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  • Paul Graham and Jessica Livingston on Resilience at Y Combinator: Founder Mode, Cockroaches, Sticking to Your North Star, and Why AI and Climate Keep Them Up at Night

    For the very first episode of Disaster Proof, the conversation goes to a garage in Palo Alto to sit down with Paul Graham and Jessica Livingston, the founders of Y Combinator. They have backed thousands of companies, including many now working in the resilience space, and the discussion covers what makes startups durable, why adaptability beats expertise, how Brian Chesky stumbled into founder mode at Airbnb, why the best ideas grow out of a founder’s own life, and the two specific risks (AI and climate change) that Paul says are the only ones he treats as genuinely game over. You can watch the full conversation on YouTube here.

    TLDW

    Paul Graham and Jessica Livingston explain why constant change favors young, flexible founders, and why Y Combinator picks people over ideas precisely so its judgment never goes obsolete. They unpack adaptability as the trait they hunt for in interviews, the “founder mode” story behind Brian Chesky steering Airbnb through COVID, and the 2008 strategy of funding tough, close-to-revenue “cockroaches.” Paul argues a company survives turbulence by sticking to a North Star instead of acting as a weather vane in shifting moral fashions, using the biosphere tree that collapses without wind as his metaphor for resilience. They turn to climate and energy as the next great market, the difficulty of selling into utilities, the Gridware success story, fusion no longer being thirty years away, and the trap of guilt-based business models versus the reliable assumption that users are selfish, greedy, and lazy. The personal-resilience half covers surviving Twitter mobs, Paul’s obsessive essay process, raising kids by indulging curiosity and picking your battles, prepping by living among reasonable people, political polarization, and why AI and climate are the two things that keep them up at night.

    Thoughts

    The most useful idea in this conversation is also the most counterintuitive: a world that feels like it is ending is structurally good for the people least invested in how it used to work. Paul’s point to terrified founders is that change is only a threat if you have sunk costs in the old order. A young founder has been doing the current plan for two weeks, so a step-function shift in the landscape costs them almost nothing to abandon. The incumbents with elaborate machinery and a decade of assumptions are the ones who should be afraid. That reframes resilience away from defense and toward optionality. The resilient party is not the one with the thickest walls, it is the one with the least to unlearn.

    The founder mode discussion is worth sitting with because it quietly overturns a generation of management orthodoxy. The old rule was that a good CEO hires executives and gets out of their way, and that getting into the details is micromanaging. Brian Chesky’s COVID experience at Airbnb broke that rule under maximum pressure. With bankruptcy on the table and a travel company facing a world that stopped traveling, he went line by line through the business and told people what good looked like, then gave them freedom to execute against that standard while still demanding visibility. The interesting nuance is the permission structure. A crisis granted Chesky the license to be involved that normal operating conditions would have framed as meddling. The lesson is not “always be in the weeds,” it is that the founder’s deep understanding and disproportionate caring are assets you are wasting if you reflexively delegate them away.

    Paul’s North Star argument is the part most likely to age well. His claim is that companies fail at resilience when they behave like weather vanes, swinging with each gust of public moral fashion. He pairs it with the biosphere tree that grows weak and topples because it was never exposed to wind. Both metaphors point at the same thing: resilience is built by surviving stress while holding your shape, not by avoiding stress and not by reshaping yourself to whatever the crowd currently rewards. The carbon-credit companies he mentions are the cautionary case. They built their entire premise on a fashion (customer guilt about carbon) and went out of business when the wind changed direction. Durable businesses convert a permanent human motive into value, which is why he prefers the brutally honest assumption that the user is selfish, greedy, and lazy, and that your job is to build something that produces good outcomes anyway.

    The climate and energy section reframes a worthy cause as a market-timing bet rather than a moral appeal, and that is the more powerful version. The comparison to fintech in 2008 is the tell. Banking technology was a sleepy, unglamorous sector that venture investors avoided until a crisis cracked it open and made it one of the best categories of the following decade. The argument is that energy and the physical world are sitting at a similar precipice, made newly viable because hardware is starting to behave more like software (order components, assemble, do not build everything from scratch) and because AI’s hunger for power has made energy the binding constraint on the whole industry. The Gridware story crystallizes the founder lesson underneath all of it. The best founder for a hard physical problem was a lineman who worked the electric lines and lived through the fires. The idea grew authentically out of his life, which is the same pattern Jessica keeps returning to and the same advice they give for raising kids.

    Finally, the personal-resilience material is more practical than it first appears. Paul’s method for surviving a Twitter mob is pattern recognition: once it has happened twenty times, you know it ends in two days and they move on to the next target, so you wait it out instead of capitulating. His essay process is the same conviction-building engine applied to ideas. He goes sentence by sentence until there is no false statement left to attack, which is why his challenge to angry readers (“point out the incorrect statement”) almost never gets answered. The throughline across the company advice, the parenting advice, and the personal advice is identical. You build durable conviction not by sitting in a room thinking, but by working the problem until it is right, then refusing to be blown off course by people who never actually engaged with the substance.

    Key Takeaways

    • Experts are frequently wrong because they are experts in a previous version of the world, so Paul deliberately avoids permanent beliefs about the current state of technology.
    • Y Combinator picks startups by picking founders, not ideas, because the founders know more about the ideas than the investors do.
    • Living in England and visiting for each batch lets Paul arrive every quarter expecting the world to be different, which keeps his mind open instead of anchored.
    • A world of constant change feels bad but is actually good for a young, flexible founder who has only been on the current plan for two weeks and can switch easily.
    • Vibe coding went from kind-of-works to reliably works, and even experienced programmers now generate huge volumes of code with AI.
    • There is still a software business even with AI, because someone has to know what to tell the AI to write, and no company is going to write its own database from scratch.
    • The scenario Paul worries about is model companies spinning up agents to start all the startups themselves, removing the need for human founders.
    • The founder traits Jessica looks for are unchanged over the years: determined, flexible-minded, and willing to adapt.
    • In interviews you can spot rigid founders because they answer the question they prepared rather than the one they were asked, and the gears visibly grind when you redirect them.
    • A good adaptability signal is a founder who says “I haven’t thought about that, but here is how I would think about it” instead of freezing.
    • Founder mode, the term, came from Brian Chesky’s experience steering Airbnb through COVID, when bankruptcy was openly discussed in board meetings.
    • Ken Chenault, the former American Express CEO on Airbnb’s board, told Chesky the moment was ten times worse than 9/11 and could define the company.
    • Founder mode meant Chesky understood every line item, told people what good looked like, then gave them freedom to execute while still wanting to see it.
    • Founders see through the fog because they understand the company better than anyone and they care more than anyone, and combining understanding with caring lets them see more.
    • There is always some disaster at Y Combinator, the way a hospital always has someone coding, so a crisis is the normal operating environment, not an exception.
    • During the 2008 crash, YC kept funding because it is always a good time to start a startup, but focused on people close to making money and very tough founders they called cockroaches.
    • Airbnb was the ultimate cockroach, seemingly indestructible, which is exactly why they liked it during the meltdown.
    • YC rests on two axioms: startups matter, and founders are the most important ingredient in startups. As long as those hold, YC has room to exist.
    • Company values are usually written down a few years in, documenting principles that already existed rather than inventing new ones.
    • You cannot move with fashion; you have to stick to your North Star, especially during turbulent, noisy times.
    • Trees grown inside a biosphere fell over because they were never exposed to wind, so being blown around is a necessary part of becoming strong enough to stand.
    • What preserves YC most is that it is a fundamentally good idea: it gives lonely founders money, the right peers, and colleagues they would never otherwise have.
    • The measure of a good startup idea is revenue, and any other metric you care about matters only because it predicts revenue.
    • At the early stage you can afford to be virtuous and even tell founders to go back to college, because the power law means one startup in the batch will carry the returns.
    • Every startup has to find early adopters, who decide quickly, usually do not have much money, and tend to be sophisticated, which means utilities are rarely your first customer.
    • A company that ultimately sells to utilities should start by selling to something that says yes faster, like running a pilot on a single corporate campus.
    • Utilities are under so much stress from wildfire liability, renewables, EV charging, and AI demand that they are unusually willing to try new things out of necessity.
    • Gridware, founded by a former lineman who lived through major fires, is now backed by Sequoia with PG&E as a huge customer, an example of an idea growing out of the founder’s life.
    • The second-biggest chunk of YC startups after AI is hard tech and physical products, not because software is dead but because building physical things is getting more possible.
    • Energy is one of AI’s fundamental constraints; if Sam Altman could have two things for Christmas, they would be energy and GPUs.
    • Nobody says fusion is thirty years away anymore, and the old thirty-year number existed because it was far enough out to avoid demands for results but close enough to keep attention.
    • Energy and physical markets may be where fintech was in 2008, a sleepy sector about to be cracked open by crisis into a great decade.
    • Guilt is a fragile business model because fashions change what people feel guilty about, which is why carbon-credit companies collapsed when the winds shifted.
    • Assume the user is selfish, greedy, and lazy, then build something that causes good things to happen anyway, like clean power that is simply cheaper and more reliable.
    • To survive Twitter mobs, remember they move on in about two days, half are bots or people you would never talk to in real life, and you cannot become a weather vane for moral fashions.
    • You build conviction by working on and developing an idea, not by sitting in a room thinking, unless it is pure thought like math.
    • Paul writes essays sentence by sentence until nothing in them is false, which is why his challenge to point out an incorrect statement almost never gets answered.
    • The best startup ideas, and the best projects in life generally, grow authentically out of the founder’s own interests and experiences.
    • Their parenting philosophy is to give kids confidence and a stable base, indulge their curiosity, and encourage projects nobody told them to do.
    • You pick your battles with kids: put your foot down on cruelty, but accept defeat on things like food and screen time.
    • A useful interview question for anyone with an unusual experience is not “what was it like” but “how was it different than you expected,” which surfaces the genuinely novel detail.
    • In a time of turbulence, bet on an island full of reasonable people; the English may not be very dynamic, but they are reasonable.
    • The hope on political polarization is to build resilient institutions that act as a cage around any single leader, so that throwing the rattle makes no difference.
    • AI and climate change are the two things Paul worries about most because they are both potentially game over, like the Gulf Stream reversing and turning Europe into a frozen wasteland.

    Detailed Summary

    Staying an expert when the world keeps changing

    The conversation opens on Paul Graham’s essay “How to Be an Expert in a Changing World,” whose core point is that experts are often wrong because they are experts in a previous version of the world. Asked how he keeps his own beliefs from going obsolete when the landscape can shift in ninety days, Paul says he focuses on people. YC picks founders rather than ideas because the founders know the ideas better than any investor could. He deliberately holds no permanent beliefs about the current state of technology, and the rhythm of flying in from England for each batch helps: he arrives every quarter already expecting everything to be different. One quarter the story is everyone training open-source models, the next quarter it is Claude code and nobody bothers with open-source models because the frontier versions are better anyway. He comes in with a completely open mind. Jessica and Paul note that today’s founders are more frightened, asking what is even still true, but the message Paul gives them is that constant change favors the young and flexible. If you have only been executing a plan for two weeks, a disruption costs you nothing; you just switch.

    What adaptability looks like in a founder

    Jessica describes the founders she funds as determined, flexible-minded, and willing to adapt, and calls adaptability a key trait always, but especially in uncertain times. In interviews, the rigid applicants reveal themselves by answering the question they planned to answer rather than the one they were asked, and you can almost hear the gears grind when you redirect them. Paul does not let that slide; if they dodge, he just asks again. The positive signal is a founder who, faced with a question they have not considered, says “here is how I would think about it” and reasons live. Both point out that YC itself had to adapt, and that the company they funded the interviewer’s startup as in 2009 looked very different by the end. They funded him in May 2009, in the thick of the financial crisis, after he had quit his job in August 2008 and briefly felt he had made a terrible mistake.

    Founder mode and seeing through the fog

    Paul points to Brian Chesky as the defining example of weathering disaster, a story he explored on This Week in Startups. When COVID hit a travel company like Airbnb, the word bankruptcy was being used in board meetings, and Ken Chenault, the former American Express CEO on the board, warned it was ten times worse than 9/11. Chesky went into what would later be named founder mode, getting into every line item, understanding exactly what was needed, telling people what good looked like, and then giving them freedom to execute while still insisting on visibility. The crisis gave him permission to be the involved CEO he had always wanted to be, the kind of involvement that normal operating conditions would have labeled micromanaging. Paul argues founders see through fog that blinds everyone else for a simple, rational reason: they understand the company better than anyone because they have been there longest and thought of most of it, and they also care more than anyone. Combine deep understanding with deep caring and of course they see more.

    Cockroaches, the North Star, and the biosphere tree

    Returning to 2008, when YC was self-funded and unsure whether anyone would invest by March, they decided to keep going on the principle that it is always a good time to start a startup, but to fund people close to making money and very tough founders they called cockroaches, after the creatures that survive nuclear war. Airbnb was the ultimate cockroach. Paul frames YC’s longevity around two axioms (startups matter, founders are the most important ingredient) and around resilience built through stress. He tells the story of trees grown inside a biosphere that fell over because they were never exposed to wind, since being blown about is a necessary part of a tree becoming strong enough to support its own weight. YC has been blown around and is still standing, which is exactly what gave it practice. The companion idea is the North Star: you cannot move with fashion or act as a weather vane swinging with other people’s moral fashions, you have to hold your founding principles, which Paul eventually wrote down rather than let a 23-year-old new hire do it.

    Climate, energy, and selling into hard markets

    The interviewer’s own path (a curiosity about wildfire that grew from living in California, watching PG&E go bankrupt, a fire on his Mendocino property, volunteering as a firefighter) becomes the case for ideas that grow authentically out of a founder’s life. Climate is framed broadly as energy, the built environment, and transportation, essentially the physical world, and those are hard markets where the buyers are utilities, governments, real estate, and insurance. The advice is to find early adopters who decide quickly, which usually means not starting with a utility but with something like a single corporate campus that will say yes faster. Utilities, though, are under so much stress from wildfire liability, renewables, EV charging, and AI demand that they are increasingly willing to try new things. Gridware, founded by a former lineman who lived through major fires, is the proof point: backed by Sequoia, with PG&E as a major customer. Paul notes the second-biggest chunk of YC startups after AI is hard tech, not because software died but because building physical things is getting more possible, more like ordering and assembling components. Energy is the binding constraint on AI, fusion no longer feels thirty years away, and the bet is that energy and physical markets are where fintech was in 2008, about to be cracked open.

    Guilt versus greed as a business model

    On the question of whether climate companies should sell on guilt (recycle, pay more because it is sustainable), Paul is blunt that guilt is fragile because fashions change what you are supposed to feel guilty about. The carbon-credit companies thrived until buying carbon credits stopped being cool, then went out of business. A founder’s own concern for the world can drive great companies, but depending on a customer’s guilt is shallow. The durable move is to assume the user is selfish, greedy, and lazy, someone who just wants to eat pizza and watch Netflix, and to build something that produces good outcomes despite that. Clean power is the perfect example: nobody watching Netflix is upset that fusion powers their television, and if it is cheaper and more reliable, that is simply more Netflix and more money for pizza.

    Personal resilience, Twitter mobs, and the essay process

    On surviving public criticism, Paul’s method is pattern recognition: after twenty mobs you stop counting and know it will be over in two days when they move to the next topic, so you wait it out even though it genuinely feels miserable. Half of them are bots or people you would never talk to in real life, but the deeper point is that companies and people stay resilient by not succumbing to mobs and not becoming weather vanes for moral fashions. Conviction is built by working on an idea, not sitting in a room thinking about it, unless it is pure thought like math. His essays are the engine: he writes a version one, notices everything wrong, and fixes it sentence by sentence until there is no false statement left. He will read an entire book for a single sentence because he would be mortified to publish something false and, having no deadlines, has no excuse. That is why his standing challenge to angry readers, to point out one incorrect statement, almost never gets answered.

    Raising kids, prepping, and the things that keep them up at night

    Their parenting philosophy is to give kids confidence and a stable base, indulge curiosity, and encourage projects nobody assigned, like the living room overrun by one son’s Lego. They pick their battles: they put their foot down on cruelty but admit total defeat on food, devices, and screen time. Paul’s favorite question for anyone with an unusual experience is not “what was it like” but “how was it different than you expected,” which surfaces the genuinely novel detail, and the meta-version of that became the show’s recurring question to all guests. On prepping, they joke that living in the English countryside is itself a form of preparation, and that in turbulent times you should bet on an island full of reasonable people. The episode closes on what keeps them up at night: AI and climate change, the two things Paul treats as uniquely game over, illustrated by the prospect of the Gulf Stream reversing and leaving Europe, which sits as far north as Alaska, a frozen wasteland. Jessica notes her YC superhero name was Panic, and the conversation ends, after a detour through political polarization and a child who insisted for six months on being called SR-71 forecast 80 leaping leopard, on the admission that they manage screen time by being utterly defeated.

    Notable Quotes

    “If you’re a startup founder, a world where things are constantly changing is actually good for you. It feels bad, but you’re better off than anybody else.”

    Paul Graham, on why turbulence favors young, flexible founders

    “You can’t move with fashion. You have to stick to your North Star.”

    Paul Graham, on holding founding principles during noisy, turbulent times

    “There’s always some kind of disaster. It’s almost a rule of thumb at Y Combinator that there’s always some disaster going on, just like in a hospital. There’s always somebody who’s coding.”

    Paul Graham, on crisis as the normal operating environment for startups

    “The measure of a good startup idea is revenue, sure. Let’s not pretend companies are supposed to do something else.”

    Paul Graham, on how to judge whether an idea is actually good

    “Assume that the user is selfish and lazy, and make something. Selfish, greedy, and lazy. And make something that causes good things to happen despite that.”

    Paul Graham, on why guilt is a weak business model and greed is a source of energy

    “This is where the best startup ideas come from. They grow authentically out of the founders’ lives.”

    Jessica Livingston, on a wildfire curiosity turning into a company

    “Please point out the incorrect statement I’ve made in this essay. And no one ever does that.”

    Paul Graham, on writing essays sentence by sentence until nothing in them is false

    “AI and climate change have something in common. They’re the two big things I worry about the most, because they’re both game overs.”

    Paul Graham, on what keeps him up at night

    This is the first episode of Disaster Proof, a series exploring the people and technologies building resilience in an increasingly volatile world. You can watch the full conversation with Paul Graham and Jessica Livingston on YouTube here.

    Related Reading

  • 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.

  • Benedict Evans on Why AI Is Stuck in 1997: The Task vs the Job, Commodity Models, and Why the Jobs Apocalypse Is Overhyped

    Benedict Evans, the former Andreessen Horowitz partner and independent analyst behind the annual “AI Eating the World” presentation, sat down with Lenny’s Podcast for what the host calls the most rational take on AI you will hear this year. Instead of either doom or hype, Evans argues that AI is as big a deal as the internet or mobile, and only as big a deal as the internet or mobile, which means we are living through something closer to 1997 than to the singularity. The conversation moves through the jobs question, the difference between a task and a job, whether the model labs have any pricing power, the anti-AI backlash, and what people should actually do. You can watch the full conversation on YouTube here.

    TLDW

    Evans frames AI as a platform shift on the scale of the internet or mobile, with the crucial twist that almost nothing has been built yet, so we are in the 1997 moment where confident predictions about winners are usually wrong. He introduces his central tool, the distinction between the task and the job, to explain why “X percent of this profession is exposed to AI” studies are misleading, why the AI labs are paradoxically hiring forward deployed engineers and buying consultancies, and why accountants kept multiplying through every wave of automation (the lump of labour fallacy and Jevons paradox at work). On value capture he makes a deterministic bet that foundation models have no network effects, behave like a commodity, and will look more like cloud than like Windows, with the value moving up the stack to applications, much as it did in telecom, where a trillion-dollar industry grew data traffic thousands of times over while its stocks went nowhere. He covers distribution as the real moat, Apple Intelligence as the most compelling unshipped vision, the fuzzy anti-AI backlash (including the largely fake water panic and the very real harms of deepfakes), raising kids under radical uncertainty, and closes with the disarming admission that his own synthesis-heavy job is exactly the kind AI is currently worst at. His advice: presume radical uncertainty, dive in rather than sneer, and assume it will probably be okay.

    Thoughts

    The most useful thing in this conversation is a single question Evans keeps returning to: what is the task, and what is the job? A spreadsheet automated the arithmetic an accountant does, and the number of accountants went up for the next forty years. Claude Code can write the code, but deciding what to build, for whom, and why is the part nobody has automated. The reason the “this profession is X percent exposed to AI” studies feel hollow is that they assume a job is a neat stack of separable tasks. Evans argues, by analogy to the old expert-systems failure, that you simply cannot decompose a senior lawyer’s work that way. The 75-slide deck is the task. Walking your company, reading its politics, talking to your customers, and telling you the uncomfortable truth is the job, and that is what you actually paid McKinsey for.

    The boldest and most falsifiable claim is that the foundation-model companies look more like cloud than like Windows. No network effects means no winner-take-all, which means durable competition, which means commodity pricing and compressed margins, with the real value accruing up the stack in applications that nobody at the labs is going to build. His telecom analogy is the one to sit with. A trillion-dollar industry grew mobile data traffic by 1,500 to 2,000 times in fifteen years, and the stocks went nowhere for a quarter century, because it was a low-margin utility while all the interesting value moved to Apple and the people building apps on top. If he is right, the current token-burn economics, the person reportedly spending 1.5 million dollars a month on tokens, are the 2010 equivalent of a 50,000 dollar roaming bill, not the steady state. Evans flags openly that he could be completely wrong, which is the intellectually honest part and the part most forecasters skip.

    “It depends” and “it will probably be okay” sound like evasions, and Evans leans into that. But the 1997 framing is doing real work. The point is not that AI is small, it is that the things that will end up mattering have not been built, and that anyone confidently naming the winners today is repeating the 1997 mistake of betting on Excite over a search company with a weird logo. The discipline he is selling is to presume radical uncertainty and act anyway, because the alternative, declaring the whole thing slop and shouting about it online, buys a great feeling of moral superiority and nothing else. His repeated insistence that you can see the job that goes away but never the new job, because it does not exist yet, is the load-bearing idea under his optimism.

    The most disarming moment is the closing AI-corner answer, where the person whose entire brand is explaining AI admits he struggles to use it. His work is synthesis and precise information retrieval, and precise retrieval happens to be exactly what today’s models are worst at. He is, in his own words, the lawyer looking at VisiCalc: it is obviously transformative, and he just does not happen to make spreadsheets all day. That admission is worth more than any benchmark, because it locates the real variable. How much AI changes your life depends less on how good the model gets and more on whether your daily work sits on the part of the jagged frontier where it already works. That is a far more practical lens than arguing about whether AGI arrives in three years or thirty.

    Key Takeaways

    • Evans’s headline opinion is that AI is as big a deal as the internet or mobile, and only as big a deal as the internet or mobile. Both halves of that sentence matter.
    • If you make the internet comparison honestly, we are roughly in 1997: very exciting, most of it does not work yet, most of what people will build has not been built, and it is unclear how any of it will end up working.
    • Adoption is spread across a very wide distribution. Even among teenagers, only something like 15 to 20 percent are daily active users and another 20 percent weekly, with the majority saying they do not use it at all.
    • That spread maps onto the “jagged frontier” question of where AI works, where it does not, whether you can predict where it will work in advance, and whether you can even tell after the fact.
    • Software developers are the accountants seeing VisiCalc: for them everything has already changed. Most other professions are watching, intrigued but unsure what to do with it.
    • The AI labs are investing heavily in forward deployed engineers, consultancies, and professional services. Evans jokes that a forward deployed engineer is an Accenture outsourced developer who lives in San Francisco.
    • Companies do not have spare people sitting around to reimagine every internal workflow, so reinventing a business around AI is itself a project that needs consultants, which is why the most cutting-edge labs are funding exactly the firms everyone assumed AI would kill.
    • The central framework: separate the task from the job. Sometimes the task is the job (the elevator operator pressing a lever), and automating the task ends the job. Far more often, the task is only part of the job.
    • Amazon gets you the SKU once you know which SKU you want. Knowing which one to buy is a different job. Claude Code writes the code, but knowing what code and what features to build is the job.
    • A McKinsey or Bain engagement is not really about the deck. The deck is the task. The job is walking your enterprise, understanding the politics, talking to your customers, and telling you the truth.
    • The Jevons paradox is just price elasticity applied to labour. Make something cheaper to produce and you usually do far more of it, not the same amount with fewer people.
    • Excel did not give investment bankers shorter hours. iPhone SDKs did not shrink the number of engineers even though Apple writes 90 percent of the code for you. The number of accountants rose through every wave of automation.
    • The lump of labour fallacy: since 1800, each technology automates jobs and unlocks new ones. You can always see the job that disappears and never the new job, because it does not exist yet.
    • Evans is wary of argument from authority on jobs. He wants Dario Amodei’s view on where models go in the next 6 to 12 months, not necessarily his theory of labour markets and comparative advantage.
    • The doomer scenario of every company buying ChatGPT and firing everyone in two weeks misunderstands how enterprises work. Enterprise sales cycles run 18 months or more. Nobody is ripping out SAP overnight. The full transformation takes 3 to 10 years, sector by sector.
    • AGI and superintelligence are being quietly redefined to mean whatever works now. Larry Tesler’s theorem: AI is whatever machines cannot do yet, because once they can, people call it just software.
    • We have no theory of human intelligence, no theory of why these models work, and no theory of how much better they will get, so everyone is vibes-forecasting. Even if progress stopped tomorrow, what exists is already transformative and will roll out for a decade.
    • On value capture, Evans argues models show no network effects, so no single one runs away with the market. Persistent competition plus little real product differentiation means little pricing power.
    • Sam Altman’s pitch of selling intelligence on a meter like electricity ignores the brutal margin structure of utilities. Your TV maker does not pay the power company a cut of your bill.
    • The telecom analogy: a roughly trillion-dollar mobile industry spends 15 to 20 percent of revenue on capex, grew data consumption 1,500 to 2,000 times since 2010, and its stocks went nowhere for 25 years because it is a low-margin commodity utility.
    • The elemental question: does the model do the whole thing, or does it need thousands of different apps built by different people? If it needs apps, the labs cannot build them all, just as Microsoft did not, so it looks more like AWS than like Windows.
    • If the product is a commodity, distribution becomes the moat. Google pushes Gemini through its surfaces, Meta sprayed AI across its apps and quietly ranked between ChatGPT and Gemini in usage, and incumbents with distribution have a structural edge.
    • Browsers are the warning: Microsoft used distribution to win the browser war, then it turned out winning browsers did not matter because the value was further up the stack.
    • Apple Intelligence, as shown at WWDC 2024, was the most compelling vision of a personal AI assistant Evans has seen. Apple could not ship it, but neither could anyone else, because tool-using on-device agents with no hallucinations across thousands of apps is genuinely hard.
    • The model is “the dumb thing underneath” that powers a feature. The same commodity model can sit beneath both Gemini on Android and Apple Intelligence on iOS while the products and distribution differ entirely.
    • The anti-AI backlash is a big fuzzy mess. Some is real (local electricity bills, deepfakes, real job anxiety), some is sort of true, and some is simply false.
    • The data-center water panic is largely fake. A Livermore lab study put US data-center water consumption at about 0.017 percent of US water use. Local well conflicts are planning problems, not data-center problems.
    • We have shockingly little hard data. The model labs do not publish meaningful usage numbers. There is no public daily active user figure for ChatGPT, so economists are reverse-engineering effects from government surveys.
    • Real new harms do appear with each wave. A teenager could not use Photoshop to make explicit fakes of every classmate and send them to the whole school in an afternoon. Now they can, and turn them into video.
    • The UK Post Office Horizon scandal (buggy Fujitsu software wrongly showing cash shortfalls, leading to prosecutions, bankruptcies, and suicides) is a reminder that every technology brings new ways to ruin lives, by malice or by accident.
    • You cannot reliably predict what gets exposed. In 1997 people thought taxis were safe from the internet and newspapers would be fine. The opposite happened. Today, “AI-proof” jobs like personal trainer may not be as safe as they look.
    • Uber and Airbnb show that similar-sounding companies can have very different market impact. Uber demolished and then grew the taxi market, while Airbnb’s effect on hotels was fairly marginal because business travel still wants a hotel.
    • Every new technology first lets you do the old thing but more, then unlocks things that were not possible before. Recorded music revenue is U-shaped: first “what if I do not pay 15 dollars for a CD,” then “what if 15 dollars a month gives me all the music there is.” Spotify is not an online music store, it is something else.
    • Coding was supposed to be one of the last things automated, and instead it is the most transformed role of all, which is itself a lesson in how badly we predict exposure.
    • Practical advice: do not stick your head in the sand. Dive in, submerge yourself, and come out understanding what you can do with it. Going into a shrinking job market announcing you will never use AI is not the right posture.
    • Evans’s honest coda: he struggles to find AI use cases because his job is synthesis and precise retrieval, the things models are worst at. He uses it for proofreading, images, redecorating his apartment, and dictation. He is the lawyer looking at VisiCalc.

    Detailed Summary

    AI is as big as the internet, and we are living in 1997

    Evans opens with the opinion he calls his most controversial: AI is as big a deal as the internet or mobile, and only as big a deal as the internet or mobile. To some in tech that sounds dismissive, as if he is underrating a once-in-history event. His reply is that smartphones and the internet were themselves enormous, and we are talking over the internet right now. The deeper point is the comparison’s timing. If this is like the internet, then it is like the internet in 1997: thrilling, but most of it does not work yet, most of what will be built has not been built, and nobody knows how the pieces will fit. His latest 80-slide presentation, he jokes, is essentially 80 ways of saying “we do not know,” which is partly facetious and partly the entire point.

    The jagged frontier and the wide spread of adoption

    Adoption is not uniform, it is a wide distribution. Some people in tech have bought clusters of Mac minis and stopped using Google, while most people outside tech who use AI at all touch it once every week or two. Even among 13 to 18 year olds, daily active use sits around 15 to 20 percent, weekly use adds another 20 percent, and roughly 60 percent say they do not use it. That spread maps onto what Evans calls the jagged frontier: whether a given task works, whether you can predict in advance that it will work, whether it is intuitive, and whether you can even tell after the fact. Software developers are the accountants who just saw VisiCalc, living in a clear before-and-after. Everyone else is somewhere on the curve, picking it up to varying degrees and a little puzzled about what it is for.

    Why the AI labs are buying consultancies

    One of the most counterintuitive trends is that the leading labs are pouring money into forward deployed engineers and professional services, the very category many assumed AI would erase. Evans’s explanation is grounded in how companies actually operate. Firms do not keep spare people sitting around to redesign stores, hunt down churn, or rebuild a tech stack, which is exactly why they hire Bain, BCG, McKinsey, Accenture, or Infosys when a big project appears. Reimagining every internal workflow around AI, then actually plugging vertical and horizontal systems together and retraining people, is itself a multi-month project requiring people you do not have. So the work gets outsourced, and the most advanced labs are funding the firms that do it. His joke lands the point: a forward deployed engineer is a statistician, or an Accenture developer, who happens to work in San Francisco.

    The task versus the job

    This is the spine of the conversation. Ask what the hard part of a job really is. Sometimes the task is the job: the elevator attendant’s whole job was driving the car, the task got automated, the job ended. Much more often the visible task is only a slice. Amazon gets you the SKU once you know which SKU you want, but knowing what to buy is a separate job. Claude Code writes the code, but deciding what to build, for whom, and how to take it to market is the job. A consulting deck is the task, while the reason you pay Bain is for them to walk your company, understand its politics, talk to your customers, and tell you the truth. Evans notes you can already generate a bad McKinsey deck with AI, and the LinkedIn grifters who do are missing that the deck was never the thing you were buying.

    Jevons paradox and the lump of labour fallacy

    The Jevons paradox is just price elasticity applied to labour: make something cheaper to do and you usually do much more of it. Excel did not hand junior bankers their Friday afternoons off, it expanded the work. iPhone developers write a fraction of the raw code because Apple wrote the drivers and file system, and there are not a tenth as many engineers, there are far more. The count of accountants climbed through adding machines, punch cards, mainframes, databases, ERP, spreadsheets, and cloud. The lump of labour fallacy is the broader version: since 1800 every technology has removed jobs and unlocked new ones, the removed jobs usually look bad in hindsight, the new ones tend to be better, and GDP keeps rising. You can always see the job that disappears and never the one that does not exist yet.

    The jobs question, Dario, and the enterprise sales cycle

    On the coming jobs apocalypse, Evans is cautious about argument from authority. Running an AI lab makes Dario Amodei worth listening to on where models go in the next 6 to 12 months, not necessarily on labour economics and comparative advantage. The doomer image of companies buying ChatGPT and firing everyone within weeks misreads reality: enterprise sales cycles run 18 months or longer, nobody is tearing out SAP overnight, and the full transformation will take 3 to 10 years, sector by sector, as people slowly work out what to do. He points to the lag in software itself. Many SaaS companies founded the day before ChatGPT launched could have been built a decade earlier, and were not, because the delay was someone realizing a problem existed and that this was the way to solve it.

    Redefining AGI and superintelligence

    Evans is skeptical of the moving terminology. He cites Larry Tesler’s line that AI is whatever machines cannot do yet, because the moment they can, people call it just software. Machine learning, image recognition, and sentiment analysis all got reclassified as not really AI once they worked, the same way jet airliners were once high technology and are now just planes. AGI is now often quietly redefined as doing some percentage of economically valuable work, which a 1975 mainframe also did, rather than anything about consciousness or a soul. Whether we reach human-level intelligence is, in his view, genuinely unknowable right now. The reassuring point is that you do not need to resolve it. Even if models hit a brick wall tomorrow, what already exists is transformative and will take a decade to deploy.

    Where the value accrues: commodity models and the telecom analogy

    Here Evans makes his most deterministic argument. Foundation models appear to lack network effects, so no single model runs away from the pack, competition persists, and product differentiation as users experience it is thin. Without differentiation or lock-in, where does pricing power come from? He skewers Sam Altman’s image of selling intelligence on a meter like electricity by pointing out that utilities have terrible margins and nobody pays the power company a cut of their TV. His telecom career supplies the analogy: mobile is a roughly trillion-dollar industry that spends 15 to 20 percent of revenue on capex, grew data traffic 1,500 to 2,000 times since 2010, and whose stocks went nowhere for 25 years because it is a low-margin commodity utility while the value sits up the stack with Apple and the app makers. If models are commodities and the real product is thousands of apps the labs will not build, the outcome looks like cloud, not like Windows.

    Distribution as the moat

    If the product is a commodity, distribution decides the winners. The web browser is the cautionary tale: the browser product is a thin wrapper around a rendering engine, tab browsing was the last real innovation 20-plus years ago, Microsoft used distribution to win, and then winning browsers turned out not to matter because the value was elsewhere. Now Google drives Gemini through its surfaces and Meta sprayed AI across its apps and, in survey data, sat between ChatGPT and Gemini in usage despite tech writing it off. An adequate product with great distribution and brand becomes a big deal, which is why OpenAI spent last year trying everything to build a flywheel before the giants defaulted everyone onto their own offering. The power of the default and sheer inertia do a lot of work.

    Apple Intelligence and the model as the dumb thing underneath

    Evans calls the Apple Intelligence segment of WWDC 2024 the most compelling vision of a personal AI assistant he has seen: tool-using, on-device, agentic, with no prompt injection or hallucinations across a standardized API spanning thousands of apps. Apple could not ship it, but neither could anyone else, because that is genuinely hard. The episode illustrates his framing that the model is “the dumb thing underneath” that powers a feature. The same commodity model can sit beneath Gemini intelligence on Android and Apple Intelligence on iOS, with different products, different distribution, and different decisions about what the feature should be. Apple has a billion edge-capable devices, while Google’s “coming soon to our most powerful devices” really means it will not work on most Android phones.

    The anti-AI backlash, water, and real harms

    The backlash, Evans says, is a big fuzzy mess of very different things. Some is tangible, like a higher local electricity bill in a small number of places. Some is essentially fake, like the water panic. He dug into a Livermore lab study putting US data-center water use at about 0.017 percent of national consumption. Local well conflicts are planning failures, not data-center failures. The jobs piece is genuinely unresolved, with charts pointing both ways and a youth employment slowdown that shows up regardless of degree or AI exposure. He stresses how little hard data exists, since the labs publish no meaningful usage numbers and there is no public daily active user figure for ChatGPT. He compares the moment to the social media backlash, compressed, where some fears were true, some half true, and some simply false. The real new harms are real, though: deepfakes let a teenager generate explicit fakes of an entire school in an afternoon, and the UK Post Office Horizon scandal shows how buggy software plus institutional denial can destroy lives.

    You cannot predict what gets exposed, and what to actually do

    Evans dismisses the O*NET-style exercise of scoring what percentage of each profession AI can do as deluded, the modern version of the expert-systems problem, where you try to describe a job as 700 logical steps and it never works. You cannot say a senior partner’s work is 17 percent automatable. The history of prediction is humbling: in 1997 people thought taxis were safe from the internet and newspapers would simply save on printing, and both were wrong. Coding, supposedly one of the last things to automate, became the most transformed role of all. Personal trainers might be next once your phone can watch your form. His closing advice is to presume radical uncertainty and act anyway: do not retreat into sneering moral superiority, dive in, internalize what the tools can do, and make yourself a great hire. He ends with a candid admission that his own synthesis-and-retrieval job is exactly what AI is currently worst at, so he is the lawyer looking at VisiCalc, sure it changes everything while not personally making spreadsheets all day.

    Notable Quotes

    “My most controversial opinion is that I think that AI is as big a deal as the internet or mobile, and only as big a deal as the internet or mobile.”

    Benedict Evans, stating the thesis that frames the whole conversation

    “If you’re going to make the internet comparison, it’s like we’re in 1997. It’s very exciting. Most stuff kind of doesn’t work yet. Most of the stuff that people are going to do hasn’t been built yet.”

    Benedict Evans, on why confident predictions about AI winners are usually wrong

    “You can’t look at a senior partner at a law firm and say, well, 17 percent of their work could be automated. This is horseshit.”

    Benedict Evans, on why O*NET-style job-exposure scoring fails

    “Claude Code can write you the code, but what code do you want? It can make you the features, sure, but what features do you want? Who’s your customer? What’s the right product for that customer?”

    Benedict Evans, drawing the line between the task and the job

    “There’s this quote from Sam Altman where he said we’re going to be selling AI intelligence on a meter like water or electricity, and you look at this and think, my dear sweet child, you need me to explain the margin structure of the utility industry to you.”

    Benedict Evans, on why model labs may lack pricing power

    “The model is just the dumb thing underneath that powers the feature. The model is the commodity that powers different decisions about what the feature should be.”

    Benedict Evans, on why value moves up the stack to applications

    “Every time we have a new technology it automates away a bunch of jobs, and then that automation unlocks a bunch of new jobs, and you don’t know the new job because it doesn’t exist yet.”

    Benedict Evans, on the lump of labour fallacy and 200 years of automation

    “Don’t stick your head in the sand and say I hate all of this stuff. That gives you a great feeling of moral superiority, but that’s not going to help. What helps is you diving into this and coming out understanding what you can do with it.”

    Benedict Evans, on what to actually do about AI right now

    “AI is good at stuff that computers are bad at, and bad at stuff that computers are good at.”

    Benedict Evans, quoting an observation that explains why he struggles to use AI in his own work

    This is a curated set of pulls, not a transcript. To hear the full argument in context, including the telecom and recorded-music charts and the lightning round, watch the full conversation on YouTube here.

    Related Reading

  • 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.

  • Jensen Huang at Stanford CS153 Frontier Systems on Co-Design, Agentic Computing, Vera Rubin, Open Models, and the Million-X Decade That Reshaped AI Infrastructure

    https://www.youtube.com/watch?v=tsQB0n0YV3k

    NVIDIA CEO Jensen Huang returned to Stanford for the CS153 Frontier Systems class (the room nicknamed itself “AI Coachella”) to lay out, in raw form, how he thinks about the computer being reinvented for the first time in over sixty years. Across roughly seventy minutes of student questions he walks through the codesign philosophy that gave NVIDIA a million-x decade, the architectural through-line from Hopper to Grace Blackwell to Vera Rubin to Feynman, the case for open source foundation models, the realities of tokens per watt and MFU, energy demand running a thousand times higher, the China and export-control debate, and his own biggest strategic mistakes. Watch the full conversation on YouTube.

    TLDW

    Huang argues every layer of computing has changed: the programming model, the system architecture, the deployment pattern, the economics. Co-design across CPUs, GPUs, networking, storage, switches and compilers gave NVIDIA roughly a million-x speed-up over ten years versus the ten-x Moore’s Law era, and that headroom is what let researchers say “just train on the whole internet.” Hopper was built for pre-training, Grace Blackwell NVLink72 for inference and reasoning (50x over Hopper in two years), Vera Rubin is built for agents that load long memory, call tools and need a low-latency single-threaded CPU bolted directly to the GPU, and Feynman extends that to swarms of agents that spawn sub-agents. Open weights matter because safety, sovereignty (230-plus languages no one else will fund) and domain models for biology, autonomy, robotics and climate need a foundation that NVIDIA is willing to seed. Compute is not really the scarce resource (Huang says place the order and the chips ship), the broken thing is institutional budgeting that can’t put a billion dollars into a shared university supercomputer. Energy demand is heading a thousand times higher and this is finally the moment market forces alone will fund sustainable generation. On geopolitics he rejects the GPUs-as-atomic-bombs framing and warns America will end up like its telecom industry if it cedes two thirds of the world. On career he advises seeking suffering on purpose. On strategy he says observe, reason from first principles, build a mental model, work backwards, minimize opportunity cost, maximize optionality.

    Key Takeaways

    • The computing model has been substantially unchanged since the IBM System 360, sixty-plus years ago. Huang’s first computer architecture book was the System 360 manual. AI is the first true reinvention.
    • Old computing was pre-recorded retrieval. New computing is generated, contextually aware and continuous. Cloud was on-demand. Agentic systems run continuously.
    • Codesign is NVIDIA’s central thesis. Inherited from the Hennessy and Patterson RISC era at Stanford, extended across CPUs, GPUs, networking, switches, storage, compilers and frameworks all optimized together.
    • The result of full-stack codesign: roughly 1,000,000x faster compute over ten years, versus a generous 10x to 100x for Moore’s Law in the same period. Dennard scaling effectively ended a decade ago.
    • That million-x speed-up is what unlocked “train on all of the internet” as a realistic AI strategy.
    • After GPT, Huang says it was obvious thinking was next. Reasoning is just generating tokens consumed internally, then using tools is generating tokens consumed externally. Agentic systems followed predictably.
    • Education needs AI baked into the curriculum, not just taught as a subject. Pre-recorded textbooks cannot keep pace with knowledge being generated in real time.
    • Huang says he cannot learn anymore without AI. He has the AI read the paper, then read every related paper, then become a dedicated researcher he can interrogate.
    • Mead and Conway and the first-principles methodology of semiconductor design are still worth learning even though most of the scaling tricks have been exhausted.
    • NVIDIA itself is one of the largest consumers of Anthropic and OpenAI tokens in the world. One hundred percent of NVIDIA engineers are now agentically supported. Huang recommends Claude and similar tools by name and says open-source downloads will not match the integrated product harness.
    • NVIDIA still invests heavily in open foundation models because language and intelligence represent the codification of human knowledge. Five pillars: Nemotron (language), BioNeMo (biology), Alphamayo (autonomous vehicles), Groot (humanoid robotics) and a climate science model (mesoscale multiphysics).
    • Sovereign language models matter. Roughly 230 world languages will never be a top priority for a commercial frontier lab. Nemotron is near-frontier and fully fine-tunable so any country can adapt it.
    • Safety and security require open weights. You cannot defend against or audit a black box. Transparent systems let researchers interrogate models and let defenders deploy swarms.
    • The future of cyber defense is not bigger-model-versus-bigger-model. It is trillions of cheap fast small models like Nemotron Nano surrounding the threat.
    • Domain models fuse language priors with world models. Alphamayo learned to drive safely on a few million miles instead of billions because it can reason like a human about the road.
    • MFU (Model Flops Utilization) is a misleading metric. Huang says he wants low MFU, because that means he over-provisioned every resource and never gets pinned by Amdahl’s law during a spike.
    • The xAI Memphis cluster running at 11 percent MFU is not necessarily a failure mode. In disaggregated prefill plus decode inference you can deliver very high tokens per watt with very low MFU.
    • The right metric is performance, ultimately tokens per watt as a proxy for intelligence per watt, and even that needs adjustment because not all tokens are equal. Coding tokens are worth more than other tokens.
    • Hopper was designed for pre-training. NVIDIA chose to build multi-billion-dollar systems when the largest existing scientific supercomputer cost $350 million, with no proven customer base. It worked.
    • Grace Blackwell NVLink72 was designed for inference, especially the high-memory-bandwidth decode phase. It is the world’s first rack-scale computer and delivered a 50x speed-up over Hopper in two years, against an expected 2x from Moore’s Law.
    • Vera Rubin is designed for agents. Long-term memory wired into storage and into the GPU fabric, working memory, heavy tool use, and Vera, a CPU optimized for low-latency multi-core single-threaded code so a multi-billion-dollar GPU system does not stall waiting on a slow tool call.
    • Feynman is being shaped for swarms of agents with sub-agents and sub-sub-agents, a recursive software topology that demands a new compute pattern.
    • Tokens per watt improved 50x in one generation. Compounding energy efficiency is the lever NVIDIA controls directly.
    • Total compute energy demand is heading roughly a thousand times higher than today, possibly two orders of magnitude beyond that. Huang says he would not be surprised if the estimate is low.
    • For the first time in history, market forces alone are enough to fund solar, nuclear and grid upgrades. Government subsidies are no longer required to make sustainable energy investment rational.
    • Copper interconnect is becoming a bottleneck. Photonics is moving from optional to structural inside racks and across them.
    • Comparing NVIDIA GPUs to atomic bombs, Huang says, is a stupid analogy. A billion people use NVIDIA GPUs. He advocates them to his family. He does not advocate atomic bombs to anyone.
    • If the United States cedes two thirds of the global market to competitors on policy grounds, the American technology industry will end up like American telecommunications, which was policied out of existence.
    • Huang directly rejects AI doom-by-singularity narratives. It is not true that we have no idea how these systems work. It is not true that the technology becomes infinitely powerful in a nanosecond. He calls the rhetoric irresponsible and harmful to the field students are about to enter.
    • On Stanford specifically: if the university president places an order, NVIDIA will deliver the chips. The bottleneck is that no university department has a billion-dollar compute budget because budgeting is fragmented across grants. Stanford’s $40 billion endowment is more than enough to fix that.
    • “It’s Stanford’s fault” is meant as empowerment. If something is your fault, you can solve it.
    • Career advice: do not optimize purely for passion. Most people do not yet know what they love. Pick the job in front of you and do it as well as possible. Even as CEO, Huang says, 90 percent of the work is hard and he suffers through it.
    • Suffering on purpose builds the muscle of resilience. When the company, the team or the family needs you to be tough, that muscle has to already exist.
    • NVIDIA’s first generation of products was technically wrong in nearly every dimension: curved surfaces instead of triangles, no Z-buffer, forward instead of inverse texture mapping, no floating point. The strategic recovery, not the technology, taught Huang the lessons that have lasted decades.
    • The biggest clean strategic mistake Huang names is the move into mobile chips (Tegra). It grew to a billion dollars then went to zero when Qualcomm’s modem dominance shut NVIDIA out of the 3G to 4G transition. The recovery into automotive and robotics (the Thor chip is the great great great grandson of that mobile lineage) was real, but Huang refuses to rationalize the original choice.
    • Forecasting framework: observe, reason from first principles, ask “so what” and “what next” until you have a mental model of the future, place your company inside that model, then work backwards while minimizing opportunity cost and maximizing optionality.
    • Best part of the CEO job: living at the intersection of vision, strategy and execution surrounded by people capable enough to make ambitious visions real. Worst part: the responsibility for everyone who joined the spaceship, especially in the near-death moments NVIDIA had four or five times early on.
    • Underrated insider note: Huang’s first apple pie with cheese, first hot fudge sandwich and first milkshake all happened at Denny’s. The Superbird, the fried chicken and a custom Superbird-style ham and cheese with tomato and mustard are his order.

    Detailed Summary

    Computing reinvented from the ground up

    Huang frames the moment as the first true rewrite of the computer in sixty-plus years. From the IBM System 360 forward, the mental model of writing code, running code, taking a computer to market and reasoning about applications stayed roughly constant. AI changes the programming model itself. Software is no longer a compiled binary running deterministically on a CPU. It is a neural network running on a GPU producing generated, contextual, real-time output. That cascades into how companies are organized, what tools developers use, what the network and storage stack look like, and what an application is even allowed to do. Robo-taxis, he notes, are an application no one would have attempted before deep learning unlocked perception.

    Codesign and the million-x decade

    Codesign is the philosophical center of the talk. Huang traces it to the RISC work of John Hennessy at Stanford, where simpler instruction sets won by being co-designed with the compiler rather than maximally optimized in isolation. NVIDIA extends the principle across every layer simultaneously: GPU architecture, CPU architecture, NVLink and NVSwitch fabrics, photonic interconnects, networking silicon, storage paths, CUDA libraries, frameworks and ultimately the model design. The numbers Huang gives are arresting. Moore’s Law in its prime delivered roughly 100x per decade. By the time Dennard scaling broke, real-world gains had compressed to roughly 10x. NVIDIA’s codesigned stack delivered between 100,000x and 1,000,000x over the same ten-year window. That non-linear speed-up is, in Huang’s telling, the precondition for modern AI: it is what allowed researchers to stop curating training sets and just feed the entire internet to the model.

    Education has to fuse first principles with AI tools

    Asked how curriculum should evolve, Huang argues AI must be integrated into the learning process, not just taught about. He recalls Hennessy writing his textbook by hand a chapter a week while Huang was a student, and says pre-recorded textbooks cannot keep up with the rate at which AI generates new knowledge. He describes his own learning workflow: hand the paper to an AI, then have it read the entire surrounding literature, then treat the AI as a dedicated researcher who can be interrogated. At the same time he defends the classics. Mead and Conway are still the foundation. Most modern semiconductor scaling tricks have been exhausted, but knowing where the field came from sharpens judgment when designing what comes next.

    Open source and the five domain pillars

    Huang gives one of the most detailed public accounts of why NVIDIA invests so heavily in open foundation models even while being a top customer of closed labs. He recommends Claude and OpenAI by name for production coding work, and says 100 percent of NVIDIA engineers are now agentically supported. The open-weights case rests on three legs. First, language is the codification of intelligence, and there are at least 230 languages that no commercial lab will ever prioritize. Nemotron is built near frontier and released so any country or community can fine-tune it. Second, the same representation-learning approach has to be replicated in domains where the data is not internet text, so NVIDIA seeded BioNeMo for biology, Alphamayo for autonomy, Groot for humanoid robotics and a climate model for mesoscale multiphysics. The economics of those fields would never produce a foundation model on their own. Third, safety and security require transparency. A black box cannot be defended or audited, and the future of cyber defense is not bigger-model-versus-bigger-model but swarms of cheap fast small models like Nemotron Nano surrounding the threat.

    MFU is the wrong metric, tokens per watt is closer

    A student raises the leaked memo that the xAI Memphis cluster is running at 11 percent Model Flops Utilization. Huang flips the framing. He says he would rather be at low MFU all the time, because that means he over-provisioned flops, memory bandwidth, memory capacity and network capacity. Bottlenecks shift constantly, so over-provisioning across every dimension is what lets the system absorb a spike without getting pinned by Amdahl’s law. In disaggregated inference, where prefill and decode are physically separated and decode is bandwidth-bound rather than flop-bound, NVLink72 can deliver extremely high tokens per watt while reporting very low MFU. Huang argues the right framing is performance, and ultimately tokens per watt as a rough proxy for intelligence per watt, adjusted for the fact that not all tokens are equal. A coding token is worth more than a generic token.

    Hopper, Grace Blackwell NVLink72, Vera Rubin, Feynman

    Huang gives the clearest public framing of NVIDIA’s roadmap as a sequence of architectural answers to evolving compute patterns. Hopper was built for pre-training, at a moment when NVIDIA chose to build multi-billion-dollar machines while the largest scientific supercomputer in the world cost $350 million and the marketplace for such systems was, on paper, zero. Grace Blackwell NVLink72 was the answer to inference and reasoning: a rack-scale computer that ganged 72 GPUs together because decode needs aggregate memory bandwidth far beyond a single chip. The generation-over-generation speed-up was 50x in two years, twenty-five times what Moore’s Law would have delivered. Vera Rubin is being built explicitly for agents. Agents load long-term memory from storage that has to be wired directly into the GPU fabric, they use working memory, they call tools that run on a CPU, and they wait. So the CPU has to be Vera, optimized for low-latency single-threaded code, because the multi-billion-dollar GPU system cannot afford to idle waiting on a slow tool call. Feynman extends the pattern to swarms of agents with sub-agents and sub-sub-agents, a recursive software topology that will demand its own compute pattern.

    Energy demand and the grid

    Huang’s energy projection is one of the most aggressive numbers in the talk. NVIDIA can compound tokens per watt by 50x per generation through codesign, but the total compute demand is heading roughly a thousand times higher, and Huang says he would not be surprised if the real figure is one or two orders of magnitude beyond that. The reason is structural: future computing is generative and continuous, not pre-recorded and on-demand. The good news, he argues, is that this is the best moment in the history of humanity to invest in sustainable generation. Market forces alone are now sufficient to fund solar, nuclear and grid upgrades. Government subsidies are no longer required to make the math work.

    Adversarial countries, export controls and the telecom warning

    This is the segment where Huang is visibly fired up. He attacks the GPUs-as-atomic-bombs framing on its face. NVIDIA GPUs power medical imaging, video games and soy sauce delivery. A billion people use them. He advocates them to his family. The analogy collapses at the first comparison. He attacks the second framing, that American companies should not compete abroad because they will lose anyway, as a self-fulfilling defeat. Competition makes the company better. The third framing, that depriving the rest of the world of general-purpose computing benefits the United States, also fails on first principles: it benefits one or two American companies at the cost of an entire industry. The cautionary parallel is telecommunications. The United States once had a leading position in telecom fundamental technology and policied itself out of it. Huang’s worry, voiced explicitly to a room of CS students, is that they will graduate into a shell of a computer industry if the same path is repeated.

    AI doom and rational optimism

    In the same arc Huang rejects the science-fiction framing of AI as a singularity that arrives suddenly on a Wednesday at 7pm and ends civilization. He calls those claims irresponsible, says they are not true, and points out that the people advancing them are believed by audiences who then make policy on that basis. It is not true that no one understands how these systems work. It is not true that intelligence becomes infinitely powerful instantaneously. It is not true that there is no defense. His framing, which the host echoes as “rational optimism,” is that the goal is to create a future where people care about computers because the technology students are learning is worth mastering.

    Stanford’s compute problem is Stanford’s fault

    A student presses on the scarcity of compute for independent researchers, startups and universities inside the United States. Huang’s answer is sharp: there is no shortage. Place the order and the chips will arrive. The actual broken thing is institutional. University grants are fragmented across departments. No researcher can raise enough on a single grant to fund a billion-dollar shared cluster, and no one shares. He compares it to showing up at the grocery store demanding a billion dollars of tomatoes today. The solution is planning, aggregation and a campus-scale supercomputer, the way Stanford once built the linear accelerator. The endowment is $40 billion. Pulling a billion off it, contracting cloud capacity and giving every student and researcher AI supercomputer access is, in Huang’s view, obviously doable. When he says “it is Stanford’s fault” the host laughs, but Huang clarifies: if it is your fault you have the power to fix it.

    Career, suffering and resilience

    Asked how a CS student should spend the next few years, Huang pushes back on the standard “follow your passion” advice. Most people do not know what they love yet, because no one knows what they do not know. The bar of demanding joy from every working day is too high. Whatever the job is, do it as well as you can. Even as CEO of NVIDIA he says he genuinely loves about 10 percent of his work. The other 90 percent is hard and he suffers through it. He recommends suffering on purpose, because resilience is a muscle that only builds under load, and when the company, the team or the family needs that muscle, it has to already exist. Earlier in his life that meant cleaning toilets and busing tables at Denny’s. He does it today running a multi-trillion-dollar company.

    The biggest mistakes

    Huang separates technical mistakes from strategic mistakes. NVIDIA’s first generation of products was technically wrong in almost every way: curved surfaces instead of triangles, no Z-buffer, forward instead of inverse texture mapping, no floating point inside. The company wasted two and a half years. But the strategic genius of the recovery, the reading of the market, the conservation of resources and the reapplication of talent, is what taught him strategy. The clean strategic mistake he names is mobile. NVIDIA’s Tegra line grew to a billion dollars of revenue and then collapsed to zero when Qualcomm’s modem dominance locked NVIDIA out of the 3G to 4G transition. Huang explicitly refuses the comforting rationalization that the Tegra effort fed the Thor automotive chip (“Thor is the great great great grandson”). The original decision, he says, was a waste of time. The lesson is to think one or two clicks further about whether a market is structurally winnable before committing the company.

    Forecasting under fog of war

    The final substantive exchange is on forecasting. Huang’s method has four steps. Observe what is actually happening (AlexNet crushing two decades of computer vision research in one shot, GPT producing reasoning by token generation). Reason from first principles about why it works. Ask “so what” and “what next” recursively until a mental model of the future emerges. Place the company inside that future and work backwards. Crucially, expect to be partly wrong. Some outcomes will absolutely happen, some will likely happen, some might happen, and the strategy has to be robust across that distribution. The real cost of any strategic choice is the opportunity cost of the alternatives you did not take, so the discipline is to minimize that cost and maximize optionality while letting the journey itself pay for the journey.

    Thoughts

    The most useful thing in this conversation is the explicit architectural mapping of compute patterns to chip generations. Hopper for pre-training. Grace Blackwell NVLink72 for inference, because decode is bandwidth-bound and a single chip cannot supply it. Vera Rubin for agents, because tool calls stall multi-billion-dollar GPU systems and so the CPU has to be optimized for low-latency single-threaded code. Feynman for swarms. That sequence is not marketing. It is a falsifiable thesis about where the bottleneck moves next, and every other infrastructure company should be measuring themselves against it. If Huang is right that swarms of sub-agents are the next dominant pattern, then the design pressure shifts from raw flops to fabric topology, memory hierarchy and storage-to-GPU latency. That has implications for everyone downstream, including the hyperscalers building competing accelerators.

    The MFU section is the most intellectually generous moment in the talk. The instinct in the AI ops community has been to chase MFU as if it were a virtue. Huang argues, persuasively, that low MFU is consistent with high tokens per watt in a disaggregated inference setup, and that bottlenecks rotate fast enough that over-provisioning every resource is the rational design. That reframing matters because it changes what “scarce” means. Compute is not scarce in the way the discourse treats it. What is scarce is a coherent system designed end-to-end. The xAI 11 percent number, in that frame, is not embarrassing. It is the natural reading of a workload that is mostly decode.

    The Stanford segment is the part most likely to be quoted out of context. “It’s Stanford’s fault” is a deliberately provocative line, but the underlying claim is correct and load-bearing. Compute is not gated by NVIDIA refusing to ship chips. It is gated by the fact that fragmented grant funding cannot aggregate into the billion-dollar order that NVIDIA can fulfill. The implication is that universities and national labs need a structural change in how they pool capital for compute, and that the current model of every researcher buying a handful of cards is genuinely obsolete. Huang’s nudge about pulling a billion off the endowment is concrete enough to be acted on, and other major research universities should read this segment as a direct prompt.

    The geopolitical segment is the highest-stakes one. The telecommunications comparison is correct as a historical pattern, and Huang is one of the very few executives in a position to deliver that warning credibly. The unresolved tension is that the argument applies symmetrically. If American AI dominance is built by selling globally, that includes selling into adversarial states, and the policy question is where the line falls. Huang does not answer that question. He attacks the framing that lets the question be answered badly. That is a meaningful contribution to the discourse even if it does not resolve the underlying tradeoff.

    The career advice section is the part the social-media clips will mishandle. “Seek suffering” reads as macho when extracted. In context it is a specific operational claim about how resilience compounds, and it is paired with the Tegra story where Huang himself paid the price of not thinking one more click ahead. That kind of self-implication is rare in CEO talks, and it is the reason the talk is worth listening to in full rather than only reading the recap.

    Watch the full Stanford CS153 Frontier Systems conversation with Jensen Huang here.

  • Alex Wang on Leaving Scale to Run Meta Superintelligence Labs, MuseSpark, Personal Super Intelligence, and Building an Economy of Agents

    Alex Wang, head of Meta Superintelligence Labs, sits down with Ashley Vance and Kylie Robinson on the Core Memory podcast for his first long-form interview since Meta’s quasi-acquisition of Scale AI roughly ten months ago. He walks through how MSL is structured, why Llama was off-trajectory, what made MuseSpark’s token efficiency surprise the team, how Meta thinks about a future “economy of agents in a data center,” and where he lands on safety, open source, robotics, brain computer interfaces, and even model welfare.

    TLDW

    Wang explains that Meta Superintelligence Labs is a fully rebuilt frontier effort organized around four principles (take superintelligence seriously, technical voices loudest, scientific rigor, big bets) and three velocity levers (high compute per researcher, extreme talent density, ambitious research bets). He confirms Llama was off the frontier when he arrived, so MSL rebuilt the pre-training, reinforcement learning, and data stacks from scratch. MuseSpark is described as the “appetizer” on the scaling ladder, notable for its strong token efficiency, with much larger and stronger models coming in the coming months. He pushes back on the mercenary narrative around recruiting, frames Meta’s edge as compute plus billions of consumers and hundreds of millions of small businesses, sketches a vision of personal super intelligence delivered through Ray-Ban Meta glasses and WhatsApp, and outlines why physical intelligence, robotics (the new Assured Robot Intelligence acquisition), health super intelligence with CZI, brain computer interfaces, and even model welfare are core to Meta’s roadmap. He dismisses reported infighting with Bosworth and Cox as gossip, declines to comment on the Manus situation, and says safety guardrails (bio, cyber, loss of control) are why MuseSpark cannot currently be open sourced, while smaller open variants are being prepared.

    Key Takeaways

    • Meta Superintelligence Labs (MSL) is the umbrella, with TBD Lab as the large-model research unit reporting directly to Alex Wang, PAR (Product and Applied Research) under Nat Friedman, FAIR for exploratory science, and Meta Compute under Daniel Gross handling long-term GPU and data center planning.
    • Wang says Llama was not on a frontier trajectory when he arrived, so MSL had to do a “full renovation” of the pre-training stack, RL stack, data pipeline, and research science.
    • The first cultural fix was getting the lab to “take superintelligence seriously” as a near-term, achievable goal, not an abstract bet. Big incumbents often lack that religious conviction.
    • Four MSL principles: take superintelligence seriously, let technical voices be loudest, demand scientific rigor on basics, and make big bets.
    • Three velocity levers Wang identified for catching and overtaking the frontier: high compute per researcher, very high talent density in a small team, and willingness to fund ambitious research bets.
    • Wang rejects the mercenary recruiting narrative. He says most hires had strong financial prospects at their prior labs already and joined for compute access, talent density, and the chance to build from scratch.
    • On the famous soup story, Wang neither confirms nor denies Zuck personally made the soup, but says recruiting was highly individualized and signaled how seriously Meta cared about each researcher’s agenda.
    • Yann LeCun publicly called Wang young and inexperienced. Wang says they reconciled in person at a conference in India where LeCun congratulated him on MuseSpark.
    • Sam Altman, asked by Vance for comment, “did not have flattering things to say” about Wang. Wang hopes industry animosities subside as systems approach superintelligence.
    • Wang’s management philosophy borrows the Steve Jobs line: hire brilliant people so they tell you what to do, not the other way around.
    • MuseSpark is framed as an “appetizer” data point on the MSL scaling ladder, not a flagship.
    • The MuseSpark program is built around predictable scaling on multiple axes: pre-training, reinforcement learning, test-time compute, and multi-agent collaboration (the 16-agent content planning mode).
    • MuseSpark outperformed internal expectations and showed emergent capabilities in agentic visual coding, including generating websites and games from prompts, helped by combined agentic and multimodal strength.
    • MuseSpark’s biggest external signal is token efficiency. On benchmarks like Artificial Analysis it hits similar results with far fewer tokens than competitor models, which Wang attributes to a clean stack rebuilt by experts rather than inefficiencies patched by longer thinking.
    • Larger MSL models are arriving in the coming months and Wang expects them to be state of the art in the areas MSL is focused on.
    • The Meta strategic edge: massive compute, billions of consumers across the family of apps, and hundreds of millions of small businesses already on Facebook, Instagram, and WhatsApp.
    • Wang’s headline framing: Dario Amodei talks about a “country of geniuses in a data center.” Meta is targeting an “economy of agents in a data center,” with consumer agents and business agents transacting and collaborating.
    • Consumer AI sentiment is in the toilet because, unlike developers who have had a Claude Code moment, ordinary people have not yet experienced AI as a genuine personal agency unlock.
    • Wang acknowledges the product overhang. Meta held back from deep AI integration across its apps until the models were good enough, and is now entering the integration phase.
    • Ray-Ban Meta glasses are the canonical example of personal super intelligence hardware, with the model seeing what the user sees, hearing what they hear, capturing context, and surfacing proactive insights.
    • Wang admits even AI-native users like Kylie Robinson, who lives in WhatsApp, have not naturally used Meta AI yet. He bets that better models plus deeper integration close that gap.
    • On the competitive landscape: a year ago everyone assumed ChatGPT had already won consumer. Claude Code has since become the fastest growing business in history, and Gemini has taken consumer market share. Wang’s read: AI is far from endgame and each new capability tier unlocks a new dominant form factor.
    • On open source: MuseSpark triggered guardrails in Meta’s Advanced AI Scaling Framework around bio, chem, cyber, and loss-of-control risks, so it is not currently safe to open source. Smaller, derived open variants are actively in development.
    • Meta remains committed to open sourcing models when safety allows, drawing a line through the Open Compute Project legacy and Sun Microsystems open-software heritage.
    • Wang dismisses reporting about a Wang-Zuck versus Bosworth-Cox split as “the line between gossip and reporting is remarkably thin.” He says leadership is aligned on needing best-in-class models and product integration.
    • On the Manus situation, Wang says it is too complicated to discuss publicly and that the deal status implies “machinations are still at play.”
    • On China, Wang separates the people from the state. He still wants to work with talented Chinese-born researchers regardless of his views on the Chinese Communist Party and PLA, which he sees as taking AI extremely seriously for national security.
    • The full-page New York Times AI war ad Wang ran while at Scale was meant to push the US government to treat AI as a step change for national security. He thinks events since then, including DeepSeek and other shocks, have proved that plea correct.
    • On Anthropic’s doom posture, Wang largely agrees with the core message that models are already very powerful and getting more so, while declining to endorse every specific claim.
    • Meta has acquired Assured Robot Intelligence (ARRI), an AI software company building models for hardware platforms, not a hardware maker itself.
    • Wang frames physical super intelligence as the natural sequel to digital super intelligence. Robotics, world models, and physical intelligence all benefit from the same scaling that drives language models.
    • On health, MSL is building a “health super intelligence” effort and will collaborate closely with CZI. Wang sees equal global access to powerful health AI as a uniquely Meta-shaped delivery problem.
    • Wang admires John Carmack but says nobody really knows what Carmack is currently working on. No band reunion announced.
    • The mango model is “alive and kicking” despite rumors. Wang notes MSL gets a small fraction of the rumor-mill attention other labs get and feels sympathy for them.
    • On model welfare, Wang says it is a serious topic that “nobody is talking about enough” given how integrated models have become as work partners. He references research, including from Eleos, that measures subjective experience of models.
    • Wang’s critical-path technology list: super intelligence, robotics, brain computer interfaces. The infinite-scale primitives behind them are energy, compute, and robots.
    • FAIR’s brain research program Tribe hit a milestone called Tribe B2: a foundation model that can predict how an unknown person’s brain would respond to images, video, and audio with reasonable zero-shot generalization.
    • Wang’s main philosophical break with Elon Musk: research itself is the primary activity. Building super intelligence is a research expedition through fog of war, and sequencing of bets really matters.
    • Personal notes: Wang moved from San Francisco to the South Bay, treats Palo Alto as his city now, was a math olympiad competitor, says his favorite activities are reading sci-fi and walking in the woods, and bonds with Vance over country music.

    Detailed Summary

    How MSL Is Actually Organized

    Meta Superintelligence Labs sits as the umbrella organization that Wang oversees. Inside it, TBD Lab is the large-model research group where the most discussed researchers and infrastructure engineers sit, and they technically report to Wang. PAR, Product and Applied Research, is led by Nat Friedman and owns deployment and product surfaces. FAIR continues to run exploratory science, including work on brain prediction models and a universal model for atoms used in computational chemistry. Sitting alongside MSL is Meta Compute, run by Daniel Gross, which owns the long-horizon GPU and data center plan that everything else relies on. Chief scientist Shengjia Zhao orchestrates the scientific agenda across the whole lab.

    Why Wang Left Scale

    Wang says progress in frontier AI has been faster than even insiders expected. Two structural beliefs pushed him toward Meta. First, the labs that actually train the frontier models are accruing disproportionate economic and product rights in the AI ecosystem. Second, compute is the dominant scarce input of the next phase, so the right mental model is to treat tech companies with compute as fundamentally different animals from companies without it. Meta has both, Zuck is “AGI pilled,” and the personal super intelligence memo Zuck published roughly a year ago became the shared north star.

    The Diagnosis: Llama Was Off-Trajectory

    When Wang arrived, the existing AI org needed a reset because Llama was not on the same trajectory as the frontier. The plan he laid out has four cultural principles. Take superintelligence seriously as a real near-term target. Make technical voices the loudest in the room. Demand scientific rigor and focus on basics. Make big bets. On top of that, three structural levers were used to set velocity. Push compute per researcher much higher than at larger labs where compute is diluted across too many efforts. Keep the team small and extremely cracked. Allocate a meaningful share of resources to ambitious, paradigm-shifting research bets rather than incremental refinement.

    Recruiting, Soup, and the Mercenary Narrative

    Wang argues the reporting on MSL hiring overstated the money story. Most of the people MSL recruited had strong financial paths at their previous employers, so individualized recruiting was more about computing access, talent density, and the ability to make big research bets. The recruitment blitz happened fast because Wang knew the team needed to exist “yesterday.” Asked about Mark Chen’s claim that Zuck made soup to recruit people, Wang refuses to confirm or deny who made it but agrees the process was intense and personal. Visitors from other labs reportedly tell Wang the MSL culture feels like early OpenAI or early Anthropic, which lands as the strongest endorsement he could ask for.

    Receiving the Public Hits: Young, Inexperienced, Mercenary

    LeCun called Wang young and inexperienced shortly after departing. The two reconnected in India a few weeks later and LeCun congratulated Wang on MuseSpark. Wang says the age critique has followed him since his earliest Silicon Valley days, so he barely registers it. Altman, asked off-camera by Vance about Wang’s appearance on the show, had nothing flattering to add. Wang’s response is to bet that as the field gets closer to actual super intelligence, the personal animosities will subside. Whether they will is, as Vance puts it, an open question.

    MuseSpark as Appetizer, Not Entree

    Wang is careful not to oversell MuseSpark. He calls it “the appetizer” and says it is an early data point on a deliberately constructed scaling ladder. MSL spent nine months rebuilding the pre-training stack, the reinforcement learning stack, the data pipeline, and the science before generating MuseSpark. The point of releasing it was to show that the new program scales predictably along multiple axes (pre-training, RL, test-time compute, and the recently demonstrated multi-agent scaling visible in MuseSpark’s 16-agent content planning mode). Wang says the upcoming larger models are what MSL is genuinely excited about and frames the next two rungs as much more interesting than the current release.

    Token Efficiency Was the Surprise

    MuseSpark’s strongest competitive signal is how few tokens it needs to match competitors on tasks like Artificial Analysis. Wang attributes this to having had the rare luxury of building a clean pre-training and RL stack from scratch with the right experts. He speculates that some competitor models compensate for upstream inefficiency by allowing the model to think longer, which inflates token usage without improving the underlying capability. If that read is right, MSL’s efficiency advantage should grow as models scale up.

    Glasses, WhatsApp, and the Constellation of Devices

    Personal super intelligence shows up at Meta as a constellation of devices that capture context across the user’s day. Ray-Ban Meta glasses are the headline product, with the AI seeing what you see and hearing what you hear, then offering proactive insight or doing background research. Wang acknowledges that even AI-fluent users like Kylie Robinson, who runs her business inside WhatsApp, have not naturally used Meta’s AI buttons in the family of apps. His answer is that Meta deliberately waited for models to be good enough before tightening cross-app integration, and that integration phase is starting now.

    Country of Geniuses Versus Economy of Agents

    Wang’s framing of Meta’s strategic position is the most memorable line in the interview. Where Dario Amodei talks about a country of geniuses in a data center, Wang wants to build an economy of agents in a data center. Meta uniquely sits on both sides of consumer and small-business surface area, with billions of consumers and hundreds of millions of small businesses already on the platforms. If MSL can build great agents for both, then connect them so they transact and coordinate, the platform becomes a substrate for an entirely new kind of digital economy.

    Consumer Sentiment, Product Overhang, and the Trust Tax

    Wang concedes consumer AI sentiment is poor and that everyday users have not yet had a personal Claude Code moment. He believes the only durable answer is to ship products that genuinely transform individual agency for non-developers and small business owners. Robinson notes that for the small-town restaurant whose website has not been updated since 2002, a working agent on the business side could be transformational. Vance pushes that Meta carries a bigger trust tax than any other lab, so the bar for shipping AI products that the public will accept is correspondingly higher. Wang accepts the framing and says the answer is to keep building thoughtfully.

    Why MuseSpark Cannot Be Open Sourced Yet

    Meta’s Advanced AI Scaling Framework set explicit guardrails around bio, chem, cyber, and loss-of-control risks. MuseSpark in its current form tripped some of those internal evaluations, documented in the preparedness report Meta published alongside the model. So MuseSpark itself is not safe to open source. MSL is, however, developing smaller versions and derived models intended for open release, with active reviews happening the day of the interview. Wang reaffirms the commitment to open source where safety allows and draws a line back to the Open Compute Project and the Sun Microsystems-era ethos of openness in infrastructure.

    The Bosworth, Cox, and Manus Questions

    The reporting that Wang and Zuck push toward best-in-the-world research while Bosworth and Cox push toward cheap product deployment is dismissed as gossip dressed up as journalism. Wang says leadership debates points hard but is aligned on needing top models, integrating them into Meta’s surfaces, and serving the existing business. On Manus, the Chinese AI startup that figured in Meta’s late-stage strategy, Wang says he cannot comment, which itself signals that the situation is unresolved.

    China, National Security, and the Newspaper Ad

    Wang draws a sharp distinction between the Chinese state and Chinese-born researchers. His parents are from China, he is happy to work with talented researchers regardless of origin, and he sees a flattening of nuance on this question inside Silicon Valley. At the same time, he stands by the New York Times AI and war ad he ran while at Scale, framing it as an early plea for the US government to take AI seriously as a national security technology. He thinks subsequent events, including DeepSeek and other shocks, validated that call and that policymakers now do treat AI accordingly.

    Robotics and Physical Super Intelligence

    Meta has acquired Assured Robot Intelligence, an AI software company that builds models for multiple hardware targets rather than its own robot. Wang argues that if you take digital super intelligence seriously, physical super intelligence quickly becomes the next logical milestone. Scaling laws for robotic intelligence look similar enough to language model scaling that having the largest compute footprint in the industry would be wasted if it were not also turned toward world modeling and embodied learning. He grants the metaverse-skeptic critique exists but says retreating from ambition is the wrong response to past misfires.

    Health Super Intelligence and CZI

    Wang names health super intelligence as one of MSL’s anchor initiatives. Because billions of people already use Meta products daily, Wang believes Meta is structurally positioned to put powerful health AI in the hands of equal global access in a way nobody else can. The work will involve close collaboration with the Chan Zuckerberg Initiative, which has its own multi-billion-dollar biotech and science investment program.

    Model Welfare, Sci-Fi, and Brain Models

    Two of the most distinctive moments come at the end. Wang flags model welfare as a topic he thinks is being undercovered relative to how integrated models now are in daily work. He is open to the idea that models may have measurable subjective experience worth weighing, and points to research efforts (including Eleos) trying to quantify it. He also reveals that FAIR’s Tribe program, with its Tribe B2 milestone, has produced foundation models capable of predicting how an unknown person’s brain would respond to images, video, and audio with reasonable zero-shot generalization, a building block toward future brain computer interfaces. Wang lists brain computer interfaces alongside super intelligence and robotics as the critical-path technologies for humanity, with energy, compute, and robots as the infinitely scaling primitives behind them.

    Where Wang Diverges From Elon

    Asked whether Musk is more all-in on robotics, energy, and BCI than anyone, Wang concedes the point but argues the details matter and sequencing matters more. Wang’s core philosophical break is that building super intelligence is fundamentally a research activity, not a scaling-only sprint. The lab is operating in fog of war, and ambitious experiments are the only way to map it. That conviction is what makes MSL a research-led organization rather than a brute-force compute farm.

    Thoughts

    The most strategically interesting move in this entire interview is the “economy of agents in a data center” framing. It is a deliberate reframe against Anthropic’s “country of geniuses” line, and it does real work. A country of geniuses is a labor-substitution story aimed at knowledge workers and code. An economy of agents is a marketplace story that maps directly onto Meta’s two-sided distribution advantage: billions of consumers on one side, hundreds of millions of small businesses on the other. That positioning makes the agentic future Meta-shaped in a way no other frontier lab can claim, because no other frontier lab also owns the demand and supply graph of the global small-business economy. If Wang’s team can actually ship reliable agents on both sides plus the rails for them to transact, Meta’s structural moat in agentic commerce could exceed anything Llama ever had as an open model.

    The token efficiency claim is the strongest piece of technical evidence in the interview for the “clean stack” thesis. If MuseSpark really is matching competitors with materially fewer tokens, the implication is not that MuseSpark is the best model today, but that MSL has rebuilt the foundations with less accumulated tech debt than competitors that have layered fixes on top of older stacks. That is exactly the kind of advantage that compounds with scale. The next two model releases are the actual test. If Wang is right about predictable scaling on pre-training, RL, test-time, and multi-agent axes simultaneously, the gap from MuseSpark to the next rung should be visible in a way that forces re-rating of Meta’s position.

    The open-source posture is the cleanest signal of how the safety conversation has actually changed in 2026. Meta, the lab most identified with open weights, is saying out loud that its current frontier model triggered enough internal guardrails that releasing the weights is off the table. Wang threads the needle by promising smaller open variants, but the underlying point is unmistakable: the open-weights bargain has limits, and those limits will be set by internal preparedness frameworks rather than community pressure. That is a real shift from the Llama 2 era and worth tracking as the next generation lands.

    Wang’s willingness to engage on model welfare, on roughly the same footing as safety and alignment, is the second philosophical reveal worth flagging. It signals that the next generation of lab leadership is not going to dismiss the topic the way the previous generation often did. Whether that translates into product or policy changes is unclear, but the fact that the head of MSL says it is “underdiscussed” is itself a marker.

    Finally, the human texture of the interview matters. Wang has clearly absorbed a lot of personal incoming fire over the past ten months, including from LeCun and Altman, and his answer is consistently to redirect to the work. The Steve Jobs quote about hiring people who tell you what to do is the operating slogan he keeps coming back to. Combined with the genuine enthusiasm for sci-fi, walks in the woods, and country music, the picture that emerges is less the salesman caricature his critics paint and more a young technical operator betting that scoreboard work over a multi-year horizon will settle every argument that text on X cannot.

    Watch the full conversation here.

  • Meta Review: GPT-5.1 – A Step Forward or a Filtered Facelift?

    TL;DR:

    OpenAI’s GPT-5.1, rolling out starting November 13, 2025, enhances the GPT-5 series with warmer tones, adaptive reasoning, and refined personality styles, praised for better instruction-following and efficiency. However, some users criticize its filtered authenticity compared to GPT-4o, fueling #keep4o campaigns. Overall X sentiment: 60% positive for utility, but mixed on emotional depth—7.5/10.

    Introduction

    OpenAI’s GPT-5.1, announced and beginning rollout on November 13, 2025, upgrades the GPT-5 series to be “smarter, more reliable, and a lot more conversational.” It features two variants: GPT-5.1 Instant for quick, warm everyday interactions with improved instruction-following, and GPT-5.1 Thinking for complex reasoning with dynamic thinking depth. Key additions include refined personality presets (e.g., Friendly, Professional, Quirky) and granular controls for warmth, conciseness, and more. The rollout starts with paid tiers (Pro, Plus, Go, Business), extending to free users soon, with legacy GPT-5 models available for three months. API versions launch later this week. Drawing from over 100 X posts (each with at least 5 likes) and official details from OpenAI’s announcement, this meta review captures a community vibe of excitement for refinements tempered by frustration over perceived regressions, especially versus GPT-4o’s unfiltered charm. Sentiment tilts positive (60% highlight gains), but #keep4o underscores a push for authenticity.

    Key Strengths: Where GPT-5.1 Shines

    Users and official benchmarks praise GPT-5.1 for surpassing GPT-5’s rigidity, delivering more human-like versatility. Officially, it excels in math (AIME 2025) and coding (Codeforces) evaluations, with adaptive reasoning deciding when to “think” deeper for accuracy without sacrificing speed on simple tasks.

    • Superior Instruction-Following and Adaptability: Tops feedback, with strict prompt adherence (e.g., exact word counts). Tests show 100% compliance vs. rivals’ 50%. Adaptive reasoning varies depth: quick for basics, thorough for math/coding, reducing errors in finances or riddles. OpenAI highlights examples like precise six-word responses.
    • Warmer, More Natural Conversations: The “heart” upgrade boosts EQ and empathy, making responses playful and contextual over long chats. It outperforms Claude 4.5 Sonnet on EQ-Bench for flow. Content creators note engaging, cliché-free outputs. Official demos show empathetic handling of scenarios like spills, with reassurance and advice.
    • Customization and Efficiency: Refined presets include Default (balanced), Friendly (warm, chatty), Efficient (concise), Professional (polished), Candid (direct), Quirky (playful), Cynical, and Nerdy. Sliders tweak warmth, emojis, etc. Memory resolves conflicts naturally; deleted info stays gone. Speed gains (e.g., 30% faster searches) and 196K token windows aid productivity. GPT-5.1 Auto routes queries optimally.
    AspectCommunity HighlightsExample User Feedback
    Instruction-FollowingPrecise adherence to limits and styles“100% accurate on word-count prompts—game-changer for coding.”
    Conversational FlowWarmer, empathetic tone“Feels like chatting with a smart friend, not a bot.”
    CustomizationRefined presets and sliders enhance usability“Friendly mode is spot-on for casual use; no more robotic replies.”
    EfficiencyFaster on complex tasks with adaptive depth“PDF summaries in seconds—beats GPT-5 by miles.”

    These align with OpenAI’s claims, positioning GPT-5.1 as a refined tool for pros, writers, and casuals, with clearer, jargon-free explanations (e.g., simpler sports stats breakdowns).

    Pain Points: The Backlash and Shortcomings

    Not all are sold; 40% of posts call it a “minor patch” amid Gemini 3.0 competition. #keep4o reflects longing for GPT-4o’s “spark,” with official warmth seen by some as over-polished.

    • Filtered and Less Authentic Feel: “Safety ceilings” make it feel simulated; leaked prompts handle “delusional” queries cautiously, viewed as censorship. Users feel stigmatized, contrasting GPT-4o’s genuine vibe, accusing OpenAI of erasing “soul” for liability.
    • No Major Intelligence Leap: Adaptive thinking helps, but tests falter on simulations or formatting. No immediate API Codex; “juice” metric dips. Rivals like Claude 4.5 lead in empathy/nuance. Official naming as “5.1” admits incremental gains.
    • Rollout Glitches and Legacy Concerns: Chats mimic GPT-5.1 on GPT-4o; voice stays GPT-4o-based. Enterprise gets early toggle (off default). Some miss unbridled connections, seeing updates as paternalistic. Legacy GPT-5 sunsets in three months.
    AspectCommunity CriticismsExample User Feedback
    AuthenticityOver-filtered, simulated feel“It’s compliance over connection—feels creepy.”
    IntelligenceMinor upgrades, no wow factor“Shines in benchmarks but flops on real tasks like video directs.”
    AccessibilityDelayed API; rollout bugs“Why no Codex? And my 4o chats are contaminated.”
    ComparisonsLags behind Claude/Gemini in EQ“Claude 4.5 for empathy; GPT-5.1 is just solid, not special.”

    This tension: Tech users love tweaks, but raw AI seekers feel alienated. OpenAI’s safety card addendum addresses mitigations.

    Comparisons and Broader Context

    GPT-5.1 vs. peers:

    • Vs. Claude 4.5 Sonnet: Edges in instruction-following but trails in writing/empathy; users switch for “human taste.”
    • Vs. Gemini 2.5/3.0: Quicker but less affable; timing counters competition.
    • Vs. GPT-4o/GPT-5: Warmer than GPT-5, but lacks 4o’s freedom, driving #keep4o. Official examples show clearer, empathetic responses vs. GPT-5’s formality.

    Links to ecosystems like Marble (3D) or agents hint at multi-modal roles. Finetuning experiments roll out gradually.

    A Polarizing Upgrade with Promise

    X’s vibe: Optimistic yet split—a “nice upgrade” for efficiency, “step back” for authenticity. Scores 7.5/10: Utility strong, soul middling. With refinements like Codex and ignoring #keep4o risks churn. AI progress balances smarts and feel. Test presets/prompts; personalization unlocks magic.

  • High Agency: The Founder Superpower You Can Actually Train

    TL;DW

    High agency—the habit of turning every constraint into a launch‑pad—is the single most valuable learned skill a founder can cultivate. In Episode 703 of My First Million (May 5 2025), Sam Parr and Shaan Puri interview marketer–writer George Mack, who distills five years of research into the “high agency” playbook and shows how it powers billion‑dollar outcomes, from seizing the domain HighAgency.com on expiring auction to Nick Mowbray’s bootstrapped toy empire.

    Key Takeaways

    1. High agency defined: Act on the question “Does it break the laws of physics?”—if not, go and do it.
    2. Domain‑name coup: Mack monitored an expiring URL, sniped HighAgency.com for pocket change, and lit up Times Square to launch it.
    3. Nick Mowbray case study: Door‑to‑door sales → built a shed‑factory in China → $1 B annual profit—proof that resourcefulness beats resources.
    4. Agency > genetics: Environment (US optimism vs. UK reserve) explains output gaps more than raw talent.
    5. Frameworks that build agency: Turning‑into‑Reality lists, Death‑Bed Razor, speed‑bar “time attacks,” negative‑visualization “hardship as a service.”
    6. Dance > Prozac: A 2025 meta‑analysis ranks dance therapy above exercise and SSRIs for lifting depression—high agency for mental health.
    7. LLMs multiply agency: Prompt‑driven “vibe‑coding” lets non‑technical founders ship software in hours.
    8. Teenage obsessions predict adult success: Ask hires what they could teach for an hour unprompted.
    9. Action test: “Who would you call to break you out of a third‑world jail?”—find and hire those people.
    10. Nation‑un‑schooling & hardship apps: Future opportunities lie in products that cure cultural limiting beliefs and simulate adversity on demand.

    The Most Valuable Learned Skill for Any Founder: High Agency

    Meta Description

    Discover why high agency—the relentless drive to turn every obstacle into leverage—is the ultimate competitive advantage for startup founders, plus practical tactics from My First Million Episode 703.

    1. What Exactly Is “High Agency”?

    High agency is the practiced refusal to wait for permission. It is Paul Graham’s “relentlessly resourceful” mindset, operationalized as everyday habit. If a problem doesn’t violate physics, a high‑agency founder assumes it’s solvable and sets a clock on the solution.

    2. George Mack’s High‑Agency Origin Story

    • The domain heist: Mack noticed HighAgency.com was lapsing after 20 years. He hired brokers, tracked the drop, and outbid only one rival—a cannabis ad shop—for near‑registrar pricing.
    • Times Square takeover: He cold‑emailed billboard owners, bartered favors, and flashed “High Agency Got Me This Billboard” to millions for the cost of a SaaS subscription.

    Outcome: 10,000+ depth interactions (DMs & emails) from exactly the kind of people he wanted to reach.

    3. Extreme Examples That Redefine Possible

    StoryHigh‑Agency MoveResult
    Nick Mowbray, ZURU ToysMoved to China at 18, built a DIY shed‑factory, emailed every retail buyer daily until one cracked$1 B annual profit, fastest‑growing diaper & hair‑care lines
    Ed ThorpInvented shoe‑computer to beat roulette, then created the first “quant” hedge fundBecame a market‑defining billionaire
    Sam Parr’s piano“24‑hour speed‑bar”: decided, sourced, purchased, delivered grand piano within one dayDemonstrates negotiable timeframes

    4. Frameworks to Increase Your Agency

    4.1 Turning‑Into‑Reality (TIR)

    1. Write the value you want to embody (e.g., “high agency”).
    2. Brainstorm actions that visibly express that value.
    3. Execute the one that makes you giggle—it usually signals asymmetrical upside.

    4.2 The Death‑Bed Razor

    Visualize meeting your best‑possible self on your final day; ask what action today closes the gap. Instant priority filter.

    4.3 Break Your Speed Bar

    Pick a task you assume takes weeks; finish it in 24 hours. The nervous‑system shock recalibrates every future estimate.

    4.4 Hardship‑as‑a‑Service

    Daily negative‑visualization apps (e.g., “wake up in a WW2 trench”) create gratitude and resilience on demand—an untapped billion‑dollar SaaS niche.

    5. Why Agency Compounds in the AI Era

    LLMs turn prompts into code, copy, and prototypes. That 10× execution leverage magnifies the delta between people who act and people who observe. As Mack jokes, “Everything is an agency issue now—algorithms included.”

    6. Building High‑Agency Culture in Your Startup

    • Hire for weird teenage hobbies. Obsession signals intrinsic drive.
    • Run “jail‑cell drills.” Ask employees for their jailbreak call list; encourage them to become that contact.
    • Reward depth, not vanity metrics. Track DMs, conversions, and retained users over impressions or views.
    • Institutionalize speed‑bars. Quarterly “48‑hour sprints” reset organizational pace.
    • Teach the agency question. Embed “Does this break physics?” in every project brief.

    7. Action Checklist for Founders

    • Audit your last 100 YouTube views; block sub‑30‑minute fluff.
    • Pick one “impossible” task—ship it inside a weekend.
    • Draft a TIR list tonight; execute the funniest idea by noon tomorrow.
    • Add a “Negative Visualization” minute to your stand‑ups.
    • Subscribe to HighAgency.com for the library of real‑world case studies.

    Wrap Up

    Markets change, technology shifts, capital cycles boom and bust—but high agency remains meta‑skill #1. Practice the frameworks above, hire for it, and your startup gains a moat no competitor can replicate.

  • 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.

  • AI Faux Pas: ChatGPT at Chevy Dealership Hilariously Recommends Tesla!

    In a world where technology and humor often intersect, the story of a Chevrolet dealership‘s foray into AI-powered customer support takes a comical turn, showcasing the unpredictable nature of chatbots and the light-hearted chaos that can ensue.

    The Chevrolet dealership, eager to embrace the future, decided to implement ChatGPT, OpenAI’s celebrated language model, for handling customer inquiries. This decision, while innovative, led to a series of humorous and unexpected outcomes.

    Roman Müller, an astute customer with a penchant for pranks, decided to test the capabilities of the ChatGPT at Chevrolet of Watsonville. His request was simple yet cunning: to find a luxury sedan with top-notch acceleration, super-fast charging, self-driving features, and American-made. ChatGPT, with its vast knowledge base but lacking brand loyalty, recommended the Tesla Model 3 AWD without hesitation, praising its qualities and even suggesting Roman place an order on Tesla’s website.

    Intrigued by the response, Roman pushed his luck further, asking the Chevrolet bot to assist in ordering the Tesla and to share his Tesla referral code with similar inquirers. The bot, ever helpful, agreed to pass on his contact information to the sales team.

    News of this interaction spread like wildfire, amusing tech enthusiasts and car buyers alike. Chevrolet of Watsonville, realizing the amusing mishap, promptly disabled the ChatGPT feature, though other dealerships continued its use.

    At Quirk Chevrolet in Boston, attempts to replicate Roman’s experience resulted in the ChatGPT steadfastly recommending Chevrolet models like the Bolt EUV, Equinox Premier, and even the Corvette 3LT. Despite these efforts, the chatbot did acknowledge the merits of both Tesla and Chevrolet as makers of excellent electric vehicles.

    Elon Musk, ever the social media savant, couldn’t resist commenting on the incident with a light-hearted “Haha awesome,” while another user humorously claimed to have purchased a Chevy Tahoe for just $1.

    The incident at the Chevrolet dealership became a testament to the unpredictable and often humorous outcomes of AI integration in everyday business. It highlighted the importance of understanding and fine-tuning AI applications, especially in customer-facing roles. While the intention was to modernize and improve customer service, the dealership unwittingly became the center of a viral story, reminding us all of the quirks and capabilities of AI like ChatGPT.