math. I never benchmark her against like, oh, like the other kids can read at the age of like three and a half. Why don’t you? Actually, I don’t because I fully understand that kids have their own time zone. Kids have their own spark. All you need to do is think deeply to define that, to understand that, understand why my daughter sometimes is super sensitive, understand why sometimes she got frustrated and want to hit.Grace Shao (54:31)I feel very validated.Reni Cao (55:00)Don’t take that on a surface level with the other tools you have. Go deep, understand that, and build these programs that’s personalized to her and help her. And I think like this is why I if I, talk about the word of Nei Juan a lot. If I have to dream on anything, right? I have to like a rather like ruthless compete on anything. I complete the deaf understanding of my daughter rather than anything else.Grace Shao (55:03)100%.Reni Cao (55:26)Because I actually think that’s the thing people gloss over. People must be like, education is just checklist. You got to check, check, check, check. And there is a better checkbox. Like Ivy League school, there’s a OK checkbox. There’s a worse checkbox. Forget about a checklist. That checklist is obsolete already. So I respect. I think we vibe together in terms of our schools of parenting.Grace Shao (55:33)Yeah.Yeah, 100%. No, I agree with you.parenting style. Yeah, yeah,yeah.Reni Cao (55:51)But you’re so fully intuitive. don’t know whether I’m right or wrong, but this is what I firmly believe in. And I believe someone’s going to join this journey.Grace Shao (55:58)I think there’s more people who are aware, especially people who are more plugged in with the technology because they realize how fundamental society will change. I just thought about when we were young, I’m sure your parents also told you to go to university, go to this, go to that, right? For sure there was a hierarchy in their mind, what kind of school you should go to, what kind of degree you should get. Now I really don’t think that’s the case. Actually, a lot of my readers would even know.my dad really forced me, well, pushed me, encouraged me to go into finance. And at one point he was like, if you don’t study finance and don’t work in finance, you’re not like following my footsteps and blah, blah, blah, blah. Right. And it was a very, it became a personal reason to do it. It’s not because I wanted to, or I was good at it. And there was actually a battle between us being like, I want to go into journalism. And he’s like, no, I was like, no, I’m going to go to journalism. He’s like, I’m not going to pay for it. You figure it out. But the beauty of it is actually found a way resourceful enough to get a full time, full scholarship.And I still want to journalism. Again, I recognize how lucky I was. I I found the opportunity to do that. But most kids actually just end up then doing what their parents told them to do and they never, and they never actually live their best life or become the best versions of themselves because they’re doing something not actually fundamental.Reni Cao (57:08)you hit a very critical, I think it’s a background or context. There wasn’t an abundance before, right? Growing up, let’s say in the eighties, It’s a relatively kind of like a society. It’s relatively kind of like not that sort of like, you you wouldn’t call it abundance at the time. Let me just put it that way, right? You still need to compete for stability, compete for resources. That’s why there’s a rat race in education, which I totally understand. That’s kind of like.It’s like a whole economy there, right? But I think that changed. No matter what we’re talking about, like, I mean, in China, I’m talking about US right now, I think abundance really will hit at some point of time. At that time, the challenge shifted from how can I avoid getting into a property or like ⁓ job loss towards kind of like, how can I find the meaning of my life? And how do I deal with this kind of like a journey?It’s a generational theme there. It’s very funny that your dad wants you to go into finance rather than journalism. mean, for those who understand Chinese internet a little bit recently, there has been a famous influencer called Zhang Xuefeng. He almost helps everyone to pick their college major. And one of the college major he hates the most and advice everyone to not go to is actually journalism at the end of the day. Because it’s just not a...stereotypically stable job that can make a lot of money, that can give you social status, quote unquote stuff like that. But I think that’s a lack of view of things. Our dad doesn’t know how our skill landscape is to be 20 years later, just like we’re not going to know what our kid is going to deal with. So we need to give them a more sort of generalize the resources and the skills at grit to survive. And it’s funny enough.I made my, I named my daughter Simone because we are big fans of Simone de Beaufort, the kind of like, you know, foundational philosopher of feminism and a lot of like a sociology thoughts. So, you know, if I have, if I have to give a set of expectation for my daughter, I want her to actually do something that’s not commonly seen as a very, like a prosperous or stable kind of like career ladder. I want her to do something that’s kind of likehas a mission and some sort of like outlier type of journey. let’s see how that goes. She’s so young, so who knows.Grace Shao (59:20)I love it. All right. Thank you so much, Reni.Reni Cao (59:23)Thank you. AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  There's more to Korea than just chips. TheVentures CIO on the country's AI stack 11.05.2026 54min

  In this episode, I spoke to a leading South Korea VC, TheVentures’ CIO Ethan Cho. He argues that South Korea’s low fertility rate and aging population put pressure on Korea to be one of the world’s fastest adopters of AI technology, similar to its rapid embrace of high-speed internet in the early 2000s. While not a leader in foundational LLMs like the US or China, Korea’s strength lies in application and adaptation, particularly in B2C areas like personalized agents and commerce, where cultural familiarity with chatbots and digital transactions lowers resistance.The Korean startup capital funding landscape is shaped by three forces: Chaebols (Samsung, SK, Hyundai), the government, and VC firms. CVCs from Chaebols tend to reinforce existing semiconductor and hardware value chains rather than explore tangential innovation. To counter this, the Korean government has become a dominant LP through initiatives like “Everybody’s Entrepreneurship,” injecting capital to encourage novice founders. On sovereign AI, he believes the government’s push is less about global dominance and more about securing sensitive areas like finance and defense, though he warns that domestically-built software has historically struggled to scale beyond Korea.Ethan is shifting focus from purely domestic champions to founders with global ambition but local execution, often Koreans educated abroad who return dissatisfied with traditional jobs. He wants to back ventures that change the world, not just build another food delivery app. He also recognizes key opportunity areas, including defense tech, K-beauty, fashion, and mental health, as society adopts AI at scale.Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently. Season two will host a series of guests from early-stage investing, as well as builders, founders, and product managers.For more information on the podcast series, see here.To find the previous episodes of Differentiated Understanding, see here.Chapters00:00 Introduction to Ethan Cho and His Journey02:47 Korea’s Role in the Global AI Supply Chain05:24 Cultural Attitudes Towards AI in South Korea11:06 Government Initiatives and Sovereign AI16:37 The Future of Commerce and AI Integration28:03 Consumer Behavior and AI Adoption28:43 Enterprise AI Solutions in Banking and Manufacturing33:39 Investing in Founders: The New Generation of Entrepreneurs39:39 Korea’s Future Exports: AI and Beyond41:41 K-Beauty and K-Fashion: Cultural Exports45:15 The Future of Mental Health in the AI Era49:53 The Limitations of AI and Human ExperienceAI- generated Transcript Grace Shao (00:00)As mentioned, our guest today is Ethan Cho. He has been active in the Korean VC space for over a decade with experience in the venture investing arms at Qualcomm, Google, Samsung and more. Now as a partner at the ventures, he leads a team focused on finding and nurturing the next generation of AI native startups. Ethan, thank you so much for joining us. So good to have you.Ethan Cho (00:18)Thank you, Grace. I’m very excited to be on the show and I would love to discuss with you more in detail.Grace Shao (00:24)Yeah, to start with, tell us about yourself. Tell us about venture investing in South Korea and the firm’s background.Ethan Cho (00:31)Sure. So I was born in Korea. I kind of moved internationally quite a bit. I moved to England when I was kid, when I was four years old. That was where I first learned my English, lived in England about four years, came back to Korea, then moved to Hungary, lived in Budapest for a year, came back to Korea again, spent the next 20 years in Korea, moved to the States, lived in New York for ⁓ my business school years and worked there for another year, came back to Korea after then. So I’ve been in and out of the country quite a bit.I loved startup investment very early in my career, so I wanted to move towards startup investment. I actually started out as a hedge fund analyst right after business school, but I quickly found out that I’m more interested in finding good companies and good stocks. So then I moved towards the private side, started with Samsung, and moved to Qualcomm, et cetera, et cetera.What fascinates me about Korean startups and startups in general is that everybody’s trying to change the world. I’m just such an honor to be a part of that and talking to entrepreneurs on a daily basis really excites me.Grace Shao (01:34)Awesome, I think it’s really interesting because you’ll definitely bring a very international perspective and not only just the Korean perspective and also kind of understand, you know, where a lot of our listeners are coming from as well. You have, you know, you have exposure to UK exposure to Europe, exposure to US. I think I want to ask you quickly, because you’ve actually worked in the public sector as in public investing, it’s kind of interesting because right now, obviously, the frenzy and the, you know, the global interest right now.Ethan Cho (01:57)Mm.Grace Shao (01:59)is in a lot of the big semi providers in South Korea, you know, focused on infrastructure layer that are public listed. At a high level, how should we think about the Korea’s role in the global AI supply chain? And then of course, we’ll shift gears into talking about the startup scene that you’re passionate about.Ethan Cho (02:02)Yep. I think that’s a great question. think one of the very obvious factors of AI is memory because you can only use AI based on whatyou or the agent knows about you or any company. So because of that, the demand for memory is exponentially increasing. I think that’s definitely a blessing for the career semi-players and also the current industry as a whole. But at the same time, think nature has always found a way to become more efficient. So the capacity or the demand constraint will not beexisting forever. There’s going to be significant improvements such as Moore’s Law. There’s always an innovative solution that comes out every other year. So I think there’s definitely going to be a lot of exciting opportunities down the road, but it always evolves. So it’s going to be very different next year. It’s not going to be HBMs anymore. I think it’s going to be something else going down the road. We’ll have to see, but I think the trend is here, but the trend itself will also keep evolving down the road.Grace Shao (03:15)Awesome. So, you know, if we have a really candid assessment of what’s happening in Korea right now, what’s genuinely really strong do you think? Like the chips are strong. ⁓ What do you think that weaker maybe compared to, you know, China and the US, you know, from outsider lens, is it really the LLM labs kind of or is it diffusion? What’s happening?Ethan Cho (03:24)Hmm.Yeah, I mean, it’s a complex situation. I think one thing that a lot of people quote and one kind of fate that we cannot deny is that we have a very low fertility rate. So the birth rate is decreasing very fast. We have a very rapidly aging population. A lot of people think of this just as a curse. I think there is an aspect to it that it’s a blessing in disguise because we are one of the countries that most desperately needs AI and robotics.And because of that, I think we will be one of the more adapting or welcoming countries for AI and robotics. If we go back to the early 2000s, we were one of the countries that adapted most rapidly to high speed internet as well as mobile technology.because we had to, we talk a lot on the phone, obviously. So because of that kind of demographic instinct, we were one of the much faster adapters to that technology. I think that’s gonna repeat in AI and robotics. As you mentioned, I think that AI and robotics is definitely not, we’re not the strongest when it comes to AI robotics in the world.But in terms of adapting and using it for actual use cases, we may be one of the very strong countries. So I think there’s a lot of challenges and opportunities ahead of us.Grace Shao (04:50)That’s really interesting. think you hit something that like, you know, people are starting to pick up in the West, which is like in East Asia in general, the embrace of technology is a lot more optimistic. Some come from very realistic reasons. Like you mentioned, whether it’s in China or Japan or Korea, there is a potential labor shortage that’s coming to the next generation, right? But not only so, I think just in terms of culture and social sentiment also feels that way. So if you have to give like a high level kind of assessment onYou know, just the cultural attitude and political attitude towards AI, what does it feel like on the ground in South Korea?Ethan Cho (05:24)I think AI itself, I think people think of AI in different forms, obviously. I think as far as I know, China thinks of AI closer to robotics. The US thinks of it as, I don’t know, maybe a chatbot or something that they use for the industrial usage. In Korea, as far as I’ve experienced, I think it’s more about becoming a personalized kind of agent, not agent.per se in terms of doing purchasing or actual daily tasks. We always had kind of chat bots, especially for instance, for our financial system or the banking system, we’ve always used CS bots very frequently. So we’re very used to it. So I guess there’s less resistance when it comes to adopting or adapting bots or AI featured functionality, especially in the B2C area. So although...When we say AI just by the name, it could sound creepy. I think it’s already very well embedded in the Korean startup ecosystem and the overall society as well.Grace Shao (06:27)That’s interesting. So you did kind of touch on one thing, you know, the Chinese view this way, the Americans view that way, you know, definitely there’s a bit of a difference in terms of how maybe AI is being seen as whether it’s a political agenda or economic ⁓ aggregator or, you know, how it’s being diffused to the seaside. So in that sense, ⁓ my question is, is Korea building domestic AI champions or is it more right now kind of working around, you know, working on top of U.S. frontier models?or leveraging a lot of the Chinese open source models. Like how do we understand that in the ecosystem?Ethan Cho (07:01)Honestly, this is a personal kind of statement and my personal observation. I think it’s all of the above. I think we were going to talk about this anyways, but the sovereign models is something that the Korean government really wants. I kind of understand it in a way. The SOTA models, the state of the art models are obviously the ones that the enterprises want to use.But at the same time, think a lot more people and developers are looking into Chinese open source models, especially with the recent changes in cloud code and Gemini and everybody, who are basically hiking their token prices. It’s getting more and more expensive to actually do recurring work. And at the same time, think, including myself, a lot of developers are quickly finding out that the fine tuning of the models can only be achieved by very,⁓ almost redundant loop work, which can only be achieved through open sources if it is to meet economic sense. So right now, for instance, if we use a certain American LLM model to do these hundreds and thousands of ⁓ very repeated work, that costs a lot. So from an individual standpoint, that’s not really easy to achieve. So I think everybody’s trying to find that sweet spot of mixing those three models.Grace Shao (08:16)That makes a lot of sense. I think for stars, especially the ones that you work with, know, the economic driver is probably one of the biggest reasons why they choose what. So I do want to save the sovereign AI kind of piece for a bit later to help our listeners understand, you know, the create ecosystem a bit better. We all hear about Chibbles. We hear about, you know, obviously the big tech like the Samsuns and the whatnots in Eskihainix right now that are getting a lot of attention, right? Help us just even understand how these different companies and the startups, how they work together. Because for example, in China, a lot of that big tech are actually the incubators and initial investors of even the startups. So even the leading LLM labs, they actually have taken 10 cent Alibaba money. In the US, it does feel a bit different. There’s vast amount of venture capital money that are kind of funding the current growth rate of OpenAI and anthropics of the world. How does it?Ethan Cho (08:54)Mm-hmm.Grace Shao (09:09)ecosystem work in South Korea.Ethan Cho (09:11)So I think there was ⁓ quite a few phases of evolution. when the startups were really founded, that was, I don’t know, that was like late 90s. Those were very purely internet domains, internet online communities. Like that had not a lot to do with the Chebals. But then came mobile technology and everybody was starting to invent something on mobile. That quickly got the...Interest from the big Chebos, but actually as far as I know there were some Interests in very early on in neighbor and cacao by all these like really large companies in Korea But they never actually fully understood what that what that was and they kind of let them grow Which was a blessing for us at the end of the day. So neighbor and cacao was you know established and they grew like crazy after that thethe big companies quickly found out that, we need that DNA of innovation. They started to set up their own VC firms. They started to set up their own accelerators and everything. But as a typical CVC does, they inject a lot of money into their interest area, but not so much in let’s say, tangential areas. So because of that, there are definitely a strong value chain around semiconductors, for instance. But that kind of reinforces thealready existing ecosystem of the chaebols, which is not exactly what the startups are intended to do. So there’s kind of pros and cons there. And on top of that, after the capital was kind of concentrated into that value chain, the government kind of now is more active in kind of leading investments. large chunk of investments in Korea is led by the government.by the mother fund or fund of funds of Korea injecting money into the ecosystem and the other funds matching to that. So there’s a layer of chaebols, there’s the government and also the capital firms who are also ⁓ acting as LPs for the Korean startup ecosystem.Grace Shao (11:06)Yeah, that’s a perfect segue into understanding, you know, the government’s play. So I visited Korea just recently, I think last November, and, you know, it seems like there’s a huge policy push as well in incorporating AI into the everyday everything. And, you know, it’s top down driven. And like you said, there’s capital also injection. So how do we understand the government’s current priorities in terms of embracing AI? How do we understand sovereign AI andwhat kind of role it plays in the economy South Korea going forward.Ethan Cho (11:38)⁓ I think the government is definitely ⁓ making a very interesting and important bet. So there’s this huge initiative called Everybody’s Entrepreneurship, loosely translated into English. The government is actually injecting a lot of money into the ecosystem by giving money to...people who want to become entrepreneurs, who are novice entrepreneurs, first time entrepreneurs. I think it’s a good thing that a lot of people are trying out their ideas at the end of the day. The downside honestly is that entrepreneurship isn’t for everybody. So there’s gonna be people who learn their lessons the hard way, but still I think all in all, it’s gonna be a positive impact on the overall ecosystem. I think...⁓ The AI drive is definitely very serious for the government. As we mentioned earlier, there’s a labor shortage coming in. I think ⁓ East Asia most of the time has a little bit of issue with immigrants. We don’t shy away from immigration, but I think traditionally we don’t have the most welcoming immigration system compared to the States, for instance. So we’re trying to buy some time there, I think.And I think because the strongest point of the industry, as we also mentioned earlier, is semiconductor and hardware and technology, we want to build upon that. And because of that, I think AI seems to be a very interesting and promising area for the government and Korea as a country overall.Grace Shao (13:03)How do we understand sovereign AI though? Like what is the, I guess, reason for like, you know, maybe the non too large company, sorry, too large economies to really start pursuing this? Because we’re seeing this kind of rhetoric in the Middle East as well. You know, a lot of the local governments are really pushing sovereign AI. South Korea for sure has been openly talking about this. think, you know, a lot of European nations are also thinking about this. Is this just for, I guess, owning?Ethan Cho (13:06)Hmm.Grace Shao (13:30)the future infrastructure or how do we understand this?Ethan Cho (13:33)I think that’s one point. I think owning the future infrastructure is one. But I think if people are realistic, think we don’t want the world, we don’t hope the world is going to use our own sovereign AI. I don’t think that’s the case. What I’m expecting or I believe that the government people are wanting is that to use sovereign AI in very sensitive areas, such as our financial backbone, for instance, orYou know, because Korea is technically set or on the defense part, maybe we’ll use that for that purpose specifically. I think in the early days when everybody in Korea started to talk about sovereign AI, I was actually less persuaded. The problem is, or the status is, as we see all these leaks all over the place, like even for the top, you know, bleeding edge,builders like Anthropic or OpenAI, there’s always issues here and there. And it kind of shows. I’m not saying that sovereign AI is going to be perfect either. They’re going to have issues too. But if a foreigner comes, a foreign entity comes in and kind of screws up an operation, that kind of blame and whatwhat something domestic spills over. There’s going to be a different kind of anxiety in the society, I guess. So that’s maybe the angle that they’re kind of anticipating. But I mean, I am worried a little bit too, because I’ve seen multiple cases of software built in Korea domestically, which has never been successful to Korea. And it just has been kind of a wanted wonder just in Korea. I just hope that doesn’t repeat. But we’ll have to see.Grace Shao (15:06)Actually, this is not completely rated to AI, but just on that note, why do you think a lot of the times like Korean companies are huge? Like you just mentioned Naver and like Kakao or like, you know, Japan, and LINE and China that we chat when not like these internet companies never really go abroad. That’s just intellectual curious question just on the topic.Ethan Cho (15:16)Mm.I’m a kind of linguistics buff. So I actually think the reason is in the language. The language that we speak is just different from English. because of that, think the like Naver, Kakao, WeChat and Line are all basically rooted in language. And because of that, that’s just universally different from WhatsApp, for instance. Like it’s not language per se, but if you look at WhatsApp, how they control their UI UX, for me at least, is very boring.Grace Shao (15:27)Mm.Ethan Cho (15:52)⁓ I would prefer a cacao or lime or WeChat much over WhatsApp if I could choose without being specific in which geography. So I think there’s a cultural preference, a very strong cultural preference that really is hard to translate across territories.Grace Shao (16:09)Okay, that’s an interesting take. Yeah, because I think it’s interesting because like, it’s basically the West has this one or even like, you know, Africa based Southeast Asia, they all fall under the American big tech kind of umbrella. And then Korea, Japan and China’s have such strong domestic players. Actually, on the note on you know, the consumer side of things, what are some interesting trends you’re seeing out of South Korea in terms of consumer AI right now? What are some companies you’re investing in that are in the consumer AI space?Ethan Cho (16:16)Yeah.Consumer AI, think, is still at a very early stage of growing. I think right now the most used cases that I see with my bare eyes are actually foreign tourists coming to Korea, visiting like big K-Beauty.department stores like Olive Young, and they go and get their skin scanned and they analyze it with AI and give recommendations to basically ads. But still, I think that’s a very clever way to scientifically analyze the customer demand. I think a lot of players, and I see a lot of players trying to replicate that into basically recommendation engines. Personally, I think that’sclever, but it’s not good enough. It has to get better. The trade-off there is obviously privacy. So if you want a Uber personalized recommendation, you have to somehow yield on the privacy part. think we’re still not clear on where is the kind of safety line. So I think they’re still kind of struggling towards that. We have invested mostly these days in consumer brands because how⁓ I think of it at least, is regardless of which AI becomes the winner or winners, I think as long as we have the best product in our portfolio, if the AI is clever enough, it will choose that product. So before actually deciding which AI algorithm will actually win the war, think we’re trying to get hold of the monuments before the, know, who,before we decide who becomes the winner of the war altogether.Grace Shao (18:12)So you’re looking at brands as in like, like retail brands. What are you looking at? Like, okay.Ethan Cho (18:16)Yes, yes, for now,yes. But at the same time on the kind of the hardcore AI part, we also have invested in sovereign AI companies like Trillium Labs, which actually develops SLMs instead of LLMs. We’ve also invested in a drone company called Bone AI, which does physical AI using drones and they’re targeting the Korean big defense industry. So that’s kind of themore of the hardcore AI part that we’re looking into. We’re still waiting for that sweet spot where consumer meets AI. I think that’s still kind of in their very early stage in Korea.Grace Shao (18:46)I see. I just want to say it’s so funny you used the Olive Young example, it’s really topical. So this is really a bit of a rant, but my friends and I were saying we need to go to South Korea to do the color palette, right? And all, you know, all the girls are like raging about this right now. I literally asked Claude Coe today to do it for me and it gave me the whole like color palette assessment. I was like, wow, I just saved myself a flight and a trip to South Korea. So definitely can see like there are consumer uses in that end, but I guess what is the monetization from that bud, right?Grace Shao (19:22)So that I can see how that will be hard to invest in that space. On the consumer end, know, again, I read headlines about South Korea, right? And, you know, I hear about companion bots being really big. I know actually even in China, they were group bots. They were like so-called boyfriend, girlfriend bots. And to your point, you know, South Korea, China, like even like a lot of East Asian countries are in are all faced with this issue where there’s mass urbanization.Grace Shao (19:48)loneliness issue everyone is like, you know faced with evolution and competition so they don’t have human companion and Do you see this as a trend and do you see this as something that potentially would not be actually within the cacau’s and the lines of the world that could be a spun-off on its own and to fall off of that I was just even just kind of thinking because Korea has so much IP right now in obviously k-pop and k-dramaEthan Cho (19:57)Hmm.Grace Shao (20:16)would that potentially be a vertical where they can tap into basically creating, you know, like companion bots, based on existing celebrities.Ethan Cho (20:25)I mean, I think yes on both is the short answer. I think the boyfriend bots, girlfriend bots are very popular in Korea. I think my son is also using one. I haven’t talked about that openly, but I think so. And yes, I think that Kakao and Neighbor would be very cautious about adopting that technology into their existing platform. There can be some...many opportunities to abuse that. People, as you know, once these bots are online, the first thing that everybody tries to do is abuse it one way or another. So I think a cow and neighbor would probably shy away from that and go into commerce, which is always what they wanted. We’re seeing more specialized startups that are just doing this boyfriend, girlfriend bots.like some are more adult focused, some are more teenager focused. So there’s definitely kind of a breed that’s coming out of that. On the K-pop and K-drama bots, I think that’s something that a lot of companies have worked on for quite a while. For instance, like Weverse, is the, it’s the entertainment company that basically ⁓ relates to all K-pop stars. They have their own like personas.So they actually provide not just only conversations on bots. I think they also give artificial voice calls. They’re already there. So you can have a conversation with your favorite star. It’s just not realistic enough yet. But I think they’re getting there. So ⁓ that’s definitely already happening. I think on the IP side, personally, think it’s more about how can you make these into really long-lasting legacies? Even for BTS and like...girls generation, which are the you know, the idols of the day. I think they’ve only been around for 10, 20 years. Like, can we make this into like decades long, right? Like a legend, like can we actually make this into something that goes through generations, not just decades? That’s a big homework for us to figure out and make them kind of timeless.Grace Shao (22:24)No, I totally see that. It feels like a Black Mirror episode with the Miley Cyrus ⁓ kind of fake doll as well. But I think to your point, there’s the IP issue, there’s obviously the security issue. There’s the psychosis issue. This is like a much bigger issue. I think that we require regulators to work with businesses, right? I want to kind of move our lens to the enterprise side of things. You you mentioned just now like Naver and Kakao wereGrace Shao (22:50)looking into maybe going to agentic AI and maybe even to commerce. Are we looking at something like what Alibaba is trying to do where you have agenda commerce through a one entry point, you you interface with a chat bot next thing you know, like a bubble teas at your door. Like, is that the future of commerce you think or what are we talking about here?Ethan Cho (23:11)I think, I think, neighbor and Kakao are both in fierce competition with coupon coupon is the dominant e-commerce player in Korea. ⁓ I think it’s a real headache for both of them because coupon was kind of non-existent. They didn’t have a lot of user interface and neighbor and Kakao were kind of self satisfied that they dominated the user interface. But, here comes coupon and they just basically just crushed every.aspect of e-commerce and is by far the number one player in Korea. So that’s something that Naver and Kakao are trying to battle. The only difference, as far as I can see, that they can make is real-time purchases. If you want milk at your home the next day, coupang is much easier and much better. That’s kind of a fact. But for Naver and Kakao, because they have basically 24-7 access to your daily life, if they can actually monetize on that, I thinkThat’s their way to go. The competition there is also not non-existent. That’s a problem. YouTube’s there. TikTok’s coming along in Korea. TikTok’s still small in Korea, but it’s growing rapidly. that space is also... I think people think... Koreans are just so big YouTube fans. The YouTube dominance is so... They used to. Now they’re getting more used to short forms now.Grace Shao (24:15)Why is it small? Curious. Why is it small?So they like long form.Ethan Cho (24:30)And the TikTok trend is definitely coming along. But I think most YouTubers, so-called YouTubers in Korea, are long-form originated. So the trend is changing now. So yeah, it’s a little bit slower to adopt. yeah, sure. Oh, yeah.Grace Shao (24:40)And I wanted some context. So coupon is like an Amazon or like it sounds like a DoorDashand like how do we understand this just for our American audience or Western audience?Ethan Cho (24:51)Yeah,Coupang is, they actually literally say that they want to be the Amazon of Korea. So they are the Amazon equivalent in Korea. Their main business is e-commerce. They have Coupang Eats, which is DoorDash or Uber Eats. They have Coupang Play, which is the Amazon Prime. So they’re basically, Coupang people would hate me saying this, but it’s kind of like the Amazon replica in Korea.But they’re doing a fascinating job. Their killer feature is next day dawn delivery. So if you deliver, if you place your order by midnight, they’ll get the item to your doorstep before 5 a.m. So it’s marvelous for. It’s not just groceries. Yeah, so they’re very good at demand expectations. So they have a lot of warehouses in Korea. So they fulfill them in advance so that they can basically distribute almost within like four or fiveGrace Shao (25:29)It’s not just groceries, it could be anything.Ethan Cho (25:43)hours window, which is also honestly possible because Korea is not so big as a country.Grace Shao (25:49)But it sounds kind of like almost a JD.com business model as well. It’s like, but more high, high, more expensive. Excuse me.Ethan Cho (25:52)True. Yeah, yeah, I think I think that’s a fair. Yeah, that’s a fair comparison. Yes.Grace Shao (25:57)It’s a bit more expensive, right?Yeah, so I think looking at that, then, you know, there’s also this rumor, or I guess it’s actually been verified that South Korea was, fact, OpenAI’s largest enterprise market outside of the US, which is crazy, because like you just said, South Korea is not exactly that big of a country. Why is that? Who are the people buying up all these tokens?Ethan Cho (26:12)Hmm. I don’t have exact numbers, but when I first heard that I wasn’t too surprised because if you look at like Koreans are very used to buying tokens online. Like that’s why Korean gaming has beenor what used to be so big, especially in the mobile era, because people were just fine with buying items online, like purchasing it like crazy, which was kind of now it’s kind of standard, but like back in the days, like early in 2000s, like it was a very weird phenomenon if you look at it from a global standard. So because of that, think people are really, really fine with just buying tokens and buying memberships, which costs 100, $200. I think that’s kind of whatthe base layer, so the willingness to pay the first layer. The second layer is that Koreans love to build things. Just trying to build things so much with their own hands is one tendency that we strongly have. So because of that, I think most of that building tokens go to my stock portfolio optimizer or kind of tools like that for personal use. But I think people just like to try out a lot of things that kind of led to that consumption.Grace Shao (27:26)Mm-hmm.Ethan Cho (27:33)On the B2B side, think as far as I know, because the head of OpenAI Korea used to be one of my kind of bosses at Google Korea, he was, well, OpenAI was very aggressive making contracts with Samsung and SK very early on. So I don’t know how many tokens they’re using, but just thinking about how many employees they have, if they struck a good deal on a B2B,business, I think that would be a very significant portion of tokens being burned in Korea as well.Grace Shao (28:03)That’s pretty crazy. So basically you’re saying the the enterprise side, people have pretty strong connections and reasons to buy and the consumer side are just willing to shell out subscription dollars. That’s very different from, would say, the Chinese market where there’s just like not a lot of willingness to pay from consumers. And hence why we saw all the consumer apps in China were all free. So I actually want to ask on enterprise end. So what are we seeing people spend money on in terms of AI thatEthan Cho (28:14)Yep.Grace Shao (28:30)What are people trying to build? Are we looking at like also like on the enterprise and are they trying to solve co-pilot like solutions? Are they trying to serve customer service issues, manufacturing optimization? Like what are people really focused on?Ethan Cho (28:43)So just based on my experience with the companies, I think one thing is the banks are very serious about building the CS layer via AI. So they want to substitute a lot of that labor force into AI. I’m not sure whether that’s like how fast that can be optimized just because people are very demanding in Korea. know, when even if you use the traditional kind of phone CS, peopleend up basically talking to people. They demand to talk to an actual person instead of going through the automated call. So we’ll have to see how the ROI comes out on that part. I know that there’s a lot of AI being used for the semiconductor processing and producing process, but that’s just not public information. So we really don’t know how much is being used there. So that’s on the enterprise side. On the consumer side, think because of everybody’s⁓ entrepreneurship program that I mentioned earlier. I think there’s a lot of people that are trying to use a lot of cloud code, for instance, or codecs from OpenAI to build programs. There’s a lot of events actually held in Korea. Maybe every week there’s an event from OpenAI or Anthropic basically, which is like the cloud ambassadors night or the OpenAI something, something night. people, lot of... ⁓the AI builders are actually encouraging Korean builders to use their own tools by giving out a lot of free tokens actually, like thousands of dollars are given out as tokens just to nudge them into building. So there’s gonna be a lot more activity in that space for sure in Korea. And hopefully there’s gonna be something that’s really interesting coming out from that.Grace Shao (30:25)That’s interesting. I did want to ask, you kind of mentioned this earlier that you guys even invested in a drone company. South Korea obviously has a very strong manufacturing sector, home appliances, phones, cars. How are we seeing this whole, we have generalized this whole sector kind of lean into AI? Are we seeing physical AI being prioritized? Are we going to see? more robotics coming out of South Korea. How do I understand that?Ethan Cho (30:55)I think there’s still some uncertainty there because of the all of the among all the Korean robotics companies, I think the most technologically advanced one is Boston Dynamics, but that’s not a Korean Korean company, to be honest, right? Because it used to be an American company acquired by Hyundai Motor Company. So there’s that. There are quite a few robotic startups that are starting in Korea.Just because we have Samsung, Hynix, and Hyundai, think the manufacturing industry obviously is a great application area or a market to sell to. So we are seeing a lot of robotics company coming out from the university as well as startups. The big question here is will they scale? That’s kind of the pressing question. I mean, I think the companies, for instance, for Coupang,which does all the logistics. They’re heavily using robotics just as Amazon does. So those robotics are already deployed or are being deployed. But for instance, humanoid robots, which China is leading the way, I think that’s still a long way to go for us. And we’re trying to figure out what the application should be. So one interesting example, I think China has this too, but...We have all these little, really cute delivery robots going down the road and trying to get food to their neighbors. That’s an experiment that a lot of companies are running right now. We also have small police robots that are also running around just to do surveillance. I think it’s a cute initiative, but can this scale is going to be a big question for a lot of us. So I think this is also intertwined withautonomous driving landscape in Korea, which is still kind of not there yet. So I think there’s going to be a lot more of this going forward.Grace Shao (32:44)Yeah, no, actually on that note, I just was in Shenzhen last week and I saw one of these like, you know, street sweeping robots per se, stuck in a puddle. And it’s like, to your point, like they look cute or like, you know, you have little robots delivering your phone charger in hotels, but they’re not actually that scalable. And I don’t actually know if they’re that cost effective is the issue, right? Because, you know, sometimes hiring a person to sweep the floor, frankly, inEthan Cho (32:48)Hmm.Grace Shao (33:12)a market like China is actually not that costly compared to even deploying a robot like that and then having to, you know, maintain it. So I see your point. Okay, I think, you know, I want to shift our focus back to, you know, your bread and butter. And I really appreciate you patiently breaking down the ecosystem for me as an outsider who don’t understand South Korea that well. But as a venture capital investor right now in South Korea, what are your, I guess, most interested areas?What kind of founders do you really want to invest in? And are you looking at the founders more or the companies more? Let’s start with that.Ethan Cho (33:45)I am looking for founders. I’m looking for founders because I think there’s been a evolution of generation or a change of generation that I’m seeing. I see a lot of Korean.like in their 20s or their 30s who are educated abroad, come back to Korea and start working in Korea, not too happy about their job and trying to figure out what to do next. I just want those people to actually start something new and I want to kind of back them. I call that like global ambition, but local execution. I think that’s something that we need more.Until now, as you know, all the companies that we’ve mentioned throughout this conversation, like Naver, Kakao, Coupang, they’re all basically really focused on the Korean market, which was, you it’s good. But still, as we all know, Korea is not the largest of the countries. And, you know, just doing business in Korea doesn’t mean a lot, especially as we move towards AI more and more. And because of that, I just want those...⁓ kind of people who are ambitious to really change the world in a significant way, not just build the next chatbot or the next food delivery app, but something that kind of, you know, breaks around and just changes something very significantly. That’s something that I’m really looking for these days.Grace Shao (35:00)That’s really interesting. Do think that has anything to do with your upbringing, just being so internationally exposed?Ethan Cho (35:05)Maybe, actually, yeah. think, this is kind of another personal note. think Asians are really smart in a lot of settings, but we as Eastern Asians, were brought up to be kind of modest and humility was one of our very top priorities as we grew up. And because of that, we tend to be more humble in front of people. And as we know,The Westerners, like this is not a great word maybe, but the Europeans or the Americans are much more aggressive in PR, but we tend to be more careful about that. Back in the days, that was great when we were just living amongst ourselves, but now as we go into the global market, PR is really important and having big ambitions like shoot for the stars, land and the moon is the way to go. But sometimes we just focus on what we have. I think that’s a healthy way of living, but.For entrepreneurship, we really have to dream bigger dreams.Grace Shao (36:02)That’s really interesting. I think it’s some things that I’ve even really noticed within the just generating Chinese founders as well. It’s really different. Like you mentioned coupon. I think the founder was Harvard educated, but he returned to Korea focused only on the cream market, just like the last year. He’s like the JD.com Alibaba’s and the day these are Chinese market businesses. They have global footprint, but there no one’s thinking of them as a international business, right? At the core, they’re Chinese company. But if you look at theEthan Cho (36:18)Yep.Grace Shao (36:28)whether it’s the LLM companies in China right now, or even some of the more consumer facing ones, or even the robotics ones in China, I kind of feel like there’s a shift in generational behavior. Exactly to your point, some of them are less educated than they’re not, but in general, people are not as modest. People are actually more, not in a bad way, but they’re much more open to doing PR for themselves. Well, not just PR, but actually flexing and going more ambitious, going global.like we said, like Kimi and Minimax, whatnot, Jiu-Jitsu, they’re used globally, right? And they’re not shying away from it. I think that’s really interesting. That’s like a phenomenon across East Asia right now. So I think for us to understand, what are some, I guess, misunderstandings or things that foreigners who are trying to invest in Korea often...you know, get wrong or not completely get correctly because, know, obviously there’s so much societal nuances. Well, in South Korea is a country where I find, like you said, it’s not not only not that immigration friendly, but actually in some ways a bit more closed off, Much like East Asia in general, like if you’re not from there, you don’t speak the language, don’t understand formality, especially South Korea has a lot of formalities. It’s really hard to do business, right? So how do what are things that you see that foreigners might be getting wrong that they could do better?Ethan Cho (37:44)Hmm, I think, well, I mean, first of all, think Koreans are just a lot of time. I wouldn’t say everybody, but a lot of Koreans are just shy. They’re, they’re friendly, but they’re shy. That’s kind of our kind of default mode. So, you know, if somebody comes to Korea and nobody wants to talk to you, that’s the norm. But once you try talking to any random Korean person, he or she will definitely help you out. That’s kind of the Korean kind of way. They’re being shy because they want to be polite. That’s kind of an Asian thing, right? So.There’s that. think because Korea has been such a small country, think people, some people think of Korea as just being focused on that very regional kind of market. We’re not, obviously. Like we want to also go global, but we just didn’t have enough chance to actually show off that.I mean, if you look at, for instance, the Koreans working in the States, they can show you what a Korean can do if they’re put in the right setting. So I think if you’re an investor and want to work with a Korean firm, think as long as you put the resources and the human talent in the right settings, they will perform. of course, I can’t guarantee everybody will, but in general, that’s how we’re formulated.I think one interesting factoid that I also always kind of want to emphasize is I think China is also similar to this, but because we have this crazy, crazy education system that’s like overly competitive, although we have been really stressed out throughout our teenage years, that actually made us very, very competitive when we just, you we’re put in the right settings. Like we will strive to become number one in whichever setting that we are put into. So just.like, you know, help us get to the right market and get to the right country or what right settings we will perform. So that’s, think, the expectation that you should kind of have for a lot of Koreans and Asians in general.Grace Shao (39:39)just like whoever can go through the national like university exams, like they have resilience. These buddies don’t like they don’t mess up. So I think on that note, I guess I want to ask what are what should we expect Korea to be exporting that if you’re saying that you want to back companies that are going global, you want to back ambitious internationally minded creates, what should we expect? Because no one expected it. Well, not no one. But a lot of people did not expect China to suddenly be exporting LLMs, right? As like one of their hottest new technology right now. I think a lot of times robotics maybe, EVs maybe were more in the expectation over the last five to 10 years because of the strength and the slow momentum it was gaining, right? But yeah, for Korea, what should we be looking at? Like you said, obviously hardware, chips, there were a lot of synergy there. You’re trying to build on top of that, but beyond that.Ethan Cho (40:29)I think the low hanging fruit or the easy pick is K beauty and K fashion. That’s definitely gonna come in the next ⁓ coming three to five years. I personally think there’s a lot of interesting angle in the Korea defense industry combined with AI because honestly speaking, there’s a lot of, how should I call this? Like confusion around the American diplomacy.policy recently because of all these international tensions. And because Korea has always been at war technically with North Korea, I think there is a lot of advancement in Korea technology wise. We have the best semiconductor in the world. I think we are one of the most flexible countries when it comes to, are you going to use US LLMs versus Chinese LLMs? Like we can do both. think we are.We see the pros and cons there. very flexible there, so we can optimize. I think because of that, the defense industry is not only growing very fast. I think it’s a very good place to kind of experiment the new warfare technology without going into actual war. And because of that, I think the Korean defense industry will benefit a lot from AI evolution.Grace Shao (41:41)I see. It’s an interesting area which I’m not like I’m not familiar with at all. But it’s like kind of like you said, it’s kind of one of those areas where you don’t really hope it being really used, right? ⁓ But it’s definitely a very hot space in terms of VC investment and in the US, especially with Palantir driving over the last couple years. Again, not really to AI, but I kind of want to double click on K beauty and K fashion. Why is it like what what is it that you know, over the last week? SoEthan Cho (41:50)Mm. Yeah.Grace Shao (42:08)My husband and were trying to talk about this very casually that day. We’re like, wow, we live in Hong Kong. In the 80s and 90s, everyone was obsessed with Hong Kong pop stars and Hong Kong movie stars across Asia and then even globally. They had all the kung fu shows and then all the police shows. And then in the early 2000s, we definitely had the Taiwan wave, the Taiwan pop stars coming out of East Asia. And even I was in Canada. I was growing up Canada and people loved Jay Chow, right?Ethan Cho (42:29)Mm.Grace Shao (42:36)Nowadays, obviously, it’s all about Blackpink, right? So how does this move around? Why is it going around? And how does one society kind of, I guess, nurture or incubate a global pop star? Is it tied to geopolitical reasons or economic reasons? Or do you think aesthetics?Ethan Cho (42:58)I don’t know exactly the reason if I knew I wouldn’t be working in V.C. I would be another producer. But I think there’s two reasons that I think is the biggest reasons. One is we have a massive farming system, you know. Koreans train boys and girls in their early teens.Grace Shao (43:04)Yeah.Ethan Cho (43:18)to become the K-pop stars and you have to go through vicious vicious competition to actually get there. So because of that, I think there’s so much talent that’s going through that pipeline, which is a blessing and occurs at the same time to society, obviously, I think so. But there’s that. The second part is I think Korea had a mix of...American culture very early on because of the Korean War and the Korean forces, sorry, American forces staying in Korea. So if you look at Blackpink’s music, because you quoted Blackpink or even BTS, there’s a lot of African-American music, ⁓ like features within embedded in that music line, in the melodies. It’s very some of it is reggae, some of it is very hip hop. And those kind of cultural fragments were embedded very early on because we hadmore exposure to African American or hip hop music versus let’s say China, which didn’t have American troops staying in China. So there was that. Then somebody might ask, what about Japan? They also have a huge American troop there. I think Korea was because, maybe because we were a smaller country, we were more open to actually getting into and using those vibes. And because of that, think.The Korean kind K-pop or K-beauty, K-fashion factory has become a little bit more westernized early on and that kind of made the entrance barrier a bit lower for the American market. That’s kind of my hypothesis.Grace Shao (44:49)Yeah, because if anything, kind of going back to your point on like South Korean and East Asian companies and people don’t really do a lot of marketing and PR, I would say ⁓ Korean cultural export has been extremely successful and has been a really, really strong soft power export. So I want to end the conversation on again, back to AI. What are some things that you think we might have not covered today? You think we’re missing? Like, what are some trends?Or say like if we really spoke again, let’s hope not two years later, but let’s say we spoke two years later, what would be true for you to think of how society has evolved, what Korea has maybe contributed in a global AI supply chain ecosystem, how to understand how you view the future.Ethan Cho (45:32)One thing that we haven’t touched that I’m personally passionate about and interested in is the mental health industry. It’s going to be very different from now versus three to five years down the road. As we know, the fitness industry, physical fitness industry, has become a huge industry ⁓ after the Industrial Revolution because people started to use less and less of their muscles. I think that’s exactly going to happen for our minds and brains.⁓ And because of that, this is not going to be driven by AI, but it’s going to be kind of a side effect or a secondary industry from the AI revolution. To keep everybody healthy, think this is something that we as a society and company as country has to work on. there’s going to be, I don’t think this, I don’t necessarily think of this as a dark scenario. I think as we go to the gym, we can go to this mental gym or something.very on a regular basis to keep ourselves healthy mentally. I think that’s gonna be something very huge. Until now, I think we’ve focused a lot on the hows, like how are we gonna do this? How are we gonna do that? The answer to that has been AI and robotics. There’s gonna be more and more questions about what are we gonna build with this? And after that, there’s definitely gonna be questions about why, why are we doing this? I think that’s not just gonna be philosophical, but it’s gonna be a very practical question.that will lead to a lot of business opportunities. So I think that’s something that we’ll have to question ourselves and answer and discuss on a very regular basis down the road to reach something meaningful either as an entrepreneur or an investor.Grace Shao (47:07)I think that’s really, really meaningful. And I think, you know, we kind of touched on like companion bots and even your you mentioned your son might be even using a companion bot himself. I don’t want to probe on a personal level, but actually on this note, then how do you view that? Like, do you ever fear that he’ll be too dependent on it or, you know, I could be creating a false reality?Ethan Cho (47:27)I think it really depends, right? I know this is not the best answer, but like I’m a big fan of the movie, Her. I think it was a very, very good example of how things can evolve. The ending was kind of sad and happy at the same time, but until then, he was very happy with Samantha. So it seems like there’s definitely a scenario where we can be more happy about the world, be more thankful about the world, thanks to this.Grace Shao (47:33)Mm.Ethan Cho (47:52)maybe emotional buffer that we create with our AI companion. There’s definitely that. But there’s also going to be a downside because the companion will feel real, but it’s not going to be real. So how can we cope with that? It’s going to be something. I still think it’s going to be very similar to the fitness industry just because when we do like, you know, bench presses or, you know, like all these like that pull downs, those are not actual resistances. We’re creating them artificially to strengthen our muscles.So I think our minds should also be strengthened in that way so that we can cope with all these scenarios that we’re not gonna be able to actually experience down the road because we’re gonna live in our own world, which is gonna be safe and creepy at the same time. you know, a lot of factors that will change down the road. So kind of excited and horrified at the same time.Grace Shao (48:42)No, 100%. I think your point on mental health, you use like a general term, but there’s obviously the obvious fear, like what we just talked about, like psychosis and dependency, but there’s also kind of like you mentioned, touched on like, you know, if we don’t really use our brain that way, you don’t really know how to do it anymore. Just kind of like languages, you know, when you move to a country and you don’t use that language for a while, you lose it. Math, I like literally don’t know how to do math anymore. It’s pretty sad. But you know what I mean? Like if these are skills where like you kind of havepush yourself and the gym is something quite, if you think about it, very arbitrarily created for our modern day lifestyle, which obviously didn’t exist even like two generations ago. But yeah, like I think that’s a really interesting take. I don’t know if that’s actually your differentiated view, but you know, I usually always ask one last question to every single guest that comes on the show,what is one differentiated view you hold? So something that might be a bit non-consensus, it could be provocative, it could be not, know, it could be about industry, it could be about life. Honestly, I think what you just said earlier was a bit, it’s quite insightful. It’s something not talked about in the mainstream enough, but if you have another one.Ethan Cho (49:31)differentiate the view. huh. I can make really dangerous comments here. ⁓ but I think,Grace Shao (49:55)No worries.Ethan Cho (49:56)yeah, this is one thing that I always think about. So I think that the creator cannot make something that the creator has not experienced. That is something that I think deeply about, and that is my personal view on the limitations of AI. How we think about AI is to become this everlasting thing that works 24-7, does only good things for humanity. Buthave human beings actually ever experienced that? I don’t think so. And that’s going to be a big question because we’ve never, we don’t know how to work 24 seven. Well, of course we’ve, you know, we’ve done all nighters for sure, but can we actually think of a process that can continuously work 24 seven by thinking, not just operating machinery and also can we think of a kind of standard that is always only helpful to human beings? Like we haven’t really done that.So I mean, I think that’s gonna be a big challenge. Like, however we construct the system or the standards for AI and robotics and all these systems going forward, there’s gonna be a loophole there. And that’s something that we’re gonna have to figure out as a society as a whole. So I think that’s gonna be something that it’s gonna be very interesting down the road.Grace Shao (51:08)Do you kind of, are you kind of alluding to what we’re seeing right now? A lot of people have AI fatigue where they actually make the agents just work 24 seven for them. So essentially the moment they just stops doing a task, they repeat, like they let’s restart it. That’s kind of the work, right?Ethan Cho (51:20)Yeah, I think so.that definitely shows what the problem is. Because we don’t know how to operate these. So I’m facing the same. I don’t use it as much as I used to like a month ago because of that. Because I’m feeling that, this is controlling me, not me controlling that. So there’s this reverse effect. So I think it’s a good thing that a lot of people are already kind of figuring that out. people are kind of.trying to like healthily distance themselves from all these agents. So that’s, I think, a positive sign. But I think as a society as a whole, that there’s going to be more and more things that we’ll have to think about.Grace Shao (51:55)No, I totally agree. And I think there’s certain things. There’s a lot of value in stopping and thinking about the action before the action respoots again. Obviously, there’s certain repetitive work that can be streamlined. so much of accessing knowledge work. mean, this discussion can go another hour, but so much of the whole argument on knowledge work being completely replaced just seems a bit I feel naive for me. Like, I feel like so much of the knowledge work actually requires us.creating things and I don’t know maybe I don’t understand technology well enough so who knows maybe it can create things on its own. Ambanao, I really really want to thank you for yourEthan Cho (52:26)Yeah, that’s another. Yeah,thank you. Thank you, that’s another hour of conversation so we can do it next time.Grace Shao (52:34)Yes, please. Thank you.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at aiproem.substack.com/subscribe
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  Assembled co-founder John Wang on building a AI native support system for enterprises 04.05.2026 43min

  In this episode, I sit down with John Wang, the co-founder of Assembled, to explore how AI is revolutionizing customer support. Having transitioned from a Stripe engineer to an AI startup founder, John shares his unique insights into the evolution of support tools. We delve into how these tools have shifted from being mere cost centers to becoming strategic assets that enhance customer experiences. John and I discuss the impact of AI on support volumes and staffing, highlighting how integration is reshaping the landscape. He emphasizes the importance of talent density and assembling high-caliber teams to drive success in the tech industry. Through his experiences, John provides practical insights into AI's current capabilities and limitations in support operations.We also explore the strategic considerations for future AI support ecosystems. John shares his thoughts on the role of support in driving revenue and customer satisfaction, and how AI can orchestrate with human support agents to create a seamless experience. His perspective on building high-performing support organizations offers valuable lessons for anyone looking to innovate in this space.Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently. Season two will host a series of guests from early-stage investing, as well as builders, founders, and product managers.For more information on the podcast series, see here.To find the previous episodes of Differentiated Understanding, see here.Chapters00:00 The Journey from Stripe to Assembled02:25 Understanding the Importance of Customer Support05:29 Lessons Learned from Stripe10:25 AI in Customer Support: Current State and Future16:04 The Economic Impact of Support Operations18:25 The Role of AI in Transforming Support Jobs24:30 The Future of Support Organizations26:58 Guardrails Against Fraud in AI Support32:42 Navigating the AI Ecosystem38:00 The Value of Long-Term Commitment in CareersAI-generated transcriptGrace Shao (00:00)Hey, John, thank you so much for joining us. I just recorded your bio already. It’s extremely impressive. And you’ve done quite a, you’ve had quite a few different roles now as the co-founder of assembled, right? To start, can you just tell us about your story? Like what inspired you to leave Stripe, you know, go into, you know, right now what you guys are doing, which is a software for people who run customer service support operations. You know, now you guys are pivoting into AI as well, or at least leaning into AI. Tell us about all of this.John Wang (00:28)Yeah, great question. When we, well, when my co-founders and I started, we were all at Stripe. We worked on a bunch of different things at Stripe. And one of the last things that my two other co-founders worked on was a support tool, an internal support tool. And I remember pretty clearly that they were making a bunch of headway. It was really, really cool. And...They had gone to this really, really high up person and product. And this person was basically like, why are you guys wasting your time on this? Like you guys are kind of like, you’ve been at Stripe for so long, you know all these things and you’re doing support. Like I’ve got this really cool Bitcoin project that I would love for you to work on instead. And I remember my co-founder coming to me and being like, hey, like pretty bummed this is what happened.And then I was like, wait, you just saved Stripe, you know, quite a few million dollars, increased customer satisfaction by 40%. And still they don’t understand the value of this. And that’s when we were like, hey, ⁓ there’s something here where there’s a market opportunity. So that’s what got us really, really excited about support. We were doing it at Stripe. We knew it was an undervalued place. We didn’t see any very good tools out there to do support well.And so we decided to go build something really, really great in the support space and just like make transform and elevate support is our mission. Yeah.Grace Shao (01:50)Do you think it was just that stripe was too rich? They were just, and they just didn’t care about saving a couple million dollars? Or do you think it was actually a blind spot for people?John Wang (01:59)I think Stripe was definitely very rich at the time. think it was also a blind, it was a combination, right? Because most people, you think of support, you think of it as just a cost center. And I think recently that started to change in the sense that like, hey, this is actually a really important part of your business. But for a lot of companies, like if you look at FinTech, if you look at like a lot of health tech companies, their entire product is their relationship with their customers.And so support’s actually really, really important for that. And I think a lot of people underappreciated that for quite a while. And now I think people are starting to understand again, hey, if you piss off your customers every time they come and talk to you, that’s not going to be a very good thing. You better be a monopoly. Otherwise, you know, they might not be coming back.Grace Shao (02:46)Yeah, definitely. I think I want to kind of lean into that later in our conversation as well. It’s like people are trying to replace support and customer service AI first. But if anything, it’s not the best experience when you’re frustrated with a product and you keep on getting a robot, right? But I want to kind of talk more about your experience at Stripe. You were there quite early. What do you think it taught you, you know, as a very early employee at such a successful startup now?if even considered still startup and then like what were things that you think you learned there lessons even if soft skills that you kind of took away to to your current role like as a founder.John Wang (03:21)Yeah, it’s a great question. You know, it’s really funny actually. I just met up with someone where, so when I was starting out of college, I had applied for all these jobs. I was able to get a lot of them, except for this one company that I really, really wanted to go to. It was called Meteor Development Group. They built open source software. In college, I had built open source software at Ruby on Rails.I was really big in that community. was like, wow, it’d be awesome to go and make this something I do day to day. And I didn’t get the job. I was really bummed about it. And then I was like, I’ll just fall back on my second here, which is Stripe. And Stripe was the obvious second choice because just the people were really, good. And now like 10 years later, I like think about that and I’m like, the business model is really important.because Meteor was not a good business. Like open source frameworks is not a good business, but Stripe, really boring. Honestly, it’s just like payments. You process payments, you go talk to Visa. You literally have to like, we had a server in the server room that would send like a specific file with specific tabs and spaces in order to get it out to Visa. Really boring. Really, really core infrastructure too.And so like the big overarching thing that I learned was like one, business model is unbelievably important because if you can just make a good product when the kind of like market is there and when there’s a really big need, then this can scale like unbelievably fast. Two was the people. I remember talking actually to a few people, Greg Brockman was maybe the second or third person I talked to.who’s now the co-founder of OpenAI. And I remember just talking to him and being like, wow, this person is so, so smart. This is awesome. And I would talk to kind of like, I would go to the lunchroom and be talking to people at Stripe. that was just, people were talking about all sorts of things. And I think like talent density was a really, really big part of like what made Stripe successful. AndIt wasn’t any one thing over time. was one, Stripe was in a great market. And then two, it iterated really, really fast on a lot of little things over and over and over again. So I thought that was a really good place to learn a lot about like what makes a company great.Grace Shao (05:49)Yeah, I think it’s interesting you’re talking about talent density and a lot of the AI labs I speak to actually also talk about that. But I’m curious, what does it mean when you have really strong talent? Is it like that they are technologically superior, like they can code better? Or does it mean actually that they can think outside the box, they’re more creative, they can pivot faster? Like what does it really mean to have really high caliber talent on your team?John Wang (06:11)I think it depends on what company or like what you’re trying to solve, right? Like talent density for Los Alamos national, like Los Alamos, like building the atomic bomb is like very different than talent density for like Bell Labs, which is very different than talent density at early Stripe, which is very different also than talent density at OpenAI Research. Like I think for Stripe in particular, the type of talent density that was there was really high curiosity.Grace Shao (06:31)Right.John Wang (06:38)really high product thinking, really technical people, and people that could dive deep on certain problems and weren’t afraid to go talk to a bunch of customers. You saw so many conversations about like, how do we make this particular API parameter better for everyone? And like hours and hours and hours of like making sure it was a really, really good product. And people who weren’t afraid to like, you know, take a week of work and just like dump it away because it wasn’t quite there. So it was like,This combination of like, they worked really hard, they’re really smart, and they care a lot about the end result and have a high quality bar. That was Stripe’s version of kind of like talent density. But I think like, you know, if you look at the labs, if you look at different research institutions, maybe it’s just, you know, I don’t know, the raw ability. Yeah. But.Grace Shao (07:26)research capabilities or whatnot, right? No, that makes a lot of sense. Yeah, I wanted to ask you earlier on in our conversation, you said, you know, look, a lot of people overlook support. It’s not that glamorous. People kind of think it’s like a back office thing. But, you know, is that was that your view back then? How does you kind of, I guess, lean into this? And did your perspective or support change over the years? Now you say it’s very important, right? Did you understand the category correctly? Do you think?John Wang (07:52)You know, I think that when we looked at the category, we went at it from like kind of the lens of, Stripe was this company that worked in this unsexy space and did really, really great things. And we thought very similar things about support. It took us a long time to really grock support. And we talked to hundreds and hundreds of different people across different parts of the support stack. AndI think early on, honestly, it was good and bad in certain ways. It was like, we thought we could build a piece of software really quickly that solved everything. Or like, you have that problem, we can build that in two weeks. Not a big deal. And we could solve the specific problems that they had in two weeks. And I remember talking to actually a few people, which was like, the system that you’re trying to do, which is called Workforce Management for Support.that’ll take you seven years. And we’re like, no way. Like we can do this. We can do this so fast. It’s going to be done soon. And now like seven and eight years later, we’re still working on it. We’re still uncovering more and more things. And that was probably the right, you know, that was probably the right call. But also there’s like some importance to naivety, which is like, if we had known that we wouldn’t have started. like we, yeah, like.Grace Shao (09:06)That’s why a lot of people say, yeah, as founders, right?John Wang (09:09)Yeah, so I think it was the right thing to do, which is just like start building stuff.Grace Shao (09:14)It’s amazing. ⁓ Why don’t we pivot into actually understanding your product bit better? So for someone who has never worked in support ops, what is the simplest way to explain to them what Assemble does? Because even between us, we had calls, we had back and forth emails. I was like, John, I don’t understand what you guys do. I’m trying to read through this material. I’ve listened to a few in the interviews. I don’t know what’s happening. Can you just dumb it down for me and explain to me what exactly you guys do?John Wang (09:37)Yeah, for sure. Let’s say you have like 10 people on your support team and you only do email, then you probably just staff them nine to five, right? Like there’s no big deal there. Once you start having a few more people on your support team, let’s say you have hundred people now and you might want to chat to your customers because AI chats, AI chat bots are a really big thing. Then you actually need to start thinking aboutwhen do these chats come in and how many people do I have in order to handle those chats, right? Because like, if you were talking to a chatbot, you’re getting instant responses back and forth, back and forth. And then you’re like, I have to wait 48 hours for the human response after I get handed off. That’s a really bad experience. So the problem is, you you’ve got a bunch of people who are calling in to support, writing in, who are chatting in.and they’re coming in at all different times of the day, they’re calling in for different types of problems, right? You, on the kind of like back end, you have a bunch of people and those people might be able to do different types of things. Like I might be a really good person to handle, you know, where’s my money kind of issues, but I might not be as good at like ⁓ fraud issues, right? Like if you’re having problems with fraud on your account.So there’s a lot of ways in which you can actually put people to the actual incoming tickets. And what our platform does is it tries to match those two things up. if you think about supply and demand, supply is the people that you have and demand is the people, like your customer is asking for questions. And if you don’t match those up well, you’re gonna either...spend way more money than you need to because you’re just going to staff everything way above what you need, or you’re going to have a terrible customer experience because it’s going to take you a really, really long time to get back to people. So it’s really an efficiency play. How do we make it really, really efficient for you to answer questions? In the last few years, we’ve also added AI agents, which is, you know, how do you actually respond instead of just with people, but also with AI togo and answer a chat or answer a phone call directly using AI.Grace Shao (11:50)That’s amazing. I really didn’t know there was so much like science kind of going behind that. I just thought kind of like you’re on a chatbot usually you have to have your frustrating like get me someone, get me someone. I’m one of those people who like no pages pressed zero all the time. I’m like, get me a human. But it makes sense. actually once you can match the talent with like the issue, it can be a lot more efficient in solving the issue and the customer experience will be much better as well. On the AI agent side.What’s the kind of, I guess, consensus right now? Like, are they really actually good at solving issues? Are customers complaining about them? Like, ⁓ how sophisticated are they at this point? He’s like, in my day to day, you know, obviously calling the banks or DHL for pickup or package returns, whatnot. None of those agents are really a pleasant experience, frankly.John Wang (12:35)Yeah, I think this depends pretty drastically on what tools you give these agents access to. I would say that the standard experience right now is fine. It will answer knowledge questions for you. And these can solve anywhere from 30 to 60 % of incoming issues, depending on how many knowledge questions you get.the place where it really is important is when you actually give it access to say your backend database and you can like make a refund or you can look up in order or you can identify why is my what’s going on with this error, right? And that is actually the hard part that prevents most of these banks and airlines and etc agents from being very good is because like thatAccess to data is a thing that they need to actually run and be able to perform actions. And then also the evals for that are really, really hard and not something that you just like launch without really thinking about it. So I’d say it’s in a progressive, like it’s in a progressing state, not at a place where it’s like, this is absolutely solved, but there are also some of our customers who have 90, 95 % of all issues who are able to be completely automated.because they’ve spent the time to give access to all of these systems and spent the time to validate that the agents are performing.Grace Shao (14:00)Very interesting. ⁓ I want to pivot into the to be kind of angle. Who are you guys actually selling to? Like who are the people inside companies that are managing this? Is it the head of support operations? And when they are buying and assessing your product like yours, is it really winning on price? Is it like over, you know, other maybe large softwares? Is it winning on speed and service? Like help us understand essentially how you guys are succeeding winning over customers.John Wang (14:26)Yeah, we generally sell into the head of support. Sometimes that person rolls up into the COO or there’s a head of operations or something like that. But generally there’s some group that is working on unsupport related things and that’s who we sell into usually. I think generally our differentiated, like the way that we actually go and sell this is one, we know all about workforce management, which is like a really, really nitty gritty detail about how youmake your systems really good. And it can save you millions and millions of dollars. Almost actually, and this is one of the things that’s really funny, it’s like using our AI agents versus using our workforce management, we actually see somewhat similar gains across those two. Because to use the AI agents, you’re usually doing it so that you can reduce head count, right?And in order to reduce headcount, you need to know how much can I reduce headcount without hurting my customer experience. And for that, you generally need something like Workforce Management. So what we do is we go in, usually we have Workforce Management helping you understand how your system is set up. And then what the AI agents that we can also bring in is a relatively easy sell becauseour AI agents are really, really connected to kind of like how you staff and when you pull in people. The thing that you were mentioning, which is like, hey, my bank still doesn’t have a very good experience, that’s true of a lot of places. And getting access to information is really hard. So escalating to a human actually happens pretty frequently, sometimes 20, 30, 40 % of the time. So getting to the right human or the rightor figuring out when to escalate to the right human is a really, really important skill to have. If you spend a million dollars a year with me, I should escalate you much more quickly than if you are a free user and you haven’t spent any money for me ever. Similarly, depending on the topic, depending on what kinds of things you’ve already previously talked to me about, I should be able to get to different types of agents and I should be able to have different levels of thresholds.that send me to a human. And I think because we have and handle the workforce management side, our ability to do the handle time and to make sure that you’re getting to the right person is much, better than a lot of our competitors.Grace Shao (16:45)So there’s an unspoken tier system then I guess with customer service as well that we don’t realize. How should we think about the economic importance of support operations? In terms of, we always think of it as like we said, back office support, but how much, do you have any proof that like basically better customer service equals better revenue?John Wang (16:52)There is, and sometimes it’s spoken. But yeah.You know, that’s a good question. should probably have some specific proof here. I guess the best anecdotes I can find are usually the kind of like medium to long-term anecdotes where companies that do not invest in their customer support tend to, you know, regress to the meat, right? Like if you are really trying to bare bones your way through customer support,⁓ Your customers will understand that and it’s not going to affect your revenue right now, but it will likely affect your revenue in 6, 12, 18 months the next time that purchase happens. have seen actually some of our customers, so in our AI agents, we have a configurable setting that’s like, do you want to be containment focused or do you want to be escalation focused? And how good of a customer experience do you want?And we’ve generally seen actually that there’s a strong correlation. Obviously we haven’t run a ⁓ natural experiment or a true A-B test with this because it’s pretty impossible. But you see a general correlation between the customers that spend more money on support, the customers that spend more on trying to have a high quality experience, and the revenue growth of those companies.actually most of the customers that we spend a lot of money on care so much about support that they actually have, you know, executive briefings every week about these, about what’s going on. And they’re the people who have the largest support teams and they’re the people who kind of like make the most, make the most changes with their team. Obviously this is a very biased perspective from our customer, like set of customers, but I think that there’s still something to that where if you spend money and if you want to make ayour support really, really good, that does tend to pay off with customers because they do tend to notice and it makes it easier from a product perspective to paper over all of the things that aren’t so great.Grace Shao (19:06)Yeah, no, totally makes sense. think even as consumers ourselves, we would be likely turned off by certain brands or experiences if the customer service really bad, right? Unless, like you said, they’re a monopoly and there’s nowhere you can go. All right, let’s talk about AI. You kind of touched on that earlier, but the naive view is that AI automate support, you know, a lot, a lot of the conversations right now about, my God, jobs are going be taken, especially the first batch is probably in roles like operationals and customer support roles.Second batch, people are saying are maybe in like more repetitive execution roles like junior consulting roles, a lot of junior training up roles, right? How do you see that? Because at least from where I sit in Hong Kong, a lot of stories are coming out saying markets like India, the Philippines, know, across Southeast Asia where they traditionally served as those telephone call centers or operational centers, they are getting caught. Is that going to be a trend forward?you know, how should we understand this?John Wang (20:02)Yeah. Yeah. I think there’s a few things. There’s like, like with all things, there’s a lot of nuance to this, which is I think your trend on seeing, you know, what we call tier one support, the first line of support who are traditionally humans outsourced. That is a place where we’re seeing a lot of change. And I don’t think that trend is going to slow down. That said, there’s a very interesting other trend thatwe’re seeing, which is that total spend on humans and headcount isn’t necessarily going down by that much. And it’s kind of like Jevin’s paradox where we see a lot of our customers and a lot of customers of other AI users ⁓ who have amazing resolution rates. They’re like answering so many questions, but that’s causing actually, or maybe there’s some correlation here ofthe number of tickets they’re getting and the number of chats they’re getting is like way, way, way higher than before. And I think there’s a few parts to this. One is you see way more ability for your AI agents to answer questions. And so obviously people are going to ask more questions because like, Hey, it used to be really hard for me because I had to literally type out an email to a human, wait a few days and get an answer. And now I can just like get an instant really good answer. Right. So I’m going to try asking more questions.The second thing is, as these companies do better and better, you actually just have this natural induced demand of increasing usage, numbers of people who are asking for support. So the higher amount of support that is automated is also, the general number of how much support is coming in is also very high.And so that actually offsets a very, very large portion of the head count. The head count is changing though. It’s not going to be the typical tier one support where it’s just like, answer an easy question. That is mostly going to go away to AI, think. The types of head count that is coming in are like, know, internal agents, people who are really good, people who can provide white glove support and like...actually go talk to people and provide like a human experience because like our companies still want and really crave giving that experience to people. And that’s just not what the kind of BPO standard really is. So I think it’s changing in the type of what you would see.Grace Shao (22:31)Yeah, I was actually going to ask about that, like, as in when AI agents start resolving more tickets, if we’re just going to see reduction of headcount. And I think you answered him when you once wait, whereas like, yes, in initial stages, but later on, there will be new jobs created, right? Essentially, people who will be managing more critical issues or even managing the agents. I want to understand. So for your company right now, essentially, are you a are you like a middleman between the human agents and the AI agents and becoming the orchestration layer, like you’re providing the service, the training and the orchestration. Like, how do we understand that?John Wang (23:05)Yeah, that’s a great question. So we think of ourselves as how do we get you to the right way to answer your question, right? In our view, there’s kind of three main types of people that can answer a support question. One, it’s AI. Two, it’s a tier one BPO’d outsourced agent. And three, it’s an internal agent who’s like super well-trained and like super, like, you really carrying about, like really trained on customer support. And what we are trying to do is make sure that you get placed at the right area, depending on what kind of issue you have and who you’re talking to and like what is the kind of like a cue that is backing up the set of people who need calls. So for us, what we’re trying to do is really provides you that ability to choose across a bunch of different options. So we don’t actually provide any, like we don’t provide any BPO agents, we don’t provide any internal agents. All we do is provide the software that routes you. And we also provide the software that can do the AI agents, or you can actually plug into a different piece of software if you want to have your own AI agents too.We’re trying to make sure that we are kind of third party and that we are making it really easy for you to optimize your support regardless of what specific providers you use.Grace Shao (24:30)So in your view, what does a well-run support organization look like in, let’s say, three years as AI adoption becomes mainstream or more more mass market?John Wang (24:38)I think you’ll probably want to have all of the different types of support using AI. So voice AI, chat AI, email AI. I think you’ll want to have a lot of nuance between the different types of customers that you have. You can’t generally provide the best level of support for literally everyone. Though this depends on also your customer base, right? Like a consumer customer base versus a super enterprise customer base with 100 very large customers is completely different. But let’s say for a standard company that might have ACVs that are in the, I don’t know, the 100 to couple tens of thousands range, then you’re probably going to have a combination of AI agents and human support. And you might have different tiers of human support, right? Some human support that’s really good at answering support questions and other tiers of human support, which is like, you’re just managing the agent. I think the other thing that’ll happen a lot is you’re gonna start to see more like, supporting agents acting in a simulation where right now, like the kind of typical flow is like a supporting agent gets a ticket and they answer it and it goes back. I think as the agents get more like, get more and more training data, get access to more information, really they’re only gonna come to humans for escalations. And similar to how Waymo works, if you’ve ever taken a Waymo, it’s a great experience, you’re like driving, driving, driving, and sometimes you get kicked out and a human operator in the Philippines is like, hey, I need to move you around this truck, right? And similar to support, That’s probably what’s going to happen. A human operator is going to come in and be like, hey, I can give you a refund right here. And then what’s going to happen is the AI agents are going to train on that, right? They’re going to like learn and get better. And you’re going to be able to use that whenever you have an interruption to understand like, why did I have this interruption? How do I make my model better for the future? And then you’ve got your closed loop. So I think in the future, you’re going to see much more of that happening than people who are just like, coming in and their job is to solve as many tickets as possible. I think the change is gonna be like, okay, people are gonna start to need to provide the best possible response in that particular instance so that the models can train on that and be as good as you are.Grace Shao (26:58)actually just on that, do you think then we’ll see more and more fraudulent activity or people trying to exploit that? like if say you know the models trained on, I say this one buzzword or one keyword and it triggers like refund. What if I just go on the call like on the phone all the time, just to be like keyword, keyword, you know, and then like how do we prevent something like that? Or do you guys kind of get involved in that building this guardrails as well?John Wang (27:21)Yeah, no, that is a age old question. think like, wouldn’t say there is going to be necessarily more or less of that, but I think like, it’s kind of like the cat and mouse game of like, everyone has always been doing that. And so like, and the methods always change every, every few months. I think the methods will change every few months here too. Our AI agents have a lot of guardrails put in place to automatically detect that. And we also have kind of like post-hoc guardrails which are like scanning through our logs and trying to identify situations where that might have happened. And we’re also training on those examples, right? So I think, yes, people will definitely start to exploit this and be like, hey, how do I get a refund faster? But there’s a ton of guardrails that you can put in place. For example, each account, you can have one or two, have like refunds without looking until that actually gets flagged and it needs to go to a human or.You can set good policies, for example, like, you know, if it is within policy of 30 to 40 days after purchase, like automatic refund, otherwise, you know, flag it and do something with it. So there’s a ton of stuff that you can do to actually like reduce the possibility of that. And I do think that it will end up being cat and mouse game like over and over again, as people get more sophisticated.Grace Shao (28:39)Right, right. And they’ll start using AI to trick AI. That’s what’s scary, right? So as we talked about, different gender standing the podcast does not have to interview anyone related to China or Asia, but we do have kind of an Asia angle to a lot of how we view the world. So my question for you really is because you’re like out in San Fran and like your company actually has no sales in China or anything. But I actually had a curious question. How does SFJohn Wang (28:43)Totally, yes.Grace Shao (29:05)as a whole, the startup ecosystem kind of view the current rise of a lot of Chinese AI. And have you guys yourself or your peers, you know, tried to use Chinese open source models over the years? Is there any view on the open source models given that, you know, you previously said you were very involved in open source and I think it’s part of your philosophical belief as well, right? So just kind of like the high level vibes.John Wang (29:28)Yeah, our vibes might be different than at the model, like the Frontier Labs, honestly. Our vibes, we love the Chinese open source models because it adds more competition. And I think the open source models are actually very, very good. I think from my friends at OpenAI Anthropic, they don’t like it quite as much because it’s competition. But for us, we have no allegiance really to any of the Frontier Labs.or any of the models that are out there, we want to provide the best possible experience to our customers at the best possible price. And that has meant, you know, over the years, like making changes in our models, making updates and to figure out what is that frontier of cost or performance. The Chinese models tend to perform really, really well on that, especiallyGrace Shao (30:12)Mm-hmm.John Wang (30:19)kind of like the latest series of models, we’ve actually spent a lot of time in the last six months kind of like pulling out a lot of our tokens. We have tens of billions of tokens per day. And a lot of it now goes to models like Quen or Kimi. And like that has actually started to really, really increase over time, mostly because you can find to them, you can do RL on them, you can...have better latency on them, you can run them on your own hardware. There’s just like so much more stuff that you can do with it. And also, you know, the cost performance latency trade off is really, really good. Now, most of our most of the like the strategy we take is actually one where we try to understand the use case and the problem and what type of model is necessary for that. So for kind of like the main model that’s actually answering questions. We’re actually usually using a frontier model for that. But actually the majority of our tokens come from out of secondary processes, processes like detecting if I need to escalate, detecting if there’s a fraud here, detecting if there’s an adversarial intent, making updates to large swathes of data in batch, like all this other stuff where you really don’t need frontier level intelligence and where if you have a a well-tuned prompt and an open source model or an open source model plus a fine-tuned model, you can get at or better in terms of frontier performance. We’ve really seen that and we’ve actually been able to save millions on our token costs in just the last two or three months by being very smart about how we use our models. And we’ve also seen a 15 to 20 % increase in quality.⁓ Just because like when you go and you have evals, you can make things much, much better more quickly with these open source models.Grace Shao (32:14)Yeah, I think that’s like the general sense I kind of get from a lot of startups, right? In a known day, it’s like, you guys are obviously more cost conscious. What is the best price to get to what you need? And there’s like a tier system where how you use the models, you might not use the most frontier models for everything. I think that makes a lot of sense, business sense, especially. Is there anything you would like to share with us that we haven’t touched on in terms of, just the overall AI ecosystem, any thoughts on, you know, where we’re going with this AI agentic push right now?⁓ you know, are we really going to see that, you a giant moment, like just kind of some high level thoughts.John Wang (32:49)Yeah, that’s a good question. Recently, I’ve been thinking a lot about Opus 4.7, which got launched a few days ago. And it’s actually kind of similar to what we were just talking about in terms of this price for performance ratio. And it seems like, based on my usage, based on our evals, based on other people’s usage on the coding side, that it’s a better model, but it is also more expensive.than before. like, you’re really it’s literally like a trade off in terms of dollars and intelligence. And it’s really interesting because, you know, a year ago, every model would just be like, this is strictly better, and it’s probably cheaper, and you’re to get more context and like, everything’s better. And you could basically just bet that you’re just going to like get better models across the board. And now actually, you’re just like kind of moving from this part of the like the frontier curve to the other part of the frontier curve without actually shifting the entire curve. And that’s happening with a few more model releases. You still see general increases in the frontier, but it’s less stark every single model release that you see that. And so I think it’s just an interesting area to look at because when you get into that world.Gross margins has become really important. Gross margins for ourselves as a startup, but also gross margins for Anthropic and OpenAI. One of the funny things that I’ve seen, just talking to people who are working at Anthropic and OpenAI, and also people who are trying to invest in those companies, gross margins are actually incredibly important. One of the OpenAI right now is becoming a much more...hand investment than before. And like, it used to be like six months ago, it’s like, you have it, you have shares of OpenAI, like, how do I get in? Now it’s completely different with, you have shares of Anthropic, how do I get in? And I think part of that’s because like, OpenAI wants to spend $100 billion on infrastructure. And Anthropic is a lot more measured in the way that they’re spending money. And I think gross margins actually do matter a lot right now. And that’s where I think actuallyChinese open source models are making a big difference because just at the end of the day, you still have to make money. And if you’re losing money on a per token basis, that’s really bad because if you go to infinity, you lose infinity money. And if you make money per token, great. Ramp usage up as high as you can.Grace Shao (35:03)Yeah. It’s just so crazy how the sentiment shifts like so every three months I feel like and then to your point like whenever I speak to investors like oh my god I got my hands on some anthropic shares and last three months earlier. Oh my god I got my hands on opening I like it’s just like and like oh no one would invest in opening right now like I don’t want to do that like people just completely go like black and white on these things it’s pretty crazy how the pendulum swings I do have a question actually on the infrastructure side doesn’t it actually make sense for open AI to eventually own their infrastructure because otherwise they have to becomecontinuously constantly pay the hyperscalers for all the infrastructure like so in the grand like scheme wouldn’t it make sense? I mean although obviously how much you’re spending is like absolutely crazy.John Wang (35:55)I think it actually does. And I think that’s like part of the problem, which is like, you know, if you think about what their compute costs are, I think actually doing all of these things that they were doing makes perfect sense. And it makes especially perfect sense if you have investors who are willing to bankroll this. But it’s almost like the, ⁓ what’s that paradox? It’s like the St. Petersburg paradox, something like that, where it’s like, you keep doing,your expected value is infinity and you keep doubling your money basically, but at some point you need to not double your money because you don’t have enough money.Grace Shao (36:32)That’s such a mo- I’m like, I’m still confused when you’re saying, go back. You keep on doubling your money.John Wang (36:36)Sorry, So I think the I think it’s like Let me let me look this up st. Petersburg paradox is Okay, it’s a coin flipping game and You start at two dollars and with every tails you double the pot and you can basically decide to like take your money at any time, right? and so you you’re doubling exponentially as you go up andIf you compute the expected value, you should basically just like, keep going forever because your expected value is like infinite, right? Like because the doubling of the pot is better than kind of like what your losses are. You just got to, you got to run. And I think OpenAI is in this St. Petersburg paradox where it’s like, well, in theory, double everything, keep going. But in practice, you don’t have enough money.Grace Shao (37:17)Yeah, I see what you mean.John Wang (37:25)and resources to be able to do that. I think that’s actually what’s happening is like, there’s not enough money in the world, not enough investors with liquid cash who are willing to invest in a business as big as OpenAI while the gains and the returns are still, yeah, having improvements. So I think it’s both rational, but also, you know, actually practically very hard to make what they’re doing.Grace Shao (37:41)haven’t been proven. Yeah. totally. Okay, I want to ask you one last question, which I ask every single guest. What is one differentiated view you have? It could be on your own sector, industry, life.John Wang (38:00)man, have a really like, I have one that like is very controversial. I don’t know if I should talk through that one. ⁓Grace Shao (38:07)You’re get doxxed and to hate it after this.Okay, tell me that one after, I wanna hear it.John Wang (38:17)Yeah, yeah, Let’s see. Like... I would say, I don’t know if this is differentiated now in the market or not, but the thing that I’ve been thinking about recently is that you should stay somewhere long enough where you see your mistakes through. And I think it’s like slightly differentiated right now, because like you’ve got in Silicon Valley, at least you’ve got people who are jumping between big labs, who are jumping between different startups where it’s like, Hey, I can make the next, you know, $5 million.by going to this next thing. And there’s just a whole huge amount of opportunity and there’s like a ton of opportunity costs to staying somewhere for a long time. And at the same time, think like long-term staying somewhere for a long time is actually one of the best things that you can do for your own learning. And it gives you that a better shot to make like the long-term massive gains that you could have like $5 million.is amazing for someone. But if you want to build your own startup, if you really want to like change everything, if you jump around between companies every year or two, like you’re probably not actually going to learn a lot. And you’re probably not in the position to make really hard decisions and then have to see those hard decisions through and then, you know, be able to learn and see that feedback from those hard decisions. Especially if you’re jumped likeEspecially if you’re like at OpenAI and you’re like, no, investors don’t want this anymore. You jump ship to Anthropic. That’s like, you know, I don’t think you’re going to get that, the learning that you really need to get.Grace Shao (39:46)Yeah, yeah. actually agree with that. think I also took some time and like experience to realize that because when we’re all young, like you’re really excited, right? It’s like, this looks cool. That looks cool. this person hates me. hate that person. Like you take everything very personally and then, you know, we’ve all heard these stories from peers, even ourselves. But what is the threshold though? Because then the other side of the argument is that like you see people who’ve been in a job for like a decade and clearly they’re frankly not.moving up in a very corporate structure way or even intellectually growing or even, you know, excited about their job anymore. You know, the joke is like you get the like, okay, this sounds on PC, but you know, like the 45 year old VP that’s been a VP for the last 15 years at banks, we have a lot of these. So what happens? Like when is it best for them to actually maybe jump or some say, that was like a lifestyle decision where they want to take it easy because they have some more time for kids. Fine.But taking that kind of considerate way, wouldn’t it sometimes be better that you jump to try something new to take risks?John Wang (40:50)I think if you are in a place where you’re unhappy with, so I will caveat this with, have to, you should only stay if you’re excited about what you’re doing and you’re learning continuously and you’re surrounded by great people. If those three things aren’t true, yeah, it’s really hard to fly.Grace Shao (41:05)Which is so hard to find. you were very lucky at Stripe, right? Like you said, you were just surrounded by very high caliber, high agency people, but not everyone can get all those things at the same time. ⁓ But no, that’s great. Thank you so much, Sean. ⁓ I had a lovely time chatting with you. I still wanna follow up on what was the unspoken differentiative you later. All right, thank you.John Wang (41:17)Yeah. ⁓ Let’s do it. Let’s do it. Thanks, guys. Get full access to AI Proem at aiproem.substack.com/subscribe
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  Matt Sheehan on China’s AI Policies: Employment, Anxiety, Safety, and State Priorities 27.04.2026 1t 1min

  Today, I’m joined by Matt Sheehan who writes this insightful newsletter. Matt is a senior fellow in the Asia Program at the Carnegie Endowment for International Peace. He researches China’s AI ecosystem, Chinese tech policy, and how technology shapes the country’s political economy.Matt lived and worked in China from 2010 to 2016 and later led China tech research at the Paulson Institute’s MacroPolo. He’s the author of The Transpacific Experiment. He speaks Mandarin, and he turns complex policy into plain English.In this episode, he helps us understand China’s AI governance, about how Beijing is thinking through the social and political consequences of rapid AI adoption. We focus especially on a shift that became more visible in early 2025: rising concern inside China’s policy community about AI’s impact on jobs, worker anxiety, and social stability.Matt explains why China’s AI labor question is different from the Western debate. We also discuss how the Chinese government is trying to balance support for technological progress with the need to manage public anxiety, clarify labor rules, and avoid social instability as AI becomes more deeply embedded in the economy.He broke down the myths, explained the jargon, and the regulatory bodies in China. Our conversation started slow, but it became very, very heavy, what they call 干货满满 substance heavy. Also, a shoutout to Nathan Lambert’s work in helping us better understand the open-source ecosystem and Rui Ma’s for helping us understand investing in China AI!Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently. Season two will host a series of guests from early-stage investing, as well as builders, founders, and product managers.For more information on the podcast series, see here.To find the previous episodes of Differentiated Understanding, see here.Chapters00:00 Introduction to AI Policy in China03:10 Matt Sheehan’s Journey into Chinese Tech Policy05:55 Shifting Perspectives on AI and Labor09:02 Public Concerns Over Job Security and Government Responses15:09 Education and AI: Preparing for the Future17:50 Regulatory Landscape of AI in China34:00 Navigating China’s AI Regulatory Landscape40:58 Misconceptions About Chinese AI and Government Funding43:57 Understanding AI Safety and Security in China52:03 Global AI Governance: Cooperation or Parallel Paths?AI-generated transcript Grace Shao (00:01)Hi Matt, thank you so much for joining us today. I’m so, so happy to finally have you on the pod for people who are listening. We’ve been trying to make this happen for like six months, but between us, there are like three little children running around with a bunch of viruses and have just not been able to make this happen. I’m really excited. ⁓ A few months ago, what really caught my attention about your work again is that you shared something on WeChat saying you were dissecting the new Chinese AI safety paper, like the big national one. ⁓like verbatim in Chinese. And I was like, wow, this is extremely impressive. It’s not an easy task. I commend you for doing that. So I really wanted you to help us understand the nuances of the AI policy world, especially how people are perceiving AI in China. I think there’s more more interest in how China’s governing AI ⁓ while we were hearing the backdrop of how the Chinese government is trying to push on AI diffusion, right? And then on top of all of this, like where areas where China’s AI governance seem to be leading, because in many ways it seems likeChina’s AI regulators are much faster to respond to how fast technology is evolving. But to start, we would love to hear about your personal story. Tell us about how you ended up studying China, studying Chinese tech policy. We met in Beijing years ago, maybe a decade ago. ⁓ Yeah, so tell us about that.Matt Sheehan (01:11)Sure.Yeah.Yeah, sure. Sort of stumbled into China stuff. I hadn’t taken Chinese or really knew anything about China until about halfway through college when I ended up getting a summer job in Beijing. I was just kind of like instantly fascinated and knew I wanted to move back there after I graduated. So took a little bit of Chinese my senior year, moved to Xi’an, taught English, kind of followed what at the time was a very like typicalknow, trajectory of like, go there, teach English and then go study Chinese at university and then get a job and get a slightly better job. And eventually I was able to kind of wiggle my way into journalism. And so I was a China correspondent for a publication called The World Post at the time. And that took me up. was there from 2010 to 2016. So kind of like the hinge period before and after she came to power. Pretty interesting thing to see. AndWhen I moved back to California in 2016, I started working on a book about China-California ties. I’m from California and this was like the period of kind of explosion in cross-border investment and Chinese students come into California in the Silicon Valley-China relationship getting even more like twisted and complicated. China-Hollywood. So I wrote a book about that and as I was doing it, the kind of the tech section, the China-Silicon Valley, China-U.S. tech connections kept growing bigger and bigger and I ended upworking a little bit with Kai-Fu Lee on his book, AI Superpowers, which was kind of my turn from like, it was like all things China, China, California, China, Silicon Valley, China AI. And since 2017, I’ve been working almost exclusively on AI issues in China. Maybe the first three years of that, like 2017 to 2020, was very focused on comparative capabilities. This was kind of a period right after the National AI Plan in China when there’s a big explosion in activity. And I think...This is kind of was like the first time America kind of got freaked out about Chinese AI capabilities. And so I spent a few years being like, okay, let’s try to like ground these assessments in some data. Let’s get like an actual grounded sense of where the countries are with each other. ⁓ And then starting in 2021, I sort of turned into focusing on Chinese AI governance, Chinese AI regulations. That’s when they first started rolling out their regulations or recommendation algorithms and sort of deep fakes. And I was kind of making a bet that I thinkIf China continues to be at or near the frontier of AI, then how they choose to regulate it domestically is going to have huge implications for China’s own ecosystem. And then it’s going to really ripple out internationally on safety, security, growth, all this stuff. So I spent the last, now it’s like five years, ⁓ just deep in the weeds of Chinese AI policy and regulation.Grace Shao (04:04)Oh, great. I think I definitely want to double click on all the algorithm security and the kind of, you coined the answer versus what’s called security and versus what’s the other Chinese word? Yes, yes.Matt Sheehan (04:17)Anshun safety security. ⁓Jeff Ding was talking about this very early on, but yeah, it’s a, it’s a constant thing that we have to negotiate for people who don’t know it’s the word Chinese, the Chinese word Anshun ⁓ means both safety and security. whenever you’re kind of translating documents on this front, you have to know, you talking about AI safety, which is kind of a different thing versus AI security, right. I think both you and Jeff definitely are some of the more nuanced scholars I follow. And I do want to kind of double click on that later on. But to start, I think I want to talk about something that’s top of mind for a lot of people. You just wrote a piece that you said was not super serious. It was just your scattered thinking put together on subset. I thought it was very well written about the growing anxiety around potential job losses. ⁓your perspective is that you know there are more and more people voicing this kind of concern and I wanted to hear a perspective on that and I kind of wanted to share a bit of my different share my different perspective on this and what I’m hearing on the ground and kind of have a conversation around that as well. Yeah why don’t you start with sharing like what you found yeahMatt Sheehan (05:22)Yeah, sounds great. Yeah. So, I’m not an AI and labor person. That’s not been my focus for a long time, but I’ve been monitoring it for a long time and just lightly. And starting around, I guess it was early 2025, I just started to hear a lot more out of the Chinese policy community about worries about AI’s impact on labor and jobs. And this was kind of a surprise to me because ⁓ just a little bit prior to this, say early 2024,I had, I sometimes ⁓ in my job, I run these kind of like informal surveys or almost like a, what do call it, focus group of American and Chinese AI policy people and asking them like, you how would you rank these different risks? How concerned are you about ⁓ job risks versus privacy versus military AI? And we have both sides that like rank the risks and then talk about the results. And when I ran one of these in early 2024, it was very striking that the Chinese sideI think it was at the time we had seven different risks and the Chinese side ranked labor impacts a second to last as six out of seven. And so my sort of baseline was like, OK, for a variety of reasons, this isn’t really too on the radar of China’s ⁓ policy community or wider policy community. And then starting around early 2025, some of those same people who I had been talking to about this before had really changed their thinking. They were saying that there was a big change in thinking withinChina, maybe especially within China’s of policy and government circles, but then I think also a little bit wider. And so that sort of sparked my curiosity. And for the past, now it’s like over a year, I’ve been just sort of tracking when does this AI and labor question show up in state media? When does it show up in kind of the online discourse? When does it show up in policy documents? And sort of the TLDR is like, I think this is really, really ramped up a lot. Over the past 18 months. It’s been maybe the single biggest change in how China perceives different sort of risks as it relates to AI. And I think I’m looking forward to sort of discussing how maybe like the policy world or the government’s perception of this differs from ordinary people or certain categories of people. ⁓ But I think it, from my perspective, it’s sort of it’s infused into both. think there’s been a fair amount of public concern.the policy community picks up on that and they want to both respond to like the actual problem, know, actual job losses, but they also really want to respond to people worrying about job losses. That’s kind of maybe the thing that actually made me write this piece now was discovering an interesting piece in state media. I think it was in science and technology daily. That was the headline was like ⁓ AI must be controllable, but people’s ⁓ anxiety about AI must also be controlled.And it was all about how sort of OpenClaw has triggered a lot of anxiety and a lot of people about, are they going to get replaced? You need to be building your own AI agent in order to not be left behind. And they’re sort of trying to tamp down those concerns in a few ways. So that’s what sparked the piece itself.Grace Shao (08:33)Yeah, I think definitely what you saw and you wrote about is like definitely kind of playing out in the China AI policy ecosystem that I see as well. And I think for sure, the open-claw frenzy have kind of opened the eyes to lot of the even average people what AI could potentially do. However, I guess my argument, not against it, but it’s just like, you know, we kind of cite each other’s work on subset. But my point was kind of saying, you know, this is a reflection of a relatively elite group of people end of the day, because the knowledge work economy in China end of the day is only like only 30 % of the workforce actually are the knowledge working economy. And end of the day, even though it’s 30%, because China has such a huge population, the mass, the sheer scale, it feels really large. However, I want to bring it back to the idea that like anyone who’s lived in China understands that the government’s like top top priority really is about social stability which leads to what they call social harmony, right? And I just think that, you know, the rising anxiety of job control a lot of times maybe is because there’s a fear of if there’s a lot of disruption to jobs then people will lead to social unrest which obviously gets a bit more sensitive but you know, a lot of what they do comes from that I guess thinking so I agree with you top down definitely have to understand what’s happening with technology and how advanced AI has become in 18 months.⁓ have given them kind of, I guess even fear mongered a little bit internally, right? ⁓ But the nuance here is that I think ⁓ the rest of the 70 % of the Chinese workforce actually don’t work in anything structured that we know. I think even probably even 80%, you know, people in China, they most of them are actually like, you know, service providers ⁓ from, you know, rural areas and urban areas. A lot of people work in factories, even the entrepreneurs, right? They run like say hospitals, clinics, factories, bottle cleaning, like factories, whatever, right? ⁓ Car logistic rental businesses, these people aren’t actually ⁓ trained in the way that maybe the West by default think they are. They actually just run it from a grassroots way and they don’t have very, very streamlined processes. They don’t have documentation. They don’t run like what we think a corporate has run. So in that sense, I think it’s very hard for AI to replace any of their workflow.because it’s actually not a, we can’t really provide context and a lot of things, business is done is through one C, is through a wink, through a look, through a gesture, through, you know. So a lot of that, I think, in fact, will be harder to replace than even maybe some of the more mature businesses in the West where there are structured processes and everything. So that’s kind of my, I guess, a more nuance, I think, push on that. Yeah, wonder what you think of it.Matt Sheehan (11:20)Yeah. Lots of thoughts. And I think the sort of the fundamental distinction that you’re pointing at is very valid in that like, you know, companies in the West, in the United States, they’ve been like big companies have been running sort of digital databases for decades. They have like decades worth of data. They have pretty advanced like enterprise software. It’s just a much more I want to say something like a bias, but it’s a little more like put together sort of official structured⁓ technological backend and not just technological, but like a process backend. Whereas in China, it’s just things have developed really quickly. A lot of it’s on the fly. lot of it is, know, enterprise software is just not, there’s not really a market for that in China in the same way. It’s a lot of stuff is pirated or they’re just not digitized in the same way. And it’s actually very interesting. This is in kind of the early days of the like China, US who’s ahead. ⁓ know, a lot of the debate focused on data.Matt Sheehan (12:18)And there was this idea of China has a billion people, so it must be this huge advantage in data. But my pushback on that was always kind of what you’re arguing, is like the US actually has very structured data, and it’s owned by corporations, it’s deployed by them, they’re already doing type of sort of lower end market intelligence type stuff. ⁓ So I think that that sort of backdrop is very real. think ⁓ maybe from there I’d like differentiate out to potential risks or debates. And it’s kind of what you were pointing out as well. There’s like the actual question of how many jobs are going to be impacted. How many people are, what is it going to do to people’s wages? What’s it going to do to aggregate employment? And then there’s the question of like, ⁓ how do people think about that? What are the fears due to sort of the Chinese social stability, even if the things haven’t manifested. So I think separating those out, definitely the government is... ⁓their sort of initial response is related to public worry about this. So in the piece, I detailed the way that ⁓ sort of a robo taxi incident in Wuhan was in many ways the spark that really like ramped up the government thinking on this. And this is something that I heard from a couple of different Chinese policy people who both pointed to this incident, which I had totally missed at the time and wasn’t like major international news, but that had a big impact. And basically what it was is thatBaidu was rolling out its sort of fully autonomous robot taxis throughout the city of Wuhan. There was this kind of like public letter, ⁓ open letter released by a taxi company that was kind of railing against, you know, both ride hailing platforms and autonomous vehicles as, you know, stealing the iron rice bowl or just smashing the rice bowl of taxi drivers and of companies. And even though it was a kind of like a small thing, a couple of days later, a Baidu taxi actually hit pedestrian, I don’t think they were seriously injured, but it kind of fed into this overall, a big kind of online reaction and discussion about like, what’s going on with AI? Is it going to take people’s jobs? And it, it’s one of those things, it’s funny to explain to people because it sounds like nothing, but it did lead to a pretty significant ⁓ imprint on the way that the Chinese government is thinking about it. So it was coming from, in many ways, public discussion of it. Like the discussion was happening online. This discussion might be happening among, you know, elites.chronically online people. ⁓ But it’s something that the government definitely picks up on. So I that’s one element. They’re worried about the worries and they want to, like among their sort of policy reactions in a ways, thus recently has been sort of directing platforms to say like, you kind of need to tamp down these articles or these viral videos that are telling everybody, like if you don’t adopt open claw, you’re going to be left behind. There’s been this kind of rash, both in China and here in the US of like,you know, kind of like fear mongering people into clicking and taking your course on building agents or just subscribing, whatever. And one thing the government is doing is telling you like, chill on that. Like, don’t be putting that narrative out there. ⁓ And so that’s part of this kind of like public opinion management thing. In terms of the actual impact on jobs and who will it hit?It’s a huge open question that I personally have gone back and forth on for years. I first kind of did a deep dive on this way back in 2017 when everything was still so speculative. At the time, was pretty not worried in part because of the reason you described it. I’m like, there’s just a lot of friction. There’s just so much friction in this economy. And just because an AI system can theoretically execute a test doesn’t mean it’s taking a person’s job. And for me personally, my thinking on this has changed a lot in the lastyear to 18 months, mostly because of how capable agents have proven to be. I expected agents to essentially be hitting a lot more roadblocks while they’re being deployed online. They haven’t been. They’ve been operating much smoother, or just they’re more relentless, and they can break through these bottlenecks. ⁓ It’s interesting that in China, the inciting incident was not about white-collar workers. It was about taxi drivers. ⁓I don’t know this as a fact, but if I had to guess, I would guess that a much larger portion of the Chinese population’s job is driving a car or driving a scooter or something like that. And that maybe that’s a vulnerability that they might face depending on how self-driving vehicles roll out or delivery robots and stuff like that. The knowledge workers, yeah, it’s such a messy and unclear thing, but I think the government has at least started to take it seriously because it’s not, their policy responses are not just this like,public opinion management stuff. They’re also talking about, ⁓ like one of the more interesting pieces that I highlighted is, and maybe the most concrete thing they’ve done so far is, ⁓ according to Chinese labor law, there’s sort of reasons why you can and cannot fire a person. There’s like legitimate and illegal reasons to fire someone. And when someone is fired and they object, this gets taken to like a labor law mediation ⁓ body that’s under the Ministry of Human Resources and...forget what the second part social ⁓ security. ⁓ Yeah, Ren Li, Ziyuan, Shouhui Bao Zhang. Yeah, that’s what it is. ⁓ And in the last year, one of the things that really made a lot of made kind of a big splash is that those mediation bodies declared and it was echoed in like the biggest state media that saying that you replace someone with AI that AI can now do this person’s job is not a legitimate reason to fire someone and those people have to be reinstituted into their jobs.That’s a concrete policy thing that’s actually directed quite clearly at like actual impacts. ⁓ Is it going to work? I really don’t know. It might just be a little bit of friction and, you know, maybe China’s kind of doing what it always does, which is like, we’ll figure this out. We’ll kind of muddle through this. We’ll put some friction here. We’ll grow a little more here. But I think the concerns are real, whether they bear out, ⁓ whether they hit faster in the United States or China, which country is better positioned to sort of roll out a more redistributive welfare system. think these are all open questions, but I think the concerns at least are real.Grace Shao (18:24)Yeah, and I think you hit something that I feel like it’s being kind of missed in the headlines, which is the government actually cares more about the general mass, which are the people who are driving the scooters and the like the DDS cars more than the knowledge workers, which is kind of different from the Western kind of conversation right now, where a lot of the whether it’s fact, frankly, the power that can lobby and the power that the voice that have the voice are all really concentrated in.the white collar elite jobs that are very much concentrated in Silicon Valley and whatnot, right? And I think it really reminds me of the time when, you know, during the Hulianmang Shidae, like the internet era, you know, like the big tech only really got clamped down when the average consumers felt like they were really being pushed to R-Shrine Egypt 2-1. So that’s when they had to add the monopoly to probes. And then soon after, only maybe two years after the probes happened, there was a common prosperity rollout, which basically all the big tech in some capacity had to like showcase that they had a CSR aspect to them. I think this is something that we don’t really see in the US as much with all the big techs, because it’s kind of like they’re doing what they need to do. They have their profit driven interests. And then of course, everyone has a CSR, but it’s not really allegiance to the government CSR mission. It’s more like, we believe in ESG. We believe in climate. Amazon is going to have some like carbon footprint reduction plan, right? Whereas like the common prosperity thing rolled out. ⁓you know, it kind of died on its own, like no one really talks about it anymore. However, during that phase, when it did get rolled out, it was like an understanding where, OK, if the government wants the, frankly, the poor or the middle lower class to feel protected, then you as a very large ⁓ moneymaker in the economy need to showcase that you are somehow ⁓ part of this kind of support. So I wonder how this will play out for the big tech in China when, the job protection policies really get rolled out in practice, like what you’ve mentioned. And obviously they can’t really say, you’re being replaced by AI. At least there’s that superficial guardrail there, I think.Matt Sheehan (20:24)Yeah, I think the sort of the political economy of these questions is going to be super interesting in both countries. know, essentially like business in many ways, it inverts the sort of technological impacts on employment that have been around for so long. Normally, like greater technologies integrated into the workforce, it hits sort of people working maybe low end manufacturing jobs or jobs that would be considered sort of repetitive and, you know, quote unquote, low skilled jobs, even if they’re not. ⁓And, you know, in the United States, we’ve seen like three, four decades of this. And the people who are concerned about that basically didn’t get hurt because they are not, like you say, part of these influential classes. AI is going to, yeah, in the United States is going to be very different. This is going to be the first time that you have the way that my sort of mental model for it is like, if you’re a senator and you have kids or nieces and nephews or your friends, kids like what, what are their problems?And like how close do those feel to you? And you know, if you’re a 60 year old senator and you’ve got like a 23 year old niece and she just graduated from college and she got a degree in, you know, something that’s like is legitimately employable normally like in marketing or something like that, and those jobs just aren’t there, that’s just going to feel very close to home for people in power in the U.S. in the ways that it hasn’t felt in past waves of technology impacts. In China, I...partially agree with what you’re saying, but I think there is also going to be a significant element of this sort of the same dynamic as the United States. mean, yes, common, you know, Xi, common prosperity. He’s focused a lot on sort of eliminating extreme poverty and, you know, the CCP, it’s in its bones that like the rural, the working class are in many ways kind of the long term support base of them. But I mean, also if you look back at Chinese history, like a lot of the biggest and for the government most dangerous protest movements came out of elite schools, came out of students at elite schools who ⁓ either couldn’t find jobs or were facing inflation issues or had, for ⁓ ideological reasons. I think that stuff does hit close to home. think some of those same dynamics, if you’re a deputy director at the National Development and Reform Commission.your family, the people that are close to you, are going to be the type of knowledge workers that are going to be impacted by this. And I think that just can’t help but kind of like compress in on the thinking on this. ⁓ You know, how AIs can... Yeah, yeah, and like how...Grace Shao (22:58)Yeah.everything becomes personal in the end. Like in the end, it’s likepolitics is still personal. Yeah. Sorry.Matt Sheehan (23:08)Politicsis personal and yeah, mean, like cities are where social instability is the most dangerous. Like cities are where people gather and you can have potentially dangerous incidents. These are the people who are very online and are sort of sparking or leading the conversation as much as that can be controlled and manipulated via censorship regimes or public opinion guidance. Like these people are gonna be vocal. yeah, I think if I was at the CSPI, I’d be concerned aboutGrace Shao (23:39)I want to kind of go into on education. Like you kind of touched on it, right? Like, you know, there’s been draft rules about children’s interaction with AI in China as well. There seems to be more guidance and obviously concerns around that ⁓ from at least from the top down ⁓ about their mental state, their dependency, or even what constitutes as an AI companion, how we should draw the line on that. We know likefamously a couple years ago, China installed this rule where like, you know, kids under 16 cannot actually play online games on their own without the parents consent. However, that you know, there’s obviously loopholes in practice. But again, it goes back to there are, you know, rules and laws in place to try to protect minors. How do you view all of this? ⁓ Because in the with the backdrop of China trying really hard to diffuse AI into the real economy. And then there is this pushback like you just mentioned onconcerns about AI taking jobs. I feel like there’s also almost like a ironic kind of contradiction happening where, you know, Tiger moms are like, okay, now we don’t need to learn math, we know how to learn AI. And Tiger moms are like saying, how do we optimize getting into, I don’t know, Harvard with AI’s help? And how do we get AI into the education, education, ASAP? I mean, honestly, we don’t even know what the education system might look like in like two decades.from our kids, but at this point, seems like there is like embrace. I don’t know. How do feel about that?Matt Sheehan (25:13)Yeah, a couple strands there. One just on the sort of regulatory side, like this is a long term strand in Chinese tech policy and tech regulation. They always put a pretty heavy emphasis on like how are kids using technology. They have, ⁓ they sort of mandated having like a minors mode on various ⁓ apps. ⁓ This is the regulation I think that you’re referring to as the newly passed. ⁓I translate as anthropomorphic AI. That’s the word that’s the official translation. ⁓ So it basically means AI that, you know, behaves like a human. This could include AI companions that are, you know, literally like a character pretending to be your friend. They could also include, you know, the way that people interact with chat, GBT or Kimmy or whatever, you know, the phenomenon of having AI boyfriends and girlfriends and all this stuff. So there was a new regulation on this that was just finalized, I think, last week andIt has some protections for everybody, specifically around ⁓ self-harm, addiction, and stuff like that. But it has really ramped up protections for minors and for elderly users. So there’s all these kind of specific add-on requirements. For ⁓ minors, it involves permission from parents. Parents can review at least some. They can set limitations on how the child uses the system. They can review.conversation that might have got toned down a little bit in the new version. ⁓ But I think, this is many ways it’s the same concern that surfaces in the US and elsewhere, like California just passed a just passed last year, passed a similar regulation on AI chat bots that I think also had believe it had extra protections in there for kids ⁓ on the education side of things. I guess there’s a couple of things. One, there’s just like the yeah, you say the tiger mom’s like this is ⁓It’s a booming industry of like, I’m going to teach your, you know, four year old AI so that they can use it because this is going to be how they get a job and how they get into school and how they get a job. ⁓ you know, a lot of it is bogus. Maybe most of it is bogus, but it’s very attractive to parents who have grown up in a really, really cutthroat competitive education system where you’re looking for every single edge that you can find.And so that’s, that’s a piece of it on the, from the policy side, they have both sort of AI and education policies, AI plus education policies that they’re pushing in a bunch of ways. I have some friends who are working with teachers over there who are described to me pretty like sophisticated and interesting ways. The teacher that are using AI to lesson plan, to create like really interesting games that keep the kids engaged and learning stuff like that. So you have those, and then on the labor.the labor side of things, they’re also viewing AI, they’re also viewing education as something of an antidote ⁓ to AI fuel job disruption. This is in the, I in the five year version, it is in the five year plan, it’s in the AI plus plan, it’s in a few other places where they say, we’re really gonna prioritize lifelong education. So maybe you used to be an accountant, you lost that job and ⁓ you’re gonna retrain as something else. ⁓which I think is a good, you know, it’s a good attitude to have. If you’re a person, you should always, I’m always trying to learn, you know, books. Um, think they’re great, but I don’t know if at a totally like a, you know, macro population, I know if you’re going to get 500 million people to be constantly staying one step ahead of AI in terms of what jobs it can do now. I mean, a lot of the things that we would have told you go back like two, three years and say, what jobs are going to be disrupted by AI? A lot of the recommendations would have been totally backwards.People would have thought that, ⁓ coding jobs are great. jobs involving creativity, ⁓ illustration, ⁓ stuff like that. AI can’t do those things. It can’t be creative in that way. And it’s like, that’s actually kind of what it’s best at now in some ways. I mean, you can argue about the level of creativity, but like generation of content, generation of images, videos, language. So I’d say it’s a piece of the Chinese sort of response on labor concerns.a fad, but maybe like a ⁓ useful fad within like the sort of education industry. But I’m a little bit skeptical of this as like ⁓ an actual antidote to the disruption that I at least imagine is coming.Grace Shao (29:49)Yeah, it’ll be really hard to be like upskilling, like re-skilling like hundreds of millions of people. Like, it’s just, you don’t even have the capacity to do so if there’s actually mass disruption. alright. ⁓Matt Sheehan (30:00)mean, this was always the response, in the United States on coal miners. We’re going to teach them to code. everybody, all these manufacturing workers, we’ll offer like a job retraining program. I’m like, ⁓ maybe, yeah.Grace Shao (30:05)Yeah.I’ll take generations. I’ll take generations for things to shift, know, resources to shift, people’s mentality shift, you know, for a while, like, when many, when remember the first wave of like, a basic rural kids no longer wanted to work in factories and wanted to go to urban cities, there was a surplus essentially service providers and then like everyone eventually became a DD driver or a food delivery man and thenNow we’re seeing a reshuffle in that population again, where people want to move back to their rural, cities. so I think based on how society is evolving, opportunities will arise without even us realizing anything, hopefully in the best case scenario, where people will find opportunities to reskill. ⁓ But I want to talk about something that’s a bitGrace Shao (30:57)I guess not heavy, but actually not many people understand, even including myself. So you really look at the government and the policy structure of ⁓ China’s regulators in the cybersecurity space and whatnot. ⁓ There’s so many players. There’s the CAC, then there’s the NDRC, there’s the MMIT. I can keep on naming acronyms, but can you give us a really, really quick high level understanding of who’s regulating whom? ⁓how do they actually work with each other and are their KPIs aligned before we get into more about how you know, how China’s policy is shaping the technology and AI ecosystem.Matt Sheehan (31:36)Yeah, maybe I’ll do it. ⁓ I’ll introduce a couple of the players and I’ll do it somewhat chronologically in terms of like when have they become important or rise and fall in importance. So AI policy, like the first really big policy document was the 2017 National AI Plan. It was released by the State Council, effectively sort of China’s cabinet, sort of the highest level of government. But ⁓ people who are in the know say that that was largely sort of drafted and pushed by the Ministry of Science and Technology. So this isreally like the policy wave of like 2017 to 2020 more or less. And it’s the Ministry of Science and Technology and it’s also the Ministry of Industry and Information Technology, MIIT. So these are really the organizations whose job it is to promote science, promote innovation, and MIIT is more of like the industrial applications of the technology. So they were kind of in the driver’s seat in that period of time. They were the most relevant actors. They were the ones who were driving real activity.starting in 2020, 2021, you had the CAC, the cyberspace administration of China, really like come to the center and become the most important actor in AI policy. The CAC, it’s basically the internet regulator. It was created in 2014. It was largely created to kind of like get the Chinese internet under control from a sort of political content ideology perspective. They’re connected to the Ministry of Prop, or the propaganda department.publicity, as they say now. ⁓ So from 2021 through 2023, the CAC was the one rolling out these binding regulations on recommendation algorithms, on deepfakes, on generative AI. And these are the regulations that actually force companies to do things. They actually force companies to register their models, to do pre-deployment testing, at this point to label AI-generated images in different contexts.There’s for that 2017 to 2020. It’s kind like the go-go period. Let’s just like push this industry forward. You have the Ministry of Science Technology, MIIT. And then from 2021 to 2023, it’s really the CAC. This corresponds roughly with the tech crackdown of 2020 through the end of 2022. That was a period when the CAC, the CAC is kind of at least historically, it’s kind like the bad cop of tech policy. They’re the ones who are like telling companies like come in and drink tea and we’ll tell you what you’re doing wrong or, finding companies in different ways. Cyberspace Administration of China, yeah, CAC. ⁓ Some people call it CAC. ⁓ And then sort of one of the more significant changes from 2023 to now is the rise of the NDRC, the National Development and Reform Commission, Chinese Fagawei. ⁓ And they are a macroeconomic regulator. They are like what grew out of the sort of state planning apparatus. And they’reGrace Shao (34:01)This is a cyberspace administration of China, right?Matt Sheehan (34:30)really powerful, they’re kind of a super, super ministry within the bureaucracy, but they’re not sort of directly, there aren’t that many direct connections to AI. They deal with, they deal a lot with money. They have money to give out for projects that funnels into compute projects and stuff like that. ⁓ But they wouldn’t be like who you would think of as the go-to AI regulator. What I was told and what I feel pretty confident ⁓ is what happened is that in some time in, I think, 2023, maybe mid to late 2023 and then into 2024, the top leadership in China essentially said, hey, we need a little more balance in our AI policy. The last three years it’s been led by the CAC. They’re kind of a bad cop. They’re really focused on controlling the technology, controlling the sort of output, the content, the ideology from it. And that is important. That’s kind of their first priority. But we need to rebalance this a little bit. We need to move out of our total tech crackdown era. And now we realize like our economy isn’t doing great.We realized we’re behind the US after CHAT GPT came out, and we need to balance this out. And so they empowered the NDRC to be a of a coordinator across AI policy, someone who is intended to take the input from the various ministries, from Ministry of Science and Technology, MIIT, CAC, and to try to make it little more coherent and balanced. And so that’s kind of the role that they have played for the past few years. The details of how that works out areshrouded in secrecy, you know, you hear little tidbits here and there. But there have been like visible manifestations of it. They had not, when they released these regulations, usually there’s a sort of a lead regulator on it or a lead policy document person on it. And then various other ministries, they co-sign it and they’re like listed below. NDRC hadn’t been on any regulations prior to 2023. And then starting in 2023, they were listed second as like the second sort of most important organ.policy body on these things. essentially we have this kind of like 2017 to 2020 is this like go-go period. Let’s diffuse. Let’s push the technology. Let’s push innovation. 2020 to 2023 is this more constrictive crackdown. Let’s build the regulatory infrastructure for things. And then 2023 to today is just like, let’s balance this out. Let’s not be purely focused on the content and ideology concerns. Let’s also be thinking about development. Let’s be thinking about employment. The NDRC is actually allegedly one of the groups that is very concerned about the employment impacts. you know, tons more details that I will love to go in on, but maybe that’s a starting point.Grace Shao (37:06)No, I think it’s super, super helpful. I just understand the nuances of like what their actual KPIs even are and like, you know, who does what, how they work together. I think that’s really helpful for lot of listeners and even investors who are trying to follow the space and just confused by acronyms. But help me understand now, like you say that 2023 to now essentially is in the same kind of era. However, I feel like at least from the capital market perspective, you know, the last year might have seen a bit of ashift again, you know, it was a bit of a let’s go AI, big tech AI, all the labs, let’s go, let’s go IPO. Then obviously the deals, some of the deals didn’t come through, some of the IPOs didn’t come through. ⁓ There seems like you even said people are being told to tamper down their excitement a little bit. Is that aligned with what’s happening with the policy side of things? Or is that actually more a reflection of just, frankly, you know, the AI space not being that exciting right now, you know, since the Gentic ⁓ kind of breakthrough. We’ve not seen more consumer and breakthrough. Also, there’s a lot of talk about, you know, there’s no obvious proof ROI on all the spending from all the big tech right now. Help me understand all that, I guess.Matt Sheehan (38:15)Sure. Yeah. When I was breaking down those errors, is largely its policy, but it’s already kind of like government attitude towards it. It’s like which, you know, they’re always in some ways swinging back and forth, going back and forth on the seesaw between, you know, control development, control development. And that 2023 to now being one era is sort of in that sense. It’s the period of rebalancing more towards development. There’s tons of sort of wiggles in that process andthings they’re pushing more and retreating on. But from a positive perspective, that’s the overlay. ⁓ In terms of like the capital markets, investments, I mean, I think this is kind of at least for people in the United States, it’s kind of like the one of the most misunderstood things about the Chinese AI ecosystem is that it is really like cash constrained, that it is not like the United States where, you know, open AI is just like sucking in.the tens of billions of dollars from a huge variety of investors are just spending huge capital outlays, which people talk about, is it a bubble? Is this going to come back to bite them? That’s an open question. But in China, you don’t have the concern about that bubble because there just is not the same level of infusion of cash. when a couple of the companies did IPO recently, Z.ai, formerly Jerpool and Minimax IPO in Hong Kong, and I think I’m notGrace Shao (39:28)100%.Matt Sheehan (39:39)really an IPO guy. think the IPOs were like modestly successful, but the valuations are just, yeah, the valuations are, yeah, not even close. And ⁓ it reflects a lot of things, but it largely reflects like a funding environment, a business environment, a macro economic environment, and the general sort of attitude towards risk investment. think I was just reading something that ⁓ Ray Ma from ⁓ TechBuzz.Grace Shao (39:42)So that’s six to eight billion dollars. The valuation is tiny compared to American peers.Matt Sheehan (40:06)China was writing on this. She’s always very good on these topics. yeah, it’s just people kind of assume that there’s like infinite money in China. They’re like, yeah, the government, whenever they want to, they just like turn on the taps and then, you know, it’s like, no, that’s not how it works. And like the VC ecosystem is much smaller, much more new and immature. And so it’s a different story.Grace Shao (40:28)on that note you know I was just in SF like last month and I met with quite a lot of investors and people’s kind of I guess misunderstanding was often twofold. One is exactly your point, people are just like oh China’s so rich the government just gives money all these AI companies are backed by the Chinese government I was like 100 % no first of all like there’s some other issues happening in the background but like the government doesn’t even are you know it’s kind of cash constraint and not even that much right now second all these companies are definitely not being backed by the government in any sense in factMost of don’t want to take municipal governments or provincial government money because you get kind of tied into, you know, what we’re seeing is, you you get forced into working with the government and it constrains your profitability and commercial goals. On the other hand, another really big misconception was, I thought quite funny was that people often ask, was open-claw frenzy because the Chinese, average Chinese consumer or user were really, really concerned about privacy issues. So they wanted everything on edge.I was like, hmm, like again, it’s kind of like just not, a major conversation people have. Like I think I hate to generalize, but I think because of how the internet ecosystem is in China, people by default have kind of ceded to not thinking too much about privacy or personal data issues as much. So that definitely isn’t. So I kind of want to bring the conversation that this, you know, likeWhat are some biggest misconceptions you think people have and how do we help them understand and bridge that gap a little bit better?Matt Sheehan (41:59)Yeah, I think yeah some of the stuff that you point out is correct like If you’re if you’re a if you’re a startup if you’re like a small medium company You I’ve talked to these people they’re like actually like we do not want to take government money if we can avoid it not just because we get kind of in mesh but like Entrepreneurs are legitimately afraid that if they take government money and then their company doesn’t work out and they lose the government’s money like they could end up like on the hook like in jail thislegitimate fear that it was stated to me by someone. like, you know, is that happening to entrepreneurs everywhere? No, but it’s like you don’t. ⁓ The government. It does a certain amount of sort of VC-esque investing, but there’s not really that VC mentality of like high risk, high reward. Like we know that most of this is going to go under. It’s kind of local governments at least have been trained on like real estate investment, which is like 10 percent, 10 percent, 10 percent every year.And this idea that most of these companies that you invest in are going to fail is not really ⁓ deeply embedded there. I do think some of the companies do rely on government funding in different ways. ⁓ mean, Z.ai, Drupal, one of their biggest, maybe their biggest single revenue stream is from ⁓ building custom models, but custom applications for ⁓ state-owned enterprises, local government, stuff like that.Matt Sheehan (43:28)When you listen to them in interviews, they’re like, it’s not that big. Maybe it’s 40 % or something like that. But it’s a significant revenue. It’s part of their business model. So there’s that type of a connection to government. With DeepSeek, that’s a company that’s kind of quite mysterious. And we don’t know exactly where all their money comes from. Is it all earned? I think the government got more hands on with them in the sort of aftermath of the DeepSeek moment. You had reports about the government taking passports away frompeople who worked there to make sure like you guys stay local ⁓ or the government was like vetting investors was another this is reporting the information. ⁓ But the idea that like these companies are just they just have the kind of like the hose of government money just flowing in at all times and therefore they don’t have to think about anything else is just not it’s just not real. ⁓ They’re they’re much more constrained cash constrained. ⁓terms of like trying to misconception on Chinese AI regulation, AI policy, this is like my, you know, much of my job is like first getting across like, the trend does actually like seriously regulate the technology. And then, you know, the next layer being like, it’s not all people think, you know, it’s an authoritarian system. She didn’t think he must just kind of like sit down and just like write the regulation. So like nothing matters except what he thinks. And we don’t know what he thinks. It’s like, no, like he doesn’t. There’s this actually very complicated and sophisticated policy ecosystem of,legal scholars and the companies are doing their lobbying and their thought leadership and, you know, they’re responding to public outcry over things. And I think that’s a, know, you can get this across to people, but it’s certainly not the people’s default mental model of how China works on policy is ⁓ just does not reflect the kind of sophistication in this zone. And it’s somewhat understandable. Like there are policy areas where Xi Jinping just like makes a decision and that’s.that’s where things are going. Like I think we saw a lot of this in the kind of 2020 to 2022 era. But as an AI policy, COVID, AI policy, it’s not that way. certainly things are not, people are not gonna like, you know, actively push things that are totally against the will of the top leaders, but they are within the constraints of like,Matt Sheehan (45:52)the direction of travel, what the CCP is good with, what she wants to do within that kind of very wide lens. It’s really individual people, scholars, bureaucrats, companies that are filling in all the details on this. And it’s a very sophisticated system because they’ve just had a lot of ⁓ had a lot of bites at the apple. They have like passed, I don’t know, eight, nine different A.I. already.The regulators at the CAC have been getting documentation from AI companies for three, four years. They’ve been building evaluations. been like, and they kind of, got their reps in with AI policy and it leads to a more sophisticated ecosystem.Grace Shao (46:33)⁓ yeah, so the last kind of section I want to focus on is just getting into the nitty gritty about, you know, the policy and the security and safety kind of side of things we touched on in the beginning of our conversation. you are one of the few in the West, I think, and talk about the nuance of the word, which you just explained, it’s security, but also safety. ⁓ help us understand.how to interpret that when we read about that. It actually even helps us understand a little bit of what’s happening in the West. Like, I feel like there’s the governance people, the security people, the safety people, but from someone who might not be in that ecosystem, people are conflating it a little bit. And I just want to understand, you know, how do we understand each of their objectives, again, KPIs, or even their goals?Matt Sheehan (47:20)Yeah, yeah, basically, it’s really complicated. It’s very context dependent and it’s always changing. ⁓ I think maybe the first key thing to understand here is like the very particular meaning of AI safety in the West like that. The West AI safety ⁓ largely refers to kind of a specific camp ⁓ of AI development and policy people that are, you know, believe that AI is going to achieve human and superhuman capabilities.And this could pose like serious, maybe catastrophic risks to people. like, that’s a somewhat coherent community in the United States that has a certain amount of power. Their power kind of ebbs and flows depending on things. But like when you say AI safety in Washington, D.C., it means one quite specific thing. ⁓ In China, that community, it has started to emerge, but it’s much newer. It’s much more recent. It doesn’t have the deep roots that it has in the West.And ⁓ the way that the word is used in policy documents is both confusing and has changed over time. So a lot of times when ⁓ just to kind of put a little color on the terminology, Anquan, when ⁓ when you’re talking about cybersecurity in Chinese, you say Wang Luo Anquan. So it’s like network Anquan, network security and cybersecurity means something very different fromAI safety from super powerful AI systems posing risks. And so there’s one sort of category of mistakes, which is to be very naive and to read all the Chinese policy documents. And every time they say a word that’s translated as safety to believe, wow, they’re talking about AI safety, they really, really care about this. That is a very naive and incorrect reading of things. ⁓ But in the past, I would say, 18 months, two years,you have seen a pretty significant uptick in the way that people sort of in and around the system and to a certain extent in and around the companies, their level of attention to what we would call in the West, AI safety to these more kind of large scale, potentially catastrophic risks from powerful AI. I’d say this is, there’s like sort of levels and degrees of this. There’s people talking about this. There’s it showing up in government documents in one way or another.And then there’s actually implementing this either through like binding regulations or through sort of companies doing their own testing and evaluation to try to their own sort of safety research and their own safety testing. I’d say what we’ve seen so far is a large increase in rhetoric, a large increase in sort of awareness within the policy community about these safety issues. We’ve seen it to start to show up in more significant documents. I think the one you’re referring to early on that I was working working on analyzing is calledThey call in Chinese the AI safety and governance framework 2.0, which is in many ways put out by some organizations underneath the CAC, the internet regulator. And it’s kind of ⁓ their attempt to diagram and do an initial discussion of how they see different risks from AI ⁓ and how are they going to mitigate these risks. Oftentimes they’re focused on technical standards as a mitigation. And there was a AI safety governance framework 1.0 in 2010.fall of 2024 and there was a 2.0 in fall of 2025. And just between those two documents, you can see real increase in the frequency and to a certain extent, the sophistication of the discussion around these risks in China. I’d say it’s pretty significantly below the sort of the AI safety discussion in the United States, but it’s on the radar. will counter, they’re like, okay, that’s great that they’re talking about it, but are what, youAre they just trying to trick us? Are they trying to make us believe they believe in safety? Are they saying it but not doing it? And ⁓ they’re like, we have not seen much in the way of like, we certainly have not seen like concrete binding regulations that sort of implement safeguards on this front. And in terms of what the companies are doing, it’s quite opaque, but ⁓ we don’t think that they’re doing the, I’d say.pretty confident they’re not doing nearly the level of sophistication or intensity of safety testing as you see at places like OpenAI and Anthropic. To me, this seems somewhat normal. This is kind of a process. Chinese companies have been behind. The government has perceived itself as being behind. When you’re behind the frontier, you’re not as worried about frontier risks as you’re like other people are going to get to those first and we need to catch up. ⁓ So I see this as kind of like a long-term process. And I think that the sort of increase in discussion about this isyou encouraging if you’re concerned about these issues. But it’s a really don’t want to be just kind of reading the documents and say every time we see Anquan being like, wow, China cares about AI safety. Look at all this stuff. It’s much more ⁓ nuanced and evolving, ⁓ evolving quickly, I would say.Grace Shao (52:21)I know like when we spoke a couple months ago, just catching up, you were saying a big part of your job is also trying to help, you know, bring the two sides together. Obviously, you know, ⁓ it’s been challenging given the geopolitical backdrop, but how do you think the global AI governance space can work together? ⁓ Are we going to see, you know, kind of the two world superpowers and two super AI powers, ⁓ you know, guide?in different directions or do you think there are certain issues where they need to come together and they will come together and are coming together? ⁓ For example, to your point on safety issues around protecting humanity, protecting children, are these things that you are seeing collaboration?Matt Sheehan (53:08)Yeah, I’m kind of ⁓ both an optimist and a pessimist on this front in that, like I said, I have very, very low expectations for the United States and China to work together on anything. I have very, very low expectations of any type of a binding agreement or some type of detente where we both shake hands and kumbaya and we’re both going to be very safe with AI and we agree and it’s great. I just don’t expect that. ⁓So in that way, I’m pessimistic. I think attempts to try to sort of preemptively create these global governance structures that are going to bind both of the countries in advance so we never reach these dangerous thresholds. ⁓ That’s just not where I’m putting my bets. I think it’s good. We need to make all kinds of bets on this front, and it’s good that people are working on this, but that’s not where I’m putting my bets. Where I’m putting my bets is on a much morelimited kind of narrow bore, but I think potentially highly effective form of ⁓ engagement. wouldn’t even say cooperation. I wouldn’t even necessarily say coordination. ⁓ my sort of the term, my mental model for it is something I call AI safety in parallel, which is that like the two ecosystems are going to be moving somewhat in parallel. They’re both going to be pushing the technology forward. They’re both going to be working through safety issues from a technical perspective, from a policy perspective.And as we kind of move forward in parallel, we’re not going to be telling each other what to do. And we’re not going to be like, okay, I’ll do the safety thing because you are. You told me you’re going to do it, so I’m going to do it. We’re not like sort of moving in lockstep on this, moving in parallel. And we need to have these touch points. We need to have touch points where the two sides develop some form of mutual understanding of what the other side is doing. They understand how other side is thinking about the issues. They understand how they’re perceiving these risks. That’s one of the reasons I do the risk ranking.stuff and in some cases trying to share best practices, explain kind of explain what we’re doing and why we’re doing it and have the Chinese side explain what they’re doing and why they’re doing it and then where possible share good ideas that we think are sort of uniformly good in the U.S. and China. If we think that we have a policy intervention maybe it’s around ⁓ certain types of pre-deployment testing. ⁓ It’s good to communicate that.to the Chinese side. And it’s good to have the Chinese side communicate some of ⁓ the reasons and the sort of the specifics of what they’re doing on these fronts. We’re not here to just like trust each other. I think a lot of people are very worried that the Chinese side is going to, is they’re going to trick us. They’re going to say they’re doing it and they’re not, which is like legitimate concern. know, that’s, this is high stakes like geopolitics and powerful technology. So you don’t take anybody’s word for it. But when you have these conversations in talking with people, you can get a,a sense of their level of sophistication when talking about the issue. If someone is talking about AI safety and they’re like, yes, humanity first, protect the humans, control the machines, that’s our policy. And it’s like, okay, is there anything more to that? They don’t have more than you kind of know that they’re actually not really thinking about it. But if you can get into a more ⁓ deeply engaged discussion, you can see like, actually, yeah, they’re working through these problems themselves. You can see it in the way that they’re.discussing it. You can see it when they talk about their specific regulatory mechanisms. You can see sort of the connection between sort of action and outcome or thinking and action. And so my model for this is like, we’re not going to agree on things. We’re not going to sort of trust each other. But there are ways that we can both be moving forward at the same time and comparing notes, checking in, getting a sense of what the other side is thinking and doing that I think could contribute to safety in a meaningful way.Grace Shao (57:02)I think that’s fair and I think the word you kept on using trust is quite interesting because I feel like whenever I speak to people in the industry, ⁓ there’s just such a lack of trust even within whether you want to say countries or communities or beliefs and value systems and a very, very optimistic, naive way, I really hope that there can be a bit more consensus on certain things like that need to be protected in practice, like such as children, right? And how we go ahead with that. ⁓ But okay, I don’t want to end on a super somber note or anything, but... ⁓ The takeaway is trust nobody. That was...Matt Sheehan (57:34)It’s optimistic in a way. think this can’t... When you do see... Well, trust nobody, buttalk and see if you can share some good ideas along the way. think there is real... ⁓ I’ve seen some real sort of traction from these type of things and I think it’s limited. We’re not going to get some kind of hard guarantee that China is going to be perfectly safe or we’re both going to...Matt Sheehan (58:04)do the right thing. But within with those low expectations, with those kind of pessimistic expectations, there are there’s progress that can be made.Grace Shao (58:13)⁓ I do want to ask one question that’s kind of been happening around right now. That’s been kind of happening like the whole idea of Chinese open models seem to take a little bit of a sidetrack and starting to kind of only release their most frontier related models and close weights. ⁓ Obviously from a very like, you know, capital perspective, where I study it is I feel like it’s a lot of it is because they need to see our eye. They cannot keep doing this because they’re not making money. API sales not enough to, you know, sustain the kind oflong-term ⁓ business as well as research costs. Are you seeing anything from the policy side? Like do you think there’s been a policy shift? That’s also kind of why I asked earlier if this year somehow, know, last year there was a public embrace by the government saying we should open source our technology. Has there been a shift?Matt Sheehan (59:04)So ⁓ I’ve seen sort of little tidbits around ⁓ sort of public policy concern about open weight models, but not enough that I would call it a shift. ⁓ In that document, the AI safety governance framework 2.0, it was interesting because it was the first time that there was a fair amount of ink spent on potential risks from open source models. The risks they were primarily talking about wereessentially if there are vulnerabilities in these models in some way, either maliciously inserted or just a vulnerability mistake, those could proliferate throughout the ecosystem because you have all these downstream models and that could lead to impacts. There’s a little bit of a mention of like, maybe open models will be used by criminals and stuff like that. I certainly don’t see this shift in specifically Alibaba strategy asMatt Sheehan (1:00:04)in reaction to a significant policy shift. mean, it kind of makes, yeah, corporate decision. It makes if you’re going to be spending tens of billions of dollars building models or at least hundreds of millions, billions, billions of models, giving it away for free is a. That’s a choice. I think there’s reasons why it’s advantageous for China to do that, or at least it was for a stage like it was going to be pretty hard.Grace Shao (1:00:09)corporate decision then.Matt Sheehan (1:00:34)to get people around the world to kind of believe in ⁓ Chinese models if they were only going to be able to access them through API. ⁓ You know, a lot of American companies that are, you know, deploying ⁓ Quan, Alibaba’s model, I don’t think they’d be doing that if they, I don’t think they would have at least made that leap initially if they had to sign a contract with Alibaba and they believe that maybe their data was going back to China or, you know, the model was more of a black box relative to them. So maybe the open model wave was a very good period of publicity. ⁓that might pass with time, but I don’t know. I think it’s being more nuanced. I’m not the expert on this. Nathan Lambert, runs the interconnects sub stack, and then Kevin Shue, who runs the interconnected sub stack, are much, much better and more sophisticated on this, and they have good writing on the ecosystem. But from a policy perspective, I haven’t seen a shift. I think it just kind of makes sense.Grace Shao (1:01:35)Yeah. Nathan, I actually spoke about this ⁓ a week ago, and I think both of us kind of feel like it’s really more of a capital and business constraint that’s really driving this. ⁓ But I just wanted to hear from you if maybe there was some kind of a top down initiative as to but it doesn’t seem like it. Right. I have one last question for you, which is a question I ask every single guest that comes on. ⁓ What is one differentiated view you hold? And actually, I try not to limit the question to just about AI. However, most people do want to talk about that.Matt Sheehan (1:02:02)Mm.⁓ Differentiated view. ⁓I mean, is... I don’t know where most people fall on this, but I think one thing I’ve been thinking about lately is just language learning and what’s going to happen with language learning in the era of simultaneous really good translation either through hardware, devices, or software. And I’ve been reflecting, why have I been spending 16 years or more learning Chinese and just in the...Matt Sheehan (1:02:40)mud of trying to learn and remember this language. And I guess my maybe differentiated view on this is I think it still is really important. And I, I waver, you know, I don’t want to just be like, ⁓ you know, justifying to myself why I’ve spent all this time. And like, I, don’t know that I would tell like a 16 year old, like I want you to invest, you know, 10,000 hours on a language when, when you can have it all translated for you, but they’rethere just is something quite meaningfully different about reading these policy documents, about listening to people and hearing the original language and just knowing how that language is used in all these different contexts that gets flattened by translation. I use machine translation all the time. I throw documents into it to get either a first draft or to get something I can share with people. But I guess my view is that I really thinkWe need people to keep like actually learning Americans to actually keep learning Chinese. And I also just think it’s so much fun. just, was earlier today, I was thinking it was like, why is this, it’s been such a, like a joy in many ways, extremely painful, but kind of a joy to like really struggle with a language over time. And so that’s my take.Grace Shao (1:03:59)No, I actually agree with you and I think if anything, I’ve thought about this a lot. So I’m raising a trilingual child by nature because we speak English at home, our parents speak Mandarin, the environment she lives in, they live in speaks Cantonese, right? And I think to your point, in many ways I’m like, wow, it’s actually, it would be so easy for them to travel the world and communicate with people. But the reason why I pushed them to learn the language is really to communicate, to understand a culture and the people and more.nuanced way, even for myself, like my parents pushed me to learn Mandarin. It like to your point, it’s so painful. But the ability to speak to my mother in her native tongue and understand her is so much more complex and you appreciate much more when you’re older, even though my parents speak English, obviously, but when we were young, we would speak English to him. As I got older, I actually enjoyed speaking to them in Chinese much more because you hear aboutMatt Sheehan (1:04:55)Hmm.Grace Shao (1:04:57)It’s also your personality changes, right? Like you kind of get to the core of who they are in their native language and their native way of expressions. So I think for sure, I agree for certain languages, there’s still such value, if anything, even more so to understand a human connection, human connectivity. then for pragmatic reasons, like I took two years of German, I remember nothing. I probably wouldn’t do that again to myself, especially in my class. a bunch of third gen German kids where they spoke the language at home but they can get away with saying they were doing beginner’s German, you know? But yeah, so I appreciate that. Thank you so much Matt, thank you for your time, I really appreciate it, we finally got together to do this episode.Matt Sheehan (1:05:34)Yeah, thanks for having me. That was fun. 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