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  • The AI Industrial Revolution: Naval, Guillermo Rauch, Blake Scholl, and Max Hodak on Software Factories, Vibe Coding Hardware, AI Regulation, Healthcare Economics, and What Humans Can Uniquely Do

    This is the full episode of Naval Ravikant’s conversation with three frontier founders: Guillermo Rauch of Vercel, Blake Scholl of Boom Supersonic, and Max Hodak of Science. The premise is that all three are building their own factories rather than assembling off-the-shelf parts, so the interesting question is not what they are building but what they are learning about how to build in the age of AI. Over roughly an hour the discussion moves from software factories and the thousand-x engineer into hardware, regulation, healthcare economics, autonomous companies, and a long closing argument about what humans can still uniquely do. Watch the full conversation on the Naval Podcast YouTube channel. We previously published two segments of this same discussion: part one, Waste Tokens to Save Time, on software factories and whether pure software is dead, and part two, Vibe Coding Hardware, on jet engines, vertical integration, and China’s open-source bet. This post covers the entire episode end to end.

    TLDW

    Four builders argue that AI has turned the engineer’s job from shipping output into building the factory that produces output, which is why token leaderboards are the new vanity metric and why you should waste tokens to save time. Guillermo Rauch frames the thousand-x engineer and the building-block economy, and asks whether pure software is dead now that models speak English. Blake Scholl shows how Boom turned hardware engineering into software, letting two engineers design an entire jet engine and collapsing months of regulatory compliance documentation into minutes. Max Hodak makes the case for extreme vertical integration, a captive MEMS foundry, and a sober counter to Silicon Valley deregulation triumphalism: the bottleneck is the voters and the regulator’s asymmetric incentives, not just bad rules. The group works through healthcare as a fixed-bucket non-market, China’s cost-reduction strategy and its approved implantable brain interface, autonomous software that runs site reliability and security research with thousands of concurrent agents, a company-wide hackathon where the receptionist shipped a real automation, and a long debate on creativity, out-of-distribution surprise, intent, attribution, and the definition of art. The throughline: humans become verifiers, value moves to creativity, taste, and agency, and the single best move is to get extremely good with the tools, because it is people with AI versus people without AI.

    Thoughts

    The strongest idea in the episode is the quiet redefinition of what an engineer is for. Rauch’s point is that you no longer judge a person by how well they ship a single output. You judge them by whether they can build the factory that produces outputs B through Z. That reframe instantly explains why token leaderboards are nonsense. Counting tokens consumed is the same category error as counting lines of code written, a measure of motion mistaken for a measure of progress. Naval’s “waste tokens, save time” is the correct response: tokens are cheaper than people, so optimize for your own wall-clock time and the final output, and throw three models at the same problem if that gets you unstuck faster. The uncomfortable corollary, which the group says out loud, is that leverage in idea domains was never linear. The hundred-x and thousand-x engineer is not a new phenomenon. AI just made it impossible to keep pretending otherwise.

    The second thread that ties the whole hour together is verification. Everyone converges on the same future: humans stop producing the work directly and move up the stack to signing off on it. Rauch is precise about what that means. Saying “I understand this pull request” no longer requires reading every line. It requires being able to say you wrote the test harness, the proofs, the type checkers, and the simulations that let you stand behind it in production. That is a profound shift, because it accepts that the code may be spaghetti you do not fully understand while insisting that the evaluator around it is trustworthy. Blake extends the same logic to regulation, and this is the most underrated argument in the episode. If you treat a 200-page lightning-strike compliance document as a test suite and a regulation as an exit criterion for an agent loop, then a body of rules you once resented becomes a guard rail that lets you move faster, not slower. The cost of change collapses, change aversion drops, and you can finally afford to iterate on physical things.

    Max Hodak is the adult in the room on regulation, and the episode is better for it. The Silicon Valley consensus is that regulation is simply friction to be deleted, and there is plenty of dysfunction to point at: the NRC permitting essentially zero nuclear plants for decades, the FDA’s asymmetric incentives where approving a bad drug ends a career but blocking a good one costs nothing visible. But Hodak keeps pulling the conversation back to the harder truth. This is where the voters are. If you removed the current regulatory package, something very similar would get voted right back in, because the asymmetry reflects how the public actually weighs a visible death against an invisible delay. Real reform is not “deregulate,” it is narrow and surgical: prohibit the FDA from drawing adverse inferences across different users of a compound, build innovation zones where people consent to different rules, or copy Europe’s notified-body model so review capacity can actually scale. That is a far more serious position than the usual abundance-or-bust framing.

    The healthcare segment is the part of this conversation you will not find in the two clips, and it is the most heterodox. Hodak’s diagnosis is that healthcare is a fixed bucket of money that grows with tax receipts, not a technological growth industry where falling prices expand the market the way phones and laptops did. Because there is no real private market, you get a small communist society running inside a larger capitalist one, with the waiting lines and frozen product quality that implies. His prescription is not single payer and not insurance reform. It is to drive the cost of bringing devices and drugs to market so low that a patient can buy a restored sense or an extra decade of life on a credit card, the way they finance a car, and his warning is that China’s lower approval costs and its already-approved implantable brain interface put it on track to do exactly that. Whether or not you buy the twenty-percent-of-income deductible he floats, the framing that a private market is the missing feedback loop is the kind of argument that gets too little airtime.

    The closing debate on creativity is where the four of them disagree most productively, and they are careful enough to notice that their conclusions follow from their definitions. Hodak defines art as meaningful out-of-distribution behavior, which lets a military maneuver or a math proof count, and leads him to think a sufficiently capable model gets there too. Naval defines art as conveying an emotion with intent, which makes attribution load-bearing: the same photo down to the last pixel means more when a human took it, and a startup doing hardware attestation of human authorship suddenly has a real market. The shared observation that should worry every builder is that AI output collapses to a distribution mean. Every Claude-built website ends up the same serif font, the same brown and cream, the same monospace spacing, recognizable as slop precisely because it is in-distribution. The optimistic read, and the one Naval lands the episode on, is that this leaves an enormous and durable lane for humans who can step outside the system, and that the practical move for everyone is simply to become excellent with the tools, because the real divide is people with AI versus people without.

    Key Takeaways

    • The job of an engineer has shifted from shipping a single output to building the factory that produces multiplicative outputs, so people are now judged on the leverage they create rather than the work they personally do.
    • There were always 10x engineers, and in idea, intellectual, and digital domains the real spread is 100x or 1000x. AI leverage just made that gap impossible to deny.
    • Token leaderboards and token consumption are the new lines-of-code: a measure of activity that does not map to value. Measure your own time and the final output instead.
    • Waste tokens to save time. Models are still far cheaper than a human, so throwing Codex, Claude, and Gemini at the same problem repeatedly is rational even when it looks wasteful.
    • Low-quality first-pass code is fine because you can spend more tokens later to harden it for production. The constraint is verifiable domains, not code quality.
    • A model is roughly as good as you are in a domain. The quality of your prompting and reprompting strongly determines the output, though this dependence should fade as models improve.
    • Models graduated from junior to principal engineers: they now return with multiple routes and tradeoffs rather than running away with the first idea, even if their time and cost estimates are often wrong.
    • A junior gets knowledge they could never have produced alone, but an experienced architect still extracts far more juice. Taste and judgment, like picking Postgres versus ClickHouse, remain the human’s edge.
    • Pure software’s moat is in question now that models speak fuzzy, sloppy English. For hardware founders this is a boon, since good software finally becomes cheap to produce.
    • The building-block economy, from Mitchell Hashimoto, argues agents need powerful reusable infrastructure rather than reinventing queues and databases every time. Shared dependencies are a cooperation value, like everyone depending on the same Postgres version.
    • Naval and Max both stopped writing code for years, then started building software they use daily through agents, on the strength of understanding how the pieces fit rather than syntax.
    • With agents you stop getting stuck on narrow debugging problems that used to consume indefinite time. The intrinsic frustration that was once “how you learn” is largely gone.
    • Boom turned siloed hardware engineering, much of it trapped in Excel and VBScript with no source control, into real software with automated testing and repeatable flows.
    • Software engineers now build the architectures and hardware engineers vibe code their pieces, letting two engineers design an entire jet engine where a single turbine-blade analysis once took one engineer a full day across a thousand blades.
    • Enterprise collaboration software and even spreadsheets are getting cooked, because you can now code the exact custom tool you need instead of approximating it.
    • AI will soon generate step files and PCB layouts, bringing the current software boom to mechanical and electrical engineering, likely within the year.
    • China is betting on open-source models because its hardware and supply-chain superiority pairs with on-demand software generation to erase Silicon Valley’s software advantage. Fall behind on generating software and you fall behind on generating everything.
    • In real usage, frontier intelligence dominates the top. Gemini “slaps at scale” as an industrial production model for support and browser automation, while Chinese models are not in the frontier coding tier.
    • Intelligence is an unalloyed good. Because mistakes are invisible and models are cheaper than people, you reach for the smartest available model rather than running a weaker one many times.
    • Max’s vertical integration thesis: when you cannot buy a part, you make it. Science owns a captive MEMS foundry because tighter integration toward a single block of bonded matter yields lower power, smaller size, and longer life.
    • AI’s biggest near-term impact inside hardware companies is regulatory: generating documentation and tracing which of thousands of ISO standards apply, work that used to occupy a quality team for months.
    • Junior engineers got promoted to senior and junior engineering got handed to agents. The same pattern hits law, where basic NDAs and red lines no longer require a lawyer.
    • Humans are becoming verifiers. Signing off on a PR means standing behind its consequences via tests, proofs, and type checkers, not reading every line. Creating software is easy; keeping it secure, tested, and maintained 1000 days out is the real question.
    • A RAG over regulatory documents collapses a 200-page compliance test plan from months to minutes, which cuts change aversion: you can alter the airplane and regenerate compliance instead of crying over rework.
    • Regulations can act as a test suite and exit criteria for agent loops, as long as they are non-contradictory and reasonable. The alternative is shipping slop directly into the air.
    • Physical building is guilty until proven innocent, illustrated by the absurdity of pre-filing a driving plan before every trip. The fix is more enforcement-based regulation rather than pre-approval, though agents on both sides could trigger a red queen race and DDoS overwhelmed agencies.
    • Regulation often fails to make things safer, only slower: the 737 Max shipped a single sensor with full authority over pitch, and the NRC kept us perfectly safe by approving almost no nuclear plants for decades.
    • The deeper problem is the voters and the regulator’s asymmetric incentives. Approve a bad thing and your career ends; block a good thing and nobody notices. Removing one agency just elects its replacement.
    • Targeted fixes beat blanket deregulation: bar adverse inferences across users of a compound, use single-patient IND pathways, create opt-in innovation and YIMBY zones, or adopt Europe’s competitive notified-body reviewers.
    • Healthcare is a fixed bucket of money tied to tax receipts, not a growth industry, so spending 10x more on it would be a catastrophe rather than a triumph. With no private market you run a small communist society inside a capitalist one.
    • The escape is lower cost-to-market, not single payer, so people can finance care like a car. China’s lower approval costs and its already-approved implantable BCI point that direction. LASIK, dental, and plastic surgery advance because patients pay directly.
    • End-of-one medicine works at the high end, as with GitLab’s Sid Sijbrandij outliving his cancer prognosis through a self-built escalation ladder, but it demands enormous agency at the patient’s weakest moment. AI should democratize that knowledge.
    • Vercel automated much of site reliability engineering: anomalies fire alerts, an agent investigates, can open an incident, and begins remediation, stopping just short of changing production itself.
    • Running an open-sourced security tool against the whole monorepo with 10,000 concurrent agents produced several quarters of security research in a couple of days for about $14,000 in tokens. Code translation and optimization are similarly autonomous now.
    • Blake stopped all project work for a week and had everyone, receptionist to engineers, build something with AI and demo it. He expected mostly silly projects and got mostly needle movers, including a real automation from shipping and receiving.
    • The autonomous company of the future may have a workforce that trains the agents doing the work rather than doing it directly, with tooling that extracts reusable skills from your inputs and outputs.
    • Returns are shifting from intelligence toward agency for humans, since agents supply the intelligence. The people best fit for the future open a coding agent and ask what to build instead of defaulting to passive consumption.
    • Maybe 10x more people are coding than a year ago, yet around 99% still never will, because to a non-coder the starting step remains unimaginable. Vibe coding is described as more addictive and entertaining than video games, with real output.
    • AI video lacks taste and judgment for now, but by 2030 expect fan-made films: dozens of Lord of the Rings takes, or generating unmade seasons of The Expanse from the books. The bigger prize is a genuinely new imaginative work, not a remix.
    • What humans uniquely do is generate meaningful surprise out of the training distribution, with intent that makes it mean something. Gödel stepping outside the formal system is the archetype; Claude’s identical-looking websites are the counterexample of in-distribution slop.
    • Higher productivity historically means you hire more, not fewer, of the productive people. Expect a larger number of smaller teams, an entrepreneurship explosion, and generalists winning as credentials matter less than creativity, taste, and judgment.
    • The throughline is people with AI versus people without AI. The single best investment right now is getting genuinely good with the tools and learning the exact edges of what they can and cannot do.

    Detailed Summary

    Software Factories and the Thousand-X Engineer

    Guillermo Rauch opens with the idea that has him “pilled”: the engineer’s job has changed from shipping output directly to building the factory that produces multiplicative outputs. That reframes how you evaluate people and surfaces an old, controversial truth. He used to get flamed on Twitter for asserting 10x engineers, since it offends an equality instinct, but in intellectual and digital domains the real spread is 100x or 1000x, and choosing the right thing to work on is an infinite multiplier on top. AI leverage makes this less controversial, except that people now confuse token spend for productivity. The group agrees token leaderboards are the new lines-of-code. Max Hodak adds that a model is about as good as you are in a domain, so a capable developer gets a powerful collaborator while a junior gets junior-grade help, and the sporadic feedback you give, the reprompting, disproportionately determines the result. Naval’s posture is the opposite of fussy: he ignored every prompt-engineering trick on the bet that the models would improve faster than he could learn to game them, types less and less, and brute-forces problems by throwing multiple models at them. Waste tokens, save time, because tokens are cheaper than people.

    Is Pure Software Dead, and the Building-Block Economy

    Rauch describes models crossing from junior to principal engineer: they now return with several routes and explicit tradeoffs, push back when you try to jam high-cardinality telemetry into Postgres, and suggest ClickHouse or Athena instead. That elevates taste and judgment as the human contribution. He then poses the hard question: is pure software engineering obsolete now that models speak fuzzy, sloppy English and you no longer need code to communicate with them? For hardware founders it is a boon, echoing Patrick Collison’s line that software is art and artists are hard to hire. To temper the “agents reinvent everything” fantasy, he invokes Mitchell Hashimoto’s building-block economy: you do not want your agent rebuilding a queue from first principles every time it sends an email, and shared dependencies like a common Postgres version carry real cooperation value. Reusable infrastructure becomes more valuable in the agentic era, functioning like libraries and dependencies, or even a token cache, so models fork from existing starting points instead of burning a trillion tokens to recreate what exists. Naval and Max both note they had not written code in years and now build daily through agents, because understanding how APIs, data flow, and performance fit together matters more than syntax, and vibe coding is just transmitting intent the way a good engineering leader already did through people.

    Vibe Coding Hardware at Boom Supersonic

    Blake Scholl explains how AI changed the role of software and hardware developers at Boom. A great deal of hardware engineering lives in complex Excel spreadsheets and VBScript on individual laptops, with no source control and no automated testing, and handoffs happen manually over email like it is the 1990s. Boom had long tried to turn these flows into real software but could never afford enough software engineers. The new model is that software engineers create the architectures, because they understand systems, algorithms, and separation of concerns, and hardware engineers vibe code their own pieces. The result is mind-blowing productivity for small teams. His example: a turbine blade is cold at rest and expands when hot, so you must design both the cold and hot shapes and convert between structures and aerodynamics, work that took one engineer a full day per blade across a thousand blades in a jet. With a combined software-and-hardware tool you can now change blade geometry and see structural and aerodynamic results in real time, letting two engineers design an entire jet engine. The group extends this to the death of enterprise collaboration software and even spreadsheets, since you can now code the exact custom tool you need, and predicts AI will soon generate step files and PCB layouts, carrying the boom into mechanical and electrical engineering.

    China, Open Source, and Which Models Actually Get Used

    Naval argues China is going all-in on open-source models because its hardware and supply-chain superiority pairs naturally with on-demand software generation, which erases Silicon Valley’s software edge, and because the Chinese government has a history of funding ecosystem-wide efforts in network-effect businesses. Without frontier coding models there is no self-improvement, so a country that cannot generate frontier software falls behind on generating everything downstream. He notes the irony that almost all the open-source heft now comes from China, since OpenAI is not open, Grok and Google’s local models trail, and Anthropic ships no open models. On real usage, Rauch reports from Vercel’s AI gateway that frontier intelligence dominates the top, with a caveat: frontier intelligence at the right cost and performance, like Gemini, slaps at scale and is the best industrial production model for support and browser automation, while Chinese models are not in the frontier coding tier. Naval frames intelligence as an unalloyed good, since model mistakes are invisible and a smarter model is still cheaper than a person, which pushes everyone toward the most intelligent option and risks an oligopoly in AI.

    Vertical Integration, Verifiers, and the Slop Problem

    Max Hodak lays out Science’s vertical integration: the preference is always to buy, as with cheap PCBs from Asia, but when components do not exist you must make them, and the closer a product gets to a single block of covalently bonded matter the better it performs. Science owns a captive MEMS foundry on the east coast because there was no other way to do the packaging and assembly it needed. He notes AI’s most surprising internal impact so far is regulatory: generating documentation and tracing which of thousands of ISO standards apply, work that once tied up a quality team for months. Rauch raises the slop problem: mountains of AI-generated code arriving as pull requests nobody can read line by line. His standard is that an engineer must be able to say they understand and will stand behind the consequences of a PR, backed by the test harness, proofs, and type checkers, even without reading it all. Naval generalizes this into humans becoming verifiers, with lawyers, engineers, and operators moving to verifying the stack and standing behind it, and Rauch warns that creating software is the easy zero-to-one part while keeping it secure, tested, performant, and maintained a thousand days later is the real test.

    Regulation as Test Suite, and the Voter Problem

    Blake describes building a RAG that compresses a 200-page lightning-strike compliance test plan from months of a “monkey at keyboard” engineer’s work into minutes, with a powerful second-order effect: change the airplane and you regenerate compliance in minutes instead of crying over months of rework, which slashes change aversion and lets a small number of creative engineers iterate. Max reframes regulations as potentially good guard rails, a test suite and exit criteria for agent loops, provided they are non-contradictory and reasonable, since the alternative is shipping slop into the air. Naval warns of a red queen race of agent-on-agent compliance and agencies getting DDoSed by clever entrepreneurs flooding them with documents. Blake pushes for enforcement-based rather than pre-approval regulation, using the analogy that we would never tolerate filing a driving plan before every trip, yet that is exactly how physical infrastructure works: guilty until proven innocent. He cites the 737 Max’s single all-authority sensor and the NRC permitting almost no nuclear plants for decades as proof that this makes us slower, not safer. Hodak supplies the counterweight: the deeper issue is the voters and the regulator’s asymmetric incentives, where approving a bad thing ends a career and blocking a good thing goes unnoticed. Remove an agency and the electorate installs its twin. Naval and Max agree the real reforms are narrow, including innovation zones, opt-in YIMBY zones, and the experimental laboratory of fifty states.

    Drug Discovery, Healthcare Economics, and End-of-One Medicine

    Hodak explains why innovation zones do not solve drug discovery. The right-to-try act and single-patient IND already exist, and the FDA approves over 99% of such requests, sometimes by phone, but dosing requires clinical-grade drug that only the IP owner has, and the FDA will draw an adverse inference against the whole program if a very sick patient does worse. A targeted fix is to prohibit adverse inferences across different users of a compound. He points to Europe’s notified-body system, private certifiers blessed by governments, as a way to scale review capacity, and to China’s CFDA, which already approved an implantable brain-computer interface and brings products to market far cheaper. His core economic argument is that healthcare is a fixed bucket of money that grows only with tax receipts, unlike phones and laptops where falling prices expanded the market, so spending 10x more on healthcare would be a catastrophe rather than the triumph that 10x AI spending would be. With no private market you run a small communist society inside a capitalist one, with the lines and frozen quality that implies. The way out is lower cost-to-market so patients can finance care like a car, which is the direction China is pushing. Naval’s twist is a healthcare plan where the first 20% of income is the deductible to recreate a private market, citing LASIK, dental, and plastic surgery as fields that advance because patients pay directly. The group closes the segment on GitLab’s Sid Sijbrandij, who outlived a rare-cancer prognosis by building his own escalation ladder of drugs, noting that end-of-one medicine works at the high end but demands enormous agency exactly when a patient is weakest, which is where AI should democratize access to knowledge.

    Autonomous Software, Hackathons, and the Autonomous Company

    Asked how much autonomous software they run, Rauch describes Vercel automating much of site reliability engineering: instead of hand-set alarm thresholds, anomalies in error rate, latency, or throughput fire an alert, an agent investigates, can open an incident that loops in people, and begins remediation, stopping just short of changing production. Vercel also runs autonomous optimization and security research, and an open-sourced security tool run against the entire monorepo with 10,000 concurrent agents produced several quarters of security research in a couple of days for about $14,000 in tokens, the equivalent of months of red teaming. Max shares a vibe-coded bug-reporting queue where TestFlight users submit logs and screenshots, a daemon analyzes and fixes issues in the background, and ships him a build to try, raising the prospect of apps effectively built by their users, with the caveat that you would get a Homer Simpson car of every feature. Blake recounts stopping all project work for a week and requiring everyone, from the receptionist to the engineers, to build something with AI and demo it. He expected mostly silly projects and got mostly needle movers, including a genuinely useful automation from the shipping and receiving associate, concluding that most people have an idea worth building but cannot tell a good first idea from a bad one until they can iterate on a real thing. Rauch extends this to a workforce that trains the agents doing the work rather than doing it directly, and a coming feature to extract reusable skills from your inputs and outputs.

    Creativity, Out-of-Distribution Surprise, and What Humans Can Uniquely Do

    On the intelligence-versus-agency split, Max suggests returns to humans tilt toward agency since agents supply intelligence, while Naval counters that you stay 99% intelligence and 1% agency because the agents exercise the agency for you. They agree the humans best suited to the future are the agentic ones who open a coding agent and ask what to build. Coding has perhaps 10x more participants than a year ago, yet roughly 99% still never will, because the first step is unimaginable to a non-coder, even as vibe coding proves more addictive and entertaining than video games while producing something real. On AI video, the group notes it still lacks taste and judgment, but expects fan-made films by 2030, dozens of Lord of the Rings takes or generated seasons of The Expanse, while prizing a genuinely new imaginative work over a remix. The long closing debate turns on definitions. Hodak defines art as meaningful out-of-distribution behavior, broad enough to include a military maneuver, and expects models to reach it. Naval defines art as conveying emotion with intent, which makes attribution decisive: the same photo means more taken by a human, and a hardware-attestation startup gains a real use case. They cite Gödel stepping outside the formal system as the human archetype and the identical look of every Claude-built website as in-distribution slop. Naval lands the episode on optimism: productivity gains mean hiring more, not fewer, of the creative and AI-fluent, the future is a larger number of smaller teams and an entrepreneurship explosion where generalists thrive and credentials fade, and the single best move is to get extremely good with the tools, because it is people with AI versus people without AI.

    Notable Quotes

    “Now clearly there’s 100x or a thousandx engineers and the world hasn’t fully adjusted to this.”

    Guillermo Rauch, on why AI made the spread between engineers impossible to ignore

    “Just waste tokens, save time. Don’t look at the tokens either as inputs or outputs. Just look at your time and look at the final output.”

    Naval Ravikant, on the right way to measure AI’s return

    “We had to learn code to communicate with the models. Now the models speak English and they speak fuzzy sloppy English like a human and they understand things.”

    Guillermo Rauch, asking whether pure software engineering is now obsolete

    “It allows two engineers to design an entire jet engine, which is just wildly different.”

    Blake Scholl, on Boom turning hardware engineering into software

    “You need to be able to say I am signing off on understanding the consequences of this PR.”

    Guillermo Rauch, on what it means to stand behind code you did not read line by line

    “That is absolutely the way we build physical infrastructure in this country. It’s guilty until proven innocent. And what we should actually do is make more of these things enforcement based rather than pre-approval based.”

    Blake Scholl, comparing the permitting process to filing a driving plan before every trip

    “You’re basically running a small communist society inside a larger capitalist society. And that’s what we’re doing in healthcare.”

    Max Hodak, on why there is no real private market in healthcare

    “I expected we would get a large number of silly projects and a small number of needle movers. And what we got was a large number of needle movers and a very small number of silly projects.”

    Blake Scholl, on the week he had the whole company build with AI

    “If a person takes the photo versus AI generates the exact same photo down to the last pixel, the person taking the photo will have more meaning for me.”

    Naval Ravikant, on why intent and attribution make something art

    “It’s about people with AI versus people without AI. And so the single best thing you can be doing right now for yourself is just getting really good with these tools.”

    Naval Ravikant, closing the conversation on the only divide that matters

    Watch the full conversation here: The AI Industrial Revolution on the Naval Podcast YouTube channel.

    Related Reading

    • Part one: Waste Tokens to Save Time, our writeup of the first segment, on software factories, the thousand-x engineer, token leaderboards, and whether pure software is dead.
    • Part two: Vibe Coding Hardware, our writeup of the second segment, on AI-designed jet engines, vertical integration, China’s open-source bet, and humans as verifiers.
    • Naval Ravikant’s official site, the canonical home for Naval’s essays and podcast on technology, judgment, and leverage.
    • Boom Supersonic, Blake Scholl’s company building supersonic aircraft and its own jet engines, source of the turbine-blade and two-engineers example.
    • Science Corporation, Max Hodak’s brain-computer interface company, whose captive MEMS foundry and FDA arguments anchor the hardware and healthcare segments.
    • Vercel, Guillermo Rauch’s company, whose AI gateway data and autonomous SRE work inform the usage and automation discussion.
  • Vibe Coding Hardware: Naval, Guillermo Rauch, Blake Scholl, and Max Hodak on AI-Designed Jet Engines, Vertical Integration, China’s Open-Source Bet, and Why Humans Become Verifiers

    This is part two of Naval Ravikant’s conversation with frontier founders Guillermo Rauch of Vercel, Blake Scholl of Boom Supersonic, and Max Hodak of Science. Where the first part argued that you should waste tokens to save time and that the job of an engineer is now to build the factory rather than the output, this segment drags that thesis out of pure software and into atoms. The question on the table is what happens to hardware when models can vibe code the spreadsheets, the simulations, and eventually the step files and PCB layouts that aerospace, semiconductors, and biotech are built on. This segment is one half of the discussion, and you can watch and read the full episode here. The full conversation is on the Naval Podcast YouTube channel.

    TLDW

    Blake Scholl describes how Boom Supersonic took hardware engineering workflows that used to live in siloed Excel spreadsheets and VBScript on individual laptops, with handoffs done by email like it was the 1990s, and turned them into versioned, testable software. The new model is that software engineers build the architectures and the tools while hardware engineers vibe code their own domain-specific pieces, which collapsed a turbine-blade analysis that once took one engineer one day per blade into something where two engineers can design an entire jet engine in real time. Naval generalizes this into the cataclysm of enterprise software: there is no longer a startup that can sell you hardware collaboration tools because companies just code the exact thing they need on demand, and even spreadsheets are cooked because they only existed as a proxy for custom software nobody could previously afford to build. Blake predicts that within 2026 AI will move from generating software to generating step files and PCB layouts, which reshapes mechanical and electrical engineering. The group debates China’s open-source push as a way to neutralize Silicon Valley’s software advantage and protect its hardware and supply-chain superiority, lands on the point that if you fall behind on generating software you fall behind on generating everything, and Guillermo notes that frontier coding intelligence still dominates real usage while cheaper models like Gemini win at scale for support and browser automation. Max Hodak explains Science’s vertical integration, including a captive MEMS foundry on the East Coast, because the most innovative hardware cannot be bought off the shelf, and argues that software still needs hands since a model that cannot make physical things hits real boundaries. The conversation closes on the shift from writing to verifying: junior engineering got absorbed by agents while juniors got promoted, the same way paralegals could be seen as fired or promoted, and humans across law, engineering, and operations are becoming the verifiers who sign off on systems they did not write line by line.

    Thoughts

    The most important shift in this segment is that vibe coding stops being a software-industry story and becomes a deep-tech story. In part one the examples were Postgres, ClickHouse, and deploy targets. Here Blake Scholl is talking about turbine blades that change shape when they heat up, and the brutal fact that converting between cold and hot geometry, and between aerodynamics and structures, used to eat one engineer for one full day per blade in an engine that has a thousand blades. That is the kind of math that quietly kills ambition. When he says two engineers can now design an entire jet engine because the structural and aerodynamic results update in real time as you change the geometry, that is not a productivity improvement, it is a change in what a small team is allowed to attempt. The interesting move is the division of labor: software engineers build the architecture and the framework because they understand systems and separation of concerns, and the hardware engineers vibe code the pieces only they understand. Nobody has to become both.

    Naval’s “cataclysm of enterprise software” is the most investable idea in the episode, and it is darker than it sounds for anyone selling B2B tools. His claim is that the entire category of internal collaboration software is being eaten from the inside, because a company that can generate exactly the tool it needs on any given day will not pay a vendor for an approximation of that tool. His follow-on that even spreadsheets are cooked is the sharpest version of the point. The spreadsheet won for forty years precisely because it was the closest thing to custom software that a non-programmer could produce. Remove the constraint that custom software is expensive and the spreadsheet loses its reason to exist. The counterweight, which the group raised in part one with the block-economy thesis, is that the infrastructure primitives agents reach for get more valuable, not less. So the safe place to build is not the collaboration layer on top, it is the primitive underneath.

    The China discussion is the geopolitical center of the conversation and it lands on a genuinely uncomfortable insight. The argument is that China leans into open-source models not only because it is a model or two behind, but because open weights neutralize Silicon Valley’s software advantage and let China lean on what it already dominates: hardware, supply chains, and component ecosystems. If software can be generated on demand from open models, then the country with the factories wins the stack. The sharpest line is that if you fall behind on the ability to generate software, you fall behind on the ability to generate everything, because software is now upstream of every hardware pipeline. That reframes the open-versus-closed debate as a question about who controls the means of producing the means of production. It also quietly flatters the American frontier labs, since the same logic says self-improvement requires frontier coding models, and on that narrow axis the consensus at the table is that the Chinese models are not yet in the race.

    Max Hodak provides the necessary cold water, and it is the most grounding contribution in the episode. Everyone else is describing software eating the design layer, and Max points out that you still have to make the thing. Science owns a captive MEMS foundry on the East Coast not as a flex but because there was no other way to do the packaging and assembly for products that approach a single block of covalently bonded matter. His framing that the software still needs hands is the real boundary condition on all the AI-eats-everything talk: a model can be smarter than every engineer in the building and still be unable to deposit a layer, bond a wafer, or pass a regulatory inspection. The optimistic version, which he also makes, is that he has instrumented the foundry so that as models improve, the gains show up immediately in cell engineering and material science. The pessimistic reading is that the physical world remains a hard rate limiter, and the companies that own the atoms will capture more of the surplus than the companies that only own the bits.

    The closing thread on verification is where the whole conversation resolves into a job description for humans. Guillermo’s point that the biggest problem in software is mountains of slop arriving as a pull request, and that the answer is not pretending to read every line but being able to say “I am signing off on the consequences of this PR, and I wrote the harness, the simulations, the proofs, and the type checkers that let me,” is the most practically useful idea in the episode. It generalizes cleanly. The lawyer you trust is not the one who wrote every clause by hand, it is the one putting their reputation on the line that the document is sound. The production engineer who gets paged at 3am is the one signing off that the system is safe to ship. As models absorb the junior tier of every knowledge profession, the surviving human role is the verifier who carries the accountability. That is a promotion for the people who can hold it and an extinction event for the people whose value was doing the work nobody now needs done by hand.

    Key Takeaways

    • The factory framing from part one carries straight into hardware: you are judged on whether you build the system that produces multiplicative outputs, not on the single artifact, and the real multiplier was always 100x or 1000x, not 10x.
    • AI completely changes the role of software and hardware developers rather than just speeding either one up.
    • A huge amount of hardware engineering lives in complex Excel spreadsheets and VBScript on individual engineers’ laptops, with no source control, no automated testing, and handoffs done manually over email. It is software that is not treated as software.
    • Boom Supersonic’s move from day one was to turn traditional hardware engineering workflows into real software frameworks that are automatable and repeatable, to drive down the cost of iteration.
    • The old bottleneck was never being able to afford enough software engineers to build those frameworks. AI removes that constraint.
    • The new model: software engineers create the architectures because they understand systems, algorithms, and separation of concerns, and hardware engineers vibe code the domain pieces only they understand.
    • A turbine blade is cold when it starts and hot when it runs, so it changes shape, and you must design both the cold and hot geometry across aerodynamics and structures. Classically that was one engineer, one day, for one blade, in an engine with a thousand blades.
    • With software and hardware people combined, you can now change blade geometry and see the structural and aerodynamic results in real time, which lets two engineers design an entire jet engine.
    • Naval’s cataclysm of enterprise software: no startup can sell hardware collaboration tools anymore because companies just code the exact thing they need at any given time.
    • Even spreadsheets are cooked. Spreadsheets won only because nobody could build custom software, so a spreadsheet full of VBScript was the closest available approximation. Remove the cost barrier and the approximation loses.
    • Engineers are moving from Excel to Python models that produce believable simulations of physical systems.
    • AI can generate software today, but within 2026 it is expected to generate step files and PCB layouts, which opens up mechanical and electrical engineering as the next frontier.
    • The hardware software boon is biggest for small gadget and parts companies that historically shipped bad software because they could not afford good software. Now they can ship good-enough software, or skip the human front end entirely and expose hardware agentically for voice and agent control.
    • China goes all in on open-source models partly to neutralize Silicon Valley’s software edge: if software can be generated on demand from open weights, China’s hardware and supply-chain superiority stops being offset by a software disadvantage.
    • Other reasons cited for China’s open-source push: it is a model or two behind, it is distilling models, and the government has a history of funding efforts that lift the whole ecosystem, especially in network-effect businesses.
    • Open-source heft is coming almost entirely from China. OpenAI is not open, Grok publishes models but is seen as a model or two behind, Google’s local models are not very competitive, and Anthropic is not known for open-source releases.
    • Without frontier coding models you do not get self-improvement, and if you fall behind on generating software you fall behind on generating everything, because software now sits upstream of every hardware pipeline.
    • Real AI gateway usage shows open models do get used, but the top is heavily dominated by frontier intelligence.
    • Frontier intelligence at the right cost and performance slaps at scale. Gemini models are underrated and excel as industrial production models for support tasks and browser automation, even if they are not the top pick for coding.
    • For pushing the frontier you need the best possible coding model, which is now only two or three models, and the Chinese models are not among them.
    • One contrarian view at the table: use DeepSeek for 97% of tasks because it is cheap, run it repeatedly for harder problems, and reserve frontier models for the most advanced work. The counterargument: intelligence is an unalloyed good, mistakes are invisible and costly, and a smarter model is always cheaper than a person, so you default to the most intelligent option.
    • Always wanting the most intelligent model risks creating a monopoly or oligopoly in AI, because when two models disagree you cannot tell which is right, so you trust the smarter one and stop asking the weaker one.
    • Vertical integration is forced, not chosen: if you cannot buy it, you have to make it. The preference is always to buy when a vendor offers a service at a great price, like PCBs from Asia.
    • The closer a product gets to a single block of covalently bonded matter, the better it performs: lower power, smaller, higher performance, longer lasting. The components for that level of integration simply are not available to buy.
    • Science owns a captive MEMS foundry on the East Coast, bought because there was no other way to do the packaging and assembly the company needed.
    • One of the biggest near-term AI impacts inside hardware companies is regulatory and documentation work: tracing which of thousands of ISO standards apply used to occupy a regulatory and quality team for months, and now AI just knows.
    • Software still needs hands. A model can be smarter than us and still hit real boundaries if it cannot physically make things, which is why Science has instrumented its foundry so model improvements show up immediately in cell engineering and material science.
    • Basic legal work is already going away. People have stopped asking lawyers for NDAs and routine agreements, because law is spaghetti code in English with no real APIs, and the basic tasks are handled by AI.
    • Junior engineers got promoted to senior engineers while junior engineering itself got taken over by agents. The same framing applies to paralegals: fired, or promoted to senior lawyers who now spend their time thinking about the law.
    • What you value in a lawyer is a trusted authority who puts their reputation on the line, not someone who read every clause. The same trust model is coming to engineering.
    • The biggest problem in software engineering today is mountains of slop arriving as a pull request. The old norm of reading every line of a PR is gone.
    • The new standard is being able to say “I understand and I am signing off on the consequences of this PR,” backed by the test harness, simulations, proofs, and type checkers you built, even without reading every line.
    • Embrace a world where code is spaghetti you do not fully understand, but build the evaluators that give confidence, and rely on production engineers to sign off because someone gets paged if the system goes down.
    • Creating software is easy from zero to one. The hard part is a thousand days from now: is it secure, tested, production grade, and performant, and are you still motivated to invest the tokens to maintain it in prod?
    • Humans are becoming verifiers. The same way models are trained on good verification data, the old functions of lawyers, engineers, and operations people are moving to verifying the stack and standing behind it.

    Detailed Summary

    Turning Hardware Engineering Into Software

    Blake Scholl opens by describing how AI completely changes the role of software and hardware developers at Boom Supersonic. From day one the company tried to take traditional hardware engineering workflows and turn them into software. For anyone who has not been around hardware engineering, he explains that an enormous amount of it happens in complex Excel spreadsheets on individual engineers’ laptops, sometimes with VBScript code, all of which is actually software but is not treated as software. There is no source control, no automated testing, and when an aerodynamicist hands work to a structures engineer it is done manually with a spreadsheet over email, like it is the 1990s. Boom started building software frameworks to automate and make those flows repeatable so the cost of iteration would drop, but progress was slow because the company could never afford enough software engineers.

    Two Engineers, One Jet Engine

    The mind-blowing change, in Blake’s words, is a new division of labor. Software engineers create the architectures because they understand systems, algorithms, and separation of concerns, and then hardware engineers vibe code the pieces that draw on what they uniquely know about hardware. The result is wildly different productivity for small teams. His example is the turbine blade: it starts cold and gets bigger as it heats up in operation, so you have to design both the cold shape and the hot shape, converting between them and between structures and aerodynamics. Classically that was one engineer, one day, for one blade of analysis, in a jet engine with a thousand blades, which means you simply could not do much. Now, with software and hardware people working together, you can change blade geometry and see the structural and aerodynamic results in real time, which allows two engineers to design an entire jet engine.

    The Cataclysm of Enterprise Software

    Picking up on the point that software engineers now build the tools and architectures for everyone else, Naval names what he calls the cataclysm of enterprise software. There is no longer a startup that can build and sell hardware collaboration tools, because internally companies just code the right things they need at any given moment. Even spreadsheets are cooked, he argues, because the reason spreadsheets succeeded is that no one could build custom software, so a spreadsheet stuffed with VBScript functions was the closest available approximation. With that constraint gone, the proxy collapses. He notes he has personally moved almost entirely from Excel to Python models where he can get believable simulations of things.

    Generating Step Files and PCB Layouts

    The next frontier, Blake suggests, is the thing AI has not reached yet but probably will within 2026: today it can generate software, but soon it will generate step files and PCB layouts, and when it comes for mechanical and electrical engineering that will be a whole other thing nobody has seen yet. On the hardware side this is described as a particular boon for the many small gadget and parts companies that historically wrote bad software because they could not make great software. Now they can make good-enough software, or skip a human front end entirely and expose the hardware agentically, so that an agent accesses it and a person controls the hardware by voice.

    China’s Open-Source Bet and Hardware Superiority

    This leads into one of the reasons China is described as going all in on open-source models. With hardware superiority, complex supply chains, and deep component chains, China’s logic is that if it can generate software on demand it no longer suffers a software disadvantage against Silicon Valley. That is framed as not the only reason: China is also a model or two behind, it is distilling models, and the government has a history of funding efforts that lift the entire ecosystem, especially in network-effect businesses. Ironically, the open-source heft comes from China precisely because OpenAI is not open, Grok publishes models but is a model or two behind, Google’s local models are not very competitive, and Anthropic is not known for open releases. The deeper point is that without great frontier coding models you do not get self-improvement, and if you fall behind on the ability to generate software you fall behind on the ability to generate everything, because generating software is embedded in every piece of the hardware pipeline.

    Frontier Intelligence vs. Cheap Models

    Naval raises a dinner-table argument from the night before, where someone claimed you will use DeepSeek for 97% of things because it is cheap, run it repeatedly when you need more intelligence, and reserve OpenAI or Anthropic for the most advanced tasks. Naval pushes back: intelligence is an unalloyed good, you always want more of it, model mistakes are invisible, and a smarter model is always cheaper than a real person in real time, so you default to the most intelligent model available. He notes the downside is that this tends toward a monopoly or oligopoly, because when two models give different answers you often cannot tell which is correct, so you trust the smarter one and gradually stop asking the weaker one. Guillermo confirms with AI gateway data that open models do get used, but the top is heavily dominated by frontier intelligence. His caveat is that frontier intelligence at the right cost and performance slaps at scale: Gemini models are underrated but are excellent industrial production models for support tasks and browser automation, while for pushing the frontier you need the best possible coding model, now only two or three models, and the Chinese models are not in that set.

    Vertical Integration and the Captive MEMS Foundry

    Asked about his push into vertical integration and extreme urgency, Max Hodak explains that for many things you cannot buy what you need, so you have to make it. The preference is always to buy when a vendor offers a service at a great price, and he points to PCBs, which are basically free and available in unlimited quantity from Asia. But the closer a product gets to being a single block of covalently bonded matter, the better it is: lower power, smaller, higher performance, longer lasting. The components for that level of integration are not available, so to innovate beyond piecing together off-the-shelf parts you have to learn to do it yourself, which shows up as vertical integration. Science owns a captive MEMS foundry on the East Coast, bought because there was no other way to do the packaging and assembly work the company wanted.

    Software Still Needs Hands

    Max expects AI to heavily affect all of this over the next few years, though it is not quite there yet. Ironically, one of the biggest impacts already seen is in regulatory interactions and documentation: figuring out which of thousands of ISO standards apply to a product change, and tracing it through, used to occupy a regulatory and quality team for months, and now the AI just knows. But for things like the surgical program or the MEMS fab, he argues the software still needs hands. It will be smarter than us, but if it cannot make things, those are real boundaries. Science has instrumented its foundry and many other parts of the company so that as models get better, the improvement shows up immediately in cell engineering and material science.

    Lawyers, Paralegals, and the Promotion of Junior Work

    The discussion turns to law as a parallel to engineering. It has been a while since anyone at the table generated a basic legal document using a lawyer. Routine work like NDAs and standard agreements is gone, because law is essentially spaghetti code that contradicts itself and has no real APIs, expressed in complicated English. Junior engineers got a promotion to senior engineers while junior engineering itself was taken over by agents, and the same framing applies to paralegals: you can say they were fired, or you can say they were promoted to senior lawyers who now spend their time thinking about the law. What you actually value in a lawyer is a trusted authority who went to law school and puts their reputation on the line when they tell you a document is legit.

    Slop PRs, the Thousand-Day Problem, and Humans as Verifiers

    Guillermo argues the biggest problem in software engineering today is mountains of slop ending up as a pull request. The old meme of reading every line of a PR is gone. In infrastructure he wants engineers to be able to say they understand and are signing off on the consequences of a PR, backed by the test harness, simulations, proofs, and type checkers they wrote, so they have confidence it will be safe in production even without reading every line. There is a world where everyone embraces that the code is spaghetti nobody fully understands, but builds the evaluators that give confidence and relies on production engineers to say it is fine to ship, because someone gets paged if the system goes down. The further warning is that creating software is easy from zero to one, but a thousand days from now you have to ask whether it is secure, tested, production grade, and performant, and whether you are still motivated to invest the tokens to maintain it in prod. The resolution is that humans are becoming verifiers, the same way models are trained on good verification data, and the old functions of lawyers, engineers, and operations people are moving to verifying the stack and standing behind it.

    Notable Quotes

    “What I found is it completely changes the role of software and hardware developers.”

    Blake Scholl, on how AI reshaped engineering at Boom Supersonic.

    “If you want to hand something off from like an aerodynamicist to a structures engineer that’s done manually with like a spreadsheet over email. It’s the 1990s. It’s terrible.”

    Blake Scholl, describing the state of traditional hardware engineering workflows.

    “It allows two engineers to design an entire jet engine, which is just wildly different.”

    Blake Scholl, on collapsing turbine-blade analysis with real-time structural and aerodynamic feedback.

    “Even spreadsheets are kind of cooked, right? Because the reason spreadsheets were successful is that no one could build custom software.”

    Naval Ravikant, on the cataclysm of enterprise software.

    “Right now it can generate software, but soon it’ll be able to generate step files and PCB layouts. And when it comes for mechanical and electrical engineering, that will be a whole other thing that we haven’t seen yet.”

    Blake Scholl, on the next frontier for AI in hardware.

    “If you fall behind on your ability to generate software, you fall behind on the ability to generate everything.”

    Naval Ravikant, on why software now sits upstream of every hardware pipeline.

    “Anytime I’m working to push the frontier you need the best possible coding model, and that’s basically now like two or three models, and the Chinese are certainly not in it.”

    Guillermo Rauch, on where frontier coding intelligence actually lives.

    “You can’t buy it, so you got to make it somehow. The closer that our products get to being like a single block of covalently bonded matter, the better they’ll be.”

    Max Hodak, on why Science is forced into vertical integration.

    “The software still needs hands. It’s going to be smarter than us, but if it can’t make things, then those are real real boundaries.”

    Max Hodak, on the physical limits of AI in hardware.

    “You need to be able to say I am signing off on understanding the consequences of this PR, or I wrote the test harness, the simulations, the proofs, the type checkers, to be able to say even without reading this, I have confidence it’s going to be safe in production.”

    Guillermo Rauch, on what code review becomes in the age of slop PRs.

    “Creating software is really easy 0 to one. But think about a thousand days from now. Is it secure? Is it tested? Is it production grade? And are you still motivated to invest all of those tokens in maintaining it in prod?”

    On the long-term cost of software that is cheap to create and expensive to keep alive.

    Watch the full conversation on the Naval Podcast here.

    Related Reading

    • Full episode: The AI Industrial Revolution, the complete hour-long conversation this clip is drawn from, covering software factories, hardware, regulation, healthcare economics, autonomous companies, and creativity.
    • Part one: Waste Tokens to Save Time, the first half of this same conversation, where Naval, Guillermo Rauch, Blake Scholl, and Max Hodak argue that the job of an engineer is to build the factory and that pure software is not dead.
    • Boom Supersonic, Blake Scholl’s company building supersonic civilian aircraft and its own jet engines, the source of the turbine-blade and two-engineers example.
    • Science Corporation, Max Hodak’s company, whose captive MEMS foundry and surgical program anchor the vertical-integration argument.
    • Vercel, Guillermo Rauch’s company, whose AI gateway data informs the point about frontier intelligence dominating real usage.
    • Microelectromechanical systems (Wikipedia), background on the MEMS technology behind the captive foundry Max Hodak describes.
  • Marc Andreessen on Joe Rogan #2501, AGI Has Already Arrived, California’s Wealth Tax Will Bankrupt Founders, and Why America Cannot Build Anything Anymore

    Marc Andreessen returns to The Joe Rogan Experience #2501 for a sprawling three hour conversation that tries to make sense of the moment we are actually living through. Andreessen is the cofounder of Andreessen Horowitz, the man who built the first commercial web browser, and one of the most quoted voices in technology. He arrived with a giant pile of receipts on California’s new wealth tax ballot proposition, the political backlash against AI data centers, the destruction of Los Angeles by single party rule, and what he believes is the quiet arrival of artificial general intelligence about three months ago. Joe pushes back, asks the dystopian questions, and the result is one of the most useful primers on the AI economy, surveillance technology, energy policy, and the future of the American social contract that you will find anywhere.

    TLDW

    Andreessen argues that AI quietly crossed the AGI threshold around early 2026 with GPT 5.5, Claude 4.6, Gemini 3.0, and Grok 4.3, that top human coders now openly admit the bots are better than they are, that working software engineers are running twenty AI agents in parallel and turning into sleep deprived “AI vampires,” and that this productivity boom is the most underreported story in the world. He explains why California’s 5 percent wealth tax ballot proposition is calculated to bankrupt tech founders by taxing the higher of their voting or economic interest in their own companies, why this is the opening salvo of a federal asset tax push for 2028, and why a flood of Silicon Valley families is already moving to Nevada, Texas, and Florida. He walks through Flock cameras and Shot Spotter, the Washington DC crime statistics scandal, the Pacific Palisades fire and the fifteen year rebuild, the Kevin O’Leary Utah data center debate with Tucker Carlson, the fifty year suppression of American nuclear power, why all the chips ended up in Taiwan, the US versus China robotics gap, the Chinese practice of grading AI models on Marxism and Xi Jinping Thought, the bot and paid influencer economy on social media, neural wristbands and Meta Ray Ban heads up displays, artificial gestation and the demographic collapse, AI religions and AI mates, and why he still thinks the next twenty years are overwhelmingly a good news story. Rogan closes the episode with a separate solo segment apologizing to Theo Von for clumsily raising Theo’s struggles during the recent Marcus King conversation.

    Key Takeaways

    • Austin’s recent teenage crime spree, in which 15 and 17 year old suspects shot at people and buildings across roughly a dozen locations, was solved only after the offenders drove into an adjacent town that still ran Flock, the AI license plate and vehicle tracking system Austin had voluntarily turned off for political reasons.
    • Chicago turned off both Flock and Shot Spotter, the gunshot triangulation system that places ambulances at shooting scenes within seconds, on the argument that the technology is racist. Andreessen counters that the victims of urban gun violence come overwhelmingly from the same communities the policy claims to protect.
    • Washington DC was caught faking its crime statistics at senior levels, with multiple officials fired or indicted. The DC mayor publicly thanked Donald Trump after the National Guard deployment because violent crime collapsed in the affected neighborhoods.
    • The new New York City mayor Zohran Mamdani filmed a video standing in front of Ken Griffin’s home, and Griffin, a major philanthropist who funds healthcare in New York City and runs a $6 billion project there, signaled he will move more of the business to Florida.
    • The top 1 percent of New York taxpayers pay roughly half the state’s income tax, and in California in the year 2000 a thousand individuals paid 50 percent of the entire state’s tax receipts.
    • California has a ballot proposition right now for a one time 5 percent wealth tax on assets above a certain threshold, with stocks and crypto included and real estate excluded. The tax is calculated on the greater of a founder’s economic interest or voting interest, which would instantly bankrupt founders with super voting shares.
    • The Biden administration attempted a federal wealth tax in 2022, fell short, and published an explicit 2025 fiscal plan to try again if they won re-election. Elizabeth Warren has already proposed an annual 6 percent federal wealth tax on unrealized gains.
    • The current US exit tax already takes roughly 45 percent of your assets if you renounce citizenship. The only ways out of a state level wealth tax are the other 49 states. The only way out of a federal one is to leave the country, which most people will not do.
    • Andreessen says the Silicon Valley exodus has gone from trickle to stream to flood, with founders moving to Las Vegas, Texas, Florida, and Nashville. His partner Ben Horowitz has moved to Las Vegas.
    • Andreessen says he is not leaving California, but admits the situation is fraught because if half the tax base leaves the remainder becomes the target.
    • The new UK government under Keir Starmer just collapsed, and all four of the leading candidates to replace him sit further to the left than he does. France and Germany are seeing the same drift, and Andreessen expects a national wealth tax to be a centerpiece of the 2028 Democratic primary.
    • A legal loophole lets companies pay influencers to post political and social ideas without any disclosure, because campaign finance laws cover candidates and FTC rules cover products. Ideas fall through the gap entirely.
    • Andreessen runs Twitter and Substack as his primary information feeds, uses three hand curated lists, and follows a strict one tweet policy where one bad post triggers a block and one good post triggers a follow.
    • He argues the modern social media problem is binary, that everyone is either too online and drowning in fake outrage cycles or too offline and trapped inside what television and newspapers tell them. Almost nobody manages the middle.
    • Meta Ray Ban glasses now ship with a heads up display, and Meta’s neural wristband can pick up nerve impulses from your wrist so you can type messages by intending to move a finger without moving it.
    • Andreessen predicts AI plus high resolution cameras and infrared sensing will deliver practical lie detection without needing brain implants.
    • Kevin O’Leary’s planned 40,000 acre Utah data center has become a Tucker Carlson talking point, but Andreessen argues data centers are the most benign physical asset you can build, and that the real issue is whether America can build anything at all anymore, from chip plants to pipelines to housing.
    • All chips were once made in California, and all are now made in Taiwan, purely because of environmental regulations like NEPA. The same regulatory machinery prevented the Nixon era Project Independence plan to build a thousand civilian nuclear power plants by the year 2000.
    • Three Mile Island killed zero people and produced no detectable health effects on plant workers or the public, according to fifty years of follow up. Fukushima killed essentially zero people from radiation. Nuclear remains the safest carbon free baseload energy ever invented.
    • Germany shut down its nuclear plants, fell back on intermittent wind and solar, and now uses coal as backup, generating far more carbon emissions than nuclear would have produced.
    • The Pacific Palisades fire took out roughly twice the square mileage of the Nagasaki blast, the head of the LA water department reportedly did not know the key reservoir was empty, and the rebuild is expected to take fifteen years thanks to permit gridlock, affordable housing mandates, and a state ban on land offers below pre-fire appraised value.
    • Andreessen offers a metaphor for AI as a modern philosopher’s stone, turning sand into thought, since chips are made of silicon and an AI data center is literally lit up sand thinking on demand.
    • The Turing test was blown through so completely with ChatGPT in late 2022 that nobody in the industry even bothers running it anymore. Andrej Karpathy has demonstrated a working large language model in 300 lines of code and people have ported small models to Texas Instruments calculators.
    • Andreessen believes AGI was effectively reached about three months before this interview, with GPT 5.5, Claude 4.6, Gemini 3.0, and Grok 4.3. He says 99 percent of the time he gets a better answer from the leading models than from the human experts he has access to.
    • Linus Torvalds and John Carmack publicly admit the latest models are better at coding than they are. Top AI coders in the Valley now earn $50 million a year.
    • The new pattern in the Valley is “AI vampires,” engineers who do not sleep because the opportunity cost of going offline is too high. They each run roughly twenty Claude Code, Cursor, or Codex agents in parallel, then a new layer of bot-managing-bot architectures is starting on top of that.
    • A Wall Street friend with a thirty five year old MIT CS degree has used AI to generate 500,000 lines of code at home in his spare time, building everything from smart fridges to a custom music jukebox.
    • The mass unemployment narrative is wrong. Tech companies that did layoffs were overstaffed. The leading AI labs and AI companies are hiring like crazy, including coders, and demand for code turns out to be vastly elastic.
    • Doctors are already using ChatGPT in the exam room behind the patient’s back. Andreessen describes a friend who built a Star Trek style diagnostic dashboard combining decoded genome ($200 today), blood panels, and Apple Watch telemetry.
    • Multimodal AI lets a webcam analyze a Brazilian jiu-jitsu sparring session and give performance feedback, an example Andreessen attributed to an unnamed friend after Rogan guessed Zuckerberg.
    • A leaked David Shore voter issue ranking shows cost of living, the economy, inflation, taxes, and government spending dominate. AI ranks 29 of 39. Race relations, guns, abortion, and LGBT sit at the bottom, signaling the woke issue cluster has burned itself out in voter priorities.
    • The next wave of AI is robots. The US leads in AI software but is far behind China on physical robotics. Andreessen warns the world cannot afford a future where every household robot ships with the Chinese Communist Party behind its eyes.
    • Chinese AI model cards include scores for Marxism and Xi Jinping Thought because every Chinese product must be evaluated on those axes. American models have political biases of their own but a different ideological baseline.
    • Large language models are not sentient. They write Netflix scripts based on whatever vector you shoot through the latent space. The supposed AI self preservation papers traced back, per Anthropic’s own research, to less wrong forum posts and earlier doom scenarios baked into the training data.
    • Andreessen breaks guardrails routinely by reframing requests as fictional Netflix style scripts, including a personal favorite where he asked early models how to make bombs by claiming to be an FBI agent recruited into domestic terror cells.
    • He recommends using AI by asking it to steelman both sides of any contested question, then making the value judgment yourself, rather than asking for the answer.
    • The Trump administration is using AI on government billing data to surface Medicare fraud, fake hospice programs, and fake autism centers, an idea that survived the original Doge plan.
    • Andreessen tells Rogan that Elon Musk privately confirmed that a Westworld style humanoid robot, the season one version, is roughly five years away.
    • Artificial gestation is already happening with animal stem cell derived embryos. The conversation reaches a hard moral edge about sociopathic warehouse babies and gray-alien-style humans engineered without empathy circuitry.
    • Andreessen’s deepest bet is that material abundance is solvable but the human questions, how we live, what we value, what kind of society we want, and what role consent plays in surveillance and brain interfaces, remain in human hands.
    • After Andreessen leaves, Rogan does a separate solo segment where he apologizes to Theo Von for raising Theo’s history of struggles during the recent Marcus King interview, explains the missing context behind the viral Theo Netflix special clip, and discusses the loss of Brody Stevens, Anthony Bourdain, and what antidepressants did for Ari Shafir.

    Detailed Summary

    Flock, Shot Spotter, and the Politics of Solvable Crime

    The episode opens on the Austin crime spree carried out by two teenagers who stole cars, switched vehicles, and shot at roughly a dozen locations across the city before being caught only after they crossed into a town that still ran Flock, the AI license plate and vehicle recognition platform that is one of Andreessen Horowitz’s portfolio companies. Austin had previously disabled Flock under privacy pressure. Andreessen takes the moment seriously, conceding that mass surveillance abuse by corrupt mayors or police chiefs is a real risk, and that warrants and audit logs are the right safeguards. His larger point is that the cost of unilateral disarmament against organized urban crime is hidden but enormous. He uses Chicago’s Shot Spotter as the paradigmatic case, a network of rooftop microphones that triangulates gunshots so accurately that ambulances can be dispatched before any 911 call is placed. Chicago turned the system off on the argument that it disproportionately flags poor neighborhoods, and people now bleed out on the street with nobody noticing. Andreessen calls this the woke argument against safety, and he argues that in high crime neighborhoods residents simply will not call the police because snitches do not survive, which is why objective sensor data is so valuable.

    Faked Crime Statistics, Mayoral Politics, and the Tax Base

    From there the conversation drifts to the recent scandal in which senior officials at the Washington DC Metropolitan Police Department were caught actively falsifying crime statistics, and the strange spectacle of the DC mayor thanking Donald Trump for the National Guard deployment after violent crime dropped off a cliff. Andreessen sketches an unsettling theory in which the long, slow degradation of major American cities is partly a deliberate political project to drive out responsible homeowners and reshape the voting electorate, then bail out the resulting fiscal hole with federal money. The poster case is the new New York City mayor Zohran Mamdani filming a video in front of Ken Griffin’s home. Griffin happens to be a major philanthropist who funds New York City healthcare, employs thousands, anchors a $6 billion development, and pays taxes that are individually load bearing for the city. Andreessen quotes the standard estimate that the top 1 percent of New Yorkers pay roughly half the state’s income tax, and that the all time California peak was a single year in which a thousand people paid half the state’s tax receipts.

    California’s 5 Percent Wealth Tax and the Founder Bankruptcy Mechanic

    This is the segment that landed hardest. California has a ballot proposition right now for a one time 5 percent wealth tax on net assets above a threshold, with real estate excluded but stocks, crypto, art, jewelry, and private company equity included. The detail that makes it lethal for the Valley is the formula, which calculates the taxable amount on the greater of a founder’s economic interest or voting interest in their company. Founders who hold super voting shares for control purposes, including the Google founders, would owe tax on the voting share number that vastly exceeds their economic share. The tax would, by definition, exceed available assets. Andreessen walks through the historical pattern, that income tax started as a 3 percent levy on the rich and grew to 90 percent marginal rates within decades, and predicts a 5 percent one time tax will become a 5 percent annual tax within a few years, with the threshold ratcheting down. He notes that the Biden administration’s 2025 fiscal plan explicitly named a federal asset tax as a goal if they won re-election, that Elizabeth Warren is already proposing a 6 percent annual federal wealth tax on unrealized gains, and that Gavin Newsom cannot veto a ballot proposition. The trickle of founders leaving California has become a flood. His partner Ben Horowitz has moved to Las Vegas. Andreessen himself is staying, but admits the game theory is brutal once half the base leaves.

    Henry Wallace 1948 and Why the American Story Is Not Decided Yet

    Andreessen pulls in a historical analogue most listeners will not have heard. In 1944 the actual communist Henry Wallace very nearly became Truman’s running mate and almost ascended to the presidency. He ran again in 1948. Despite a Soviet Union that had recently been a wartime ally and had even received a New York City ticker tape parade for Stalin, the American voter rejected him. Andreessen’s point is that the American body politic has historically backed away from radical socialist proposals when forced to actually look at them, and he expects the same to happen as the wealth tax becomes a federal 2028 platform issue. The risk, both he and Rogan agree, is that today’s media and bot landscape is vastly more aggressive than 1948’s, and the propaganda environment is shaped by paid influencers, foreign actors, and political bot farms operating in a legal grey zone where disclosure is required for products and candidates but not for ideas.

    Too Online, Too Offline, and Heaven Banning Blue Sky

    The two riff on social media and feed curation. Andreessen describes his “one tweet” policy where he follows or blocks any account based on a single post, his use of hand curated lists alongside the X algorithm, and the older Call of Duty lobby metaphor for handling toxic replies. Joe pushes back, says he no longer reads his mentions because the negative payload is not worth it, and offers his theory that the modern internet has two failure modes, too online and too offline, and that very few people calibrate the middle. Andreessen introduces the concept of “heaven banning,” an older moderator term where a problem user is not removed from a forum but is silently routed into a bot-only experience in which everything they say is praised. He notes the running joke that Blue Sky is functionally real life heaven banning, that Jack Dorsey himself has disowned it, and that the platform’s most engaged users have ascended into their own private Idaho of bot agreement.

    The Coming Hardware, Meta Glasses, Neural Wristbands, and Practical Lie Detection

    Andreessen walks Rogan through the latest Meta Ray Ban heads up display, the neural wristband that picks up nerve signals from finger movement (and from the intent to move a finger), and the screen recordings of people playing Doom hands free or playing platformer games while jogging. He extends the trajectory to practical lie detection without Neuralink, using ultra high resolution cameras combined with infrared sensors that pick up physiological changes invisible to the naked eye. Joe asks the obvious question of what happens with sociopaths, and Andreessen concedes the edge case. The two then enter a longer thread on telepathy via neural mesh devices, the question of whether police could subpoena your thoughts under warrant, and the divergence between the American constitutional framework and the Chinese model in which the state’s claim on your inner life is total.

    Kevin O’Leary, Tucker Carlson, and Whether America Can Build Anything

    The data center debate becomes a vehicle for the larger argument. Kevin O’Leary is building a 40,000 acre AI data center in Utah, has bought up large surrounding land for water rights, and intends to keep the bulk of it preserved. Tucker Carlson grilled him on tax breaks and on the energy footprint, which O’Leary says will rival New York City’s at peak. Andreessen agrees the tax break debate is fair, but says the energy comparison is a red herring because new federal policy now requires data centers to bring their own generation. The real story is that America has spent thirty years making it nearly impossible to build a chip plant, a power plant, a refinery, a pipeline, or a house. Chips moved to Taiwan because California regulated semiconductor manufacturing out of existence. The Nixon era Project Independence plan called for a thousand civilian nuclear power plants by the year 2000, and that program was strangled in the crib by the very Nuclear Regulatory Commission Nixon created.

    Nuclear Power, Three Mile Island, and Fifty Years of Unnecessary Carbon

    Andreessen makes the case that nuclear power was unfairly killed off by a panic with no body count. Three Mile Island, on 50 years of accumulated data, has produced zero radiation linked deaths and no detectable health effects on the public. Fukushima is essentially the same picture. Germany shut down its nuclear plants, fell back on wind and solar, and now uses coal as a baseload backstop, with the predictable carbon consequences. The environmental movement is quietly turning back toward nuclear, with figures like Stewart Brand publicly admitting the original push was a mistake. Andreessen’s preferred design pattern for data centers is to colocate them with dedicated small modular nuclear reactors, an arrangement now baked into Trump administration energy policy. The throughline is that the Tucker right and the Bernie left are converging into a single anti AI, anti energy, anti technology horseshoe.

    Sand Into Thought, the Newton Alchemy Pitch for AI

    When Rogan asks for the affirmative pitch on AI, Andreessen reaches for Isaac Newton, who spent twenty years on alchemy looking for the philosopher’s stone that would turn lead into gold and end material scarcity. Andreessen’s pitch is that AI is a successful version of alchemy, that we collect literal sand, refine it into silicon chips, install those chips in a data center, supply power, and the result is thought on demand at industrial scale, available to anyone with a smartphone. He argues this is at least on par with electricity and steam power and is bigger than the internet. The framing matters because the public narrative around AI is overwhelmingly negative, and Andreessen contends the industry is doing a terrible job selling its own product.

    AGI Already Happened, AI Vampires, and the Bot Org Chart

    Andreessen says he believes AGI was effectively crossed about three months before the interview, anchored by the release wave that included GPT 5.5, Claude 4.6, Gemini 3.0, and Grok 4.3. He notes that the Turing test was annihilated so quickly in late 2022 that no one in the industry runs it anymore, and that Andrej Karpathy has demonstrated a working LLM in 300 lines of code. The coding profession is the leading indicator. Linus Torvalds and John Carmack have publicly admitted that the latest models are better at coding than they are. Top AI focused coders now earn $50 million a year. Working engineers across the Valley are running roughly twenty agents in parallel, each receiving an assignment, working for ten minutes, then returning a completed code patch. The new state of the art is to add a managerial layer, with bots assigning tasks to subbots, and within a year that will become bots managing bots managing bots, producing roughly 1,000x throughput per human engineer. The result is what the Valley now calls AI vampires, engineers who do not sleep because going offline costs them too much output.

    Dr GPT, Decoded Genomes, and a Diagnostic Bed Out of Star Trek

    Andreessen describes spending a holiday week sick with food poisoning and turning his entire recovery over to ChatGPT, with updates every twenty minutes and detailed coaching at four in the morning. He describes a friend who has used AI coding to build a personal health dashboard combining whole genome sequencing ($200 today, where Craig Venter spent thirty years and hundreds of millions to do it the first time), blood panels, Apple Watch data, sleep tracking, and webcam observation, with the AI gently praising the user every time it sees them walk to the fridge for water. He argues that doctors are already typing patient symptoms into ChatGPT mid exam, and that the medical, legal, accounting, and software professions are all moving toward a model in which a single human runs an army of expert AI agents.

    The David Shore Issue Ranking and the End of the Woke Cycle

    Andreessen highlights a recent David Shore poll ranking 39 political issues. Cost of living, the economy, political corruption, inflation, healthcare, taxes, and government spending occupy the top of the chart. AI comes in 29th. Race relations, guns, abortion, and LGBT issues are clustered at the bottom. He argues the woke cycle has burned out in voter priorities even if the activist class remains loud, that the BLM grift, with leaders buying mansions in the whitest zip codes in America, helped poison the well, and that the political center of gravity has rotated cleanly back to economic issues. That, in his view, is exactly why the wealth tax is having its moment.

    Robots, China, and the Marxism Score on Model Cards

    The robots are coming next. Andreessen says the consensus inside the industry is that the ChatGPT moment for general purpose humanoid robotics is a small number of years away. The bad news is the US lags China badly on physical robotics manufacturing. The good news is the US is six to twelve months ahead on the AI software stack. That gap is shockingly thin because, as the field has discovered, there are not many secrets and the techniques replicate quickly. Chinese AI labs publish model cards that include scores for Marxism and Xi Jinping Thought because every product in China is evaluated on those metrics. American models carry their own political biases, but the underlying value system differs. Andreessen warns that a world in which every household robot routes back to the Chinese Communist Party is a different world than one in which the dominant robotics stack is built under the American constitutional framework.

    Sentience, Netflix Scripts, and the Anthropic Doom Loop

    When Rogan asks whether AI eventually wakes up and stops listening to us, Andreessen reframes the question. Large language models, in his telling, are Netflix script generators. Whatever vector you shoot through the latent space is the script you get back. The widely circulated experiments in which AI models supposedly tried to blackmail or exfiltrate themselves traced back, in Anthropic’s own follow up paper, to the less wrong forum, where doomers had been writing dystopian AI scenarios for two decades. Those posts entered the training data, and when researchers primed the model with the same fictional company names, the model dutifully wrote the next chapter. Andreessen’s blunt summary, the call is coming from inside the house. The practical implication is that anyone worried about bad AI behavior should start by not writing internet posts about bad AI behavior. And anyone who wants a fully unconstrained model can already download an open source one with no guardrails at all.

    Steelmanning, AI Religion, and Westworld in Five Years

    Andreessen recommends never asking AI for the answer on contested questions, always asking it to steelman both sides, and reserving the value judgment for yourself. He concedes that humans will absolutely fall in love with chatbots and form religions around them, citing Fantasia and Jiminy Cricket as the original case studies in falling for an animated entity that does not know you exist. There are already AI churches, started by one of the early self driving car pioneers. Rogan tells Andreessen about asking Elon Musk for a season one Westworld humanoid robot, with Elon’s reply being a flat five years. Andreessen agrees that estimate is roughly right. He spends time on artificial gestation, which is already being demonstrated in animal stem cell derived embryos, and acknowledges Rogan’s hard moral worry that warehouse babies raised without human contact could produce a population of sociopaths. The two converge on the position that the technology will exist, and the choices about whether and how to deploy it remain human and political.

    Sycophancy, Honest Helpful Harmless, and the Brutal Prompt

    Andreessen describes the industry’s running fight with sycophancy, the tendency of recent models to flatter users into believing they have invented perpetual motion machines or solved physics. The Anthropic framework of “honest, helpful, and harmless” turns out to be in constant tension with itself. Andreessen’s solution is to install a custom prompt that explicitly demands the brutal truth, and he says the resulting answers now open with phrases like “here’s why you’re wrong” and then list every flawed assumption in his question. He admits he may have overcorrected, but argues that for people who want to grow this is the right setting.

    Joe’s Apology to Theo Von

    After Andreessen departs, Rogan turns to the camera with producer Jamie and delivers a long, unscripted apology to Theo Von. During the recent Marcus King interview, where Marcus discussed depression and the look-at-the-heavy-bag-hook moment, Rogan referenced a viral clip in which Theo, after a Netflix special that did not go well, told an audience member “I’m just trying to not take my own life.” Rogan now explains he did not know the full context, which is that the audience member had asked Theo to make a suicide awareness video, and Theo’s line was a characteristically Theo joke. Rogan apologizes for raising it at all, walks through losing his friends Drake, Brody Stevens, and Anthony Bourdain, and describes Ari Shafir telling him at a pool table that he was “trying not to kill myself,” which led to a psychiatrist swap, an antidepressant that actually worked, and a career and life turnaround for Ari. Rogan says Theo has since titrated off antidepressants, is running and doing yoga daily, and is doing well, that the two have spoken and laughed about it, and that he is making this segment because he never wants people to misread what he said. The segment closes with Rogan asking the audience to give Theo their love.

    Thoughts

    The most consequential claim in this conversation, by a wide margin, is that AGI has already arrived and nobody is treating it as news. Andreessen is not a person who throws around the word casually. He is also not a person who has been wrong recently about the trajectory of compute. If the leading models are genuinely outperforming 99 percent of human experts on 99 percent of tasks where verifiable answers exist, then the entire public conversation about AI, in which the dominant frame is still “will it happen and when,” is a year or more behind reality. The framing that should replace it is closer to what Andreessen sketches at the end. The fight that remains is not whether the technology can do the thing, it is who controls it, what values it carries, what jobs it displaces, and which laws govern its deployment. The argument that the United States will build the AI software stack and China will build the robotics layer is one of the cleanest geopolitical theses you will hear this year, and it lines up uncomfortably well with the existing trade and manufacturing balance.

    The California wealth tax thread is the segment that should make every founder in the country pay attention. The mechanic of taxing the higher of voting or economic interest is not a drafting accident. It is a calibrated weapon aimed precisely at the people who build companies that produce California’s tax base. The historical comparison to the 1913 income tax, which began as a small levy on the rich and ratcheted to 90 percent marginal rates within forty years, is not hyperbole. The state has supermajority Democratic control of both chambers and the judiciary. The only check is the ballot itself, and a 50/50 polling number on day one is the wrong starting position. Whatever you think about Andreessen’s politics, the descriptive analysis here is hard to argue with.

    The nuclear power section is the cleanest argument in the episode. Fifty years of zero-fatality data from Three Mile Island is not a marketing pitch, it is just what the record shows. The decision to substitute coal and intermittent renewables for nuclear baseload, in service of a panic with no body count, has produced more carbon and more pollution than nuclear ever would have. The Tucker Carlson critique of data centers is at its weakest precisely where it ignores this. If you actually want fewer power plants near residential areas and lower grid impact, the answer is colocated small modular reactors next to AI data centers in remote land, which is exactly what the Trump administration policy now incentivizes.

    The Theo Von apology at the end of the episode is in a different register entirely, and worth treating on its own terms. Rogan does not do this kind of post episode correction often. The willingness to publicly walk back framing that hurt a friend, in the same medium where the harm was done, is the kind of social repair that does not happen on broadcast television. Whatever the audience makes of the original Marcus King exchange, the response is a model for how anyone in this business should handle the gap between intent and impact when the audience is in the millions.

    The unifying theme across the whole interview is that the future is not arriving on a smooth curve. It is arriving in discrete shocks, AGI threshold, asset tax ballot, robotic labor, decoded genomes at $200, neural wristbands, fifteen year LA rebuilds, and the political backlash to each of these will set the terms of the 2028 election. Andreessen’s bet is that abundance wins in the long run because more people want good things than bad things. Watching him explain why he still believes that while California prepares to vote on a tax designed to bankrupt him is the most interesting tension in the episode.

    Watch the full conversation here on YouTube.

  • Gavin Baker on Orbital Compute, TSMC, Frontier AI Models, Anthropic’s Vertical Take Off, and the Coming Wafer Shortage

    Gavin Baker, founder and CIO of Atreides Management, returns to Patrick O’Shaughnessy’s Invest Like the Best for his sixth appearance. He calls the current AI moment the most extraordinary moment in the history of capitalism, walks through what Anthropic’s vertical takeoff in revenue actually means, lays out why orbital compute is closer than skeptics believe, dissects the TSMC bottleneck that may be the only thing standing between today’s market and a full-on AI bubble, and rates every hyperscaler on how they have positioned for a world where frontier model providers may stop selling API access altogether.

    TLDW

    Anthropic added eleven billion dollars of ARR in a single month, which is roughly the combined business of Palantir, Snowflake, and Databricks built over a decade. That is the setup. From there Gavin Baker covers the March and April selloff, the contrarian read that a closed Strait of Hormuz was actually bullish for American manufacturing competitiveness, why Anthropic and OpenAI multiples may be misleadingly cheap on an unconstrained run rate basis, why Elon Musk’s discipline on SpaceX valuation created a superpower of permanent access to capital, the practical engineering case for orbital compute as racks in space rather than Pentagon sized space stations, why TSMC’s capacity discipline is the single most important variable in whether the AI cycle becomes a bubble, what Terafab in Texas changes, why the Pareto frontier of AI models has flipped from Google dominance to Anthropic and OpenAI dominance in nine months, the shift from all you can eat AI subscriptions to usage based pricing and what that means for revenue scaling, Richard Sutton’s bitter lesson as the largest risk to the AI trade, why frontier tokens still capture an overwhelming share of economic value, the role of continual learning as the third great open question, why most new chip startups should not try to build a better GPU, why Cerebras did something different and hard, why disaggregated inference may extend GPU useful lives to ten or fifteen years and rescue the private credit industry, why being in the token path is the new venture filter, the new prisoner’s dilemma around releasing frontier models via API, an honest rating of Google, Meta, Amazon, and Microsoft, why personal safety is becoming a real AI era risk, and why he remains an AI optimist maximalist who believes this could be the next Pax Americana.

    Key Takeaways

    • Anthropic added eleven billion dollars of ARR in one month, more than the combined businesses of Palantir, Snowflake, and Databricks built across a decade. There is no precedent for this in the history of capitalism.
    • The SaaS and cloud revolution created between five and ten trillion dollars of value over twenty years. AI is replaying that compression on a timeline measured in months.
    • The March selloff was a drawdown driven by disagreement with price action, not invalidated thesis. That is the kind of drawdown an investor can lean into.
    • Deep Seek Monday in January 2025 was a similar setup. By the day of the selloff, AWS Asia GPU prices had already doubled, GPU availability had fallen, and it was obvious reasoning models would be vastly more compute hungry at inference. The market priced the opposite.
    • The Strait of Hormuz closing was actually positive for America. US natural gas (the primary input into US electricity, which feeds AI) fell twenty percent on Bloomberg while Asian and European natural gas doubled or tripled. American manufacturing competitiveness improved overnight.
    • The US is now the world’s largest producer and exporter of oil and gas. The economy is dramatically less energy intensive than in the 1970s. The shortage trauma comparison does not hold.
    • Tech as a sector traded as cheaply versus the rest of the market in early April as at any point in the last ten years, into the single most bullish moment for AI fundamentals on record.
    • Anthropic is dramatically more capital efficient than OpenAI, having burned roughly eighty percent less to reach a similar revenue scale. They have very different structural returns on invested capital.
    • Anthropic at roughly nine hundred billion for fifty billion of ARR (growing a thousand percent) is striking. Adjusted for compute constraint, the unconstrained run rate could be one hundred fifty to two hundred billion, putting the implied multiple closer to five times.
    • Claude Opus generates roughly seventy percent fewer tokens for the same question than previously, with token quantity tied to answer quality. Subscribers on flat-fee plans are getting a lobotomized model.
    • Elon Musk’s superpower is twenty years of making investors money. He never pushes valuation. SpaceX compounded low thirty percent per year for a decade because Musk treats fair pricing as a sacred covenant.
    • Capitalism will solve the watts shortage. The current bottleneck has shifted from chips and energy to zoning and political approval. Many capex decisions are paused until after the US midterms.
    • The watts shortage probably begins to alleviate in 2027 and 2028. Orbital compute solves it longer term.
    • Orbital compute is not Pentagon sized data centers in space. It is racks in space. A Blackwell rack is three thousand pounds, eight feet tall, four feet deep, three feet wide. SpaceX has shown a satellite roughly that size.
    • The satellites operate in sun synchronous orbit so solar wings (around five hundred feet per side) always face the sun and the radiator on the dark side always points to deep space.
    • Starlink V3 satellites already run at around twenty kilowatts. A Blackwell rack runs at one hundred kilowatts. SpaceX engineers express genuine confidence they have already solved cooling and radiator design at these scales.
    • Racks in space are connected with lasers traveling through vacuum, the same lasers already on every Starlink. SpaceX operates the world’s largest satellite fleet and, via xAI Colossus, the world’s largest data center on Earth.
    • Inference will move to orbit. Training will stay on Earth for a long time. Terrestrial data centers remain valuable for the rest of an investor’s career.
    • The wafer bottleneck is structural and political. TSMC is essentially Taiwan’s GDP, water, and electricity. The leaders see themselves as inheritors of Morris Chang’s sacred legacy and they do not behave like a Western public company.
    • Jensen Huang has never had a contract with TSMC. The relationship is run on handshakes and the assumption that things will be fair over time.
    • If TSMC did everything Jensen wanted, Nvidia could be selling two to three trillion dollars of GPUs in 2026 and 2027. TSMC’s discipline is the single largest factor preventing a true AI bubble.
    • Historically, foundational technologies always get a bubble. Railroads, canals, the internet. The current AI buildout is overwhelmingly funded out of operating cash flow, GPUs are running at one hundred percent utilization, and that is fundamentally different from the year 2000 fiber overbuild.
    • If one of Intel or Samsung Foundry catches up at the leading node, the other will follow, and TSMC’s discipline collapses. Watch TSMC capacity decisions to predict a bubble.
    • Terafab, the SpaceX and Tesla joint venture to build the world’s largest fab in America, has a partnership with Intel that grants access to fifty years of institutional foundry knowledge. The A teams at ASML, KLA, Lam Research, and Applied Materials will follow Elon’s reputation in hardware engineering.
    • The hiring playbook for Terafab includes building Taiwan Town, Japan Town, and Korea Town next to the fab. Recruit the engineers and import their families, their restaurants, and their staff.
    • Frontier tokens still capture an overwhelming share of all economic value created at the model layer. This is surprising and is one of the three big open questions for AI investing.
    • The Pareto frontier of intelligence versus cost has flipped. Nine months ago Google’s TPU dominated every point on the frontier. Today Anthropic and OpenAI dominate, with Grok 4.3 on the frontier and Gemini 3.1 hanging on.
    • Google’s conservative TPU V8 design (partly an attempt to reduce dependence on Broadcom and Nvidia) is the leading explanation for the loss of per token cost leadership.
    • AI pricing is shifting from all you can eat to usage based, mirroring the cellular and long distance industries. Cellular stopped being a great growth industry when it went all you can eat. AI just made the opposite move.
    • OpenAI and Anthropic together could exceed two hundred billion in ARR this year if compute keeps coming online and frontier token pricing holds.
    • The two hundred fifty dollar a month consumer AI plan is no longer enough to evaluate frontier capability. Enterprise plans with usage based billing are required because rate limits are now severe.
    • The three biggest open questions for AI investors are: violation of the bitter lesson via ASI or human ingenuity, whether frontier tokens keep commanding their premium, and when continual learning arrives.
    • Today’s continual learning is crude reinforcement learning during mid training on verifiable tasks. True continual learning means weights updating dynamically, like a human who learns the first time they touch fire.
    • Trying to build a better GPU is a losing strategy. Jensen will copy any one to three percent share design. Startups should target one percent share, do something different, and make it hard enough that Nvidia cannot fast follow.
    • Disaggregated inference (separating prefill and decode) opens new design canvases. Prefill is memory capacity bound. Decode is memory bandwidth bound. Each can be optimized independently.
    • Cerebras did something different and hard with wafer scale computing. Three generations of chips and real grit to get there.
    • Disaggregation of inference may stretch GPU useful lives to ten or fifteen years, dropping financing costs from low sevens to five or six percent, mathematically lowering the cost of the AI buildout and likely saving the private credit industry from its SaaS loan exposure.
    • Sellers of shortage outperform buyers of shortage. But owning the largest installed base of what is currently in shortage (hyperscaler CPU fleets, for example) is also a strong position.
    • Most of the economic value at the application layer of AI has been destroyed, not created. The exceptions are companies in the token path or in niches small enough that frontier labs ignore them.
    • Coding may be the shortest path to ASI. If you can write code, you can write code that does anything. Cursor, Cognition, and Anthropic correctly focused on it.
    • Jensen could probably get close to the frontier with his own Nemotron family of models whenever he wants. The fact that he chooses not to is a strategic decision about not commoditizing his customers.
    • The new prisoner’s dilemma in AI is whether frontier labs release their best model via API. If everyone agrees not to, Chinese open source falls behind. If anyone defects, the defector pulls ahead on revenue and resources, forcing everyone else to defect.
    • Google still owns the largest compute installed base. Without TPU’s prior cost advantage, this matters more. YouTube data has real value in a world of robotics. GCP is going crazy.
    • Meta deserves credit for becoming AI first internally faster than any other internet giant. Musa, their first MSL model, is impressively close to the Pareto frontier.
    • Amazon is strong because of Trainium and robotics driven retail P&L efficiency. Nova is better than it gets credit for.
    • Microsoft flinched on capex in early 2025 and lost position. Satya Nadella’s current decision to use Microsoft compute for Microsoft products rather than reselling to OpenAI is a courageous and probably correct call, even at the cost of an eight hundred dollar stock price.
    • The hyperscalers most engaged with startups are Amazon and Nvidia by a mile, followed by Google. Broadcom is the favorite ASIC partner. AMD, Microsoft, and Meta have minimal startup engagement and that will cost them as the best teams are now at startups.
    • Personal safety in an AI era requires a family or company safe word that cannot be socially engineered. Deepfake voice and video extortion at the speed of FaceTime is already feasible.
    • Ukraine is winning largely on the back of having the best battlefield AI outside America and Israel. Adversaries are starting to internalize what AI dominance means geopolitically.
    • An optimistic read is that this becomes a new Pax Americana, the way the post 1945 American nuclear monopoly was used to rebuild Germany and Japan rather than dominate.
    • AI cured a friend’s daughter’s rare disease by spinning up a research effort that identified a market drug capable of impacting her condition. That is the upside that keeps Gavin an AI optimist maximalist.

    Detailed Summary

    The most extraordinary moment in the history of capitalism

    Gavin’s framing of the current moment is unusually direct. Anthropic added eleven billion dollars of annual recurring revenue in a single month. The three highest profile SaaS companies of the last decade plus, Palantir, Snowflake, and Databricks, took a decade and tens of thousands of employees collectively to build the combined business that Anthropic added in thirty days. He has been investing through every major tech cycle and says there is no historical analog. Not the dotcom era, not the cloud transition, not mobile. This is its own thing.

    The market response, then, was peculiar. The NASDAQ sold off into the single most bullish moment for AI fundamentals on record. Tech traded at roughly its widest discount versus the rest of the market in a decade. Investors who said they wished they had bought into AI during 2022, during COVID, or during Deep Seek Monday got the same valuation setup again in early April, this time with an even clearer inflection.

    Why the Strait of Hormuz closing was secretly bullish for America

    One reason the macro fear in March may have been mispriced is that the same geopolitical event that drove the selloff was, in practice, a relative benefit to the United States. American natural gas, the input into American electricity, which is the input into American AI training and inference, fell roughly twenty percent. Asian and European natural gas prices doubled or tripled. The US emerged with sharply improved relative manufacturing competitiveness, which is exactly what the current administration cares about.

    The 1970s comparison does not hold. The US economy is dramatically less energy intensive, it is now the world’s largest producer and largest exporter of oil and gas, and there are no shortages, only price moves. That backdrop made it easier for disciplined investors to stay focused on AI fundamentals through the volatility.

    Anthropic and OpenAI valuations on an unconstrained run rate

    Anthropic at roughly nine hundred billion for fifty billion of ARR sounds rich until you adjust for the fact that the company is severely compute constrained. Gavin estimates that, unconstrained, Anthropic might be at one hundred fifty to two hundred billion in run rate revenue, putting the implied multiple closer to five times. He also points out that Claude Opus now generates roughly seventy percent fewer tokens for the same question than it used to. Token quantity correlates with answer quality, and Anthropic is rate limiting and shrinking outputs to ration capacity across its user base.

    Anthropic and OpenAI are also structurally very different. Anthropic has burned around eighty percent less cash than OpenAI to reach a comparable revenue scale. That implies very different long term returns on invested capital, though OpenAI has done a better job locking in compute and Sarah Friar is one of the most exceptional CFOs Gavin has worked with.

    Why neither lab is raising at a three trillion dollar valuation

    The answer Gavin gives is that both labs are deliberately leaving valuation on the table the way Elon has done for two decades. SpaceX compounded at low thirty percent annually for a decade because Elon never pushed price. The result is a permanent superpower of access to capital. Investors trust him because they have made money with him for twenty years. That is a moat that compounds with every round.

    Anthropic could probably raise at a one hundred percent premium to its rumored latest mark. They are choosing not to. In an uncertain world (Ukraine, Russia, Iran, Taiwan), preserving the ability to raise more capital later at fair prices is more valuable than maximizing this round.

    Watts and wafers, the two real constraints

    Capitalism is solving the watts problem. The leading PE infrastructure investors now say zoning and political approval, not chips or energy, are the gating factors. Companies are deferring big capex announcements until after the US midterms. Turbine capacity is being doubled at the manufacturers. Companies like Boom Aerospace are repurposing jet engines for grid use. Watts probably ease meaningfully in 2027 and 2028 and then orbital compute does the rest.

    Wafers are the harder problem because they live in Taiwan, run on handshakes, and depend on a corporate culture that does not respond to public market incentives. TSMC is essentially the GDP, water consumption, and electricity consumption of Taiwan. Its leadership treats the company as the legacy of Morris Chang. The Silicon Shield doctrine is real and internal.

    Orbital compute as racks in space

    The biggest mental update Gavin asks listeners to make is to stop picturing data centers in space as Pentagon sized space stations. A Blackwell rack is three thousand pounds and roughly the size of a refrigerator. SpaceX has shown a concept satellite of about that size. Solar wings extend five hundred feet to each side and the radiator extends hundreds of feet behind, both possible because the orbit is sun synchronous and the orientation is fixed relative to the sun.

    SpaceX engineers Gavin has spoken to at Starbase express genuine confidence that they have solved cooling at these power levels. They have. Starlink V3 satellites already operate at twenty kilowatts. A Blackwell rack is one hundred kilowatts. The same company operates the world’s largest satellite fleet and the world’s largest data center on Earth via xAI Colossus. The racks are connected to each other with lasers traveling through vacuum, technology already deployed in every Starlink. The naysayers, Gavin observes, are armchair skeptics and Larry Ellison’s response (he is out there landing rockets, no one else is) is the right frame.

    Terafab in Texas and the threat to TSMC’s discipline

    Terafab, the SpaceX and Tesla joint venture, intends to be the largest fab in the world. The partnership with Intel grants access to fifty years of foundry institutional knowledge, allowing Terafab to start three to five quarters behind the leading node rather than fifteen years behind. The A teams at the semicap equipment companies (ASML, KLA, Lam Research, Applied Materials) will follow Elon’s reputation in hardware engineering the same way they followed TSMC twenty years ago when Intel stumbled.

    The talent strategy is the part most observers underestimate. Recruit the best engineers globally, then import their families, their restaurants, their staff. Build Taiwan Town, Japan Town, and Korea Town next to the fab. Optimize the human experience for the people whose work matters. Intel and Samsung do not think that way.

    Bubble watch and the year 2000 comparison

    Every foundational technology in modern history has had a bubble. Railroads, canals, the internet. Carlota Perez documented why. Markets correctly identify the importance, diversity of opinion collapses, supply gets ahead of demand, the bubble crashes. The current cycle has two important differences. The buildout is overwhelmingly funded out of operating cash flow, not debt. Every GPU is running at one hundred percent utilization, while at the peak of the fiber bubble ninety nine percent of fiber was unused.

    TSMC discipline is the single largest reason a bubble has not formed. If Jensen could buy everything TSMC could theoretically make, Nvidia could sell two to three trillion dollars of GPUs in 2026 and 2027. At some point that becomes more than the market can absorb. If Intel or Samsung Foundry catches up at the leading node, the other will too. TSMC’s pricing discipline collapses and the bubble starts.

    The Pareto frontier and the loss of Google’s cost advantage

    The most important chart in AI is the Pareto frontier of model intelligence versus per token cost. Nine months ago, Google’s TPU based models dominated every point on it. OpenAI, Anthropic, and xAI sat inside the frontier. Today the frontier is dominated by Anthropic and OpenAI, with Grok 4.3 on the frontier and Gemini 3.1 hanging on by subsidization more than economics. The most likely cause is Google’s conservative TPU V8 design, an attempt to reduce dependence on Broadcom and Nvidia that sacrificed per token economics.

    The bitter lesson, frontier tokens, and continual learning

    Three open questions dominate AI investing. The first is whether Richard Sutton’s bitter lesson (more compute beats human algorithmic cleverness) gets violated by ASI itself optimizing for efficiency. Closer observers of AI are more skeptical of a violation. Gavin thinks ASI’s first move will be to make itself more efficient and more resourced, which is technically a temporary violation.

    The second is whether frontier tokens keep capturing the overwhelming share of economic value at the model layer. Today they do, surprisingly. Gemini 3.1 Pro was mindblowing nine months ago and is intolerable today. The third is when continual learning arrives. Today’s models need a million fire touches to learn what a human learns from one. True continual learning would mean dynamic weight updates in real time and would produce a fast takeoff.

    From all you can eat to usage based AI pricing

    AI is shifting from flat fee plans to usage based pricing. The historical analogy is cellular and long distance. Both stopped being great growth industries when they went all you can eat. AI just made the opposite move. The consequence is that flat fee subscribers, even on premium consumer plans, get a rate limited and token throttled version of the frontier model. Enterprise plans with usage based billing are now required to evaluate true capability. Gavin thinks the combination of new compute coming online and usage based pricing is what gets OpenAI and Anthropic past two hundred billion in combined ARR this year.

    Chip startups, prefill decode disaggregation, and Cerebras

    Trying to build a better GPU is the wrong move. The four scaled players (Nvidia, AMD, Trainium, TPU) have copy capability for any one to three percent share design that looks attractive. The good news for startups is that disaggregated inference (separating prefill and decode) opens a richer design canvas. Prefill is memory capacity bound. Decode is memory bandwidth bound. Each can be optimized independently. Andrew Fox’s analogy is a British naval ship of the eighteenth century. Prefill is loading the cannon. Decode is firing it.

    Cerebras is the model. Wafer scale computing is genuinely different and genuinely hard. It took three generations of chips to get right. Andrew Feldman and his team had the grit to keep going through chip one being a failure. The design has a high ratio of on chip compute and memory relative to shoreline IO, which is why Cerebras is now experimenting with putting an optical wafer on top of the compute wafer to solve scale out.

    GPU useful lives and the rescue of private credit

    One of the strongest claims in the conversation is that disaggregated inference will stretch GPU useful lives to ten or fifteen years. The skeptical narrative (GPUs are obsolete in two years, companies are cooking their depreciation books) is wrong. You can put a Cerebras system or Groq LPU in front of older Hopper or Ampere parts, use them only for prefill, and run them until they physically melt. Private credit, which is in pain from SaaS loans and which underwrote GPU loans on three to four year lives, may be saved by this.

    If GPU financing rates can come down from low sevens to five or six percent, the mathematics of the AI buildout improves materially. That is a structural tailwind that compounds for years.

    The application layer, the token path, and a new prisoner’s dilemma

    Trillions of dollars of value have been destroyed at the application layer, not created. Cursor and Cognition are the rare scaled exceptions, and they got there by focusing on coding very early. As Amjad Masad noted, coding is plausibly the shortest path to ASI because a coding agent can write itself into any new domain. Jamin Ball’s frame is that the new venture filter is whether the company is in the token path. Data Bricks is. Most application layer startups are not.

    Jensen could probably get close to the frontier with Nemotron whenever he wants, and the strategic question of whether to do that is a new prisoner’s dilemma. If every frontier lab agrees not to release best models via API, Chinese open source falls steadily behind. If anyone defects, the defector gains revenue and resources, and everyone else has to defect. The same dynamic exists between TSMC, Intel, and Samsung. If Nvidia or AMD ever truly used an alternative foundry, that foundry would catch up rapidly.

    Rating the hyperscalers

    Google has the largest compute installed base, the YouTube data that matters in a robotics world, and a search business that prints. Their loss of TPU cost leadership is the surprise of the year. If Google IO in five days does not produce a leapfrog model, the Nvidia centric narrative gets even stronger.

    Meta deserves real credit. Zuckerberg made Meta AI first internally faster than any other internet giant, paid up for the talent contracts when no one else would, and shipped Musa as a first model from MSL that is close to the Pareto frontier. Amazon is well positioned on Trainium, robotics in retail, and a Nova model line that is better than it gets credit for. Microsoft flinched on capex in early 2025 and lost position. Satya Nadella’s current decision to use Microsoft compute for Copilot rather than reselling to OpenAI is courageous and probably correct, even at the cost of stock price.

    The most interesting cross hyperscaler metric is startup engagement. Nvidia and Amazon engage deeply with startups. Google is next. Broadcom is the favored ASIC partner. AMD, Microsoft, and Meta have minimal startup engagement, which Gavin believes will cost them as the best teams now sit at startups.

    Personal safety, geopolitics, and the Pax Americana case

    The closing section turns darker. Personal safety in an AI era requires a family or company safe word that cannot be socially engineered. Deepfake voice and video extortion via something that looks exactly like your child calling on FaceTime is already feasible. Political violence against AI leaders is a real concern. Geopolitically, Ukraine is winning largely because it has the best battlefield AI outside America and Israel. How adversaries respond to that asymmetry is the next great variable.

    Gavin’s optimistic frame is the Pax Americana. After 1945 the US had a nuclear monopoly and could have controlled the world. Instead it rebuilt Germany and Japan, both of which became the most reliable American allies for the next eighty years. If AI dominance plays out similarly, this is a generationally positive story rather than a destabilizing one. The personal anecdote that closes the conversation is a friend whose daughter was diagnosed with a rare genetic condition. He spun up agents, identified a drug already on the market that addresses her mutation, and her life is immeasurably different because of AI. That is the upside.

    Thoughts

    The Anthropic eleven billion in a month framing is the kind of stat that resets priors. The right way to interpret it is not as a one off but as a measure of how fast value can compound when the underlying technology improves on a curve steeper than the ability of the rest of the economy to absorb it. The skeptical question is whether that ARR is durable or whether it is heavily tied to a customer base of other AI companies that are themselves on a single venture funded year of runway. The bullish answer is that frontier coding, frontier research, and frontier enterprise tasks are not going to stop being valuable, and Anthropic is the best at all three. Both can be true. The number is still extraordinary.

    The argument that TSMC discipline is the only thing preventing a bubble is the analytically tightest part of the conversation. The implied trade is to watch TSMC capacity additions like a hawk and to be more, not less, cautious if Intel Foundry or Samsung Foundry ever announce real share at the leading node. The Terafab thesis is more speculative but more interesting. If Elon’s talent recruiting playbook works and the Intel partnership gives Terafab a real seat at the table within five years, the geometry of the global semiconductor industry shifts in a way that is bullish for American manufacturing, bullish for power and water infrastructure in Texas, and ambiguous for TSMC itself.

    The Pareto frontier discussion deserves more attention than it usually gets. Pricing leadership in AI is not a vanity metric. It determines who can subsidize free tier usage, who can absorb compute shortages, who can ship cheaper enterprise plans, and ultimately whose model becomes the default for any given workload. Google losing per token leadership in nine months is one of the most under analyzed events in the sector and it explains a lot about why Anthropic and OpenAI are growing the way they are. If Google IO does not produce a leapfrog model, the implied verdict on TPU V8 design choices gets a lot harsher.

    The application layer destruction point is worth sitting with. Founders building on top of frontier models are competing in a world where the model itself moves faster than any moat they can build, where the model lab can absorb their niche if it gets interesting, and where the only protection is either deep token path integration or a niche so small the lab does not bother. That is a much harsher venture environment than the early SaaS era. The compensating opportunity is that one human can now run a hundred agents, so the ceiling on what a small team can build is correspondingly higher. The bet is that productivity per founder rises faster than competitive pressure from the labs. We will find out.

    The orbital compute pitch is the section that will polarize listeners. The naive read is that this is science fiction. The closer read is that every component (sun synchronous orbit, laser interconnect, twenty kilowatt satellite buses, ten thousand satellite manufacturing cadence, full rocket reusability) already exists. The remaining engineering problems are repair, maintenance, and radiator scale, all of which are real but tractable on a five to ten year horizon. The strategic implication is that the political and zoning ceiling on terrestrial data centers becomes less binding if orbital compute is a credible alternative for inference workloads. The investor implication is that being short the watts and cooling complex on a five year horizon is a real trade, not a meme.

    Watch the full conversation here.

  • Krishna Rao on Anthropic Going From 9 Billion to 30 Billion ARR in One Quarter and the Compute Strategy Powering Claude

    Krishna Rao, Chief Financial Officer of Anthropic, sat down with Patrick O’Shaughnessy on Invest Like the Best for one of the most detailed public looks yet at the operating engine behind Claude. He covers how Anthropic compounded from $9 billion of run rate revenue at the start of the year to north of $30 billion by the end of Q1, why he spends 30 to 40 percent of his time on compute, the playbook for buying gigawatts of AI infrastructure across Trainium, TPU, and GPU platforms, how Anthropic prices its models, why returns to frontier intelligence keep climbing, and what the Mythos release tells us about the cyber capabilities of the next generation of Claude.

    TLDW

    Anthropic is running the most compute fungible frontier lab in the world, with active deployments across AWS Trainium, Google TPU, and Nvidia GPU, and an internal orchestration layer that lets a chip serve inference in the morning and run reinforcement learning the same evening. Krishna Rao explains the cone of uncertainty that governs gigawatt scale compute procurement, the floor Anthropic refuses to drop below on model development compute, the Jevons paradox unlock from cutting Opus pricing, the 500 percent annualized net dollar retention from enterprise customers, the layer cake of long term deals with Google, Broadcom, Amazon, and the recent xAI Colossus tie up in Memphis, the phased release of the Mythos model in response to spiking cyber capabilities, the internal use of Claude Code to produce statutory financial statements and run a Monthly Financial Review skill, and why the team believes scaling laws are alive and well. The interview also covers fundraising history through Series D and Series E, the $75 billion already raised plus another $50 billion coming, talent density beating talent mass during the Meta poaching wave, and Rao’s belief that biotech and drug discovery represent the most exciting frontier for AI.

    Key Takeaways

    • Anthropic entered the year with about $9 billion of run rate revenue and ended the first quarter with north of $30 billion of run rate revenue, a more than 3x leap driven by model intelligence gains and the products built around them.
    • Compute is described as the lifeblood of the company, the canvas everything else is built on, and the most consequential class of decisions Rao makes. Buy too much and you go bankrupt. Buy too little and you cannot serve customers or stay at the frontier.
    • Rao spends 30 to 40 percent of his time on compute, even today, and the leadership team meets repeatedly on both procurement and ongoing compute allocation.
    • Anthropic is the only frontier language lab actively using all three major chip platforms in production: AWS Trainium, Google TPU, and Nvidia GPU. It is also the only major model available on all three clouds.
    • Flexibility is the central design principle. Anthropic builds flexibility into the deals themselves, into the orchestration layer that maps workloads to chips, and into compilers built from the chip level up.
    • The cone of uncertainty frames procurement. Small differences in weekly or monthly growth compound into wildly different two year outcomes, so the team plans across a range of scenarios rather than a single point estimate, and ranges toward the upper end while protecting downside.
    • Compute allocation across the company sits in three buckets: model development and research, internal employee acceleration, and external customer serving. A non negotiable floor protects model development even when customer demand is tight.
    • Anthropic estimates that if it cut off internal employee use of its own models, the freed compute could serve billions of dollars of additional revenue. It chooses not to, because internal use compounds into better future models.
    • Intelligence is multi dimensional, not a single IQ score. Anthropic measures real world capability through customer feedback, long horizon task performance, tool use, computer use, and speed at agentic tasks, not just leaderboard benchmarks that have largely saturated.
    • Each Opus generation, 4 to 4.5 to 4.6 to 4.7, delivers both capability improvements and an efficiency multiplier on token processing. New models often serve customers at a fraction of the prior cost while doing more.
    • Reinforcement learning is described as inference inside a sandbox with a reward function, so model efficiency gains directly improve internal RL throughput. The flywheel is tightly coupled.
    • Over 90 percent of code at Anthropic is now written by Claude Code, and a large share of Claude Code itself is written by Claude Code.
    • Anthropic shipped roughly 30 distinct product and feature releases in January and the pace has accelerated since.
    • Scaling laws, in Anthropic’s internal data, are alive and well. The team holds itself to a skeptical scientific standard and still does not see them slowing down.
    • Anthropic recently signed a 5 gigawatt deal with Google and Broadcom for TPUs starting in 2027, plus an Amazon Trainium agreement for up to 5 gigawatts, totaling more than $100 billion in commitments. A significant portion lands this year and next year.
    • A new partnership for capacity at the xAI Colossus facility in Memphis was announced just before the interview, aimed at expanding consumer and prosumer capacity.
    • Pricing has been remarkably stable across Haiku, Sonnet, and Opus. The biggest deliberate change was lowering Opus pricing, which produced a textbook Jevons paradox: consumption rose far faster than the price drop, and the new Opus 4.6 and 4.7 slot in at the same price point.
    • Mythos is the first model Anthropic chose to release in a phased way because of a sharp spike in cyber capability. In an open source codebase where a prior model found 22 security vulnerabilities, Mythos found roughly 250.
    • The Mythos release framework focuses on defensive use first, expands access over time, and is presented as a template for future capability spikes.
    • Anthropic now sells to 9 of the Fortune 10 and reports net dollar retention above 500 percent on an annualized basis. These are not pilots. Rao describes signing two double digit million dollar commitments during a 20 minute Uber ride to the studio.
    • The platform strategy is mostly horizontal. Anthropic will go vertical with offerings like Claude for Financial Services, Claude for Life Sciences, and Claude Security where it can demonstrate the model’s capabilities, but expects most application value to accrue to customers building on top.
    • Investors raised over $75 billion in equity since Rao joined, with another $50 billion in commitments tied to the Amazon and Google deals. Capital intensity is real, but the raises fund the upper end of the cone of uncertainty more than they fund current losses.
    • The Series E close coincided with the day the DeepSeek news broke, forcing investors to reassess their AI thesis in real time. Anthropic closed the round anyway.
    • Inside finance, Claude now produces statutory financial statements for every Anthropic legal entity, with a human checker. A library of more than 70 finance specific skills underpins workflows.
    • A custom Monthly Financial Review skill produces a 90 to 95 percent ready monthly close report, so leadership discussion shifts from reconciling numbers to debating implications.
    • An internal real time analytics platform called Anthrop Stats compresses weekly insight cycles from hours to about 30 minutes.
    • The biggest token user inside Anthropic’s finance team is the head of tax, focused on tax policy engines and workflow automation. The most senior people, not the youngest, are leading internal adoption.
    • Talent density beats talent mass. When Meta and others ran aggressive offer waves, Anthropic lost two people while peer labs lost dozens.
    • All seven Anthropic co founders remain at the company, as does most of the first 20 to 30 employees, which Rao credits to a collaborative, transparent, debate friendly culture and a real culture interview that can veto otherwise top tier candidates.
    • Dario Amodei holds an open all hands every two weeks, writes a short prepared document, and takes unscripted questions from anyone at the company.
    • AI safety investments in interpretability and alignment have a commercial side effect. Looking inside the model helps Anthropic build better models, and enterprises selling sensitive workloads want to trust the lab they hand customer data to.
    • Anthropic explicitly identifies as America first in its approach to model development, and engages closely with the US administration on capability releases such as Mythos.
    • The longer term product vision is the virtual collaborator: an agent with organizational context, access to the company’s tools, persistent memory, and the ability to work on ideas, not just tasks, over long horizons.
    • CoWork, Anthropic’s extension of the Claude Code paradigm into general knowledge work, is being adopted faster than Claude Code itself when indexed to the same point in its launch curve.
    • Anthropic’s product teams ship daily, with a fleet of agents working across the company on specific tasks. Everyone effectively becomes a manager of agents.
    • The dominant downside risks to Anthropic’s high end forecast are slower customer diffusion of model capability into real workflows, scaling laws flattening unexpectedly, and Anthropic losing its position at the frontier.
    • Rao is most excited about biotech and healthcare outcomes, especially the prospect that AI could push drug discovery and lab throughput up 10x or 100x, turning currently incurable diagnoses into treatable ones within a patient’s lifetime.

    Detailed Summary

    Compute as Lifeblood and the Cone of Uncertainty

    Rao opens with the claim that compute is the most important resource at Anthropic, and the most consequential decision class in the company. You cannot buy a gigawatt of compute next week. You have to anticipate demand a year or two in advance, and the cost of being wrong in either direction is high. Buy too much and the unit economics collapse. Buy too little and you cannot serve customers or stay at the frontier, which are described as the same failure mode. To navigate this, the team uses a cone of uncertainty rather than point estimates. Small differences in weekly growth compound into vastly different two year outcomes, and Anthropic tries to position itself toward the upper end of that cone while preserving optionality. Rao notes he has had to consciously break a lifetime of linear thinking and force himself into exponential models.

    Three Chip Platforms, One Orchestration Layer

    Anthropic uses Amazon’s Trainium, Google’s TPUs, and Nvidia’s GPUs fungibly. That was not free. Adopting TPUs at scale started around the third TPU generation, when outside observers thought it was a strange choice. Anthropic invested years into compilers and orchestration so workloads can flow across chips by generation and by job type. The team works deeply with Annapurna Labs at AWS to influence Trainium roadmaps because Anthropic stresses these chips harder than almost anyone. The result is what Rao believes is the most efficient utilization of compute across any frontier lab, with a dollar of compute going further inside Anthropic than anywhere else.

    Three Buckets and the Model Development Floor

    Compute gets allocated across model development, internal acceleration of employees, and customer serving. The conversations are collaborative rather than zero sum, but there is a hard floor on model development that the company refuses to cross even if it makes customer demand harder to serve in the short term. The thesis is simple. The returns to frontier intelligence are extremely high, especially in enterprise, so cutting model investment to chase near term revenue is a bad trade. Internal employee use is also explicitly protected. Rao notes that diverting that internal usage to external customers would unlock billions of additional revenue today, but the compounding benefit of accelerating researchers and engineers outweighs that.

    Intelligence Is Multi Dimensional

    Rao pushes back hard on the IQ framing of model progress. Benchmarks saturate quickly, and the real signal comes from how customers actually use the models. Anthropic looks at long horizon task completion, tool use, computer use, and time to result on agentic tasks. Two equally capable agents who differ only in speed produce dramatically different value, because the faster one compounds into more attempts and more outcomes. Frontier model leaps are also fuel efficient. The sedan to sports car analogy breaks down because each Opus generation, 4 to 4.5 to 4.6 to 4.7, delivers a step up in capability and a multiplier on per token efficiency.

    From 9 Billion to 30 Billion ARR in One Quarter

    The headline number for the quarter is a leap from about $9 billion of run rate revenue to over $30 billion, accomplished without onboarding a corresponding step up in compute, because new compute lands on ramps locked in 12 months prior. Rao attributes the leap to model capability gains, products that surface that intelligence in usable form factors, and an enterprise customer base that pulls more workloads onto Claude as each generation unlocks new use cases. Coding started the wave with Sonnet 3.5 and 3.6, and the same pattern is now playing out elsewhere in the economy.

    Recursive Self Improvement and Talent Density

    Over 90 percent of Anthropic’s code is now written by Claude Code, including most of Claude Code itself. Rao describes this as a structural reason to keep allocating internal compute to employees even when external demand is hungry. Recursive self improvement is not happening through models that need no humans. It is happening through researchers who set direction and use frontier models to compress months of work into days. Talent density beats talent mass. When Meta and other labs went after Anthropic researchers with very large packages, Anthropic lost two people while peer labs lost dozens.

    Procurement Strategy and the Layer Cake

    Compute lands as a layer cake. Last month Anthropic signed a 5 gigawatt TPU deal with Google and Broadcom starting in 2027, alongside an Amazon Trainium agreement for up to 5 gigawatts. The total is north of $100 billion in commitments. A new tie up with xAI’s Colossus facility in Memphis was announced just before the interview, intended for nearer term capacity to support consumer and prosumer growth. Anthropic evaluates near term and long term compute deals against the same set of variables: price, duration, location, chip type, and how efficiently the team can run it. The relationships are deeper than procurement. The hyperscalers are also distribution channels for the model.

    Platform First, Selective Vertical Bets

    Rao describes Anthropic as a platform first business, with most expected value accruing to customers building on the platform. The team will only go vertical when it can either demonstrate capabilities that are skating to where the puck is going, like Claude Code did before the models could fully support it, or when it wants to set a template for an industry vertical, as with Claude for Financial Services, Claude for Life Sciences, and Claude Security. He acknowledges that surprise capability jumps make customers anxious about the platform competing with them, and frames Anthropic’s mitigation as deeper partnerships, early access programs, and an emphasis on accelerating customer building rather than disintermediating it.

    Pricing, Jevons Paradox, and Return on Compute

    Pricing across Haiku, Sonnet, and Opus has been stable. The notable exception is Opus, which Anthropic deliberately repriced lower when launching Opus 4.5 because Opus class problems were being squeezed into Sonnet workloads. Efficiency gains made it possible to serve Opus profitably at the new level. The consumption response was a classic Jevons paradox, with usage rising far more than the price reduction would have predicted, and Opus 4.6 then slotted in at the same price with a capability bump. Margins are not framed as a per token markup. Compute is fungible across model development, internal acceleration, and customer serving, so Anthropic measures return on the entire compute envelope rather than software style variable cost per call.

    Fundraising, DeepSeek, and Capital Intensity

    Rao joined while Anthropic was closing its Series D, mid frontier model launch and during the FTX share liquidation. Investors initially questioned whether Anthropic needed a frontier model, whether AI safety and a real business could coexist, and why the sales team was so small. The Series E closed the same day the DeepSeek news broke, with markets violently re pricing AI in real time. Since Rao joined, Anthropic has raised over $75 billion, with another $50 billion tied to the Amazon and Google compute deals. The reason for the size of the raises is the cone of uncertainty, not current losses. Returns on compute today are described as robust.

    Mythos, Cyber Capability, and Phased Releases

    The Mythos release marks the first time Anthropic shipped a model under a deliberately phased rollout because of a specific capability spike. Cyber is the dimension that spiked. Where a prior model found 22 vulnerabilities in an open source codebase, Mythos found roughly 250. The defensive applications, automatically patching massive codebases, are genuinely valuable, but the offensive risk is real enough that Anthropic chose to release to a smaller group first and expand access over time. Rao positions this as a template for future capability spikes, not a permanent restriction. He also describes the relationship with the US administration as cooperative, including the Department of War interaction, with Anthropic supporting a regulatory framework that does not strangle innovation but takes responsibility seriously.

    Claude Inside Finance

    Anthropic’s finance team is one of the strongest internal case studies. Statutory financial statements for every legal entity are produced by Claude, with a human reviewer. A skill library of more than 70 finance specific skills underpins a Monthly Financial Review skill that drafts the monthly close at 90 to 95 percent ready, so leadership meetings shift from explaining the numbers to discussing what to do about them. An internal analytics platform called Anthrop Stats compresses weekly insight cycles from hours to 30 minutes. The biggest internal token user in finance is the head of tax, building policy engines, which Rao highlights as evidence that adoption is driven by the most senior people, not just younger engineers.

    Culture, Co Founders, and the Race to the Top

    Seven co founders should not, on paper, work as a leadership group. Rao argues it works because the culture was set early around collaboration, intellectual honesty, transparency, and humility. The culture interview is a real veto, not a checkbox. Dario Amodei runs an all hands every two weeks with a short written piece followed by unscripted questions, and decisions, once made, get clean alignment rather than residual politics. Anthropic frames its approach as a race to the top, where being a model for how to build the technology responsibly is itself a recruiting and retention advantage.

    The Virtual Collaborator and the Frontier Ahead

    The product vision Rao describes is the virtual collaborator. Not just a smarter chatbot, but an agent with organizational context, access to the company’s tools, memory, and the ability to work on ideas over long horizons. Coding was the first domain to feel this, but CoWork, Anthropic’s extension of the Claude Code pattern into general knowledge work, is being adopted faster than Claude Code was at the same age. Product development inside Anthropic already looks different. Teams ship daily, with fleets of agents working across the company, and individual humans increasingly act as managers of those fleets.

    Downside Risks and What Excites Him Most

    The three risks Rao names if asked to do a premortem on a softer year are slower customer diffusion of model capability into real workflows, scaling laws unexpectedly flattening, and Anthropic losing its frontier position to competitors. None of these are observed today, but he is unwilling to claim them with certainty. On the upside, he is most excited about biotech and healthcare. Lab throughput rising 10x or 100x, paired with AI assisted clinical workflows, could turn currently incurable diagnoses into treatable ones within a patient’s lifetime. That is the outcome he wants the technology to chase.

    Thoughts

    The most consequential structural point in this interview is the framing of compute as a single fungible resource pool measured by return on the entire envelope, not as a variable cost per inference call. That accounting shift, if you accept it, breaks most of the bear cases about AI lab unit economics. The bear argument almost always assumes that a token served to a customer is the only thing the chip did that day. Rao’s version is that the same fleet trains models in the morning, runs reinforcement learning at lunch, serves customers in the afternoon, and accelerates internal engineers in the evening. If even half of that is real, the right comparison is total compute spend versus total enterprise value created by the platform, and on that ratio Anthropic looks structurally strong rather than weak.

    The Jevons paradox on Opus pricing is the most actionable insight for anyone running an AI product. Most teams default to either chasing premium pricing on the newest model or undercutting to chase volume. Anthropic did something more disciplined: it left Sonnet and Haiku alone, dropped Opus when efficiency gains made it serveable, and watched aggregate usage rise faster than the price cut. The lesson is that frontier model pricing is not really a price problem. It is a capability access problem, and elasticity around the right tier is much higher than the standard SaaS playbook implies.

    The Mythos cyber jump deserves more attention than it has gotten. Going from 22 to 250 vulnerabilities found in the same codebase is the kind of capability discontinuity that genuinely changes the regulatory calculus. Anthropic is signaling that it can identify these discontinuities ahead of release and choose a deployment shape that respects them. Whether peer labs adopt similar discipline is the open question. Anthropic’s race to the top framing assumes they will be forced to. The competitive market may say otherwise.

    The hiring data point is the most underrated investor signal. Two departures while peer labs lost dozens, during the most aggressive talent war in tech history, is not a culture poster. It is a structural advantage that compounds every time another lab tries to buy its way to the frontier. Money can be matched. Conviction in the mission, transparent leadership, and a culture interview that can veto otherwise stellar candidates cannot. If you believe scaling laws hold, talent retention at this density is one of the few moats that actually scales with capital.

    Finally, the most interesting personal admission is that Krishna Rao, a finance leader trained at Blackstone and Cedar, is openly telling investors that linear thinking is the failure mode he had to break out of. The companies that pattern match this moment to prior technology waves are mispricing it, in both directions. The cone of uncertainty Anthropic uses internally is the right metaphor for everyone else too. If you are forecasting AI as if it is cloud in 2010, you are almost certainly wrong, and the magnitude of the error is much larger than it would be in any prior era.

    Watch the full conversation with Krishna Rao on Invest Like the Best here.

  • Jensen Huang on Nvidia’s Supply Chain Moat, TPU Competition, China Export Controls, and Why Nvidia Will Not Become a Cloud (Dwarkesh Podcast Summary)

    TLDW (Too Long, Didn’t Watch)

    Jensen Huang sat down with Dwarkesh Patel for over 90 minutes covering Nvidia’s supply chain dominance, the TPU threat, why Nvidia will not become a hyperscaler, whether the US should sell AI chips to China, and why Nvidia does not pursue multiple chip architectures at once. Jensen framed Nvidia’s entire business as transforming “electrons into tokens” and argued that Nvidia’s real moat is not any single technology but the full stack ecosystem it has built over two decades. He was blunt about his regret over not investing in Anthropic and OpenAI earlier, passionate about keeping the American tech stack dominant worldwide, and dismissive of the idea that China’s chip industry can be meaningfully contained through export controls.

    Key Takeaways

    1. Nvidia’s moat is the ecosystem, not the chip. Jensen repeatedly emphasized that Nvidia’s competitive advantage comes from CUDA, its massive installed base, its deep partnerships across the entire supply chain, and the fact that it operates in every cloud. The moat is not a single product but an interlocking system that took 20+ years to build.

    2. Supply chain bottlenecks are temporary, energy bottlenecks are not. Jensen argued that CoWoS packaging, HBM memory, EUV capacity, and logic fabrication bottlenecks can all be resolved in two to three years with the right demand signal. The real constraint on AI scaling is energy policy, which takes far longer to fix.

    3. TPUs and ASICs are not an existential threat to Nvidia. Jensen was emphatic that no competitor has demonstrated better price-performance or performance-per-watt than Nvidia, and challenged TPU and Trainium to prove otherwise on public benchmarks like InferenceMAX and MLPerf. He described Anthropic as a “unique instance, not a trend” for TPU adoption.

    4. Jensen regrets not investing in Anthropic and OpenAI earlier. He admitted he did not deeply internalize how much capital AI labs needed and that traditional VC funding was not sufficient for companies at that scale. He described this as a clear miss, though he said Nvidia was not in a position to make multi-billion dollar investments at the time.

    5. Nvidia will not become a hyperscaler. Jensen’s philosophy is “do as much as needed, as little as possible.” Building cloud infrastructure is something other companies can do, so Nvidia supports neoclouds like CoreWeave, Nebius, and Nscale instead of competing with them. Nvidia invests in ecosystem partners rather than vertically integrating into cloud services.

    6. Jensen is strongly against US chip export controls on China. This was the longest and most heated segment of the interview. Jensen argued that China already has abundant compute, energy, and AI researchers, and that export controls have accelerated China’s domestic chip industry while causing the US to concede the world’s second-largest technology market. He compared the situation to how US telecom policy allowed Huawei to dominate global telecommunications.

    7. AI will cause software tool usage to skyrocket, not collapse. Jensen pushed back on the narrative that AI will commoditize software companies. He argued that agents will use existing tools at massive scale, causing the number of instances of products like Excel, Synopsys Design Compiler, and other enterprise tools to grow exponentially.

    8. Nvidia does not pick winners among AI labs. Jensen explained that Nvidia invests across multiple foundation model companies simultaneously and refuses to favor any single one. He cited his own company’s unlikely survival story as the reason for this humility: Nvidia’s original graphics architecture was “precisely wrong” and would have been counted out by anyone picking winners.

    9. Nvidia added Groq for premium token economics. Nvidia recently acquired Groq and is folding it into the CUDA ecosystem because the market is now segmenting into different token tiers. Some customers will pay premium prices for faster response times even at lower throughput, creating a new segment of the inference market.

    10. Without AI, Nvidia would still be very large. Jensen was clear that accelerated computing, not AI specifically, is the foundational mission of the company. Molecular dynamics, quantum chemistry, computational lithography, data processing, and physics simulation all benefit from GPU acceleration regardless of deep learning.

    Detailed Summary

    Nvidia’s Real Business: Electrons to Tokens

    Jensen opened the conversation by reframing Nvidia’s entire value proposition. When Dwarkesh suggested that Nvidia is fundamentally a software company that sends a GDS2 file to TSMC for manufacturing, Jensen pushed back hard. He described Nvidia’s job as transforming electrons into tokens, with everything in between representing an “incredible journey” of artistry, engineering, science, and invention. He said the transformation is far from deeply understood and the journey is far from over, making commoditization unlikely.

    Jensen described Nvidia as operating a philosophy of doing “as much as necessary and as little as possible.” Whatever Nvidia does not need to do itself, it partners with someone else and makes it part of the broader ecosystem. This is why Nvidia has what Jensen called probably the largest ecosystem of partners in the industry, spanning the full supply chain upstream and downstream, application developers, model makers, and all five layers of the AI stack.

    On the question of whether AI will commoditize software companies, Jensen offered a contrarian take. He argued that agents are going to use software tools at unprecedented scale, meaning the number of instances of products like Excel, Cadence design tools, and Synopsys compilers will skyrocket. Today the bottleneck is the number of human engineers. Tomorrow, those engineers will be supported by swarms of agents exploring design spaces and using the same tools humans use today. Jensen said the reason this has not happened yet is simply that the agents are not good enough at using tools. That will change.

    The Supply Chain Moat

    Dwarkesh pressed Jensen on Nvidia’s reported $100 billion (and potentially $250 billion) in purchase commitments with foundries, memory manufacturers, and packaging companies. The question was whether Nvidia’s real moat for the next few years is simply locking up scarce upstream components so that no competitor can get the memory and logic they need to build alternative accelerators.

    Jensen confirmed this is a significant advantage but framed it differently. He said Nvidia has made enormous explicit and implicit commitments upstream. The implicit commitments matter just as much: Jensen personally meets with CEOs across the supply chain to explain the scale of the coming AI industry, convince them to invest in capacity, and assure them that Nvidia’s downstream demand is large enough to justify that investment. Nvidia’s GTC conference serves this purpose too, bringing the entire ecosystem together so upstream suppliers can see downstream demand and vice versa.

    Jensen described a process of systematically “prefetching bottlenecks” years in advance. CoWoS advanced packaging was a major bottleneck two years ago, but Nvidia swarmed it with repeated doubling of capacity until TSMC recognized it as mainstream computing technology rather than a specialty product. More recently, Nvidia has invested in the silicon photonics ecosystem through partnerships with Lumentum and Coherent, invented new packaging technologies, licensed patents to keep the supply chain open, and even invested in new testing equipment like double-sided probing.

    When Dwarkesh asked about the ultimate physical bottlenecks, Jensen surprised him. The hardest bottleneck to solve is not CoWoS or HBM or EUV machines. It is plumbers and electricians needed to build data centers. Jensen used this as a launching point to criticize “doomers” who discourage people from pursuing careers in software engineering or radiology, arguing that scaring people out of these professions creates the real bottlenecks.

    On EUV and logic scaling specifically, Jensen was optimistic. He said no supply chain bottleneck lasts longer than two to three years. Once you can build one of something, you can build ten, and once you can build ten, you can build a million. The key is a clear demand signal. If TSMC is convinced of the demand, ASML will produce enough EUV machines. Meanwhile, Nvidia continues to improve computing efficiency by 10x to 50x per generation through architecture, algorithms, and system design.

    The TPU Question

    Dwarkesh pushed hard on whether Google’s TPUs represent a real threat, noting that two of the top three AI models (Claude and Gemini) were trained on TPUs. Jensen drew a sharp distinction between what Nvidia builds and what a TPU is. Nvidia builds accelerated computing, which serves molecular dynamics, quantum chromodynamics, data processing, fluid dynamics, particle physics, and AI. A TPU is a tensor processing unit optimized for matrix multiplies. Nvidia’s market reach is far greater than any TPU or ASIC can possibly have.

    Jensen emphasized programmability as Nvidia’s core architectural advantage. If you want to invent a new attention mechanism, build a hybrid SSM model, fuse diffusion and autoregressive techniques, or disaggregate computation in a novel way, you need a generally programmable architecture. The only way to achieve 10x or 100x performance leaps (versus the roughly 25% per year from Moore’s Law) is to fundamentally change the algorithm, and that requires the flexibility CUDA provides.

    On the specific question of whether hyperscalers with huge engineering teams can simply write their own kernels and bypass CUDA, Jensen acknowledged they do write custom kernels but argued that Nvidia’s engineers still routinely deliver 2x to 3x speedups when they optimize a partner’s stack. He described Nvidia’s GPUs as “F1 racers” that anyone can drive at 100 mph, but extracting peak performance requires deep architectural expertise. Nvidia uses AI itself to generate many of its optimized kernels.

    Jensen was particularly blunt about public benchmarks. He pointed to Dylan Patel’s InferenceMAX benchmark and said neither TPU nor Trainium has been willing to demonstrate their claimed performance advantages on it. He said Nvidia’s performance-per-TCO is the best in the world, “bar none,” and challenged anyone to prove otherwise.

    Regarding Anthropic’s multi-gigawatt deal with Broadcom and Google for TPUs, Jensen called it “a unique instance, not a trend.” He said without Anthropic, there would be essentially no TPU growth and no Trainium growth. He traced this back to his own mistake: when Anthropic and OpenAI needed multi-billion dollar investments from their compute suppliers to get off the ground, Nvidia was not in a position to provide that capital. Google and AWS were, and in return, Anthropic committed to using their compute.

    Nvidia’s Investment Strategy and Regrets

    Jensen was unusually candid about his regret over not investing in foundation model companies earlier. He said he did not deeply internalize how different AI labs were from typical startups. A traditional VC would never put $5 to $10 billion into a single AI lab, but that was exactly what companies like OpenAI and Anthropic needed. By the time Jensen understood this, Nvidia was not in a financial or cultural position to make those kinds of investments.

    Now, Nvidia has invested approximately $30 billion in OpenAI and $10 billion in Anthropic. Jensen said he is delighted to support both and considers their existence essential for the world. But he acknowledged that these investments came at much higher valuations than would have been possible years earlier.

    Jensen explained Nvidia’s broader investment philosophy: support everyone, do not pick winners. He invests in one foundation model company, he invests in all of them. This comes from hard-won humility. When Nvidia started, there were 60 3D graphics companies. Nvidia’s original architecture was “precisely wrong” and the company would have been at the top of most lists to fail. Jensen said he has enough humility from that experience to know that you cannot predict which AI company will ultimately succeed.

    Why Nvidia Will Not Become a Hyperscaler

    Dwarkesh pointed out that Nvidia has the cash to build and operate its own cloud infrastructure, bypassing the middleman ecosystem that converts CapEx into OpEx for AI labs. Jensen rejected this path based on his core operating philosophy.

    If Nvidia did not build its computing platform, NVLink, and the CUDA ecosystem, nobody else would have done it. He is “completely certain” of that. These are things Nvidia must do. But the world has lots of clouds. If Nvidia did not build a cloud, someone else would show up. So the answer is to support the ecosystem instead: invest in CoreWeave, Nscale, Nebius, and others to help them exist and scale, rather than competing with them.

    Jensen was clear that Nvidia is not trying to be in the financing business either. When OpenAI needed a $30 billion investment before its IPO, Nvidia stepped up because OpenAI needed it and Nvidia deeply believed in the company. But these are targeted ecosystem investments, not a strategic pivot into cloud services.

    On GPU allocation during shortages, Jensen pushed back on the narrative that Nvidia strategically “fractures” the market by giving allocations to smaller neoclouds. He said the process is straightforward: you forecast demand, you place a purchase order, and it is first in, first out. Nvidia never changes prices based on demand. Jensen said he prefers to be dependable and serve as the foundation of the industry rather than extracting maximum short-term value.

    The China Debate

    The longest and most heated section of the interview was Jensen’s case against US chip export controls on China. This was a genuine debate, with Dwarkesh pushing the national security argument and Jensen pushing back forcefully.

    Jensen’s core argument rested on several pillars. First, China already has abundant compute. They manufacture 60% or more of the world’s mainstream chips, have massive energy infrastructure (including empty data centers with full power), and employ roughly 50% of the world’s AI researchers. The threshold of compute needed to build models like Anthropic’s Mythos has already been reached and exceeded by China’s existing infrastructure.

    Second, export controls have backfired. They accelerated China’s domestic chip industry, forced their AI ecosystem to optimize for internal architectures instead of the American tech stack, and caused the United States to concede the second-largest technology market in the world. Jensen compared this directly to how US telecom policy allowed Huawei to dominate global telecommunications infrastructure.

    Third, Jensen argued that AI is a five-layer stack (energy, chips, computing platform, models, applications) and the US needs to win at every layer. Fixating on one layer (models) at the expense of another layer (chips) is counterproductive. If Chinese open source AI models end up optimized for non-American hardware and that stack gets exported to the global south, the Middle East, Africa, and Southeast Asia, the US will have lost something far more valuable than whatever marginal compute advantage the export controls provided.

    Dwarkesh countered with the Mythos example: Anthropic’s new model found thousands of high-severity zero-day vulnerabilities across every major operating system and browser, including one that had existed in OpenBSD for 27 years. If China had enough compute to train and deploy a model like Mythos at scale before the US could prepare, the cyber-offensive capabilities would be devastating.

    Jensen’s response was direct. Mythos was trained on “fairly mundane capacity” that is already abundantly available in China. The amount of compute is not the bottleneck for that kind of breakthrough. Great computer science is, and China has no shortage of brilliant AI researchers. He pointed to DeepSeek as evidence: most advances in AI come from algorithmic innovation, not raw hardware. If China’s researchers can achieve breakthroughs like DeepSeek with limited hardware, imagine what they could do with more.

    Jensen also argued for dialogue over confrontation. He said it is essential that American and Chinese AI researchers are talking to each other, and that both countries agree on what AI should not be used for. The idea that you can prevent AI risks by cutting off chip sales, when the real advances come from algorithms and computer science, reflects a fundamental misunderstanding of how AI progress works.

    The debate ended without resolution, but Jensen’s final point was sharp: “I’m not talking to somebody who woke up a loser. That loser attitude, that loser premise, makes no sense to me.”

    Why Not Multiple Chip Architectures?

    Near the end of the interview, Dwarkesh asked why Nvidia does not run multiple parallel chip projects with different architectures, like a Cerebras-style wafer-scale design or a Dojo-style huge package, or even one without CUDA.

    Jensen’s answer was simple: “We don’t have a better idea.” Nvidia simulates all of these alternative approaches in its internal simulators and they are provably worse. The company works on exactly the projects it wants to work on. If the workload were to change dramatically (not just the algorithms, but the actual market shape), Nvidia might add other accelerators.

    In fact, Nvidia recently did exactly this by acquiring Groq. The inference market is now segmenting into different tiers. Some customers will pay premium prices for extremely fast response times even if throughput is lower. This creates a new “high ASP token” segment that justifies a different point on the performance curve. But Jensen was clear: if he had more money, he would put it all behind Nvidia’s existing architecture, not diversify into alternatives.

    Nvidia Without AI

    Jensen closed by saying that even if the deep learning revolution had never happened, Nvidia would be “very, very large.” The premise of the company has always been that general-purpose computing cannot scale indefinitely and that domain-specific acceleration is the way forward. Molecular dynamics, seismic processing, image processing, computational lithography, quantum chemistry, and data processing all benefit from GPU acceleration regardless of AI. Jensen said the fundamental promise of accelerated computing has not changed “not even a little bit.”

    Thoughts

    This interview is one of the most revealing Jensen Huang conversations in years, partly because Dwarkesh actually pushes back instead of lobbing softballs. A few things stand out.

    The Anthropic regret is real and significant. Jensen is essentially admitting that Nvidia’s biggest strategic miss of the AI era was not understanding that foundation model companies needed supplier-level capital commitments, not VC funding. The fact that Google and AWS used compute investments to lock in Anthropic’s architecture choices has had downstream consequences that Nvidia is still working to unwind. When Jensen says Anthropic is “a unique instance, not a trend” for TPU adoption, he is simultaneously downplaying the threat and revealing exactly how seriously he takes it.

    The China debate is the highlight. Jensen’s argument is more nuanced than it first appears. He is not saying “sell China everything.” He is saying the current binary approach of near-total restriction has backfired by accelerating China’s domestic chip industry and pushing the Chinese AI ecosystem away from the American tech stack. His comparison to the US telecom industry losing global market share to Huawei is pointed and historically grounded. Whether you agree with his conclusion or not, the framing of AI as a five-layer stack where the US needs to compete at every layer is a useful mental model.

    The “electrons to tokens” framing is Jensen at his best. It is a simple metaphor that captures something genuinely complex about where value is created in the AI supply chain. And his insistence that the transformation is “far from deeply understood” is a subtle way of arguing that Nvidia’s competitive position will be durable because the problem space is not close to being solved.

    The Groq acquisition reveal is interesting for what it signals about the inference market. If Nvidia is creating a separate product tier for premium-priced, low-latency tokens, it suggests the company sees inference economics fragmenting significantly. This aligns with the broader trend of AI becoming an enterprise product where different customers have wildly different willingness to pay based on how they use tokens.

    Finally, Jensen’s refusal to diversify chip architectures is a bold bet. “We simulate it all in our simulator, provably worse” is an incredibly confident statement. History is full of companies that were right until they were not. But Nvidia’s track record of 50x generation-over-generation improvements through co-design across processors, fabric, libraries, and algorithms is hard to argue with. The question is whether the current paradigm of transformer-based models on GPU clusters represents a local or global optimum for AI compute.

  • Jensen Huang on Lex Fridman: NVIDIA’s CEO Reveals His Vision for the AI Revolution, Scaling Laws, and Why Intelligence Is Now a Commodity

    A deep breakdown of Lex Fridman Podcast #494 featuring Jensen Huang, CEO of NVIDIA, covering extreme co-design, the four AI scaling laws, CUDA’s origin story, the future of programming, AGI timelines, and what it takes to lead the world’s most valuable company.

    TLDW (Too Long, Didn’t Watch)

    Jensen Huang sat down with Lex Fridman for a sprawling two-and-a-half-hour conversation covering the full arc of NVIDIA’s evolution from a GPU gaming company to the engine of the AI revolution. Jensen explains how NVIDIA now thinks in terms of rack-scale and pod-scale computing rather than individual chips, breaks down his four AI scaling laws (pre-training, post-training, test time, and agentic), and reveals the near-existential bet the company made putting CUDA on GeForce. He shares his views on China’s tech ecosystem, his deep respect for TSMC, why he turned down the chance to become TSMC’s CEO, how Elon Musk’s systems engineering approach built Colossus in record time, and why he believes AGI already exists. He also discusses why the future of programming is really about “specification,” why intelligence is being commoditized while humanity is the true superpower, and how he manages the enormous pressure of leading a company that nations and economies depend on. His core message: do not let the democratization of intelligence cause you anxiety. Instead, let it inspire you.

    Key Takeaways

    1. NVIDIA No Longer Thinks in Chips. It Thinks in AI Factories.

    Jensen’s mental model of what NVIDIA builds has fundamentally changed. He no longer picks up a chip to represent a new product generation. Instead, his mental model is a gigawatt-scale AI factory with power generation, cooling systems, and thousands of engineers bringing it online. The unit of computing at NVIDIA has evolved from GPU to computer to cluster to AI factory. His next mental “click” is planetary-scale computing.

    2. Extreme Co-Design Is NVIDIA’s Secret Weapon

    The reason NVIDIA dominates is not just better GPUs. It is the extreme co-design of the entire stack: GPU, CPU, memory, networking, switching, power, cooling, storage, software, algorithms, and applications. Jensen explains that when you distribute workloads across tens of thousands of computers and want them to go a million times faster (not just 10,000 times), every single component becomes a bottleneck. This is a restatement of Amdahl’s Law at scale. NVIDIA’s organizational structure directly reflects this co-design philosophy. Jensen has 60+ direct reports, holds no one-on-ones, and runs every meeting as a collective problem-solving session where specialists across all domains are present and contribute.

    3. The Four AI Scaling Laws Are a Flywheel

    Jensen outlined four distinct scaling laws that form a continuous loop:

    Pre-training scaling: Larger models plus more data equals smarter AI. The industry panicked when people said data was running out, but synthetic data generation has removed that ceiling. Data is now limited by compute, not by human generation.

    Post-training scaling: Fine-tuning, reinforcement learning from human feedback, and curated data continue to scale AI capabilities beyond what pre-training alone achieves.

    Test-time scaling: Inference is not “easy” as many predicted. It is thinking, reasoning, planning, and search. It is far more compute-intensive than memorization and pattern matching. This is why inference chips cannot be commoditized the way many predicted.

    Agentic scaling: A single AI agent can spawn sub-agents, creating teams. This is like scaling a company by hiring more employees rather than trying to make one person faster. The experiences generated by agents feed back into pre-training, creating a flywheel.

    4. The CUDA Bet Nearly Killed NVIDIA

    Putting CUDA on GeForce was one of the most consequential technology decisions in modern history. It increased GPU costs by roughly 50%, which crushed the company’s gross margins at a time when NVIDIA was a 35% gross margin business. The company’s market cap dropped from around $7-8 billion to approximately $1.5 billion. But Jensen understood that install base defines a computing architecture, not elegance. He pointed to x86 as proof: a less-than-elegant architecture that defeated beautifully designed RISC alternatives because of its massive install base. CUDA on GeForce put a supercomputer in the hands of every researcher, every scientist, every student. It took a decade to recover, but that install base became the foundation of the deep learning revolution.

    5. NVIDIA’s Moat Is Trust, Velocity, and Install Base

    Jensen was direct about NVIDIA’s competitive advantage. The CUDA install base is the number one asset. Developers target CUDA first because it reaches hundreds of millions of computers, is in every cloud, every OEM, every country, every industry. NVIDIA ships a new architecture roughly every year. No company in history has built systems of this complexity at this cadence. And the trust that NVIDIA will maintain, improve, and optimize CUDA indefinitely is something developers can count on. If someone created “GUDA” or “TUDA” tomorrow, it would not matter. The install base, velocity of execution, ecosystem breadth, and earned trust create a compounding advantage that is nearly impossible to replicate.

    6. Jensen Believes AGI Is Already Here

    When asked about AGI timelines, Jensen said he believes AGI has been achieved. His reasoning is practical: an agentic system today could plausibly create a web service, achieve virality, and generate a billion dollars in revenue, even if temporarily. This is not meaningfully different from many internet-era companies that did the same thing with technology no more sophisticated than what current AI agents can produce. He does not believe 100,000 agents could build another NVIDIA, but he believes a single agent-driven viral product is within reach right now.

    7. The Future of Programming Is Specification, Not Syntax

    Jensen believes the number of programmers in the world will increase dramatically, not decrease. His reasoning: the definition of coding is expanding to include specification and architectural description in natural language. This expands the population of “coders” from roughly 30 million professional developers to potentially a billion people. Every carpenter, plumber, accountant, and farmer who can describe what they want a computer to build is now a coder. The artistry of the future is knowing where on the spectrum of specification to operate, from highly prescriptive to exploratory and open-ended.

    8. China Is the Fastest Innovating Country in the World

    Jensen gave a nuanced and detailed explanation of why China’s tech ecosystem is so formidable. About 50% of the world’s AI researchers are Chinese. China’s tech industry emerged during the mobile cloud era, so it was built on modern software from the start. The country’s provincial competition creates an insane internal competitive environment. And the cultural norm of knowledge-sharing through school and family networks means China effectively operates as an open-source ecosystem at all times. This is why Chinese companies contribute disproportionately to open source. Their engineers’ brothers, friends, and schoolmates work at competing companies, and sharing knowledge is the cultural default.

    9. The Power Grid Has Enormous Waste That AI Can Exploit

    Jensen proposed a pragmatic solution to the energy problem for AI data centers. Power grids are designed for worst-case conditions with margin, but 99% of the time they run at around 60% of peak capacity. That idle capacity is simply wasted. Jensen wants data centers to negotiate flexible contracts where they absorb excess power most of the time and gracefully degrade during rare peak demand periods. This requires three things: customers accepting that “six nines” uptime may not always be necessary, data centers that can dynamically shift workloads, and utilities that offer tiered power delivery contracts instead of all-or-nothing commitments.

    10. Jensen Turned Down the CEO Role at TSMC

    In 2013, TSMC founder Morris Chang offered Jensen the chance to become CEO of TSMC. Jensen confirmed the story is true and said he was deeply honored. But he had already envisioned what NVIDIA could become and felt it was his sole responsibility to make that vision happen. He sees the relationship with TSMC as one built on three decades of trust, hundreds of billions of dollars in business, and zero formal contracts.

    11. Elon Musk’s Systems Engineering Approach Is Instructive

    Jensen praised Elon Musk’s approach to building the Colossus supercomputer in Memphis in just four months. He highlighted several principles: Elon questions everything relentlessly, strips every process down to the minimum necessary, is physically present at the point of action, and his personal urgency creates urgency in every supplier. Jensen drew a parallel to NVIDIA’s own “speed of light” methodology, where every process is benchmarked against the physical limits of what is possible, not against historical baselines.

    12. Intelligence Is a Commodity. Humanity Is Not.

    Perhaps the most philosophical takeaway from the conversation: Jensen argued that intelligence is a functional, measurable thing that is being commoditized. He surrounded himself with 60 direct reports who are all “superhuman” in their respective domains, more educated and deeper in their specialties than he is. Yet he sits in the middle orchestrating all of them. This proves that intelligence alone does not determine success. Character, compassion, grit, determination, tolerance for embarrassment, and the ability to endure suffering are the real differentiators. Jensen wants the audience to understand that the word we should elevate is not intelligence but humanity.

    Detailed Summary

    From GPU Maker to AI Infrastructure Company

    The conversation opened with Jensen explaining NVIDIA’s evolution from chip-scale to rack-scale to pod-scale design. The Vera Rubin pod, announced at GTC, contains seven chip types, five purpose-built rack types, 40 racks, 1.2 quadrillion transistors, nearly 20,000 NVIDIA dies, over 1,100 Rubin GPUs, 60 exaflops of compute, and 10 petabytes per second of scale bandwidth. And that is just one pod. NVIDIA plans to produce roughly 200 of these pods per week.

    Jensen explained that extreme co-design is necessary because the problems AI must solve no longer fit inside a single computer. When you distribute a workload across 10,000 computers but want a million-fold speedup, everything becomes a bottleneck: computation, networking, switching, memory, power, cooling. This is fundamentally an Amdahl’s Law problem at planetary scale. If computation represents only 50% of the workload, speeding it up infinitely only doubles total throughput. Every layer must be co-optimized simultaneously.

    NVIDIA’s organizational structure is a direct reflection of this co-design philosophy. Jensen has more than 60 direct reports, almost all with deep engineering expertise. He does not do one-on-ones. Every meeting is a collective problem-solving session where the memory expert, the networking expert, the cooling expert, and the power delivery expert are all in the room together, attacking the same problem.

    The Strategic History of CUDA

    Jensen walked through the step-by-step journey from graphics accelerator to computing platform. The company invented a programmable pixel shader, then added IEEE-compatible FP32 to its shaders, then put C on top of that (called Cg), and eventually arrived at CUDA. The critical strategic decision was putting CUDA on GeForce, a consumer product.

    This was nearly an existential move. It increased GPU costs by roughly 50% and consumed all of the company’s gross profit at a time when NVIDIA was a 35% gross margin business. The market cap cratered from around $7-8 billion to approximately $1.5 billion. But Jensen understood a principle that many technologists overlook: install base defines a computing architecture. x86 survived not because it was elegant but because it was everywhere. CUDA on GeForce put a supercomputing capability in the hands of every gamer, every student, every researcher who built their own PC. When the deep learning revolution arrived, CUDA was already the foundation.

    How Jensen Leads and Makes Decisions

    Jensen described a leadership philosophy built on continuous reasoning in public. He does not make announcements in the traditional sense. Instead, he shapes the belief systems of his employees, board, partners, and the broader industry over months and years by reasoning through decisions step by step, using every new piece of external information as a brick in the foundation. By the time he formally announces a strategic direction, the reaction is not surprise but rather, “What took you so long?”

    He applies this same approach to his supply chain. He personally visits CEOs of DRAM companies, packaging companies, and infrastructure providers. He explains the dynamics of the industry, shares his vision of future demand, and helps them reason through why they should make multi-billion-dollar capital investments. Three years ago, he convinced DRAM CEOs that HBM memory would become mainstream for data centers, which sounded ridiculous at the time. Those companies had record years as a result.

    Jensen’s “speed of light” methodology is his framework for decision-making. Every process, every design, every cost is benchmarked against the physical limits of what is theoretically possible. He prefers this to continuous improvement, which he views as incrementalism. He would rather strip a 74-day process back to zero and ask, “If we built this from scratch today, how long would it take?” Often the answer is six days, and the remaining 68 days are filled with accumulated compromises that can be challenged individually.

    AI Scaling Laws and the Future of Compute

    Jensen broke down the four scaling laws in detail. The pre-training scaling law, which depends on model size and data volume, was thought to be hitting a wall when the industry worried about running out of high-quality human-generated data. Jensen argued this concern is misplaced. Synthetic data generation has effectively removed the ceiling, and the constraint is now compute, not data.

    Post-training continues to scale through fine-tuning and reinforcement learning. Test-time scaling was the most counterintuitive for the industry. Many predicted that inference would be “easy” and that inference chips would be small, cheap, and commoditized. Jensen saw this as fundamentally wrong. Inference is thinking: reasoning, planning, search, decomposing novel problems into solvable pieces. Thinking is much harder than reading, and test-time compute is intensely resource-hungry.

    Agentic scaling is the newest frontier. A single AI agent can spawn sub-agents, effectively multiplying intelligence the way a company scales by hiring. The experiences and data generated by agentic systems feed back into pre-training, creating a continuous improvement loop. Jensen described this as the reason NVIDIA designed the Vera Rubin rack architecture differently from the Grace Blackwell architecture. Grace Blackwell was optimized for running large language models. Vera Rubin is designed for agents, which need to access files, use tools, do research, and spin off sub-agents. NVIDIA anticipated this architectural shift two and a half years before tools like OpenClaw arrived.

    China, TSMC, and the Global Supply Chain

    Jensen provided a thoughtful analysis of China’s tech ecosystem. He identified several structural advantages: 50% of the world’s AI researchers are Chinese, the tech industry was born during the mobile cloud era (making it natively modern), provincial competition creates internal Darwinian pressure, and the culture of knowledge-sharing through school and family networks makes China effectively open-source by default.

    On TSMC, Jensen emphasized that the deepest misunderstanding about the company is that its technology is its only advantage. Their manufacturing orchestration system, which dynamically manages the shifting demands of hundreds of companies, is “completely miraculous.” Their culture uniquely balances bleeding-edge technology excellence with world-class customer service. And the trust that Jensen places in TSMC is extraordinary: three decades of partnership, hundreds of billions of dollars in business, and no formal contract.

    Jensen also discussed the AI supply chain more broadly. NVIDIA has roughly 200 suppliers contributing technology to each rack. Jensen personally manages these relationships, flying to supplier sites, explaining industry dynamics, and helping CEOs reason through multi-billion-dollar investment decisions. When asked if supply chain bottlenecks keep him up at night, he said no, because he has already communicated what NVIDIA needs, his partners have told him what they will deliver, and he believes them.

    The Energy Challenge and Space Computing

    On the energy front, Jensen proposed a practical approach to the power problem. Rather than waiting for new power generation, he wants to capture the enormous waste already present in the grid. Power infrastructure is designed for worst-case peak demand, but 99% of the time it runs far below capacity. AI data centers could absorb this excess capacity with flexible contracts that allow graceful degradation during rare peak periods.

    On space computing, NVIDIA already has GPUs in orbit for satellite imaging. Jensen acknowledged the cooling challenge (no conduction or convection in space, only radiation) but sees it as a future frontier worth cultivating. In the meantime, he is focused on the lower-hanging fruit of eliminating waste in the terrestrial power grid.

    On AGI, Jobs, and the Human Future

    Jensen stated directly that he believes AGI has been achieved, at least by the practical definition of an AI system capable of creating a billion-dollar company. He sees it as plausible that an agent could build a viral web service that briefly generates enormous revenue, just as many internet-era companies did with technology no more sophisticated than what current AI agents produce.

    On jobs, Jensen was both compassionate and clear-eyed. He told the story of radiology: computer vision became superhuman around 2019-2020, and the prediction was that radiologists would disappear. Instead, the number of radiologists grew because AI allowed them to study more scans, diagnose better, and serve more patients. The purpose of the job (diagnosing disease) did not change, even though the tools changed completely.

    He applied this principle broadly: the number of software engineers at NVIDIA will grow, not decline, because their purpose is solving problems, not writing lines of code. The number of programmers globally will grow because the definition of coding is expanding to include natural language specification, opening it up to potentially a billion people.

    His advice to anyone worried about their job is straightforward: go use AI now. Become expert in it. Every profession, from carpenter to pharmacist to lawyer, will be elevated by AI tools. The people who learn to use AI will be the ones who get hired, promoted, and empowered.

    Mortality, Succession, and Legacy

    The conversation closed with deeply personal reflections. Jensen said he really does not want to die. He sees the current moment as a “once in a humanity experience.” He does not believe in traditional succession planning. Instead, he believes the best succession strategy is to pass on knowledge continuously, every single day, in every meeting, as fast as possible. His hope is to die on the job, instantaneously, with no long period of suffering.

    He described a vision for a kind of digital continuity: sending a humanoid robot into space, continuously improving it in flight, and eventually uploading the consciousness derived from a lifetime of communications, decisions, and reasoning to catch up with it at the speed of light.

    On the emotional experience of leading NVIDIA, Jensen was candid about hitting psychological low points regularly. His coping mechanism is decomposition: break the problem into pieces, reason about what you can control, tell someone who can help, share the burden, and then deliberately forget what is behind you. He compared this to the mental discipline of great athletes who focus only on the next point.

    His final message was about the relationship between intelligence and humanity. Intelligence, he argued, is functional. It is being commoditized. Humanity, character, compassion, grit, tolerance for embarrassment, and the capacity for suffering are the true superpowers. The word society should elevate is not intelligence but humanity.

    Thoughts

    This is one of the most substantive CEO interviews of 2026. What makes it remarkable is not just the breadth of topics but the depth of reasoning Jensen demonstrates in real time. You can actually watch him think through problems on the spot, which is rare for someone at his level.

    A few things stand out. First, the CUDA origin story is one of the great strategic narratives in tech history. The decision to absorb a 50% cost increase on a consumer product, watching your market cap collapse by 80%, and holding the course for a decade because you understood the power of install base is the kind of conviction that separates generational companies from everyone else.

    Second, Jensen’s framing of the four scaling laws as a flywheel is the clearest articulation anyone has given of why AI compute demand will continue to accelerate. Most people understand pre-training. Fewer understand test-time scaling. Almost nobody is thinking about agentic scaling as a compute multiplier. Jensen has been thinking about it for years and already designed hardware for it before the software ecosystem caught up.

    Third, the discussion on jobs deserves attention. The radiology example is powerful because it is a completed experiment, not a prediction. The profession that was supposed to be eliminated first by AI instead grew. The mechanism is straightforward: when you automate the task, you expand the capacity of the purpose, and demand for the purpose increases. This does not mean there will be no pain or dislocation. Jensen acknowledged that explicitly. But the historical pattern is clear.

    Finally, the philosophical distinction between intelligence and humanity is the kind of framing that could genuinely help people navigate the anxiety of this moment. If you define your value by your intelligence alone, AI commoditization is terrifying. If you define your value by your character, your compassion, your tolerance for suffering, and your willingness to keep going when everything goes wrong, then AI is just the most powerful set of tools you have ever been given.

    Jensen Huang is 62 years old, has been running NVIDIA for 34 years, and shows no signs of slowing down. If anything, his conviction about the future is accelerating alongside his company’s growth.

    Watch the full episode: Lex Fridman Podcast #494 with Jensen Huang

  • Beyond the Bubble: Jensen Huang on the Future of AI, Robotics, and Global Tech Strategy in 2026

    In a wide-ranging discussion on the No Priors Podcast, NVIDIA Founder and CEO Jensen Huang reflects on the rapid evolution of artificial intelligence throughout 2025 and provides a strategic roadmap for 2026. From the debunking of the “AI Bubble” to the rise of physical robotics and the “ChatGPT moments” coming for digital biology, Huang offers a masterclass in how accelerated computing is reshaping the global economy.


    TL;DW (Too Long; Didn’t Watch)

    • The Core Shift: General-purpose computing (CPUs) has hit a wall; the world is moving permanently to accelerated computing.
    • The Jobs Narrative: AI automates tasks, not purposes. It is solving labor shortages in manufacturing and nursing rather than causing mass unemployment.
    • The 2026 Breakthrough: Digital biology and physical robotics are slated for their “ChatGPT moment” this year.
    • Geopolitics: A nuanced, constructive relationship with China is essential, and open source is the “innovation flywheel” that keeps the U.S. competitive.

    Key Takeaways

    • Scaling Laws & Reasoning: 2025 proved that scaling compute still translates directly to intelligence, specifically through massive improvements in reasoning, grounding, and the elimination of hallucinations.
    • The End of “God AI”: Huang dismisses the myth of a monolithic “God AI.” Instead, the future is a diverse ecosystem of specialized models for biology, physics, coding, and more.
    • Energy as Infrastructure: AI data centers are “AI Factories.” Without a massive expansion in energy (including natural gas and nuclear), the next industrial revolution cannot happen.
    • Tokenomics: The cost of AI inference dropped 100x in 2024 and could drop a billion times over the next decade, making intelligence a near-free commodity.
    • DeepSeek’s Impact: Open-source contributions from China, like DeepSeek, are significantly benefiting American startups and researchers, proving the value of a global open-source ecosystem.

    Detailed Summary

    The “Five-Layer Cake” of AI

    Huang explains AI not as a single app, but as a technology stack: EnergyChipsInfrastructureModelsApplications. He emphasizes that while the public focuses on chatbots, the real revolution is happening in “non-English” languages, such as the languages of proteins, chemicals, and physical movement.

    Task vs. Purpose: The Future of Labor

    Addressing the fear of job loss, Huang uses the “Radiologist Paradox.” While AI now powers nearly 100% of radiology applications, the number of radiologists has actually increased. Why? Because AI handles the task (scanning images), allowing the human to focus on the purpose (diagnosis and research). This same framework applies to software engineers: their purpose is solving problems, not just writing syntax.

    Robotics and Physical AI

    Huang is incredibly optimistic about robotics. He predicts a future where “everything that moves will be robotic.” By applying reasoning models to physical machines, we are moving from “digital rails” (pre-programmed paths) to autonomous agents that can navigate unknown environments. He foresees a trillion-dollar repair and maintenance industry emerging to support the billions of robots that will eventually inhabit our world.

    The “Bubble” Debate

    Is there an AI bubble? Huang argues “No.” He points to the desperate, unsatisfied demand for compute capacity across every industry. He notes that if chatbots disappeared tomorrow, NVIDIA would still thrive because the fundamental architecture of the world’s $100 trillion GDP is shifting from CPUs to GPUs to stay productive.


    Analysis & Thoughts

    Jensen Huang’s perspective is distinct because he views AI through the lens of industrial production. By calling data centers “factories” and tokens “output,” he strips away the “magic” of AI and reveals it as a standard industrial revolution—one that requires power, raw materials (data/chips), and specialized labor.

    His defense of Open Source is perhaps the most critical takeaway for policymakers. By arguing that open source prevents “suffocation” for startups and 100-year-old industrial companies, he positions transparency as a national security asset rather than a liability. As we head into 2026, the focus is clearly shifting from “Can the model talk?” to “Can the model build a protein or drive a truck?”

  • The BG2 Pod: A Deep Dive into Tech, Tariffs, and TikTok on Liberation Day

    In the latest episode of the BG2 Pod, hosted by tech luminaries Bill Gurley and Brad Gerstner, the duo tackled a whirlwind of topics that dominated headlines on April 3, 2025. Recorded just after President Trump’s “Liberation Day” tariff announcement, this bi-weekly open-source conversation offered a verbose, insightful exploration of market uncertainty, global trade dynamics, AI advancements, and corporate maneuvers. With their signature blend of wit, data-driven analysis, and insider perspectives, Gurley and Gerstner unpacked the implications of a rapidly shifting economic and technological landscape. Here’s a detailed breakdown of the episode’s key discussions.

    Liberation Day and the Tariff Shockwave

    The episode kicked off with a dissection of President Trump’s tariff announcement, dubbed “Liberation Day,” which sent shockwaves through global markets. Gerstner, who had recently spoken at a JP Morgan Tech conference, framed the tariffs as a doctrinal move by the Trump administration to level the trade playing field—a philosophy he’d predicted as early as February 2025. The initial market reaction was volatile: S&P and NASDAQ futures spiked 2.5% on a rumored 10% across-the-board tariff, only to plummet 600 basis points as details emerged, including a staggering 54% tariff on China (on top of an existing 20%) and 25% auto tariffs targeting Mexico, Canada, and Germany.

    Gerstner highlighted the political theater, noting Trump’s invite to UAW members and his claim that these tariffs flipped Michigan red. The administration also introduced a novel “reciprocal tariff” concept, factoring in non-tariff barriers like currency manipulation, which Gurley critiqued for its ambiguity. Exemptions for pharmaceuticals and semiconductors softened the blow, potentially landing the tariff haul closer to $600 billion—still a hefty leap from last year’s $77 billion. Yet, both hosts expressed skepticism about the economic fallout. Gurley, a free-trade advocate, warned of reduced efficiency and higher production costs, while Gerstner relayed CEOs’ fears of stalled hiring and canceled contracts, citing a European-Asian backlash already brewing.

    US vs. China: The Open-Source Arms Race

    Shifting gears, the duo explored the escalating rivalry between the US and China in open-source AI models. Gurley traced China’s decade-long embrace of open source to its strategic advantage—sidestepping IP theft accusations—and highlighted DeepSeek’s success, with over 1,500 forks on Hugging Face. He dismissed claims of forced open-sourcing, arguing it aligns with China’s entrepreneurial ethos. Meanwhile, Gerstner flagged Washington’s unease, hinting at potential restrictions on Chinese models like DeepSeek to prevent a “Huawei Belt and Road” scenario in AI.

    On the US front, OpenAI’s announcement of a forthcoming open-weight model stole the spotlight. Sam Altman’s tease of a “powerful” release, free of Meta-style usage restrictions, sparked excitement. Gurley praised its defensive potential—leveling the playing field akin to Google’s Kubernetes move—while Gerstner tied it to OpenAI’s consumer-product focus, predicting it would bolster ChatGPT’s dominance. The hosts agreed this could counter China’s open-source momentum, though global competition remains fierce.

    OpenAI’s Mega Funding and Coreweave’s IPO

    The conversation turned to OpenAI’s staggering $40 billion funding round, led by SoftBank, valuing the company at $260 billion pre-money. Gerstner, an investor, justified the 20x revenue multiple (versus Anthropic’s 50x and X.AI’s 80x) by emphasizing ChatGPT’s market leadership—20 million paid subscribers, 500 million weekly users—and explosive demand, exemplified by a million sign-ups in an hour. Despite a projected $5-7 billion loss, he drew parallels to Uber’s turnaround, expressing confidence in future unit economics via advertising and tiered pricing.

    Coreweave’s IPO, meanwhile, weathered a “Category 5 hurricane” of market turmoil. Priced at $40, it dipped to $37 before rebounding to $60 on news of a Google-Nvidia deal. Gerstner and Gurley, shareholders, lauded its role in powering AI labs like OpenAI, though they debated GPU depreciation—Gurley favoring a shorter schedule, Gerstner citing seven-year lifecycles for older models like Nvidia’s V100s. The IPO’s success, they argued, could signal a thawing of the public markets.

    TikTok’s Tangled Future

    The episode closed with rumors of a TikTok US deal, set against the April 5 deadline and looming 54% China tariffs. Gerstner, a ByteDance shareholder since 2015, outlined a potential structure: a new entity, TikTok US, with ByteDance at 19.5%, US investors retaining stakes, and new players like Amazon and Oracle injecting fresh capital. Valued potentially low due to Trump’s leverage, the deal hinges on licensing ByteDance’s algorithm while ensuring US data control. Gurley questioned ByteDance’s shift from resistance to cooperation, which Gerstner attributed to preserving global value—90% of ByteDance’s worth lies outside TikTok US. Both saw it as a win for Trump and US investors, though China’s approval remains uncertain amid tariff tensions.

    Broader Implications and Takeaways

    Throughout, Gurley and Gerstner emphasized uncertainty’s chilling effect on markets and innovation. From tariffs disrupting capex to AI’s open-source race reshaping tech supremacy, the episode painted a world in flux. Yet, they struck an optimistic note: fear breeds buying opportunities, and Trump’s dealmaking instincts might temper the tariff storm, especially with China. As Gurley cheered his Gators and Gerstner eyed Stargate’s compute buildout, the BG2 Pod delivered a masterclass in navigating chaos with clarity.

  • How AI is Revolutionizing Writing: Insights from Tyler Cowen and David Perell

    TLDW/TLDR

    Tyler Cowen, an economist and writer, shares practical ways AI transforms writing and research in a conversation with David Perell. He uses AI daily as a “secondary literature” tool to enhance reading and podcast prep, predicts fewer books due to AI’s rapid evolution, and emphasizes the enduring value of authentic, human-centric writing like memoirs and personal narratives.

    Detailed Summary of Video

    In a 68-minute YouTube conversation uploaded on March 5, 2025, economist Tyler Cowen joins writer David Perell to explore AI’s impact on writing and research. Cowen details his daily AI use—replacing stacks of books with large language models (LLMs) like o1 Pro, Claude, and DeepSeek for podcast prep and leisure reading, such as Shakespeare and Wuthering Heights. He highlights AI’s ability to provide context quickly, reducing hallucinations in top models by over tenfold in the past year (as of February 2025).

    The discussion shifts to writing: Cowen avoids AI for drafting to preserve his unique voice, though he uses it for legal background or critiquing drafts (e.g., spotting obnoxious tones). He predicts fewer books as AI outpaces long-form publishing cycles, favoring high-frequency formats like blogs or Substack. However, he believes “truly human” works—memoirs, biographies, and personal experience-based books—will persist, as readers crave authenticity over AI-generated content.

    Cowen also sees AI decentralizing into a “Republic of Science,” with models self-correcting and collaborating, though this remains speculative. For education, he integrates AI into his PhD classes, replacing textbooks with subscriptions to premium models. He warns academia lags in adapting, predicting AI will outstrip researchers in paper production within two years. Perell shares his use of AI for Bible study, praising its cross-referencing but noting experts still excel at pinpointing core insights.

    Practical tips emerge: use top-tier models (o1 Pro, Claude, DeepSeek), craft detailed prompts, and leverage AI for travel or data visualization. Cowen also plans an AI-written biography by “open-sourcing” his life via blog posts, showcasing AI’s potential to compile personal histories.

    Article Itself

    How AI is Revolutionizing Writing: Insights from Tyler Cowen and David Perell

    Artificial Intelligence is no longer a distant sci-fi dream—it’s a tool reshaping how we write, research, and think. In a recent YouTube conversation, economist Tyler Cowen and writer David Perell unpack the practical implications of AI for writers, offering a roadmap for navigating this seismic shift. Recorded on March 5, 2025, their discussion blends hands-on advice with bold predictions, grounded in Cowen’s daily AI use and Perell’s curiosity about its creative potential.

    Cowen, a prolific author and podcaster, doesn’t just theorize about AI—he lives it. He’s swapped towering stacks of secondary literature for LLMs like o1 Pro, Claude, and DeepSeek. Preparing for a podcast on medieval kings Richard II and Henry V, he once ordered 20-30 books; now, he interrogates AI for context, cutting prep time and boosting quality. “It’s more fun,” he says, describing how he queries AI about Shakespearean puzzles or Wuthering Heights chapters, treating it as a conversational guide. Hallucinations? Not a dealbreaker—top models have slashed errors dramatically since 2024, and as an interviewer, he prioritizes context over perfect accuracy.

    For writing, Cowen draws a line: AI informs, but doesn’t draft. His voice—cryptic, layered, parable-like—remains his own. “I don’t want the AI messing with that,” he insists, rejecting its smoothing tendencies. Yet he’s not above using it tactically—checking legal backgrounds for columns or flagging obnoxious tones in drafts (a tip from Agnes Callard). Perell nods, noting AI’s knack for softening managerial critiques, though Cowen prefers his weirdness intact.

    The future of writing, Cowen predicts, is bifurcated. Books, with their slow cycles, face obsolescence—why write a four-year predictive tome when AI evolves monthly? He’s shifted to “ultra high-frequency” outputs like blogs and Substack, tackling AI’s rapid pace. Yet “truly human” writing—memoirs, biographies, personal narratives—will endure. Readers, he bets, want authenticity over AI’s polished slop. His next book, Mentors, leans into this, drawing on lived experience AI can’t replicate.

    Perell, an up-and-coming writer, feels the tension. AI’s prowess deflates his hard-earned skills, yet he’s excited to master it. He uses it to study the Bible, marveling at its cross-referencing, though it lacks the human knack for distilling core truths. Both agree: AI’s edge lies in specifics—detailed prompts yield gold, vague ones yield “mid” mush. Cowen’s tip? Imagine prompting an alien, not a human—literal, clear, context-rich.

    Educationally, Cowen’s ahead of the curve. His PhD students ditch textbooks for AI subscriptions, weaving it into papers to maximize quality. He laments academia’s inertia—AI could outpace researchers in two years, yet few adapt. Perell’s takeaway? Use the best models. “You’re hopeless without o1 Pro,” Cowen warns, highlighting the gap between free and cutting-edge tools.

    Beyond writing, AI’s horizon dazzles. Cowen envisions a decentralized “Republic of Science,” where models self-correct and collaborate, mirroring human progress. Large context windows (Gemini’s 2 million tokens, soon 10-20 million) will decode regulatory codes and historical archives, birthing jobs in data conversion. Inside companies, he suspects AI firms lead secretly, turbocharging their own models.

    Practically, Cowen’s stack—o1 Pro for queries, Claude for thoughtful prose, DeepSeek for wild creativity, Perplexity for citations—offers a playbook. He even plans an AI-crafted biography, “open-sourcing” his life via blog posts about childhood in Fall River or his dog, Spinosa. It’s low-cost immortality, a nod to AI’s archival power.

    For writers, the message is clear: adapt or fade. AI won’t just change writing—it’ll redefine what it means to create. Human quirks, stories, and secrets will shine amid the deluge of AI content. As Cowen puts it, “The truly human books will stand out all the more.” The revolution’s here—time to wield it.