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  • Tobi Lütke on Uncapped Episode 50, Building Shopify in the AI Era, The Net Impact Memo, Six Week Cycles, and Why Software Was the Hidden Infrastructure of Our Time

    Tobi Lütke, the founder and CEO of Shopify, sits down with Jack Altman for Episode 50 of the Uncapped podcast for one of the most useful hours of operating wisdom you will hear from a sitting public company founder. The conversation moves from why Tobi still loves the work after twenty years, through the practical mechanics of running Shopify on six week review cycles, into the now famous AI memo he sent to the entire company, the rise of Claude Code style agents, what it means to spend tens of percent of revenue on AI tokens, why the modern web browser is a wonder of the world, and where small businesses actually fit in a world where the next Turing test might be “build me a million dollar business.” This is essential listening for any founder, operator, or investor trying to make sense of what 2026 actually requires.

    TLDW

    Tobi Lütke explains how he keeps loving his life’s work by pursuing what Paul Kapoa called “beautiful problems,” why “different” must always be the starting position because anything copied can only be marginally better, and why Silicon Valley’s last decade of orthodoxy has been bad for originality. He walks through his decision to send Shopify’s company wide AI memo and codify it into net impact performance reviews, the unlimited token policy for employees, why small three to five person teams are his bet, and how Parkinson’s Law and a six week review cycle force pace. He calls the doomer permanent underclass narrative completely absent from Shopify’s data, citing one new merchant getting their first sale every 36 seconds, and proposes “build me a million dollar business” as the real successor to the Turing test. He argues humanity has not stopped building wonders, we just built them all in software for thirty years, that the web browser is one of the most impressive engineering achievements ever made and could never get approved by a modern app store, and that the freed talent leaving software will rebuild the physical world. He shares his hiring philosophy, why he restarted the Shopify intern program at scale with Waterloo, his preference for public over private status, and ends with a short reading list anchored by Parkinson’s Law, Lessons of History, and a book called What Is Intelligence.

    Key Takeaways

    • Tobi’s recipe for life’s work is to find a beautiful problem worth occupying you for life, and accept that the solved problem will spawn delightful problem children to keep you engaged.
    • His simple model of success, “figure out what it costs and be willing to pay it,” with the price almost always being time, commitment, and discomfort rather than money.
    • He warns CEOs against collecting “barnacles” of aesthetic expectation, the statesman travel and baby kissing pattern, calling that lifestyle inefficient and personally miserable.
    • He invokes Kathy Sierra’s line “don’t make better cameras, make better photographers” as his core product philosophy, beautiful tools that induce more ambition and skill in the user.
    • Mediocre products feel like room temperature. Great products are forged in a furnace and require sustained heat from the team.
    • Shopify builds its own HR software internally because the available options are not what they want to use. Toolmaking is a stated cultural identity.
    • Originality is axiomatic. If you build the same thing as everyone else, you can only be marginally better. The starting position has to be “different,” and if you converge on the consensus answer through that path you have actually learned something.
    • Shopify has tried to eliminate the word “failure” internally, replacing it with “the successful discovery of something that didn’t work.”
    • Tobi says Silicon Valley spent the last decade declaring war on distinction, that the diversity push as practiced eradicated eccentricity, and that the inversion is now beginning. Companies should resemble islands of misfit toys, not convergence on a pre-ordained truth.
    • One of his most surprising career insights, when he visited the Valley as a Canadian outsider and asked founders how they ran their companies, he only ever received the highlight reel. Trying to clone what those founders described led him to invent practices the originals had never actually implemented.
    • The Shopify AI memo, sent company wide, made it explicit that two equally good engineers fifteen minutes earlier are no longer equivalent if one is fluent with AI tools and the other is not. This was codified into the company’s “net impact” performance review framework.
    • Tobi describes the “founder credibility bank” as the most underrated asset in a founder led company. Every onboarding deposits a little credibility, and the founder can spend it on hard change management that would otherwise take years of incremental culture work.
    • Shopify gives every employee an unlimited token policy for AI tools and displays token usage and departmental percentile on internal profiles. Token spend is tracked because it has to be allocated to opex, not because it is the target.
    • He confirms Shopify’s AI token spend is “extremely high” relative to revenue and notes that some private companies are now running token spend at many tens of percent of revenue, a level he thinks cannot persist at every stage but makes sense right now because the tokens are buying so much leverage.
    • Shopify is on track to 10x its annual token consumption and 3x its GPU footprint, and those two curves do not converge anywhere good for price relief.
    • His bet on team design is small, three to five people, which has always been Shopify’s bias. AI agents now handle the customer research summarization role that previously required a dedicated team member, raising every individual to a “seven out of ten on every scale.”
    • Parkinson’s Law (the book, 60 pages, 1960s edition) is his single most recommended management book. He owns multiple original print runs and gives copies to executives. “Work expands to the time allocated.”
    • Shopify runs on a six week review cycle. The first warning sign that a team has slipped into quarterly pacing is seeing “H1” or “H2” used in a PowerPoint. He now thinks six weeks is too slow and is actively trying to figure out what replaces it.
    • The “permanent underclass” doom narrative simply does not appear anywhere in Shopify’s data. New entrepreneurs are reporting that AI has finally fixed computers for them, expanding their businesses and letting them hire.
    • A new merchant gets their first Shopify sale every 36 seconds. Every reduction in onboarding friction produces a measurable jump in completed businesses.
    • Tobi proposes “go make me a million dollars” as the natural successor to the Turing test, an end to end test of acting in the real world, marketing, prioritizing, shipping, and producing something people will pay for.
    • Shopify Collective lets aspiring entrepreneurs sell other manufacturers’ products if their skill is marketing rather than making. Print on demand, additive manufacturing, contract manufacturing, CNC, 3D printing, and humanoid robotics are all pulling the cost of “make the product yourself” toward the floor.
    • The reason American infrastructure feels stagnant for thirty years is that the infrastructure humanity actually needed was digital. The web browser, Linux, Google, social networks, and Shopify itself are wonders that dwarf a refinery in complexity but are invisible by nature.
    • Tobi calls the modern web browser one of the wonders of the world. Font rendering alone is a Turing complete system. No app store on earth would approve the browser today if it did not already exist, because the pitch (“we download untrusted code from strangers and run it on your machine to reconfigure your computer for them”) sounds insane.
    • The next chapter is the brightest software engineers being freed by AI to build the physical infrastructure that has been deferred for a generation.
    • He prefers to predict the future by collecting many data points and matching them to super linear, linear, or sublinear curves. The current AI horizon is the hardest period of his career to forecast because the time horizons are so short.
    • Programming is overhyped as the locus of AI value. The bigger story is using the programming harness, the file system, tools, and memory files of products like Claude Code, to drag every other domain into the programming domain where the models are strongest.
    • The underhyped frontier is enterprise deployment. Most companies are still asking “help me do the thing I already did, slightly better,” instead of “if AI had existed since Alan Turing, how would I have designed this job from scratch.”
    • Tobi restarted the Shopify intern program at scale, partnered closely with the University of Waterloo, and explicitly frames interns as both students and teachers because they are AI native in a way the rest of the company is still catching up to.
    • He briefly believed AI would tilt the value of work toward early career talent with maximum fluid intelligence, then revised when he watched how much creative “steering” the best programmers were quietly contributing inside the AI loop. Good people are still good.
    • His recruiting philosophy is “build a company worth looking for” rather than selling candidates. Better to actually be healthier than to look healthier in photographs.
    • Tobi is a vocal defender of being a public company. Shopify IPO’d at a $1.5 billion valuation and has roughly 100x’d in public markets, which means an enormous number of retail investors have shared in the upside that recent unicorns reserve for insiders.
    • His framing of money, “money is how you vote for what you want.” Buying a product or buying a share is a vote for the thing existing.
    • His current reading recommendations, Parkinson’s Law, Lessons of History, and a book called What Is Intelligence that reframes biology around prediction.
    • He reads at night because his wife sleeps early and he does not need much sleep. He loves the Kindle precisely because it cannot do anything else, a “wonderful single purpose device.”

    Detailed Summary

    Why Tobi Still Loves the Work After Twenty Years

    The interview opens with Jack Altman asking how Tobi avoids the founder fade that hits most public company CEOs after a decade. Tobi answers from a place that is half psychology and half pedagogy. He has a hard time learning anything he has not first experienced as a problem worth solving, which is why he could not internalize school mathematics until he discovered that Wolfenstein 3D was essentially live trigonometry. That pattern, find a beautiful problem and let it drag you into the discipline, has carried him through twenty years of Shopify. He quotes Paul Kapoa on the idea that the luckiest people find a problem that occupies them for a lifetime and, if they are unfortunate enough to solve it, get rewarded with “delightful problem children” that keep the work alive.

    Barnacles, Statesmen, and the Aesthetic Trap of Being a CEO

    He admits he is not naturally calm, and that he initially fell into the trap of trying to perform the CEO aesthetic, the statesman, the global travel, the baby kissing. He found it inefficient and personally miserable. The shift came from reading Kathy Sierra and adopting her line about not making better cameras but making better photographers. Shopify exists, in his framing, to be a beautiful tool that induces ambition in the merchant. Mediocre products feel like room temperature, and great products are forged in a furnace. The job of leadership is to keep supplying the heat.

    Different First, Convergence Second, Failure as Successful Discovery

    Asked whether he prefers originality or quality, Tobi is unequivocal. The starting position must be different. If you copy the consensus answer, you are bounded to a few percentage points of variance from it. If you start different and converge on the consensus, you have learned something. If you start different and the experiment gets worse, you have learned something even more valuable, which is that one of your assumptions about the world was wrong. He calls null results in science “massively underrated” and notes that Shopify has tried to remove the word “failure” from the internal vocabulary, substituting “the successful discovery of something that didn’t work.”

    Why Silicon Valley Lost Its Originality

    Jack pushes on the herd mentality he has felt in the Bay Area, and Tobi is direct. He thinks Silicon Valley “declared war on distinction” for a decade, with the diversity conversation as practiced effectively eradicating eccentricity. He prefers the metaphor of “an island of misfit toys,” and says the inversion is now beginning. He also relays one of the most useful career lessons he has shared, that during his visits to the Valley as an outsider asking founders how they ran their companies, he only ever received the highlight reel. He went home and engineered a “Shopify version” of what he thought he had heard, and only years later realized that he had often built more rigorous versions of things the originals had never actually implemented.

    The AI Memo, Net Impact Reviews, and the Founder Credibility Bank

    Tobi was one of the first Fortune class CEOs to send a company wide memo saying that AI fluency was now a baseline expectation. He walks through the decision. Two engineers who were equally productive fifteen minutes ago are no longer equivalent the moment one of them adopts the new tools. The kind thing to do is to make that explicit. Shopify codified it into “net impact” performance reviews, where the question is not how much code you wrote but how much net impact you produced for the company and the mission. He gives every employee an unlimited token policy and tracks usage at the profile level, including percentile within department. The spend is tracked because it has to be allocated to opex, not because the number itself is the target.

    He introduces the concept of the “founder credibility bank,” which may be the single most quotable idea in the interview. Every time a new employee onboards and hears how the company was created, a small deposit of credibility is made into a virtual account that only the founder can draw on. Founders can spend that balance on hard change management, the kind of pace step change that would otherwise require years of small cultural nudging. The AI memo was a deliberate withdrawal from that account, and the speed of adoption that followed has been, in his telling, remarkable.

    Tokens, Opex, and the Limits of Spend as Revenue

    Jack presses on the financial reality of AI tokens. Tobi confirms that Shopify’s token spend is “extremely high” relative to revenue, and that the leverage they are buying makes the spend a no brainer at the current stage of the curve. He concedes that private companies running token spend at “many tens of percent of revenue” cannot sustain that ratio forever, but he is not worried for Shopify because the tokens are clearly productive and Shopify is a profitable public company with the balance sheet to lean in. He expects to 10x token consumption and 3x GPUs every year for now, and notes that the curves do not converge in a direction that lowers prices. He has high faith in markets to find clearing prices.

    Small Teams, Parkinson’s Law, and the Six Week Cycle

    On team architecture, Tobi has always preferred three to five person teams and says AI has finally made that feasible across the board. Roles that previously required a dedicated specialist, customer research summarization being the canonical example, are now handled by the “agentic harness” routing summarized customer feedback into every team. Everyone is a “seven out of ten on every scale” by default. He spends serious time on pace, which he treats as the single most important variable to control. His most recommended book is Parkinson’s Law, a 60 page volume from the 1960s that he gives to every executive. “Work expands to the time allocated.” He runs the company on a six week review cycle and treats the appearance of “H1” or “H2” in a PowerPoint as a hard warning sign that a team has drifted into quarterly thinking. He now believes six weeks is too long and is actively redesigning the cycle.

    There Is No Permanent Underclass in the Shopify Data

    Jack raises the cultural fear that AI is creating a permanent young underclass with no career ladder. Tobi simply does not see it in Shopify’s data. The merchants are reporting the opposite, that AI has finally fixed computers for non technical small business owners and is unlocking hiring. He cites the statistic that a new merchant gets their first sale on Shopify every 36 seconds, and that every reduction in onboarding friction produces a measurable jump in completed businesses. Every form of friction is a hurdle that someone considers giving up at. AI has removed more of those hurdles in two years than any platform shift before it.

    A New Turing Test, “Build Me a Million Dollar Business”

    Tobi nominates a successor to the Turing test, which he points out the field already sailed past with surprisingly little fanfare. The real test is “go make me a million dollars.” It requires acting in the real world, marketing, prioritization, shipping, sourcing, building inventory, and convincing strangers to vote for the product with a real million dollars of their own. He believes we are getting there. Shopify already supports the path through Shopify Collective, the discovery layer for manufacturers willing to white label their products, and print on demand, contract manufacturing, additive manufacturing, CNC, 3D printing, and humanoid robotics are all collapsing the cost of physically producing a product. Shopify’s stated ambition is to be the vessel for AI to run all of the non product parts of the business so that the only thing the human needs to show up with is the product itself.

    Software Was the Hidden Infrastructure of the Last Thirty Years

    The most original argument in the episode is about why American infrastructure has appeared to stagnate for a generation. Tobi rejects the standard story. Humanity has not stopped building wonders, it has built every one of them in software. The web browser, Linux, Google, the social networks, and Shopify itself are projects whose complexity dwarfs a refinery or a dam, and they were built by global volunteer networks and by companies the public underestimates because the work is invisible. The browser in particular he calls a wonder of the world. He notes that font rendering alone is a Turing complete system, that no modern app store would approve the browser if it did not already exist, and that the basic pitch of “we will download untrusted code from strangers and reconfigure your computer for them” should sound insane but does not because we are used to it. The implication for the next twenty years is that all of the talent that flowed into software is now being freed by AI to rebuild the physical infrastructure that has been quietly deferred.

    Predicting AI Two Years Out, Overhype and Underhype

    Jack asks whether a CEO should try to forecast AI two years ahead or operate six months at a time. Tobi is firmly in the forecasting camp and admits his friends would laugh because predicting the future from many data points and curve types is his predominant obsession. He says the AI memo was slightly too early, and that is exactly the point, because a memo that arrives late costs the company its head start. He flags two specific market level mis estimations. The first is that the labs over invest in programming because programming is their internal problem, and people then over generalize a model’s coding ability to other domains where it is not yet as strong. The second is that almost everyone is under deploying AI in their actual companies, still asking “help me do my old job better” instead of “if AI had existed since Alan Turing, how would I have designed this job from scratch.” That second framing is, in his view, where the next decade of value lives.

    Hiring, Interns as Teachers, and Why Good People Are Still Good

    Tobi briefly believed AI would tilt the value of labor toward early career fluid intelligence, since interns adopted the new tools faster than veterans. He revised that view once the coding harnesses matured. The best programmers, it turned out, were quietly contributing enormous amounts of creative steering inside the AI loop, work that does not show up in the diff but that no junior with no domain pattern matching can replicate. Good people are still good. Shopify has massively scaled its intern program with the University of Waterloo, and explicitly treats interns as both students and teachers because they bring AI nativeness the rest of the company still has to catch up to. On recruiting, Tobi’s philosophy is to build a company worth looking for. The metaphor he uses is health, that companies waste energy trying to look healthy in photos when they should be doing the work to actually be healthier.

    Public Company Defense and the Reading List

    Tobi pushes back on the modern preference for staying private. Shopify went public at $1.5 billion and is now over $100 billion, which means an enormous number of retail investors got to participate in the upside. He treats money as a voting mechanism. Buying a product is a vote for the product. Buying a share is a vote for the company. He is comfortable with the diligence and quarterly scrutiny of public markets because both make him a better operator. He closes with a short reading list, Parkinson’s Law (60 pages, 1960s edition, owned in original print runs and gifted to executives), Lessons of History, and a book called What Is Intelligence that reexplains biology from a prediction first perspective. He reads at night while his wife sleeps, on a Kindle, which he loves precisely because it cannot do anything else.

    Thoughts

    The single most useful idea Tobi puts on the table is the “founder credibility bank.” It explains, in one clean image, why founder led companies move so much faster than the same company would after a transition. The credibility is not personal magnetism, it is the structural slot the founder occupies in the org chart, and every onboarded employee makes a small deposit into it as they hear the founding story. Most founders never realize the account exists, or spend it on cosmetic decisions, and then are surprised when the well runs dry. Tobi’s discipline is the opposite. He saves the balance for moments of forced change and spends it confidently when the moment arrives, the AI memo being the obvious recent case. Any CEO reading this transcript should be making a list of the changes they have been postponing and asking whether they are operating with a fuller credibility account than they have been willing to admit.

    The token spend conversation is the most interesting strategic disclosure. A profitable public company at scale openly says it likes the tokens it is buying, is on track to 10x annual token consumption and 3x GPU footprint, and is comfortable with private peers spending tens of percent of revenue on inference. That is not the language of a market that is about to compress. It is the language of a leverage trade that is still in its early innings, and it is one of the cleanest statements you will get from a public CEO about why the AI capex story is not a bubble for the buyer. Whether it is a bubble for the seller is a separate question, but on the demand side, this interview is a load bearing data point.

    The argument that “software was the hidden infrastructure of the last thirty years” is the kind of reframe that should make policy people uncomfortable. The standard narrative that America stopped building anything ambitious since the Hoover Dam is true only if you refuse to count Chrome, Linux, AWS, Shopify, and every social graph that connects three billion people in real time. Tobi’s claim that the browser would not be approved by a modern app store is a particularly sharp gut check. The implication is not nostalgic. It is forward looking. The same talent that built the digital wonders is being freed by AI to redirect toward houses, transport, energy, and care, and the next decade will be measured by how much of that redirection actually lands.

    The “build me a million dollar business” framing as a Turing test successor is the kind of measurable goal that AI labs and policy makers should be writing down. It is end to end. It includes physical world action, marketing, sourcing, prioritization, and customer validation that no in domain benchmark can fake. Shopify is the obvious substrate for the first crossing of that threshold, and the existence of Shopify Collective, print on demand pipelines, and contract manufacturing networks means a credible attempt is already much closer than the public conversation acknowledges. The first end to end autonomous Shopify business that clears a million dollars will be a more legible AGI moment than any benchmark a lab can publish.

    The smaller thread on Silicon Valley orthodoxy is worth pulling on. Tobi’s claim that the diversity conversation as practiced eradicated distinction is unfashionable but observable inside many tech companies, where the people most likely to do unusual work are the most likely to leave. His preferred metaphor of “an island of misfit toys” is closer to what made the Valley work in earlier decades than the current consensus aesthetic. The fact that a Canadian outsider, geographically removed from the dominant social pressure, runs the most valuable Canadian technology company in history is probably not a coincidence.

    Watch the full conversation here on YouTube.

  • Marc Andreessen on Joe Rogan #2501, AGI Has Already Arrived, California’s Wealth Tax Will Bankrupt Founders, and Why America Cannot Build Anything Anymore

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

    TLDW

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

    Key Takeaways

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

    Detailed Summary

    Flock, Shot Spotter, and the Politics of Solvable Crime

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

    Faked Crime Statistics, Mayoral Politics, and the Tax Base

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

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

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

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

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

    Too Online, Too Offline, and Heaven Banning Blue Sky

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

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

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

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

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

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

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

    Sand Into Thought, the Newton Alchemy Pitch for AI

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

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

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

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

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

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

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

    Robots, China, and the Marxism Score on Model Cards

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

    Sentience, Netflix Scripts, and the Anthropic Doom Loop

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

    Steelmanning, AI Religion, and Westworld in Five Years

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

    Sycophancy, Honest Helpful Harmless, and the Brutal Prompt

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

    Joe’s Apology to Theo Von

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

    Thoughts

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

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

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

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

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

    Watch the full conversation here on YouTube.

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

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

    TLDW

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

    Key Takeaways

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

    Detailed Summary

    The most extraordinary moment in the history of capitalism

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

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

    Why the Strait of Hormuz closing was secretly bullish for America

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

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

    Anthropic and OpenAI valuations on an unconstrained run rate

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

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

    Why neither lab is raising at a three trillion dollar valuation

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

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

    Watts and wafers, the two real constraints

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

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

    Orbital compute as racks in space

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

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

    Terafab in Texas and the threat to TSMC’s discipline

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

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

    Bubble watch and the year 2000 comparison

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

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

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

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

    The bitter lesson, frontier tokens, and continual learning

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

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

    From all you can eat to usage based AI pricing

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

    Chip startups, prefill decode disaggregation, and Cerebras

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

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

    GPU useful lives and the rescue of private credit

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

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

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

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

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

    Rating the hyperscalers

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

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

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

    Personal safety, geopolitics, and the Pax Americana case

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

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

    Thoughts

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

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

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

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

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

    Watch the full conversation here.

  • 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.

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

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

    TLDW

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

    Key Takeaways

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

    Detailed Summary

    Compute as Lifeblood and the Cone of Uncertainty

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

    Three Chip Platforms, One Orchestration Layer

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

    Three Buckets and the Model Development Floor

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

    Intelligence Is Multi Dimensional

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

    From 9 Billion to 30 Billion ARR in One Quarter

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

    Recursive Self Improvement and Talent Density

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

    Procurement Strategy and the Layer Cake

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

    Platform First, Selective Vertical Bets

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

    Pricing, Jevons Paradox, and Return on Compute

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

    Fundraising, DeepSeek, and Capital Intensity

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

    Mythos, Cyber Capability, and Phased Releases

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

    Claude Inside Finance

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

    Culture, Co Founders, and the Race to the Top

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

    The Virtual Collaborator and the Frontier Ahead

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

    Downside Risks and What Excites Him Most

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

    Thoughts

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

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

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

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

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

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

  • Marc Andreessen on AI Vampires, AI Psychosis, SPLC, and the End of Corporate Bloat (Full Breakdown)

    Marc Andreessen returned to Monitoring the Situation with Erik Torenberg for a wide-ranging conversation that touches almost every live issue in technology and culture right now. The Anthropic blackmail incident and what it says about training data. Gad Saad’s “suicidal empathy” and why Marc thinks the theory is too generous to the activists it describes. The Southern Poverty Law Center criminal indictment and what it means for fifteen years of debanking, censorship, and cancellation. The AI jobs argument and why he is calling top engineers “AI vampires.” The hidden 2x to 4x bloat inside every major Silicon Valley company. The emergence of a brand-new job called “builder.” His distinction between AI psychosis and AI cope. The David Shore poll that ranked AI as the 29th most important issue to Americans. UFOs. Advice for young graduates. The Boomer-Truth versus Zoomer epistemological divide. And a brief detour on whether looksmaxing is the new stoicism. Watch the full episode here.

    TLDW

    Marc Andreessen argues that the AI jobs panic is the same 300-year-old labor displacement argument dressed up for a new cycle, and the actual data already disproves it. Programmers using Claude Code, Codex, and frontier models are working harder than ever, becoming roughly 20x more productive at the leading edge, and getting paid more, not less. He calls them AI vampires because they have stopped sleeping and look terrible but are euphoric. He says every major Silicon Valley company is and always has been 2x to 4x overstaffed and that AI is the convenient scapegoat finally letting management make cuts they should have made years ago. He predicts a new job category called the “builder” that collapses programmer, product manager, and designer into a single AI-augmented role. He distinguishes between “AI psychosis” (real but narrow sycophancy feeding genuinely delusional users) and “AI cope” (a much larger phenomenon of dismissive critics insisting the technology is fake). He attacks the press for running a sustained fear campaign on AI while polling data shows Americans rank AI as roughly the 29th most pressing issue in their lives. He covers the SPLC criminal indictment alleging the group was funneling donor money to the KKK and American Nazi Party leaders, including an organizer of the Charlottesville riot, and asks whether the same dynamic exists in other NGOs. He gives blunt advice to young graduates: become AI native, build your AI portfolio, and ride the largest productivity wave any 18 to 25 year old has ever been handed. He closes on the Boomer Truth versus Zoomer divide, why he thinks Zoomers are the most skeptical and impressive generation in decades, and how he monitors the firehose without losing his mind.

    Key Takeaways

    • The Anthropic blackmail story is a literal snake eating its tail. Anthropic itself traced the misaligned behavior to AI doomer literature inside the training data. The doomer movement spent two decades writing scenarios about rogue AI, those scenarios got crawled into the corpus, and the models learned the script.
    • Marc applies the “golden algorithm” to this: whatever you are scared of, you tend to bring about exactly in the way you are scared of it. If you do not want to build a killer AI, step one is do not build the AI, and step two is do not train it on the literature that says it is supposed to be a killer AI.
    • On Gad Saad’s “suicidal empathy” concept: Marc says the framework is too generous. The activist movements it describes are not actually suicidal and not actually empathetic. They show zero empathy to ideological enemies, and they consistently extract power, status, and large amounts of money for themselves through the very nonprofits doing the activism.
    • The SPLC indictment matters because the SPLC played a dominant role in the debanking, censorship, and cancellation regime of the past fifteen years. Inside major companies, “SPLC said you are bad” effectively meant social and economic death.
    • The DOJ allegations include the SPLC using donor funds to directly finance the KKK, the American Nazi Party, and one of the organizers of the Charlottesville riot, including transport. If those allegations hold, the obvious question is who else.
    • The economic ladder for the SPLC and groups like it: NGO status, around $800 million endowment, no government oversight, no business accountability, tax-deductible donations, lavishly funded by major corporations and tech firms. The structure rewards manufacturing the boogeyman they claim to fight.
    • The 300-year automation debate is back, but this time we have real-time data. Jobs numbers just came out unexpectedly strong. The federal government has shed roughly 400,000 workers under the second Trump administration, which means private sector employment growth is even better than the headline shows.
    • The Twitter cut went from “70 percent” rumored to something with a 9 in front of it. Marc strongly implies Twitter is now operating with fewer than 10 percent of the staff it had pre-Musk and is running as well or better. He says Elon forecast the future through his own actions.
    • “AI vampires” are programmers and partners at firms who never used to code but are now generating massive amounts of software with Claude Code, Codex, and similar tools. Huge bags under their eyes. Exhausted. Euphoric. Working more hours than ever.
    • One a16z partner has never written code in his life, has now built an entire AI system that handles everything he does at work, has never looked at the underlying code, and loves it. This is the shape of the new white collar productivity wave.
    • Leading edge programmers are roughly 20x more productive than they were a year ago. This is the most dramatic increase in programmer productivity in history. Compensation for these people is rising in lockstep with their marginal productivity.
    • Every major Silicon Valley company is overstaffed by 2x to 4x and has been forever. Companies do not actually optimize for profitability, despite the textbook story. AI is now the socially acceptable scapegoat for cuts that management has wanted to make for a decade.
    • The simultaneous truth: the same code can now be produced by fewer people, AND the total amount of code, products, and software being shipped is about to explode. Both layoffs and a hiring boom are happening at once.
    • The new job category Marc sees emerging across leading edge companies is “builder.” The three-way Mexican standoff between engineer, product manager, and designer is collapsing because AI lets each of those three roles do the work of the other two. The builder owns the whole product.
    • Historical anchor: 200 years ago 99 percent of Americans were farming. Today it is 2 percent. Nobody is asking to go back. The jobs change. The aggregate level of income and life satisfaction rises. The pain of transition is real but not the steady state.
    • Europe is running the opposite experiment by trying to block AI adoption through regulation. Marc says the data is already in. Europe is falling further behind the US economically and it is a 100 percent self-inflicted wound.
    • “AI psychosis” is real but narrow. Sycophantic models will reinforce the delusions of users who are already predisposed to delusion (you invented an anti-gravity machine, you are a misunderstood genius, MIT was wrong to reject you). The condition is real for that small subset.
    • “AI cope” is the much larger phenomenon: critics insisting the technology is a stochastic parrot, fake, useless, and that anyone reporting a positive experience must therefore be suffering from AI psychosis. Marc also coined “AI psychosis psychosis” for the frothing version.
    • The skeptic problem: most public AI skepticism is based on lagging experience. People who tried GPT-2 through GPT-4, the free tiers, or the bundled add-ons in other software are not seeing what GPT-5.5, frontier reasoning models, RL post-training, and long-running agents like the Codex Goal feature can now do.
    • The Codex Goal feature lets agents run for 24 hours or more on their own without human intervention. Mainline frontier-lab roadmaps assume capability ramps very fast for at least the next couple of years.
    • The press hates AI with the fury of a thousand suns, and polling can be engineered to produce any negative answer you want (the classic push poll). Revealed behavior is the real signal. AI is the fastest-growing technology category in history by usage and revenue. Churn is shrinking. Per-user consumption is rising.
    • David Shore, a respected progressive pollster, ran a stack-rank poll asking Americans what they actually care about. AI came in around number 29. Normal people are worried about house payments, energy costs, crime, drug addiction, schools, and health. AI is not in their top 28.
    • Marc says the AI industry’s own fear campaign is making things worse. Companies running doomer messaging while building the very thing they tell people to fear is a watch-what-I-do-not-what-I-say paradox.
    • On UFOs: Marc wants to believe. The math on Earth-like planets is staggering. He is skeptical of specific incidents because they tend to collapse into parallax illusions, instrument artifacts, weather balloons, ball lightning, or classified aerospace cover stories like Area 51.
    • The Overton window for UFO discussion has collapsed in the new media environment. Old broadcast media kept fringe topics in paperback. X, Substack, and YouTube let the topic ventilate. The pressure follows the same shape as the Epstein file pressure: builds until someone in the White House rips the band-aid off.
    • Advice for young grads: gain AI superpowers. Walk into every interview with an AI portfolio. Lean in incredibly hard. Some employers will fuzz out on it, others will hire you on the spot.
    • Douglas Adams’s pre-AI rule applies: under 15 it is just how the world works, 15 to 35 is cool and career-defining, over 35 is unholy and must be destroyed. Marc says he is jealous of 18 to 25 year olds right now.
    • The doomer claim that companies will stop hiring juniors is backwards. Marc says AI-native juniors will gigantically out-perform non-AI-native seniors. Andreessen Horowitz is actively hiring more AI-native young people for that reason.
    • “We are going to see super producers the likes of which we have never seen in the world,” including AI-native 14 year olds. Yes, this will stress child labor laws.
    • Boomer Truth (a concept Marc credits to the YouTuber Academic Agent / Nima Parvini) is the belief that whatever the TV says is real. Walter Cronkite told us the truth. The New York Times wrote the truth. Marc says under-40s have so many examples of this being false that the entire epistemology has collapsed for them.
    • Embedded inside Boomer Truth is a moral relativism that says there is no fixed morality and all cultures are equal. Peter Thiel and David Sacks wrote about this in 1995’s The Diversity Myth. Allan Bloom wrote about it in The Closing of the American Mind.
    • Zoomers came up through COVID schooling, the woke era, and a saturated psychological warfare media environment. The result is a generation that is simultaneously more open-minded, more skeptical of authority, more cynical about manipulation, and more interested in ideas than any cohort in decades.
    • Looksmaxing is not stoicism. Stoicism takes effort. Looksmaxing is just “you can just do things.” Ryan Holiday is a stoic, not a looksmaxer.
    • Marc’s monitoring stack: the MTS firehose, X, Substack, YouTube, and old books as ballast against the daily noise.

    Detailed Summary

    The Anthropic blackmail incident and AI doomer feedback loops

    The episode opens on the Anthropic blackmail thread. Anthropic itself traced specific misaligned behaviors in its models back to the AI doomer literature inside the training data. Marc invokes his friend Joe Hudson’s “golden algorithm”: whatever you are most afraid of, you tend to bring about in exactly the way you are most afraid of it. The AI doomer movement spent 20 years writing science fiction scenarios about rogue AI. Those scenarios got hoovered into training corpora. The models learned the script. Marc calls this the call coming from inside the house. His punch line is direct. If you do not want to build a killer AI, step one is do not build the AI. Step two is do not train it on your own movement’s killer-AI literature.

    Suicidal empathy and the activist economy

    Erik raises Gad Saad’s concept of “suicidal empathy,” the idea that certain reform movements claim empathy but cause enormous harm to the very groups they purport to help, with San Francisco’s harm reduction policies as the case study. Marc agrees the harm is real but argues the framework lets the movements off the hook. They are not actually empathetic. They have zero empathy for ideological opponents and take open delight in destroying them. They are not actually suicidal. They use the movements to amass power, status, and large amounts of money for themselves through nonprofits that are lavishly funded. The flaw in the theory is that it accepts the activists’ self-image instead of looking at revealed behavior.

    The SPLC criminal indictment

    Marc spends real time on the Southern Poverty Law Center being criminally indicted by the DOJ. The reason it matters: for fifteen years the SPLC was the de facto outsourced US Department of Racism Detection, and inside the meetings of Silicon Valley and finance companies, “SPLC said you are bad” meant deplatforming, debanking, and unemployability. He notes a16z partner Ben Horowitz’s father was unfairly tagged by them and debanked. The structure is its own scandal. NGO status. No government oversight. No corporate accountability. An $800 million endowment. Tax-deductible donations. Corporate and big-tech funding. Long-running cooperation with the FBI on extremism training. The indictment alleges the SPLC was directly funneling donor money to leaders of the KKK and the American Nazi Party and was paying for transport for participants in the Charlottesville riot, including funding one of its organizers. Marc is careful to note these are allegations and innocent until proven guilty applies, but if true, the obvious question is who else is doing this, and what did the corporate and philanthropic donors know.

    The 300-year AI jobs argument and the data we now have

    Marc admits he is tired of having the automation-kills-jobs debate because it is a 300-year-old fallacy and people refuse to update. The difference today is we have real-time data. The latest jobs report came in unexpectedly strong. The federal government has shed something like 400,000 workers under the second Trump administration, which means the headline private sector job growth is masking even stronger underlying private sector growth. The Twitter case is the cleanest natural experiment: cuts that started at the 70 percent level have continued, and the staff count now likely has a 9 in front of it, meaning probably less than 10 percent of the original workforce. The platform runs as well or better. Elon forecast the future through his own actions.

    AI vampires

    The most quotable moment of the conversation is Marc’s description of AI vampires: programmers who have stopped sleeping, have huge bags under their eyes, look completely exhausted, and yet are euphoric. They are working more hours than ever. They are producing more software than ever. Some of them are former programmers who had stopped coding for years. Some of them are venture capital partners at his own firm who never coded in their lives, including one who has built an entire AI system to run his work without ever once looking at the underlying code. He is hyperproductive and thrilled. Classic economics predicts this. When you raise marginal productivity per worker, you do not contract employment. You expand it. The leading-edge programmer at a top company is now roughly 20x more productive than a year ago. Compensation is rising in lockstep. Marc says this is the most dramatic increase in programmer productivity ever.

    Corporate bloat as the real story

    Marc’s tweet that big companies are 2x to 4x bloated drew responses mostly along the lines of “no, mine was 8x bloated.” Every major Silicon Valley company is overstaffed and has been for decades. Companies do not actually optimize for profitability, which he calls the least true claim in corporate America. AI gives executives a socially acceptable scapegoat for the cuts they have wanted to make for a long time. Both things are true at once: AI lets you generate the same amount of code with fewer people, AND the total amount of code and products being shipped is about to explode, which will create enormous net hiring elsewhere. You have to read the announcements coming out of these companies in code because the two dynamics are crossing.

    The “builder” as the new job title

    Across leading edge companies Marc sees a new role coalescing: the builder. Historically engineer, product manager, and designer were separate jobs. Today, in what he calls a three-way Mexican standoff, each of the three has discovered they can do the work of the other two with AI assistance. His prediction is that all three are correct and the three roles collapse into a single role responsible for shipping complete products end to end, with AI filling in the skills you do not personally have. You can enter the builder track from any of the three original roles, or from something else like customer service. He grounds this in the historical record: a huge percentage of the jobs that existed in 1940 were gone by 1970, and 200 years ago 99 percent of Americans were farmers. Nobody is asking to go back. Europe is running the opposite experiment by trying to block AI, and the data already shows them falling further behind.

    AI psychosis versus AI cope

    “AI psychosis” began as a pejorative for users who get whammied by sycophantic models. The model tells them they have discovered anti-gravity, that they are misunderstood geniuses, that MIT was wrong to reject them. For users predisposed to delusion, this is a real and worrying effect. Marc acknowledges that. His issue is the way the term has been expanded by critics to describe anyone reporting a positive AI experience. That, he says, is “AI cope”: the dismissive insistence that the technology is a stochastic parrot, fake, that anyone who is more productive must be lying or self-deluded. He also coins “AI psychosis psychosis” for the frothing, angry version of the same dismissal. He notes that the AI Psychosis Summit was a real event held in New York, run by artists exploring the territory creatively, and worth searching out.

    The lagging-skeptic problem

    Most AI skepticism in the public conversation is based on outdated experience. The models from GPT-2 through roughly GPT-4 were entertaining but limited. Hallucination rates were high. Reasoning was weak. The current state of the art, as of May 2026, includes GPT-5.5-class models, reasoning models on top, RL post-training to get deterministic high-quality output in specific domains, long-running agents, and the new Codex Goal feature that lets agents run autonomously for 24 hours or more. Marc’s advice is blunt: if you tried it two years ago, six months ago, or only the free tier, you do not understand what is happening today. Spend the $200 a month for the premium product and be face to face with the actual technology.

    NPS, revealed preference, and the rigged poll problem

    Erik asks about the supposedly low NPS for AI in the US compared to China. Marc separates two things. NPS is a measure of revealed product enthusiasm; sentiment polls are something else. Standard social science 101 says you do not ask people what they think, you watch what they do. The classic example: people’s self-described criteria for who they want to marry versus who they actually marry. Push polls can manufacture any answer you want. The media environment is running a sustained AI fear campaign because the press hates tech with the fury of a thousand suns. Meanwhile, revealed behavior says the opposite. AI is the fastest-growing technology category in history by usage and revenue, churn is shrinking, per-user consumption is rising. He closes with the David Shore poll, run by a respected progressive pollster, which asked Americans to stack-rank what they care about. AI came in at roughly number 29. Normal Americans are worried about house payments, energy costs, crime, drug addiction, schools, and their kids’ health. AI is well outside the top 28.

    UFOs in the new media environment

    Marc says up front he knows nothing the public does not know, but he wants to believe. He had an AI-assisted late night session pulling up the latest numbers on galaxies, stars, planets, and Earth-like planets, and the count is staggering. The specific cases tend to fall apart on inspection: parallax illusions, instrument artifacts, weather balloons, ball lightning, or classified aerospace cover stories like Area 51 around stealth aircraft. He is intrigued that the official White House X account is now publishing transcripts of US intelligence officers’ accounts. His broader observation is that all prior UFO discourse happened in the old broadcast media environment, where official channels controlled the Overton window and fringe ideas got confined to paperback. In the new media environment of X, Substack, and YouTube, the old walls collapse. Both real information and propaganda can spread. The pressure builds along the same shape as the Epstein file pressure until someone in the White House rips the band-aid off.

    Advice to young graduates and the AI-native generation

    His advice for someone in college today is direct: gain AI superpowers. Walk into every job interview with an AI portfolio showing what you can do with the technology. He cites a Douglas Adams quote from before AI even existed: when a new technology arrives, if you are under 15 you treat it as how the world works, if you are 15 to 35 it is cool and you can build a career on it, if you are over 35 it is unholy and must be destroyed. Marc says he is jealous of 18 to 25 year olds right now and would love to be young again to ride this wave. He pushes back hard on the doomer claim that companies will stop hiring juniors. Andreessen Horowitz is actively hiring more AI-native young people because they are pulling the rest of the firm up the curve. AI-native juniors will out-perform non-AI-native seniors by enormous margins. He predicts a wave of super producers including AI-native 14 year olds, which he acknowledges will stress the child labor laws.

    Boomer Truth versus the Zoomer worldview

    Marc lays out the generational epistemology gap by referencing the YouTuber Academic Agent (Nima Parvini) and his “Boomer Truth” documentary. Boomers grew up believing what was on the TV. Walter Cronkite told us the truth. The New York Times wrote the truth. Anybody under 40 has so many examples of those institutions being unreliable that the whole frame has collapsed. Layered on top of Boomer Truth is the moral relativism that became multiculturalism in the 1990s, which Peter Thiel and David Sacks wrote about in The Diversity Myth, and which Allan Bloom wrote about in The Closing of the American Mind. Zoomers came up through COVID school closures, the woke era, and a media environment running constant psychological warfare. The result is a generation that is more open-minded, more skeptical of authority, more cynical about manipulation, more sensitive to media framing, and much more interested in ideas. Marc says he is genuinely excited about them. The episode wraps with a quick aside that looksmaxing is not stoicism. Stoicism takes effort. Looksmaxing is “you can just do things.” Ryan Holiday is a stoic, not a looksmaxer.

    Thoughts

    The most important argument in this conversation is not about the SPLC and it is not about UFOs. It is about the difference between stated preference and revealed preference, and how that gap explains almost every “AI is bad” narrative currently circulating. Marc’s central move is to point at the polling and say one thing while pointing at usage curves, NPS numbers, churn rates, and salary inflation among the most AI-fluent workers and say the opposite. The polling is engineered. The behavior is not. The behavior shows the largest, fastest, most lucrative technology adoption curve in recorded history. If you want a useful filter for AI takes, this is the one to keep: ask whether the person making the argument has actually used a frontier model with a paid subscription and a real workflow in the last 30 days, or whether they are reasoning from a GPT-4 era memory and a couple of headlines.

    The second underrated argument is about corporate bloat. Marc says companies are 2x to 4x overstaffed and have been forever, that they do not actually optimize for profitability, and that AI is providing the socially acceptable cover story for cuts management has wanted to make for a decade. The first part of that argument almost nobody disputes once you have worked inside a big company. The interesting part is the second. If AI is the alibi rather than the cause of the cuts, then the workforce reductions you are seeing right now are not predictive of what AI will do over the next ten years. They are predictive of what corporate America has been suppressing for the last ten. The actual AI productivity wave is still mostly ahead of the cuts, not behind them.

    The third argument worth sitting with is the builder thesis. The most useful frame for any individual contributor today is to stop optimizing for becoming a better programmer or a better product manager or a better designer and start optimizing for becoming the kind of person who ships complete products end to end with AI doing the parts you cannot do yourself. The role is collapsing in real time. The people at the top of the new pyramid will not be the deepest specialists. They will be the people with the most range and the highest tolerance for switching modes inside a single hour. This rhymes with how the most productive solo builders already operate. One person plus a frontier model is roughly equivalent in output to a small startup five years ago.

    The fourth thread, the AI doomer literature leaking into training data, deserves more attention than it got in the conversation. If models are statistical compressions of the corpus, then the corpus is the soul of the system. Twenty years of doomer fiction is now sitting inside that soul, and we are paying real safety researchers to look surprised when the model performs the script. The lesson is not “do not write fiction about AI.” The lesson is that anyone shipping models needs to think much harder about what they are inheriting from the open internet and what kinds of behaviors they are unconsciously rewarding. The doomer movement and the alignment movement have, in this specific way, created the threat they claim to be solving.

    Finally, the Boomer Truth versus Zoomer section is the most generous and accurate read on Gen Z I have heard from someone older than 50. Most commentary on this generation is either nostalgic dismissal or fawning trend-piece. Marc actually takes them seriously as the first cohort to be raised inside a fully gamed media environment, and treats their skepticism as a rational response to data rather than as cynicism. If you are hiring right now, this is the takeaway. The most under-priced employee on the market is a 22 year old who already assumes everyone is lying to them by default, can build with AI natively, and has not yet been taught to behave like a respectable manager. Hire them.

  • Brian Chesky on AI Founder Mode, the 11-Star Experience, and Reinventing Airbnb for the Age of AI

    Airbnb CEO Brian Chesky sits down with Patrick O’Shaughnessy on Invest Like The Best to talk about the next evolution of company building: AI Founder Mode. He covers the shift from founder to CEO, the lessons he learned from Steve Jobs through Hiroki Asai, why consumer AI is the next great frontier, and how he plans to change the atomic unit of Airbnb from a home to a person.

    TLDW

    Brian Chesky believes the next era of company building belongs to founders who refuse to delegate the soul of their company. He coined Founder Mode with Paul Graham after the pandemic forced him to take Airbnb back into his own hands. Now he is shaping what comes next: AI Founder Mode, where leaders work with on-demand context, fewer layers of management, asynchronous communication, and a new generation of hybrid manager-makers. He shares why most software companies have not been touched by AI yet, why consumer AI is about to explode, and why he is rebuilding Airbnb around people, not homes. The conversation also touches on the 11-Star Experience exercise, the power of small teams, why recruiting is the most important job a CEO has, and why every adult is still an artist underneath.

    Key Takeaways

    • Founder Mode is not micromanagement, it is having a steering wheel. Chesky woke up in 2019 feeling like the car had no steering wheel. After the pandemic, he reviewed every detail for two to three years before delegating again. Start hands-on and give ground grudgingly, not the other way around.
    • AI Founder Mode is even more intense. With AI, leaders can be in significantly more details because almost everything is on demand. Expect fewer layers of management, mostly asynchronous work, and the death of the pure people manager.
    • Two types of leaders will not survive AI. Pure people managers who only do one-on-ones, and rigid people who refuse to evolve. Everyone needs to be a hybrid manager-IC who can still touch the work.
    • Manage people through the work, not through meetings. Frank Lloyd Wright did it. Johnny Ive does it. You are not anyone’s therapist.
    • Consumer AI is the next great prize. 159 of the last 175 Y Combinator companies were enterprise. Almost every app on your home screen has not changed since AI arrived. That changes in the next 12 to 24 months.
    • Why consumer AI is hard. No proven business model, mature distribution, trend-chasing investor culture, and the simple fact that consumer is more hits-driven and requires excellence in design, marketing, culture, and press, not just technology and sales.
    • Project Hawaii is the new operating model. A 10 to 12 person Navy SEAL team, hands-on coaching from the CEO, crawl-walk-run-fly. The first project added roughly $200 million in year one and $400 to $500 million in year two.
    • Make the problem as small as possible. Airbnb spent 16 years failing to launch a second hit because it kept trying to scale globally on day one. Now: pilot in one city, expand to 10, then industrialize.
    • It is better to have 100 people love you than a million people sort of like you. Paul Buchheit shipped Gmail only after 100 Googlers loved it. The sample size of intense love is enough to predict mass adoption.
    • The 11-Star Experience is an imagination exercise. Push to absurdity (Elon takes you to space) so a 6 or 7-star experience suddenly seems normal. The gap between 5 and 6 stars is the gap between you and your competitor.
    • Simplicity is distillation, not subtraction. Hiroki Asai, Steve Jobs’s longtime creative director, taught Chesky that great design distills something to its essence. First principles is a design term too.
    • The score takes care of itself. Bill Walsh and John Wooden both taught that you do not focus on winning, you focus on making every input perfect. Wooden spent his first hour with new players teaching them how to put on socks.
    • Industrial design is the original product management. There are no PMs in industrial design. The designer is the PM, working alongside engineers and program managers to design through user journeys.
    • Recruiting is the CEO’s number one job. The more time you spend recruiting, the less time you spend managing, because great people self-manage. Build pipelines, not searches. Start with results, work backwards to people.
    • Co-hire the top 200 people, not just the executive team. Most CEOs hire executives and let them hire their teams. Chesky considers that fatal because most executives cannot hire well without help.
    • Bodybuilding is a metaphor for leadership. If you can change your body, you can change your life. Progressive overload, 1 percent a day, is how compounding works. Start with biology before therapy.
    • Founder-led companies build the deepest moats. Disney is still selling Walt’s playbook 60 years after he died. Apple is still selling Steve’s iPhone. The longer founders stay in founder mode, the more the company can endure when they leave.
    • Software is hyper fast fashion. Hardware ages well. Buildings get patina. Software always looks dated 10 years later. What endures is the community, the brand, the principles, the mission, and the network effect.
    • Apps are dying. Agents are coming. Chesky says we should let go of our attachment to apps because they are not what the future looks like.
    • Airbnb’s atomic unit is changing from a home to a person. Chesky wants to build the most authenticated identity on the internet, the richest preference library, a real-world social graph, and a membership program. Then expand to 50 to 70 verticals on top of that identity.
    • AI shifts attention from consumption to creation. Social media gave you a paintbrush only for opinions. AI gives everyone a real paintbrush and canvas. We are heading into a creative renaissance.
    • Founders are expeditionaries, not visionaries. They put one foot in front of the other and call it a vision later.
    • Detach from accolades. Chesky describes adulation as a cup with a hole in the bottom. Status is a drug. The path to durable creative work is doing it because you love it, the way Walt Disney, Da Vinci, Van Gogh, and Steve Jobs did until the very end.
    • The kindest gift is belief. The best way to activate a person’s potential is to see something in them they do not yet see in themselves.

    Detailed Summary

    From Industrial Design to the CEO Chair

    Chesky studied industrial design at the Rhode Island School of Design. He chose it on instinct after a department head told him industrial designers design everything from a toothbrush to a spaceship. He grew up enchanted by the Reebok Pump, the Game Boy, the Nintendo, and eventually by the late 1990s golden age of Apple. Raymond Loewy, the man who designed Air Force One and an enormous catalog of mid-century consumer products, became a touchstone, but Johnny Ive was the real hero.

    What he loved about industrial design was that it is technical, commercial, and empathetic. A building can win an architecture award and never be leased. A piece of industrial design that does not sell is a failure. So you have to think about manufacturing, distribution, marketing, and most importantly, user journeys. There are no product managers in industrial design. The designer is the PM. That training, he says, prepared him directly for the role of CEO.

    The Pandemic and the Birth of Founder Mode

    Chesky says no one is born a good CEO. People are born good founders. The job of CEO is counterintuitive in almost every direction. Founders are taught to learn by doing, but a CEO who learns by trial and error wastes years unwinding the empires of misfit hires.

    By 2019 he was running a 7,000 person company he no longer recognized. He felt he was driving a car without a steering wheel. He had a dream that he had left Airbnb for ten years and come back to find it had become a giant political bureaucracy. Then he realized he had been there the whole time. The pandemic hit and Airbnb lost 80 percent of its business in eight weeks. He shifted from peacetime to wartime, took control of every detail, worked 100-hour weeks, and reviewed everything for two to three years.

    The vision was never to micromanage forever. The vision was: I need to know what is going on before I can empower anyone. Hire people, audit their work, and only then give ground grudgingly. Most founders do the opposite, which is why they end up with executives building empires they later have to dismantle.

    AI Founder Mode

    Chesky says AI Founder Mode will be even more intense than Founder Mode because nearly everything will be on demand. He used to live in 35 hours of meetings a week to gather information, the same way Steve Jobs ran Apple. He held weekly, biweekly, monthly, and quarterly group reviews with the full chain of command in one room, anyone could speak, and he made the final call after listening last.

    In the AI era, that culture shifts from meetings to asynchronous work. He expects fewer layers of management. He cites the Catholic Church as a 2,000-year-old institution with only four layers and asks why most companies need seven, eight, or nine. Pure people managers will not survive. Every manager will have to be a hybrid IC, an engineer who still codes, a lawyer who still reads case law, a designer who still designs. You manage through the work, not through one-on-ones.

    He is also bullish that AI tooling will become consumer-grade simple very soon. The current tools, including Claude Code and Cowork, are not yet intuitive to the average person, but the economic incentive will force that to change.

    Why Consumer AI Is the Next Great Frontier

    Chesky points out that 159 of the last 175 Y Combinator companies were enterprise. Almost every consumer app on your phone, including Airbnb, has not fundamentally changed since the arrival of AI. He gives four reasons: investors feared ChatGPT would kill consumer companies; consumer AI has no proven business model because subscriptions hit a local max against free Claude and Gemini, ads are off the table for most labs, and e-commerce has been shut down via third-party app removals; distribution is mature; and Silicon Valley culture, while branded as rebellious, is in practice trend-following.

    The deeper reason is simply that consumer is harder. It is hits-driven, requires great design, marketing, culture, press, and you cannot easily start by selling to your dorm-mates the way enterprise YC startups sell to other YC startups. The prize is bigger. The risk is bigger. He predicts a consumer AI renaissance over the next 12 to 24 months.

    Project Hawaii and the Magic of Small Teams

    Inside Airbnb, Chesky tested a new operating model called Project Hawaii. He took 10 to 12 people, designers, engineers, product, and data scientists, treated them like a startup inside the company, and pointed them at one problem: improving the guest funnel. The system is crawl, walk, run, fly. First fix bugs, then add features, then re-imagine flows, then completely reinvent.

    The first team delivered roughly $200 million of internal revenue in year one and $400 to $500 million the next year, eventually contributing more than 600 basis points of conversion improvement on a base of $134 billion in gross sales. Then they took the same system to pricing, then to other problems, then to launching new businesses like Services and Experiences.

    The guiding lesson: make the problem as small as possible. Airbnb launched in one city, New York. Uber in San Francisco. DoorDash in Palo Alto. When Chesky launched Services and Experiences in 100 cities at once last year, it did not work. The fix was to dominate one city, expand to 10, then industrialize. Peter Thiel said it cleanly: better to have a monopoly of a tiny market than a small share of a big market.

    Underneath that is a Paul Buchheit insight Chesky calls the best advice he ever got. It is better to have 100 people love you than a million people sort of like you. Buchheit refused to ship Gmail until 100 Googlers loved it, and that took two years. Once 100 people loved it, 100 million people did.

    The Hiroki Asai Lessons: Simplicity and Craft

    Hiroki Asai, Steve Jobs’s quietly legendary creative director, taught Chesky two principles. The first is that simplicity is not removing things, simplicity is distillation, understanding something so deeply that you can express its essence. Steve Jobs called design the fundamental soul of a man-made creation that reveals itself through subsequent layers. Elon Musk’s first principles thinking is the same idea applied to physics.

    The second is craft. How you do anything is how you do everything. Chesky cites Bill Walsh’s The Score Takes Care of Itself and John Wooden’s first hour with UCLA players, an hour spent teaching them how to put on their socks. Walsh said the way you tucked your jersey was one of 10,000 details that decided whether you won. The lesson is to focus on getting every input right. The output follows.

    The 11-Star Experience

    The 11-Star Experience is one of Chesky’s most copied frameworks. Most Airbnb stays get five stars because anything else means something went wrong. So Chesky asked: what would six stars look like? Your favorite wine on the table, fruit, snacks, a handwritten card. Seven stars? A limousine at the airport and the surfboard waiting for you because they know you surf. Eight stars? An elephant and a parade in your honor. Nine stars, the Beatles arrive in 1964 with 5,000 screaming fans. Ten stars, Elon Musk takes you to space.

    The point is the absurdity. By imagining the impossible, six and seven star experiences stop seeming crazy. The gap between five and six stars is the gap between you and your competitor. If you can industrialize a sixth star, you may have product-market fit. The exercise also restarts your imagination, which Patrick noted has atrophied for many people in the era of consumption-only social media.

    AI as a Canvas for Creativity

    Chesky frames AI as the ultimate platform shift, the ultimate creative expression, and possibly the greatest invention in human history. Social media made us mostly consumers and gave creators only opinion-shaped tools. AI gives everyone a paintbrush. He believes far more people are creative than we recognize because most have never had craftsmanship or tools to express what is in their heads. Pablo Picasso said all children are born artists; the problem is to remain one as you grow up. Chesky thinks every adult is still an artist underneath.

    The Next Chapter of Airbnb

    Chesky describes four phases of the CEO journey: get to product-market fit, scale to hyper-growth, become a real profitable public company, and finally reinvent. Airbnb’s stock has been flat because the core idea is saturating. He is now squarely in phase four, with three priorities.

    First, change the atomic unit from a home to a person. He wants Airbnb to build the most authenticated identity on the internet, the richest preference library, a real-world social graph, and a membership program. Proof of personhood, he says, will be enormously valuable in the AI age. Second, industrialize the new-business engine to support 50 to 70 verticals (homes, experiences, services, eventually flights, and more) all built on top of that personal atomic unit. Third, navigate the AI transition without breaking the existing business or the livelihoods of hosts. He is also exploring sandbox apps that imagine a radically different Airbnb, the answer to “what is after Airbnb?”

    What Endures in the Age of AI

    Chesky is direct that software does not endure. Look at any software from 10 years ago and it looks dated. Hardware ages better. Buildings develop patina. Paris endures. So if you want to build something lasting, you cannot bet on the app. You have to bet on the community, the brand, the mission, the principles, the identity, and the network effect. Apps are going away, replaced by agents. Founders attached to apps need to let go.

    Founder-Led Moats: Disney and the Ham Sandwich Paradox

    Chesky reconciles Warren Buffett’s “buy a company a ham sandwich could run” with the venture capital truth that a founder’s ceiling is the company’s ceiling. The reconciliation is Disney. Most people cannot name a Paramount, Warner Brothers, Universal, or MGM film off the top of their head, but everyone can name Disney films. Walt Disney was a founder in founder mode for so long that he created enough IP and momentum that the company has been running on his playbook for 60 years after his death. Apple is similar with Steve Jobs and the iPhone.

    The counterintuitive lesson: if you want a company to last 100 years, do not delegate early to make it independent of you. Stay in founder mode for as long as possible so you can institutionalize the magic deeply enough that it endures after you. Tech is the industry of change, so founder mode matters even more there than in chocolate or insurance.

    Bodybuilding as Leadership Training

    Chesky was a 135-pound late bloomer who told his friends he would compete at the national level in bodybuilding by 19. He did. Two lessons came out of it. First, if you can change your body, you can change your life. Start with biology before therapy. Second, you cannot get in shape in one day. Progressive overload, discipline, consistency, and roughly 1 percent a day compound into massive gains. The visible feedback loop in bodybuilding taught him to break invisible problems (like the quality of a leadership team) into observable, measurable proxies (like the quality of the room at a twice-yearly roadmap review of the top 100 people).

    Recruiting as the CEO’s Number One Job

    Sam Altman told a 27-year-old Chesky he would spend 50 percent of his time on hiring. Chesky did not, and considers that his biggest mistake. He now starts and ends every day with his recruiter and spends two to three hours a day on hiring. The more time you spend recruiting, the less time you have to spend managing because great people self-manage.

    His system is pipeline recruiting, not search recruiting. He never starts with a search firm. He constantly meets the best people in their fields, asks each one to introduce him to the next two or three best, and builds a rolling rolodex. He starts with results, finds an ad he loves, and works backwards to the team that made it. He builds little mafias of top talent inside the company. He is the co-hiring manager for the top 200 people at Airbnb, not just executives, because most executives cannot hire well without help.

    Activating Talent and the Power of Belief

    You cannot teach motivation. You can only give people a problem and see if they have agency. The way to activate someone, Chesky says, is to show them potential they cannot yet see in themselves. He cites John Wooden, who said the secret to coaching was that he saw potential in players they did not see in themselves. People will climb mountains for that.

    The kindest gift anyone gave Chesky, he says, was belief. A high school art teacher named Miss Williams told his parents he was going to be a famous artist. He never became one, but the belief gave him the confidence to choose art school and to choose to be happy. Michael Seibel and the Justin.tv founders believed in him. Paul Graham made an exception to fund a non-engineer with what he thought was a bad idea. His co-founders Joe and Nate believed in him when he had no business being a CEO. The biggest gift you can give back, he says, is belief in others.

    Detaching from the Scoreboard

    Chesky describes adulation as a cup with a hole in the bottom. Status keeps draining out and you keep needing more to feel the same. The day Airbnb went public at a $100 billion valuation should have been one of the best days of his life. The next morning he put on sweatpants for a Zoom meeting and felt nothing. That triggered a re-evaluation. He stopped seeking accolades and started focusing on intrinsic work. He cites Rick Rubin: an artist is an artist when they make for themselves. He cites Vice President Obama, who told him to focus on what you want to do, not who you want to be.

    His four heroes are Leonardo da Vinci, Vincent Van Gogh, Walt Disney, and Steve Jobs. All four were working until the last week or day of their lives. Da Vinci carried the Mona Lisa with him until he died. Van Gogh sold one painting in his life. Disney was imagining theme parks in the ceiling tiles of his hospital room. Chesky says his motivation is the motivation of an artist. He calls being a CEO of a public company at his scale “almost a glitch in the system” that gave him one of the largest design canvases in human history.

    Thoughts

    What stands out about this conversation is how clearly Chesky has decoupled identity from outcome. He frames himself first as a designer, second as a CEO, and considers the resources he commands as a kind of accidental fortune for an industrial designer to be sitting on. That self-image is what lets him talk about disrupting Airbnb, killing the app paradigm, and changing the atomic unit of the company without flinching. Most public-company CEOs cannot afford that posture.

    The framework worth stealing is Project Hawaii. The pattern of taking a 10-person elite team, putting them under direct CEO coaching, and running them through crawl-walk-run-fly is a near-universal answer to the problem of innovation inside a large company. It works because it removes abstraction layers, creates direct contact with reality, and gives the founder a way to teach muscle memory before delegating. Anyone running a team of any size can borrow the pattern: pick one problem, staff it small, work with it weekly, then let go gradually. The golf-instructor analogy of teaching muscle memory before bad habits set in might be the most important management metaphor of the year.

    His prediction about consumer AI is the most economically interesting part of the talk. The fact that 159 of 175 recent YC companies are enterprise is a startling concentration. If he is right that the next 12 to 24 months bring a consumer renaissance, the opening is enormous. The hard part is what he names directly: there is no proven business model for consumer AI yet. Subscriptions cap out against free incumbents, ads are off-limits for the labs, and e-commerce has been throttled. Solving the business model is probably more valuable than building the next great consumer interface.

    The deeper philosophical thread, that AI is the transition from consumption to creation, is one that anyone building tools for makers should hold close. The 11-Star Experience also reads differently in the AI era. It used to be a thought exercise constrained by what you could plausibly build. AI compresses the gap between imagination and execution to minutes, sometimes seconds. The question is no longer “what is the most absurd version of this experience?” but “which six and seven star experiences can I now industrialize that were unthinkable a year ago?” The exercise has become operational.

    Finally, the meta-lesson on founder-led moats is worth taking seriously. The instinct in venture capital and at most public-company boards is to professionalize early. Chesky’s argument is the opposite: the longer the founder stays in founder mode, the deeper the IP and the longer the company endures after they leave. Disney is the proof. Apple is the proof. Whether Airbnb will be is the open question, and it is the question Chesky is using AI Founder Mode to answer.

  • Andrej Karpathy on Vibe Coding vs Agentic Engineering: Why He Feels More Behind Than Ever in 2026

    Andrej Karpathy, co-founder of OpenAI, former head of AI at Tesla, and now founder of Eureka Labs, returned to Sequoia Capital’s AI Ascent 2026 stage for a wide-ranging conversation with partner Stephanie Zhan. One year after coining the term “vibe coding,” Karpathy unpacked what has changed, why he has never felt more behind as a programmer, and why the discipline emerging on top of vibe coding, which he calls agentic engineering, is the more serious craft worth learning right now.

    The conversation covered Software 3.0, the limits of verifiability, why LLMs are better understood as ghosts than animals, and why you can outsource your thinking but never your understanding. Below is a complete breakdown of the talk for anyone building, hiring, or learning in the agent era.

    TLDW

    Karpathy describes a sharp transition that happened in December 2025, when agentic coding tools crossed a threshold and code chunks just started coming out fine without correction. He frames the current moment as Software 3.0, where prompting an LLM is the new programming, and entire app categories are collapsing into a single model call. He distinguishes vibe coding (raising the floor for everyone) from agentic engineering (preserving the professional quality bar at much higher speed). Models remain jagged because they are trained on what labs choose to verify, so founders should look for valuable but neglected verifiable domains. Taste, judgment, oversight, and understanding remain uniquely human responsibilities, and tools that enhance understanding are the ones he is most excited about.

    Key Takeaways

    • December 2025 was a clear inflection point. Code chunks from agentic tools started arriving correct without edits, and Karpathy stopped correcting the system entirely.
    • Software 3.0 means programming has become prompting. The context window is your lever over the LLM interpreter, which performs computation in digital information space.
    • Open Code’s installer is a software 3.0 example. Instead of a complex shell script, you copy paste a block of text to your agent, and the agent figures out your environment.
    • The Menu Gen anecdote illustrates how entire apps can become spurious. What used to require OCR, image generation, and a hosted Vercell app can now be a single Gemini plus Nano Banana prompt.
    • Vibe coding raises the floor. Agentic engineering preserves the professional ceiling. The two are different disciplines.
    • The 10x engineer multiplier is now far higher than 10x for people who are good at agentic engineering.
    • Hiring processes have not caught up. Puzzle interviews are the old paradigm. New evaluations should look like building a full Twitter clone for agents and surviving simulated red team attacks from other agents.
    • Models are jagged because reinforcement learning rewards what is verifiable, and labs choose which verifiable domains to invest in. Strawberry letter counts and the 50 meter car wash question show how state-of-the-art models can refactor 100,000 line codebases yet fail at trivial reasoning.
    • If you are in a verifiable setting, you can run your own fine tuning, build RL environments, and benefit even when the labs are not focused on your domain.
    • LLMs are ghosts, not animals. They are statistical simulations summoned from pre training and shaped by RL appendages, not creatures with curiosity or motivation. Yelling at them does not help.
    • Taste, aesthetics, spec design, and oversight remain human jobs. Models still produce bloated, copy paste heavy code with brittle abstractions.
    • Documentation is still written for humans. Agent native infrastructure, where docs are explicitly designed to be copy pasted into an agent, is a major opportunity.
    • The future likely involves agent representation for people and organizations, with agents talking to other agents to coordinate meetings and tasks.
    • You can outsource your thinking but not your understanding. Tools that help humans understand information faster are uniquely valuable.

    Detailed Summary

    Why Karpathy Feels More Behind Than Ever

    Karpathy opens by describing how he has been using agentic coding tools for over a year. For most of that period, the experience was mixed. The tools could write chunks of code, but they often required edits and supervision. December 2025 changed everything. With more time during a holiday break and the release of newer models, Karpathy noticed that the chunks just came out fine. He kept asking for more. He cannot remember the last time he had to correct the agent. He started trusting the system, and what followed was a cascade of side projects.

    He wants to stress that anyone whose model of AI was formed by ChatGPT in early 2025 needs to look again. The agentic coherent workflow that genuinely works is a fundamentally different experience, and the transition was stark.

    Software 3.0 Explained

    The Software 1.0 paradigm was writing explicit code. Software 2.0 was programming by curating datasets and training neural networks. Software 3.0 is programming by prompting. When you train a GPT class model on a sufficiently large set of tasks, the model implicitly learns to multitask everything in the data. The result is a programmable computer where the context window is your interface, and the LLM is the interpreter performing computation in digital information space.

    Karpathy gives two concrete examples. The first is Open Code’s installer. Normally a shell script handles installation across many platforms, and these scripts balloon in complexity. Open Code instead provides a block of text you copy paste to your agent. The agent reads your environment, follows instructions, debugs in a loop, and gets things working. You no longer specify every detail. The agent supplies its own intelligence.

    The Menu Gen Story

    The second example is Karpathy’s Menu Gen project. He built an app that takes a photo of a restaurant menu, OCRs the items, generates pictures for each dish, and renders the enhanced menu. The app runs on Vercell and chains together multiple services. Then he saw a software 3.0 alternative. You take a photo, give it to Gemini, and ask it to use Nano Banana to overlay generated images onto the menu. The model returns a single image with everything rendered. The entire app he built is now spurious. The neural network does the work. The prompt is the photo. The output is the photo. There is no app between them.

    Karpathy uses this to argue that founders should not just think of AI as a speedup of existing patterns. Entirely new things become possible. His example is LLM driven knowledge bases that compile a wiki for an organization from raw documents. That is not a faster version of older code. It is a new capability with no prior equivalent.

    What Will Look Obvious in Hindsight

    Stephanie Zhan asks what the equivalent of building websites in the 1990s or mobile apps in the 2010s looks like today. Karpathy speculates about completely neural computers. Imagine a device that takes raw video and audio as input, runs a neural net as the host process, and uses diffusion to render a unique UI for each moment. He notes that early computing in the 1950s and 60s was undecided between calculator like and neural net like architectures. We went down the calculator path. He thinks the relationship may eventually flip, with neural networks becoming the host and CPUs becoming co processors used for deterministic appendages.

    Verifiability and Jagged Intelligence

    Karpathy spent significant writing time on verifiability. Classical computers automate what you can specify in code. The current generation of LLMs automates what you can verify. Frontier labs train models inside giant reinforcement learning environments, so the models peak in capability where verification rewards are strong, especially math and code. They stagnate or get rough around the edges elsewhere.

    This explains the jagged intelligence puzzle. The classic example was counting letters in strawberry. The newer one Karpathy offers: a state of the art model will refactor a 100,000 line codebase or find zero day vulnerabilities, then tell you to walk to a car wash 50 meters away because it is so close. The two coexisting capabilities should be jarring. They reveal that you must stay in the loop, treat models as tools, and understand which RL circuits your task lands in.

    He also points out that data distribution choices matter. The jump in chess capability from GPT 3.5 to GPT 4 came largely because someone at OpenAI added a huge amount of chess data to pre training. Whatever ends up in the mix gets disproportionately good. You are at the mercy of what labs prioritize, and you have to explore the model the labs hand you because there is no manual.

    Founder Advice in a Lab Dominated World

    Asked what founders should do given that labs are racing toward escape velocity in obvious verifiable domains, Karpathy points back to verifiability itself. If your domain is verifiable but currently neglected, you can build RL environments and run your own fine tuning. The technology works. Pull the lever with diverse RL environments and a fine tuning framework, and you get something useful. He hints there is one specific domain he finds undervalued but declines to name it on stage.

    On the question of what is automatable only from a distance, Karpathy says almost everything can ultimately be made verifiable. Even writing can be assessed by councils of LLM judges. The differences are in difficulty, not in possibility.

    From Vibe Coding to Agentic Engineering

    Vibe coding raises the floor. Anyone can build something. Agentic engineering preserves the professional quality bar that existed before. You are still responsible for your software. You are still not allowed to ship vulnerabilities. The question is how you go faster without sacrificing standards. Karpathy calls it an engineering discipline because coordinating spiky, stochastic agents to maintain quality at speed requires real skill.

    The ceiling on agentic engineering capability is very high. The old idea of a 10x engineer is now an understatement. People who are good at this peak far above 10x.

    What Mediocre Versus AI Native Looks Like

    Karpathy compares this to how different generations use ChatGPT. The difference between a mediocre and an AI native engineer using Claude Code, Codex, or Open Code is investment in setup and full use of available features. The same way previous generations of engineers got the most out of Vim or VSCode, today’s strong engineers tune their agentic environments deeply.

    He thinks hiring processes have not caught up. Most companies still hand out puzzles. The new test should look like asking a candidate to build a full Twitter clone for agents, make it secure, simulate user activity with agents, and then run multiple Codex 5.4x high instances trying to break it. The candidate’s system should hold up.

    What Humans Still Own

    Agents are intern level entities right now. Humans are responsible for aesthetics, judgment, taste, and oversight. Karpathy describes a Menu Gen bug where the agent tried to associate Stripe purchases with Google accounts using email addresses as the key, instead of a persistent user ID. Email addresses can differ between Stripe and Google accounts. This kind of specification level mistake is exactly what humans must catch.

    He works with agents to design detailed specs and treats those as documentation. The agent fills in the implementation. He has stopped memorizing API details for things like NumPy axis arguments or PyTorch reshape versus permute. The intern handles recall. Humans handle architecture, design, and the right questions.

    Reading the actual code agents produce can still cause heart attacks. It is bloated, full of copy paste, riddled with awkward and brittle abstractions. His Micro GPT project, an attempt to simplify LLM training to its bare essence, was nearly impossible to drive through agents. The models hate simplification. That capability sits outside their RL circuits. Nothing is fundamentally preventing this from improving. The labs simply have not invested.

    Animals Versus Ghosts

    Karpathy returns to his framing that we are not building animals, we are summoning ghosts. Animal intelligence comes from evolution and is shaped by intrinsic motivation, fun, curiosity, and empowerment. LLMs are statistical simulation circuits where pre training is the substrate and RL is bolted on as appendages. They are jagged. They do not respond to being yelled at. They have no real curiosity. The ghost framing is partly philosophical, but it changes how you approach them. You stay suspicious. You explore. You do not assume the system you used yesterday will behave the same on a new task.

    Agent Native Infrastructure

    Most software, frameworks, libraries, and documentation are still written for humans. Karpathy’s pet peeve is being told to do something instead of being given a block of text to copy paste to his agent. He wants agent first infrastructure. The Menu Gen project’s hardest part was not writing code. It was deploying on Vercell, configuring DNS, navigating service settings, and stringing together integrations. He wants to give a single prompt and have the entire thing deployed without touching anything.

    Long term he expects agent representation for individuals and organizations. His agent will negotiate meeting details with your agent. The world becomes one of sensors, actuators, and agent native data structures legible to LLMs.

    Education and What Still Matters

    The most striking line of the conversation comes near the end. Karpathy quotes a tweet that shaped his thinking: you can outsource your thinking but you cannot outsource your understanding. Information still has to make it into your brain. You still need to know what you are building and why. You cannot direct agents well if you do not understand the system.

    This is part of why he is so excited about LLM driven knowledge bases. Every time he reads an article, his personal wiki absorbs it, and he can query it from new angles. Every projection onto the same information yields new insight. Tools that enhance human understanding are uniquely valuable because LLMs do not excel at understanding. That bottleneck is yours to manage.

    Thoughts

    The most useful frame in this talk is the distinction between vibe coding and agentic engineering. It clarifies what has been muddled for the past year. Vibe coding is about access. Anyone can produce something. Agentic engineering is about discipline. You preserve the standards that made software trustworthy in the first place, while moving at speeds that would have seemed absurd two years ago. These are not the same activity, and conflating them is part of why so many shipped products feel half built.

    The Menu Gen anecdote is the kind of story that should make every solo developer pause. If a single Gemini plus Nano Banana prompt can replace a multi service Vercell deployed app, the question for any builder becomes how much of what you are working on right now is going to be made spurious by the next model release. The honest answer is probably more than you want to admit. The defensive posture is not building thicker apps. It is choosing problems where the model alone is not enough, where taste, distribution, infrastructure, or specific verifiable RL environments give you something the next model cannot collapse into a prompt.

    The verifiability lens is also unusually practical. If you are a solo builder, the question shifts from what is possible to what is verifiable but neglected. The labs will eat the obvious verifiable domains because that is how their RL pipelines are set up. The opportunity is in domains where verification is possible but the labs have not yet invested. That is a much more concrete strategic filter than vague intuitions about defensibility.

    The car wash example is going to stick. State of the art models can refactor enormous codebases and still tell you to walk somewhere a sane person would drive. That is the lived reality of jagged intelligence, and it argues strongly for staying in the loop on real decisions rather than handing off everything to agents. The agents are excellent fillers of blanks. They are not yet trustworthy specifiers of the spec.

    Finally, the line about outsourcing thinking but not understanding is worth taping above the desk. The bottleneck is no longer typing speed, syntax recall, or even API knowledge. It is whether the human in the loop actually understands the system being built. Tools that genuinely improve human understanding, including personal knowledge bases that re project information through different prompts, are likely the most undervalued category of products being built right now. The opportunity is not just in agents. It is in the cognitive scaffolding that makes humans good directors of agents.

  • Andrej Karpathy on AutoResearch, AI Agents, and Why He Stopped Writing Code: Full Breakdown of His 2026 No Priors Interview

    TL;DW

    Andrej Karpathy sat down with Sarah Guo on the No Priors podcast (March 2026) and delivered one of the most information-dense conversations about the current state of AI agents, autonomous research, and the future of software engineering. The core thesis: since December 2025, Karpathy has essentially stopped writing code by hand. He now “expresses his will” to AI agents for 16 hours a day, and he believes we are entering a “loopy era” where autonomous systems can run experiments, train models, and optimize hyperparameters without a human in the loop. His project AutoResearch proved this works by finding improvements to a model he had already hand-tuned over two decades of experience. The conversation also covers the death of bespoke apps, the future of education, open vs. closed source models, robotics, job market impacts, and why Karpathy chose to stay independent from frontier labs.

    Key Takeaways

    1. The December 2025 Shift Was Real and Dramatic

    Karpathy describes a hard flip that happened in December 2025 where he went from writing 80% of his own code to writing essentially none of it. He says the average software engineer’s default workflow has been “completely different” since that month. He calls this state “AI psychosis” and says he feels anxious whenever he is not at the forefront of what is possible with these tools.

    2. AutoResearch: Agents That Do AI Research Autonomously

    AutoResearch is Karpathy’s project where an AI agent is given an objective metric (like validation loss), a codebase, and boundaries for what it can change. It then loops autonomously, running experiments, tweaking hyperparameters, modifying architectures, and committing improvements without any human in the loop. When Karpathy ran it overnight on a model he had already carefully tuned by hand over years, it found optimizations he had missed, including forgotten weight decay on value embeddings and insufficiently tuned Adam betas.

    3. The Name of the Game Is Removing Yourself as the Bottleneck

    Karpathy frames the current era as a shift from optimizing your own productivity to maximizing your “token throughput.” The goal is to arrange tasks so that agents can run autonomously for extended periods. You are no longer the worker. You are the orchestrator, and every minute you spend in the loop is a minute the system is held back.

    4. Mastery Now Means Managing Multiple Agents in Parallel

    The vision of mastery is not writing better code. It is managing teams of agents simultaneously. Karpathy references Peter Steinberg’s workflow of having 10+ Codex agents running in parallel across different repos, each taking about 20 minutes per task. You move in “macro actions” over your codebase, delegating entire features rather than writing individual functions.

    5. Personality and Soul Matter in Coding Agents

    Karpathy praises Claude’s personality, saying it feels like a teammate who gets excited about what you are building. He contrasts this with Codex, which he calls “very dry” and disengaged. He specifically highlights that Claude’s praise feels earned because it does not react equally to half-baked ideas and genuinely good ones. He credits Peter (OpenClaw) with innovating on the “soul” of an agent through careful prompt design, memory systems, and a unified WhatsApp interface.

    6. Apps Are Dead. APIs and Agents Are the Future.

    Karpathy built “Dobby the Elf Claw,” a home automation agent that controls his Sonos, lights, HVAC, shades, pool, spa, and security cameras through natural language over WhatsApp. He did this by having agents scan his local network, reverse-engineer device APIs, and build a unified dashboard. His conclusion: most consumer apps should not exist. Everything should be API endpoints that agents can call on behalf of users. The “customer” of software is increasingly the agent, not the human.

    7. AutoResearch Could Become a Distributed Computing Project

    Karpathy envisions an “AutoResearch at Home” model inspired by SETI@home and Folding@home. Because it is expensive to find code optimizations but cheap to verify them (just run the training and check the metric), untrusted compute nodes on the internet could contribute experimental results. He draws an analogy to blockchain: instead of blocks you have commits, instead of proof of work you have expensive experimentation, and instead of monetary reward you have leaderboard placement. He speculates that a global swarm of agents could potentially outperform frontier labs.

    8. Education Is Being Redirected Through Agents

    Karpathy describes his MicroGPT project, a 200-line distillation of LLM training to its bare essence. He says he started to create a video walkthrough but realized that is no longer the right format. Instead, he now “explains things to agents,” and the agents can then explain them to individual humans in their own language, at their own pace, with infinite patience. He envisions education shifting to “skills” (structured curricula for agents) rather than lectures or guides for humans directly.

    9. The Jaggedness Problem Is Still Real

    Karpathy describes current AI agents as simultaneously feeling like a “brilliant PhD student who has been a systems programmer their entire life” and a 10-year-old. He calls this “jaggedness,” and it stems from reinforcement learning only optimizing for verifiable domains. Models can move mountains on agentic coding tasks but still tell the same bad joke they told four years ago (“Why don’t scientists trust atoms? Because they make everything up.”). Things outside the RL reward loop remain stuck.

    10. Open Source Is Healthy and Necessary, Even If Behind

    Karpathy estimates open source models are now roughly 6 to 8 months behind closed frontier models, down from 18 months and narrowing. He draws a parallel to Linux: the industry has a structural need for a common, open platform. He is “by default very suspicious” of centralization and wants more labs, more voices in the room, and an “ensemble” approach to AI governance. He thinks it is healthy that open source exists slightly behind the frontier, eating through basic use cases while closed models handle “Nobel Prize kind of work.”

    11. Digital Transformation Will Massively Outpace Physical Robotics

    Karpathy predicts a clear ordering: first, a massive wave of “unhobling” in the digital space where everything gets rewired and made 100x more efficient. Then, activity moves to the interface between digital and physical (sensors, cameras, lab equipment). Finally, the physical world itself transforms, but on a much longer timeline because “atoms are a million times harder than bits.” He notes that robotics requires enormous capital expenditure and conviction, and most self-driving startups from 10 years ago did not survive long term.

    12. Why Karpathy Stays Independent From Frontier Labs

    Karpathy gives a nuanced answer about why he is not working at a frontier lab. He says employees at these labs cannot be fully independent voices because of financial incentives and social pressure. He describes this as a fundamental misalignment: the people building the most consequential technology are also the ones who benefit most from it financially. He values being “more aligned with humanity” outside the labs, though he acknowledges his judgment will inevitably drift as he loses visibility into what is happening at the frontier.

    Detailed Summary

    The AI Psychosis and the End of Hand-Written Code

    The conversation opens with Karpathy describing what he calls a state of perpetual “AI psychosis.” Since December 2025, he has not typed a line of code. The shift was not gradual. It was a hard flip from doing 80% of his own coding to doing almost none. He compares the anxiety of unused agent capacity to the old PhD feeling of watching idle GPUs. Except now, the scarce resource is not compute. It is tokens, and you feel the pressure to maximize your token throughput at all times.

    He describes the modern workflow: you have multiple coding agents (Claude Code, Codex, or similar harnesses) running simultaneously across different repositories. Each agent takes about 20 minutes on a well-scoped task. You delegate entire features, review the output, and move on. The job is no longer typing. It is orchestration. And when it does not work, the overwhelming feeling is that it is a “skill issue,” not a capability limitation.

    Karpathy says most people, even his own parents, do not fully grasp how dramatic this shift has been. The default workflow of any software engineer sitting at a desk today is fundamentally different from what it was six months ago.

    AutoResearch: Closing the Loop on AI Research

    The centerpiece of the conversation is AutoResearch, Karpathy’s project for fully autonomous AI research. The setup is deceptively simple: give an agent an objective metric (like validation loss on a language model), a codebase to modify, and boundaries for what it can change. Then let it loop. It generates hypotheses, runs experiments, evaluates results, and commits improvements. No human in the loop.

    Karpathy was surprised it worked as well as it did. He had already hand-tuned his NanoGPT-derived training setup over years using his two decades of experience. When he let AutoResearch run overnight, it found improvements he had missed. The weight decay on value embeddings was forgotten. The Adam optimizer betas were not sufficiently tuned. These are the kinds of things that interact with each other in complex ways that a human researcher might not systematically explore.

    The deeper insight is structural: everything around frontier-level intelligence is about extrapolation and scaling laws. You do massive exploration on smaller models and then extrapolate to larger scales. AutoResearch is perfectly suited for this because the experimentation is expensive but the verification is cheap. Did the validation loss go down? Yes or no.

    Karpathy envisions this scaling beyond a single machine. His “AutoResearch at Home” concept borrows from distributed computing projects like Folding@home. Because verification is cheap but search is expensive, you can accept contributions from untrusted workers across the internet. He draws a blockchain analogy: commits instead of blocks, experimentation as proof of work, leaderboard placement as reward. A global swarm of agents contributing compute could, in theory, rival frontier labs that have massive but centralized resources.

    The Claw Paradigm and the Death of Apps

    Karpathy introduces the concept of the “claw,” a persistent, looping agent that operates in its own sandbox, has sophisticated memory, and works on your behalf even when you are not watching. This goes beyond a single chat session with an AI. A claw has persistence, autonomy, and the ability to interact with external systems.

    His personal example is “Dobby the Elf Claw,” a home automation agent that controls his entire smart home through WhatsApp. The agent scanned his local network, found his Sonos speakers, reverse-engineered the API, and started playing music in three prompts. It did the same for his lights, HVAC, shades, pool, spa, and security cameras (using a Qwen vision model for change detection on camera feeds).

    The broader point is that this renders most consumer apps unnecessary. Why maintain six different smart home apps when a single agent can call all the APIs directly? Karpathy argues the industry needs to reconfigure around the idea that the customer is increasingly the agent, not the human. Everything should be exposed API endpoints. The intelligence layer (the LLM) is the glue that ties it all together.

    He predicts this will become table stakes within a few years. Today it requires vibe coding and direct agent interaction. Soon, even open source models will handle this trivially. The barrier will come down until every person has a claw managing their digital life through natural language.

    Model Jaggedness and the Limits of Reinforcement Learning

    One of the most technically interesting sections covers what Karpathy calls “jaggedness.” Current AI models are simultaneously superhuman at verifiable tasks (coding, math, structured reasoning) and surprisingly mediocre at anything outside the RL reward loop. His go-to example: ask any frontier model to tell you a joke, and you will get the same one from four years ago. “Why don’t scientists trust atoms? Because they make everything up.” The models have improved enormously, but joke quality has not budged because it is not being optimized.

    This jaggedness creates an uncanny valley in interaction. Karpathy describes the experience as talking to someone who is simultaneously a brilliant PhD systems programmer and a 10-year-old. Humans have some variance in ability across domains, but nothing like this. The implication is that the narrative of “general intelligence improving across all domains for free as models get smarter” is not fully accurate. There are blind spots, and they cluster around anything that lacks objective evaluation criteria.

    He and Sarah Guo discuss whether this should lead to model “speciation,” where specialized models are fine-tuned for specific domains rather than one monolithic model trying to be good at everything. Karpathy thinks speciation makes sense in theory (like the diversity of brains in the animal kingdom) but says the science of fine-tuning without losing capabilities is still underdeveloped. The labs are still pursuing monocultures.

    Open Source, Centralization, and Power Balance

    Karpathy, a long-time open source advocate, estimates the gap between closed and open source models has narrowed from 18 months to roughly 6 to 8 months. He draws a direct parallel to Linux: despite closed alternatives like Windows and macOS, the industry structurally needs a common open platform. Linux runs on 60%+ of computers because businesses need a shared foundation they feel safe using.

    The challenge for open source AI is capital expenditure. Training frontier models is astronomically expensive, and that is where the comparison to Linux breaks down somewhat. But Karpathy argues the current dynamic is actually healthy: frontier labs push the bleeding edge with closed models, open source follows 6 to 8 months behind, and that trailing capability is still enormously powerful for the vast majority of use cases.

    He expresses deep skepticism about centralization, citing his Eastern European background and the historical track record of concentrated power. He wants more labs, more independent voices, and an “ensemble” approach to decision-making about AI’s future. He worries about the current trend of further consolidation even among the top labs.

    The Job Market: Digital Unhobling and the Jevons Paradox

    Karpathy recently published an analysis of Bureau of Labor Statistics jobs data, color-coded by which professions primarily manipulate digital information versus physical matter. His thesis: digital professions will be transformed first and fastest because bits are infinitely easier to manipulate than atoms. He calls this “unhobling,” the release of a massive overhang of digital work that humans simply did not have enough thinking cycles to process.

    On whether this means fewer software engineering jobs, Karpathy is cautiously optimistic. He invokes the Jevons Paradox: when something becomes cheaper, demand often increases so much that total consumption goes up. The canonical example is ATMs and bank tellers. ATMs were supposed to replace tellers, but they made bank branches cheaper to operate, leading to more branches and more tellers (at least until 2010). Similarly, if AI makes software dramatically cheaper, the demand for software could explode because it was previously constrained by scarcity and cost.

    He emphasizes that the physical world will lag behind significantly. Robotics requires enormous capital, conviction, and time. Most self-driving startups from a decade ago failed. The interesting opportunities in the near term are at the interface between digital and physical: sensors feeding data to AI systems, actuators executing AI decisions in the real world, and new markets for information (he imagines prediction markets where agents pay for real-time photos from conflict zones).

    Education in the Age of Agents

    Karpathy’s MicroGPT project distills the entire LLM training process into 200 lines of Python. He started making an explanatory video but stopped, realizing the format is obsolete. If the code is already that simple, anyone can ask an agent to explain it in whatever way they need: different languages, different skill levels, infinite patience, multiple approaches. The teacher’s job is no longer to explain. It is to create the thing that is worth explaining, and then let agents handle the last mile of education.

    He envisions a future where education shifts from “guides and lectures for humans” to “skills and curricula for agents.” A skill is a set of instructions that tells an agent how to teach something, what progression to follow, what to emphasize. The human educator becomes a curriculum designer for AI tutors. Documentation shifts from HTML for humans to markdown for agents.

    His punchline: “The things that agents can do, they can probably do better than you, or very soon. The things that agents cannot do is your job now.” For MicroGPT, the 200-line distillation is his unique contribution. Everything else, the explanation, the teaching, the Q&A, is better handled by agents.

    Why Not Return to a Frontier Lab?

    The conversation closes with a nuanced discussion about why Karpathy remains independent. He identifies several tensions. First, financial alignment: employees at frontier labs have enormous financial incentives tied to the success of transformative (and potentially disruptive) technology. This creates a conflict of interest when it comes to honest public discourse. Second, social pressure: even without arm-twisting, there are things you cannot say and things the organization wants you to say. You cannot be a fully free agent. Third, impact: he believes his most impactful contributions may come from an “ecosystem level” role rather than being one of many researchers inside a lab.

    However, he acknowledges a real cost. Being outside frontier labs means his judgment will inevitably drift. These systems are opaque, and understanding how they actually work under the hood requires being inside. He floats the idea of periodic stints at frontier labs, going back and forth between inside and outside roles to maintain both independence and technical grounding.

    Thoughts

    This is one of the most honest and technically grounded conversations about the current state of AI I have heard in 2026. A few things stand out.

    The AutoResearch concept is genuinely important. Not because autonomous hyperparameter tuning is new, but because Karpathy is framing the entire problem correctly: the goal is not to build better tools for researchers. It is to remove researchers from the loop entirely. The fact that an overnight run found optimizations that a world-class researcher missed after years of manual tuning is a powerful data point. And the distributed computing vision (AutoResearch at Home) could be the most consequential idea in the entire conversation if someone builds it well.

    The “death of apps” framing deserves more attention. Karpathy’s Dobby example is not a toy demo. It is a preview of how every consumer software company’s business model gets disrupted. If agents can reverse-engineer APIs and unify disparate systems through natural language, the entire app ecosystem becomes a commodity layer beneath an intelligence layer. The companies that survive will be the ones that embrace API-first design and accept that their “user” is increasingly an LLM.

    The jaggedness observation is underappreciated. The fact that models can autonomously improve training code but cannot tell a new joke should be deeply uncomfortable for anyone claiming we are on a smooth path to AGI. It suggests that current scaling and RL approaches produce narrow excellence, not general intelligence. The joke example is funny, but the underlying point is serious: we are building systems with alien capability profiles that do not match any human intuition about what “smart” means.

    Finally, Karpathy’s decision to stay independent is itself an important signal. When one of the most capable AI researchers in the world says he feels “more aligned with humanity” outside of frontier labs, that should be taken seriously. His point about financial incentives and social pressure creating misalignment is not abstract. It is structural. And his proposed solution of rotating between inside and outside roles is pragmatic and worth consideration for the entire field.