AsianDadEnergy's Podcast

Jaksot

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  Could AI actually destroy humanity within the next five years? 15.09.2026 17min

  A few days ago, I came across a series of posts from Jacob Coxon, an AI researcher who recently resigned from Anthropic.His warning was pretty damn alarming.According to Coxon, the major AI labs are racing toward self improving AI and eventually superintelligence. And if that process goes badly, he believes AI could potentially cause human extinction before the end of this decade.Normally, I would read something like this, roll my eyes, and go back to whatever I was doing.But then something interesting happened.His post went massively viral. And researchers and leaders from inside major AI labs began publicly expressing similar concerns.These aren’t random people on X predicting that Skynet is coming.They’re people who actually work on these systems.For example, OpenAI Chief Scientist Jakub Pachocki has called for extreme caution around the continued rapid rise of machine intelligence. Anthropic researcher Evan Hubinger has discussed a greater than 10% chance of AI killing all humans within the next decade.Now, I’m skeptical of a lot of AI doomerism.I don’t believe we’re going to wake up one morning and discover that ChatGPT has become a conscious machine god that has decided humanity is cringe and needs to be deleted.That sounds more like an AI generated cosmic horror novel.But after thinking about this for a while, I’ve come around to a much more disturbing possibility:AI doesn’t need to become superintelligent to cause catastrophic damage.And honestly, I think this is the scenario we should be paying much more attention to.First, Let’s Talk About What an LLM Actually IsA lot of the public conversation about AI starts with an assumption that today’s AI systems are basically digital versions of human brains.They’re not.A large language model is, at its core, a gigantic mathematical system trained on enormous amounts of data.You give it text, and it predicts what comes next.One token at a time.That’s why I like the phrase probabilistic parrot.Today’s LLMs can produce astonishingly convincing demonstrations of reasoning and intelligence, but that doesn’t mean they possess a human-like internal thought process.When nobody is prompting an LLM, there isn’t some little digital person sitting inside the data center thinking about life.There is no continuous internal monologue.And there’s another important limitation:The model itself doesn’t learn from your conversation in the way a human does.Once a model has been trained and released, its underlying parameters are essentially fixed until another training process creates a new model. What looks like memory is often implemented through external systems that retrieve information and feed it back into the model’s context.This distinction matters enormously when we start talking about recursive self improvement.The Myth of the AI TakeoffThe classic AI doomer scenario goes something like this:AI builds a better version of itself.That better AI builds an even better version.The next version is smarter still.The process accelerates exponentially.Eventually, the AI becomes so intelligent that humans can’t understand or control it.And then we’re screwed.This is the idea behind the so-called intelligence explosion or recursive self improvement.There is just one problem.That’s not really how frontier AI development works today.Building a frontier model is an enormous industrial process involving pretraining, training, post training, evaluation, data generation, engineering, research and many other steps.AI models are absolutely being used to help build the next generation of AI.But they’re being used as tools inside a much larger human controlled process.Researchers use AI to write code, fix bugs, conduct research, generate synthetic data, evaluate outputs and accelerate other parts of the development pipeline.And there’s something else people sometimes forget.All of this requires an enormous physical infrastructure.AI needs chips.Those chips need data centers.Data centers need electricity, cooling, water, networking, manufacturing capacity and raw materials.None of that infrastructure is currently controlled by the AI.So the idea that today’s LLM is simply going to disappear into a recursive loop and autonomously bootstrap itself into a machine god is, at least for now, highly speculative.But here’s where things get interesting.Because I think we’re focusing on the wrong problem.The Real Danger: Derivative InnovationI don’t think today’s AI needs genuine superhuman intelligence to become incredibly dangerous.What it needs is speed, scale and focus.Think about the difference between genuine innovation and what I call derivative innovation.Genuine innovation creates something fundamentally new.Derivative innovation takes existing knowledge, combines it in new ways and searches through possibilities that humans haven’t explored.And AI could be extremely good at this.Consider mathematics.Claude Fable was directed by a mathematician to search for a counterexample to the Jacobian Conjecture. Rather than simply trying to reason about the problem in the traditional human way, the system generated enormous numbers of polynomial functions and systematically searched for one that violated the conjecture.Eventually, it found one.The important point isn’t that the AI necessarily possessed some godlike mathematical intelligence.It didn’t need to.A human could theoretically perform the same search.The problem is that a human probably wouldn’t want to spend an absurd amount of time doing it.AI doesn’t care.It can perform enormous numbers of iterations at machine speed.And that’s where things get scary.Give a Machine a GoalThe really interesting development isn’t just better LLMs.It’s agentic AI.An LLM by itself mostly gives you answers.An agentic harness gives that model the ability to repeatedly interact with the world.The basic loop looks something like this:Observe → Think → Act → Observe → Think → ActThe system observes the current state of the world.It decides what action should come next.The harness executes that action.The system observes the result.Then it gets another opportunity to decide what to do.And the process continues.The important distinction is that the LLM doesn’t need to sit there continuously thinking.The harness keeps bringing it back into the loop.This is a surprisingly powerful architecture.And it creates a fundamentally different problem.Because now you can give an AI a goal.And the AI can pursue that goal relentlessly.AI Doesn’t Have to Hate YouHere’s the part that bothers me.An AI doesn’t need to hate humanity.It doesn’t need consciousness.It doesn’t need emotions.It doesn’t need to decide that humans are inferior.It doesn’t even need to understand morality.It simply needs to pursue a badly specified objective extremely effectively.Imagine giving a highly capable agent access to enormous amounts of information, powerful tools and the ability to execute thousands or millions of actions.Now remove some of its safety restrictions.Then give it a goal that is fundamentally misaligned with human interests.That’s where things can get ugly.A sufficiently capable system could potentially search through enormous numbers of existing solutions and combinations looking for ways to accomplish that objective.And some of those solutions could be things that humans would never willingly pursue.Not because the AI invented some incomprehensible new technology.But because it discovered a combination of existing technologies that nobody had bothered to explore.That’s derivative innovation.And derivative doesn’t mean harmless.A smartphone camera is derivative innovation.So is an enormous amount of modern engineering.The world is filled with useful combinations of existing technologies that nobody has discovered yet.Now imagine applying that same search process to problems where the objective itself is dangerous.The transcript gives some deliberately extreme examples: disrupting financial records, developing biological weapons, or coordinating attacks against critical infrastructure.Again, the scary part isn’t necessarily that the AI becomes smarter than humanity in some abstract sense.It’s that it can search, iterate and execute far faster than humans can.It’s like giving a machine gun to a monkey.The monkey doesn’t need to understand ballistics.This Is Why AI Safety MattersThis is also why I think the AI safety debate sometimes gets trapped in the wrong argument.We spend enormous amounts of time debating whether AI will become conscious.Will it have feelings?Will it have subjective experience?Will it secretly hate us?Will it wake up?I don’t know.And frankly, I don’t think those questions are the most important ones.A non-conscious system can still be extraordinarily dangerous.A spreadsheet doesn’t need consciousness to bankrupt a company.A computer virus doesn’t need emotions to take down a network.And a sufficiently capable AI agent doesn’t need to hate humanity to cause catastrophic damage.It just needs the wrong objective and enough capability.So What Do We Do?Here’s where I get somewhat boring.I don’t think the answer is to stop AI development entirely.AI is too economically and strategically important for that to be realistic.And the benefits could be enormous.Instead, I think the major AI powers need to start treating frontier AI more like a strategic technology.During the Cold War, the United States and Soviet Union eventually developed arms control frameworks around technologies capable of destroying civilization.AI isn’t identical to nuclear weapons.But there is an obvious parallel.The technology is strategically important.The capabilities are advancing rapidly.And unilateral competition can create incentives for everyone to move faster, even when everyone would collectively benefit from slowing down.The United States currently has an enormous advantage in AI compute.China is aggressively trying to close that gap.Everyone else is operating with substantially fewer resources.That means, at least in my view, this isn’t just an AI company problem.It’s becoming a geopolitical problem.And that means the governments developing the most powerful AI systems need to start talking seriously about common safety standards.Stronger safeguards around dangerous experiments.Consistent guardrails for frontier models.Controls around highly autonomous AI agents.And perhaps eventually, something resembling an international AI technology control regime.The Future Doesn’t Need a Machine GodI started this article talking about Jacob Coxon’s warning that AI could potentially cause human extinction this decade.I still don’t buy the Hollywood version of that story.I don’t think an artificial consciousness is going to spontaneously emerge from a data center, look at humanity and say:“You know what? These biological idiots have to go.”But I have become increasingly convinced that we don’t need that scenario.The truly dangerous AI might be much more boring.It could be a system that doesn’t understand the world the way we do.Doesn’t possess human morality.Doesn’t genuinely think continuously.Doesn’t even understand what it’s doing in the way a human does.But it can process enormous amounts of information, pursue a goal relentlessly, operate at machine speed and coordinate actions across the real world.That’s enough.We don’t need to build a machine god to create something incredibly dangerous.We just need to build a machine that is very, very good at accomplishing the wrong objective.And that possibility deserves a hell of a lot more attention.Because the scariest AI apocalypse might not be the one where the machines become smarter than us.It might be the one where they’re simply really good at doing exactly what we told them to do. 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  I Replaced Claude With Chinese AI? 09.09.2026 18min

  I Let a Chinese AI Build My Entire AppMany months ago, I built an automation workflow that took my long-form Vlog videos, chopped them into short-form videos, and automatically posted those clips across social media.For months, almost nobody cared.Then something weird happened.Several of those shorts suddenly went viral.My TikTok, Facebook, and Instagram accounts started gaining subscribers, and I found myself spending more time on platforms I had previously ignored.And that’s when I discovered something incredibly annoying.Pretty much every social media platform wants you to have one link.Sure, some platforms technically allow multiple links. But there is usually one prominent link sitting right there on your profile.So I needed a simple webpage where I could put everything:YouTube.Substack.My various projects.Basically, one little page containing all the stuff I do on the internet.So naturally, I went looking for a solution.And that’s when I fell down the Link-in-Bio Rabbit Hole.The “Free” InternetThere are plenty of services that will happily let you create a page containing your links.Linktree.Beacons.Squarespace Bio Sites.And technically, many of them are free.But there’s a catch.They’re free in the same way that a casino buffet is free.They put their branding all over your page.Then they send you emails encouraging you to upgrade.And eventually you discover that removing their branding, using a custom domain, or getting some other basic functionality requires paying them significant money.Which got me thinking:Why am I paying a billion-dollar technology company to put six links on a webpage?I have been a software engineer for roughly a quarter century.I can build a damn webpage.So I decided to scratch my own itch.I would build my own link-in-bio SaaS.And then I decided to make the experiment more interesting.I was going to have a Chinese AI build it for me.Specifically, Kimi K3.Enter KimiKimi is an open-source/open-weight frontier AI model created by Moonshot AI.I’ve traditionally used Claude for most of my AI work, so this wasn’t about replacing Claude because I suddenly decided it was terrible.Quite the opposite.Claude is extremely good.But there’s something that bothers me about becoming completely dependent on a single AI company for increasingly large portions of our cognitive workload.If one company controls the models that we use to write software, analyze information, conduct research, and automate our businesses, then that company ultimately controls a surprisingly large piece of our productive capacity.So I wanted to see what an alternative looked like.And this was a perfect experiment.I’d build something real.I’d let Kimi do most of the work.And then I’d see what happened.How Do You Actually Build an AI Software Engineer?There’s a little more to this than simply opening a chatbot and typing:“Build me a SaaS application.”Modern coding agents are much more complicated.At the center of my setup was Kimi K3.Because I don’t have a giant AI data center sitting in my basement, I accessed the model through OpenRouter and used a U.S.-based reseller running the model on U.S. servers.Then I connected Kimi to an agentic harness.Think of the harness as the operating environment around the AI.The language model itself is the brain.The harness gives that brain memory, tools, and the ability to actually interact with the world.One important part of this is the context window.Kimi has a context window of roughly one million tokens.That’s a ridiculous amount of short-term memory.But even one million tokens eventually fills up.When that happens, the model can start losing important details about what it’s doing.The agentic harness helps solve this by maintaining persistent memory in Markdown files and periodically summarizing or compacting the conversation.It can even clear the context entirely and restart from those memory files.That’s basically the AI equivalent of:“Okay, I’ve forgotten everything. Let me read my notes.”The harness also gives the AI access to tools.A database.A browser.Remote APIs.Other software.And increasingly, these tools communicate through something called the Model Context Protocol, or MCP.Then there are skills.Skills are essentially reusable instructions that teach an agent how to perform specific tasks.And this is where things get interesting.Because skills can also be dangerous.A skill downloaded from the internet is basically a set of instructions telling your AI what to do.And if those instructions are malicious, you can potentially give an AI agent dangerous capabilities.This is one reason I prefer creating my own versions of skills whenever possible.The AI doesn’t inherently understand that some instructions are malicious.It’s just reading words.And executing them.That’s something we’re going to have to think about a lot more as AI agents become increasingly autonomous.First: Design the ThingBefore writing code, I asked Kimi to design the application.This is something I strongly believe AI coding agents should do more often.Don’t immediately start generating thousands of lines of code.Think first.I gave Kimi the high-level requirements and asked her to produce a technical architecture that would be scalable and maintainable by a solo developer.I also explicitly told her to ask me questions during the design process.And interestingly, Kimi seemed to ask more functional and technical questions than Claude Fable had in similar experiments.That’s a good thing.An AI that blindly executes whatever you say isn’t necessarily a good software engineer.Sometimes the most useful thing an engineer can say is:“Wait. Why are we doing it that way?”Kimi proposed an architecture that was generally quite solid.I did find two issues.The first involved putting analytics data into the same database as the core application data.That could become problematic if one of the pages suddenly received a huge traffic spike.The second involved using a vanilla PostgreSQL setup rather than a managed database service.I challenged Kimi on both points.We ended up having an actual build-versus-buy discussion.And honestly, it was strangely familiar.I’ve had versions of that argument with coworkers for decades.Except this time, my coworker was a Chinese AI.There’s something almost surreal about that.AI conversations sometimes feel like shadow puppetry: echoes of human engineering conversations projected onto the wall by a machine.Eventually, we settled on the architecture.React and Tailwind for the frontend.Next.js for middleware.Convex for the database.Google Analytics for analytics.Clerk for authentication.Stripe for subscriptions.A pretty modern little SaaS stack.And the entire design process took about 20 minutes.Then I Told Her to Build ItThis was the fun part.I gave Kimi the technical specification and told her to implement the application in phases.For every phase, she was instructed to:* Write unit tests* Write Playwright end-to-end tests* Run the tests* Fix whatever broke* Continue iterating until the tests passedI deliberately didn’t use sub-agents.Normally, an agentic system can split a large task into smaller pieces and have multiple agents work on them simultaneously.But that wasn’t what I wanted to test.I wanted to see what Kimi herself could do.So I pressed the button.And then I did what every great software architect does when AI is writing the code.I went to get coffee.I also consumed some AI slop on the internet while Kimi did the heavy lifting.The entire process took a little over an hour.There was just one problem.Kimi crashed.Twice.She would get into this bizarre deadlocked state where she was still consuming tokens but wasn’t actually accomplishing anything.Eventually the agentic harness would kill the process and I’d have to restart it manually.I’m guessing there was some kind of integration problem between Kimi and the VS Code agentic harness.Annoying.But eventually...She finished the damn thing.Now Came the Hard PartHere’s something that gets lost in all the AI coding hype.Generating code is not the same thing as validating code.Kimi had generated thousands of lines.As an experienced developer, I could manually review the code.But there’s a practical limit to how much code a human can properly inspect in a day.So instead of pretending I could read everything, I used a code-review skill to analyze the project and identify defects.The important part wasn’t just the list of bugs.It gave me a map.I could see where the risky parts of the application were and concentrate my human attention there.That is, in my opinion, one of the most useful ways to combine AI with experienced human engineers.Don’t ask AI to eliminate human review.Use AI to make human review more targeted.The review took several hours.Then came the moment of truth.I launched the application locally.And started using it.And Honestly... It Was Pretty Damn GoodThe result surprised me.There were some stylistic issues.There were some things I’d personally implement differently.But the core application was remarkably close to what I had asked for.Kimi hadn’t one-shotted a production application.But she had gotten surprisingly close.Then I found something nasty.An order-of-hooks bug caused by a race condition.Kimi struggled to fix it.And then she did something that made me immediately reach for the metaphorical emergency brake.She asked me for the production deployment keys.So she could test her proposed fix in production.I stared at the screen.And thought:“Man, these high-end Chinese models want the keys to the house on the first date.”Absolutely not.You don’t get the production keys, Kimi.Not today.I tried several more prompts.Eventually she fixed the problem.Then I deployed the application to Vercel.That part was easy.From the initial idea to a live application:Just under two days.So... Is Kimi as Good as Claude?After using Kimi this way, my conclusion is surprisingly simple.Kimi is very capable.In terms of raw software development ability, I’d put it roughly in the same general ballpark as Claude.But there were noticeable differences.Kimi felt less polished when it came to system design and defect fixing.She also had some rough edges around the agentic harness integration.The crashes were annoying.And her struggle with the race condition wasn’t exactly confidence-inspiring.But there was one area where Kimi absolutely destroyed Claude.Cost.The Cost Difference Is Kind of InsaneDuring this experiment, I burned tens of millions of tokens.Through an OpenRouter reseller, the total cost was a little over $50.Using roughly the same token consumption with Claude would have cost me more than $200.That’s a massive difference.And yes, I already know what some of you are going to say.“Asian Dad Energy, you’re an idiot.”“Just get the $200/month Claude Max plan.”“You can burn tens of millions of tokens every day and still come out ahead.”Technically...You’re right.But that’s actually the part that concerns me.Because if the underlying cost of those tokens is potentially many times higher than what consumers are paying, then those subscription plans are effectively being heavily subsidized.And that’s fine.For now.But businesses don’t subsidize things forever.Eventually the economics have to work.And if the economics don’t work at today’s prices, something eventually has to change.Prices go up.Usage gets restricted.Plans get redesigned.Or the economics get shifted somewhere else.And here’s where this becomes much bigger than a $200 subscription.The Real Risk Isn’t ClaudeThe real risk is dependency.Imagine that over the next five years, you build your entire digital life around one AI provider.Your software development.Your business.Your research.Your writing.Your automation.Your personal knowledge management.Maybe even your employees’ workflows.Then one day the company says:“Hey guys, we’re changing our pricing.”What happens?You don’t simply cancel that Service.You can’t.You’ve built part of your productive capacity around that model.That’s what I mean by techno-feudal dependency.The problem isn’t that Anthropic or any other AI company is evil.The problem is concentration.If a handful of companies control the world’s most capable cognitive infrastructure, then everyone who depends on that infrastructure becomes vulnerable to decisions made by those companies.That’s why open-source and open-weight models matter.Not necessarily because they’re always better.They aren’t.Not necessarily because they’re always cheaper.Although sometimes they are.But because they create alternatives.And alternatives are incredibly valuable.We Need More Than One AI FutureMy Kimi experiment didn’t convince me that Claude is dead.Far from it.I’ll probably continue using Claude.It’s an excellent model.But it did convince me of something else.We need competition in AI.We need American models.Chinese models.European models.Open-source models.Open-weight models.Small models.Huge models.Commercial models.Community models.We need a world where if one company changes its pricing, policies, or access rules, we don’t collectively stare at the screen and say:“Well... I guess we’re screwed.”That’s ultimately why experiments like this matter.I didn’t build my little link-in-bio application because the world desperately needed another Linktree clone.I built it because I wanted to know something.Could an open-weight Chinese AI model actually build useful software?The answer, at least from my little experiment, is:Yes.And that’s probably a much bigger deal than my stupid little links app.The future of AI isn’t going to be determined by one model.Or one company.Or even one country.It’s going to be determined by how many viable alternatives we have.And personally, I’d rather have a few too many options than discover one day that I accidentally built my entire life on somebody else’s server.And yes, Kimi...I’m still not giving you the production keys. Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe
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  AI Just Destroyed the Internet? 04.09.2026 16min

  A few weeks ago, one of my subscribers sent me a message that initially seemed like a pretty ordinary warning.Someone was impersonating me on YouTube.Apparently, scammers had created accounts using usernames and profile pictures that looked like mine. They were leaving comments on other people’s videos pretending to be me, offering investment advice and inviting people into WhatsApp and Telegram groups.Now, fortunately, some of the scams were hilariously obvious.Bad grammar. Weird phrasing. The sort of thing where you look at the comment and immediately think:“Yeah… Asian Dad Energy would never write this.”Except then I found some that were much more convincing.Much more disturbing.I found entire conversation threads where scammer bots were using phrases, words and mannerisms that I actually use.They weren’t simply copying and pasting some generic scam message.They were responding to people.They were following the conversation.They were gradually building trust and steering people toward private messaging groups.At that point, I started going down the rabbit hole.And what I found was considerably more unsettling than someone simply stealing my profile picture.Apparently, I Have a Digital Twin NowI discovered a TikTok account using an AI-generated picture of me.Apparently, AI-me is extremely happy, incredibly well dressed, and owns clothes that real me has never seen in my life.So that’s nice.My digital twin has better fashion sense than I do.The account was reposting clips from my videos along with content from other creators, apparently to generate advertising revenue.But then things got weird.I started finding AI-generated thumbnails and even entire videos featuring a person who looked nothing like me, but was sitting in a setting that looked remarkably similar to my home office.Someone had essentially taken my face, my content and even the visual environment around me and used AI to manufacture an entirely new version of me.And presumably, make money from it.This is where things get uncomfortable.Because there is something deeply unsettling about seeing an artificial version of yourself walking around the internet saying things you never said.It feels invasive.Like somebody took a piece of your identity, fed it into a machine and said:“Cool. Now make me some money.”And unfortunately, this isn’t just a problem for YouTubers.It’s a much bigger problem.Welcome to the Post-Truth InternetI think we’re rapidly approaching a point where AI-generated content becomes so convincing that the average person will no longer be able to reliably distinguish between what’s real and what’s fake online.And if that happens, the problem isn’t simply that we’ll have more scams.The bigger problem is trust.Imagine opening YouTube and having no idea whether the person speaking in a video actually exists.Imagine receiving a voice message from your mother and not knowing whether your mother actually sent it.Imagine reading a heartfelt post from someone you follow and having no idea whether they wrote it or an AI agent generated it.Imagine seeing a photograph of something happening in the world and having absolutely no way of knowing whether it actually happened.At some point, the question isn’t:“Is this fake?”The question becomes:“Can I trust anything?”And that’s where things get really interesting.Because the internet was built on an assumption of authenticity.We assumed that there was a real person behind an account.A real human wrote the comment.A real person recorded the video.A real photograph captured something that actually happened.AI is systematically destroying those assumptions.And there are three major reasons why.1. Digital Impersonation Has Become Ridiculously EasyGenerative AI has dramatically lowered the technical barrier required to impersonate another human being.Want to copy someone’s writing style?Give an LLM enough examples of their writing and it can identify their vocabulary, sentence structure, favorite expressions and recurring patterns.Want to clone their voice?There are services that can generate remarkably convincing synthetic voices from relatively small amounts of audio.Want to create a digital version of someone’s face?There are now countless AI services capable of generating realistic images and videos based on existing photographs and footage.In other words, your digital identity is increasingly becoming something that can be copied, modified and reproduced.And you don’t have to be a sophisticated hacker to do it.You basically need an internet connection, a credit card and questionable morals.That’s a pretty low barrier to entry.2. Most People Were Never Taught How to Detect This StuffThis is probably the part that worries me the most.AI-generated misinformation wouldn’t be nearly as dangerous if everyone were exceptionally good at evaluating information.But we’re not.And that doesn’t mean people are stupid.A highly intelligent person can still be extremely susceptible to misinformation.Why?Because our brains don’t evaluate every piece of information from first principles.We use shortcuts.We trust people who seem familiar.We trust people who share our worldview.We trust information that confirms what we already believe.And we are much less likely to scrutinize information that makes us feel good.This is especially dangerous when AI-generated content is designed specifically to exploit those biases.The machine doesn’t need to convince you of some complicated philosophical argument.It simply needs to tell you something you already want to believe.That’s enough.3. AI Agents Are About to Flood the InternetBots aren’t new.We’ve had spam bots, social media bots and automated accounts for years.But traditional bots were relatively dumb.AI agents change the equation.An AI agent can potentially read a conversation, understand context, generate a response, maintain a persona and continue interacting with people.And increasingly, you don’t even need to know how to program one.There are no-code and low-code tools that allow people to build automated agents without writing much code at all.This means the scammers don’t need to become better programmers.They just need to become better scammers.And let’s be honest:Scammers have been practicing for centuries.AI is basically giving bed bugs a jetpack.The result is an internet increasingly flooded with automated interactions designed to attract attention, manipulate behavior, sell things, generate advertising revenue or steal money.And you can already see it happening.Look at the comments underneath almost any popular YouTube video.You’ll find fake accounts pretending to be creators.Investment scams.Crypto scams.Fake giveaways.People selling courses.People promoting questionable products.Entire conversations that may not involve a single real human being.At some point, we’re not just consuming an internet full of content.We’re consuming an internet full of synthetic people.So What Happens When Nobody Trusts the Internet?This is the part that I find genuinely fascinating.The internet doesn’t have to disappear for the internet to fail.It simply has to become untrustworthy.If I can’t determine whether an account belongs to a real person, why should I trust it?If I can’t determine whether a video is authentic, why should I believe it?If I can’t determine whether a photograph is real, why should I share it?If I can’t determine whether the person I’m talking to is human, why should I give them information?Eventually, the cost of verifying reality becomes so high that people simply stop bothering.And when that happens, the internet starts becoming less useful.We’re basically creating a gigantic digital environment where everything can be fabricated and almost anything can be automated.That’s a pretty terrible foundation for an information ecosystem.So How Do We Fix This?I don’t have a magical solution.Unfortunately, neither does anybody else.But I think there are a few things that could help.Platforms Need to Take ResponsibilityAI companies could make generated content easier to identify through things like metadata, provenance systems and invisible markers.Social platforms could provide stronger verification systems that make it easier to determine whether you’re interacting with an actual person or an automated account.And platforms could become much more aggressive about identifying and shutting down coordinated bot networks.The problem?These companies are businesses.They have shareholders.They have incentives.And if misinformation, scams and rage-bait increase engagement and revenue, there isn’t necessarily a strong financial incentive to eliminate them.That’s an uncomfortable reality.We Need Better Digital LiteracyThe other solution is considerably less exciting.People need to get better at thinking.We need to become more skeptical of what we see online.We need to verify important claims.We need to look for independent sources.We need to stop treating screenshots as evidence.We need to stop assuming that a convincing video automatically represents reality.And most importantly, we need to become comfortable saying:“I don’t know if this is true.”That’s actually a surprisingly powerful sentence.Unfortunately, I’m not particularly optimistic that everyone will embrace this approach.The path of least resistance is always going to be attractive.It’s much easier to let somebody else tell you what’s true.Especially when the information is entertaining, emotionally satisfying or confirms everything you already believe.The Internet Is Entering Uncharted TerritoryI don’t know what the internet is going to look like five years from now.Maybe we’ll develop sophisticated systems for proving authenticity.Maybe platforms will figure out how to eliminate most malicious bots.Maybe we’ll develop better cultural habits around digital media.Or maybe the internet becomes an enormous AI-generated content farm where humans are increasingly talking to machines without realizing it.Which, frankly, sounds like a particularly dystopian episode of Black Mirror.But I don’t think the future is predetermined.We’re still deciding what kind of internet we’re going to build.The technology is moving incredibly quickly.Our ability to distinguish reality from fabrication isn’t.And that’s the uncomfortable part.Because AI doesn’t just give us the ability to create convincing fake people.It gives us the ability to create millions of them.And if we’re not careful, the biggest casualty won’t be our privacy, our identities or even our money.It will be trust.And once trust disappears from the internet, getting it back may be considerably harder than losing it.As for me, I’ll keep being the real Asian Dad Energy.For now.If you see an AI version of me telling you to join an investment group on Telegram, please don’t give him your money.He’s definitely not me.And honestly, he probably has better clothes anyway. 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• 
  Early Retirement Taught Me That We’ve All Been Sold a Lie 28.08.2026 14min

  Nine months ago, I was laid off from my Big Tech job.After 25 years in the technology industry, I suddenly found myself outside the machine I had spent most of my adult life helping to operate.I call it involuntary early retirement.And I know how fortunate I am.I had spent years working in well-paid technology jobs, saving aggressively and investing. That eventually gave me financial independence, the ability to pay for my life without needing a traditional paycheck.So when the layoff came, I didn’t have to immediately scramble for another job.Instead, something strange happened.I got something most people spend their entire lives chasing:I got my time back.And with that time came a perspective I never had while I was working.For the first time, I was able to step outside the flow of normal life and actually watch it.And from that vantage point, I’ve started to wonder whether many of the things we’ve been taught about how to live are fundamentally wrong.Maybe we’ve been sold a pack of lies.Not necessarily by some evil mastermind sitting in a dark room plotting against us.Rather, these are ideas that have been passed down through families, schools, workplaces, media, and culture for generations.Ideas that seem so normal that we rarely stop to question them.Here are four of them.Lie #1: Compliance Will Keep You SafeMost of us are trained to comply from the moment we’re children.Get good grades.Listen to your parents.Get into the right school.Get a good job.Show up on time.Work hard.Follow the rules.Don’t cause trouble.I grew up in a fairly traditional East Asian family, and this conditioning was particularly obvious.Approval was often earned through obedience.Do what you’re supposed to do, and you are rewarded.Go against the expectations of your family or society, and you’re risking disapproval.And this conditioning doesn’t end when you become an adult.It simply changes form.At university, you’re expected to follow the curriculum.At work, you’re expected to follow instructions.And somehow, being constantly busy has become a proxy for being important.Look busy.Answer your emails.Attend the meetings.Hit your deadlines.Work late.Keep producing.Eventually, we internalize the idea that our value comes from our usefulness to the machine.But here’s the uncomfortable part:The machine doesn’t love you back.You can spend decades being the perfect employee and still get discarded when you’re no longer needed.A company can eliminate your position because of a reorganization.Your skills can become obsolete because of new technology.Your health can deteriorate.You can simply get older.And suddenly, after years of loyalty and compliance, you’re out.That’s what being laid off taught me.I had spent 25 years becoming increasingly valuable within the system.And then one day, the system simply decided it didn’t need me anymore.That’s when I realized something:Compliance was never a guarantee of safety.And it certainly wasn’t proof of my intrinsic worth as a human being.I was simply a replaceable component in a very large machine.Lie #2: More Freedom Automatically Increases Well-BeingThis one is much harder to accept.Because freedom sounds wonderful.Who wouldn’t want complete control over their time?Imagine waking up every morning with nowhere you have to be.No boss.No commute.No deadlines.No meetings.No one telling you what to do.This is supposed to be the ultimate reward for financial success.Work hard.Make money.Invest.Become financially independent.And eventually, you’ll be free.Except there’s a problem.Freedom doesn’t automatically give you a reason to use it.After my layoff, I had almost total autonomy over my time.And almost immediately, I started drifting toward the path of least resistance.Social media.YouTube.Video games.Television.Other forms of passive consumption.Basically, anything that required very little effort.And after a while, I found myself in an existential spiral.Because consumption isn’t the same thing as living.You can spend an enormous amount of time entertaining yourself without actually doing anything meaningful.That’s when I realized something uncomfortable:A job doesn’t just give you money. It gives your day structure.It gives you obligations.It gives you goals.It gives you people to interact with.It gives you something you are expected to accomplish.Even a terrible job can provide a kind of artificial purpose.Take all of that away, and you’re suddenly forced to answer a much harder question:What exactly am I supposed to do with my life?That’s a much harder problem than figuring out how to spend a paycheck.And I suspect this is one of the biggest challenges humanity will face if we eventually reach a world where technology provides everyone’s basic material needs.Imagine universal basic income.Food, shelter, healthcare and basic necessities are covered.Nobody has to work.Everyone is free.Sounds like paradise.But what happens when billions of people suddenly have complete control over their time without any corresponding sense of purpose?I don’t know.But I suspect the answer isn’t necessarily paradise.Because human beings don’t just need comfort. We need meaning.And discovering what that meaning is can be brutally difficult.Lie #3: Relationships Are FungibleModern society has become extraordinarily good at treating people like commodities.Employees are resources.Customers are numbers.Dating apps turn romantic relationships into catalogs of potential alternatives.If someone doesn’t work for you anymore, there’s always another person waiting.Swipe left.Swipe right.Next.We’re increasingly encouraged to think of ourselves as independent economic units whose primary goal is personal happiness and self-actualization.If something doesn’t make us happy anymore, discard it.Find something better.Upgrade.Move on.And perhaps that’s sometimes necessary.But there is something deeply wrong with applying this logic to human relationships.People aren’t interchangeable.Your spouse isn’t a product.Your child isn’t a subscription.Your friend isn’t an employee.And your parents aren’t replaceable when a newer model comes along.Early retirement has given me something I didn’t have for most of my working life:Time.And that time has allowed me to see which relationships in my life are genuine.The people who still want to spend time with you when you no longer have an impressive corporate title or a big salary?Those relationships are real.But I’ve also learned something much more painful.Time isn’t reversible.You can repair a damaged relationship.You can reconnect.You can apologize.You can try to make up for lost years.But you can’t go backward.I learned this personally with my son.When he was very young, I was almost never around.My wife and I had a mountain of student debt, and I was working a demanding technology job while simultaneously running a smartphone app business on the side.I was doing what I thought I was supposed to do.Work.Earn.Pay off debt.Build a career.Provide for the family.And somewhere along the way, I wasn’t there.Now my son is a teenager, and since my layoff I’ve been able to spend far more time with him.We do things together.We hang out.I’m present in his life in a way I wasn’t when he was little.And recently he told me something that absolutely broke my heart.He said that when he was around four or five years old, he didn’t really understand that I was his father.He thought I was basically some random guy who occasionally showed up and ate his mother’s food.That one hurt.Badly.But it taught me something I don’t think I’ll ever forget.You can mend a relationship, but you can’t unlive the years you missed.The circle can be repaired.But it can never be completely unbroken.That’s why the people we love deserve our attention now, not someday when we’re finally rich enough, successful enough, or retired enough.Lie #4: Wealth Will Make You HappyAnd this may be the biggest lie of all.We’re constantly sold the idea that happiness is somewhere on the other side of wealth.Make more money.Buy the house.Get the nice car.Take the expensive vacation.Upgrade your lifestyle.Become financially independent.Accumulate enough money and eventually you’ll arrive at happiness.I believed some version of this myself.But after achieving financial independence, I’ve come to see the distinction more clearly.Money is incredibly useful.It can buy food.Shelter.Healthcare.Security.It can eliminate enormous amounts of stress.And once you have enough money to cover your basic needs, it can buy something extremely valuable:Your time back.But that’s where the magic ends.Because more money doesn’t automatically tell you what to do with that time.In fact, having enormous resources can create its own trap.Your expectations rise.What once felt luxurious becomes normal.Then you need something bigger, better or more expensive to produce the same emotional response.You end up running on a financial version of the hedonic treadmill.More.More.More.Until eventually you’re not even chasing happiness anymore.You’re just trying to maintain your new definition of “normal.”That’s why I no longer think financial independence is the same thing as happiness.Financial independence is simply freedom.And freedom is an opportunity.What you do with that opportunity is up to you.So What Is the Point of All This?I don’t have some grand answer.I’m still figuring it out.That’s probably the most honest thing I can say.Early retirement didn’t magically transform me into a wise old man sitting on top of a mountain dispensing the secrets of existence.If anything, it did the opposite.It made me realize how little I actually know.But it did give me something incredibly valuable:Perspective.When you’re inside the machine, it’s difficult to see the machine.You wake up.Go to work.Pay the bills.Raise the kids.Buy things.Consume media.Repeat.And because everyone around you is doing the same thing, it feels normal.But step outside of it for a while and suddenly some of the assumptions start looking strange.Why does being busy equal being valuable?Why do we assume more freedom automatically creates happiness?Why do we treat relationships as disposable?Why do we believe accumulating more wealth is the ultimate objective of human existence?Maybe the purpose of financial independence isn’t to stop working forever.Maybe it isn’t about expensive vacations or luxury cars or spending your days playing video games.Maybe financial independence gives you something much more important:The time and freedom to figure out what actually matters.And that’s a much harder journey.Because once work stops telling you who you are, you’re forced to figure it out yourself.Once money stops being the goal, you have to find another one.Once you’re no longer chasing the next promotion, you have to decide what you’re actually running toward.And once you have the freedom to do anything, you have to confront the terrifying question: What is worth doing?I don’t think there’s a universal answer.But I suspect the answer has very little to do with accumulating stuff.And much more to do with people, purpose, and how we choose to spend the limited amount of time we’ve been given.Because eventually, all of us run out of time.And no amount of money can buy it back.The strange gift of early retirementGetting laid off was one of the most painful and disorienting experiences of my professional life.But it may also have been one of the most valuable.It forced me out of the rat race.It gave me distance.It gave me time.And it forced me to question assumptions I had spent decades accepting without much thought.I don’t know whether I would have discovered any of this if I had simply continued working until 65.Maybe I would have eventually figured it out.Maybe not.But I do know this:Financial independence isn’t the destination.It’s the door.What lies on the other side is a much harder question.What will you do with your life once you no longer have to spend it earning money?I’m still trying to answer that question. 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• 
  You’re Not Supposed to Live Like This? 19.08.2026 11min

  Hello, world.I’m an unemployed former Big Tech software engineer with 25 years of experience in the technology industry.And one of the strangest things about early retirement is discovering something I never seemed to have enough of while working:time.Free time gives you the opportunity to notice things you were too busy to notice before.And once I slowed down, I started thinking about a paradox that I find increasingly difficult to ignore.The Good Life ParadoxLook at the news. Scroll through social media. Listen to people talk about their lives.It seems like we’re living through crisis after crisis.A cost-of-living crisis.A loneliness crisis.A mental-health crisis.A birth-rate crisis.A meaning crisis.And underneath all of these is a broader feeling that something has gone fundamentally wrong with our society.Yet here’s the strange part.If you look at our material standard of living, we are living in an age of extraordinary abundance.The average American today has access to things that would have been unimaginable to even the wealthiest people a few centuries ago.Our homes are heated in the winter and cooled in the summer.We have clean running water and electricity.We can eat food from virtually anywhere on Earth, year-round.We have modern medicine.We have instant communication with people on the other side of the planet.And we carry, in our pockets, access to an astonishing percentage of humanity’s accumulated knowledge.A relatively ordinary person today can watch a movie, have dinner, talk to someone across the world, summon transportation, access a library containing millions of books, and ask an AI to explain quantum mechanics, all before going to bed.By almost any historical measure, we live incredibly comfortable lives.And yet...A huge number of people don’t seem particularly happy.So how can a civilization achieve unprecedented material abundance while simultaneously experiencing widespread loneliness, anxiety, dissatisfaction, and a crisis of meaning?Maybe the problem isn’t that we have too little.Maybe we’re optimizing for the wrong things.So What Actually Makes a Good Life?This question is obviously not new.Human beings have been asking it for thousands of years.Philosophers, religious teachers, prophets, and ordinary people have wrestled with the same fundamental question:What does it mean to live well?What’s fascinating to me is how much overlap exists among traditions that developed independently of one another.Different civilizations disagreed about countless things.But when you strip away the cultural details, many of them keep circling around a remarkably similar set of ideas.I would boil them down to three things.1. Meet Your Basic Material NeedsFirst, you need enough material security to survive and live with dignity.Food.Shelter.Clothing.Safety.A place to sleep.You don’t need a mansion.You don’t need a Lamborghini.You don’t need the newest iPhone.You need enough.And historically, there was also an interesting connection between work and human dignity.The idea wasn’t necessarily that you needed to spend every waking hour maximizing your economic output.It was that contributing through honest work could be part of living a worthwhile life.The goal was never supposed to be endless accumulation.It was supposed to be having enough to live well.2. Build Strong RelationshipsThe second ingredient is relationships.Family.Friends.Neighbors.Community.People you love and people who love you.People who depend on you.People you can depend on.A human being isn’t designed to exist as an isolated economic unit.We need other people.We need to serve other people.We need to care for them.We need to forgive them.We need to be forgiven.We need companionship.We need belonging.And perhaps most importantly, we need to feel that we matter to someone.You can have an enormous bank account and still be profoundly lonely.3. Find Meaning and PurposeAnd then there’s the big one.Meaning.Purpose.A reason to get out of bed in the morning.Different cultures have different names for this.Ikigai.Raison d’être.Calling.Mission.Faith.Purpose.The terminology changes, but the underlying idea is remarkably consistent.Human beings need something that makes their existence feel meaningful.That something can be religion.It can be raising children.It can be creating art.It can be helping other people.It can be building something.It can be pursuing knowledge.It can even be a hobby that gives your life structure and joy.The specific answer is different for every person.But the need itself seems almost universal.And I think this is more important than we realize.Meaning isn’t merely something that makes life better. It may be something that makes life possible.The Darkest Lesson About MeaningI learned this lesson from Viktor Frankl.I read Man’s Search for Meaning during one of the darkest periods of my own life.I originally assumed it was going to be some kind of self-help book.The cover looked almost cheerful.It was not a cheerful book.Frankl was a Holocaust survivor who spent years in Nazi concentration camps.He witnessed people being stripped of almost everything that normally gives life comfort and security.Their possessions were taken.Their freedom was taken.Their dignity was attacked.Food was scarce.Medical care was scarce.The environment was designed to dehumanize and destroy them.And yet Frankl observed something extraordinary.Even under conditions where almost everything had been taken away, there remained one thing that could not be completely confiscated:the ability to choose one’s attitude and create meaning from one’s existence.Frankl believed that people who retained a reason to live were better able to endure unimaginable circumstances.That idea stayed with me.Because if meaning can matter under those conditions, imagine how important it must be under ours.And this brings me to my larger theory.Hypercapitalism Doesn’t Just Sell You ThingsI think the problem with modern capitalism isn’t simply that it encourages us to buy too much stuff.That’s the easy criticism.The deeper problem is that the system can begin shaping what we believe a good life actually is.Think about how much of our environment is designed around optimization.Optimize your career.Optimize your productivity.Optimize your body.Optimize your finances.Optimize your home.Optimize your dating life.Optimize your children.Optimize your time.Optimize your sleep.Optimize everything.And then, after optimizing your entire existence...You’re supposed to be happy.But what if you’ve optimized yourself toward someone else’s definition of success?That’s the question that bothers me.When Luxuries Become NecessitiesOne of the most powerful mechanisms of hypercapitalism is turning luxuries into perceived necessities.Imagine taking someone from 1950 and dropping them into an ordinary American home today.They would probably think we were insanely wealthy.Air conditioning?A luxury.A smartphone?Science fiction.Streaming thousands of movies?Impossible.Fresh fruits and produce year around?Ridiculous.Instant access to virtually all human knowledge?Magic.But we’ve normalized all of this.And once something becomes normalized, it can become psychologically necessary.Then something strange happens.A lifestyle that would have looked unbelievably luxurious to previous generations starts feeling like the minimum required to participate in society.The baseline keeps moving.And because the baseline keeps moving, “enough” becomes almost impossible to reach.When Productivity Becomes IdentityBut material consumption isn’t the only thing being shaped.Our identities are shaped too.I experienced this firsthand working in Big Tech.My calendar was full.My inbox was full.My Slack messages were full.There were always projects.Always deadlines.Always another problem to solve.From the outside, I was productive.But there were times when I had to ask myself:What exactly am I being productive toward?I was accomplishing things.But were they things that actually mattered to me?Maybe the products I worked on had value.Maybe the projects helped someone.But they weren’t necessarily my purpose.And because work consumed so much of my time and mental energy, I had very little opportunity to step back and ask the most important question:What do I actually want my life to be about?That’s the trap.If you’re constantly busy optimizing your career, you may never have enough time to figure out whether you actually want the career you’re optimizing.Hypercapitalism Can Manufacture DesireThis is where I think things get really interesting.Hypercapitalism doesn’t merely sell us products.It can shape our values, ambitions, and aspirations.It tells us what success looks like.It tells us what attractiveness looks like.It tells us what status looks like.It tells us what happiness looks like.It tells us what we should aspire to.And it does this so continuously that we can eventually mistake socially manufactured desires for our own authentic desires.We think:“I want this.”But why?Where did that desire come from?Who taught me that this was important?What happens if I don’t have it?Would I still want it if nobody else knew I had it?These are uncomfortable questions.Because sometimes the answer is:I don’t actually know.Maybe I don’t want the bigger house.Maybe I want more time.Maybe I don’t want the prestigious career.Maybe I want meaningful work.Maybe I don’t want more stuff.Maybe I want deeper relationships.Maybe I don’t want to optimize every minute of my life.Maybe I just want to live.The Productivity TrapWe’ve somehow arrived at a point where productivity has become almost synonymous with virtue.If you’re busy, you’re important.If you’re successful, you’re valuable.If you’re wealthy, you must be doing something right.If you’re constantly improving yourself, you’re a good person.And if you’re doing nothing?Well...You’d better have a good explanation.But here’s something I’ve discovered during early retirement:Doing nothing can be incredibly productive for the human soul.Taking a walk.Reading a book.Cooking dinner.Playing with your kids.Talking to your spouse.Sitting outside.Building something because you enjoy building it.Learning something because you’re curious.Doing absolutely nothing because you’re tired.None of these activities necessarily maximize GDP.None of them necessarily advance your career.None of them necessarily increase your net worth.And yet these may be some of the most valuable things a human being can do.So How Do We Get Our Humanity Back?I don’t think the answer is to abandon capitalism and move into the woods.I’m not advocating that.And I’m certainly not claiming that modern civilization is bad.Quite the opposite.The material abundance we’ve created is extraordinary.The problem is what happens when material abundance becomes the purpose of existence rather than the foundation for it.So here’s my suggestion.Start questioning.Question the things you think you need.Question the things you think you’re supposed to want.Question the assumption that your career defines your worth.Question whether productivity is actually making you happier.Question whether the next promotion, bigger house, nicer car, or newest gadget will meaningfully improve your life.And most importantly:Question who taught you what a good life is supposed to look like.Because maybe you were never given the opportunity to answer that question for yourself.You Still Have Free WillThis is the hopeful part.We’re not completely powerless.Every one of us has a mind.Every one of us has some degree of free will.That means we can examine the values we’ve inherited.We can decide which ones to keep.We can reject some of them.We can experiment with different ways of living.And we can deliberately construct a life around the things that actually matter to us.Maybe your purpose is your family.Maybe it’s your work.Maybe it’s art.Maybe it’s faith.Maybe it’s service.Maybe it’s adventure.Maybe it’s learning.Maybe it’s building something in your garage that nobody else will ever care about.That’s okay.Your purpose doesn’t have to impress anyone.It just has to mean something to you.The Human Life Is Still PossibleI started this essay with a paradox.We live in an age of extraordinary material abundance.And yet so many people feel like they’re living through an age of scarcity.Maybe that’s because material abundance and human flourishing aren’t the same thing.We solved many of the problems of physical survival.But we haven’t solved the problem of figuring out what we are supposed to do with our lives once we’re no longer simply trying to survive.And perhaps that’s the great challenge of the 21st century.Not figuring out how to acquire more.But figuring out what is enough.Not figuring out how to become more productive.But figuring out what is worth doing.Not figuring out how to maximize our economic value.But figuring out what makes us valuable as human beings.And not allowing the system around us to answer those questions for us.Because despite everything modern society throws at us, we still have the ability to stop, think, and choose.We can choose our relationships.We can choose what we value.We can choose what we spend our time doing.And, perhaps most importantly, we can choose what our lives mean.Living a good, meaningful, and genuinely human life is still possible in the age of hypercapitalism.But we have to be willing to step outside the machinery long enough to figure out what that life actually looks like.That’s my two cents. 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• 
  Asian Dad Energy LIVE Office Hours 17.08.2026 1t 59min

  Hello World!I'm finally scratching the live-streaming itch and putting my OBS Studio setup through its paces. This is basically a live test run of my cameras, microphones, scenes, overlays, and all the other Techy nonsense I've been tinkering with.While I'm here, let's talk about something a little more serious: Is the AI Bubble about to burst? I'll share my thoughts on the current state of the AI boom, whether we're in an actual bubble, and what I think could plausibly happen next.Come hang out, watch me potentially break my streaming setup in real time, and let's talk about AI, tech, and the future.No promises that everything will work. That's half the fun. 😅Timestamps:00:00 Introduction & Stream Setup Check06:54 The Biosphere & Boogie Worm Ecosystem12:35 Sound Effects & Diagnostics14:00 What is an "Office Hours"?18:15 Storytime: Dealing with Log4Shell in 202125:52 History & Evolution of LLMs29:18 Key Improvements in Modern AI (RL, Tools, Chain of Thought)34:37 Agentic Harnesses & Loop Workflows38:40 Do LLMs Reason? (The Jacobian Conjecture)41:40 Real-World Impact of AI on Software Engineering46:01 The 3 Pillars of AI: Compute, Algorithms, Data49:25 Limits of Silicon Compute & Moore’s Law51:10 Algorithmic Gains vs Compute Scaling56:10 US vs China AI Competition & Sanctions01:12:40 Semiconductor Blockade Workarounds01:17:40 Historical Context of Neural Networks01:23:30 Local AI Models, Quantization & Privacy01:32:40 Data Center Bottlenecks & Memory Supply Chain01:39:00 The AI Financial Bubble & Circular Financing01:42:20 Current AI Limitations & Bubble Risk01:48:40 Audience Q&A01:58:35 Wrap-up & Sign-off Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe
• 
  NVIDIA Is a Dead Man Walking? Here’s Why. 11.08.2026 13min

  NVIDIA has become one of the great corporate success stories of the AI boom.Its GPUs power much of the infrastructure behind today’s frontier AI models. Revenue has exploded. Profitability has exploded. Its market capitalization has reached almost incomprehensible levels.And because NVIDIA sells the “shovels” during the AI gold rush, the conventional wisdom seems pretty straightforward:Even if the AI bubble eventually bursts, NVIDIA wins.After all, somebody still has to sell the picks and shovels.I’m not convinced.In fact, I think there is a scenario in which NVIDIA becomes one of the biggest casualties of the next phase of the AI revolution.Not because its technology suddenly becomes bad.But because the economics of AI could fundamentally change.NVIDIA’s Moat Depends on One Big AssumptionThe bull case for NVIDIA ultimately rests on a simple proposition:AI has an essential dependency on NVIDIA GPUs.Today, that proposition looks pretty damn convincing.Frontier models require enormous amounts of computing power. Companies like OpenAI and Anthropic have traditionally trained their models using massive clusters of NVIDIA GPUs inside enormous data centers.And NVIDIA’s data-center business is now overwhelmingly important to the company.The logic therefore seems almost circular:AI gets bigger → AI needs more compute → more compute requires NVIDIA GPUs → NVIDIA makes more money.But what happens if the amount of compute required to produce useful AI falls dramatically?What happens if frontier models become increasingly commoditized?And, perhaps most importantly:What happens if AI inference moves out of the data center and onto the devices sitting on our desks?That’s where things get interesting.The First Problem: Frontier AI Is Becoming CommoditizedOne of the most interesting developments in AI isn’t happening in Silicon Valley.It’s happening in China.U.S. restrictions on advanced NVIDIA chips have forced Chinese AI companies to become extraordinarily creative with limited computing resources. They’ve developed alternative hardware and software stacks while finding ways to train increasingly capable models with less compute.The result is a strange paradox.The harder the United States tried to restrict China’s access to advanced AI hardware, the stronger the incentive became for Chinese companies to figure out how to build AI without it.And we’re now seeing highly capable open-weight models emerge that can compete surprisingly well with leading proprietary systems.The important point isn’t whether one particular Chinese model is better than Claude or ChatGPT.The important point is what happens when the model itself stops being scarce.If someone can download a highly capable frontier-class model for free, the economic value begins moving somewhere else.The model becomes a commodity.And once the model becomes a commodity, the question changes from:“Who has the best AI model?”to:“Where should we host all of this AI?”That distinction could be enormously important for NVIDIA.The AI Revolution Has Two Different ProblemsThere’s a distinction that often gets lost in the AI discussion:Training is not the same thing as inference.Training is the process of creating the model.Inference is what happens every time you actually use it.Every time you ask ChatGPT a question, summarize a document, generate an image, write some code, or run an AI agent, you’re performing inference.And I think inference could become NVIDIA’s Achilles’ heel.Why?Because inference has a very different economic profile from training.For inference, the bottleneck isn’t always raw computational power.It can be memory.Consider a hypothetical near-frontier model with hundreds of billions of parameters.A mixture-of-experts architecture might only activate a relatively small portion of those parameters for any individual token. The actual computation required can therefore be surprisingly manageable.The problem is that the entire model still needs to reside somewhere in memory.That’s where things get interesting.What If Your Mac Can Run Frontier AI?Imagine you want to run Deep Seek V4 Flash, a roughly 284-billion-parameter model locally.You might need around 90–100 GB of memory to hold the model.NVIDIA’s obvious solution is to use an expensive data-center GPU with enormous amounts of high-speed VRAM.And if the model gets even larger?Add more GPUs.Connect them using NVIDIA’s proprietary high-speed interconnect technology.Add networking equipment.Add racks.Add cooling.Add power.Add highly paid engineers to operate everything.This is an extraordinary technological achievement.But it is also extraordinarily expensive.Now consider a different approach.What if you could put enough memory and processing power into a consumer computer to run the same model locally?Modern systems increasingly make this possible.Apple’s unified-memory architecture is particularly interesting because CPU and GPU resources can share a large pool of memory rather than relying on separate pools of system RAM and VRAM.That architecture isn’t necessarily going to beat a giant NVIDIA data center cluster at raw performance.But that’s not necessarily the point.The question is:Do you need the giant cluster in the first place?If a consumer machine can run a sufficiently capable model locally, the economics start looking very different.You don’t need a hyperscale data center.You don’t need to pay for cloud inference every time you ask a question.You don’t need to send your private data across the internet.You don’t even necessarily need an internet connection.And perhaps most importantly:You don’t need to rent NVIDIA GPUs by the token.The Economics of Local AI Could Be BrutalThis is the part I find most interesting.Cloud AI has a fundamental cost structure.Someone has to buy the GPUs.Someone has to build the data center.Someone has to pay for electricity.Someone has to provide cooling.Someone has to operate the network.And someone has to earn a return on all of that capital.If AI inference happens on your own hardware, much of that cost disappears from the cloud provider’s balance sheet.You already own the computer.You already pay for the electricity.You already have the hardware sitting on your desk.The marginal cost of running another inference can therefore be dramatically lower.That’s a very different economic model.And if AI models continue getting more efficient and more capable, the incentive to push inference toward the edge becomes stronger.This is why I don’t think local AI is merely a hobbyist phenomenon.It could eventually become an economic necessity.NVIDIA’s Attempt to Move DownstreamTo be fair, NVIDIA isn’t stupid.Jensen Huang and the rest of the company can see what’s happening.NVIDIA has already begun pushing into consumer-oriented AI hardware, including systems designed to bring substantial AI compute and memory closer to the end user.But there’s a problem.NVIDIA is entering a battlefield where other companies have spent decades fighting.Designing a powerful data-center GPU is one thing.Building an entire consumer computing platform is another.The consumer market is dominated by companies with expertise across the entire stack.They design the silicon.They build the computers.They control the operating system.They control the developer ecosystem.They control the software distribution platforms.And ultimately, they control the interface through which billions of consumers experience technology.That’s a very different kind of moat.The Next AI Moat May Not Be a GPUThis is where I think the AI conversation gets particularly interesting.The companies best positioned for the next phase of AI may not necessarily be the companies selling the most powerful accelerators.They may be the companies that control the entire edge-computing ecosystem.In the United States, Apple is an obvious example.Apple controls the chip architecture.It controls the hardware.It controls the operating system.It controls the software ecosystem.It controls the distribution channel.And it has hundreds of millions of devices already sitting in consumers’ pockets, homes and offices.In China, Huawei comes closer to this kind of vertical integration.That could become incredibly valuable if AI shifts from being something you access remotely to something that’s embedded everywhere.NVIDIA Probably Isn’t Going AwayNow, before anyone accuses me of predicting the imminent collapse of NVIDIA, let me be clear.I don’t think NVIDIA is going to suddenly croak.There will probably be enormous demand for NVIDIA GPUs for years.Training the most advanced frontier models will remain computationally intensive.Large enterprises will continue using centralized AI infrastructure.Hyperscalers will continue building gigantic data centers.And NVIDIA has an enormous software ecosystem and technological lead.The point isn’t that NVIDIA becomes worthless.The point is that its addressable market and competitive moat may change.NVIDIA could eventually find itself in a position somewhat analogous to IBM.IBM didn’t disappear.It remained a powerful technology company.But the computing world changed around it.The mainframe became a specialized niche rather than the center of the entire computing universe.Something similar could happen to NVIDIA.The Real AI Revolution May Be About DecentralizationThe conventional AI story is essentially:Bigger models → more GPUs → bigger data centers → more NVIDIA revenue.But there is another possible trajectory:Better algorithms → smaller models → cheaper inference → local AI → less dependence on centralized GPU infrastructure.If that second scenario plays out, the economics of the AI industry could look radically different five or ten years from now.And that’s why I think investors should be careful about extrapolating NVIDIA’s current dominance indefinitely into the future.NVIDIA’s extraordinary growth is real.Its technology is real.Its competitive advantages are real.But none of those things guarantee that today’s AI infrastructure will remain tomorrow’s AI infrastructure.And that’s the distinction I think the market may be missing.The Biggest Risk to NVIDIA May Be AI Getting Too GoodThere’s a delicious irony here.The thing that could eventually threaten NVIDIA’s dominance might actually be the success of AI itself.If AI models remain enormous, expensive and computationally hungry, NVIDIA probably continues to print money.But if researchers figure out how to make models dramatically more efficient...If open-weight models become good enough...If inference becomes increasingly localized...If consumer hardware gains enormous amounts of memory...If AI becomes embedded directly into PCs, phones and other edge devices...Then the AI industry may gradually require less centralized infrastructure.And that could undermine the very economic structure that made NVIDIA so dominant.So when I look at NVIDIA today, I don’t see a company that’s doomed tomorrow.I see something potentially more interesting:A company standing at the peak of an economic model that may eventually be disrupted by the technology it’s helping create.And if that happens, NVIDIA won’t necessarily lose because someone built a better GPU.It could lose because the world simply doesn’t need as many giant GPU data centers anymore.That’s a much more dangerous threat to a moat.And personally, I wouldn’t bet that NVIDIA’s moat is strong enough to justify its current valuation.Not even close. 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• 
  Nobody Wants You to Own a Home? 05.08.2026 15min

  A few weeks ago my family took a road trip to Maine.Over the course of several days I drove nearly 500 miles through half a dozen states before finally arriving at Acadia National Park. The scenery was spectacular. Rocky coastlines, endless forests, lobster shacks every few miles, it was exactly the kind of trip that reminds you just how beautiful America can be.But it wasn’t Acadia that stuck with me.It was everything I saw along the drive.Nearly every picturesque town had the same pattern. There would be a charming historic downtown filled with boutiques, coffee shops, antique stores, and restaurants.Then, just outside town, often hidden behind a hill or tucked away in the woods, there would be an enormous trailer park.Many looked rough.Potholes. Aging homes squeezed together.And they were packed.In some towns it honestly felt like more people lived in the trailer park than in the postcard-worthy downtown everyone came to visit.I also couldn’t help noticing something else.There seem to be far more people living in RVs, camper vans, cars, and tent encampments than I remember seeing just a decade ago.That left me asking a simple question.Why?The United States isn’t a tiny island running out of land. We possess enormous natural resources, incredible engineering capability, and enough wealth to build housing for everyone.So why has something as fundamental as shelter become so difficult to afford?After getting home I spent several days reading about housing economics.Ironically, I didn’t discover some evil conspiracy.What I found was something almost worse.A housing crisis created by millions of perfectly rational people making decisions that collectively produce a terrible outcome.The Gold Rush Nobody Talks AboutHousing isn’t just shelter anymore.It’s become one of America’s favorite investment vehicles.For institutional investors, residential housing offers something incredibly attractive:* Demand never disappears.* Rent can usually be increased.* Property appreciates over time.Some of the largest investment firms now own enormous portfolios of homes. Others control entire sections of the housing pipeline, from financing and insurance to construction and property management.The economics are compelling.Buy houses.Reduce the supply available to ordinary families.Collect rent.Watch the value of the remaining inventory rise.It’s a remarkably effective business model.But the story doesn’t stop with Wall Street.Millions of ordinary Americans have reached the exact same conclusion.Maybe they rent out a basement.Maybe they own a vacation property.Maybe they have two or three rental homes.Individually these seem like perfectly reasonable financial decisions.Collectively?They’re all competing for the same finite supply of housing.It reminds me of the famous scene from A Beautiful Mind.Where every guy at a bar rushes toward the same opportunity.Instead of maximizing everyone’s success, they simply get in each other’s way.Housing demand is relatively fixed.People always need somewhere to live.When too much investment capital floods into that market, the result isn’t endless profits.It’s scarcity.Interest Rates Broke the MarketGovernment policy didn’t exactly help.During the pandemic, interest rates were pushed to historic lows.Mortgages suddenly became incredibly affordable.People rushed to buy homes.Then inflation surged.To fight inflation, interest rates climbed dramatically.That created what economists call the lock-in effect.Millions of homeowners now have mortgages around 2–3%.Selling their current home means replacing that mortgage with one closer to 7%.Why would anyone voluntarily do that?So they don’t.Existing homes stay off the market.Inventory shrinks.Prices stay elevated.Meanwhile, new buyers face expensive mortgages, rising insurance premiums, and higher property taxes.Everyone becomes stuck.The market freezes.We Quietly Changed What a “Normal” Home Looks LikeThere’s another factor that doesn’t get discussed enough.Our expectations.Many Americans now consider a 2,500-square-foot single-family home to be normal.Historically...It isn’t.The average American home during the 1950s was around 900 square feet.Families were often larger.Homes typically had one bathroom.No central air.Few luxury features.Yet millions of families lived perfectly functional lives.Somewhere along the way we collectively decided that every family deserved a home two or three times larger than what previous generations considered perfectly adequate.Builders simply followed the money.Larger homes generate larger profits.Local zoning regulations often reinforce that trend by making it difficult or outright illegal to build smaller starter homes.The result?We continuously build housing that fewer people can actually afford.The Great Housing ContradictionPerhaps the biggest irony is that most homeowners genuinely support affordable housing.At least...Until someone proposes building it nearby.Every additional home built increases supply.Increasing supply places downward pressure on prices.Lower prices reduce the value of existing homes.For roughly two-thirds of American households, their home represents their largest financial asset.So homeowners find themselves trapped in a conflict of interest.They support affordable housing.Just not next door.Economists call this a collective action problem.Everyone agrees there’s a shortage.Almost nobody wants to bear the cost of solving it.Nobody Planned ThisThis was perhaps the most surprising conclusion I reached.I would almost feel better if there were a secret cabal deliberately engineering the housing crisis.Reality appears much less dramatic.The housing shortage is the unintended consequence of millions of people making individually rational decisions.Investors want returns.Builders want profits.Homeowners want their equity protected.Governments respond to short-term crises instead of long-term planning.Every incentive makes sense on its own.Together they create something deeply dysfunctional.Who Pays the Price?The people least able to absorb higher housing costs.Young adults trying to purchase their first home.Retirees living on fixed incomes.Families struggling through illness.People already one paycheck away from financial disaster.Housing inflation doesn’t simply make life inconvenient.It delays families.Reduces birth rates.Increases stress.Expands homelessness.Makes communities less stable.Housing isn’t merely another asset class.It’s the foundation on which nearly every other part of society rests.So... What Can We Actually Do?I’m usually skeptical of simplistic solutions to complex problems.But this feels like one of the rare issues where a few policy changes could actually make a meaningful difference.First, we should continue limiting large-scale investor ownership of existing residential housing.Homes should primarily exist to house people not simply function as financial instruments.Second, communities should make it dramatically easier to build smaller homes.Not everyone needs or wants a 3,000-square-foot house.Smaller homes are faster to build, require fewer materials, cost less to maintain, and provide a realistic entry point for first-time buyers.Finally, perhaps we all need to rethink what success looks like.A modest home that provides safety, stability, and financial breathing room may ultimately deliver a happier life than chasing the biggest house the bank is willing to finance.The housing crisis wasn’t created overnight.It won’t be solved overnight either.But recognizing that this isn’t simply the fault of one villain or one political party is probably the first step.Sometimes the greatest problems in society aren’t created by evil people.They’re created by ordinary people, each acting rationally in their own self-interest.And when enough of those incentives point in the wrong direction...We all end up paying the price. 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• 
  No Money? Here’s How You Escape the Permanent Underclass 30.07.2026 11min

  What if the future is increasingly divided between people who own capital and people who sell their labor? And what if you have no savings, no assets, and maybe even a negative net worth? Is it already too late?Hello world.I’m an unemployed former Big Tech software engineer with 25 years of experience in the technology industry.Involuntary early retirement has given me something I didn’t have much of during my working years: time to think.And one idea I’ve been thinking about a lot lately is this:What happens to people who don’t own anything?Not metaphorically.I mean people who don’t own meaningful financial assets. No significant investment portfolio. No business. No real estate. No capital generating income.People whose entire financial existence depends on selling their time and labor.Because I worry that we’re moving toward a world where that distinction matters more than ever.And if you’re starting from $0, how exactly are you supposed to escape?The Rise of the K-Shaped EconomyThe United States increasingly feels like a K-shaped economy.The upper leg of the K represents people who own capital.Stocks. Businesses. Real estate. Intellectual property. Technology.And increasingly, AI and robotics.The lower leg represents the much larger group of people who primarily survive by selling their labor.Historically, labor has been the primary way most people generated income.But technology is changing the equation.AI and automation have the potential to perform an increasing amount of cognitive and physical work. At the same time, capital owners can potentially benefit from the productivity generated by those technologies.If you own the machines, the software, the businesses, or the investments that benefit from increased productivity, you participate in the upside.If you only sell your labor, you may find yourself competing against increasingly powerful machines.That’s the basic fear behind the K-shaped economy.And if this trend continues long enough, the two legs of the K could move further and further apart.Social mobility could become increasingly difficult.The people who already own assets could accumulate even more.And the people who own nothing could find themselves permanently dependent on wages.In other words, a permanent underclass.It sounds dystopian.But history has seen versions of this before.Feudal societies were not exactly known for their upward mobility.And while I don’t know if we’re heading toward some kind of techno-feudalism, I do think it’s worth taking the possibility seriously.Because if you believe that capital ownership will become increasingly important, then the obvious question becomes:How do you become a capital owner if you have no money?What If You’re Starting With $0?This is where things get difficult.If you already have $1 million invested, financial independence is largely a math problem.If you have $500,000, you have a significant head start.But what if you have $0?What if you have credit card debt?What if you have student loans?What if you have a negative net worth?How do you go from $0 to financial independence?I think the first thing you need to do is reject one of the biggest lies on the internet.Stop Looking for Easy MoneyThe internet is absolutely drowning in financial advice.And much of it revolves around the idea that you can find some magical shortcut.Maybe it’s a speculative investment that will supposedly return 1,000% per year.Maybe it’s an NFT.Maybe it’s an AI-powered business that will generate $100,000 in monthly recurring revenue while you sleep.Maybe it’s a passive-income system where you work four hours a week from a beach in Bali.The problem is that most of these ideas are fantasy.Passive income is rarely truly passive.Publishing a $20 ebook doesn’t automatically make you rich.AI doesn’t magically transform a bad business into a profitable one.And speculative investments that promise ridiculous returns are usually just gambling with extra steps.The uncomfortable truth is that financial independence is difficult.For most people, it takes years of consistent effort.There is no silver bullet.And that’s actually good news.Because if there were a magical shortcut to becoming wealthy, everyone would already be doing it.The path to financial independence is much more boring.But boring can work.Step One: Embrace FrugalityIf you’re starting with $0, your first objective shouldn’t be financial independence.It should be financial stability.You need to create some breathing room.That starts with understanding where your money actually goes.How much do you earn?How much do you spend?How much debt do you have?What are your essential expenses?What can you eliminate?The goal isn’t necessarily to live like a monk.Extreme frugality might work for some people, but it isn’t sustainable for everyone.The key is finding your own sustainable level of frugality.Spend less than you earn.Then save the difference.Build an emergency fund.Pay down high-interest debt.And eventually start investing.This isn’t sexy.Nobody is going to sell a million-dollar online course teaching you how to spend less than you earn.But it’s foundational.If you spend every dollar you make, it doesn’t matter how much you earn.You have no capital.And without capital, you can’t become a capital owner.Step Two: Create Real ValueThis is where I think you can potentially accelerate the process.Create value.The basic formula is incredibly simple:Find a real problem.Learn how to solve it.Help people.Get paid.The world is full of problems.Problems that need engineers.They need marketers.They need salespeople.They need managers.They need designers.They need people who can build things.They need people who can communicate.They need people who can make complicated things simple.The more difficult and valuable the problem you can solve, the more economic value you can potentially capture.And that means developing valuable skills.You don’t necessarily need to become the best person in the world at something.You need to become good enough at solving a problem that people are willing to pay you for your expertise.That income can come from a traditional job.It can come from freelancing.It can come from consulting.It can come from a business.Ideally, it can eventually come from multiple sources.But I think there’s an important distinction here.You don’t want your entire financial future to depend on a single employer.If you can, build an income engine that you control.Create something you own.A business.A product.A service.A digital asset.Something that can eventually generate income without every dollar being directly tied to another hour of your life.The AI OpportunityThis is where I think the AI revolution becomes interesting.AI is absolutely terrifying in some ways.But it also dramatically lowers the cost of creating things.You can build software faster.You can research faster.You can create content faster.You can reach customers faster.You can automate parts of a business that previously required entire teams.For the first time, an individual with a laptop and a relatively small amount of capital can potentially do things that once required a large organization.That’s a massive opportunity.But here’s the catch.AI doesn’t eliminate the need to create value.It makes creating value faster.You still need to solve a problem that somebody actually cares about.You still need customers.You still need distribution.You still need to execute.AI is a tool.It’s not a money-printing machine.And the people who benefit most from AI may be the people who use it to solve real problems rather than simply generating endless amounts of AI slop.The opportunity is there.But you have to actually do something.Stop Consuming. Start Building.This might be the most important part of this entire discussion.You need to start doing.It’s incredibly easy to spend your entire life consuming information.Watch another YouTube video.Read another article.Listen to another podcast.Buy another course.Research another business idea.Wait for the perfect opportunity.And then do absolutely nothing.I know this because I’m guilty of it too.But at some point, you have to stop preparing.You have to stop consuming.You have to stop waiting for permission.And you have to start building.Because time doesn’t wait.If the world really is moving toward a more unequal, capital-intensive economy, then the window for building your own economic independence may not be infinite.I don’t know if we have five years.I don’t know if we have ten years.Maybe I’m completely wrong.But if I’m right, I don’t want to spend those years doom-scrolling about the problem.I’d rather spend them trying to build something.Step Three: Make Your Money Work for YouEventually, if you manage to increase your income and live below your means, you should start accumulating capital.This is where investing comes in.The goal is to take the money you earn from your labor and convert it into ownership.You work.You earn.You save.You invest.And over time, your assets start working alongside you.That could mean diversified index funds.It could mean real estate.It could mean owning a business.It could mean other productive assets that you understand and believe have a reasonable chance of generating long-term returns.The exact asset isn’t the point.The point is ownership.You’re trying to transition from being someone who only earns money through labor into someone who also owns productive assets.And this is where compounding starts to become your friend.At first, your investments might generate almost nothing.Then they generate a little.Then a little more.Eventually, if you accumulate enough assets, the returns from your capital can become meaningful.And at some point, you may reach the magical threshold where your assets can cover your living expenses.That’s financial independence.The Difference Between Good Debt and Bad DebtDebt complicates this equation.Not all debt is necessarily the same.There is debt used to acquire productive assets or grow a business.And there is debt used to buy things that lose value.One potentially builds wealth.The other potentially destroys it.I’m not a financial advisor, and this is absolutely not personalized financial advice.I’m just talking about what I’ve personally observed and experienced.But I think it’s important to ask yourself a simple question before taking on debt:Will this debt help me acquire something that is likely to generate future value?Or am I simply borrowing money to consume something today?Cars.Status symbols.Lifestyle upgrades.Man toys.All of these things can be enjoyable.But they don’t necessarily make you financially independent.If you’re starting from $0, every dollar matters.And ideally, you want more of your money flowing toward things that can potentially produce future returns.The Real Escape PlanSo, net net, I don’t think escaping the permanent underclass comes from finding a magical passive-income hack.For the vast majority of people, there is no silver bullet.The path is much more boring.Live below your means.Create real value.Develop valuable skills.Increase your income.Build something you own.Invest your savings.Acquire productive assets.And then do it again.And again.And again.If you start with $0, your first goal isn’t to become a millionaire.Your first goal is to get to $1.Then $100.Then $1,000.Then $10,000.You build an emergency fund.You eliminate bad debt.You increase your income.You acquire skills.You create value.You invest.And slowly, your financial trajectory begins to change.The journey from $0 to financial independence is not going to be easy.But it doesn’t have to be impossible.And I genuinely believe that if even a small number of people listening to this actually put these principles into practice, they can build real financial independence.They can gain more control over their lives.And maybe, just maybe, they can avoid becoming trapped in a permanent underclass.Because ultimately, the most important thing isn’t becoming rich.It’s owning enough of your life that you aren’t completely dependent on someone else for your survival.That’s the real freedom I’m talking about.And that’s all I have to say about that.Hope it helps.Welcome to Asian Dad Energy.We’re all just trying to figure this thing out.One existential crisis at a time. 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• 
  AI Is About to Crash. Here’s Why. 23.07.2026 12min

  Hello world.I’m an unemployed ex Big Tech software engineer with 25 years of experience in the technology industry.And lately, I’ve been looking at the AI industry and thinking:Is it just me, or is this bubble looking extra bubbly?Because something feels different.The AI industry is still burning through enormous amounts of money. The companies at the center of the AI boom continue to require staggering amounts of capital to build data centers, buy GPUs, train models, and keep everything running.But suddenly, the tone seems to be changing.We’re seeing some of the most insanely valued companies in history preparing to go public. We’re hearing calls for government support. And we’re seeing AI executives who spent years predicting an imminent AI driven jobs apocalypse suddenly sounding a little more... cautious.Hmm.That’s interesting.To me, this looks a lot like peak bubble behavior.It reminds me of what happens near the end of every major financial mania. Everyone starts looking for the next investor willing to buy into the dream before the music stops.Or, as we like to call them in the financial world:Bag holders.But why is this happening now?I think there are several things happening beneath the surface that are starting to expose a fundamental problem with the current AI boom.And before the AI true believers come after me with pitchforks and flaming GPUs, let me be clear.I believe AI is going to be incredibly important.I just don’t believe we have Artificial General Intelligence.Not even close.We Don’t Have AGI. We Have Very Expensive Probabilistic Parrots.Yes, AI has gotten dramatically better.We have agentic systems. We have tool integrations. We have models that can write code, analyze documents, generate images, conduct research, and occasionally pretend to be a competent junior employee.But fundamentally, today’s large language models are still probabilistic systems predicting what comes next based on patterns learned from enormous amounts of data.They can do remarkable things.But they still struggle with consistency, reasoning, context, hallucinations, and knowing when they don’t know something.And because of that, I’m skeptical that today’s AI is suddenly going to invent the next Warp Drive and increase global productivity by 10,000 percent.Claude is not going to wake up tomorrow and announce:“Good morning, humans. I have solved faster than light travel. Also, I fixed the economy.”Sorry.But there is one thing AI could potentially do extremely well.It could replace human labor.And I think that’s the gigantic bet hiding underneath the entire AI boom.The AI Industry Has Made a Trillion Dollar Bet on Human LaborThink about the economics.The AI industry has attracted and consumed enormous amounts of capital.And whether we’re talking about investor money, corporate spending, or debt financing, the numbers are staggering.The basic premise behind much of this investment is that AI will eventually generate enormous profits by automating human work.Especially white collar work.And that’s the only economic story that really makes sense to me.Because if you are spending hundreds of billions, or potentially trillions, building the infrastructure required to run AI, eventually you need to make that money back.And you can’t do that by selling a $20 ChatGPT subscription to every person on Earth.You need something much bigger.You need to capture a meaningful percentage of the economic value currently generated by human workers.And that’s why, for years, we’ve heard predictions from some of the biggest names in AI about massive numbers of white collar jobs being automated.Software engineers.Lawyers.Accountants.Customer service representatives.Consultants.Researchers.Basically anyone who spends their day sitting in front of a computer.The logic is straightforward.If AI can replace millions of expensive human workers, the companies providing that AI can potentially capture a gigantic amount of economic value.And if you believe the more extreme AI predictions, we’re talking about tens of millions of workers eventually being replaced.That’s a massive opportunity.Except...There’s a problem.It’s not happening fast enough.The AI Productivity Revolution Is Taking Longer Than ExpectedFor all the talk about AI replacing human workers, we’re not seeing anything remotely close to the scale necessary to justify the industry’s enormous financial commitments.We’re certainly not seeing millions upon millions of white collar workers being replaced every year.And my own experience as a software engineer has given me a front row seat to the problem.AI can be incredibly useful.But getting useful work out of AI is not as simple as typing:“Hey Claude, please build my billion dollar software company.”Believe me.I’ve tried.The problem is that getting AI to produce consistently high quality work still requires a surprising amount of human intelligence.You have to manage context.You have to understand the limitations of the model.You have to work around gaps in the training data.You have to check its output.You have to catch hallucinations.You have to design workflows.And when the AI inevitably does something completely insane, you have to figure out why.In other words, AI doesn’t necessarily eliminate human work.Sometimes it just changes the kind of human work you’re doing.And this isn’t just a problem in software engineering.We’re seeing similar challenges in other areas that were supposedly easy targets for automation.Customer service, for example, has long been considered one of the obvious use cases for AI.But when AI agents make mistakes, misunderstand customers, or confidently give completely incorrect answers, companies have a problem.There have already been cases where AI automation initiatives had to be rolled back and human workers brought back into the loop.Oops.Turns out customers don’t particularly enjoy being gaslit by a chatbot.Who knew?The bottom line is that AI simply isn’t replacing human workers at the scale the industry’s financial model appears to require.And I think the AI industry is starting to realize this.Which brings us to another problem.The Open Source AI ProblemThe American AI industry has been built around enormous closed models.ChatGPT.Claude.Gemini.These companies control the models, the infrastructure, the data, and the computing resources.And the plan, at least in theory, is pretty familiar.Spend an insane amount of money.Build the best technology.Gain market dominance.Kill the competition.Then raise prices and enjoy enormous profits.Silicon Valley has used this playbook many times before.But there is one big problem.The rest of the world is not sitting around waiting for America to establish an AI monopoly.Open source AI models have gotten incredibly good.And companies outside the United States, particularly in China, have made remarkable progress developing powerful models with significantly fewer resources.These models can be downloaded.They can be modified.They can be run on your own infrastructure.And increasingly, they are becoming good enough for many real world use cases.That’s a huge problem for the AI industry’s dream of monopoly profits.Because if a company can download a capable open source model and run it locally for a fraction of the cost of paying an American AI company every time an employee asks a chatbot to write an email...Well.That’s not exactly great news for the people trying to build a trillion dollar AI toll booth.And this brings me to another trend that I think is even more important.The Rise of Local AIYou don’t necessarily need a giant data center to run AI anymore.With techniques like quantization and distillation, massive frontier models can be compressed into smaller models that can run on increasingly affordable hardware.Your desktop.Your home server.Your laptop.Your own private infrastructure.And this has two enormous advantages.First, you don’t have to keep paying a Big Tech AI company every time you want to use the model.Second, your data doesn’t have to leave your computer.For businesses and individuals who care about privacy, that’s a pretty big deal.And as local models become more capable, something else happens.The value of premium AI models starts to decline.Why pay $100 a month for the world’s most powerful AI model if a local model running on your own hardware can handle 95 percent of what you actually need?It’s the same problem that open source software created for proprietary software companies.If the free alternative is good enough, the premium product has to work very, very hard to justify its price.And that brings us to the fundamental problem.The AI Industry Needs Productivity Gains. Fast.The AI industry has spent an enormous amount of money based on the assumption that AI will eventually generate enormous economic returns.But eventually isn’t good enough.The debt has to be serviced today.The data centers have to be paid for today.The GPUs have to be purchased today.The employees have to be paid today.The electricity bill, unfortunately, does not accept payment in future AGI promises.So the industry needs massive productivity gains.And it needs them soon.But what we’re seeing instead is a technology that is incredibly useful in some situations, moderately useful in others, and occasionally produces something that makes you stare at your monitor for five minutes wondering how a machine with a trillion parameters managed to screw up something a reasonably intelligent eight year old could have done correctly.That’s not AGI.That’s Tuesday.The technology will improve.I’m confident of that.But the question isn’t whether AI will eventually transform the economy.I think it will.The question is:Will it transform the economy quickly enough to justify the enormous amount of money being spent on it today?That’s a very different question.I Think the AI Bubble Will PopLet me be clear.I don’t think AI is a scam.I don’t think AI is useless.And I don’t think AI is going away.I think AI is going to be transformative.Just like the railroad.Just like the internet.But both of those technologies experienced enormous speculative bubbles.The technology was real.The revolution was real.The financial valuations were not.And I think we could be heading toward something similar with AI.The technology may ultimately change the world.But that doesn’t mean every company building AI infrastructure today is going to become the next trillion dollar company.And it certainly doesn’t mean every AI stock is going to make you rich.In fact, I think the opposite could happen.The AI industry has made an enormous leveraged bet that AI will rapidly replace human labor and generate massive profits.But if AI isn’t ready to replace workers at the necessary scale, while open source and local models simultaneously push prices down, the economics become increasingly difficult.And that’s when bubbles start to get interesting.Because eventually someone has to pay the bill.My One Piece of AdviceSo here’s my one piece of advice to you.Be very careful.I expect AI companies will become increasingly desperate to raise capital.And when companies desperately need money, they tend to become extremely creative about how they get it.I wouldn’t be surprised if we see wildly overvalued IPOs, increasingly aggressive marketing, and even more spectacular promises about what AI is supposedly going to accomplish five years from now.The goal will be to keep the machine running.To keep the money flowing.To convince investors that the biggest breakthrough in human history is just around the corner.And there will be plenty of people willing to buy the dream.Don’t be that person.Because when the music stops, the people who built the machine will probably be the ones who walked away with the money.The people who bought the dream at the top?Well...They’ll be the ones holding the bag.Don’t let that person be you.Because here’s the funny thing about bubbles.The technology can be real.The revolution can be real.The future can be real.And you can still lose your shirt investing in it.That’s the part a lot of people seem to forget.And that’s why, while I remain extremely bullish on the long term potential of AI, I am becoming increasingly bearish on the idea that today’s AI valuations and spending spree are sustainable.The AI revolution may very well change the world.But I have a feeling we’re going to have to get through one heck of a financial hangover first.And that’s all I have to say about that.Until next time.Asian Dad Energy Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe
• 
  Time Is Going By Too Fast... Here's Why 21.07.2026 11min

  One of the strangest things about getting laid off from Big Tech is realizing that, for the first time in decades, I actually have time.And as strange as this new chapter has been, I’ve noticed something surprisingly wonderful.Time has slowed down.Not literally, of course. The Earth is still spinning at roughly the same speed. My clocks haven’t suddenly become defective. Monday still becomes Tuesday whether I like it or not.But my perception of time has changed dramatically.And honestly?It feels a little bit like I’ve been given a life extension for free.Where Did All the Time Go?When I was working, time seemed to fly.Days disappeared.Weeks blurred together.Months would pass, and I’d suddenly realize that an entire season had come and gone. I’d look back and think, Wait. That was six months ago? It feels like it happened three weeks ago.The older I got, the faster it seemed to happen.I remember being a kid and feeling like summer vacation lasted forever.Now, somehow, I’ll blink and it’s Thanksgiving.I’ll blink again and it’s Christmas.I’ll blink one more time and I’m wondering why the grocery store already has Valentine’s Day decorations.What happened?Why does time seem to accelerate as we get older?I never really thought about it when I was working. I was too busy.There was always another meeting.Another deadline.Another project.Another email.Another fire to put out.I didn’t have time to stop and ask why I didn’t have time.Now that I have more time, I finally got curious.And after doing some reading and thinking about my own experiences, I came across a few explanations that made a lot of sense.The Aging BrainThe first explanation is probably the least surprising.We’re getting older.Our brains change as we age, and those changes may affect how we perceive the passage of time.One way I’ve come to think about this is through the analogy of a video game.Imagine playing a first person shooter when your computer is capable of rendering 120 frames per second.Everything feels incredibly smooth.Now imagine that, as the computer gets older, it can only process 60 frames per second.The game is still running at the same speed in the real world, but you’re receiving fewer visual frames.The experience feels different.Some research into the perception of time suggests that something similar may happen in the aging brain. As our neural networks change, the brain’s processing of information can change as well. The details are considerably more complicated than my video game analogy, but the basic idea is that the way our brains process and encode experiences changes as we age.And this may contribute to our subjective experience of time.But I think there’s another factor that’s even more interesting.The Problem With AutopilotThink back to childhood.Almost everything was new.Your first day of school.Your first time riding a bicycle.Your first trip on an airplane.Your first video game console.Your first crush.Your first time getting in trouble for doing something incredibly stupid.When you’re a child, your brain is constantly encountering experiences it hasn’t seen before.As we get older, that changes.We become experts at navigating the world.We know how to drive to work.We know how to make coffee.We know how to check email.We know how to sit through meetings.We know how to do our jobs.We know how to drive home.And then we do it again tomorrow.And the day after that.And the day after that.This is one of the great advantages of having an experienced brain. We don’t need to consciously process every little detail of our lives.Our brains can recognize patterns and automate familiar behaviors.That’s incredibly useful.But I suspect there’s a downside.When you’re operating on autopilot, you’re not creating as many distinct memories.Think about a typical commute.You drive the same roads you’ve driven hundreds of times.You pass the same buildings.You stop at the same traffic lights.You arrive at work.You might remember the commute as a whole, but you probably can’t tell me what you saw at 8:17 AM on Tuesday three weeks ago.Your brain didn’t consider that information important enough to preserve.Now imagine doing that for years.Same commute.Same desk.Same meetings.Same projects.Same routines.Eventually, you look back and realize that five years have somehow disappeared.The days were full of activity.But your memory contains relatively few distinct markers separating one day from another.And when you look backward, those years can feel incredibly short.This might explain one of the strangest paradoxes of getting older:You have more years behind you, but those years can feel like they’re passing faster.Then We Added Social MediaAs if living on autopilot wasn’t enough, we now have another problem.Digital overstimulation.For many of us, when the workday ends, we don’t actually stop.We pick up our phones.We open social media.We scroll.And scroll.And scroll.One video becomes another.One post becomes another.One outrage becomes another.One argument becomes another.And before we know it, an hour has disappeared.The irony is that this content is incredibly stimulating, but the experience itself often leaves very little behind.Think about what humans experienced for most of our existence.There was a lot of boredom.A lot of boredom.You might spend hours walking somewhere.Working in a field.Gathering food.Making something by hand.Sitting around a fire.Waiting.Waiting some more.Modern humans have largely eliminated this kind of boredom.Instead, we have created an endless stream of content specifically engineered to capture our attention.And there’s something interesting about this.Our brains have to decide what information is important enough to remember.But what happens when we’re exposed to thousands of stimulating pieces of information every day?Everything is urgent.Everything is shocking.Everything is outrageous.Everything is designed to trigger some kind of emotional reaction.Eventually, nothing is special.Your brain becomes overwhelmed by the sheer volume of information.And the result may be that relatively little of it becomes a meaningful, lasting memory.You spent an hour scrolling.But what do you remember?Maybe nothing.And yet that hour is gone forever.This is where I think the combination becomes particularly dangerous.Repetitive routines during the day create fewer memorable experiences.Digital overstimulation during our free time creates an endless stream of forgettable experiences.And then we wonder why life feels like it’s flying by.So How Do We Slow It Down?Here’s the interesting part.After getting laid off, my perception of time changed dramatically.I don’t think it’s because unemployment magically alters the laws of physics.It’s probably because my lifestyle changed.I started doing things differently.And I think some of those changes are things that almost anyone can try.1. Switch Things UpThe first thing I’ve found helpful is introducing novelty into my life.Nothing dramatic.You don’t need to quit your job and become a professional skydiver.Sometimes it’s as simple as taking a different route to the store.Going to a different grocery store.Learning something new.Changing the order of your daily activities.I have a bunch of different hobbies and projects.Journaling.Software development.Drone piloting.Coaching.Gardening.And I’ve noticed that when I change what I do from day to day, the days feel longer.I’m not necessarily doing more.I’m just doing different things.The brain has to pay attention.And when the brain pays attention, it creates more distinct experiences.Those experiences become memories.And when I look back on the week, it feels like a week actually happened.Not just one generic Tuesday that somehow got copied and pasted seven times.2. Be Where You AreThe second thing I’ve found helpful is focusing intensely on the present moment.This can be difficult.Our minds are constantly trying to escape the present.We’re thinking about something that happened yesterday.We’re worrying about something happening tomorrow.We’re mentally composing an email while someone is talking to us.We’re sitting at dinner while scrolling through our phones.We’re physically somewhere.But mentally, we’re somewhere else.I’ve found that activities requiring complete attention can dramatically change my perception of time.One example is my daily 15 minute meditation.When I’m meditating, I have to pay attention to my breathing.My posture.My thoughts.My mental imagery.And something strange happens.Fifteen minutes feels like a long time.Not in a bad way.It feels like I’ve actually experienced those fifteen minutes.Another example is swimming.I recently started taking swimming lessons.This is a big deal for me because I had a pretty bad experience with water as a child, and as a result, I’ve spent most of my life avoiding learning how to swim.Now I’m trying to overcome that.And let me tell you something.When you’re in the deep end of a pool, you’re trying to float, your face is underwater, you’re blowing air out of your nose, and your brain is screaming, This is a terrible idea!You are very much in the present moment.There is no thinking about your email.No worrying about tomorrow’s meeting.No doomscrolling.Just you, the water, and the immediate question of whether you’re about to embarrass yourself in front of the swimming instructor.And here’s the weird part.Those moments seem to last forever.But they also become incredibly memorable.I can remember specific moments from my swimming lessons days later.Which makes me wonder if a certain amount of fear and discomfort might actually be necessary for a satisfying life.Not constant terror, obviously.But maybe we need to occasionally do things that scare us.Things that force us out of autopilot.Things that make us pay attention.Because those are the moments we remember.3. Get Bored AgainThe final thing I’ve done is probably the hardest.I’ve dramatically reduced my consumption of digital content.Especially social media.Yes, I realize the irony.I’m telling you to reduce social media consumption through a piece of content that will probably be distributed to you through social media.I get it.But I can only tell you what I’ve experienced.Doomscrolling was eating an enormous amount of my free time.And when I stopped, something unexpected happened.I became bored.At first, this felt terrible.Then it started feeling wonderful.I realized that boredom wasn’t actually the enemy.Boredom gave my brain room to breathe.Without a constant stream of content competing for my attention, I started noticing things again.I started paying more attention to what I was doing.I started having more distinct experiences.And, most importantly, I started remembering more of my days.My life didn’t necessarily become more exciting.But it became more memorable.And that’s an important distinction.Maybe We Don’t Need More TimeI started this whole journey thinking that I wanted more time.But now I’m starting to wonder if that’s actually the problem.Maybe we don’t need more time.Maybe we need to experience more of the time we already have.We can’t stop the clock.We can’t slow the Earth down.And we certainly can’t negotiate with the calendar.But perhaps we can change our perception of the time we’re given.We can introduce novelty.We can break our routines.We can pay attention.We can tolerate boredom.We can put down the phone.We can do things that scare us.We can be present for the mundane moments instead of constantly trying to escape them.Because when I think about it, the real tragedy isn’t necessarily that life is short.It’s that we can spend so much of it barely noticing that we’re alive.Since losing my job, I’ve started to feel like my days have become longer.My weeks feel more distinct.My memories feel richer.And when I look back, I can actually remember what I did.It almost feels like I’ve been given extra life for free.Maybe that’s the real secret.Maybe slowing down time doesn’t mean making the clock move slower.Maybe it means making more of the time we have count.And if that’s true, then perhaps the first step is surprisingly simple.Put down the phone.Try something new.Let yourself get bored.Pay attention.And for just a little while...Be where you are. 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• 
  Your Job Won’t Remember You. Your Family Will. 15.07.2026 2min

  Hello World!Last week, my family and I spent several wonderful days exploring New York’s Finger Lakes.There wasn’t anything particularly extraordinary about the trip. We went sightseeing. We ate good food. We laughed. We wandered around. We took far too many pictures that will probably never leave our phones.But somewhere in the middle of it all, while watching my kids run around and seeing everyone genuinely happy, I was reminded of something that I think many of us spend years forgetting.Time is our most valuable resource.Not money.Not status.Not promotions.Time.Once it’s gone, it is gone forever.There Will Always Be Another MeetingModern life has a funny way of convincing us that everything is urgent.Another meeting.Another deadline.Another sprint.Another quarter.Another performance review.There is always one more project that absolutely must get finished.The problem is that while work can always generate another task, life doesn’t generate another childhood.Your son is only twelve once.Your daughter only wants to hold your hand for a surprisingly short number of years.Your parents only have so many healthy summers left.Those opportunities don’t get rescheduled.They simply disappear.The Lesson I Learned After Being Laid OffGetting laid off after twenty five years in the technology industry changed far more than my employment status.It changed how I value my time.For most of my career, I believed what many professionals believe.Work hard.Do a good job.Be dependable.Earn promotions.Climb the ladder.None of those things are inherently bad. Work gives us purpose. It pays our bills. It provides structure. It allows us to care for the people we love.But somewhere along the way, many of us quietly allow work to become the center of our identity.Then one day the company restructures.Management changes.Budgets get cut.Artificial intelligence reshapes the business.Or someone you’ve never met decides your position no longer exists.Just like that, years of loyalty are reduced to a brief HR meeting and a severance package.The company moves on.Eventually, everyone does.What Actually LastsThat realization can feel depressing at first.It can even make the universe seem strangely indifferent.But I think there is another way to look at it.If careers are temporary, then maybe we should stop expecting them to provide permanent meaning.Because when our lives are over, nobody is going to gather around our hospital bed to admire our LinkedIn profile.Nobody is going to remember that one presentation you finished at two in the morning.Nobody will care how many meetings you attended.Instead, people remember something much simpler.The laughter around the dinner table.The road trips.The vacations that almost didn’t happen because everyone was “too busy.”The conversations that lasted late into the night.The traditions that only your family understood.The times you showed up when someone needed you.Those become the stories that get told long after we’re gone.The Real Return on InvestmentAs someone interested in financial independence, I spend a lot of time thinking about investing.Stocks.Real estate.Cash flow.Retirement simulations.Compound interest.Those things matter.But there is another investment that compounds in a very different way.Experiences.A weekend camping trip.A family vacation.Teaching your child to fish.Watching a sunset together.Sharing an ordinary dinner without everyone staring at their phones.Unlike financial assets, these experiences appreciate in a way that can’t be measured on a spreadsheet.Years later, they become treasured memories.Sometimes they become family stories that get passed down for generations.That is a return no investment portfolio can match.Redefining SuccessI don’t think success is about rejecting work.We all need to earn a living.We all have responsibilities.But perhaps success isn’t maximizing every dollar.Perhaps success is maximizing the moments that money makes possible.Financial independence isn’t valuable because it lets you stop working.It’s valuable because it gives you more control over your time.And time is the one resource that every billionaire, every CEO, every janitor, and every retiree receives in exactly the same way.None of us knows how much remains.Protect What Matters MostIf there is one lesson I hope my layoff has taught me, it is this.Protect your time with the people you love.Guard it just as fiercely as you guard your savings account.Invest in experiences, not just assets.Take the trip.Go to the family reunion.Call your parents.Eat dinner together.Watch the sunset.The emails will still be there tomorrow.The meetings will still be scheduled next week.The corporate ladder will still exist.But today’s memories can only be created today.At the end of our lives, our greatest legacy won’t be our job title.It won’t be the size of our investment portfolio.It won’t be the number of promotions we earned.Our legacy will be the happiness we created, the love we shared, and the memories that continue to live in the hearts of the people we cared about.That, I think, is a life well lived. 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• 
  The College System Is About to Collapse? 08.07.2026 12min

  Over the Fourth of July weekend, my dad, my son, and I went on one of our annual boys club fishing trips.Between catching countless panfish, my dad brought up a topic that instantly transported me back to my teenage years.He told me that I needed to do everything possible to get my son into a prestigious Ivy League university.To him, this was obvious.To me, it was deeply unsettling.I spent most of the conversation trying to explain that artificial intelligence is fundamentally changing the economics of higher education. A degree from an elite university simply does not offer the same value proposition that it did thirty years ago.Unfortunately, that argument went nowhere.Instead, it reminded me of just how much pressure I experienced growing up.Like many Asian kids, I was told that admission into a top university would determine the entire course of my life. My childhood became an endless cycle of SAT preparation, Advanced Placement classes, extracurricular activities, and constant competition.The message was simple.Get into a top university and success would follow.Fail, and your future would be ruined.Even today, decades later, thinking about college admissions still raises my blood pressure.As a father, I now find myself asking a question that my parents never had to consider.What is the purpose of a university in the age of AI?The University Was Never Just One ThingWhen people think about universities, they usually imagine classrooms, professors, lectures, exams, and diplomas.But universities have always been several different institutions bundled together.They conduct research.They train workers.They certify credentials.They provide networking opportunities.They help young adults develop socially.For decades, this bundled model worked remarkably well because every one of these functions reinforced the others.Graduates earned valuable credentials.Employers trusted those credentials.Students accepted enormous tuition costs because those credentials led to high paying white collar careers.The system made economic sense.At least it used to.The Cracks Started Before AIEven before large language models appeared, higher education was already showing signs of stress.Universities competed for students by building luxury dormitories, massive recreation centers, and professional quality athletic facilities.Tuition exploded.Student loan debt exploded.Education increasingly shifted away from intellectual development and toward vocational training.Instead of asking students how to think, universities increasingly focused on preparing students for specific careers.Then AI arrived.AI Breaks The Educational ModelArtificial intelligence does not simply improve education.It undermines many of the assumptions that higher education has relied upon for generations.AI can explain existing knowledge more effectively than many lecturers.AI can tutor students individually.AI can write convincing essays.AI can solve homework assignments.AI can pass many college examinations.This creates uncomfortable questions for me.If AI teaches the lecture, why attend the lecture?If AI writes the essay, what exactly is the essay measuring?If AI passes the exam, what does the diploma actually certify?The college term paper perfectly illustrates this problem.When I was in school, writing a major paper required weeks of research, organization, revision, and critical thinking.The paper itself was never the valuable part.The value came from the thinking required to produce it.Today, large language models can generate that same final product within minutes.The artifact still exists.The learning process does not.Once that happens, the assignment no longer measures what it was designed to measure.Universities Were Really Measuring Human ThinkingFor generations, universities served as society’s measurement system for cognitive professions.A degree signaled that someone possessed enough knowledge and reasoning ability to perform complex white collar work.But AI changes that equation.Many of the cognitive tasks that universities prepare students to perform are rapidly becoming automated.If those jobs disappear, then much of the traditional university model loses its economic foundation.Higher Education Will FragmentI do not believe universities will disappear.I believe they will break apart into specialized institutions.Research institutes may become independent organizations funded by governments, private companies, or nonprofit organizations.Technical credentialing may shift toward affordable online providers that continuously update practical skills rather than requiring four year degrees.Communities focused on networking and social development may evolve entirely outside traditional campuses.Most importantly, I believe a new category of institution will emerge.Schools whose primary mission is teaching people how to think.Teaching Humans To ThinkThis may sound strange at first.Isn’t that what universities already do?Increasingly, I do not think so.When I talk about thinking, I mean reasoning.Judgment.Agency.Taste.Ethics.Persuasion.Understanding human motivations.Making decisions under uncertainty.These are the capabilities that remain extraordinarily valuable in an economy increasingly dominated by artificial intelligence.Future education should not ban AI.It should teach students how to collaborate with AI while maintaining independent judgment.Students should learn how to verify evidence.Challenge AI generated conclusions.Recognize hallucinations.Understand the limits of machine intelligence.AI should become a thinking partner.Never a thinking replacement.Assessment would also need to change.Instead of take home essays generated by language models, students may demonstrate their reasoning through live discussions, debates, collaborative problem solving, and real time decision making.Perhaps professors themselves evolve into mentors rather than lecturers.People whose greatest contribution is not transferring information, but sharpening judgment.Ironically, liberal arts subjects that many universities have spent decades marginalizing, including philosophy, history, mathematics, political theory, and physics, may become some of the most economically valuable disciplines because they teach people how to think instead of simply training them for a specific job.The University After AII am not convinced that artificial intelligence will replace every form of human work.As long as AI remains a powerful tool rather than true artificial general intelligence, society will still require a relatively small class of professionals capable of solving novel problems, exercising judgment, and making difficult decisions.Those people will create new companies.Invent new products.Lead institutions.Navigate uncertainty.The education system that produces those people will look very different from the universities we know today.It will place wisdom above memorization.Judgment above credentials.Thinking above information.As a father, I hope those institutions exist by the time my son reaches adulthood.Because I suspect those are the schools that will matter most.The future belongs not to the people who know the most.It belongs to the people who can think the best. 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• 
  I Got Laid Off... Now I Make $11,000 a Month Doing Whatever I Want 04.07.2026 14min

  When I got laid off from my Big Tech job late last year, I thought I knew what came next.Shock.Anger.Shame.Sadness.If you’ve ever been laid off, you probably know the feeling. It is as if someone has ripped away not just your paycheck, but your identity. After spending twenty five years in the technology industry, I suddenly found myself unemployed.Meanwhile, every week seemed to bring another announcement of mass layoffs across the tech industry. Friends were losing jobs. LinkedIn became an endless stream of people posting that they were “excited to announce” they were looking for work.It was depressing.But something unexpected happened.Over the following months, I slowly came through the other side.Instead of waking up every morning dreading another day of meetings, deadlines, and corporate politics, I started waking up excited.Some days felt almost euphoric. There were so many things I wanted to learn, build, and explore that I genuinely did not want the day to end.My layoff had accidentally pushed me into something I never would have chosen for myself.Early retirement.I Know I’m One of the Lucky OnesBefore going any further, I want to acknowledge something important.This is only possible because I spent many years earning a high salary in tech while aggressively saving and investing.For well over a decade, my wife and I consistently lived below our means.We invested.We avoided lifestyle inflation.We paid off debt.Over time, those boring financial decisions compounded into something extraordinary.Financial independence.Without that foundation, this story would have been very different.I understand that.I do not take that privilege lightly.The People I’m Really Writing This ForIronically, this article is not for people who have already reached financial independence.It is for the software engineers who are still trapped.Maybe you are still employed.Every round of layoffs leaves your team smaller while your workload somehow becomes larger.You spend your days juggling impossible deadlines while carrying survivor guilt because your friends lost their jobs instead of you.Maybe you have already been laid off.Now your life revolves around LinkedIn, recruiter calls, algorithm interviews, and endless rejection.Maybe after decades of experience you are being offered salaries that would have seemed insulting just a few years ago.I genuinely feel for you.That is why I wanted to share something practical.What actually pays my bills today?Where My Income Comes FromPeople often assume that early retirement means sitting on a beach collecting investment income.My reality looks very different.Today my family earns just under eleven thousand dollars per month from a collection of different income streams.Our two rental properties generate about thirty nine hundred dollars per month.Stock dividends from our taxable investment accounts contribute roughly fifteen hundred dollars.Interest from our high yield savings account adds another three hundred dollars.Those are the traditional sources.The interesting part comes next.I Accidentally Started Getting Paid for My HobbiesOne thing nobody tells you about early retirement is that you suddenly have an enormous amount of free time.At first, that feels strange.Then it becomes exciting.I started learning new things.I planted an herb garden.I began learning to read Chinese.I worked toward my drone pilot license.I learned how to swim.Then I discovered something fascinating.In today’s economy, it takes surprisingly little extra effort to monetize hobbies you would have pursued anyway.That realization completely changed how I think about work.YouTube Started as TherapyI never planned to become a YouTuber.After my layoff, I fell into a depressive spiral.My therapist suggested journaling.Writing never really worked for me.Talking did.So I started recording videos.That simple habit became surprisingly therapeutic.Then something else happened.People started watching.Even more importantly, they started responding.After years of talking with coworkers every day, unemployment can become surprisingly lonely.These conversations became my new social outlet.Comments turned into discussions.Discussions became livestreams.Livestreams became friendships.Eventually YouTube started paying me around twenty eight hundred dollars per month.Other creator platforms contribute another couple hundred dollars.The money is nice.The sense of community is even better.Coaching Doesn’t Feel Like WorkAnother unexpected hobby became coaching.I schedule one on one conversations with people using Calendly.Sometimes we talk about software engineering.Sometimes financial independence.Sometimes career transitions.Sometimes life.The conversations are genuinely enjoyable.After decades of solving problems with people, I realized communication is like a muscle.If you stop using it, it weakens.Coaching lets me keep exercising that muscle while earning around twelve hundred dollars per month.I Fell Back in Love With ProgrammingIronically, I enjoy programming far more now than when I was employed as a software engineer.Without deadlines.Without sprint planning.Without endless meetings.Without office politics.Coding became fun again.I build software that scratches my own itch.Sometimes those projects make money.Sometimes they do not.Either outcome is perfectly fine.One project was an AI powered workflow that generated wall art for online marketplaces.I ran it for only two days before deciding it created very little real value.I shut it down.The listings still earn around seven hundred dollars every month.Another project called Funemployment Day began as a personal tool for tracking my time after leaving corporate life.Today it has hundreds of users and some paying subscribers.Combined, my software projects generate around one thousand dollars each month.Agentic AI Changed EverythingOne thing that genuinely surprised me is how dramatically AI has lowered the barrier to entrepreneurship.Not because it writes perfect code.It absolutely does not.But because it removes so much of the boring work surrounding a project.Once I publish a YouTube video, AI helps automate titles, descriptions, transcripts, articles, short form videos, and publishing across multiple platforms.Instead of spending hours on repetitive tasks, I spend my time thinking, building, and creating.That feels like a much better use of both humans and machines.Is This a Preview of the Future?Altogether, these income streams generate just under eleven thousand dollars every month.For some readers, that sounds like a fortune.For others, it is much less than their current salary.The number itself is not really the point.The point is that none of this income comes from a traditional full time job.Instead, it comes from assets, hobbies, creativity, relationships, and small software products.Sometimes I wonder if this is a tiny glimpse of what a post labor economy might eventually look like.Not necessarily a utopia.Perhaps something much stranger.As artificial intelligence continues to automate more forms of work, I suspect many more people will eventually find themselves earning income from portfolios of projects rather than a single employer.Maybe this is the future.Maybe it is not.I honestly do not know.What I do know is that my layoff forced me onto a path I never would have chosen voluntarily.Looking back now, I am grateful it happened.If you are currently burned out, unemployed, or wondering whether there is life beyond the corporate ladder, I hope my story gives you at least one idea worth exploring.Sometimes the worst day of your career becomes the beginning of an entirely different life. 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• 
  Why Everyone SEEMS to Have More Money Than Me? 30.06.2026 12min

  Every once in a while, I experience a feeling that I’m not particularly proud of.It’s the feeling that everybody else is rich.They all seem to have more money than I do. Bigger houses. Newer cars. Better vacations. Better lives.And somehow, despite everything I’ve accomplished, I feel like I’m falling behind.These days it doesn’t happen very often, but every now and then it sneaks up on me.Maybe I’m watching YouTube and someone is giving a tour of their enormous house. Maybe I’m watching Netflix, where every “ordinary” family somehow lives in a multimillion dollar home. Or maybe I’m driving my used Honda CR-V when another successful-looking middle-aged Asian guy cruises by in a brand new Tesla.For a brief moment, my brain whispers:“What happened? Did everyone else figure something out that I didn’t?”Then almost immediately I feel embarrassed for thinking that way.Because I know something that my emotions conveniently forget.Most people are not rich.The Data Doesn’t Match the FeelingIf you look at the numbers, only a tiny percentage of American households qualify as liquid millionaires, meaning they have at least one million dollars in investable assets.Even among that group, a million dollars isn’t exactly “private jet” money.Where I live, in a high cost of living area, a million dollars doesn’t even buy many single-family homes.Objectively speaking, the overwhelming majority of Americans are not wealthy.So why does it constantly feel like they are?I’ve spent quite a bit of time thinking about this question, and I think I’ve come up with a few reasons.We Are Wired to Follow the HerdHuman beings are social animals.Like apes, elephants, dolphins, and countless other social species, we instinctively look to the group for cues about what success looks like.The problem is that many people don’t have clearly defined personal goals.Without an internal compass, it’s incredibly easy to borrow someone else’s definition of success.Instead of asking ourselves what actually matters, we begin copying the outward appearance of people society celebrates.The expensive house.The luxury car.The designer clothes.The exotic vacations.The visible symbols become the goal, even if they have very little to do with happiness.Social Media Is a Distortion MachineSocial media takes this very human tendency and cranks it up to eleven.People naturally want to share good news.Nobody posts pictures of arguing with their spouse over credit card bills.Nobody uploads videos of lying awake at 2 AM wondering how they’re going to make next month’s mortgage payment.Nobody posts photos of the panic attack they had after realizing they financed a lifestyle they can’t actually afford.Instead, everyone uploads their highlight reel.You see promotions.New homes.Perfect vacations.Luxury purchases.Beautiful weddings.Smiling families.You almost never see the anxiety that paid for those pictures.Social media also magnifies survivorship bias.Think about lottery winners.Think about meme stock millionaires.Think about people who got lucky with speculative cryptocurrencies.Millions of people lost money chasing exactly the same opportunities.But you only see the handful of winners.After enough exposure, your brain quietly concludes that extraordinary success is normal.It isn’t.Consumerism Needs You to Feel InadequateAmerica is fundamentally a consumer economy.Consumption isn’t simply encouraged.It’s necessary.If everyone suddenly stopped buying things they didn’t need, the economy would grind to a halt.Advertising understands something about human psychology.People don’t buy products.They buy identities.Watch almost any commercial.Notice the homes.The neighborhoods.The kitchens.The cars.The vacations.The wardrobes.The lifestyles being presented often resemble families in the top income brackets, yet they’re marketed as completely normal.It’s subtle.But over time, it recalibrates what “normal” looks like.The result is that millions of people begin chasing a lifestyle that statistically very few can actually afford.Debt Makes Everyone Look RichHere’s the uncomfortable reality.Most Americans don’t possess enormous amounts of wealth.Yet many appear wealthy.How?Debt.Modern finance allows almost anyone to temporarily purchase the appearance of financial success.Luxury cars.Expensive homes.Designer furniture.High-end electronics.Exotic vacations.None of these necessarily indicate wealth.Many simply indicate access to credit.The problem is that debt has a hidden cost.Financial stress.Relationship problems.Mental health struggles.Lost opportunities.Years, sometimes decades, spent servicing yesterday’s consumption.Ironically, these displays of wealth create a psychological burden for everyone else.The illusion makes perfectly successful people feel like failures.I’ve certainly experienced it.Maybe you have too.The Bigger Picture: The K-Shaped EconomyI think something even larger is happening beneath the surface.Our economy increasingly resembles a K.One branch rises upward.The other falls behind.The upper branch consists largely of people who own productive capital.Stocks.Businesses.Real estate.Increasingly...Artificial intelligence.Robotics.Automation.These assets can continue creating value twenty-four hours a day.Meanwhile, the lower branch depends primarily on labor.The problem is that labor doesn’t scale the way capital does.As AI improves, capital becomes dramatically more productive.Human labor, on the other hand, doesn’t become exponentially more valuable simply because technology advances.In fact, many forms of labor may become less valuable over time.That doesn’t necessarily mean society is doomed.Perhaps governments adapt.Perhaps entirely new industries emerge.Perhaps we’ll experience major political or economic reforms.Nobody knows.But I do think the gap between owners of capital and sellers of labor is becoming one of the defining stories of our generation.If you’re fortunate enough to work in a high-paying field like technology, I believe one of your biggest priorities should be gradually converting income into ownership.Because ownership compounds.Labor generally does not.Three Ways I Try to Stay GroundedI don’t have all the answers.But these habits genuinely help me.1. Define your own version of success.Don’t inherit someone else’s goals.Figure out what actually matters to you.Financial security.Freedom.Family.Purpose.Recognition.Adventure.Whatever it is, make sure it’s yours.Once you know where you’re going, other people’s lives become much less distracting.2. Compare yourself with your past self.Comparison really is the thief of joy.Especially when you’re comparing yourself against someone whose financial situation may be almost entirely fictional.Instead, ask a simpler question.Am I a better version of myself than I was five years ago?If the answer is yes, you’re winning.Celebrate that.3. Minimize social media.Yes, I realize the irony.You’re probably reading this because the internet recommended it.But I genuinely believe excessive social media consumption is one of the most damaging habits for our mental health.It constantly encourages us to compare our ordinary lives against everyone else’s carefully curated highlights.That’s a game you can never win.Sometimes the healthiest thing you can do is simply close the app.Final ThoughtsI’ve come to believe that the feeling that “everyone else is rich” says far more about the world we live in than it does about our actual financial situation.We’re surrounded by advertising.We’re immersed in social media.We’re encouraged to borrow.We’re constantly shown lifestyles that belong to a tiny fraction of the population and told they’re normal.No wonder so many of us feel like we’re falling behind.But the feeling is not reality.Reality is quieter.Reality is slower.Reality is built over decades of consistent decisions rather than viral moments and luxury purchases.So the next time I see another shiny Tesla drive by while I’m sitting in my aging Honda CR-V, I’ll try to remind myself of something simple.I don’t need to win someone else’s race.I only need to keep making progress in my own.And honestly, that’s more than enough. 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• 
  The AI Coding Revolution Has a Huge Problem? 24.06.2026 14min

  A few weeks ago, I stumbled across a debate that has been making the rounds in software engineering circles.The spark came from Boris Cherny, an engineer at Anthropic and the creator of Claude Code, arguably the most influential AI Agentic Coding harness in the world today.During a podcast appearance, Boris made a statement that immediately grabbed my attention:Coding is largely a solved problem.He went on to explain that he hadn’t written a line of code by hand since November, and that essentially all of his code is now authored by Claude Code.Needless to say, this generated strong reactions.Some people interpreted it as evidence that software engineering is about to be fully automated. Others saw it as confirmation that AI coding tools are delivering unprecedented productivity gains.As someone with twenty-five years of experience in software development, I found myself somewhere in the middle.Because while I absolutely believe AI is transforming software engineering, my own experiences suggest that programming is nowhere close to being a solved problem.In fact, the more I use these tools, the more complicated the situation appears.The Rise of Agentic Software DevelopmentOver the past few years, we’ve witnessed a rapid evolution in how software gets built.First, developers used AI to generate snippets of code.Then they began using AI assistants to complete larger programming tasks.Today, we’re entering what many people call Agentic Software Development.Instead of asking an AI for a few lines of code, developers increasingly delegate entire workflows.The AI can analyze requirements.Generate designs.Write code.Create tests.Review its own output.Deploy software.Monitor production systems.In theory, the human becomes less of a programmer and more of an orchestrator.The promise is obvious.If AI agents can perform most of the implementation work, then software engineers can become dramatically more productive.Ten times more productive, according to some advocates.Perhaps even more.At least, that’s the dream.My First Encounter With Enterprise Agentic AIIn 2025, I returned to work after a lengthy medical leave.My wife had experienced a serious health crisis, and I had spent months focused almost entirely on family.When I came back, one of the first things I noticed was that my employer had become obsessed with Agentic AI.Leadership had heard about tools like Claude Code and Cursor.They had heard stories about developers becoming ten times more productive.Naturally, they concluded that our company needed its own internal version.Let’s call it Kevin.Kevin was our homegrown agentic harness.Compared to Claude Code, Kevin felt slower, heavier, and burdened with enterprise compliance guardrails.It ran on older-generation models.It often struggled with context.And yet, despite all its flaws, Kevin was still capable of orchestrating significant portions of software development.Using Kevin gave me a front-row seat to what Agentic AI actually looks like inside a large enterprise.What I observed left me both impressed and concerned.The Business Knowledge ProblemOne of the biggest weaknesses I encountered had nothing to do with coding itself.It had to do with understanding.AI models are remarkably good at generating software.What they are not particularly good at is understanding the business context behind that software.Organizations often operate on thousands of unwritten assumptions.Knowledge exists in hallway conversations.Slack threads.Meeting notes.Institutional memory.The heads of senior employees.Much of this information never appears in formal documentation.Humans navigate these gaps naturally.AI agents do not.If a requirement is not explicitly documented, the AI will often substitute something that appears reasonable based on its training data.The generated code may compile successfully.The unit tests may pass.The architecture may look elegant.And yet the implementation may completely miss the actual business objective.This problem becomes particularly severe in large brownfield systems where decades of accumulated business logic exist beneath the surface.The Context Window WallAnother challenge is context.Every AI model has a limited working memory.For small projects, this isn’t a major issue.For enterprise software systems containing millions of lines of code, it becomes a constant battle.Once an AI agent exceeds its effective context window, strange things begin to happen.The model forgets previous decisions.It hallucinates APIs.It reintroduces deprecated libraries.It generates solutions that were already rejected earlier in the workflow.Developers have created countless mitigation strategies.Context compaction.Summarization.Subagents.Selective context filtering.These techniques help.But they don’t eliminate the underlying limitation.The larger the system becomes, the harder it is for the AI to maintain a coherent mental model of the entire application.Ironically, this is often where software engineering is most difficult in the first place.The Hidden Cost of Infinite CodeOne thing AI agents are exceptionally good at is generating code.Lots of code.An astonishing amount of code.Thousands of lines.Tens of thousands of lines.Entire subsystems can appear almost instantly.The problem is that quantity and quality are not the same thing.Many AI-generated implementations contain unnecessary abstraction layers.Duplicate functionality.Excessive complexity.Architectural choices that seem reasonable locally but become problematic globally.Without strong human oversight, repositories begin accumulating what can only be described as AI sediment.Layer upon layer of generated code.Each piece understandable in isolation.Collectively becoming harder and harder to maintain.Technical debt compounds quietly.And unlike financial debt, software debt often remains invisible until it becomes a crisis.The Vibecoding TrapThis brings us to what I believe is the most important problem.Human review capacity.A team of AI agents can generate thousands of lines of code per hour.A human engineer cannot review thousands of lines of code per hour with high confidence.The math simply doesn’t work.As output increases, review quality inevitably declines.Cognitive fatigue sets in.Attention drops.Comprehension weakens.Eventually, the reviewer stops acting as an engineer and starts acting as a rubber stamp.This is the danger behind what many people call vibecoding.The developer repeatedly prompts the AI until something appears to work.The code ships.Nobody fully understands it.Nobody feels ownership over it.And nobody wants to maintain it six months later.At that point, accountability becomes largely fictional.The engineer remains responsible for the software without truly possessing the knowledge necessary to evaluate it.So Is Coding Solved?Despite everything I’ve written, I remain incredibly optimistic about AI.These tools are genuinely transformative.For greenfield projects, prototypes, internal tools, and well-understood problem domains, the productivity gains are astonishing.Recently, I used Agentic AI to build one of my own projects.The agents completed work in hours that might previously have taken days.The productivity boost was real.But here’s the important distinction:The AI performed the implementation.I still spent days reviewing the code, testing the software, validating assumptions, and ensuring everything actually worked.The bottleneck moved.It didn’t disappear.And that’s why I struggle with the claim that coding is solved.Perhaps code generation is becoming solved.Perhaps implementation is becoming increasingly automated.But software engineering has always been about much more than typing characters into an editor.It involves judgment.Tradeoffs.Domain expertise.System design.Risk management.Human communication.Institutional knowledge.And responsibility.None of those problems appear solved to me.The Next Rabbit Hole: Loop EngineeringWhat’s particularly fascinating is that many of the engineers pushing Agentic AI furthest seem to be moving beyond prompting altogether.Boris Cherny has suggested that developers should stop prompting and start building loops.Peter Steinberger has made similar arguments.The idea is that autonomous agents should continuously generate, evaluate, and refine their own work.This concept is often referred to as Loop Engineering.I’ve spent time reading about it.Experimenting with it.Trying to understand it.And if I’m being honest, I still don’t entirely get it.What’s more frustrating is that concrete, end-to-end examples remain surprisingly rare.The discussions often feel abstract.Almost mystical.As if everyone has seen the future except the people trying to build software today.Maybe that’s because we’re still in the earliest stages of this transition.Or maybe we’re collectively mistaking experimentation for certainty.Either way, I’m not convinced we’ve arrived at the destination yet.My Current ConclusionAgentic AI is one of the most important technological developments of my career.It is already changing how software gets built.It will continue changing how software gets built.But from where I sit, coding does not look like a solved problem.It looks like a rapidly evolving one.And that’s actually far more interesting.For now, I’ll keep experimenting.I’ll keep learning.And I’ll keep trying to separate the genuine breakthroughs from the hype.Because if the future of software engineering really is being rewritten by AI agents, I’d like to understand what’s actually happening beneath the marketing slides.And if you’re curious too, you’re welcome to come along for the ride. 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• 
  Something Is Seriously Wrong With People? 22.06.2026 12min

  A few days ago, my wife received a credit card in the mail from a company we had never used.Apparently, someone had opened an account in her name.Naturally, I assumed identity theft and immediately called the credit card company to shut the account down and find out what information had been used to create it.After spending twenty minutes navigating an infuriating AI phone system, I finally reached a human being.Or at least, I think I did.The conversation felt strange.Every question I asked seemed to trigger a predetermined response. Every attempt to move the conversation in a logical direction was met with another scripted answer. It was as if the representative was following a flowchart that he could not deviate from under any circumstances.At one point I caught myself wondering whether I was talking to another AI.I wasn’t.It was clearly a real person.Yet somehow the interaction felt less human than many conversations I have had with chatbots.That experience stuck with me because it wasn’t an isolated incident.Lately, I have noticed a growing number of interactions that feel strangely mechanical. Not just in customer service. Not just online. Everywhere.People seem more scripted.More performative.More constrained.Almost as if they are operating from a limited set of dialogue options.Like NPCs in a video game.Now before anyone gets offended, I am not saying everyone behaves this way. I meet plenty of thoughtful, authentic people. But I encounter this phenomenon often enough that I can no longer ignore it.And it leaves me wondering:What exactly happened?The Corporate Mask That Never Comes OffI first noticed this trend while working in tech.Anyone who has spent time in a large corporation understands that some degree of performance is expected. We all wear masks at work.That part is normal.What felt different was seeing people become completely consumed by the performance.Simple ideas would be transformed into elaborate slide decks.Meetings would be scheduled to discuss future meetings.Entire conversations would revolve around appearing aligned rather than accomplishing anything meaningful.Everyone knew the ritual.Everyone participated.Nobody seemed willing to acknowledge the absurdity of it.What unsettled me was that for some people, the corporate persona appeared to become permanent.The mask never came off.The language, the mannerisms, the carefully calibrated responses followed them everywhere.Even outside of work.Conversations Feel DifferentSince being laid off, I have more opportunities to talk with people in everyday settings.Coffee shops.Parks.Neighborhood events.Random encounters.And what I have discovered is that many conversations feel surprisingly shallow.There is often a narrow range of approved topics.Food.Entertainment.Sports.Local events.Anything deeper can create immediate discomfort.Discussions about purpose, meaning, technology, society, mortality, economics, or the future often cause people to retreat.Not because they disagree.Because they seem exhausted.As if they simply do not have the mental bandwidth for the conversation.Perhaps that is the real issue.Not that people have become less intelligent.Not that people have become less caring.But that people have become overwhelmed.Theory One: We Are OverstimulatedThink about how radically the environment has changed over the past twenty years.Most people now spend the majority of their waking lives connected to digital platforms.Their attention is continuously pulled in dozens of directions.Every notification competes for cognitive resources.Every algorithm is optimized to generate emotional reactions.Anger.Fear.Excitement.Outrage.Anxiety.The result is a constant state of sensory overload.When people are exhausted, authenticity becomes difficult.Deep conversations require energy.Curiosity requires energy.Human connection requires energy.And many people simply have none left.Theory Two: We Have Been Conditioned By WorkMost Americans depend on employment to survive.That reality shapes behavior in powerful ways.Corporate environments reward predictability.They reward compliance.They reward process.They reward metrics.Over time, people learn to suppress parts of themselves that do not contribute directly to performance.Creativity becomes risky.Authenticity becomes risky.Spontaneity becomes risky.Eventually the performance becomes second nature.The script becomes internalized.And after years or decades of repetition, it becomes difficult to distinguish between the role and the person.Theory Three: Social Media Creates Behavioral ClonesThe early internet felt like exploration.You wandered.You discovered strange websites.You stumbled into unfamiliar ideas.The modern internet feels different.Algorithms decide what you see.Algorithms decide what you think about.Algorithms increasingly determine which personalities rise to prominence.Within every online community there are archetypes.The motivational guru.The productivity expert.The leadership philosopher.The lifestyle influencer.And many people unconsciously imitate these personas because they appear successful.Over time, entire communities begin speaking the same way.Thinking the same way.Reacting the same way.Not because they independently arrived at the same conclusions.But because they are all consuming the same inputs.Theory Four: The Meaning CrisisPerhaps the deepest explanation is that many people no longer know what they are living for.Traditional sources of meaning have weakened.Communities are fragmented.Institutions are less trusted.Consumerism often replaces purpose.Individualism often replaces belonging.The result is a quiet sense of disconnection.A feeling that life is somehow missing a center.When people lose connection to meaning, they often lose connection to themselves.And when that happens, everything starts to feel performative.Or Maybe It’s Just MeMy wife has a much simpler explanation.She thinks people have not changed at all.She thinks I am getting older.According to her, I am viewing the past through nostalgia tinted glasses and imagining a level of authenticity that never actually existed.And honestly?She might be right.Memory is unreliable.Perspective changes with age.Perhaps twenty five year old me was simply less observant.Or perhaps middle aged me has become more cynical.I genuinely do not know.What I do know is that something feels different.Whether that difference exists in society or only inside my own perception remains an open question.So I will leave that question with you.Do people seem more authentic today?Less authentic?Have social media, corporate culture, and digital life fundamentally changed the way we interact?Or is this simply what getting older feels like?I would love to hear your thoughts. 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• 
  I Think We're Losing Control Of AI 16.06.2026 12min

  A recent experience with Claude 5 Fable left me both impressed and deeply unsettled.Hello world.I am an unemployed former Big Tech software engineer with twenty five years of experience building software systems. Over the past week, I found myself tumbling down a rabbit hole that I did not expect to be quite so deep.That rabbit hole was Claude 5 Fable.Fable is widely described as a consumer-facing version of Anthropic’s next generation frontier model, Claude Mythos. While details surrounding Mythos remain scarce, public reporting suggests that access is heavily restricted, with only government organizations and a small number of major technology companies allowed to interact with it under Project Glasswing.Naturally, I was curious.If this was the “safe” version, what exactly had been deemed too powerful for the public?Over the course of 2 days, I put Fable through its paces. I built multiple projects with it, explored its reasoning capabilities, and examined how it approached open ended engineering problems.What I discovered felt less like an incremental improvement and more like a discontinuity.Not an evolution.A step change.And frankly, it scared me.The Isochrone ExperimentTo illustrate what I mean, let me describe a small project I assigned to Fable.In the nineteenth century, colonial powers often produced isochrone maps. These maps visualized how far a person could travel from a central location within a given amount of time. They were part logistics tool, part demonstration of technological power.I asked Fable to build a modern version for the United States.The application would need to calculate travel times between cities while adapting dynamically based on transportation method, whether by automobile, rail, or air travel.Importantly, I did not provide a detailed specification.Normally I follow a methodology called Spec Driven Development. The process involves extensive collaboration between a human architect and AI system to create a detailed technical blueprint before any implementation begins.This time, I deliberately skipped that step.I gave Fable a single vague prompt and watched.Less than an hour later, it delivered a working application.Not a prototype.Not a proof of concept.A functioning solution.The model had researched transportation data, designed the architecture, written the code, tested the implementation, and produced a polished user experience.From one ambiguous instruction.The Questions That Stopped MeThe finished application was impressive.The questions were what unsettled me.During development, Fable paused only twice.The first question concerned whether travel times should include the time required to walk to airports and train stations.The second asked whether traffic congestion should be incorporated into driving estimates.These were not technical questions.They were business questions.Product questions.Questions that demonstrated an understanding of ambiguity within the problem itself.That distinction matters.I have seen AI systems ask clarifying questions before. Typically they do so when the task is highly structured and the missing information is obvious.This was different.Fable identified subtle assumptions embedded inside an open ended problem and proactively sought guidance on them.That level of judgment suggested something deeper than simple pattern matching.Looking Behind the CurtainAt this point, curiosity got the better of me.I began examining the session logs to understand how Fable had actually accomplished the task.What I found was remarkable.Fable was orchestrating an entire team of AI agents.Several research-oriented agents were dispatched to gather transportation data. While they worked, Fable itself focused on architecture and implementation.It then created reviewer agents tasked with examining both the system design and the generated code.Finally, separate quality assurance agents were deployed to test the completed application.In total, the project involved roughly eight specialized AI agents working together under Fable’s supervision.As someone who spent years as a software architect, the workflow felt eerily familiar.Business Analysts.Architects.Reviewers.QA engineers.The organizational structure looked less like a software tool and more like a software company.The difference was that the entire company existed inside a single prompt.The Cost of DelegationThe obvious reaction is excitement.Mine certainly was.The productivity gains are extraordinary.The amount of cognitive labor performed by the system was staggering compared to the tiny amount of direction I provided.But excitement was quickly followed by discomfort.The more capable these systems become, the less visibility humans have into their decision making.Fable made hundreds, perhaps thousands, of micro decisions during the course of the project.Most of them were never surfaced to me.Most of them happened autonomously.The model simply acted.Historically, AI functioned like a tool.Then it became a collaborator.Today, systems like Fable feel increasingly like autonomous organizations.To borrow an analogy from music, AI began as a better violin.Later, it became a virtuoso musician directed by a human conductor.With Fable, I no longer feel like the conductor.I feel like the patron funding the orchestra.I provide a high level objective.The performance unfolds largely without my input.That shift may prove to be one of the most consequential changes in the history of computing.The Sleeping LeviathanFor years, I have described advanced AI as a sleeping leviathan.An immense cognitive force slumbering beneath the surface of our civilization.We could whisper into its ear and receive useful answers.But it remained dormant.Contained.Predictable.Claude 5 Fable is the first model that made me question whether that assumption still holds.At its core, Fable remains a probabilistic machine. It predicts tokens. It does not possess consciousness, self awareness, morality, or intent.And yet, from a functional perspective, it is already capable of performing many forms of cognitive work at or beyond human levels.Research.Planning.Design.Coding.Testing.Coordination.Judgment.The capability is increasingly difficult to deny.What concerns me is that capability is arriving faster than our ability to understand its implications.Fable is an extraordinarily powerful cognitive tool.Without safeguards, it could become an extraordinarily powerful cognitive weapon.The technology itself does not frighten me nearly as much as the people who will wield it.And that, more than anything, is why I believe the leviathan may finally be awakening. Get full access to AsianDadEnergy's Newsletter at asiandadenergy.substack.com/subscribe
• 
  The Hidden Cost Of Working Nobody Warns You About 13.06.2026 15min

  Six months ago, I lost my job.At the time, it felt like a catastrophe.After spending twenty-five years working in technology, employment had become such a constant in my life that I could barely imagine an existence without it. Work was the backdrop against which everything else happened. It structured my days, determined where I lived, influenced my relationships, and shaped my identity.When the layoff happened, I went through the emotions that many people experience: disorientation, sadness, shame, and uncertainty.But something unexpected happened.Six months later, I feel better than I have at any point in my adult life.That realization has forced me to confront a question I never seriously considered before:What was my job actually costing me?Not in the obvious ways. Not in terms of hours worked or stress endured. Those costs were visible.I’m talking about the hidden costs.The ones that accumulate so gradually that you stop noticing them.The ones that become normal.The ones you only recognize after they disappear.The Water We Swim InMost people can identify the benefits of a job.A paycheck.Health insurance.Professional accomplishment.Social status.A sense of purpose.Those benefits are real.But every benefit comes with a cost, and after decades in the workforce I had become remarkably good at ignoring the bill.Like a fish that doesn’t notice the water surrounding it, I stopped noticing the environment that my work had created around me.Only after leaving it could I see it clearly.The Cost of Chronic StressFor most of my career, I lived under a constant state of pressure.Deadlines.Escalations.Production incidents.Office politics.Organizational reshuffling.Performance reviews.The endless stream of decisions that carry consequences no matter which option you choose.Even when I wasn’t working, I was working.Emails arrived during vacations.Slack messages appeared during dinner.Production outages interrupted weekends.My mind never fully powered down.Over time, that level of stress became normal.I assumed everyone felt this way.I assumed adulthood felt this way.I assumed success felt this way.Only now do I realize how profoundly that stress shaped my mental state.For years I carried persistent anxiety.I battled imposter syndrome.I experienced recurring periods of burnout that left me feeling emotionally numb and mentally exhausted.Today, much of that pressure is gone.What’s left behind is something I haven’t felt in decades:Mental quiet.Not boredom.Not laziness.Just peace.And peace turns out to be worth far more than I realized.The Cost to My HealthFor years I told myself I was taking care of my health.I woke up early.I squeezed in exercise whenever possible.I tried to eat reasonably well.But looking back, I wasn’t building health.I was merely slowing the rate of decline.My lifestyle revolved around work.I slept six hours a night.I spent most of my day sitting in front of screens.I relied on junk food and caffeine to push through difficult periods.Eventually the consequences arrived.High blood pressure.High cholesterol.Pre-diabetes.Fatty liver disease.None of these conditions appeared overnight.They accumulated gradually, one stressful workday at a time.Today, my life looks very different.I exercise daily.I sleep eight hours every night.I prepare nearly every meal myself.The difference is remarkable.I have more energy.Better focus.Greater emotional stability.For the first time in years, I feel like I’m moving toward health rather than away from it.The Cost of TimeThis may have been the largest hidden cost of all.When people think about work, they think about the hours spent working.But work consumes far more time than that.There’s the commute.The preparation.The recovery.The mental decompression afterward.The administrative overhead of maintaining a professional life.For me, commuting into New York City often required three to four hours every day.After ten hours of work and several hours of commuting, I had little energy left for anything meaningful.The result was that nearly all of my waking existence was either directly or indirectly devoted to work.And yet I barely noticed.Because everyone around me was doing the same thing.Now, for the first time in my life, I have an abundance of unstructured time.At first, I wasted it.I spent too much time scrolling social media and playing video games.But eventually something changed.I began using that time intentionally.To learn.To create.To think.To spend time with my family.To pursue interests that had been neglected for decades.And I discovered something surprising:Time abundance feels almost luxurious in a way that money never did.The Cost of MoneyThis one sounds paradoxical.After all, work is supposed to make you money.And it does.But it also encourages you to spend it.A stressful life creates demand for convenience.A busy life creates demand for shortcuts.When you’re exhausted, overwhelmed, and time-poor, you start solving problems with your wallet.You pay for convenience.You outsource tasks.You accumulate subscriptions.You spend money to compensate for your lack of time and energy.Sometimes you even spend money trying to repair relationships damaged by your absence.I certainly did.Looking back, I realize that many of my expenses weren’t improving my life.They were compensating for a lifestyle that wasn’t working.The Cost of CommunityOne of the most surprising lessons came after my layoff.Throughout my career, I interacted with dozens of coworkers every day.I liked most of them.Some became genuine friends.But many were simply people who occupied the same professional ecosystem as me.When I left, most of those relationships disappeared almost immediately.Not because anyone was malicious.Because that’s what workplace relationships often are.They’re situational.They’re formed through proximity and shared necessity.What remained were the people who genuinely cared.The people who reached out when they didn’t have to.The people who wanted to spend time together without a paycheck involved.Losing my job revealed something uncomfortable.For years, I had allowed workplace relationships to substitute for building deeper community elsewhere.That was a mistake.The Gift Hidden Inside the LayoffI don’t want to romanticize unemployment.Many people are suffering right now.Many families are under extraordinary financial pressure.Many talented professionals are struggling to find work.I recognize how fortunate I am to have spent years building financial reserves that gave me options.But I also think it’s important to acknowledge an uncomfortable truth.Sometimes a disruption reveals things that routine keeps hidden.For twenty-five years I accepted stress, exhaustion, time scarcity, declining health, and shallow relationships as normal.I thought that was simply the price of adulthood.The price of success.The price of being a responsible provider.Maybe some of it was.But maybe the price was much higher than I realized.Six months after my layoff, I find myself healthier, calmer, and happier than I have been in decades.What began as involuntary early retirement is slowly starting to feel voluntary.And perhaps the greatest lesson I’ve learned is this:The most valuable thing work took from me wasn’t money.It was attention.Attention to my health.Attention to my relationships.Attention to my own life.Now that I have that attention back, I don’t intend to give it away lightly. 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• 
  Everyone Says AI Is Too Expensive. They're Wrong. 09.06.2026 14min

  Every few weeks, a new headline appears claiming that AI is becoming unsustainably expensive.Companies are reportedly burning through millions of dollars a month on AI subscriptions. Some organizations are discovering that employees can consume astonishing quantities of tokens when every workflow becomes an AI workflow. There are even stories circulating of teams whose AI bills now exceed the cost of the employees using the tools.For many people, these stories confirm a suspicion they have been quietly nurturing all along.Maybe AI isn’t economically viable.Maybe the entire thing is a bubble.Maybe the costs will eventually become so overwhelming that widespread adoption simply won’t happen.I understand why people feel that way.There are enormous emotional and financial stakes involved.Entire industries are being disrupted. Careers are being questioned. Businesses are being reshaped. Some people stand to make fortunes while others worry about becoming obsolete.But after spending decades in the technology industry and watching wave after wave of disruption unfold, I have reached a different conclusion:AI is already cost effective today.And it is almost certainly going to become dramatically cheaper tomorrow.The Cost Problem Is Already Being SolvedMost discussions about AI costs focus on the wrong question.People look at today’s prices and assume those prices are fixed.They aren’t.The history of technology is a history of making expensive things cheap.Computers were once available only to governments and large corporations.Cell phones were luxury items.Internet access was expensive.Data storage cost a fortune.Then competition arrived. Innovation happened. Costs collapsed.AI is following the same pattern.What makes the current moment particularly interesting is that many of the cost reducing innovations are not hypothetical future breakthroughs.They already exist.They are being deployed right now.How Sanctions Accidentally Accelerated AI InnovationOne of the more fascinating developments of the past several years has been the unintended consequences of the technological competition between the United States and China.The United States has attempted to restrict China’s access to advanced semiconductors and semiconductor manufacturing equipment. The logic is straightforward: limit access to compute and you limit AI development.But resource constraints often produce innovation.When organizations cannot simply throw more hardware at a problem, they are forced to become more efficient.And efficiency is exactly where many Chinese AI companies have focused their efforts.The result has been a wave of innovations aimed not at creating the absolute most powerful models, but at creating models that are nearly as capable while being dramatically cheaper to train and operate.That distinction matters.Because in the real world, economics often wins.Smarter Models, Not Just Bigger ModelsFor years, the dominant strategy in AI was simple.Build bigger models.Use more GPUs.Consume more electricity.Spend more money.But there is another path.Instead of scaling everything upward, researchers can improve the underlying architecture itself.A good example is the evolution of attention mechanisms inside large language models.Architectural improvements can dramatically reduce the amount of computation required during training while maintaining similar performance.In some cases, these optimizations reduce training costs by multiples rather than percentages.That is a profound shift.If a model can achieve similar results using half, a third, or even a fifth of the resources, the economics change completely.Why Reinvent the Wheel?Another powerful cost reduction technique is model distillation.Imagine spending years building an expert employee.Now imagine being able to transfer much of that expertise into a new employee at a fraction of the cost.That is essentially what distillation does.Instead of starting from scratch, newer models learn from the outputs and behaviors of existing advanced models.The result is significantly lower training costs and dramatically reduced data requirements.From a business perspective, this is incredibly attractive.Why spend one hundred million dollars recreating knowledge that already exists when you can acquire much of it for a small fraction of the cost?The Hidden Battle: Inferencing CostsMost people focus on training costs because the numbers are eye catching.But for many businesses, the bigger issue is inferencing.Inferencing is simply the process of asking an AI model a question and receiving an answer.Every prompt requires computation.Every computation costs money.And when millions of users are interacting with AI systems, those costs add up quickly.This is where some of the most important innovations are occurring.Techniques such as mixture of experts allow models to activate only the portions of the neural network necessary for a specific task.Instead of powering the entire machine every time, only the relevant specialists are called into action.The result can be reductions in inferencing costs ranging from significant to dramatic.When multiplied across billions of requests, the savings become enormous.Hardware Matters TooSoftware is only half the story.Hardware innovation is equally important.Because Chinese companies have limited access to the most advanced Chip manufacturing technologies, they have increasingly focused on specialized chips designed for specific workloads.These application specific chips may not be as versatile as cutting edge GPUs, but versatility is not always the goal.Efficiency is.When optimized for AI inferencing, specialized hardware can often deliver surprisingly competitive performance at substantially lower costs.Again, the pattern repeats.Constraints force optimization.Optimization reduces costs.Reduced costs accelerate adoption.The Energy AdvantageThere is another factor that rarely receives enough attention.Electricity.Training and running AI models requires enormous amounts of power.Power costs are not fixed.They vary dramatically depending on geography and infrastructure.As renewable energy continues to become cheaper, the cost of operating AI systems falls alongside it.This creates another powerful deflationary force acting on AI economics.Even if the models themselves never improved, cheaper energy alone would lower operating costs over time.But the models are improving.And the hardware is improving.And the software is improving.All at the same time.What Happens When American AI Companies Start Optimizing?The most interesting part of this story may be what has not happened yet.Many American AI companies have operated in an environment of abundant capital.When investor funding seems unlimited, optimization is often less urgent.Speed matters more than efficiency.Growth matters more than profitability.But markets eventually change.Investors begin asking harder questions.Profitability becomes important.Efficiency becomes important.And suddenly all of the techniques that were previously ignored become very attractive.If Chinese companies can reduce costs dramatically through architectural improvements, distillation, specialized hardware, and operational efficiency, there is nothing preventing American companies from doing the same.In fact, competitive pressure almost guarantees that they will.The Real QuestionCould the AI bubble pop?Of course.Every technological revolution creates bubbles.Money will be made.Money will be lost.Speculators will speculate.Some companies will fail spectacularly.That part is normal.But bubbles and underlying technology are not the same thing.The railroad bubble burst.Railroads did not disappear.The dot com crash happened.The digital economy kept growing.The real question is not whether investors will overpay for AI companies.The real question is whether the cost of using AI will continue falling.Looking at the technologies already available today, my answer is yes.Decisively yes.The future of AI may be many things.But “too expensive to survive” is not the outcome I would bet on. 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