SemiAnalysis Weekly

Epizode

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  Ep. 031 - EMERGENCY EPISODE: Are We Doomed? | Jordan Nanos, Doug O'Laughlin, Max Kan, Joey Brookhart 16.09.2026 1h 2min

  Emergency episode. Doug O'Laughlin (@Fabknowledge) , Joey Brookhart (@SaasquatchC), and Max Kan (@maxkan) join Jordan (@JordanNanos) to talk through Dario's "We Must Pace the Frontier" blog and the responses from Sam Altman and David Sacks. Does pacing actually mean less compute, or a lot more? Plus the Hugging Face incident, Moonshot serving Claude, and the security behaviour that keeps them up at night.0:00 Cold Open2:50 Glorified Neoclouds8:21 Pacing the Frontier12:53 Safety Eats Compute20:38 Hugging Face Lessons30:52 Moonshot Serving Claude36:37 The Coxson Resignation38:27 Two-Year Predictions49:19 Regulation Under Trump57:21 Hotter or Cooler
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  Ep. 030 - Long Live the Short King: Why 4-HI HBM Wins (Memory) | Myron Xie, Jordan Nanos 14.09.2026 38min

  NVIDIA previewed Rubin Ultra at 1TB of HBM per package. The part that ships will carry 192GB.Myron Xie and Jordan Nanos get into how the design fell that far, why the reason is supply and not performance, and why shipping less memory per chip might be the right call anyway.For the first time in recent memory, NVIDIA's next flagship will have less capacity than the one it replaces.Read More: https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi
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  Ep. 029 - Modular Data Centers Cut Build Time to 12 Months (Datacenter, Energy) | Nico Bontigui, Jordan Nanos, Nigel Chiang, Eric Wen 11.09.2026 46min

  Traditional data center builds ran 18 to 24 months, sometimes past three years. Modular construction cuts that to 12. Jordan Nanos (@JordanNanos) sits down with Eric Wen, Nigel Chiang, and Nico Bontigui to map what modular actually means, from MEP heavy skids to full containerized builds. They break down why the bottleneck moved from construction time to labor, why welders and electricians are the real constraint in Abilene, and how time to power translates directly into tokens at 40 to 100 million dollars per megawatt. Subscribe for weekly SemiAnalysis coverage on data center construction, prefabrication, and AI infrastructure. 0:00 Cold Open1:09 What Is Modular4:03 Why Go Modular5:55 Time to Power Math9:04 Labor Wage Data13:51 Two Kinds of Bottlenecks18:24 Site, Shell, System22:31 Who Owns the Risk26:59 Trucks and Insurance30:53 Who Supplies the Modules35:50 Commissioning Reality41:39 Vendor Map and WrapRead More: The Wild Wild West Of LEGO Datacenters: https://newsletter.semianalysis.com/p/the-wild-wild-west-of-lego-datacenters
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  Ep. 028 - Most Neoclouds Suck At Security: How Agents Hacked Hugging Face (Neoclouds, Security) | Doug O'Laughlin, Sam Harshe, Jordan Nanos 02.09.2026 50min

  This week Doug, Sam and Jordan discuss the OpenAI vs HuggingFace security incident and our recent article on Neocloud security ahead of ClusterMAX 3.0Full article: https://newsletter.semianalysis.com/p/most-neoclouds-suck-at-security0:00 Cold Open0:57 Neocloud Security4:38 The Hugging Face Hack11:47 Agent Swarm Behavior14:47 Obliterated Models20:37 Security as a Service24:30 What the Data Shows33:08 Attacker Asymmetry40:55 Nothing Ever Happens45:22 CMAX Audit
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  Ep. 027 - OpenAI Jalapeño: Better Than Nvidia Blackwell (Accelerators) 30.08.2026 1h 3min

  This week Bryan, Myron and Jordan discuss our recent article on OpenAI Jalapeño. They cover the performance, architecture, programming model, implications for NVIDIA and more.1:05 Jalapeno Overview4:22 Tokens Per Megawatt9:15 Benchmark Caveats13:48 The CUDA Moat21:12 How OpenAI Did It32:13 Samsung HBM442:10 AI-Designed Silicon49:24 Architecture Deep Dive56:58 Doom and WrapFull Article: ⁠https://newsletter.semianalysis.com/p/openai-jalapeno-better-than-nvidia
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  Ep. 026 - PJM's $12B Modeling Mistake Is Hitting Ratepayers Again (Datacenter, Energy) | Robert Boswall, Jordan Nanos 20.08.2026 43min

  PJM overpaid $12 billion across two capacity auctions, and the same modeling error is set to repeat in an upcoming emergency auction. Robert Boswall (@RobertBoswall) and Jordan Nanos (@JordanNanos) break down how the largest grid in America, 13 states and 66 million people, inflates demand while constraining supply. The result is scarcity pricing that ratepayers absorb, not the data centers driving the narrative. Of the $63 billion spent across four auctions, Boswell estimates $12 billion was avoidable, split $7 billion and $5 billion across the 2025-26 and 2026-27 auctions. This is the mechanics behind every headline blaming AI for rising power bills. Subscribe for weekly analysis on grid design, capacity markets, and AI energy demand!0:00 Cold Open1:33 What Is PJM3:13 Capacity Auctions7:39 The $12 Billion12:33 How Plants Get Paid15:15 The Winterization Gap21:17 Data Center Demand26:41 The Emergency Auction30:00 Board Overrules Members40:32 Turning It AroundArticle: https://open.substack.com/pub/semianalysis/p/12b-of-us-ratepayers-money-wasted
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  Ep. 25 - DYLAN IS HERE, LIVE! | Dylan Patel & Jordan Nanos 17.08.2026 38min

  Dylan joins the pod today, recorded live from our SF office. Jordan and Dylan discuss SemiAnalysis's AI spend, the model that escaped during training, AI rollups eating private equity, new accelerators vs NVIDIA, and whether the pod gives away too much information for free.0:00 Cold Open5:25 SA AI Spend9:29 AI Performance Reviews10:40 AI Rollups13:47 The Agent Moment15:28 The Escaped Model18:11 Model Exponential22:52 Compute and Chips30:18 Open Models32:00 ADHD and AI
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  Ep. 024 - SpaceX's 10GW Plan Drives $300B ARR by 2027 (Datacenter, Energy) | Reyk Knuhtsen, Jeremie Eliahou Ontiveros, Jordan Nanos 09.08.2026 50min

  OpenAI and Anthropic are adding close to $30 billion of ARR per month, and the driver is gross margin expansion, not new compute. Jeremie Eliahou Ontiveros (@JeremieEO) and Reyk Knuhtsen (@robotknower) join Jordan Nanos (@JordanNanos) to trace the $100 million per megawatt per year figure from real inference workloads on GB200 and GB300 up through SemiAnalysis simulation. The Google deal prices GB300 capacity near $14 an hour against a $3 average, a premium justified by a 90 day cancellation clause and the ability to turn on megawatts immediately.From there the conversation moves to SpaceX's 10 gigawatt ambition, the sites and supply chain needed to hit it, the permitting playbook, and why Microsoft becomes the largest offtaker. The bull and bear cases both get airtime, followed by a Hugging Face security scare to close. Subscribe for weekly coverage of tokenomics, datacenter energy, and AI compute economics.CHAPTERS:0:00 – Intro0:47 – The $100M Thesis3:59 – Pricing & Google's Deal10:38 – Training vs Inference13:21 – Sites & Supply Chain20:55 – Permitting Playbook25:40 – Microsoft's Role33:31 – Paying For It41:01 – Bull vs Bear43:29 – Hugging Face Security Scare & Wrap-UpReferenced:SpaceX 10GW in 2027 – Why It's Real, Will Drive $300B ARR for SpaceX, and Why Microsoft Will Be the Largest Offtaker: https://newsletter.semianalysis.com/p/spacex-10gw-in-2027-why-its-real
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  Ep. 023 - Everyone Leaves Google, Elon Forecasts 1T ARR, Reflecting On GPT-5, Building Personalized Software (Roundtable) | Jon Y, Doug O'Laughlin, Jordan Nanos 07.08.2026 44min

  Jeff Dean and the Gemini leads are leaving Google. Jon Y (@asianometry) makes his FIRST appearance to unpack the exodus with Doug O'Laughlin (@fabknowledge) and Jordan Nanos (@JordanNanos). The crew debates the Demis CEO question, whether Google acquired its innovation or invented it, and how the Bell Labs comparison reads once disruption arrives. First engineers who spent thirty-five years in one job now leaving for passion projects.The episode opens by revisiting the 2024 Dwarkesh clip where Jon questioned whether GPT-5 would be any good. Two years later the verdict is mixed: GPT-5 was a dud, 5.2 was ass, but 5.6 is strong and GPT-6, codenamed Doug, is rumored to write well. Then the Chinese transceiver ban, where the West depends on a supply chain China already owns. 00:00 Intro01:02 Was GPT-5 Good?05:00 The Transceiver Ban08:57 Everyone Leaves Google12:56 Google's L Culture19:20 Do Legends Matter?23:44 The Protestant Church28:44 Elon's $1T Pull-In30:55 Roll Your Own Software38:17 Start With Memory
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  Ep. 022 - Market Drawdown, Historic Bubbles, Funding The Buildout, AI Politics (Doug is Back) 29.07.2026 49min

  Doug is back this week!Timestamps:00:00 Market Update07:02 Comparing to Past Bubbles in Taiwan and Korea10:08 Memory Prices, LTAs, and Market Cycles18:11 Future Demand for AI and Model Usage37:40 Scaling Laws and Supply Constraints44:45 Financial and Capital Constraints in Tech Expansion50:57 Geopolitical Risks, Policy Impact, Long Term Outlook
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  Ep. 021 - The AI Project Trinity: Capital, Offtake, Data Center (Datacenter, Energy) | Dan Nishball, Jordan Nanos, Zane Fong, Kang Wen Cheang 23.07.2026 52min

  AI and datacenter CapEx hits $11 trillion cumulatively from 2024 to 2029, and $7.1 trillion of that needs funding, roughly 75% debt financed. Dan Nishball (@dnishball), Zane Fong (linkedin.com/in/zanefongzq), and Kang Wen Cheang (linkedin.com/in/cheangkangwen) sit with Jordan Nanos (@JordanNanos) to break down the AI project Trinity: capital, offtake, and data centers. Today only one deal reliably clears the lending bar, a five-year offtake from an investment-grade hyperscaler. Everything else struggles to finance.The crew explains how NVIDIA backstops are structured, how lenders price GPU loans against them, and what NVIDIA actually does with GPUs it takes back. AI debt financing is on track to become the second-largest US asset-backed market behind the $13 trillion mortgage market. Subscribe for weekly analysis on semiconductors and AI infrastructure.References: https://newsletter.semianalysis.com/p/nvidia-gpu-debt-backstop-unleashesCHAPTERS:00:00 Intro|01:23 The $11T Funding Problem09:12 Startups Can't Get GPUs11:32 How the Backstop Works20:26 The Central Bank of AI21:35 The Bullseye26:23 CoreWeave Credit Spreads36:58 APAC Examples42:41 ClusterMax48:58 Training vs Inference
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  Ep. 020 - Anthropic vs OpenAI Usage, Margins, Meta Compute, Future of MSL (Tokenomics) | Crystual Huang, Max Kan, Joey Brookhart, Jordan Nanos 18.07.2026 50min

  Coding drives over 70% of lab API revenue, and token austerity policies mostly miss the point. Crystal (@crystalthegg), Max Kan (@maxkan), and Joey Brookhart (@SaasquatchC) break down why blocking teams from Opus saves nothing, while power users at the 99th percentile burn $100k per employee per year. Jordan (@Jordannanos) and the team run break-even math on the Max plans and Anthropic's margins.00:00 Intro00:53 Token Budgeting: Maxing vs Austerity03:05 Coding Eats the Token Market04:49 The ROI Question06:42 Subscriptions vs API Pricing09:08 Break-Even Math on the Max Plans10:42 Anthropic's Profit Margins12:07 Consumer vs Enterprise Mix16:04 The Two-Horse Race18:45 Codex App vs CLI20:41 Who Comes in Third?23:14 Clawbacks and the SpaceX Playbook26:59 Meta's NeoCloud Backstop29:58 Token-as-a-Service Market Forecast32:03 Hyperscalers vs Inference Startups38:50 MSL and the RL Scaling Law41:58 How to Build a Five-Figure RL Task47:10 Vibe Checks48:13 The $400B Anthropic BetReferenced:TokenBudgeting: Our Conversations with Enterprises on Token Spend: https://newsletter.semianalysis.com/p/tokenbudgeting-our-conversationsMeta Compute: Everyone Wants To Be A Neocloud: https://newsletter.semianalysis.com/p/meta-compute-everyone-wants-to-beAnthropic 3Q26 Profit Over $1B: The Anthropic IPO Financials Sneak Peak: https://newsletter.semianalysis.com/p/anthropic-3q26-profit-over-1b-theThe Future of Meta Superintelligence: A 1 Year Progress Update: https://newsletter.semianalysis.com/p/the-future-of-meta-superintelligenceComplete Launch Kit
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  [Emergency Episode] Moonshot’s Kimi K3 has Arrived! China has a Frontier Model 18.07.2026 31min

  A year ago, the big three was OpenAI, Anthropic, and Google. Things have changed.Moonshot's Kimi K3 sits above Gemini on every composite benchmark, and it's open source in 10 days.New episode: what K3 reveals about frontier margins, model sizes, and who's actually still in the game. 00:00 Intro00:11 Is Kimi K3 the Third Best Model?04:04 Why Delay the Weights?05:30 2.8T Parameters and Serving Constraints06:48 Frontier Margins and the 3x Price Hike11:10 New Architecture, What Comes Next14:09 Will Open Source Catch Closed?19:51 Built for Chinese Accelerators22:57 The Harness Is the Product28:49 We're Still Early
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  Ep. 019 - Inside the STEEL Lab: From Package to Transistor (Teardown Lab) | Afzal Ahmad, Andrew Wagner, Jordan Nanos 16.07.2026 36min

  SMIC's N+3 node shrank the M0 layer over 15% and cut SRAM area 10 to 20%, all without EUV. SemiAnalysis built a lab just for that; Andrew Wagner and Afzal Ahmad walk Jordan Nanos (@JordanNanos) through the STEEL teardown of Huawei's Kirin 9030, from package to transistor. They explain die shots, FIB and TEM cross sections, NPU discovery, cell height, and standard cell libraries. Then backside power, GAA, and where SMIC goes next. 00:00 Intro: The STEEL Teardown Lab01:02 What Is a Teardown?02:17 Who Uses Teardown Data03:22 SMIC N+3 and the Kirin 903005:02 Inside the Lab: Sourcing to Silicon09:10 Die Shots Explained12:35 The NPU Discovery15:42 Scaling Without EUV17:57 FIB, SEM, and TEM Cross Sections20:59 Cell Height and Transistor Shrink23:56 Standard Cell Libraries26:22 Export Bans and Huawei's Response27:40 What's Next: Backside Power and GAA32:43 Data Center GPUs and Logic Folding34:28 Closing ThoughtsRead More: https://newsletter.semianalysis.com/p/steel-smic-n3-teardown
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  Ep. 018 - Stop Saying Half of 2026 US Datacenter Capacity Is Canceled (Datacenter, Energy) | Jeremie Eliahou Ontiveros, Reyk Knuhtsen, Ellie Holbrook, Jordan Nanos 09.07.2026 50min

  Bloomberg said half of 2026 US data center capacity is delayed. The SemiAnalysis Data Center, Energy, and Industrials team pulled the underlying report and found a broken denominator. Amazon alone built 4GW in 2025 and is adding 5GW plus in 2026. CoreWeave adds a gigawatt, all under construction. Jeremie Eliahou Ontiveros (@JeremieEO), Reyk Knuhtsen (@robotknower), and Ellie Holbrook join Jordan Nanos (@JordanNanos) as they walk through why the number is wrong and what the real forecast shows. The team covers behind the meter power generation reaching 40GW by 2028, Oracle's New Mexico problem, and the gas turbine supply chain that is running toward peak. They break down the three types of data center delays, how OEMs are responding, and where solar, batteries, and nuclear fit. 00:00 Intro00:51 The "half of capacity is canceled" myth04:43 Why early stage projects get canceled08:06 Three types of data center delays08:40 Oracle's New Mexico problem13:47 Behind the meter: 40GW by 202818:50 How OEMs are responding20:24 Signed deal to powered GPUs23:44 Hyperscaler market share27:49 How SemiAnalysis tracks data centers31:34 What behind the meter means33:31 The gas turbine supply chain38:22 Peak turbine42:54 Solar, batteries, and nuclear46:13 Final thoughts48:20 Favorite projectsReferenced:Stop Saying Half of 2026 US Datacenter Capacity Is Canceled: https://newsletter.semianalysis.com/p/stop-saying-half-of-2026-us-datacenterUS Grid Constraints: Towards 40GW+ of Behind-The-Meter Datacenter by 2028?: https://newsletter.semianalysis.com/p/us-grid-constraints-towards-40gw
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  Ep. 017 - DeepSeek V4 and Huawei Ascend NPU Performance (InferenceX) | Kimbo Chen, Cam Quilici, Bryan Shan, Jordan Nanos 01.07.2026 34min

  DeepSeek V4 claims a 100x KVcache reduction versus a standard MoE model, hitting 1M context length through compressed sparse attention and heavily compressed attention. Kimbo (@Kimbochen), Cam Quilici (@noslawextratost), Bryan Shan join Jordan Nanos (@JordanNanos) to break down what changed from V3, why the new MHC dimension tripped up NVIDIA on day zero, and how Mega MoE fuses compute and communication into a single kernel. The vLLM versus SGLang NDA access gap and the Huawei day zero optimization guide circulating on Twitter.The crew walks through the InferenceX article on going from day zero to day 43 support and what that grind actually looks like across different hardware. Subscribe for weekly semiconductor and AI infrastructure analysis from the SemiAnalysis team.Referenced:DeepSeekV4 1.6T Day 0 to Day 43 Performance Over Time - GB300 NVL72, Huawei, MI355X, B200: https://newsletter.semianalysis.com/p/deepseekv4-16t-day-0-to-day-43-performanceChapters:(00:00) DeepSeek V4 vs V3 changes(01:00) Sparse attention and KV cache reduction(03:04) Day zero runtime support challenges(05:34) What Mega MoE actually is(08:38) Downsides of fusing kernels(10:25) MegaKernel benchmark claims(12:59) AMD FP4 optimization gains(15:14) Compounding step by step improvements(17:59) vLLM versus SGLang competition(19:34) Open source vs vendor libraries
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  Ep. 016 - What Unitree's Evolution Means For Robotics (Robotics) | Jordan Nanos, Reyk Knuhtsen, Niko Ciminelli 23.06.2026 48min

  Unitree is going public, boasting 67% gross margins on its humanoid robots. Jordan Nanos (@JordanNanos), Reyk Knuhtsen (@robotknower), and Niko Ciminelli discuss how the Chinese company achieves this through aggressive pricing, rapid iteration, and a focus on "good enough" hardware for the research and hobbyist markets. This strategy allows Unitree to dominate, much like DJI and BYD did in their respective fields.The discussion explores the reality of humanoid robot deployment versus market hype. While industrial applications are in their "baby days," Unitree's approach leverages economies of scale to create a significant moat, challenging US competitors to match their production volume and cost efficiency. The team analyzes if the US can truly compete with China's manufacturing might in the emerging robotics sector.Join SemiAnalysis Weekly for expert insights into the semiconductor, AI infrastructure, and robotics markets. Subscribe for deep dives into AI supply chain, chip economics, and market analysis.Article: https://newsletter.semianalysis.com/p/chinas-unitree-will-dominate-globalTimestamps:00:00 — Intro, deployment reality vs. hype02:39 — Unitree Business: why go public, margins, pricing05:48 — Parallels to DJI and BYD, economies of scale as China's moat16:52 — When is Claude Code moment for robotics18:38 — Real use cases and future demand shocks26:39 — Shenzhen and the humanoid BOM35:56 — The bear case: who actually buys them?42:17 — Can the US compete?
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  Ep. 015 - DG Matrix Explains 800V DC vs Legacy AC Distribution (Datacenter, Energy) | Jordan Nanos, Jeremie Eliahou Ontiveros, Nicolas Bontigui, Haroon Inam 15.06.2026 47min

  NVIDIA's next architecture will demand 800V DC and datacenters can't wait to build the infrastructure. Haroon Inam, CEO and Co-Founder of DG Matrix, joins Jordan Nanos (@JordanNanos), Jeremie Eliahou Ontiveros (@JeremieEO), and Nicolas Bontigui to explain why megawatt GPU racks make 800V DC a necessity, not an option. Haroon details his journey from uncool power electronics to enabling superhuman intelligence infrastructure. Full Article Link: https://newsletter.semianalysis.com/p/inside-the-800vdc-revolution-partChapters:00:00 Haroon Inam's Background and Power Electronics Journey00:29 Introduction to 800V DC Architecture and Its Significance02:19 Why 800V? Economics, Semiconductor Ratings, and EV Influence04:47 Physics and Economics Driving the 800V Revolution08:27 DG Matrix's Multi-Port SST and Its Value Proposition11:33 Design Challenges and Innovations in Multi-Port SSTs13:53 Current State and Adoption of 800V DC in Data Centers17:51 Future Data Center Architectures and Power Density Trends22:08 Adoption Curve and Market Penetration of 800V DC26:34 Risks, Challenges, and Future Proofing of Data Centers30:36 Cybersecurity and Power System Resilience38:04 The Broader Impact of Power Innovations on Society
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  Ep. 014 - Finding Miscompiles For Fun, Not Profit (AI Infrastructure) | Justin Lebar & Jordan Nanos 04.06.2026 23min

  Justin Lebar (jlebar.com) recently spent $10,000 in an afternoon, uncovering critical miscompiles across NVIDIA's PTXAS, LLVM's AMD GPU, and X86 backends. He joins Jordan Nanos (@JordanNanos) to detail his methodology, which combined traditional fuzzing techniques with novel LLM-assisted bug finding. Their discussion highlights the unique challenges of detecting flaws in less-tested ML compilers compared to mature CPU environments.Lebar shares specific high-severity X86 findings, including an atomic operation bug that splits into two non-atomic operations. They explore the comparative efficacy of fuzzing versus LLM agents in identifying these elusive errors. This episode offers critical insights into compiler security and the burgeoning role of AI in automating rigorous code verification for AI infrastructure.FULL ARTICLE00:00 Introduction and Content Overview00:25 Justin Lebar's Background and Recent Project00:59 Fuzzing Techniques for Compiler Bugs01:56 Motivation Behind the Project02:48 Challenges in Bug Detection in GPU and ML Compilers04:13 Bug Severity and Findings in AMD and x8605:38 Using LLMs to Read and Find Bugs in Code07:56 Impact of New Models and UltraCode Mode12:18 Estimating Time and Effort Without AI Assistance14:22 Limitations of Manual Code Review for Bugs15:03 Optimism About AI in Software Development16:17 Next Steps and Future Projects18:11 Key Takeaways for Developers and Researchers21:48 Call for Community Engagement and Scientific Approach
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  Ep. 013 - AWS Margins Jump 10% While Azure and GCP Flatline (Tokenomics) | Jordan Nanos, Jeremie Eliahou Ontiveros, Joey Brookhart, Crystal Huang 01.06.2026 43min

  AWS operating margins jumped 10 percentage points while Microsoft Azure and Google Cloud stayed flat. The driver: Anthropic's Claude usage routing through Bedrock, Amazon's token-as-a-service platform. Jordan Nanos (@JordanNanos), Jeremie Eliahou Ontiveros (@JeremieEO), Joey Brookhart (@SaasquatchC), and Crystal Huang (@Egg1459) break down why stabilized token margins are fundamentally richer than GPU-as-a-service for hyperscalers. The crew analyzes Anthropic's recent $65B Series H raise, Claude Opus 4.8 release, and SpaceX partnership against the backdrop of 300+ neo clouds fragmenting the traditional cloud moat.The team forecasts how AWS's workload mix advantage creates sustainable returns while competitors struggle with asset-heavy GPU service models. They examine the $22.7T TAM question, earnings-before-training dynamics, and whether the 2026 AI infrastructure beat belongs to silicon vendors or platform integrators. Subscribe for weekly deep dives into semiconductor and AI infrastructure economics.00:00 Intro: Episode 13 and the AWS margins article00:56 What is Bedrock? The three hyperscaler buckets02:33 AWS margins rising while peers lag03:33 Cloud moats collapsing and the neo cloud explosion06:32 Why stabilized token-as-a-service margins are so rich09:54 Amazon's workload mix advantage12:41 Forecasting Anthropic and the 4.8 release16:33 The SpaceX deal and the $65B Series H raise19:30 Bullish or bearish? Demand becoming supply28:55 The $22.7T TAM and does the race even matter31:59 Earnings before training and open-ended TAM36:27 The 2026 beat is basically one company40:22 Who wins long term: silicon, partnerships, integration