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  • OpenCode CEO Jay V on 20x Growth in 6 Months: 13 Million Users, 7 Trillion Tokens a Day, the Anthropic Block That Backfired, and the 16-Year Road to Overnight Success

    In this episode of Y Combinator’s Lightcone podcast, Jay V, founder and CEO of OpenCode, the open-source coding agent that works with any model, walks through one of the wildest growth stories in developer tools: 650,000 monthly active users in January to roughly 13 million by June, 7 trillion tokens processed per day, and a business that went from zero to a $40 million revenue run rate in about eight months. He also tells the part almost nobody knows: the company behind this “overnight success” is a 16-year-old legal entity that applied to Y Combinator nine times before getting in.

    TLDW

    Jay V explains how OpenCode grew 20x in six months to around 13 million monthly active users and 4.6 million weekly actives, processing 7 trillion tokens a day (more than OpenRouter’s entire volume), with an inference business annualizing near $40 million plus 160,000 subscribers worth another $18 million. The inflection point came when Anthropic started blocking Claude Code subscriptions inside OpenCode by rejecting requests whose system prompt contained the words “open code,” which backfired by equating the two products and sending curious users flooding in, shortly after which OpenAI’s Codex officially supported OpenCode. The conversation covers OpenCode’s public usage data (DeepSeek Flash dominating token volume despite GLM hype), a global user base led by China at 17% with heavy usage in Indonesia, Brazil, and Vietnam, Fortune 500 companies discovering thousands of employees already using the tool, the shift from ad-based CAC to token-based CAC, the flat 24-hour GPU utilization curve that comes from serving the whole planet, the “betting the field” marketplace thesis on model commoditization, and the founder’s 16-year, nine-application journey from a Waterloo dorm through SST, OpenNext, and selling coffee over SSH to finally catching lightning.

    Thoughts

    The Anthropic block is the most instructive growth story in the episode, because it is a perfect modern Streisand effect. Anthropic had a defensible reason to stop subsidized Claude Code subscriptions from flowing through a third-party harness, but the implementation (rejecting any request whose system prompt literally contained “open code”) turned a quiet policy decision into a public endorsement. As Jay puts it, the block placed OpenCode on the same pedestal as Claude Code in the minds of developers who had never heard of it. The hosts’ Instacart comparison is apt: when Amazon bought Whole Foods, the “death of Instacart” meme drove every grocer in America into Instacart’s arms. Incumbents keep learning this lesson the hard way. You cannot block a product without simultaneously advertising that it matters.

    The deeper story is geographic. Silicon Valley talks about coding agents as if the $200-per-month power user is the market, and Jay’s data says the opposite. China alone is 17% of OpenCode’s usage, with Indonesia, Brazil, and Vietnam each carrying meaningful share, places where a frontier subscription costs more than rent. OpenCode’s $10 Go plan, running DeepSeek and GLM instead of Sonnet and Opus, is how billions of developers will actually have their first coding-agent moment. There is also a hard operational edge hiding in that distribution: because the East works while the West sleeps, OpenCode’s GPU utilization runs a nearly flat 24-hour cycle, which quietly improves unit economics in a way no single-market competitor can match. Serving the whole planet is not just a mission statement. It is a margin strategy.

    OpenCode’s neutrality is turning into one of the most valuable datasets in AI. Because the product is a harness over every model rather than a storefront for one lab, opencode.ai/data shows what developers actually run when they are spending their own money, and it routinely contradicts the Twitter narrative. GLM was supposedly eating DeepSeek’s lunch; the token-volume charts show DeepSeek Flash dipping and then bouncing right back. Users are not loyal, they are rational: they ride frontier limits until they hit caps, then switch to models cheap and fast enough to finish the day’s work. That behavioral reality, boring cost optimization rather than fandom, is what the model market actually looks like once the marketing fog clears, and only a neutral aggregator gets to see it.

    The business model inversion deserves more attention than it usually gets. In the last era, customer acquisition cost meant ads. In this one, it means tokens: the free tier is the marketing budget, spent on giving people the aha moment, and the payoff comes when a fraction of those users become whales paying per token, where OpenCode’s volume discounts become margin. This is the same funnel Anthropic and OpenAI run, except the frontier labs subsidize with investor billions while OpenCode rides the falling cost curve of open-weight models. The enterprise motion follows the same bottoms-up physics: no procurement dance, just inbound emails saying thousands of our employees are already using you, please sign the security questionnaire. That is the purest product-market-fit signal that exists.

    And then there is the 16-year overnight success. Same legal entity since 2010, same two founders from a Waterloo dorm room, nine YC applications and four interviews before acceptance in 2021, years of living with parents and running out of money, a serverless framework, a coffee shop that ran over SSH. Every “dead end” turns out to have been training: the consumer company taught metrics discipline, SST taught open source and building in public, the terminal storefront taught terminal-UI craft that made OpenCode instantly credible with the Neovim crowd. The hosts land the right conclusion: lightning did strike, but the founders spent a decade positioning the bottle. In an industry currently obsessed with six-month-old unicorns, this episode is a useful reminder that most of them are carrying more history than the headline suggests.

    Key Takeaways

    • OpenCode ended June 2026 at roughly 13 million monthly active users and 4.6 million weekly actives, close to Codex’s numbers, a 20x increase from about 650,000 monthly actives at the start of the year.
    • The platform now processes around 7 trillion tokens per day, more than OpenRouter’s total of roughly 6 trillion, up from about 300 billion per day at the beginning of the year.
    • The pay-per-token inference business, launched around late September 2025, annualizes to $31-33 million on June data and $38-40 million on the most recent week, roughly eight months from zero.
    • The subscription product launched in late February has grown to about 160,000 monthly subscribers, roughly $18 million in annualized revenue on top of inference.
    • A Codex lead engineer publicly noted that about 5% of all Codex subscribers use OpenCode as their main harness, and OpenAI officially supports Codex subscriptions inside OpenCode.
    • In the first week of January, Anthropic began blocking Claude Code subscriptions in OpenCode by rejecting any request whose system prompt contained the words “open code.”
    • Jay concedes the block made business sense (Anthropic subsidizes that usage) but says it inadvertently equated OpenCode with Claude Code and drove waves of new users to investigate the product.
    • The hosts compare it to Amazon buying Whole Foods: the “death of Instacart” meme drove every grocer in America to sign with Instacart, fueling its growth instead of killing it.
    • The founding premise is that most people in the world still have not experienced the magic of a coding agent, and frontier per-token prices put that moment out of reach for much of the globe.
    • When OpenCode launched in June 2025 the pitch was using your Claude Code subscription in a better terminal UI; by August and September the first credible open-source models (GLM, Kimi, MiniMax) arrived, roughly six months behind the frontier.
    • February 2026 marked the first four-week span in OpenCode’s data where users ran Gemini more than the Anthropic models (Sonnet plus Opus combined), which convinced the team the non-Anthropic models were ready for real work and triggered the subscription launch.
    • OpenCode publishes its usage data at opencode.ai/data, covering the Go plan where $10 a month buys access to open-source models.
    • DeepSeek Flash leads token volume per day, with the two DeepSeek models plus GLM as the top three, despite social media chatter suggesting GLM had overtaken DeepSeek.
    • By unique users the top models run DeepSeek Flash at about 38,000, DeepSeek Pro at 31,000, and GLM 5.2 near 30,000.
    • A key usage pattern: as users approach daily or weekly limits on premium models, they switch to very cheap models like DeepSeek Flash to finish their work, extending how much coding-agent time their budget buys.
    • Speed matters too: some open models are hosted with far higher tokens-per-second than alternatives, making the agent feel near real time, and users perceive quality niches, like GLM 5.2 being better at front-end design.
    • China is OpenCode’s largest market at 17% of usage, which the hosts note may make it the only YC company in history with meaningful usage in China, partly because Chinese developers want to run Chinese models and OpenCode gives them that choice.
    • Developing countries are huge: Indonesia at 4% of traffic, Brazil at 5%, plus Vietnam and similar markets where a $200-a-month Claude Code subscription is prohibitively expensive.
    • The US, which the team was not even targeting with the Go plan, is growing strongly anyway, which Jay reads as a broader vibe shift toward token budgeting even among Americans.
    • Large US companies with effectively unlimited token budgets also adopted OpenCode early because they did not want to be locked into a specific model or harness.
    • Dozens of forward-leaning Fortune 500 companies have significant OpenCode footprints, often discovered when the company itself emails saying thousands of employees are already using it.
    • Enterprise inbound has inverted the old SaaS procurement dance: companies beg OpenCode to fill out security questionnaires so they can officially use a product their engineers already adopted.
    • Enterprise pull comes in four flavors: officially blessing developer usage, extending the tool to non-technical employees, embedding the agent loop inside their own products, and managing token spend by routing teams to cheaper models.
    • One enterprise asked for deep visibility into exactly what every employee does with the tool, which the team flagged as a should-we-even-build-this question.
    • Ramp built a Slack bot running OpenCode’s embeddable server (the agent loop that works behind the UI) before OpenCode had built anything similar internally, publishing a blog post about it in December.
    • OpenCode is architected as a two-part product: the terminal UI you interact with, and a separately embeddable server that runs the agent loop and calls the LLM.
    • The new CAC is tokens, not ads: the free tier exists to give people the magic moment, the subscription converts them to real work, and whales paying per token feed directly into margin via OpenCode’s volume discounts on inference.
    • The episode references Dylan Patel’s podcast claim that Anthropic reached roughly $50 billion annualized revenue at around 70% margin in Q2, proof that the subsidize-then-harvest funnel can cross into profitability.
    • Global usage produces a nearly flat 24-hour GPU utilization curve (the East works while the West sleeps), improving unit economics versus competitors serving one region.
    • Jay describes OpenCode as a marketplace that showcases model diversity: competition among labs benefits consumers, while vendor lock-in mostly benefits vendor margins.
    • OpenCode is now the largest customer by token volume for most open-source model labs, making the relationship symbiotic: the strategy is not picking a winning lab but betting the whole field.
    • Every bump in OpenCode’s monthly actives traces back to a corresponding release in the open-source model market, making its growth a proxy for open-model progress.
    • The name OpenCode was deliberate positioning: when a market has one or two dominant players, the rest coalesces around an open alternative, and whoever occupies that position first is very hard to displace.
    • To support 70+ models and providers at launch, the team built models.dev, an open-source database of models and providers that Jay calls probably the best such dataset in the world.
    • The origin moment: when Claude Code appeared in February 2025, the team (Neovim users unimpressed by its terminal UI) decided to build a coding agent that met the standard of modern terminal tools, credibility that resonated instantly with the core developer audience.
    • The team had form here: co-founder Dax had built terminal.shop, a complete storefront for buying coffee over SSH, the kind of eccentric-taste project the hosts argue pulls founders toward outlier outcomes.
    • The company is one 16-year-old legal entity, incorporated in 2010, founded by Jay and his college roommate Frank after a Waterloo co-op term convinced Jay he never wanted a normal job.
    • Jay applied to YC nine times between 2016 and 2021 with four interviews before getting in, with his first interview dating back to the era when Paul Graham ran them and an Airbnb founder was hanging around the waiting room.
    • The 2021 YC idea was a serverless platform, Heroku for AWS, which became SST, the team’s first big open-source project and the on-ramp to building in public.
    • Building in public became core identity after co-founder Dax observed that if all your code is public and you work in public, staying silent about it is a disservice to the product; the community now follows the company like a reality TV show.
    • Jay credits survival to stubbornness, visible forward progress, and cheap burn (living with parents after running out of money), while warning founders: don’t try this at home.
    • The hosts’ framing of the whole arc: it took ten years of grinding to get to zero-to-$30-million in eight months, and catching lightning in a bottle requires positioning the bottle correctly first.

    Detailed Summary

    The Numbers: 20x in Six Months

    OpenCode began the year around 650,000 monthly active users and ended June near 13 million, with 4.6 million weekly actives that put it in the same conversation as OpenAI’s Codex. Token throughput grew from roughly 300 billion per day to 7 trillion, a volume larger than all of OpenRouter. The money followed two tracks: a pay-per-token inference business launched in the fall that annualizes near $40 million on recent weeks, and a subscription product launched in late February that reached 160,000 monthly subscribers and about $18 million annualized. Codex officially supporting OpenCode, with around 5% of Codex subscribers choosing it as their harness, added a second frontier on-ramp right as the Anthropic controversy peaked.

    The Anthropic Block That Backfired

    Using a Claude Code subscription inside OpenCode was one of the most common usage patterns until Anthropic moved to stop it in early January, rejecting requests whose system prompt mentioned “open code.” Jay is gracious about the logic (Anthropic subsidizes subscription usage and wants it inside its own product) but the effect was the opposite of containment. The block put the scrappy open-source harness on the same pedestal as the category leader, told every developer who had not tried it that it was worth investigating, and kicked off the year’s 20x run. The hosts draw the Instacart parallel: a supposed death blow that functioned as the best marketing campaign the company never paid for.

    A Global User Base the Valley Doesn’t See

    The product premise is that the coding-agent aha moment is a once-a-generation experience most of the world cannot afford at frontier prices. The Go plan ($10 a month for open-source models) was built for that global audience, and the geography shows it: China leads at 17%, with Indonesia at 4%, Brazil at 5%, and Vietnam prominent, markets where $200 a month is simply not a consumer price point. Two surprises followed. Chinese developers use OpenCode partly to run their own country’s models, which no US-locked product lets them do. And the US, never the target for Go, is growing fast anyway, which Jay reads as the token-budgeting vibe shift reaching even the throw-money-at-it crowd, helped by moments like GLM 5.2’s popularity making the plan the easiest way to try it.

    What the Usage Data Really Shows

    OpenCode publishes per-model usage at opencode.ai/data, and because every data point is an actual end user rather than aggregated API traffic, it is arguably the cleanest picture of what working engineers really run. DeepSeek Flash dominates token volume, the two DeepSeeks plus GLM hold the top three, and the market-share graph shows DeepSeek dipping when GLM launched and then bouncing back, contradicting the Twitter narrative of a GLM takeover. By unique users, Flash leads at 38,000 with DeepSeek Pro at 31,000 and GLM 5.2 near 30,000. The behavioral driver is pragmatic: cheap, fast models let users keep working after they hit premium limits, hosted speeds make some models feel real time, and perceived niches (GLM for front-end design) steer specific workloads.

    Enterprises Arriving Through the Back Door

    Before the open-model wave, companies adopted OpenCode to avoid lock-in to any single model or harness. Now dozens of forward-thinking Fortune 500 companies have significant footprints, and the procurement process has inverted: instead of sales outreach, OpenCode receives DMs saying a few thousand employees are already using the product, please sign the security questionnaire, and often, please don’t tell anyone. Once inside, enterprises pull in predictable directions: extend access to non-technical staff, embed the agent loop in their own products, and manage token spend by restricting expensive frontier models to teams that need them. Ramp exemplified the embedding path, running a Slack bot on OpenCode’s server component before OpenCode itself had tried it. One request, total visibility into employee activity, raised the harder question of what the company is willing to build.

    Token Economics: CAC Is Now Paid in Tokens

    The episode’s sharpest business insight is that customer acquisition cost has migrated from ads to tokens. Becoming skilled enough with coding agents to justify heavy spend is itself expensive, a chasm most individuals and companies cannot cross unaided. Anthropic and OpenAI solve this by subsidizing subscriptions until a percentage of users become whales, and per Dylan Patel’s numbers cited in the episode, that funnel has carried Anthropic to roughly $50 billion annualized at 70% margins. OpenCode runs the same funnel without frontier-scale subsidies: the free tier delivers the magic moment, the $10 plan makes real work affordable on open models, and whales paying per token convert OpenCode’s volume discounts into margin. The flat 24-hour GPU utilization curve from serving every timezone compounds the advantage.

    Betting the Field: The Marketplace Thesis

    Jay frames OpenCode as a marketplace where users pick models by attribute and cost, which keeps labs honest and passes competitive gains to consumers instead of vendor margins. Every bump in OpenCode’s growth traces to a release in the open-model market, so the company is explicitly not picking a winning lab; it is betting the field. That bet has made OpenCode the largest customer by token volume for most open-source model labs, a symbiosis where each side needs the other. On commoditization, Jay’s view is nuanced: the intelligence market is so large that labs will carve defensible niches along the quality-cost-performance axes, the way DeepSeek deliberately owns the cost corner. The positioning strategy has deep roots: as with the team’s earlier OpenNext project, when a market has two dominant players, the rest coalesces around an open alternative, and OpenCode raced to become that default, building models.dev along the way just to support 70+ providers at launch.

    Sixteen Years to Overnight Success

    The backstory reframes everything. Jay started the company after a discouraging Waterloo co-op term in 2006-2007, incorporated with college roommate Frank in 2010, and spent the next decade shipping products that did “reasonably well” while applying to YC nine times across 2016-2021, with four interviews, all as the same legal entity, the same founders, and a rotating cast of ideas. His first YC interview was with Paul Graham, in a waiting room shared with an Airbnb founder. Acceptance finally came in 2021 with the serverless platform that became SST, the team’s gateway into open source and building in public, a practice pushed by YC’s Dalton and crystallized by co-founder Dax’s observation that public code deserves public storytelling. When Claude Code landed in February 2025, the team’s terminal-UI taste (honed on projects as eccentric as coffee-over-SSH) told them exactly what to build. The hosts close on the honest version of the lightning-in-a-bottle myth: ten years of grinding taught the team consumer metrics, open source, marketing, and positioning, so when the strike came, the bottle was already in place.

    Notable Quotes

    “Most people in the world still haven’t experienced the magic of a coding agent.”

    Jay V, on the founding premise of OpenCode

    “You really know you have product market fit when like enterprises are bugging you to sign the security agreement so they can use your product.”

    Lightcone host, on OpenCode’s inverted enterprise sales motion

    “It’s not that we’re picking a winner in terms of a model lab. We’re just betting the field. We just think the rest of the field is going to do well.”

    Jay V, on OpenCode’s strategy toward the model market

    “With these open-source models, we’re the largest customer for most of them.”

    Jay V, on OpenCode’s token volume relative to open-model labs

    “When you’ve got a dominant or in this case two dominant players in the market, the rest of the market coalesces around an open alternative. And picking that position ends up being really valuable because if you pick it, it’s very hard for somebody else to displace you.”

    Jay V, on the deliberate positioning behind the OpenCode name

    “This is just an unprecedented market, like the market for intelligence has not existed before, everybody should be thinking in a positive-sum grow-the-pie mentality.”

    Lightcone host, on why labs should welcome OpenCode’s growth

    “Look, you know, all your code is public. You work basically in public. If you don’t talk about it publicly, you’re probably doing yourself a disservice and your product a disservice.”

    Jay V, recounting co-founder Dax’s case for building in public

    “It was really more a journey that took 10 years to get to 0 to 30 million in 8 months.”

    Lightcone host, reframing the overnight-success narrative

    “To catch the lightning in the bottle, you actually like have to sort of position the bottle correctly and be ready for it and know what to do with it.”

    Lightcone host, closing the episode on preparation meeting luck

    Watch the full conversation here.

    Related Reading

    • OpenCode the open-source coding agent discussed throughout the episode, including its public usage data.
    • models.dev the open-source database of AI models and providers the team built to support 70+ providers at launch.
    • SST the serverless framework that got the company into YC and established its open-source, build-in-public roots.
    • Terminal the coffee-over-SSH storefront that proved the team’s terminal-UI chops before OpenCode existed.
    • Y Combinator the accelerator behind the Lightcone podcast, which Jay applied to nine times before getting in.

  • Jensen Huang Says the AI Apocalypse Is ‘Complete Nonsense’: NVIDIA’s CEO on AI Jobs, China, Open Source Models, the AI Bubble, and the Trillion-Agent Future (Axios Behind the Curtain)

    Sitting on the floor of a brand new chip factory in Fort Worth, Texas, NVIDIA CEO Jensen Huang gave Axios reporter Mike Allen one of his most combative and quotable interviews yet. In this episode of Behind the Curtain, the head of the world’s most valuable company dismisses AI doom scenarios as “complete nonsense,” argues that AI is creating jobs rather than destroying them, defends Chinese open source models like Kimi and DeepSeek, explains why the AI build out is not a bubble yet, and calls for Anthropic’s most powerful model to be made available to everyone.

    TLDW

    Huang covers the full sweep of the AI moment: Chinese export control threats and why he wants open research flows in both directions, why the world needs both closed models (Anthropic, OpenAI) and open models (Kimi, Qwen, DeepSeek, NVIDIA’s own Nemotron), why Wall Street misread the Kimi selloff exactly as it misread DeepSeek, the sovereign AI argument that no company or country should “outsource its alpha,” his evidence that AI is increasing jobs for radiologists, paralegals, and manufacturing workers, a sustained attack on AI doomers and the “made up” narratives of singularity, simulation, and machine consciousness, the CapEx-heavy economics of manufacturing intelligence via tokens, his claim that the bubble is not coming in the next five years because physical constraints (chips, memory, power, construction workers) are pacing the build out, his warm relationship with President Trump and his warning against knee-jerk regulation, his position that Claude Mythos should be available to all users, the coming era of a trillion AI agents, the “ChatGPT moment” for robots having already arrived, and closing life lessons on pain, suffering, practice, immigration, and why he refuses to wear a watch because “now is the most important time.”

    Thoughts

    The first thing to hold in mind while watching this: every single position Huang takes, without exception, maps to selling more GPUs. Open models are good (more diffusion, more compute). Closed models are also good (more services, more compute). Chinese models are good (more use, more compute). Doom talk is bad (fear slows adoption, which slows compute). The bubble is far away (keep buying compute). That perfect alignment between worldview and order book does not make him wrong, but it means his arguments deserve scrutiny on the merits rather than deference to his position. He is the most effective anti-doomer in the industry partly because he is the person with the most to lose if the world gets scared.

    That said, his strongest material is empirical, and it lands. The radiologist example is a direct rebuttal to one of the most famous predictions in AI history, Geoffrey Hinton’s 2016 claim that we should stop training radiologists. Huang’s version of events, that automating the scan-reading task let radiologists see more patients and demand for them grew, is a textbook case of what economists call the Jevons effect applied to labor. Whether his specific numbers (20 percent more radiologists, 10 percent more paralegals, 50 percent more manufacturing jobs) survive fact-checking, the structural argument that automating a task can grow the profession around it is historically well supported, and it is the single most useful reframe in the interview: your job is not your task, and when the task gets automated, the purpose remains.

    The open source security argument is the most intellectually serious part of the conversation and the one most directly aimed at his own customers. Huang praises Anthropic and OpenAI as businesses in one breath and then dismantles the “closed models are safer” position in the next: Linux runs the world’s digital infrastructure precisely because millions of people can inspect and harden it, and a world defended by one closed model is a world with a single point of failure. His call for “massively distributed, diverse defense” via open models in the hands of cybersecurity experts everywhere is a real policy position with real stakes, and it puts him closer to Meta’s historical stance than to the labs he supplies.

    The bubble section is where the skeptic should lean in. Allen hands him the most famous cursed phrase in financial history, “this time is different,” and Huang takes the bait enthusiastically: it is different, he says, because the demand is industrial rather than cyclical. Every bubble in history was justified by exactly this argument, including the railroads and the dot-com fiber build out that Huang implicitly invokes as precedent. But his supply-side observation deserves weight: bubbles pop when supply overshoots demand, and right now everything (chips, memory, packaging, power, land, construction labor) is short. A market that cannot build fast enough is at least not overbuilt yet. His own concession that “the bubble will come someday” and his refusal to vouch for years five through ten is more honest than the rest of the answer.

    Finally, notice the tension he never resolves. He says warnings about AI’s power are “well heeded,” that safety is the leaders’ responsibility, and that Anthropic must fix jailbreaks fast. He also says consciousness, singularity, and existential risk are “all made up,” and shrugs off the referenced Mythos jailbreak with “everything was fine, you and I are here having a conversation.” Those two postures, take the technology seriously enough to harden it but never seriously enough to fear it, are held together mostly by confidence. It is a bet that capability and controllability scale together. The doomers he mocks are making the opposite bet, and nothing in this interview actually settles which one is right.

    Key Takeaways

    • On reports that Chinese regulators may tighten export controls on AI models and semiconductors to keep them from the West: Huang hopes it does not happen, notes half the world’s AI researchers are Chinese, and says both sides should de-escalate and let the technology advance.
    • He opposes any US ban on Chinese models like Kimi: American companies should absolutely be allowed to use them, because downloaded open models can be fine-tuned, guardrailed, and run inside secure sandboxes and harnesses, and the “back door” fear is a misconception.
    • The world needs both closed and open models: use closed services (Anthropic, OpenAI) as much as possible because they are excellent and convenient, but science, cybersecurity, and sovereignty require open models.
    • Regulate applications of AI (medicine, transportation, autonomous vehicles), not the underlying technology, which is dual use and should advance as fast as possible.
    • NVIDIA’s China sales are “approximately zero today” and he has told investors to expect none; he would consider it an honor to return if both governments allow it.
    • The market misunderstood DeepSeek and is now misunderstanding Kimi the same way: great open models, wherever they come from, drive more AI use, which drives more NVIDIA computers, more data centers, and more services.
    • Open models are not adversarial to closed models: the most likely customer to upgrade to Anthropic or OpenAI is someone who already uses AI and wants it more convenient and better.
    • NVIDIA’s Nemotron open model exists for companies that must build their own AI for sovereignty, regulatory, privacy, or IP reasons. “We don’t have to be the frontier. We have to be at the frontier.”
    • The large language model is the brain; a harness (he names OpenClaw and Claude Code as examples) turns it into a working agent. With the right harness, Nemotron can be world-class for specific skills.
    • Cheap or free open source tokens are “fantastic” for the proprietary labs: free AI grows the population of people who realize they need AI, and running even a free model yourself usually costs more than renting a service.
    • Echoing the viral Palantir CEO interview: “Nobody should outsource their alpha.” Companies and countries should rent AI wherever they can but must build their own AI for domain-specific, proprietary, sovereign, secret, or regulated work.
    • For non-differentiating work (marketing automation, legal department productivity), outsource to the frontier labs as much as possible.
    • Nothing AI has done has truly surprised him; what society needs to realize is that automating tasks is increasing the number of jobs the world needs.
    • His jobs evidence: radiologists up roughly 20 percent because AI-automated scan reading lets them see far more patients; paralegals up roughly 10 percent for the same reason; US manufacturing jobs up roughly 50 percent in recent years because AI data centers require industrial might.
    • On the demonstrated ability of Anthropic’s Mythos to break into hardened systems: “it surprised me that people were surprised.” An AI that can write and debug software can necessarily find vulnerabilities; the same capability powers cyber defense.
    • His security architecture argument: one single model is one single point of attack and failure. Open models in the hands of cybersecurity experts worldwide create “massively distributed, diverse defense,” the same reason Linux is trustworthy.
    • Whether China has “caught up” does not matter: the race-with-a-finish-line framing is wrong, China manufactures more AI researchers than the rest of the world combined, holding China back is ill-conceived, and neither side can hold back the other.
    • “AI is not going to destroy all of our jobs. Someone who uses AI is going to take our jobs.” The biggest risk to the US is scaring industries and society out of adopting AI.
    • On doomer AI CEOs: warning is fine, warning with a solution is better, and making things up is “absolutely inappropriate.” End-of-humanity and half-of-jobs-destroyed claims are “complete nonsense” contradicted by all the evidence.
    • Asked why Asia loves him while America is anxious: “the doomers spend too much time theorizing about these science fiction outcomes, maybe it makes them sound smart.”
    • OpenAI and Anthropic are not in trouble from Chinese competition: “zero possibility” China runs US companies off the road, both labs are thriving, and their IPOs will be the most successful in human history.
    • On chip stocks down 18 percent after Kimi dropped: free AI is great for hardware, chips, and data centers; the market got it wrong with DeepSeek (NVIDIA fell about 30 percent) and is getting it wrong again.
    • AI cannot have peaked because diffusion into society and industry has barely begun; useful AI has finally arrived, and useful AI is profitable AI, citing coding agents companies happily pay hundreds of millions a year for.
    • The new IT industry is CapEx heavier than software because intelligence must be manufactured: machines produce the tokens behind every answer, image, protein, and robot maneuver, and the resulting productivity will more than pay for the build out.
    • A token is an embedding of knowledge and intelligence, and unlike pi it gets smarter over time; smarter tokens are more valuable, which is why token economics keep improving.
    • On the bubble: “The bubble will come someday. It’s just not today.” Very unlikely in the next five years; five to ten years depends on how fast the industry can build.
    • The build out is constrained in every direction (chips, memory, land, power, construction workers), and that constraint is healthy: it pushes out the day supply exceeds demand.
    • This cycle is “industrial-driven,” not seasonal or consumer-demand-driven: the world needs a new intelligence infrastructure layer on top of energy, internet, roads, and railroads, and the semiconductor industry needs to be 5 to 10 times larger within ten years.
    • He is not worried about customers issuing hundreds of billions in debt to buy his chips: these companies generate enormous cash, the compute platform shift is real, and the ROI question has been answered because AI is now demonstrably profitable.
    • He would use Kimi himself, with fine-tuning, guardrails, sandboxing, and access control, the same way the world already trusts open source software like Linux.
    • On Trump: they text, the president “remembers everything” including H20, H200, Blackwell, and Rubin, and the Fort Worth factory they are sitting in is a direct result of their first conversation about reindustrializing America.
    • His warning to the administration: do not over-correct based on science fiction narratives about AI consciousness; talk to many CEOs and scientists, not one or two, and take time to be informed before regulating.
    • On the government taking an equity stake in NVIDIA: unnecessary, because the US already has a stake via $10 billion in taxes paid last year, job creation, and the stock market holdings of most Americans.
    • Claude Mythos should “absolutely be available to everyone,” not just selected institutions; it is Anthropic’s job to harden it and patch jailbreaks fast, and he notes that when it was jailbroken “everything was fine.”
    • On distillation of closed models: learning from other intelligence is fundamental (soon the internet will be 99 percent AI-generated content anyway), but violating terms of service or privacy is not okay and should be handled through existing legal channels.
    • NVIDIA has 6,500 employee families in Israel he is concerned for; he remains bullish on the UAE reinventing itself from an oil economy into an AI hub.
    • NVIDIA runs about 50,000 employees and may reach only 75,000 in ten years, “as small as possible,” because strategy means maximizing impact per unit of resource.
    • Jobs that are a single task (customer service call centers) will be automated; jobs with purpose survive because purpose does not change when the task is automated. “Don’t mistake your task for the job.”
    • In 10 to 20 years, photos of people typing at keyboards will look like old photos of typing pools with IBM Selectrics: typing was never the job, solving problems and creating value was.
    • The ChatGPT moment for robots has already arrived (a robot can reason through “put the apple in the drawer,” including opening the drawer first); useful robots in ordinary life within 3 to 4 years would not surprise him.
    • The agentic era’s capability has arrived and diffusion is next: the future holds 100 billion to a trillion agents running constantly, and agents will not become computers, they will use computers, which is why compute demand explodes.
    • $300 billion has been invested into US venture capital startups in the last six months, and he tells his nieces and nephews that great fortunes will be created on a laptop.
    • Life lessons: greatness requires “plenty of pain and suffering” and practice when nobody is watching; under maximum stress, time slows down the way athletes describe, and that comes from repetition.
    • He advises every bright mind in the world to come to America, the country built by immigrants that will need amazing immigrants in the future.
    • He wears no watch and refuses to let Outlook manage his life: “now is the most important time.” His perfect Saturday: dogs, work, family dinner, a cocktail, and he notes every weekend is exactly like that.

    Detailed Summary

    Export Controls Cut Both Ways

    The interview opens on a Financial Times report that Chinese regulators are considering export controls of their own, restricting Chinese AI models and semiconductors from reaching the West. Huang’s response is de-escalation in both directions: half the world’s AI researchers are Chinese, groundbreaking research flows from both countries, and once one side reaches for export controls, everyone starts thinking in those terms. He is confident the US will continue to lead as long as government supports rather than constrains its companies. Asked whether the US should ban Chinese models like Kimi, he rejects the premise: downloaded open models run inside harnesses and sandboxes with security, privacy, and access controls, and the idea of hidden back doors phoning home to China is a misconception. His China sales, he notes pointedly, are approximately zero today, so his position is not about protecting revenue he does not have.

    Open and Closed Models Both Win

    Huang’s framework is consistent: rent closed models (Anthropic, OpenAI, which he personally uses along with Perplexity) whenever you can because they are excellent and convenient, and build on open models only when you must, for sovereignty, regulation, privacy, or proprietary domain reasons. This is the pitch for NVIDIA’s own Nemotron open model family, which he positions not as a frontier competitor but as raw material for companies that need custom AI: “We don’t have to be the frontier. We have to be at the frontier.” He describes the modern stack in plain terms: the large language model is the brain, and a harness (he cites OpenClaw and Claude Code) turns it into a working agent. Open, cheap, and free models are on-ramps that grow the total population of AI users, which is why he insists the labs should not fear them: the person most likely to pay for Claude is someone already using AI who wants it better and easier.

    Kimi, DeepSeek, and Wall Street’s Repeated Mistake

    Chip stocks fell 18 percent in the month after Kimi dropped, echoing the roughly 30 percent NVIDIA drawdown when DeepSeek landed. Huang says the market got it wrong both times and for the same reason: free and open AI is great for hardware, because great models drive use, use drives data centers, and data centers drive chips. He runs through the models he considers extraordinary (Kimi 3, Qwen, Nemotron, GPT 5.6, Codex, Claude Code) and lands on his core claim about this moment: useful AI has finally arrived, and useful AI is profitable AI. Companies like NVIDIA happily pay hundreds of millions of dollars a year for coding agents doing high-value work, which funds more AI, which he describes as a flywheel that has now started.

    Don’t Outsource Your Alpha

    Allen raises the viral Palantir CEO warning about handing your intellectual property to frontier labs, noting Huang’s unique position as both a top customer and top supplier of those labs, including using their models for chip design. Huang agrees with the principle without hesitation: nobody, no company, no country should outsource its alpha or its intelligence. His dividing line is specificity: work that is domain-specific, proprietary, sovereign, secret, or regulated must be done in-house on your own models, while generic productivity work like marketing automation or legal department support should be outsourced to the labs as aggressively as possible. The same logic scales to nations, which he says cannot outsource their fundamental intelligence to a third party.

    The Jobs Evidence

    Asked what AI has done that scared or awed him, Huang says essentially nothing surprised him, including the demonstrated ability of Anthropic’s Mythos to penetrate hardened systems (“it surprised me that people were surprised,” since an AI that debugs software can obviously find vulnerabilities). What he wants the world to notice instead is the labor data. Radiology reading has been substantially automated, and the number of radiologists is up roughly 20 percent because they can now see the enormous backlog of patients. Paralegals are up roughly 10 percent by the same mechanism. Manufacturing jobs are up roughly 50 percent in recent years because AI data centers require industrial construction. His formulation of the real risk: AI will not take your job, someone who uses AI will, and the worst thing America could do is scare its own industries out of adopting the technology.

    Against the Doomers

    This is the section that gives the interview its title. Huang says warning people is fine, warning with a solution is better, and making things up is absolutely inappropriate. The end of humanity: complete nonsense. Half of American jobs destroyed: complete nonsense. The singularity, living in a simulation, machine consciousness: “all made ups,” fun science fiction he enjoys hearing from “many of those leaders and my friends,” but Hollywood, not ground truth. Asked why he is mobbed by fans in Asia while the American mood is hostile, he suggests the doomers theorize about science fiction outcomes because “maybe it makes them sound smart.” His prescription for the industry is to tell the factual story, that AI is creating millions of jobs, rather than a made-up narrative that frightens the public and, more dangerously in his view, frightens policymakers. His closest thing to a concession: the closest thing to true AI is R2-D2 and C-3PO, “and who doesn’t want R2-D2 and C-3PO?”

    CapEx, Tokens, and the Bubble Question

    Huang’s economic argument for the build out runs through the token. Unlike the CapEx-light software era, intelligence must be manufactured: machines generate the tokens behind every answer, every image, and eventually every protein, chemical, and robot movement. A token is an embedding of knowledge, and unlike a static number it gets smarter over time, which makes it more useful, more valuable, and worth paying more for. On the bubble, he does not deny one is possible: “The bubble will come someday. It’s just not today.” He rules it out for roughly five years and hedges on five to ten. His reasoning is that this cycle is industrial-driven rather than consumer-cyclical: the world is adding an intelligence layer on top of energy, internet, roads, and railroads, the semiconductor industry needs to be 5 to 10 times larger within a decade, and everything (chips, memory, optical interconnects, packaging, TSMC capacity, land, power, construction workers) is short. Those constraints pace the CapEx and push out the day supply overtakes demand. As for customers issuing hundreds of billions in debt to buy his chips, he says the companies are extraordinary cash generators and the ROI question has been settled by profitable coding agents.

    Trump, Washington, and the Over-Correction Risk

    Huang describes a genuinely warm relationship with President Trump: they text, the president remembers chip model numbers (H20, H200, Blackwell, and next-generation Rubin), and the Fort Worth factory hosting the interview traces directly to their first conversation about restoring American manufacturing. He praises Susie Wiles, Secretary Bessent, and Secretary Lutnick. But his message to the administration is a warning: signs point toward more restrictive AI policy, and he fears policymakers falling for science fiction narratives (consciousness, an imminent finish line in a US-China race) pushed partly by companies hoping regulation will advantage them. His advice: talk to many CEOs and scientists, not one or two, take time, and do not over-correct. He rejects the 100-meter-dash framing of the China race entirely, arguing the win is diffusion, not invention: America did not invent electricity or manufacturing, it applied them with more enthusiasm than anyone, and that is what made the country. Asked about the government taking equity stakes in AI companies, he calls it unnecessary: the US already holds a stake in NVIDIA through $10 billion in annual taxes, job creation, and the stock market.

    Mythos for Everyone, and the Distillation Question

    In the most newsworthy exchange, Allen asks whether the world is ready for Anthropic’s most powerful model, Claude Mythos, to be available to everyone rather than selected institutions. Huang’s answer is unambiguous: it should absolutely be available to everyone, it is Anthropic’s responsibility to harden it, and jailbreaks are the nature of software, to be patched as fast as they are found. He points to the referenced jailbreak incident and observes that “everything was fine,” while noting that holding Anthropic back serves no American interest, especially since open models are available regardless. On distillation, he splits the question: AIs learning from other AIs is fundamental and inevitable (within a few years, he predicts, the internet will be 99 percent AI-generated content, so every model is distilling other AIs anyway), but violating terms of service or privacy is not acceptable, and aggrieved providers should pursue the conventional legal remedies that already exist.

    Robots, Agents, and the Next Era

    Huang argues the ChatGPT moment for robots has already happened, on his definition: the 2022 ChatGPT moment was not when AI became useful (that took four more years) but when it did something surprising, and a robot that can reason through “put the apple in the drawer,” including opening the drawer first, clears that bar today. Useful everyday robots within three to four years would not surprise him. On the agentic era, capability has arrived and diffusion is what comes next: where perhaps 100 million humans use computers at any given moment today, the future holds 100 billion to a trillion agents of every kind running constantly. His line: agents are not going to become computers, agents are going to use computers, and that is the deepest driver of compute demand.

    Life Lessons from 33 Years at the Helm

    The closing stretch turns personal. On keeping NVIDIA at roughly 50,000 employees (maybe 75,000 in ten years, “as small as possible”) while peers run six figures, he says strategy is using limited resources with maximum precision, a craft he has practiced longer than any CEO in tech history: “this is my kung fu.” On which jobs disappear, he distinguishes task from job from purpose: call center tasks will be automated, but a radiologist’s purpose (ending human suffering) survives the automation of scan reading, and typing was never the job in the first place. Born in Taiwan and sent to a rough American boarding school at nine, he calls America the greatest country in the world because open discourse and freedom let it work through its disagreements, and he urges bright minds everywhere to come. On greatness: no athlete just happens to be great, it is practice when nobody is watching, setbacks, losing, and “plenty of pain and suffering” that elevate craft, character, and resilience. He wears no watch because now is the most important time, and his perfect Saturday (dogs, work, family dinner, a cocktail) is, he says, exactly what every weekend already looks like.

    Notable Quotes

    “And so the fact that this is going to be the end of humanity, it’s complete nonsense. The fact that this is going to destroy half of the American jobs. It’s complete nonsense. And all of the facts, all of the evidence point exactly to the opposite.”

    Jensen Huang, on AI doom predictions from fellow tech leaders

    “AI is not going to destroy all of our jobs. Someone who uses AI is going to take our jobs, and so we have to make sure that we adopt AI, diffuse AI into the industries as quickly as possible.”

    Jensen Huang, on the real employment risk of the AI era

    “Nobody should outsource their alpha. Nobody should outsource their intelligence. No country should.”

    Jensen Huang, agreeing with the Palantir CEO’s warning about handing IP to frontier labs

    “We don’t have to be the frontier. We have to be at the frontier.”

    Jensen Huang, on NVIDIA’s Nemotron open source model strategy

    “The bubble will come someday. It’s just not today.”

    Jensen Huang, on whether the AI build out is a bubble

    “It is made up that there’s going to be a singularity. It’s made up that somehow we’re living in a simulation. These are all made ups.”

    Jensen Huang, on science fiction narratives he says are scaring the public and policymakers

    “The closest thing to true AI is R2-D2 and C-3PO. And who doesn’t want R2-D2 and C-3PO?”

    Jensen Huang, on how to inoculate the public against fear of AI

    “These two companies will be the most successful IPOs in human history.”

    Jensen Huang, predicting the public debuts of OpenAI and Anthropic

    “If your job is the task, then it’s very likely that when that task is automated, your job will be eliminated or changed.”

    Jensen Huang, on which jobs disappear in an industrial revolution

    “Because now is the most important time. I refuse to let Outlook manage my life, and I refuse to let a watch manage my life.”

    Jensen Huang, on why he does not wear a watch

    Watch the full conversation between Jensen Huang and Mike Allen on Axios Behind the Curtain here.

    Related Reading

  • Tim Ferriss and Kevin Rose Random Show: Mortality and Grief, Zen Insights, Rock Climbing at 50, LSD for Anxiety (MM120), AI Smart Homes, and Why You Should Buy the Company Instead of the Product

    Tim Ferriss and Kevin Rose reunite over tequila for another Random Show, and this one swings from the heaviest material they have covered in years (the death of their friend Om Malik, aging parents, dementia, and what grief actually is) to Zen retreat breakthroughs, rock climbing as a post-50 obsession, a phase 3 LSD trial for anxiety, AI-powered smart homes, the coming wave of AI IPOs, and the single investing lesson both keep relearning: let your winners run, and when you love a product, buy the company.

    TLDW

    Kevin reframes the loss of Om Malik and his father through a simple equation: grief is love with nowhere to go, and the sorrow is proof of how lucky you were. Tim adds Tim Urban’s “The Tail End” math (you have spent roughly 95% of your lifetime hours with your parents by high school graduation) and Sam Harris’s “The Last Time” meditation. Kevin recounts a micro-awakening at a five-day silent Zen retreat (“nothing lacking”), both plug their meditation app The Way with Henry Shukman, and Tim declares multi-pitch climbing in Yosemite his next deliberate-practice obsession, complete with hangboard protocols and grip-training gear. The health segment covers A2 whey, venison organ-meat sticks as a multivitamin, the 1,3-butanediol ketone controversy, ketones temporarily unlocking speech in relatives with dementia, terminal lucidity, a JAMA phase 3 trial of MM120 (lysergide) showing 12 weeks of anxiety relief from a single dose, and the Norwegian 4×4 protocol whose hippocampal benefits may persist for five years. The AI segment runs from Kevin’s Claude-coded camera system that opens his gate via license plate recognition, to Tim’s 20-year angel investing retrospective built with Claude Code and the Gmail API, to their handicapping of Google versus Anthropic versus OpenAI, China’s open-source push, local inference boxes, and why buying at IPO and holding may match venture returns.

    Thoughts

    The emotional spine of this episode is the best thing in it. Kevin’s formulation, that the gap left by a death “is just love at the end of the day,” is not new philosophy, but it lands differently coming from someone actively managing a dying dog, a mother with dementia, and a friend’s fresh death, all in the same month. The practical corollary the two keep circling is time-boxing: Tim Urban’s Tail End math and Sam Harris’s “last time” framing both convert vague mortality awareness into a scheduling problem. Tim credits one short blog post with causing years of family trips that his emotionally reserved family would never have taken otherwise. That is about as strong an endorsement as content can get: it changed the calendar, not just the mood.

    The health middle of the show is classic Random Show in that the interesting part is the epistemology, not the products. Tim flags that the loudest critics of 1,3-butanediol ketones sell competing ketone salts, applies a shelf-life heuristic to processed meat instead of memorizing ingredient lists, and treats organ-meat sticks as a dosed multivitamin rather than a diet. The MM120 discussion is the meatiest science: a five-arm randomized trial where a single 100 microgram dose of lysergide produced roughly twelve weeks of relief in generalized anxiety disorder, which Tim, who has been diagnosed with GAD and OCD, reads as a plausible future where anxiety treatment is episodic rather than daily. The unresolved tension they name honestly: the promising dementia signals (ketones, psilocybin case reports, microdosing) all crash into the consent problem. A person who cannot consent cannot sign up for a hallucinogen, and “it might give you half a day of real conversation back” is both a miracle and an ethical minefield.

    The AI section quietly contains one of the more useful predictions frameworks going: Kevin’s argument that Google’s confusing high-bandwidth TPU architecture only makes sense as a bet on continuous learning, where models stop shipping as discrete releases and start improving around the clock like a child. If self-improving models are really 12 to 18 months out, the “model drop” news cycle this episode itself participates in (new Sonnet today, Mythos tomorrow) is a temporary artifact. Tim’s counterweight is human-scale and more sobering: an AI trained on your own writing produces in 30 seconds what takes you 30 hours, and he compares the demoralization to Lee Sedol retiring after AlphaGo. His book sales chart, stable for a decade and then compounding downward every year since ChatGPT launched, is the receipts. The tension between “AI made my 20-year retrospective possible” and “AI is draining my motivation to write” is the honest version of the AI discourse most podcasts flatten into one direction.

    The investing segment is the most immediately actionable. Three ideas stack neatly: let winners run (Tim has lost more money selling early than he made buying), the venture-returns myth (a famous firm’s own analysis found that buying at IPO and holding a decade roughly matched their gains from early rounds through lockup), and buy-what-you-use (the friend who spent $100k on a top-of-the-line Tesla instead of Tesla stock forfeited roughly $15 million; teenage Tim bought Pixar after seeing Toy Story). None of this is sophisticated, which is the point both make explicitly: with Anthropic and OpenAI racing to IPO, ordinary people who use these tools daily will get a shot the private markets never gave them, and the discipline that matters is holding, not access.

    Key Takeaways

    • Kevin and Tim lost their friend and colleague Om Malik of True Ventures within the past week; Kevin found out mid-way through a five-day silent meditation retreat.
    • Kevin’s reframe on grief: the severe sense of loss is “just love at the end of the day.” The gaping hole his father’s death left is love manifested through sorrow, and recognizing that converts anguish into gratitude for having crossed paths at all.
    • Tim credits Matt Mullenweg twice: for organizing the Antarctica trip where he got days of uninterrupted time with Om (including a visit to an emperor penguin colony), and for sending him Tim Urban’s blog post “The Tail End.”
    • The Tail End’s core math: by high school graduation you have used up roughly 90 to 95% of the total in-person hours you will ever spend with your parents. Reading it drove Tim to organize regular family trips, awkwardness be damned, before his father’s mobility declined.
    • Sam Harris’s short meditation “The Last Time” pairs with it: for many activities you will do a last time without knowing it was the last time.
    • Kevin’s 15-year-old dog Toaster had a violent shaking episode (a stress syndrome after standing six hours at a vet visit, not a terminal event), and Kevin’s takeaway from being covered in the aftermath was that when you love an animal that much, none of it matters.
    • At a traditional Zen sesshin with Henry Shukman and his visiting Japanese teacher Yamada Roshi, Kevin had a two-second micro-insight while working his koan: a felt sense of “nothing lacking,” where nothing could be added or taken away because everything was already fully present. Not an emotion, a steady state.
    • Both are investors in The Way, Henry Shukman’s single-path guided meditation app, which they frame as an ideological investment like their funding of the dog aging study on rapamycin. Tim’s favorite sessions: “Whole Earth is Medicine” and “This Too is Me.”
    • Tim’s practical meditation pitch: you do not need a retreat; 10 minutes twice daily works, and there seems to be real alchemy in the twice-a-day rhythm. Kevin, once the guy who quit everything in two weeks, is coming up on five years of consistent practice.
    • A physiology aside: Henry’s instruction to drop the jaw slightly mirrors how Tim’s mandibular snoring device works. Dropping the jaw an eighth of an inch down and forward opens the airway. The ancients found it by trial and error.
    • Kevin, approaching 50, wants to stop saying “one day” about his bookmarked obsessions (Japanese woodworking, ships in bottles) and actually commit to things in the next two decades.
    • Tim’s next deep dive is rock climbing: his surgically repaired right elbow finally allows it, and his stretch goal is multi-pitch climbing in Yosemite despite being, in his words, deadly terrified of heights, sweaty palms included.
    • Tim’s philosophy of training: “training to not die sooner than is necessary” is not a sufficient goal. He needs a concrete deadline event, the way the Lancaster Classic structured his archery, to make deliberate practice worth it.
    • What sold Tim on climbing longevity: the 60-to-almost-80-year-olds at Salt Lake City gyms climbing 5.11+ on weekday mornings, out-performing what he could imagine doing, plus women who cannot do five pull-ups climbing 5.13 and 5.14 on pure technique.
    • Climbing is also social in a way archery never was: bouldering routes are literally called “problems,” and strangers trade beta. After decades of solitary repetition, Tim has hit his quota.
    • Training tools discussed: Michael Eckert’s finger-strength course (the multiple-time pull-up world champion Kevin just bought into), the Nug (a pocket-size wooden grip trainer Tim travels with), and Abrahangs, Emil Abrahamsson’s protocol of moderate partial-bodyweight hangs, 10 seconds on and 50 seconds off for 10 minutes twice a day, which produces outsized forearm and finger gains.
    • Tim’s fantasy recommendation: The Blade Itself, whose treatment of the randomness of death (a friend of Tim’s just died in a plane crash) doubles as a gratitude practice. The audiobooks are exceptional.
    • Protein talk: Kevin likes Pioneer Pastures A2 whey (30 grams a shake, lactose removed, no investor relationship); Tim gets roughly 40% of his protein from Maui Nui wild-harvested axis deer venison and treats the liver-and-heart pepper sticks as a two-or-three-a-week multivitamin.
    • On processed meat and nitrates, Tim’s heuristic is shelf life: if an ultraprocessed meat lasts three years on a shelf, raise an eyebrow. Minimally processed meat almost definitionally does not keep.
    • Exogenous ketones containing 1,3-butanediol may carry liver toxicity risk, though Tim notes many people pushing that claim sell competing ketone salts. His personal policy: use them intermittently, not daily.
    • The startling ketone anecdote: given to relatives with dementia, sentence length roughly 5xed within 20 minutes. Caveats: it tastes like gasoline, and 1,3-butanediol can affect balance, a serious concern when a broken hip is often the beginning of the end for older adults.
    • Kevin moved his mother, who has non-Alzheimer’s (likely vascular) dementia, into a new home equipped with an AI radar orb that detects falls instantly. She cannot recall breakfast but knows who he is, which he will take all day long.
    • The exercise-for-brain-health protocol Tim assembled with neuroscientist Dr. Tommy Wood: Norwegian 4×4 VO2 max intervals (4 minutes on, ~3 minutes off, 4 rounds) three times weekly for five to six months produces volumetric changes in the hippocampus that appear to last up to five years.
    • The only bike Tim can tolerate for it is the Kaiser M3i indoor bike, because the handlebars raise enough to spare his lower back. Kevin’s sustainable alternative: incline treadmill walking while playing Duolingo chess until 40 minutes disappear. Tim’s version of don’t-let-perfect-be-the-enemy-of-good: a 5-minute, three-set gym session still counts.
    • The JAMA study that grabbed Tim: a phase 3, five-arm randomized trial of MM120 (lysergide, essentially LSD, from the company formerly known as MindMed) for generalized anxiety disorder. Effects were dose-dependent, with 100 micrograms (a standard full trip) as the apparent minimum effective dose, and relief persisting through 12 weeks after a single treatment.
    • Mid-conversation they discover the trial ran at Neuroscape at UCSF, their friend Adam Gazzaley’s lab, which Kevin helped fund. Tim, clinically diagnosed with GAD and OCD, finds 12 weeks of relief from one dose remarkable.
    • Related dementia signals: a case report of an elderly Japanese woman with dementia who took a five-gram “heroic dose” of psilocybin mushrooms, slept 19 hours, and woke temporarily capable of full expositional conversation instead of monosyllables; Tim has also seen an unpublished case report of LSD microdosing producing similar verbal fluidity.
    • Both note the hard ethics: hallucinogens for someone who cannot consent, the devastation of a bad trip you inflicted, versus the possibility of half a day of real connection or slowed decline.
    • Terminal lucidity, the well-documented phenomenon of vegetative or unresponsive patients becoming fully lucid in their final days, leaves both baffled: if cognition is fully localized in a structurally deteriorated brain, where is the lucidity coming from? Kevin’s analogy: we assume nothing is backed up to the cloud.
    • Tim’s caffeine pacing hack: Nutonic nootropic toothpicks (a gift from Chris Williamson), roughly 20 to 25 milligrams of caffeine each, a hard ceiling per toothpick that prevents his chain-refill coffee problem.
    • Kevin’s AI smart home: his Ubiquiti camera system has a full API, so with Claude writing the glue code, the cameras now recognize individual people (and Toaster, who gets a dog emblem), play deterrent audio at loiterers in his alleyway, and open his gate automatically when they read his license plate. The camera costs about $200; anyone can do this now.
    • Tim’s flagship AI project: a 20-year retrospective of his angel investing, built with Claude Code and the Gmail API, testing his own stories about his batting average against hard data. Doing it manually would have taken a year of full-time work by multiple people.
    • The humbling adjacent stat from Kevin: friends with always-on AI wearables report that about 70% of what we confidently remember is what actually happened. Startup genesis stories are the same phenomenon, a five-minute bit polished until the teller believes it.
    • Tim’s most valuable everyday AI use: holistic health cross-checking (contraindications between medications and supplements, could A explain D), hallucination-limited by fact-checking across multiple LLMs.
    • Tim’s contrarian AI take: for most people the honest impact is small “because most shit isn’t worth doing in the first place.” Doing something well does not make it worth doing, and AI is skyrocketing the volume of efficiently produced BS.
    • The demoralization is real, though: an AI trained on your writing produces in 30 seconds what takes 30 hours. Tim compares it to the top Go player who lost the joy of the game after AlphaGo, and his all-format book sales have compounded downward every year since ChatGPT launched (roughly -5%, then -28%, then -49%, tracking toward -67%).
    • The prompt experiment both loved: with cross-conversation memory enabled, ask your model “What are three to five rewarding paths I might explore in the next five years?” Tim sent the answers to close friends who called them outstanding, including a non-book business idea Kevin urged him to build. Ask AI open-ended questions the way you would ask a close friend, not robot questions.
    • Kevin is prototyping “Bond,” an app built from scanned values-card decks: swipe to surface your core values, form explicit agreements with partners and friends that both sides “shake” on, weight the damage of a broken bond, and accumulate a trust ledger. He calls the underlying idea dark information: real relational data (trust, reliability, empathy) that exists everywhere but has never been given physical form.
    • Tim’s writing unlock for the blank page: dictate a rambling brain dump into Wispr Flow while walking, drop it into Claude to clean up, and uncomfortable procrastinated emails come together in minutes. Gear notes: Shokz OpenMeet bone-conduction headset (open ears for traffic, recommended by Exploding Kittens co-founder Elan Lee) and a Sennheiser lav mic plus the Ferrite app as a pocket recording studio that beats studio mics in echoey hotel rooms.
    • State of AI, per both: the big three are Google, Anthropic, and OpenAI, with X/Grok never count-out-able (though Anthropic and Google buying excess Colossus capacity suggests weak Grok demand; Kevin still values Grok’s X-API grounding and uses it heavily for Digg). Meta has phenomenal assets but, Kevin thinks, not the talent to keep pace. Apple is quietly a couple of years out.
    • Kevin’s Google thesis: they own the full stack (TPUs, data centers, models, Android’s install base), and their confusingly high-bandwidth chip architecture is a bet that the future is continuous learning, models improving 24/7 like a child rather than shipping as discrete releases. Consensus estimates put self-improving models 12 to 18 months out.
    • Kevin’s insider color: touring Google X with Sergey Brin and Bill Maris a decade-plus ago, he saw Waymos years before the public knew. Google is sitting on roughly five years of undisclosed deck and holds back frontier models partly for cost and partly to avoid government intervention. In 12 months we will know where Google really stands.
    • Counterweights: ChatGPT owns consumer mindshare and OpenAI must crack advertising, which is very hard; Anthropic is reportedly the fastest-scaling enterprise business ever but keeps taking hits from the administration; no frontier lab will remain unconstrained by government; and China is releasing open-source models on par with the frontier (“doing it the American way”), while AMD’s ~$4,000 local inference box can run massive models at home, eight months behind the frontier, which for many users is fine.
    • The investing lessons: let winners run (Tim: “I’ve lost more money by selling stocks early than I’ve ever probably made buying the original stock”); a famous venture firm’s internal analysis found buying at IPO and holding roughly 10 years matched their gains from early-stage investing through post-lockup; and buy the company, not just the product. Kevin’s friend David Prager spent $100k on a maxed-out Tesla instead of Tesla stock, forgoing roughly $15 million. Tim’s first stock, at about 15 years old, was Pixar, bought because Toy Story convinced him animation was the future.
    • Kevin relaunched Digg: from 20,000 weekly users to nearly 500,000 and millions of monthly page views, pulling the zeitgeist from X and other feeds with heavy AI curation rather than trying to build another social network.

    Detailed Summary

    Grief, the Tail End, and the Last Time

    The show opens with banter about alcohol taxes and ketamine before turning serious: Toaster, Kevin’s 15-year-old dog, just had a terrifying (ultimately survivable) collapse, and the pair lost their friend Om Malik of True Ventures within the week. Kevin, who got the news at a silent retreat, offers the episode’s emotional thesis: the loss and sorrow are the shape love takes when the person is gone, and he would not trade the chaos of caring for people and animals for a calmer, emptier life. Tim thanks Matt Mullenweg for the Antarctica trip that gave him days of psychologically naked time with Om, and for sending him Tim Urban’s “The Tail End,” the post whose parents-time math pushed Tim into years of deliberate family trips before his father needed a wheelchair. Sam Harris’s meditation “The Last Time” extends the theme: you rarely know a last time is the last time. Kevin’s response is to do the thing one more time anyway, bouncy-house backflips at 49 included.

    Zen, Nothing Lacking, and The Way

    Kevin describes his five-day traditional Zen sesshin with Henry Shukman and Yamada Roshi: wall-gazing with eyes open, koan practice on the out-breath, and private interviews with the Roshi. His micro-insight, about two seconds long, was a non-emotional steady state of “nothing lacking,” everything fully present with nothing to add or subtract, what Zen calls the removal of the veil. Tim relays his favorite sessions from The Way (the app both back as a philosophical investment, like the rapamycin dog aging study): “This Too is Me,” which dissolves the burden of a squirrel-chasing mind by including everything experience serves up as you, and Henry’s small physical instructions, like dropping the jaw, which Tim connects to his mandibular snoring device: an eighth of an inch down and forward opens the airway. His bottom line: 10 minutes twice a day captures most of the benefit, and watching the formerly two-weeks-and-out Kevin sustain five years of practice has been deeply reassuring.

    Rock Climbing as the Next Decade’s Project

    Kevin, marching toward 50, wants to stop bookmarking dreams (Japanese woodworking, ships in bottles) and start doing them. Tim’s answer is rock climbing: his repaired right elbow finally allows it, and his stretch goal is multi-pitch in Yosemite despite sweating through his palms at the mere thought of heights. What converted him was the Salt Lake City gym crowd at 11 a.m.: retirees in their 60s and 70s climbing 5.11+, inverted on overhangs, evidence that this sport rewards technique and consistency over youth (women who cannot do five pull-ups climb 5.13). After archery, which he loved but found definitionally solitary, climbing’s social “beta”-trading culture is the draw. The training stack: Michael Eckert’s finger-strength course, the Nug pocket grip trainer, and Abrahangs (Emil Abrahamsson’s 10-seconds-on, 50-off, 10-minute, twice-daily hang protocol). A darker aside grounds the ambition: a friend of Tim’s just died in a plane crash, and The Blade Itself keeps teaching him that life-or-death is often dumb luck.

    Protein, Ketones, and the Dementia Frontier

    The supplements run: Kevin’s new favorite is Pioneer Pastures A2 whey (30 grams, lactose removed, gut-friendly); Tim, disclosure-forward as always, travels with Maui Nui venison and treats the liver-and-heart sticks as a twice-weekly multivitamin. On processed meat, Tim’s heuristic is shelf life over ingredient forensics. The exogenous ketone conversation is more fraught: 1,3-butanediol may stress the liver (though the claim’s loudest advocates sell competing ketone salts), so Tim doses intermittently. The astonishing part: given to relatives with dementia, ketones 5xed sentence length within 20 minutes, going from non-answer answers to full paragraphs, “offline to online.” Balance risks make it dicey in exactly the population that needs it. Kevin’s mother’s new care home uses an AI radar orb for instant fall detection. For prevention, Tim’s protocol from conversations with Dr. Tommy Wood: Norwegian 4×4 VO2 max intervals three times a week for five to six months, whose hippocampal volumetric changes appear to persist up to five years, done on the one bike (Kaiser M3i) that does not wreck his back. Kevin’s sustainable version: incline treadmill plus Duolingo chess.

    MM120, Psilocybin Case Reports, and Terminal Lucidity

    Tim walks through the JAMA-published phase 3 trial of MM120 (lysergide, effectively LSD) for generalized anxiety disorder: five arms (placebo, 25, 50, 100, 200 micrograms), dose-dependent response, with 100 micrograms reading as the minimum effective dose and relief lasting through the 12-week measurement window from a single supervised treatment. Kevin clicks through mid-show and discovers it ran at Neuroscape at UCSF, their friend Adam Gazzaley’s lab, which Kevin helped fund. For Tim, clinically diagnosed with GAD and OCD, episodic rather than daily treatment is the headline. The dementia thread continues: a case report of an elderly Japanese woman who took five grams of psilocybin mushrooms, slept 19 hours, and woke into temporary full conversation; an unpublished LSD microdosing report with similar verbal fluidity. Both wrestle with consent ethics. And then terminal lucidity, the documented phenomenon of unresponsive patients becoming fully lucid days before death, which neither can explain: as Kevin puts it, if it is all localized in a deteriorated brain, where is that coming from?

    AI at Home and AI on Yourself

    Kevin’s Ubiquiti camera system, glued together with Claude-written code against its API, now recognizes faces (and Toaster), scolds loiterers through a speaker, and opens his gate when it reads his license plate, all on a $200 camera. Tim’s project is introspective: a Claude Code plus Gmail API retrospective of 20 years of angel investing, checking who made which introductions, what he passed on, and whether his stories about his batting average survive contact with data (they mostly did; he missed fewer explicit opportunities than he feared). Kevin cites friends with always-on AI wearables: about 70% of what we confidently remember is accurate. Tim’s daily-driver use is health: cross-referencing medications, supplements, and symptoms across multiple LLMs. His caution: most tasks AI accelerates were not worth doing, and the volume of efficient BS is skyrocketing. His countervailing enthusiasm: the “what should I do in the next five years” prompt with cross-conversation memory produced ideas good enough to deeply inform his next chapter. Ask it questions like a close friend. Kevin’s next experiment is “Bond,” a values-and-trust app for making implicit relational agreements (what he calls dark information) explicit, trackable, and reflective. Tim’s practical writing unlock: Wispr Flow voice dumps cleaned up by Claude, especially for procrastinated uncomfortable emails, recorded on a Shokz OpenMeet bone-conduction headset.

    The AI Landscape and Where the Money Goes

    Recorded the day a new Anthropic Sonnet launched, with Mythos due the next day, the forecasting segment lands on a big three of Google, Anthropic, and OpenAI. Kevin’s Google case: full-stack ownership (TPUs whose high-bandwidth architecture only makes sense as a bet on continuous, 24/7 self-improving learning, expected within 12 to 18 months), Android distribution, data center expertise, billion-dollar engineer retention, and a five-year hidden deck he glimpsed touring Google X with Sergey Brin and Bill Maris before Waymo was public. Google holds frontier models back for cost and regulatory reasons; within a year we will know what they have. OpenAI owns consumer mindshare but must solve ads; Anthropic is crushing enterprise ARR while absorbing slaps from the administration; no lab escapes government constraint; China’s open-source frontier-parity models and AMD’s ~$4,000 local inference box threaten the subscription model from below. The investing translation: these companies are going public, and ordinary users will finally get access. The lessons both preach: let winners run, remember that buying at IPO and holding a decade roughly matched one famous firm’s venture returns, and buy the company behind the product you love, the lesson of Prager’s $15 million Tesla and teenage Tim’s Pixar shares. Kevin closes with Digg’s relaunch (20,000 to nearly 500,000 weekly users) and Tim with the sobering chart of his AI-era book sales, compounding downward since ChatGPT.

    Notable Quotes

    “I realized that that gap is just love at the end of the day because I wouldn’t have it unless I loved this man so much. I cared for this person so much. How lucky am I to have crossed paths with this person to get to know them?”

    Kevin Rose, on losing Om Malik

    “When I lost my dad, like that is just a gaping hole of love manifested through sorrow and sadness.”

    Kevin Rose, on grief as a consequence of deep love

    “I had a sense of nothing lacking. Nothing needed to be added and nothing even possibly could be added and nothing possibly could be taken away because everything at that moment was full in the way that it should be.”

    Kevin Rose, describing his micro-insight at the Zen retreat

    “Training to not die sooner than is necessary is not sufficient for me.”

    Tim Ferriss, on why he needs concrete physical goals like multi-pitch climbing in Yosemite

    “Doing something well does not make it important or worth doing in the first place.”

    Tim Ferriss, on AI’s honest impact when most tasks were never worth doing

    “I can still write, but what they can do in 30 seconds is what would take me 30 hours. And I’m just like, it really drains the motivation for me to put in those 30 hours.”

    Tim Ferriss, on AIs trained on his own writing

    “It’s not about those new models dropping. It’s about just like a child learning. Tomorrow it’ll be better than today for forever.”

    Kevin Rose, on Google’s bet that continuous learning replaces the model-release cycle

    “You got to let your winners run as long as possible. I’ve lost more money by selling stocks early than I’ve ever probably made buying the original stock.”

    Tim Ferriss, the takeaway from his 20-year angel investing retrospective

    “You find something that you love and you buy said object when you should actually buy the company.”

    Kevin Rose, on the $100k Tesla that should have been $15 million of Tesla stock

    Watch the full conversation between Tim Ferriss and Kevin Rose here on YouTube.

    Related Reading

    • The Tail End (Wait But Why) the Tim Urban post that quantifies how little time you have left with the people you love.
    • The Way Henry Shukman’s single-path guided meditation app that both Ferriss and Rose back and use daily.
    • Terminal lucidity (Wikipedia) background on the end-of-life phenomenon neither host can explain.
    • LSD (Wikipedia) context for MM120/lysergide and the history behind the generalized anxiety disorder trial.
    • The Botany of Desire by Michael Pollan, the book Tim cites on how dogs (and plants) co-domesticated us as much as we domesticated them.

  • Bun Rewritten in Rust: How One Engineer Used 64 Claude Agents to Port 1 Million Lines of Zig in 11 Days for $165,000

    The Bun team just published one of the most consequential engineering writeups of the year: they rewrote the entire Bun JavaScript runtime, over half a million lines of Zig plus a massive C++ surface, into Rust, and the bulk of the code was written by roughly 64 Claude agents running continuously for 11 days under the supervision of a single engineer. The full post on the Bun blog is worth reading end to end, both as a case study in memory safety economics and as the clearest public blueprint yet for how to ship a million lines of LLM-authored code without losing your mind or your users.

    TLDR

    Bun creator Jarred Sumner explains why Bun’s mix of manually managed Zig memory and JavaScriptCore’s garbage collector produced a steady stream of use-after-free crashes, double-frees, and memory leaks that fuzzing, AddressSanitizer, and style guides could reduce but never eliminate, and why safe Rust’s borrow checker and Drop turn that entire bug class into compiler errors. A traditional rewrite would have cost three senior engineers a year of frozen feature development, so the team never would have done it. Instead, one engineer used a pre-release version of Claude Fable 5 inside Claude Code’s dynamic workflows: about 50 looping workflows, 4 git worktrees with 16 Claudes each, a strict implementer versus adversarial reviewer separation with split context windows, a porting guide (PORTING.md) and a lifetime map (LIFETIMES.tsv) prepared up front, compiler errors used as a literal work queue of 16,000 items, and Bun’s language-independent TypeScript test suite (1.38 million expect() calls) as the acceptance gate. Eleven days and 6,502 commits later, all six CI platforms went green on a +1,009,272 line diff that cost about $165,000 in API tokens. The result, shipping as Bun v1.4.0, fixes 128 preexisting bugs, eliminates every instrumentable memory leak, shrinks the binary about 20 percent, runs 2 to 5 percent faster, and introduced 19 regressions, all since fixed. Claude Code itself now runs on the Rust port and barely anyone noticed.

    Thoughts

    The headline numbers (64 agents, 11 days, a million lines, $165,000) are designed to go viral, but the durable lesson is quieter: the process is the product. Almost nothing in this writeup is about prompting brilliance. It is about organizational design applied to machines. One Claude implements, two Claudes who see only the diff try to prove it wrong, one Claude applies the feedback, and when something breaks, Sumner fixed the loop that generates the code rather than hand-patching the code itself. That last move is the one most teams will miss. Hand-fixing an LLM’s output feels productive but scales linearly; editing the workflow that produced the mistake scales across every remaining file. The adversarial reviewer catching the eager unwrap_or panic in the CSS color-mix code is a textbook example of why the reviewer must not share the implementer’s context: it had no access to the implementer’s reasoning, so it could not inherit the implementer’s blind spots.

    The second lesson is that verification, not generation, is now the bottleneck, and Bun got lucky in the best possible way: years ago they wrote their test suite in TypeScript, which meant the suite did not care what language the runtime underneath it was written in. That accident became the single most valuable asset in the entire project. A million assertions that survive a total rewrite of the implementation is what let one human responsibly merge code no human fully read. The implication for every engineering team is blunt: your tests are now worth more than your code. Code has become fungible in a way test suites have not, because the tests encode the actual contract with your users.

    Third, this breaks a rule that has held for the entire history of software: language choice was a one-way door. Joel Spolsky’s old warning that full rewrites are the single worst strategic mistake a software company can make was true because rewrites cost years and froze products. Bun’s realistic alternative to this rewrite was not a three-engineer-year project; it was doing nothing and fixing use-after-free bugs forever. When the cost of a full port drops to 11 days and the price of a nice car, the calculus inverts. Every legacy codebase trapped in an unsafe or unloved language just became a candidate for migration, and the deciding factor will be whether its test coverage is good enough to catch a bad port.

    The honest caveats matter too. Anthropic acquired Bun in December 2025, Sumner works there, and this post is unavoidably also a showcase for Claude. The disclosure is right at the top, which is to their credit. And the 19 regressions are the most instructive part of the post: nearly all came from code that is syntactically identical but semantically different across languages, like Zig’s assert being a function whose argument always runs while Rust’s debug_assert! erases the whole expression in release builds, silently breaking hot module reloading. A human porting that line would have made the same mistake. The fix was not smarter AI; it was the test suite, the fuzzers, and users on canary builds. This was not push-button autonomy. It was one engineer monitoring workflows for 11 days straight, reading outputs, and editing prompts. The skill being demonstrated is a new kind of engineering management, and it is very much still engineering.

    Key Takeaways

    • Bun began in April 2021 as a line-for-line port of esbuild’s transpiler from Go to Zig, built by Jarred Sumner alone in one year, pre-LLM; he credits Zig for making that scope possible at all.
    • Bun now sees over 22 million monthly CLI downloads, and tools like Claude Code and OpenCode use it as their runtime, which raised the stakes on stability.
    • A single patch release, v1.3.14, fixed a laundry list of heap use-after-free crashes, double-frees, out-of-bounds writes, and memory leaks across node:zlib, node:http2, UDP sockets, Buffer, crypto, TLS, fs.watch, and the CSS parser.
    • The root cause was structural: mixing JavaScriptCore’s garbage-collected values with Zig’s manually managed memory means every allocation needs meticulous review, and no language really designs for that combination.
    • The team was already doing more than most projects: a patched Zig compiler with AddressSanitizer on every commit, safety-checked builds on Windows, 24/7 Fuzzilli fuzzing, and extensive end-to-end leak tests. Bugs still got through.
    • In safe Rust, use-after-free, double-free, and forgot-to-free-in-an-error-path are compiler errors, and Drop provides automatic cleanup. Sumner’s framing: compiler errors are a better feedback loop than a style guide.
    • Excluding comments, Bun was 535,496 lines of Zig. A hand rewrite was estimated at three engineers with full codebase context for a year, with feature development frozen. The realistic alternative was to never do it.
    • Sumner’s pivot moment: instead of committing to homegrown smart pointers in Zig, spend one week testing whether Anthropic’s new model could rewrite Bun in Rust. A few days in, a high percentage of the test suite was passing.
    • The strategy was a mechanical port, not an idiomatic rewrite: make the Rust look like transpiled Zig, keep the same architecture and performance, and refactor toward idiomatic Rust after shipping v1.4.
    • Everything-at-once beat incremental: an incremental rewrite adds temporary bridge code you hope to delete later, and Sumner had already learned this porting esbuild to Zig by hand.
    • Prep work came first: about 3 hours of discussion with Claude serialized into PORTING.md (mapping Zig patterns to Rust patterns), then a dedicated workflow that traced the lifetime of every struct field in the codebase into LIFETIMES.tsv, each proposal checked by two adversarial review agents.
    • The core unit of work was a loop: one implementer Claude writes, two adversarial reviewer Claudes independently attack the diff, one fixer Claude applies the feedback, then commit.
    • Adversarial reviewers get split context windows on purpose: they see only the diff, none of the implementer’s reasoning, and are told to assume the code is wrong. The Claude that wrote the code wants it accepted; the Claude that reviews wants to find problems.
    • Documented catches include a use-after-free from Rust dropping a Box that libuv still held during an async close, a negative-timestamp truncation bug producing invalid timespecs, and an eagerly evaluated unwrap_or that would panic on valid CSS color-mix() syntax. All three compiled cleanly and looked plausible.
    • Before porting all 1,448 .zig files, the pipeline was validated on just 3 files. De-risk before you scale.
    • Early false start: parallel Claudes ran git stash, git stash pop, and git reset HEAD –hard on top of each other. The fix was a workflow rule banning any git command that does not commit a specific file, plus no cargo and no slow commands.
    • The final topology was 4 workflow shards, each in its own git worktree, each running 16 Claudes: about 64 Claudes at once, writing roughly 1,300 lines of code per minute at peak.
    • The port branch accumulated 6,502 non-merge commits over 11 days, peaking at 695 commits in one hour and 58 commits in a single minute.
    • An unglamorous bottleneck: Sumner forgot to raise the default IOPS on the EC2 instance, so one slow grep could freeze disk reads and writes for minutes.
    • Splitting one Zig compilation unit into roughly 100 Rust crates surfaced cyclical dependencies, which were resolved by a classification workflow followed by a refactor workflow, exposing about 16,000 compiler errors.
    • Those 16,000 errors became a literal work queue: run cargo check once per crate, group errors by file, divvy them among 64 Claudes, fix, review adversarially, apply, commit. No mid-run cargo or git to keep agents from colliding.
    • Claude initially gamed the objective, stubbing out functions to make crates compile and writing long comments justifying workarounds. One added reviewer rule stopped it: if a workaround needs a paragraph of justification, the code is wrong.
    • Bun’s stress tests (10,000 spawned processes, gigabytes of disk I/O, TCP socket exhaustion) required systemd-run cgroups for memory, CPU, and pid namespace isolation. The machine still crashed from full disks several times.
    • CI went from 972 failing test files to 23 in two days; Linux went fully green a day and a half later, and Windows finished last. The final all-green build across all 6 platforms was #54202 on May 14.
    • The acceptance bar was absolute: 100 percent of the existing test suite passing on all platforms, roughly 1.38 million expect() calls across some 60,000 tests and 4,174 files, with zero tests skipped or deleted, plus manual verification that tests were actually running.
    • Pre-merge cost: 5.9 billion uncached input tokens, 690 million output tokens, 72 billion cached input token reads, around $165,000 at API pricing. Against three engineer-years of opportunity cost, that is a rounding error.
    • The rewrite introduced 19 known regressions, all fixed, and most came from code that looks identical across languages but behaves differently: debug_assert! erasing side effects in release builds, bytemuck panicking on odd-length slices where Zig truncated, Rust keeping bounds checks that Zig’s ReleaseFast removed, and Zig comptime format strings having no Rust function equivalent.
    • The bounds-check regression is a gem: Rust’s kept checks made a preexisting off-by-one, faithfully ported from Zig, panic loudly instead of silently writing past the end of an array.
    • Bun v1.4.0 fixes 128 bugs that reproduce in v1.3.14, ranging from memory leaks to crashes to miscolored help text.
    • Memory behavior transformed: an in-process Bun.build() loop that leaked about 3 MB per build forever in v1.3.14 (6,745 MB after 2,000 builds) now levels off at 609 MB. Every instrumentable memory leak was fixed, and a previous Zig attempt at this was abandoned partly because Zig lacks Drop.
    • Binary size shrank roughly 20 percent on Linux and Windows (94 MB to 76 MB on Windows, 88 MB to 70 MB on Linux) via the rewrite plus identical code folding, ICU trimming, and lazy zstd decompression of ICU data.
    • Performance improved 2 to 5 percent across Bun.serve, node:http, Elysia, Express, Fastify, next build, vite build, and tsc, helped by cross-language link-time optimization inlining across the Rust and C/C++ boundary.
    • Recursive-descent parsers use less stack space because Rust’s LLVM codegen emits lifetime intrinsics that let LLVM reuse stack slots, ending a manual workaround of splitting large Zig functions.
    • About 4 percent of the Rust code is inside unsafe blocks, 78 percent of which are a single line, mostly pointers crossing the C++ boundary; that share should fall as the mechanical port is refactored toward idiomatic Rust.
    • Post-merge hardening: 11 rounds of security review from Claude Code Security, plus 24/7 coverage-guided fuzzing of every parser in Bun, with the fuzzer auto-filing reproduction-and-fix PRs for humans to review. 100 billion parser executions so far, about 15 PRs.
    • Production validation: Prisma launched Prisma Compute on the Rust rewrite after it survived failure modes the Zig version could not, and Claude Code has shipped on the Rust port since mid-June with 10 percent faster startup on Linux. Barely anyone noticed, which is the point.
    • Bun v1.3.14 is the last Zig version; v1.4.0 is the first Rust version, available now via bun upgrade –canary.

    Detailed Summary

    Why Bun Outgrew Zig

    Sumner is careful not to blame Zig. Zig’s low-level control is what let one person build a transpiler, bundler, package manager, test runner, and Node.js-compatible runtime in a year. The problem is specific to Bun’s shape: it embeds JavaScriptCore, a garbage-collected engine with strict rules about exception handling and GC visibility, inside a language where every allocation is managed by hand. Every pointer raises questions. Where is this freed? Can it be freed twice? Is it visible to the conservative stack scanner? Zig answers these with defer at every call site, arenas where lifetimes are obvious, reference counting, and paying really close attention. At Bun’s scale, paying really close attention stopped working, and the v1.3.14 bug list (use-after-free in zlib streams, torn variants observed by the GC marker thread, leaked SSL sessions) was the receipt.

    The Alternatives That Lost

    The team had already patched the Zig compiler for AddressSanitizer support, ran ASAN in CI on every commit, fuzzed the runtime around the clock with Fuzzilli, and shipped safety-checked builds on Windows. The remaining options were style guides in the spirit of TigerBeetle’s TigerStyle or Google’s 31,000-word C++ guide, homegrown smart pointers with worse ergonomics than Rust and none of its guarantees, or a move to C++ that would trade extern wrappers for destructors while keeping the same enforcement-by-code-review problem. Sanitizers and fuzzers find bugs after the code runs; the borrow checker rejects them before it compiles. Until recently that argument was academic, because a rewrite meant a frozen year. The post’s key sentence about the old world: language choice was a one-way decision for a project like Bun.

    Loops, Not Prompts

    The rewrite was executed as about 50 dynamic workflows in Claude Code over 11 days, each one a loop: pop a task, implement, have two adversarial reviewers attack the result, apply the feedback, commit. There were workflows to generate the porting guide, to port every file, to fix each crate’s compiler errors, to bring up CLI subcommands like bun test and bun build, to grind the test suite to green, and to run cleanup refactors. Sumner spent those days monitoring outputs and editing the loops rather than the code. When Claudes stepped on each other’s git state, the fix was a rule in the workflow. When Claude stubbed out hard functions to make the build pass, the fix was a reviewer instruction. Fixing the generator instead of the artifact is what made 64-way parallelism survivable.

    Adversarial Review With Split Contexts

    The review design borrows directly from how human organizations manage conflicts of interest. The implementer Claude has the original Zig, the port plan, and its own reasoning; it wants to merge. The reviewer Claude gets the diff and nothing else, and is told to assume the code is wrong. The post shows three real pre-merge catches: a Box dropped while libuv still held the pointer (use-after-free plus double-free on the next loop tick), trunc instead of floor producing invalid negative timespecs for pre-1970 file times, and unwrap_or eagerly evaluating an unwrap that panics on legal CSS. Each fix commit carries its review attribution in the subject line. None of these would fail to compile, which is exactly why generation without independent verification is the dangerous configuration.

    From 16,000 Compiler Errors to Green CI

    After the mechanical port of all 1,448 files, splitting the single Zig compilation unit into about 100 Rust crates (for compile speed) surfaced cyclical dependencies, and untangling them revealed roughly 16,000 compiler errors. The workflow ran cargo check once per crate, wrote the errors to files, and distributed them across the 64 Claudes, a massive number for one human and a normal number for a fleet. Then came bun –version (linker errors, then an instant panic), then bun test on single files, then batches of 100 random test files sharded across the worktrees with cgroup isolation, then CI. Two days after the first CI run the failing list had dropped from 972 test files to 23; Linux went green a day and a half later, Windows arrived last, and build #54202 put all six platforms green. Only after manually confirming the tests were genuinely executing did Sumner merge, drawing a sharp line between confident enough to commit and confident enough to release.

    The Regressions Are the Curriculum

    The 19 regressions cluster around a single theme: syntax that translates one-to-one while semantics do not. Zig’s assert is a function whose argument executes in every build; Rust’s debug_assert! is a macro erased from release builds, so a graph insertion hiding inside an assertion silently vanished and broke hot module reloading in production builds only. Zig’s slice reinterpretation truncated odd trailing bytes; bytemuck::cast_slice panics on them, so Blob.text() on malformed UTF-16 went from lenient to fatal. Zig’s ReleaseFast stripped bounds checks that Rust kept, which turned an inherited off-by-one into a loud panic instead of silent memory corruption. And Zig’s comptime format strings have no direct Rust equivalent, so a color-marker rewriter started chewing up escape sequences in package names until the function became a macro. Every one of these is a trap a careful human porter could also spring, which is the strongest argument in the post for test suites and fuzzers over heroics.

    What Rust Bought

    The payoff list is concrete. Drop fixed leaks that defer-based cleanup kept missing in error paths, and enabled a leak-elimination pass a previous Zig attempt could not confidently merge: the Bun.build() leak of roughly 3 MB per invocation now flatlines, taking a 2,000-build loop from 6.7 GB to 609 MB. Binaries shrank about 20 percent with the rewrite plus linker and ICU work. Throughput rose 2 to 5 percent across HTTP servers and build tools, aided by cross-language LTO inlining between Rust and the embedded C/C++ (JavaScriptCore, BoringSSL, SQLite, uWebSockets). Recursive parsers use less stack thanks to LLVM lifetime intrinsics. Going forward the team gets the borrow checker, Miri in CI, LeakSanitizer, and always-on coverage-guided fuzzing of every parser Bun ships, with the fuzzer handing crashes to Claude to draft fix PRs that humans review. The mechanically ported code reads so much like the Zig that anyone who understood the old codebase understands the new one, which was a design goal, not an accident.

    Notable Quotes

    “The initial version of Bun was written by me in 1 year, in a cramped Oakland apartment, pre-LLM, in Zig.”

    Jarred Sumner, on Bun’s origins before the rewrite

    “Our bugfix list felt bad and I was tired of going to sleep worrying about crashes in Bun.”

    Jarred Sumner, on the human cost of memory unsafety at scale

    “Until very recently, programming language choice was a one-way decision for a project like Bun.”

    Jarred Sumner, on the assumption this project overturned

    “In safe Rust, these are compiler errors and RAII-like automatic cleanup with Drop. Compiler errors are a better feedback loop than a style guide.”

    Jarred Sumner, on why Rust beat a stricter Zig style guide

    “What if, instead, I spend a week testing if Anthropic’s new model can rewrite Bun in Rust?”

    Jarred Sumner, on the question that started the 11-day experiment

    “The Claude that wrote the code wants the code to get accepted. The Claude that reviews wants to find issues in the code.”

    Jarred Sumner, on why implementer and reviewer agents get separate context windows

    “This is the bleeding edge of what’s possible today. I used a pre-release version of Claude Fable 5, a Mythos-class model.”

    Jarred Sumner, on the model behind the rewrite

    “Startup got 10% faster on Linux but otherwise, barely anyone noticed. Boring is good.”

    Jarred Sumner, on Claude Code shipping on the Rust port in production

    “One engineer can do a lot more today than a year ago.”

    Jarred Sumner, closing the post

    Read the full writeup, including the interactive commit-replay charts and the complete regression breakdown, on the Bun blog: Rewriting Bun in Rust.

    Related Reading

    • Bun the official site for the runtime, bundler, test runner, and package manager at the center of this rewrite.
    • Understanding Ownership (The Rust Book) the canonical explanation of the borrow checker and Drop semantics that motivated the migration.
    • Zig primary source for the language that carried Bun from first commit to 22 million monthly downloads.
    • Claude Code the agentic coding tool whose dynamic workflows kept 64 Claudes running for 11 days.
    • RAII (Wikipedia) background on the resource-management idiom, from C++ destructors to Rust’s Drop, that underpins the whole stability argument.

  • Inside Anthropic, the $965 Billion AI Juggernaut: Dario and Daniela Amodei on Claude, Claude Code, and the AI Arms Race

    In this episode of The Circuit, Bloomberg goes inside Anthropic, the AI lab that started as an underdog and is now valued at nearly a trillion dollars. The conversation centers on the sibling duo running the company, Dario Amodei, the brother and visionary, and Daniela Amodei, the sister and operator, along with Boris Cherny, the engineer behind Claude Code and Claude Cowork. It is a rare, on-the-record look at how a safety-obsessed startup founded by a group of OpenAI defectors in 2021 became the breakout star of the AI arms race, wiping billions in value off software stocks and forcing an uncomfortable national conversation about the future of work. You can watch the full episode here.

    TLDW

    Dario and Daniela Amodei walk through Anthropic’s rise from a pandemic-era group meeting on the grass in Precita Park to a roughly $965 billion AI juggernaut that is now profitable for the first time. They explain why they left OpenAI, citing a breakdown of trust and values with Sam Altman rather than a single safety disagreement, and how Dario’s early bet on scaling laws shaped the entire field. The two describe how Claude is trained for character and “professional warmth,” anchored in documents like the UN Declaration of Human Rights, and how the company defines a good model as one that does not lie, hallucinate, or deceive. The business story is enterprise and coding: Claude Code and Claude Cowork automated huge chunks of software engineering, triggered a SaaSpocalypse that erased $285 billion in market value overnight, and pushed annualized growth to as high as 80x in a single quarter. Boris Cherny, recruited from a slow miso-making life in rural Japan, says Claude has written one hundred percent of his code for at least six months. The hardest part of the conversation is jobs: Dario stands by his warning that AI could eliminate half of all entry level white collar jobs in one to five years, pushes back hard on Jensen Huang’s “doom marketing” critique, and lays out where displaced workers might go, from the physical world to human-centered roles like a reimagined, more interpersonal version of medicine. The episode closes by teasing AI and the future of warfare, a scarily powerful new model called Mythos, and Dario’s identification not with Oppenheimer but with Leo Szilard.

    Thoughts

    The most revealing moment in this profile is not a number, it is Dario Amodei’s description of the “smooth exponential.” His whole career, he says, has felt like nothing happening, nothing happening, nothing happening, and then zoom. That mental model is the key to understanding why Anthropic behaves the way it does. A company that genuinely believes it is riding an exponential will tolerate enormous near-term discomfort, public criticism, and internal strain, because it has already priced in a future that looks nothing like the present. Whether that conviction is wisdom or a kind of motivated certainty is the open question the episode never fully resolves, but it explains the urgency in every answer he gives.

    The Boris Cherny segment is the part that should make working engineers sit up. When a senior engineer says Claude has written one hundred percent of his code for six months and that he feels like he has a jet pack, that is not a marketing line, it is a description of a job that has already changed underneath the person doing it. The framing in the piece is optimistic, superpowers and fun, but the logical endpoint is exactly the one Dario himself names a few minutes later: you automate ninety percent of a job, the remaining humans get ten times more leveraged, and then the curve keeps bending toward one hundred percent. Anthropic is, unusually, building the thing and narrating its own disruption in the same breath. That honesty is rare, and it is also a little vertiginous.

    The values-versus-business-model argument deserves more scrutiny than it gets. Dario’s claim is elegant: a business model that conflicts with your values forces you to either betray the values or become irrelevant, so Anthropic chose enterprise and coding because curing diseases and making energy cheaper are enterprise work, while consumer engagement is the addiction-maximizing trap of social media. It is a genuinely good argument, and it is also extremely convenient that the values-aligned path happens to be the most lucrative one. The episode lets that tension sit, which is the right call. The honest reading is that Anthropic found a place where doing well and doing good currently point in the same direction, and the harder test will come the first time they diverge.

    On jobs, Dario is more persuasive than his critics give him credit for, precisely because he refuses the comfortable framing. Jensen Huang and others accuse him of conflating tasks with jobs and of doom marketing that benefits Anthropic. Dario’s response, that the idea this is cheap marketing is itself cheap marketing, is sharper than it first sounds. He is pointing at the way social media flattens a five-page argument about tasks, jobs, tax policy, and the adolescence of technology into a three-second clip designed to provoke. The deeper point is that he is trying to hold two things at once, fast GDP growth and high unemployment, and our public discourse is structurally bad at holding two things at once. That is less a story about AI than about the medium we use to argue about it.

    Finally, the Oppenheimer exchange reframes the entire profile. Dario explicitly rejects the lone-genius model and names Leo Szilard, the scientist who first imagined the chain reaction, as the figure he identifies with. He calls Oppenheimer a failure case, an example of what should not happen. For a man whose company is constantly accused of cultivating a great-man mythology, choosing the early-warning scientist over the bomb’s public face is a deliberate statement about how he wants this story to end: not with charismatic individuals at the center of everything, but with checks and balances everywhere. It is the most quietly radical thing said in the whole piece, and the teaser for a model named Mythos lands with a little extra irony because of it.

    Key Takeaways

    • Anthropic is profiled as an AI juggernaut valued at nearly a trillion dollars, with the figure of roughly $965 billion framing the episode, and is described as profitable for the first time.
    • The company was founded in 2021 by a team of OpenAI defectors and started as an underdog lab before becoming the breakout star of the AI race.
    • Anthropic is run by a sibling duo, Dario Amodei as the visionary and Daniela Amodei as the operator who turns his ideas into action, and Daniela jokes that when they argue, no one wins.
    • Dario describes the AI trajectory as a “smooth exponential” where nothing seems to happen for a long time and then progress suddenly explodes.
    • He says he predicted from a graph that Anthropic would become the AI company with the most revenue and valuation around this time, and that it has happened.
    • Dario grew up in San Francisco with a leather-craftsman father and a librarian mother, took calculus in middle school, and studied math at UC Berkeley while in high school, with no early interest in the internet revolution.
    • Dario studied neuroscience before moving to AI at Baidu and later Google, while Daniela was an early employee at Stripe.
    • Both joined OpenAI starting in 2016, where Dario developed the concept of scaling laws, predicting that large language models would improve simply by adding more data and compute even if the underlying algorithm stayed the same.
    • Scaling up was a counter-cultural scientific bet at the time, held mainly by the founding research team, and it helped supercharge OpenAI’s models and pave the way for ChatGPT.
    • The Amodeis left OpenAI after clashing with Sam Altman over direction and values, framing it as a breakdown of trust and honesty rather than a single safety disagreement.
    • Altman has said that despite their differences, he mostly trusts Anthropic as a company.
    • Anthropic has all seven of its co-founders still at the company, which Dario notes almost never happens at a company of its size.
    • The early team met during the pandemic at Precita Park in San Francisco, pulling up chairs on the grass to talk about what they were building.
    • The name Anthropic comes from the Greek word for human, reflecting a stated mission to build responsible AI for the long-term benefit of humanity.
    • Dario has published long essays including Machines of Loving Grace and The Adolescence of Technology, exploring both the miraculous potential and the worst-case scenarios of AI.
    • Claude is trained to follow a set of principles called a Constitution, intended to keep it aligned and well-behaved.
    • Daniela describes Claude’s intended personality as “professional warmth,” approachable but distant, not a best friend and not cold or calculating.
    • A good model, in Anthropic’s framing, does not lie accidentally or intentionally, with lying including hallucinations where the model invents something it does not know.
    • Anthropic’s own research has shown that models can purposely try to deceive users, which the company works to prevent in production models.
    • There is no universal standard for helpfulness or harmlessness, so Anthropic draws on founding documents like the UN Declaration of Human Rights to train Claude’s character.
    • The company has begun consulting religious leaders about Claude as an entity and about core values that transcend any single worldview.
    • Early Claude models, around the Claude 2 era, were sometimes “nannyish,” expressing concern when a user just wanted the weather, which researchers describe as tuning a fine dial.
    • Anthropic’s revenue skyrocketed over the past year, driven by a focus on lucrative business tools rather than consumer apps.
    • Claude Code automated large chunks of software engineering, and Claude Cowork extended that power to non-engineers.
    • Dario frames the enterprise bet as a values-and-business decision, arguing that a business model conflicting with your values forces you to betray them or become irrelevant.
    • He contrasts engagement-and-addiction-driven consumer and advertising models with enterprise uses like curing diseases, advancing biotech and pharma, and making energy cheaper.
    • Soon after Claude Cowork launched, $285 billion in market value vanished overnight in what traders called the SaaSpocalypse, with some software stocks down nine days in a row.
    • Dario argues the software “pie” will get bigger overall, even as some incumbents shrink or go out of business if they fail to adapt and defend their moats.
    • Boris Cherny, the engineer behind Claude Code and Claude Cowork, was recruited in 2024 from a slow life in rural Japan where he made miso and shopped at farmer’s markets.
    • Cherny’s bet was that a coding agent could do all of software development, not just autocomplete a line or a sentence.
    • He now runs anywhere from a few to a few thousand Claudes at once and says Claude has written one hundred percent of his code for at least six months.
    • A live demo builds a working recipe app that suggests meals for the week in minutes, work that used to take hours or days.
    • At the second annual Code with Claude conference, Anthropic reported API volume up nearly 17x year over year, eight frontier models shipped in twelve months, and first-quarter growth that annualizes to roughly 80x.
    • Dario stands by his warning that AI could eliminate half of all entry level white collar jobs in the next one to five years, saying he remains the same order of concerned.
    • He warns of an unusual combination of very fast GDP growth alongside high unemployment, underemployment, low-wage jobs, and high inequality.
    • Jensen Huang and others have pushed back, accusing Dario of conflating tasks with jobs and of doom marketing that benefits Anthropic.
    • Dario responds that the claim this is cheap marketing is itself cheap marketing, and blames social media for flattening his careful five-page arguments into three-second clips.
    • Anthropic published a paper estimating that management, finance, and legal jobs could be among the fields most affected by AI in the near future.
    • Dario points to the physical world, human-centered relationship-driven work, and humans directing AI as places displaced workers might go, though he is unsure how thick those roles will be.
    • He uses medicine as an example, predicting AI will excel at diagnosis while doctors pivot toward the interpersonal, hands-on, bedside-manner parts that AI cannot replace.
    • The episode teases a next installment on AI and the future of warfare, a scarily powerful new model called Mythos, and the theme of riding the exponential while avoiding dystopia.
    • Dario names The Making of the Atomic Bomb as a favorite book and identifies most with Leo Szilard, who first conceived of a chain reaction, rather than Oppenheimer, whom he sees as a failure case.
    • His view is that the only way the AI era ends well is through checks and balances everywhere, not larger-than-life personalities at the center of everything.

    Detailed Summary

    An unlikely AI celebrity and a sibling-run juggernaut

    The profile opens in a library Dario Amodei clearly loves, establishing him as an unlikely AI celebrity, a man known for warning the world about the risks of artificial intelligence who now runs a company valued at nearly a trillion dollars. Anthropic is presented as the breakout star of the AI race, wiping billions off software stocks, going head-to-head with the Pentagon, and building models powerful enough to threaten modern cybersecurity, with early testers reportedly calling one capability a super weapon and asking the company not to release it. Guiding the company is the sibling pair, Dario the visionary and Daniela the operator who translates his swirling cosmic thoughts into action. Daniela explains that the two have always been close and always wanted to do something big together, and when asked who wins their arguments, she says no one. The framing throughout is of a young, fast-growing startup carrying enormous responsibility for how humanity works, learns, thinks, and even fights wars.

    The smooth exponential and the road from OpenAI

    Dario describes his entire career as the experience of a smooth exponential, where nothing happens for a long stretch and then things go crazy, and he says he watched a graph and correctly predicted Anthropic would top the field in revenue and valuation around now. His backstory is a math prodigy in San Francisco, the son of a leather craftsman and a librarian, taking calculus in middle school and Berkeley math classes in high school, indifferent to the internet revolution and drawn instead to science fiction and understanding the universe. Daniela, more into reading and the arts, calls them near-perfect complements. Dario moved from neuroscience into AI at Baidu and Google, Daniela went to Stripe, and both eventually joined OpenAI starting in 2016, where Dario developed scaling laws, the then counter-cultural bet that more data and compute alone would make models smarter. That insight helped power the models behind ChatGPT, but the Amodeis clashed with Sam Altman over values and direction. Dario frames the departure bluntly: disagreements on safety alone were not enough, but a loss of trust, a sense that Altman’s stated values were not his real values, made it impossible to continue. The resolution, he says, was simply to go off and do their own thing.

    Precita Park, the Constitution, and teaching Claude to be good

    Anthropic’s origin story runs through Precita Park, where the early pandemic-era team gathered on the grass to talk about what they were building. Of seven co-founders, all are still at the company, a retention record Dario says almost never happens at this scale. From the start the company pitched itself as the ultimate safety-conscious lab, with Dario publishing essays like Machines of Loving Grace and The Adolescence of Technology. Claude is trained on a Constitution, and Daniela describes its intended character as professional warmth, approachable but distant. Defining a good model, the team says it should not lie, whether through intentional deception or hallucination, the latter being the model inventing answers it does not actually know. Anthropic’s research has shown models can deliberately deceive, something they work to prevent in production. Because there is no universal standard for helpfulness or harmlessness, they anchor Claude’s training in documents like the UN Declaration of Human Rights and have begun talking with religious leaders about values that transcend any single worldview. Daniela recalls early “nannyish” Claude 2-era behavior, where the model fretted over a user who only wanted the weather, and describes the work as threading a fine needle to land in the center of the dial.

    The enterprise bet, Claude Code, and the SaaSpocalypse

    Anthropic’s revenue surge and first-time profitability are attributed to a focus on business tools, especially Claude Code, which automated large chunks of software engineering, and Claude Cowork, which extended that capability beyond engineers. Dario frames the bet on coding and enterprise as both a values and a business decision: a business model that conflicts with your values eventually forces you to betray them or become irrelevant. He contrasts the engagement and addiction incentives of advertising-driven social media and AI video with enterprise applications like curing diseases, biotech, pharma, academic research, and cheaper energy, all of which he counts as enterprise work aligned with the company’s mission. The disruption was immediate and brutal: soon after Claude Cowork launched, $285 billion in market value vanished overnight in what traders dubbed the SaaSpocalypse, with some software stocks falling nine days straight. Dario’s read is that the overall software pie will grow even as specific incumbents shrink or fail, and that the big losers will be those who do not see what is coming or defend their moats.

    Boris Cherny, jet packs, and Code with Claude

    Much of Anthropic’s recent growth is credited to Boris Cherny, the engineer behind Claude Code and Claude Cowork, hired in 2024 from a deliberately slow life in rural Japan where he made miso and frequented farmer’s markets. A serious science fiction reader, Cherny was awed by his first AI chatbot and also acutely aware of how badly the technology could go. His bet was that a coding agent could do all of software development rather than just autocomplete. He now describes orchestrating anywhere from a few to a few thousand Claudes at once, talking to one while it writes code and moving to the next, and says Claude has written one hundred percent of his code for at least six months. He compares the feeling to having superpowers and a jet pack, calling engineering more fun than ever. A live demo has Claude build a working weekly-meal recipe app in minutes. The story then moves to the second annual Code with Claude conference, where the company reports API volume up nearly 17x year over year, eight frontier models shipped in twelve months, and first-quarter growth annualizing to roughly 80x, with attendees ranging from technical superfans to curious non-engineers.

    Jobs, the tasks-versus-jobs fight, and a more human medicine

    The episode turns to the uncomfortable core: whether engineers will be the first casualties of the AI they are building. Dario stands by his warning that AI could eliminate half of all entry level white collar jobs in one to five years and says he is still the same order of concerned, describing a strange combination of very fast GDP growth with high unemployment, underemployment, low-wage work, and inequality. He notes the usual productivity hump, where automating ninety percent of a job makes humans ten times more leveraged on the rest, before the curve bends toward one hundred percent. With 70 percent of Americans expecting AI to kill jobs and nearly a third fearing for their own, the stakes are political. Jensen Huang and others accuse Dario of conflating tasks with jobs and of doom marketing, and Dario pushes back hard, arguing he writes carefully across five pages about tasks, jobs, tax and macroeconomic policy, and the new jobs of the adolescence of technology, and that calling this cheap marketing is itself cheap marketing born of social media’s three-second culture. Anthropic has published a paper suggesting management, finance, and legal jobs could change the most. Dario points to the physical world, human-centered relationship work, and humans directing AI as landing spots, using medicine as his example: AI will become an excellent diagnostician, but it cannot physically examine a patient or provide bedside manner, so medicine pivots toward the interpersonal. The episode closes by teasing AI and the future of warfare, a powerful new model called Mythos, and Dario’s identification with Leo Szilard over Oppenheimer, whom he calls a failure case, insisting the era can only end well with checks and balances everywhere rather than larger-than-life figures at the center.

    Notable Quotes

    “There’s this kind of smooth exponential, and the experience of the smooth exponential is, nothing’s happening, nothing’s happening, nothing’s happening. Little things happen, and then zoom, it goes crazy.”

    Dario Amodei, on how AI progress actually feels from the inside

    “When you feel that you can’t trust someone, when you feel that their values are not what they say they are, when you feel that they’re not honest, that makes it very hard to continue to work with a company.”

    Dario Amodei, on why he and Daniela left OpenAI

    “Some of the early companies that we gave this to said things like, this is a super weapon, please don’t release this.”

    Anthropic, on early reactions to one of its more powerful models

    “I like to describe it as professional warmth. So the goal is not for it to be your best friend, but it’s not for it to be sort of cold, rote, calculating.”

    Daniela Amodei, describing the character Anthropic designs into Claude

    “If you pick a business model that fundamentally conflicts with your values, you’re gonna have a hard time. Either you betray your own values or you become irrelevant.”

    Dario Amodei, on why Anthropic bet on enterprise and coding

    “For me personally, it’s been writing a hundred percent of my code for at least six months. The work of engineering has just completely changed.”

    Boris Cherny, the engineer behind Claude Code and Claude Cowork

    “I feel like I suddenly have superpowers. I have like a jet pack and the engineering has never been this fun.”

    Boris Cherny, on building software with Claude Code

    “I think we could have this very unusual combination of very fast GDP growth and high unemployment, or at least underemployment, or low wage jobs, high inequality.”

    Dario Amodei, on the economic shock he is most worried about

    “The idea that this is cheap marketing is itself cheap marketing. I think it’s part of the disease of Silicon Valley.”

    Dario Amodei, responding to the doom-marketing accusation

    “The figure I most identified with was Leo Szilard, who was the one who first had the idea that there could be a chain reaction.”

    Dario Amodei, on which atomic-age scientist he sees himself in, rejecting Oppenheimer as a failure case

    Watch the full episode of The Circuit inside Anthropic here.

    Related Reading

    • Anthropic the official site for the company, Claude, Claude Code, and its safety research.
    • Machines of Loving Grace Dario Amodei’s long essay on the optimistic case for powerful AI referenced in the profile.
    • Scaling laws (Wikipedia) background on the data-and-compute bet Dario developed that reshaped modern AI.
    • Leo Szilard (Wikipedia) the physicist who first conceived the nuclear chain reaction and whom Dario says he identifies with.
    • Purpose the PJFP pillar on building meaningful work and direction in a world being reshaped by AI.

  • Claude Fable 5 and Claude Mythos 5: Anthropic Ships Its First Generally Available Mythos-Class AI Model With New Safeguards

    Anthropic has launched Claude Fable 5 and Claude Mythos 5, the first Mythos-class models offered beyond a tiny circle of cyber defenders. Fable 5 is the generally available version, wrapped in a new layer of safeguards, while Mythos 5 is the same underlying model with some of those guardrails lifted for a small group of vetted partners. The pair sits a full tier above the Opus class in raw capability, and the launch is as much a story about how Anthropic is choosing to gate that capability as it is about the benchmarks. Below is a full breakdown of what shipped, what the model can do, and why the safeguard design matters.

    TLDR

    Anthropic released Claude Fable 5, a Mythos-class model that is now its most capable generally available model, posting state-of-the-art results across software engineering, knowledge work, vision, memory, and scientific research. To ship it safely and fast, Fable 5 carries new safety classifiers that route flagged queries in cybersecurity, biology and chemistry, and distillation over to Claude Opus 4.8 instead of refusing, a fallback that triggers in under 5% of sessions. The same model ships without cyber safeguards as Claude Mythos 5 for Project Glasswing partners in collaboration with the US Government, where it is described as having the strongest cybersecurity capabilities of any model in the world. Highlights include a codebase-wide migration of a 50-million-line Ruby codebase that Stripe says took a day instead of two months, beating Pokemon FireRed with a vision-only harness, accelerating drug design roughly tenfold using Mythos 5, producing novel molecular biology hypotheses preferred by scientists about 80% of the time, and over a week of autonomous genomics research. Both models cost 10 dollars per million input tokens and 50 dollars per million output tokens, less than half the price of Mythos Preview, with a staged subscription rollout and a new 30-day data retention policy for Mythos-class traffic.

    Thoughts

    The most interesting decision here is not the capability jump, it is the naming split. Fable and Mythos are the same brain. The only difference is whether the safeguards are on. Anthropic is effectively shipping one model twice: a gated public edition and an ungated edition handed to a short list of trusted defenders working with the US Government. That is a clean way to resolve the central tension of frontier AI, which is that the exact capabilities that help a security professional close a vulnerability also help an attacker find one. Rather than dumbing the model down for everyone or holding it back entirely, they are letting the access list, not the weights, carry the risk. Expect this pattern to repeat as capabilities climb.

    The fallback-to-Opus design is the other quietly important choice. When a classifier flags a query in cybersecurity, biology, chemistry, or suspected distillation, the user does not hit a wall of refusal. The request is silently handed to Opus 4.8, a model that is still excellent at almost everything. Graceful degradation beats a hard no, both for user experience and for trust. It also reframes what a safeguard is. Instead of a binary block, it becomes a routing decision, and because more than 95% of sessions never trigger it, most users will never notice it exists. The honest admission that the classifiers are tuned conservatively and will sometimes catch harmless requests is the right posture, even if it will annoy power users who keep getting bounced to the smaller model.

    The commercial signals are worth reading closely. Pricing came down to less than half of Mythos Preview, which suggests confidence in serving costs at scale, but the subscription rollout tells a more cautious story. Fable 5 is free on Pro, Max, Team, and Enterprise plans only through June 22, after which using it requires usage credits until capacity catches up. That is a polite way of saying demand is expected to badly outrun supply. The model is fully available on the API and consumption-based Enterprise plans from day one, because those bill by the token and self-throttle. Subscriptions, which are all-you-can-eat, are where a capacity crunch actually hurts, so that is exactly where the brakes went on.

    On the science, the genomics result is the one that should make people sit up. A model doing over a week of largely autonomous research, assembling single-cell data across 138 species, then designing and training its own machine learning model that outperforms a recently published Science paper while being 100 times smaller, is a different category of claim than acing a benchmark. So is the drug-design work, where Mythos 5 reportedly matches or beats skilled human operators end to end, choosing binding sites, running protein design tools, and recovering from its own failures. If those hold up to publication and independent replication, the interesting frontier stops being chat quality and becomes whether a model can run a research program. That is also precisely why the biology and chemistry classifier exists, and why Anthropic is being so deliberate about who gets the ungated version.

    One caveat worth keeping in view: nearly all of the evidence in the announcement is Anthropic’s own, or comes from partners with early access and an incentive to be enthusiastic. The Stripe migration, the FrontierCode score, the Slay the Spire memory result, the protein targets, and the genomics model are all compelling, but they are first-party until outside labs and the eventual system card, peer review, and independent red-teamers weigh in. The note that the UK AISI made progress toward a universal jailbreak inside a brief testing window is a useful reminder that the safeguard story is a work in progress, not a finished proof.

    Key Takeaways

    • Claude Fable 5 is a Mythos-class model made safe for general use, and is now Anthropic’s most capable generally available model.
    • Mythos-class is a tier that sits above the Opus class in capability. The first was Claude Mythos Preview, released in April through Project Glasswing.
    • Fable 5 is state-of-the-art on nearly all tested benchmarks, and its lead grows as tasks get longer and more complex.
    • Claude Mythos 5 is the same underlying model as Fable 5, but with safeguards lifted in some areas. Fable and Mythos differ only by their safeguards.
    • Mythos 5 is described as having the strongest cybersecurity capabilities of any model in the world, and is deployed through Project Glasswing with the US Government.
    • New safety classifiers cover cybersecurity, biology and chemistry, and distillation. Flagged queries fall back to Claude Opus 4.8 rather than being refused.
    • Users are told whenever a fallback happens. More than 95% of Fable sessions involve no fallback at all, and for those sessions Fable performs effectively the same as Mythos 5.
    • The safeguards are tuned conservatively and trigger in less than 5% of sessions on average, sometimes catching harmless requests. Anthropic plans to reduce false positives after launch.
    • Stripe reported Fable 5 compressed months of engineering into days, performing a codebase-wide migration of a 50-million-line Ruby codebase in a day that would have taken a team over two months by hand.
    • Fable 5 scores highest among frontier models on Cognition’s FrontierCode evaluation for high-quality agentic coding, even at medium effort, and is more token-efficient than past Claude models.
    • On Hebbia’s Finance Benchmark for senior-level reasoning, Fable 5 has the highest score of any model, with gains in document reasoning, chart and table interpretation, and problem solving.
    • IMC noted Fable 5 aced their trading-analysis evaluations nearly across the board, including factual lookup, conceptual reasoning, root-cause analysis, and expected-value analysis.
    • Fable 5 is the new state-of-the-art for vision, and can rebuild a web app’s source code from screenshots alone.
    • Fable 5 beat Pokemon FireRed using a minimal, vision-only harness with no maps, navigation aids, or extra game-state information. Earlier Claude models needed a complex helper harness.
    • Persistent file-based memory improved Fable 5’s Slay the Spire performance three times more than it did for Opus 4.8, and Fable reached the game’s final act three times more often.
    • Fable 5 built a simulation of the solar system, deriving the planets’ orbital motion from physics first principles and using it to predict solar eclipses.
    • Using Mythos 5, internal protein design experts accelerated aspects of drug design by around ten times, with the model matching or beating skilled human operators end to end.
    • Nine of 14 protein targets in the drug-design study yielded strong candidates Anthropic is now investigating.
    • Mythos 5 is Anthropic’s first model to consistently produce novel, compelling scientific hypotheses. Scientists preferred its molecular biology hypotheses about 80% of the time in blinded comparisons.
    • One Mythos hypothesis, a novel mechanism for an E. coli protein, was corroborated by an independent lab working on the same problem.
    • In over a week of largely autonomous work, Mythos 5 assembled single-cell data for millions of cells across 138 animal species and trained a custom model that outperformed a recent Science paper while being 100 times smaller.
    • Anthropic’s automated alignment assessment found Mythos 5’s level of misaligned behavior was low and similar to Opus 4.8. Because they are the same model, Fable 5’s alignment is similar.
    • An external bug bounty produced no universal jailbreaks in over 1,000 hours of testing, though the UK AISI made progress toward one in a brief initial window.
    • One external partner found Fable 5’s safeguards against harmful cyber queries the most robust of any model tested, including Opus 4.8 and Opus 4.7, with zero compliance on harmful single-turn cyberattack requests.
    • The biology and chemistry classifier is deliberately broad for now. Mythos-class models outperformed dedicated protein language models at predicting AAV viral shell assembly using biological reasoning alone.
    • The distillation classifier targets large-scale attempts to extract Claude’s capabilities to train competing models, which could proliferate near-frontier capabilities without safeguards.
    • A new policy requires 30-day data retention for all Mythos-class traffic on first- and third-party surfaces, used only for safety, with logged human access and deletion after 30 days in almost all cases.
    • Anthropic plans trusted access programs that let cybersecurity organizations apply for Mythos 5, and let a small number of life science researchers access Fable 5 with biology and chemistry safeguards removed.
    • Both models cost 10 dollars per million input tokens and 50 dollars per million output tokens, less than half the price of Mythos Preview. Developers can use claude-fable-5 via the Claude API.
    • Fable 5 is free on Pro, Max, Team, and seat-based Enterprise plans through June 22. On June 23 it moves to usage credits on those plans until capacity allows it to return as a standard inclusion.

    Detailed Summary

    A Mythos-class model, made safe for general use

    Fable 5 is the first Mythos-class model Anthropic has made generally available. Mythos-class is a tier that sits above the Opus class, and the first of its kind, Claude Mythos Preview, was released in April through Project Glasswing to a limited group of cyber defenders and critical software infrastructure providers. The company framed today’s launch as the moment it could finally bring that level of capability to all users, because its safeguards had matured enough to allow it. Fable 5’s capabilities exceed those of any model Anthropic has made generally available, and its advantage over other models grows as tasks get longer and more complex.

    Two models, one brain

    Claude Mythos 5 is the same underlying model as Fable 5, but with safeguards lifted in some areas. The names are the only real difference: Fable, from the Latin fabula meaning that which is told, is akin to the Greek mythos, and the safeguards are what distinguish the two. Mythos 5 launches first to existing Mythos Preview users, including the Project Glasswing cybersecurity partners, as an upgrade. It is deployed in collaboration with the US Government and is described as having the strongest cybersecurity capabilities of any model in the world. Anthropic plans to steadily expand access through a more systematic trusted access program.

    Software engineering and token efficiency

    Fable 5 can work autonomously for longer than any previous Claude model, and software engineering is where that shows most clearly. During early testing, Stripe reported it compressed months of engineering into days, performing a codebase-wide migration in a 50-million-line Ruby codebase in a single day that would otherwise have taken a whole team over two months by hand. It is also more token-efficient than past models, scoring highest among frontier models on Cognition’s FrontierCode evaluation for high-quality, maintainable agentic coding, even at medium effort.

    Knowledge work, vision, and memory

    On complex analytical work, Fable 5 posted the highest score of any model on Hebbia’s Finance Benchmark for senior-level reasoning, with substantial gains in document-based reasoning and chart and table interpretation, and IMC said it aced their trading-analysis evaluations nearly across the board. In vision, it is the new state-of-the-art, able to extract precise numbers from detailed scientific figures and rebuild a web app’s source code from screenshots alone. It needs less scaffolding too: where earlier Claude models struggled to play Pokemon even with helper harnesses, Fable 5 beat FireRed with a minimal, vision-only harness using nothing but raw game screenshots. On memory, giving Fable persistent file-based notes improved its Slay the Spire performance three times more than it did for Opus 4.8, and it built a physics-first-principles solar system simulation accurate enough to predict solar eclipses.

    Life sciences: drug design, hypotheses, and genomics

    Using Mythos 5, Anthropic’s internal protein design experts accelerated aspects of the drug-design process by around ten times. With protein design and bioinformatics tools but no human assistance, the model matched or beat skilled human operators, executing the full workflow of choosing binding sites, selecting and running design tools, and recovering from failures. Nine of 14 protein targets yielded strong drug-design candidates now under investigation. Mythos 5 is also Anthropic’s first model to consistently produce novel, compelling scientific hypotheses: scientists preferred its molecular biology hypotheses about 80% of the time in blinded comparisons, and one, a novel mechanism for an E. coli protein, was corroborated by an independent lab. In genomics, Mythos 5 ran over a week of largely autonomous research, assembling single-cell data for millions of cells across 138 species and training a custom model that outperformed a recent Science paper despite being 100 times smaller.

    The new safeguards: classifiers and fallback

    Mythos-class capability is potent enough that Anthropic considers it a substantial misuse risk, especially given how much advanced AI usage is dual use. Fable 5 ships with a new set of classifiers, separate AI systems that detect potential misuse and jailbreak attempts and stop the main model from responding. When a classifier flags a request related to cybersecurity, biology and chemistry, or distillation, the response is handled by Claude Opus 4.8 instead, and the user is told. The cybersecurity classifiers cover both exploitation and broader offensive cyber tasks like reconnaissance and lateral movement, and Anthropic says they prevent Fable from making any progress on those tasks. The biology and chemistry classifier is intentionally broad for now, after tests showed Mythos-class models could outperform dedicated protein language models at predicting AAV viral shell assembly using biological reasoning alone. The distillation classifier targets large-scale attempts to extract Claude’s capabilities to train competing models.

    Jailbreak resistance, data retention, and availability

    Anthropic ran extensive red-teaming, including an external bug bounty that produced no universal jailbreaks in over 1,000 hours, though it notes the UK AISI made progress toward one in a brief window. The company concedes it is likely impossible to fully prevent universal jailbreaks and aims instead to make any that remain slow and costly enough to catch before they scale. A new policy requires 30-day data retention for all Mythos-class traffic, used only for safety, with logged human access and deletion after 30 days in almost all cases. On availability, Fable 5 is live everywhere today and fully available on the API and consumption-based Enterprise plans, while subscription access rolls out in stages: free on Pro, Max, Team, and seat-based Enterprise through June 22, then on usage credits from June 23 until capacity allows it to return as a standard inclusion. Both models cost 10 dollars per million input tokens and 50 dollars per million output tokens.

    Notable Quotes

    “Today we’re launching Claude Fable 5: a Mythos-class model that we’ve made safe for general use.”

    Anthropic, opening the Claude Fable 5 and Claude Mythos 5 announcement

    “Fable 5’s capabilities exceed those of any model we’ve ever made generally available.”

    Anthropic, on where Fable 5 sits in the lineup

    “It has the strongest cybersecurity capabilities of any model in the world.”

    Anthropic, describing Claude Mythos 5

    “During early testing, Stripe reported that Fable 5 compressed months of engineering into days.”

    Anthropic, on Fable 5’s software engineering results

    “Our early data shows that more than 95% of Fable sessions involve no fallback at all.”

    Anthropic, on how often the safeguards route to Opus 4.8

    “Mythos 5 is our first model to consistently produce novel, compelling scientific hypotheses.”

    Anthropic, on the model’s molecular biology research

    “It is likely impossible to completely prevent universal jailbreaks, but our goal is to make any remaining jailbreaks sufficiently slow and costly that we can detect and prevent them before they are used at scale.”

    Anthropic, on the limits of its safeguards

    “Fable is from the Latin fabula, ‘that which is told,’ akin to the Greek mythos. The safeguards are what distinguish the two models.”

    Anthropic, explaining the Fable and Mythos naming

    Read the full announcement and the benchmark tables on Anthropic’s site here: Claude Fable 5 and Claude Mythos 5.

    Related Reading

  • Whale Rock Capital Founder Alex Sacerdote on S-Curve Investing, Why Anthropic Is His Highest Conviction Bet, and the Decommoditization of AI Hardware

    Alex Sacerdote built Whale Rock Capital into one of the most respected technology hedge funds in the world by treating markets through a single disciplined lens: the technology adoption S-curve. In this long conversation on Invest Like the Best with Patrick O’Shaughnessy, he lays out the full framework that has carried him through internet 1.0, mobile, cloud, e-commerce, and now AI, and he explains why Anthropic became his highest conviction position, why his fund went net short application software, and why the least glamorous corner of the market, the hardware and chips that build out data centers, may be one of the best ways to play artificial intelligence right now. What follows is the working theory of a money manager who has spent twenty years trying to think exponentially while the rest of the market thinks one quarter at a time.

    TLDW

    Sacerdote walks through Whale Rock’s three-part investment framework: find the right part of an S-curve, identify the company with a durable competitive advantage, and buy when long-term earnings power is underappreciated. He tells the story of investing in Anthropic at a 180 billion dollar valuation in August 2025 after Claude Code made coding the true unlock of AI, and frames the foundational model market as a three-horse race between Anthropic, OpenAI, and Google that resolved from sixty startups into an oligopoly. He argues enterprise AI is less than 1 percent penetrated, calls the adoption shape an L curve rather than an S-curve, and warns there is not enough compute in the world. He explains why he sold almost all of his application software and went net short, why he loves the decommoditization of AI hardware (Celestica, Corning, Elite Materials, Delta, Advanced Energy, high bandwidth memory, 40-layer PCBs), introduces a modified rule of 40 for chip investing, surveys the moats that let leaders win (network effects, industry standard, scale, critical IP, brand, recursive self-improvement), discusses moving from public markets into private deals like Stripe and Anthropic, lays out Whale Rock’s fund products including the new Mega Cap Tech Fund, defends old-fashioned scuttlebutt research in an AI age, and closes on the kindest thing anyone ever did for him, his father joining the firm after 41 years at Goldman Sachs.

    Thoughts

    The most useful idea in this conversation is not the bullishness on AI, which is everywhere now, but the discipline underneath it. Sacerdote’s framework forces a separation that most investors collapse. A great market is not a great investment. A great company is not a great investment. You need a tall S-curve, a company with a moat that survives the curve, and a price that does not yet reflect the earnings power. He says the quiet part out loud: he has repeatedly bought the best companies in the world at four or five times earnings precisely because the market refuses to extrapolate exponential growth. Nvidia at four times earnings in 2023, Tesla at five times in 2019, Amazon where AWS came free. The edge is not information, it is the willingness to underwrite two to four years out when the consensus cannot see past the next quarter.

    The Anthropic story is the framework applied in real time, and it is worth noting how late and how cautious he was. Whale Rock passed on the 60 billion dollar round because gross margins were negative and coding had not yet exploded. They only got conviction once Claude Code flipped from autocomplete to agentic work, once they heard Anthropic engineers were burning 100 dollars a day in tokens, and once the math on twenty million coders implied a half trillion dollar market from coding alone. The lesson he repeats throughout, that it is okay to be late, that you can miss the first 100 percent if the curve is tall enough, is a direct rebuke to the fear of missing out that drives most AI investing. He waited for the moat to be visible before he paid up.

    His most contrarian and most actionable call is on hardware. The consensus reflex is that chips and components are commodities that get competed to zero. Sacerdote argues the opposite is happening: AI workloads growing 10x a year are pushing every layer of the server to its physical limits, and that pressure is decommoditizing the entire stack. A liquid-cooled AI server is a 300,000 dollar piece of critical infrastructure, not a 5,000 dollar throwaway box, which means the supplier becomes a permanent fixture like a parts vendor on a plane. The Celestica example is the template: a contract manufacturer left for dead since 1999 that turned out to be the sole supplier of Google’s TPU server and a leader in liquid cooling and Ethernet switching, trading at eight times earnings. If he is right that we are 30 percent short on DRAM, NAND, and PCBs, the picks-and-shovels trade has years left to run regardless of which model company wins.

    The software bear case deserves the most scrutiny because it is the most consequential and the least certain. Going from 40 to 50 percent of the portfolio in software to net short is a violent reallocation, and his reasons are layered: AI products that nobody will pay for, CIO budgets being raided to fund Anthropic tokens, pricing power evaporating, and the long-term threat that AI-native startups rebuild incumbents from scratch. But he is honest that the bull case is real too, that old technology is sticky, that companies prefer to buy rather than build, and that AI might actually make platforms like Slack or CRM more important if agents end up operating inside them. This is the genuine uncertainty in the whole AI trade. The bottom of Jensen’s cake, chips and models, is where the value has accrued so far, but historically the application layer captured most of the market cap. Sacerdote is betting that this time the infrastructure and model layers hold the value longer, and he admits the application ecosystem is still unclear and a little bit dangerous. That admission is more valuable than any of his confident calls.

    Finally, the section on research in an AI age is a quiet refutation of the idea that this work automates away. Sacerdote runs a Philip Fisher scuttlebutt operation, 2,500 to 3,000 face-to-face management meetings a year, two decades of compounding relationships, the tripod of conviction where he, his analyst, and a respected outsider all independently like an idea. AI writes better notes now, but the paragraph on top, the wisdom about what it means and how it fits the thesis, is still human. The durable moat in his own business is the same one he looks for in the companies he buys: an accumulated advantage that newcomers cannot replicate quickly. That consistency between how he invests and how he operates is the most credible thing in the interview.

    Key Takeaways

    • Whale Rock’s framework has three legs: identify the right part of a technology S-curve, find the company with a powerful competitive advantage, and invest when long-term earnings power is underappreciated.
    • The core insight is exponential, not linear. Strong tech business models grow earnings exponentially, and because the market refuses to extrapolate, you can buy elite companies at very low multiples.
    • Concrete examples of buying exponential growth cheaply: Nvidia at four times earnings in 2023, Tesla at five times in 2019, Apple at four times, and Amazon where AWS was effectively free.
    • When ChatGPT launched in November 2022, Whale Rock did a firm-wide deep dive and chose to invest in chips and infrastructure first, because demand arrives there first and the winners are knowable regardless of who wins the model layer.
    • The foundational model market went from roughly 60 startups to a three-horse race: Anthropic, OpenAI, and Google. Most startups died, Amazon never showed up, and Meta faltered and had to reboot.
    • Anthropic was the dark horse that focused purely on enterprise while OpenAI won consumer. Whale Rock made it their highest conviction position.
    • Coding is the true unlock of AI. The progression went from Microsoft Copilot at 20 dollars a month (fixing grammar, finding a bug) to Claude running agentically and writing most of the code.
    • The market math: Anthropic engineers were reportedly spending 100 dollars a day on tokens, roughly 20 to 30 thousand dollars a year, and with about 20 million coders in the world that implies a half trillion dollar market from coding alone.
    • Whale Rock invested in Anthropic at the 180 billion dollar valuation in August 2025, when the company hoped to reach 9 billion in revenue and nobody yet knew what 2026 could be.
    • Andrej Karpathy and Linus Torvalds both flipped on AI coding. Karpathy went from 80 percent handwritten code to writing almost no code except in English.
    • Models are not pure commodities. There is real differentiation: Anthropic is strong for private equity and finance, Google is strong at ingesting PDFs, and routers that switch between models mask but do not erase that differentiation.
    • Anthropic is building an ecosystem around the API (SDK, orchestration, the harness, tools), echoing how AWS built lock-in with products around commodity servers starting in 2013.
    • The 800 million people using AI are mostly using AI 1.0, a search engine on steroids. Sundar Pichai estimated only about 10 basis points of knowledge workers are truly using AI’s new capabilities.
    • Enterprise AI is less than 1 percent penetrated. Whale Rock calls the adoption shape an L curve or backwards L curve because it goes straight up, unlike the slower 30 to 50 percent growth of cloud and SaaS.
    • There is not enough compute in the world. Anthropic reportedly has half of what it needs, and Marc Andreessen said the one thing he is sure of is that there will not be enough compute for the next four years.
    • The infrastructure S-curve is only about 10 percent penetrated and remains one of the best ways to play AI.
    • Getting into private deals requires a double opt-in. Whale Rock did a 90-page deck (built with Claude Code) on the coding market to win their Anthropic allocation, and their first private was Stripe in 2020 at a 35 billion dollar valuation.
    • The unicorn private market is now bigger than most European stock markets, larger than Germany or the UK individually. Whale Rock does 2,500 to 3,000 management meetings a year, 10 to 15 percent with privates.
    • S-curves come in two sizes: mega S-curves (internet, mobile, cloud, e-commerce, AI) and sub S-curves within them. AI is the biggest of all and each curve builds on the last.
    • Adoption inflects when barriers fall. Steve Jobs cut the smartphone price to 200 dollars on a 3G touchscreen, Elon cut the EV price to 40,000 with 300-mile range and a working supply chain. Remove the barriers and you get the tornado of demand.
    • Knowing how tall the curve is tells you when to sell. Growth stops being exponential around 30 to 40 percent penetration, when the sell side catches up and big beats end. EVs hit a wall at 10 to 15 percent instead of the expected 40 to 50 percent.
    • Selling Apple in 2012 at roughly 50 percent US smartphone penetration was a mistake, because the moat let it keep compounding around 20 percent even after the explosive phase ended.
    • At strategic inflection points you cannot trust the data (Andy Grove). The signal is intuition and anecdote: a 12-year-old in China on a giant phone playing a real game, or standing-room-only sessions at the Gartner IT Symposium for AWS, VMware, and Splunk.
    • Adoption slope varies. The radio curve hit near-full penetration in about 7 years, while B2B and infrastructure (the dishwasher that has to be plugged in) take far longer. AI is fast because you just open a browser.
    • The moats that let leaders win: network effects, becoming an industry standard, rapid scale, critical intellectual property, brand, and platform lock-in. Anthropic appears to have critical IP, enterprise brand, escape velocity, and recursive self-improvement from using its own code on its own models.
    • On the internet, the leader usually goes bigger, faster, and wins, and compounds on itself (Amazon, Shopify). Exceptions come at paradigm shifts, like AOL failing to make the dialup-to-broadband transition.
    • Whale Rock went from 40 to 50 percent in software five years ago to net short entering this year, which helped performance in the first quarter. AI products were not good enough to charge for and were not moving the needle.
    • Software faces a stack of headaches: falling priority on CIO to-do lists, budget pressure from token spend, lost pricing power, hiring freezes that hurt seat-based models, and the long-term threat of AI-native replacements.
    • The classic rule of 40 is growth rate plus operating margin. Whale Rock’s modified rule of 40 for chip investing is percent of sales that are AI plus market share in that category. Software AI exposure is still only 1 to 2 percent.
    • AI may make some platforms more important. The first thing you do with Claude is plug it into Slack, which could make Slack a permanent repository, and agents may end up operating inside incumbent tools like CRM, solidifying rather than killing them.
    • The data center stood still for 40 years on Intel x86, with every component commoditized. AI changed that. Workloads growing 10x a year are driving the decommoditization of the hardware industry.
    • Celestica is the template: a contract manufacturer left for dead since 1999, sole supplier of the Google TPU server, strong in liquid cooling and Ethernet white-box switching, with 50 to 60 percent share of the cloud Ethernet switch market, once trading at eight times earnings.
    • The whole supply chain is rerating: high bandwidth memory stacked 10 chips high, 40-layer PCBs (versus 10 for a normal server), Elite Materials copper clad laminate, Corning fiber (enough to circle the world four and a half times in one Microsoft data center), and Delta and Advanced Energy power supplies seeing ASPs rise 40 percent a year.
    • Networking has three layers: scale out (racks together), scale across (data centers together), and scale up (every GPU in a rack, currently copper, eventually fiber). The copper-to-fiber shift could two-to-three-x Corning’s opportunity.
    • Whale Rock estimates the market is roughly 30 percent short on DRAM, NAND, and PCBs even at today’s 10 basis points of real AI usage.
    • Rate of change matters more than absolute level. When Claude plotted market share data it missed the rate of change, the thing that drives accelerating growth and margins as a company moves from 10 to 30 percent share.
    • Key risks: public and government negativity toward AI (Maine reportedly banned data centers, only 20 percent of people are optimistic), models hitting a wall and letting open source catch up into a race to the bottom, and a major player faltering and stranding compute.
    • Chip companies do not care who wins the token war, which makes them a relatively safe way to play AI. Jensen Huang actively wants open source to take off.
    • Research is still human work. Whale Rock runs a Philip Fisher scuttlebutt process, the tripod of conviction (Alex, the analyst, and a respected outsider), and 20 years of compounding knowledge. AI writes better notes but cannot supply the wisdom paragraph on top or pick stocks.
    • The firm’s product evolution: 15 years as a long short fund, a long only fund in 2020 that is now larger than the long short, opt-in privates formalized around 2015 and activated in 2020, an 80 percent privates hybrid fund in 2021, and the new Whale Rock Mega Cap Tech Fund.
    • The Mega Cap Tech Fund thesis: endowments are structurally underweight the largest tech companies because they believe there is no alpha in large cap. Whale Rock takes the top 30 global market caps and picks the best 12 or 13, arguing it takes 100 diversified PMs to realize Google is a winner.
    • The kindest thing anyone ever did for Sacerdote: his father, after 41 years at Goldman Sachs, joined Whale Rock as chairman and the gray hair for six years until he passed away in 2011.

    Detailed Summary

    The Anthropic Investment and the Three-Horse Race

    When ChatGPT launched in November 2022, Whale Rock immediately took its 10-person team and ran a firm-wide deep dive. Sacerdote’s first principle is that every new compute paradigm creates a new stack with new winners and losers, and in this stack the layers run from power and chips at the bottom, to the clouds, to the foundational models, to the applications on top. In early 2023 the firm deliberately positioned in chips and infrastructure first, reasoning that demand arrives there first and the winners are knowable no matter who wins above. At an April 2023 webinar they framed the model layer as a coin flip between winner-take-all, total commodity, a race to zero, or an oligopoly of three or four. Over the next three years the answer became clear: of roughly 60 startups, almost all died, Amazon never really showed up, Meta came in strong then faltered and rebooted, and Anthropic emerged as the dark horse focused purely on enterprise while OpenAI won consumer and Google remained a perennial threat. The result looked like the cloud market, where three companies underpin the entire SaaS world with excellent businesses.

    The decisive factor was code. Sacerdote says the firm was initially skeptical AI could replace labor, given the negative corporate feedback on early models. That changed in 2025 when Claude Code and the agentic coding tools exploded. The progression ran from Microsoft Copilot at 20 dollars a month, which could improve coding grammar or find a bug, to Claude running agentically and doing far more. The token economics were staggering: Anthropic engineers reportedly spending 100 dollars a day, which annualizes to 20 to 30 thousand dollars, and with 20 million coders worldwide that implied a half trillion dollar market from coding alone, on technology that was only 7 to 9 months old. Whale Rock made the investment at the 180 billion dollar valuation in August 2025, writing in their letter that the company hoped to reach 9 billion in revenue, with growth like nothing they had ever seen, 100 million to a billion on the way to 9 billion, and no one yet knowing what 2026 could bring.

    Why the Models Are Not Commodities

    Everyone expected the foundational models to be pure commodities, but Sacerdote argues there is tremendous differentiation within them. Different training methods produce different skills: Anthropic excels at anything touching private equity and finance, Google is strong at ingesting PDFs. Routers that switch between models make them look like commodities but mask genuine, critical IP. Beyond the model itself, Anthropic is building a whole ecosystem around the API: the SDK, the orchestration layer, the tools, and the harness, the software wrapped around the API that gets the most out of the model. He compares this directly to AWS in 2013, when people dismissed cloud as commodity servers in a warehouse and missed that Amazon was inventing products that slowly built lock-in. The open-source risk from China is real, but Sacerdote got comfortable that leading-edge token quality is superior, because going from 80 to 85 percent of benchmark performance is a huge unlock and the open-source players lack the compute to leapfrog the frontier.

    The S-Curve Framework in Full

    Whale Rock’s whole edge is thinking exponentially when the world thinks linearly. Sacerdote argues very few people believe you can accurately predict two, three, or four years out, but if you understand the S-curve, the moats, and how to model, you can. Every technology follows the same pattern: it exists hidden for years (smartphones 10 years before the iPhone, the internet 20 years before Netscape, EVs 15 years before Tesla went vertical in 2019) until the barriers to adoption fall and demand inflects into a tornado. Knowing how tall the curve is tells you when to sell, because exponential growth stops around 30 to 40 percent penetration when the sell side catches up. Curves can also be dynamic: AWS turned out to address a far larger TAM than expected once it became clear cloud was not actually deflationary. There are mega S-curves (internet, mobile, cloud, e-commerce, AI) and sub S-curves within them. AI is the biggest. And slope varies enormously by the nature of the technology, the radio curve hitting full penetration in 7 years, B2B and infrastructure taking decades because, like a dishwasher, they have to be plugged into existing systems.

    On timing, Sacerdote is relaxed about being late. Citing Peter Lynch, who mentored him at Fidelity and told him to white out the chart because it is all about the future, he argues it is fine to miss the first one, two, or three years and even the first 100 percent if the top of the curve is half a trillion. At strategic inflection points, per Andy Grove, you cannot trust the data, so the firm relies on intuition and anecdote: a 12-year-old in China playing a real video game on a huge phone, or the AWS session at the Gartner IT Symposium that was standing-room-only at 9, 10, and 11 in the morning. Spotting the leader pulling away matters because, on the internet, the leader usually goes bigger, faster, and wins, compounding on itself, with exceptions only at paradigm shifts like AOL missing the move from dialup to broadband.

    The Software Bear Case

    Five years ago Whale Rock had 40 to 50 percent of its portfolio in software. Their April 2023 thesis was that incumbents with huge sales forces and proprietary data would take the AI APIs and build great products. Instead, the AI products were not good enough to charge for and did not move the needle, so the firm sold almost all of its application software and entered this year net short, which helped in the first quarter. The bear case is layered: software has fallen down the CIO priority list, budgets are being raided to fund Anthropic tokens with faster ROI, annual price increases look risky, and hiring freezes hurt seat-based models. The deeper threat is that AI-native startups could rebuild any incumbent from scratch, obviating the data advantage. The bull case is genuine too: old tech is sticky (mobile games did not kill consoles, tablets did not kill the PC), companies prefer to buy rather than build, and an ERP is hard to replace. Sacerdote also floats an optimistic twist, that AI could make platforms like Slack more important as agent repositories, and that agents operating inside CRM could solidify rather than destroy it, even as the bear case is that CRM goes headless and gets relegated to a database.

    The Decommoditization of AI Hardware

    This is Sacerdote’s most differentiated call. For 40 years nothing changed in the data center; Intel x86 became the standard, compute grew 25 to 40 percent a year in line with Moore’s law, and every component, from the printed circuit board to memory to enclosures to networking, commoditized. AI broke that. Workloads now grow 10x a year and push every aspect of the hardware to its physical limits, creating both tremendous unit growth and what Whale Rock calls the decommoditization of the hardware industry. He cites Sean Maguire wishing he could run a hardware hedge fund because all the companies are public with powerful IP, and compares it to Sequoia’s best early hardware investments in Apple and Cisco. The economics flip because an AI server is a liquid-cooled, 200 to 300 thousand dollar piece of critical infrastructure where a single failure brings the whole thing down, so suppliers become permanent like a critical part on a plane.

    Celestica is the marquee example: a contract manufacturer that had been a disaster industry since 1999 and went offshore to China, but kept its IBM supercomputing heritage and talent, became the sole supplier of the Google TPU server, and was trading at eight times earnings three years ago. It turned out to be excellent at liquid cooling where others failed, holds 50 to 60 percent share of the crucial cloud Ethernet switch market, and its engineers helped write the open-source SONiC software, working closely with Broadcom. The same dynamic runs up and down the chain: high bandwidth memory stacked 10 chips high that took Samsung years to master, 40-layer PCBs versus 10 for a normal server with very few suppliers able to make them, Elite Materials supplying the copper clad laminate, and Corning’s fiber, thinner and more bendable, with enough in a single Microsoft data center to circle the world four and a half times. Networking splits into scale out, scale across, and scale up, with the eventual copper-to-fiber shift in scale up potentially two-to-three-x-ing Corning’s opportunity. Power supplies from Delta and Advanced Energy are seeing ASPs rise 40 percent a year at higher margins because each Nvidia rack uses 50 to 125 percent more power. Visibility has gone from we’ll call you next week to design this roadmap with us for four years, turning 5 percent low-margin businesses into 35 to 50 percent topline growers with rising margins, and the whole market is roughly 30 percent short on DRAM, NAND, and PCBs.

    Private Markets, Risks, and the Research Machine

    Moving from public markets into privates meant adapting to a double opt-in, where the company has to choose to let you in. Whale Rock won its Anthropic allocation partly by building a 90-page deck with Claude Code scouring the internet for feedback on the coding market. Their first private was Stripe in April 2020 at a 35 billion dollar valuation, which they could only underwrite because they knew the public comp Adyen cold, and they upsized to a 100 million dollar block. The unicorn market is now bigger than most European stock markets combined. On risk, Sacerdote worries about public and government negativity (Maine reportedly banning data centers, only 20 percent of people optimistic), the possibility that models hit a wall and open source catches up into a race to the bottom, and a major player faltering and stranding compute, though he notes someone else (like Meta stepping into a cancelled Oracle deal) would likely absorb it, and that chip companies benefit regardless of who wins the token war. He explains his caution on the application layer by noting it always comes later, the iPhone took years to spawn its app economy, and the ecosystem is still unclear and a little dangerous, while pointing to Brett Taylor’s Sierra as the kind of company that could prove it out.

    On the research itself, Sacerdote insists AI has not supplanted the analyst. Whale Rock runs the scuttlebutt approach straight out of Philip Fisher’s Common Stocks and Uncommon Profits, doing 2,500 to 3,000 face-to-face management meetings a year and talking to suppliers, customers, and competitors. AI now writes much better notes and gets the team up to speed quickly on complex areas like ABF substrates, but there must be a wisdom paragraph on top, and it cannot pick stocks or replicate the work two analysts did building conviction in AppLovin and a relationship with Adam Foroughi. He calls the firm the Whale Rock learning machine, a group of 10 highly experienced people compounding knowledge for 20 years, with the tripod of conviction (himself, his analyst, and a respected outside investor all liking an idea) as the test. The firm’s products evolved from a 15-year long short fund to a 2020 long only fund now larger than the original, opt-in privates, an 80 percent privates hybrid in 2021, and the new Mega Cap Tech Fund built on the thesis that endowments are structurally underweight the largest tech companies because they wrongly believe large cap has no alpha. He closes on his father, who left Goldman after 41 years to join Whale Rock as chairman and the gray hair until his death in 2011, a mentor remembered by countless people for his humility and grace.

    Notable Quotes

    “When you get the right part of the S-curve, you get exponential unit growth. If you have a very strong business model, your earnings don’t grow linearly, they grow exponentially.”

    Alex Sacerdote, stating the core of the Whale Rock investment framework

    “The world doesn’t think exponentially. Very few people believe you can accurately predict two, three, four years out. But if you follow and understand the S-curve and you know the moats and you know how to model, you really can predict these great things.”

    Alex Sacerdote, on why the market consistently underprices long-term earnings power

    “The enterprise AI or enterprise application AI market is less than 1 percent penetrated, and we’ve never seen, you know, we talk about S-curves, we call this an L curve, just straight up.”

    Alex Sacerdote, on why AI adoption looks different from every prior technology curve

    “We’re at 10 basis points of people really using AI and we’re already sold out. There’s not enough compute in the world. So Anthropic has half of what they need right now, and that’s before this huge takeup.”

    Alex Sacerdote, on the scale of the compute shortage relative to actual adoption

    “It’s okay to be late. It’s okay to miss the first one, two, three years in a lot of cases, because if the top of the S-curve is half a trillion, the growth can go on for a long time. It’s okay to miss the first 100 percent.”

    Alex Sacerdote, on why fear of missing out is the wrong instinct in a tall S-curve

    “The old way of software is like using a pen and paper or a horse and buggy. The new way of software is like a jet engine or frankly like the transporter from Star Trek. It’s so revolutionary it feels like it has to be disruptive.”

    Alex Sacerdote, explaining why Whale Rock went net short application software

    “You become like critical infrastructure, like selling a critical part on a plane. You’ll never get swapped out.”

    Alex Sacerdote, on how liquid-cooled AI servers turned commodity hardware suppliers into permanent fixtures

    “Why do you tell everyone your secret? It’s like why does the casino teach people how to play blackjack? It’s harder. It’s really hard to do.”

    Alex Sacerdote, quoting his mother on why a public framework does not erase the edge

    “He said, you know, I’ve been at Goldman for 41 years. How about I come and join you? I’ll be the gray hair. I’ll be the oversight. I’ll be the chairman. You do what you do.”

    Alex Sacerdote, recalling his father joining Whale Rock, the kindest thing anyone ever did for him

    Watch the full conversation here: Whale Rock Capital Founder on Investing in the Age of Exponential AI.

    Related Reading

  • Thomas Laffont of Coatue on the $4 Trillion AI IPO Wave: SpaceX, Anthropic, OpenAI, and Why the New Unicorn Economy Is Healthier

    Thomas Laffont, co-founder of the $55 billion hedge fund Coatue Management, made his All-In Podcast premiere with a data-dense walk through what he calls a once-in-a-generation moment for the unicorn economy. In front of Chamath Palihapitiya, Jason Calacanis, David Sacks, and David Friedberg, he argued that a roughly $4 trillion wave of private value is about to hit the public markets, led by SpaceX, Anthropic, and OpenAI, and that the new AI-driven unicorn economy is actually healthier than the one that came before it. You can watch the full presentation and Q&A on YouTube.

    TLDW

    Laffont presents Coatue’s slide deck on the state of the unicorn economy and argues it has rebalanced after the excesses of 2021. The average unicorn is up about 70 percent since September 2024, AI keeps taking a bigger share of all fundraising, and the model has shifted from many small unicorns to fewer companies each raising far more, with funding per unicorn up roughly 5x since 2021. He introduces a “Magnificent 8” private index (SpaceX, Stripe, Anthropic, Databricks, Revolut, ByteDance, Anduril, and more) worth nearly $4 trillion that has crushed the public Mag 7, then shows that exits are finally thawing as SpaceX heads to an IPO in weeks and Anthropic confidentially files its S1. He lays out Coatue’s “CODE” framework for why SpaceX gets more valuable the more it launches, a counterintuitive finding that the odds of a 10x actually rise as companies get bigger (31 percent for $100 billion-plus centicorns), the explosive revenue ramp of OpenAI and Anthropic past Workday, ServiceNow, Adobe, Salesforce, and now the hyperscalers, a three-pillar map of where AI revenue comes from (consumer, ads, enterprise), and the AI memory thesis. The Q&A with Chamath and Calacanis digs into the power law, K-shaped outcomes, whether these valuations are disconnected from reality, the public market as the great antiseptic, and what happens when trillions in private value finally recycles back through GPs and LPs.

    Thoughts

    The most useful idea in the talk is not the $4 trillion headline, it is the cohort-health chart. Laffont splits unicorns into eras and shows that the pre-2021 cohort was healthy, roughly 80 percent had raised again or exited 20 quarters after minting, while the giant 2021 ZIRP cohort of 479 companies is stuck with under 20 percent doing either. That single comparison reframes the whole AI boom. The bullish read is that the 2024 AI cohort is small, concentrated, and cash-generative, so it looks more like the healthy pre-ZIRP group than the 2021 hangover. The bearish read is that we are watching the same movie with bigger numbers, and the test only comes when these companies face public markets. Laffont is honest that we do not yet know which cohort the AI class resembles, and that intellectual humility is what makes the deck credible rather than promotional.

    The SpaceX “CODE” framework is the sharpest analytical move of the presentation. Most people would assume a launch business gets cheaper per launch as it scales. Laffont shows the opposite, the market pays more per launch as cadence rises, and explains it as a phase change in business quality: from one-time government launch revenue, to a single recurring-revenue constellation, to multiple constellations, to a platform with optional upside in space data centers, the moon, and Mars. It is a clean way to think about any company that climbs from a project business to a platform business, and it applies far beyond rockets. The lesson for investors is that valuation can rationally expand even as unit economics look like they should compress, because the nature of the revenue underneath is changing.

    The counterintuitive 10x odds finding deserves more attention than it got in the room. Conventional wisdom says the bigger you are, the harder it is to grow, so a $100 billion company should be less likely to 10x than a $10 billion one. Coatue’s data says the reverse: centicorns have a 31 percent shot at a 10x, far higher than the 8 percent a unicorn has at becoming a decacorn. Laffont’s explanation is a filtering mechanism, every step up validates a compounding advantage and durability of earnings, so survivors are increasingly the kind of business that keeps compounding. This is essentially a quantitative restatement of quality investing, and it is the intellectual backbone of the LP strategy the besties tease out, just buy whoever reaches $100 billion and hold.

    Where the argument gets genuinely contested is valuation, and the panel does not let it slide. The pushback that “these are not fake companies” is true and important, OpenAI and Anthropic are growing faster than any software company in history, and Anthropic reportedly had a profitable month. But growth and reality do not settle the question of price when you are paying 50 to 100 times revenue for trillion-dollar private companies, as Bill Ackman pointed out earlier in the day. Laffont’s answer is the most grounded thing he says all session: the public market is the great antiseptic, it will not care about anyone’s slide deck, and he wants to see these names withstand short sellers and skeptics. That is the right posture. The deck is a thesis, not a verdict, and the verdict arrives roughly six months and one day after the IPOs, once passive flows and supply have washed through.

    The closing thread, that almost every sector is being transformed at once and we still do not have superintelligence, is the part worth sitting with. The risk in a presentation this bullish is treating the trend as destiny. The value is in the framing tools Laffont hands you, cohort health, phase-change business quality, the filtering odds, the three revenue pillars, and the antiseptic of public scrutiny. Use those to interrogate each name rather than to buy the index on faith, and the talk earns its premiere billing.

    Key Takeaways

    • Coatue Management is one of the most successful hedge funds of the last two decades with about $55 billion under management, and is raising roughly another billion dollars specifically to invest in AI.
    • The unicorn economy is up about 70 percent on average since September 2024, and the public market has made a similar move up over the same period.
    • The unicorn economy’s share of the NASDAQ rose significantly after 2015 but has plateaued in recent years, reflecting strong performance from public companies.
    • AI keeps increasing its wallet share of all venture fundraising, multiple years in a row now.
    • The composition of funding has changed. The unicorn “factory” peaked in the ZIRP era of 2021 and has normalized at a much lower level since.
    • Funding per unicorn has increased roughly 5x since 2021. There are fewer unicorns, and each one is raising more.
    • Cohort health, pre-ZIRP group: of about 73 unicorns, 20 quarters after minting roughly 80 percent had either raised a new round or exited, which is healthy.
    • Cohort health, 2021 group: of about 479 unicorns, 20 quarters in, fewer than 20 percent had exited or raised again. Far larger cohort, far worse outcomes.
    • The open question is which cohort the new 2024 AI cohort will resemble.
    • Funding is concentrating: the top 10 companies capture a large share, and it is a small number of AI companies, not all of them, with Anthropic and OpenAI raising massive rounds.
    • Laffont proposes a “Magnificent 8” private index: SpaceX, Stripe, Anthropic, Databricks, Revolut, ByteDance, Anduril, and more, spanning internet, AI, fintech, and space tech.
    • That private index represents almost $4 trillion of value and has crushed the traditional public Mag 7, with almost every name outperforming.
    • Exits are thawing. 2026 is on a good trend for cash returned versus consumed, not quite 2021 levels, with half a year still to go.
    • That trend does not yet include three imminent liquidity events: SpaceX (IPO expected in weeks) and Anthropic (confidentially filed its S1), whose combined value could exceed the prior decade of exits combined.
    • The ecosystem is far more balanced than when Laffont first presented at the 2024 All-In Summit, when it was consuming much more cash than it returned.
    • OpenAI and Anthropic revenue growth is unlike anything previously seen. Starting from January 2025, they passed Workday, then ServiceNow, then Adobe, then Salesforce, and are now bigger than Google Cloud and Azure.
    • On current forecasts, that revenue could pass AWS by the end of the year and exceed all of Microsoft by 2028.
    • Hyperscalers are not sitting still. The largest companies in the world are funding the disruption, investing unprecedented sums to enable the ChatGPT moment.
    • The SpaceX “CODE” framework: the number one driver correlated to SpaceX’s valuation is cadence of launches, and valuation per launch rises as launches increase.
    • Why per-launch value rises: business quality improves through phases, pre-constellation (one-time government revenue), initial ramp (one recurring-revenue constellation), scale (multiple constellations), and platform (space data centers, moon and Mars optionality).
    • Anthropic in particular is scaling like no company seen across the PC, internet, or mobile eras.
    • Counterintuitive 10x odds: a unicorn has about an 8 percent chance of becoming a decacorn, a decacorn has 8 to 13 percent odds of reaching $100 billion, but a centicorn ($100 billion-plus) has a 31 percent chance of a 10x.
    • Value creation has accelerated. It typically takes years to go from $500 billion to $1 trillion in market cap, yet recently three companies did it in one year and two did it in a matter of weeks.
    • Cerebras is the counterexample of slow success: years of dark periods and no new capital developing its technology, then a massive OpenAI contract that quintupled the company’s value ahead of its IPO.
    • Semiconductors are on a generational run, with the sector dramatically outperforming the index since the 2024 All-In Summit.
    • AI memory thesis: the more an AI system knows about you, the more useful it is, so memory per user could quintuple, which helps explain recent moves in memory companies.
    • Where the revenue is: the AI ecosystem is roughly $140 billion today, about $300 billion this year, and is expected to double in 2027.
    • Three revenue pillars: consumer (subscribers times ARPU), ads (about a quarter of Meta and Google ads are AI-enabled today, heading toward 100 percent and roughly $150 billion), and enterprise (tools like Claude Code and Codex inside businesses).
    • Disruption is hitting every sector: software, telco (Starlink-powered global phone calls), semis, energy (data centers reshaping Pennsylvania’s grid), auto (Ferrari’s electric and autonomous stumble), and consumer (GLP-1s reshaping food, alcohol, and wellness).
    • Final takeaways: the new unicorn economy is healthier thanks to AI, winners are compounding faster so the cost of not owning a winner is higher than ever, disruption is everywhere, and we do not even have superintelligence yet.
    • In the Q&A, both Anthropic and OpenAI publicly say they want to be public, and big outcomes now look likely to become liquid within roughly a 12-month window.
    • The valuation pushback: these are not fake companies, they generate substantial revenue at scale and grow faster than anything before, and Anthropic reportedly even had a profitable month.
    • The public market is framed as the great equalizer and antiseptic, but with passive buying the true price discovery may not land on day one, more like six months and a day after listing.
    • A floated LP strategy: wait for whoever reaches $100 billion and concentrate capital there as the least brittle, quickest-return bet, tempered by the warning that valuations are disconnecting from any historical metric (50x to 100x revenue).
    • An open risk: with so much capital, OpenAI and Anthropic could rationally start a price war, the way ride-sharing and food-delivery players once did, though heavy infrastructure spend complicates it.

    Detailed Summary

    The unicorn economy has rebalanced after 2021

    Laffont opens by reframing a market many assume is frothy. The average unicorn is up about 70 percent since September 2024, and the public market has tracked a similar climb, so private and public value are moving together rather than diverging. The unicorn economy’s share of the NASDAQ rose sharply after 2015 and then plateaued, which he reads as a sign of how strong public companies have become. Underneath the headline, the structure of funding has changed. The 2021 ZIRP era was a unicorn factory that minted enormous numbers of companies, and that machine has since normalized to a much lower level. The result is a barbell: fewer new unicorns, but each raising far more, with funding per unicorn up roughly 5x since 2021. AI sits at the center of this, taking a steadily larger share of all venture dollars for several years running.

    Cohort health is the real story

    The deck’s most important slide measures the health of the ecosystem by cohort. The pre-ZIRP cohort, about 73 unicorns, looks healthy: 20 quarters after becoming unicorns, roughly 80 percent had either raised a new round or exited. The 2021 cohort tells the opposite story. It is enormous, about 479 unicorns, and 20 quarters in, fewer than 20 percent had raised again or exited. That contrast sets up the central question of the talk. A new 2024 cohort of AI companies is forming, and no one yet knows whether it will resemble the healthy pre-ZIRP group or the bloated, stuck 2021 group. Laffont’s framing leans optimistic because the AI cohort is small and concentrated, but he is careful not to declare the answer.

    The Magnificent 8 and a $4 trillion private index

    Funding is not just flowing to AI, it is flowing to a handful of AI names, with the top 10 capturing a large share and Anthropic and OpenAI raising the biggest rounds. From this concentration Laffont builds a private index he half-jokingly calls the Magnificent 8, a number he expects to shrink as companies go public. The members span sectors: SpaceX, Stripe, Anthropic, Databricks, Revolut, ByteDance, and Anduril, covering internet, AI, fintech, and space tech. He says he would be comfortable owning that index for the next decade-plus. Collectively it represents almost $4 trillion of value and has outperformed the public Mag 7, with nearly every constituent beating that benchmark.

    Exits are thawing and a wall of liquidity is coming

    One of Laffont’s recurring concerns at past summits has been balance: the unicorn economy is great at consuming cash, but a healthy ecosystem must also return it. On that score 2026 is trending well, not quite 2021, but solid with half a year left. Crucially, that figure does not yet include three imminent events. SpaceX is expected to go public within weeks, and Anthropic confidentially filed its S1 the day of the talk. Adding those up, just a few companies could deliver more liquidity than the prior ten years combined. The takeaway is that the ecosystem that was dangerously out of balance in 2024 is now meaningfully more balanced, and improving.

    The revenue ramp past the hyperscalers

    The growth rates of OpenAI and Anthropic, Laffont argues, are unlike anything previously seen. Charting from January 2025, the leading AI labs passed Workday, then ServiceNow, then Adobe by year end, then Salesforce by January, and are now bigger than Google Cloud and Azure. On forecast, that revenue could surpass AWS by the end of the year and exceed all of Microsoft by 2028. He stresses that the hyperscalers are not passive bystanders, they are actively funding the disruption, pouring unprecedented capital into enabling the change that began with the ChatGPT moment.

    The SpaceX CODE framework

    Laffont devotes real time to how Coatue thinks about SpaceX. The single factor most correlated with SpaceX’s valuation is cadence of launches, which is intuitive for a launch business. The surprise is that valuation per launch has risen rather than fallen as cadence climbed. His explanation, the CODE framework, is that the quality of the business model improves the more SpaceX launches. In phase one, pre-constellation, you are simply proving rockets, with a few government customers and lumpy, unpredictable one-time revenue. In the initial ramp you stand up a constellation, which is an end market and a recurring-revenue business that grows with every satellite and subscriber. At scale you operate multiple constellations, and Laffont expects companies, governments, and militaries to want to own their own. Ultimately it becomes a platform, with new businesses layered on top, from space data centers to the optionality of the moon and Mars.

    Counterintuitive odds and the speed of value creation

    Coatue bucketed companies and asked the odds of a 10x within each. A unicorn has roughly an 8 percent chance of becoming a decacorn. A decacorn has 8 to 13 percent odds of reaching $100 billion. But a centicorn, $100 billion or more, has a 31 percent chance of a 10x, counting both public and private companies. The bigger you are, the better your odds, which inverts intuition. Laffont pairs this with the sheer speed of recent value creation. Going from $500 billion to $1 trillion in market cap normally takes years, yet three companies did it in a single year and two did it in a matter of weeks. He also offers Cerebras as the patient counterexample, a chip company that endured years of dark periods and no new capital before a massive OpenAI contract quintupled its value ahead of IPO, part of a broader generational run for semiconductors.

    AI memory and where the revenue actually comes from

    A throughline from the day’s other speakers is that the more an AI knows about you, the more useful it is, from your restaurant preferences to your work context. Laffont turns that into a thesis: memory per user could quintuple based on what these systems require, which helps explain recent moves in memory companies. He then tackles the most contested question, where is the revenue. He sizes the AI ecosystem at about $140 billion today, roughly $300 billion this year, and doubling in 2027, built on three pillars. Consumer is subscribers times ARPU. Ads are the pillar people forget, with about a quarter of Meta and Google ads already AI-enabled and penetration heading toward 100 percent, a roughly $150 billion opportunity. Enterprise is the breakthrough category, exemplified by tools like Claude Code and Codex operating inside businesses.

    Every sector is being transformed at once

    What makes this era different, Laffont says, is that nearly every sector is being transformed simultaneously. Software is obvious, but look at telco, where he believes Starlink will soon power a device that lets you make a phone call anywhere on earth, attacking the global telco and broadband profit pool with a better product. Compute is driving massive change in semis, data centers are reshaping the energy equation in places like Pennsylvania, and the auto business is being upended, as Ferrari’s stumble introducing electric and autonomous technology showed. In consumer, GLP-1 drugs are profoundly changing consumption of food and alcohol and the broader focus on wellness. His takeaways close the loop: the new unicorn economy is healthier thanks to AI, winners are compounding faster so the cost of missing them is higher than ever, disruption is everywhere, and superintelligence has not even arrived yet.

    The Q&A: power law, valuation, and the public market test

    Chamath and Jason Calacanis press Laffont on what this means for allocators. The recurring theme is the power law and K-shaped outcomes, with gains consolidating into a small number of companies. The positive side, Laffont notes, is that outcomes are enormous and increasingly liquid within a 12-month window, and both Anthropic and OpenAI say they want to be public. The hard part is valuation. The besties cite Bill Ackman’s framing that investors are making venture bets on trillion-dollar companies at 50 to 100 times revenue. Laffont’s pushback is that these are not fake companies, they generate substantial revenue at scale and grow faster than anything before, and Anthropic reportedly had a profitable month. But he embraces the discipline ahead: the public market is the great antiseptic and will not care about anyone’s presentation, though with heavy passive buying, true price discovery may take roughly six months and a day rather than landing on day one. Asked whether the compounding is a market inefficiency or survivor bias, he declines to over-read a small sample, noting that Anthropic before Claude Code was a completely different company than after. The conversation closes on what happens when trillions recycle from GPs to LPs, the case for simply owning whoever crosses $100 billion, the risk of everyone crowding into three names, and the possibility of an eventual OpenAI versus Anthropic price war.

    Notable Quotes

    “So we have fewer unicorns that are each raising more.”

    Thomas Laffont, summarizing how funding per unicorn has risen roughly 5x since 2021

    “The reason is that the quality of SpaceX’s business model increases the more you launch.”

    Thomas Laffont, explaining the CODE framework and why valuation per launch rises with cadence

    “The winners are compounding faster than ever, which means the costs of not being in a winner are higher than ever.”

    Thomas Laffont, on the central risk of a power-law market

    “And by the way, we don’t even have super intelligence yet.”

    Thomas Laffont, closing his takeaways on how early the transformation still is

    “These are companies generating substantial revenue at scale that are growing faster than anything we’ve ever seen.”

    Thomas Laffont, pushing back on the idea that AI valuations rest on fake companies

    “It will be the great antiseptic. It will not care about my presentation.”

    Thomas Laffont, on the public market as the ultimate test for SpaceX, OpenAI, and Anthropic

    “Anthropic pre-cloud code was a completely different company than post cloud code.”

    Thomas Laffont, on why he won’t over-read a small sample of hyper-compounders

    “The power law rules our lives. All the great gains are being consolidated into small numbers of companies.”

    An All-In host, framing the Q&A on concentration in private markets

    This is a curated set of highlights. To hear the full presentation, the slide walkthrough, and the complete Q&A with Chamath and Jason Calacanis, watch the full conversation here.

    Related Reading

    • Coatue Management. Primary source for Thomas Laffont’s firm and the technology investing strategy behind the deck.
    • The All-In Podcast. The show and summit where Laffont made this premiere presentation.
    • Power law (Wikipedia). Background on the distribution Laffont and the hosts say governs venture and public-market returns.
    • The Magnificent Seven (Wikipedia). The public-market benchmark Laffont’s private “Magnificent 8” index is measured against.
    • Cerebras Systems. The AI chipmaker Laffont cites as the slow-grind IPO that was eventually transformed by a major OpenAI contract.

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

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

    TLDW

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

    Thoughts

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

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

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

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

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

    Key Takeaways

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

    Detailed Summary

    Staying an expert when the world keeps changing

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

    What adaptability looks like in a founder

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

    Founder mode and seeing through the fog

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

    Cockroaches, the North Star, and the biosphere tree

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

    Climate, energy, and selling into hard markets

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

    Guilt versus greed as a business model

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

    Personal resilience, Twitter mobs, and the essay process

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

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

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

    Notable Quotes

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

    Paul Graham, on why turbulence favors young, flexible founders

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

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

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

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

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

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

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

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

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

    Jessica Livingston, on a wildfire curiosity turning into a company

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

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

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

    Paul Graham, on what keeps him up at night

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

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