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  • Jonathan Ross on Groq’s $20 Billion NVIDIA Deal, Faster Inference, and Why Asking the Right Questions Wins the AI Age

    Jonathan Ross, the founder of Groq and the inventor of Google’s Tensor Processing Unit (TPU), sits down with David Senra (host of the Founders podcast) to walk through Groq’s roughly $20 billion partnership with NVIDIA and the decade of near-death struggle that preceded it. You can watch the full conversation here. Ross, now a senior executive at NVIDIA following the deal, is unusually candid about being one of the world’s worst leaders when he started, about coming three weeks from running out of money, and about the single contrarian bet (that faster inference would make AI both faster and smarter) that almost everyone, including his own engineers, told him was pointless.

    TLDW

    Ross explains the structure of the NVIDIA deal (a call to Jensen Huang about buying 100,000 GPUs turned, in three weeks, into NVIDIA’s largest deal by nearly 3x) and why pairing Groq’s LPU with the GPU defeats the many different bottlenecks inside an LLM the way you would use both 18-wheelers and delivery vans in a logistics network. He unpacks the AlphaGo moment that revealed faster inference makes models smarter, the shift from the information age (answering questions) to the AI age (asking the right questions), and a leadership philosophy built on autonomy, one brutally clear priority (25 million tokens per second on a challenge coin), and giving people the fewest constraints so they can surprise you. He shares hard-won lessons from Jensen and NVIDIA (the least political large org he has seen, no secret one-on-ones), his concepts of reality quotient and the dominant game, return on luck and the GitHub opportunity he let his team talk him out of, intentional leadership (“I intend to do this”), the Grok bonds that traded salary for equity and saved the company, hiring for negatives instead of positives, loss bias and manufactured discontent, and a closing case for radical optimism: code is becoming free, software creation is being democratized like literacy, and education should stop teaching kids to answer questions and start teaching them to ask.

    Thoughts

    The technical spine of this interview is a genuinely counterintuitive claim: you can make a model smarter by making it faster. Ross’s proof is the AlphaGo anecdote, where the exact same model, ported from GPUs to his TPU, saw its ELO jump by hundreds of points and beat the world champion, because more compute per unit of time let it search deeper and surface moves like the famous Move 37 that were too far down the tree to find otherwise. Once you internalize that inference speed is not a convenience but a capability multiplier, the entire Groq thesis, and the logic of the NVIDIA deal, snaps into focus. The industry spent years treating fast inference as a nice-to-have. Ross treated it as the whole game, and was nearly alone in doing so for a very long time.

    The most transferable material is the leadership arc, precisely because Ross is willing to say he was bad at it. His core insight is that there is no single correct way to lead, any more than there is one way to invest, and the founder’s first job is to know which way is true to them. Ross is a delegator who hires autonomous people and gives them a single, poetically compressed objective, then gets out of the way. The reason that matters is subtle: if you over-constrain the goal, your team can never surprise you with a better answer than the one you already had, which means they can never actually innovate. The Kelly Johnson line Senra offers (“extreme performance often comes from one brutally clear priority”) is the same idea from the Skunk Works side. A challenge coin that reads “25 million tokens per second” is not a slogan, it is a mechanism that lets every engineer connect their work to one dominant game.

    Two ideas deserve to be lifted out and used directly. The first is intentional leadership, borrowed from David Marquet’s submarine turnaround: replace “should I do this?” with “I intend to do this.” Asking for opinions invites pessimism and hands your most timid people a veto. Declaring intent still lets someone shout “the hatch is open” when it truly matters, but it stops the reflexive no. Ross traces years of stalled progress to the simple error of asking instead of declaring. The second is his inversion of hiring: hire for negatives, not positives. Growing talent means showing people the path, so you emphasize positives. Selecting talent means screening people out, so you hunt for the disqualifying negatives, because one person’s negative trait infects the whole team. Most founders, Ross included for years, are clever enough to talk themselves into any candidate. A versioned “people spec” and a deliberate loss-averse posture are the antidote.

    The Grok bonds story is the emotional center and a small masterpiece of change management. Facing a layoff list that would have killed the company (because the people slated to be cut were exactly the ones needed to make the product work at all), Ross instead asked the team to trade salary for equity, framed with World War II war-bond imagery. Eighty percent participated, half went to statutory minimum wage, and attrition actually fell. His phrase for why is “put everyone’s hands on the steering wheel.” Passengers fear a windy road, drivers feel in control. It is a reminder that morale under existential stress is often a function of agency, not comfort, and that the Phil Knight move of converting employee sacrifice into ownership is a recurring pattern in company survival stories for a reason.

    Where the conversation turns almost spiritual is manufactured discontent. Ross observes that the entrepreneurs in a room of successful people were the least happy with their wealth, and that this very dissatisfaction was the fuel that kept them building. His own current discontent is stark and worth sitting with: the world does not have enough compute, and if it takes an extra year to cure cancer or slow aging because of that shortage, he considers it his fault. Whether or not you accept the moral weight he assigns himself, the mechanism is instructive. Edwin Land wrote “300 people died today” on the whiteboard while inventing anti-glare technology. A concrete, human cost attached to delay is a far more durable motivator than a revenue target. Paired with his closing optimism about code becoming free and software creation democratizing like literacy, it makes for one of the more clear-eyed and yet hopeful founder conversations in recent memory.

    Key Takeaways

    • The NVIDIA deal began as a request to buy about 100,000 GPUs; Jensen saw what Groq had built pairing GPUs and LPUs and decided to make it available to all NVIDIA customers, closing what Ross calls the firm’s biggest deal by nearly 3x in roughly three weeks from first call to wired money.
    • GPUs and LPUs are complementary: inside an LLM’s decoder layer, the GPU is better at the compute-bound attention portion and the LPU is better at the memory-throughput-bound weights, so combining them defeats bottlenecks across the whole performance curve, like using both 18-wheelers and last-mile vans.
    • As AI increasingly talks to AI, speed dominates, because agents kick off other agents and compound; a human tolerates a one-second wait, but AI is just sitting there idle.
    • Agentic micro payments will make the number of payments skyrocket, but payments infrastructure is not yet built for AI operating inside an allocated budget.
    • Ross prototypes cutting-edge ideas as personal hobby projects first, then brings them to work; his personalized “daily brief” evolved from long text into headlines he can interrogate with follow-up questions, like the game of 20 questions.
    • The information age rewarded answering questions; the AI age rewards asking the right ones, as everyone shifts from individual contributor to leader of AI, and good leaders ask the question no one else did.
    • There is no single right way to lead, just as there are many ways to invest; the founder’s job is to know themselves and pick the leadership form that is true to them (inspiration versus fear, control versus delegation).
    • Ross was, by his own account, one of the world’s worst leaders at the start, which cost Groq three to four years; his fix was to define one goal simple enough to fit on a challenge coin: 25 million tokens per second.
    • The fewer constraints you give a person (or an AI agent), the more freedom they have to surprise you with a better solution; over-constraining the goal makes real innovation impossible.
    • Lessons from Jensen and NVIDIA: it is the least political large organization Ross has seen, Jensen never runs secret one-on-ones (tell everyone at once, copy everyone on email), and the whole strategy reduces to “what does the customer actually need?”
    • Jensen manages around 60 direct reports, each smarter than him in their own domain, which he offers as the model for orchestrating AI agents that may be smarter than you.
    • Asking a sharp question that makes an expert say “I didn’t think of that” is a universal founder skill (it appears in every Bezos book) and can be honed.
    • Confidence, not competence, was Ross’s early bottleneck: shadowing a leader of 2,000 people, he realized he would have made the same decisions, and acting with confidence made people follow his direction without changing the decisions themselves.
    • The better and more creative your people, the harder they are to manage; running 450 highly creative scientists felt more like managing 5,000.
    • Reality quotient (RQ), distinct from IQ, is the ability to recognize reality and, in its extreme form, to choose the dominant game; MySpace optimized accounts signed up while Facebook optimized monthly active users and won.
    • The first principle of change management is to make it feel like it is not a change; people who seem fine with change are usually anchored to something that did not change.
    • Return on luck (from Jim Collins): the most successful companies do not get more lucky breaks, they seize the ones they get; Ross let his team talk him out of powering GitHub’s LLMs on Groq chips, then vowed never again.
    • People adopt fast inference only when they experience it personally; an Anthropic demo three months before ChatGPT drew no reaction because the answers were not the audience’s own, and Groq later went viral off a fast-LLM video posted on X.
    • Great innovators often experience a problem before others do; the future is already here, just not evenly distributed, and Ross saw fast inference’s value first because of AlphaGo.
    • Intentional leadership (from David Marquet’s USS Santa Fe turnaround): say “I intend to do this” instead of asking for an opinion, which stops reflexive pessimism while still letting people flag a real problem.
    • Grok bonds: three weeks from running out of money, Ross swapped a layoff for a war-bond-style salary-for-equity exchange; 80% participated, about half took statutory minimum wage, and it bought roughly two months of runway.
    • “Put everyone’s hands on the steering wheel”: participation in saving the company cut attrition to under 10% during the crisis, echoing Phil Knight converting employee loans into Nike equity.
    • West Coast VCs behave like lemmings (one pass triggers all passes), while East Coast VCs run independent analysis; the herd missed what became NVIDIA’s biggest deal ever, a live example of the Keynesian beauty contest.
    • For the first time, top startups are not starved for cash, so putting in more money is no longer an advantage even though investors still behave as if it is.
    • Hiring flip: move from hiring for positives (how you grow talent) to hiring for negatives (how you select talent), because one negative trait poisons the team; write a versioned “people spec” like a product spec.
    • Loss bias (a loss feels roughly six times more painful than an equal gain) can be a hiring signal: Ross looks for people who “book the win early,” treating any missed improvement as a loss.
    • Poetic design (maximum meaning in minimal expression, “every word matters”) was a positive on the people spec; its negative is maximalist, cluttered design.
    • Michael Jordan manufactured pressure by taunting opponents so a loss would be humiliating, forcing superhuman performance (per his trainer Tim Grover), a deliberate version of throwing your keys over the fence.
    • Manufactured discontent (David Ogilvy’s “divine discontent”): the best entrepreneurs never rest on wins; the least happy people with their wealth were the ones who kept building.
    • Ross’s discontent today is the world’s lack of compute; he treats every delayed medical breakthrough as partly his responsibility, the way Edwin Land wrote a daily death count on the whiteboard while fighting headlight glare.
    • Software has run on “code rationing” because code was expensive to write, enforced by “no engineers”; as the marginal cost of code approaches zero, you just implement, experience, and re-implement.
    • AI democratizes software creation like the alphabet democratized literacy: Ross’s executive assistant now builds working apps, and individual founders with taste but no coding background will create valuable companies.
    • Education should be revamped around asking questions and solving real community problems; if a kid can look up or prompt the answer, the assignment taught nothing, but making them ask the right questions to get AI to solve a real problem does.

    Detailed Summary

    The $20 Billion NVIDIA Deal and Why LPUs and GPUs Belong Together

    The deal’s most striking feature is speed: the idea was first floated on a call roughly three weeks before the money was in the bank. Groq had been integrating GPUs and LPUs and went to Jensen Huang wanting to buy about 100,000 GPUs to deploy themselves. Jensen saw the combined system and decided it should be offered to all of NVIDIA’s customers. The technical logic is that processing an LLM token involves many matrix multiplies with different bottlenecks, some compute-constrained (better on the GPU, especially the attention portion) and some memory-throughput-constrained (better on the LPU, applying the trained weights). There is no single perfect architecture, so putting the two together defeats bottlenecks across the whole curve. Ross adds that as AI talks to AI, speed becomes everything, because agents spawn agents and compound exponentially.

    Asking Questions, Daily Briefs, and the Shift to Leading AI

    Ross builds cutting-edge tools as personal hobby projects before bringing them to work, including a personalized “daily brief” that functions like a presidential daily brief. He redesigned it from long text into headlines he can interrogate, because interactivity, like 20 questions, distills straight to what you actually care about. This grounds one of his signature ideas: success in the information age meant answering questions, but success in the AI age means asking the right questions. As people move from individual contributors to leaders of AI, the skill that matters is the leader’s skill of asking the question everyone else missed or was afraid to raise, since the question you ask determines the output you get.

    Knowing Your Leadership Style and the Challenge Coin

    Ross frames leadership like investing: the first principle is simply having followers, but there are infinite valid styles. New founders fail by copying advice that is not true to them. Ross is a natural delegator (he has not held a driver’s license since his teens because he would rather think than control the car) who hires unusually autonomous people. Early on this backfired badly, because he entrusted people who needed direction, and he calls himself one of the world’s worst early leaders, a gap that cost Groq years. His breakthrough was distilling the mission onto a challenge coin reading “25 million tokens per second,” which let everyone connect their work to one dominant game. He references David Marquet’s Turn the Ship Around later, but the coin embodies Kelly Johnson’s Skunk Works principle that extreme performance comes from one brutally clear priority, plus the rule that fewer constraints give people more room to surprise you, turning a team from Superman into the Avengers.

    Lessons from Jensen: Killing Politics and Serving the Customer

    Working at NVIDIA taught Ross how much further he could have pushed lessons he half-learned at Groq. NVIDIA is, in his experience, the least political large organization anywhere, and a big reason is that Jensen never tells different people different things in private one-on-ones. When you address a room, everyone hears the same message; separate conversations breed side cliques. Ross’s practical rules: hold big meetings for anything you want a group to know, and copy everyone on email so no one can route politics through you. The other Jensen lesson is to stop playing 3D chess and just ask what the customer needs, tell them only what you believe and can support, and refuse to sell them something they do not need. Senra notes he has covered roughly 19 ideas from The Nvidia Way on his Founders podcast, and Jensen’s line that he already manages 60 reports smarter than him is the template for managing AI agents.

    Reality Quotient, the Dominant Game, and Change Management

    Groq hired for reality quotient, not just IQ, because plenty of very smart people construct elaborate stories disconnected from reality. In its extreme form, RQ is the ability to choose the dominant game, the way Facebook’s focus on monthly active users beat MySpace’s focus on accounts signed up. The founder’s job is to help everyone connect their activity to that dominant game (for Groq, tokens per second), then manage the change. Ross’s first principle of change management is to make it feel like it is not a change: nobody likes change, and people who tolerate it well are usually focused on something that stayed constant. If your team is anchored to the dominant goal, a new tactic does not feel like change; if they are anchored to a narrow task, it does.

    Return on Luck, the AlphaGo Insight, and the GitHub Miss

    From Jim Collins’s Great by Choice, Ross took the idea that winners seize luck better, not that they get more of it. He experienced it first-hand with AlphaGo: after a DeepMind team asked whether his TPU was as fast as rumored (he said yes, Ghostbusters-style), porting the identical model from GPUs to TPUs pushed its ELO from around 3,200 to roughly 3,900 and it crushed the world champion. As Thinking Fast and Slow by Daniel Kahneman frames it, more compute lets the model virtually play out more moves and occasionally find a better second-best line, which is how the famous Move 37 surfaced. Faster thinking is smarter thinking. Yet Ross also let his own engineers talk him out of powering GitHub’s LLMs on Groq chips, twice, because they focused on why it could not be done rather than why it could. He eventually did the math himself, hit the numbers, and learned to stop inviting that pessimism.

    Selling Speed and Intentional Leadership

    Customers could not grasp fast inference until they felt it. Ross recalls an Anthropic demo three months before ChatGPT that drew no reaction, because seeing someone else’s answer appear is not magical, but getting your own question answered instantly is. So Groq simply put fast inference online, and it went viral after someone posted a video of a blazing-fast LLM on X (Ross noticed his own demo slowing in Norway because usage had skyrocketed). The deeper fix for internal resistance came from Turn the Ship Around, David Marquet’s account of turning the USS Santa Fe from worst to best in nuclear readiness by replacing command-and-control with intentional leadership. Saying “I intend to do this” rather than “should I?” stops people from reflexively supplying negative opinions, while still letting someone shout “the hatch is open” when there is a genuine problem.

    Grok Bonds: Three Weeks From Zero

    With three weeks of cash left and a layoff list on the table, Ross realized the cuts targeted exactly the people needed to finish an unprecedented compiler and reach the critical mass where the product would even work. Layoffs would not save the company; only reducing burn without losing people could. So Groq held an all-hands, put up World War II war-bond imagery, and launched “Grok bonds,” an exchange of salary for equity. Ross expected heavy attrition; instead 80% participated and about half dropped to statutory minimum wage, real pain for engineers used to six-figure salaries. It bought closer to two months of runway. His framing, “put everyone’s hands on the steering wheel,” explains why attrition actually fell below 10%: drivers feel more in control than passengers, and it echoes Phil Knight in Shoe Dog converting employee loans into Nike equity on the edge of collapse.

    Hiring for Negatives, Loss Bias, and Manufactured Discontent

    Ross was good at spotting smart, talented people but kept hiring ones who caused organizational problems, because he could always talk himself into a candidate. Watching a sharp head of HR screen people out, he realized he had been hiring wrong: growing talent means showing positives, but selecting talent means hunting for disqualifying negatives, since one bad trait spreads to the whole team. He formalized a versioned “people spec” with positives like return on luck and poetic design, each paired with a negative. He also hired for loss bias, the fact that a loss feels roughly six times more painful than an equal gain, seeking people who “book the win early.” That competitive, pressure-seeking wiring links to Michael Jordan manufacturing humiliation stakes (per Tim Grover in Relentless) and to David Ogilvy’s divine discontent. Ross’s own manufactured discontent today is the world’s shortage of compute, which he frames in life-and-death terms.

    The Optimistic Close: Free Code and Universal Software Literacy

    Ross ends on aggressive optimism. Software has long run on “code rationing” because code was expensive to write, policed by “no engineers” whose job is to say no. As the marginal cost of code approaches zero, the workflow flips to implement, experience, then re-implement. More important is accessibility: just as alphabets and universal education turned reading and writing from a scribe’s monopoly into a question of quality, AI is making software creation universal. His executive assistant now builds working apps, and a wave of individual founders with taste but no coding background will create valuable companies. The corollary for education is to stop teaching kids to answer questions and start teaching them to ask, revamping curricula around real community problems where the point is asking the right questions to get AI to solve something that matters.

    Notable Quotes

    “Success in the information age was about being able to answer questions. Success in the AI age will be about being able to ask the right questions.”

    Jonathan Ross, on the fundamental shift AI creates

    “The fewer constraints that you give someone, the more freedom they have to solve the problem, and the more freedom they have to surprise you with the solution.”

    Jonathan Ross, on leading creative teams

    “Being able to think faster makes you think smarter.”

    Jonathan Ross, on why faster inference produces more capable models

    “There are plenty of really smart people who wouldn’t recognize reality if it tapped them on the shoulder.”

    Jonathan Ross, defining reality quotient versus IQ

    “If you express intentional leadership, you say, ‘I intend to do this.’ People don’t tend to offer their opinion, but if it’s very wrong and there’s a reason, they will push back.”

    Jonathan Ross, on the lesson from Turn the Ship Around

    “When people are passengers in a car, they’re more nervous about a windy road or a scary road. But when they’re the driver, they feel more in control.”

    Jonathan Ross, on why Grok bonds kept the team together

    “The biggest flip in my hiring was when I went from looking for positives, which is what you do when you’re trying to grow talent, to looking for negatives, which is what you do when you’re trying to select talent.”

    Jonathan Ross, on inverting his approach to hiring

    “If it takes us an extra year to cure cancer because we don’t have enough compute, that’s my fault.”

    Jonathan Ross, on the discontent that drives him today

    Watch the full conversation between Jonathan Ross and David Senra here on YouTube.

    Related Reading

    • Groq the company Ross founded and the LPU behind the fast-inference story and the NVIDIA partnership.
    • AlphaGo versus Lee Sedol (Wikipedia) the match, including Move 37, that showed Ross how much faster hardware raises a model’s capability.
    • The Keynesian Beauty Contest (Wikipedia) the dynamic Ross uses to explain why West Coast VCs herded past what became NVIDIA’s biggest deal.
    • Zero to One by Peter Thiel, the source of the first-principles thinking Ross applied to the contrarian bet on fast inference.
    • Founders podcast by David Senra the host’s biography-driven show, source of the Jensen, Michael Jordan, and Edwin Land ideas referenced throughout.

  • OpenAI and Broadcom Unveil Jalapeño, a Custom LLM Inference Chip to Cut Compute Costs and Reduce Nvidia Dependence

    OpenAI and Broadcom pulled the wrapper off Jalapeño on Wednesday, June 24, 2026, a custom silicon accelerator that OpenAI is calling its first “Intelligence Processor” and its first real move into designing the hardware underneath its own models. Broadcom President and CEO Hock Tan and President Charlie Kawwas physically handed the wafer to OpenAI CEO Sam Altman and President and Co-Founder Greg Brockman, a staged moment meant to signal that the ChatGPT maker is no longer just a models-and-products company but is now reaching all the way down to the chip. Jalapeño is purpose-built for large language model inference, the compute-intensive job of actually serving answers to users rather than training the model in the first place, and OpenAI plans to deploy it at gigawatt scale by the end of 2026 as the first step in a multi-generation platform built with Broadcom and Canadian electronics manufacturer Celestica. You can read the announcement straight from the source in OpenAI’s official post.

    TLDR

    OpenAI and Broadcom unveiled Jalapeño, OpenAI’s first custom AI chip, an ASIC designed from a blank slate specifically for LLM inference rather than training, manufactured by TSMC and integrated into server systems by Celestica that only OpenAI will use. OpenAI claims the chip went from initial design to manufacturing tape-out in just nine months, what it calls the fastest ASIC development cycle ever in high-performance advanced semiconductors, accelerated in part by using its own AI models to design the silicon. Engineering samples are already running ML workloads in the lab, including GPT-5.3-Codex-Spark, and OpenAI says early testing shows performance per watt “substantially better” than current state-of-the-art, a self-reported and not yet independently verified claim with a full technical report promised in the coming months. Broadcom CEO Hock Tan told Reuters the chip matches Nvidia’s Blackwell and Google’s TPUs, framing the launch as part of a flywheel where OpenAI owns the full stack from chip to model to product. The chip slots into a broader infrastructure strategy targeting 10 gigawatts of custom accelerator capacity between 2026 and 2029 with deployments alongside Microsoft and other partners, and The Decoder reported Microsoft is expected to buy 40 percent of the chips, a guarantee Broadcom reportedly demanded to secure the first phase. The move is widely read as OpenAI diversifying away from Nvidia, continuing a procurement spree that already includes AWS Trainium, AMD, and Cerebras, as inference quietly becomes the company’s real cost center.

    Thoughts

    The single most important word in this announcement is “inference,” and it is the word doing the heavy lifting. Training a frontier model is a capital expense that happens in bursts. Inference is the bill that arrives every single day, forever, scaling linearly with usage. Every ChatGPT reply, every Codex task, every API call, every agent step is an inference event, and as OpenAI’s product surface explodes that recurring cost is the thing that actually threatens the unit economics. A custom chip aimed squarely at inference is therefore not a vanity project or a research flex. It is OpenAI attacking the largest variable cost in its business at the root, trying to bend its cost-per-token curve below what it pays renting Nvidia GPUs. If Jalapeño lands anywhere near its claims, the payoff is not faster benchmarks, it is gross margin.

    The performance-per-watt claim, though, deserves the most skeptical reading in the room. OpenAI says Jalapeño will deliver performance per watt “substantially better” than current state-of-the-art, but it has not finalized the numbers, has not said which chips it tested against, on what tasks, or under what conditions, and the full technical report is somewhere in the indefinite “coming months.” These are self-reported figures from a company with an enormous interest in convincing the market it has a credible alternative to Nvidia. Hock Tan’s line that the chip is “as good as” Blackwell and Google’s TPUs is a CEO talking his own book in an interview, not a measured result. The honest posture is to treat the figures as marketing until the technical report lands. A chip running engineering samples in a lab at target frequency is real progress, but it is a very long way from a chip that holds those numbers across a production fleet under messy real-world load.

    OpenAI left the most revealing detail out of its own press release: the report, via The Decoder, that Broadcom demanded Microsoft guarantee it will buy 40 percent of the chips to secure the first phase. That single sentence tells you who is actually carrying the risk. Building gigawatt-scale custom silicon is brutally capital-intensive, and Broadcom is not willing to commit manufacturing capacity on the strength of OpenAI’s demand alone. It wants a balance sheet behind the order, and Microsoft, OpenAI’s largest backer, is the balance sheet. That detail quietly reframes the whole “OpenAI owns the stack” narrative. OpenAI may design the chip, but the deployment is underwritten by Microsoft’s purchasing commitment, which means Microsoft also gets leverage and supply security out of an OpenAI-branded part. Ownership of the design is not the same as ownership of the risk.

    The flywheel framing is genuinely interesting and probably the most defensible strategic claim OpenAI is making. OpenAI says it used its own models to accelerate parts of the chip design and optimization, compressing a normally multi-year ASIC cycle into nine months. If that is even partly true, it is a meaningful loop: the models help design the chips, the chips run the models more cheaply, the cheaper models drive more usage and revenue, and the revenue funds the next chip. That is a compounding advantage that is hard for a pure hardware vendor to replicate and hard for a pure software lab to replicate. The catch is that nine months from design to tape-out is a claim about speed, not about whether the resulting chip is actually competitive in volume. Fast tape-out and great silicon are different achievements, and the industry has seen plenty of chips that taped out quickly and underwhelmed in production.

    Strip away the “Intelligence Processor” branding and this is a playbook we have already watched run three times. Google built TPUs, Amazon built Trainium and Inferentia, Meta built MTIA, and all of them turned to Broadcom or Marvell for the design IP that is hard to replicate in-house. OpenAI is doing the same thing with the same partner, just later and louder. The diversification arc is unmistakable: OpenAI was one of the biggest Nvidia GPU buyers on earth, and in the span of a year it has signed deals for AWS Trainium, AMD accelerators, and Cerebras inference hardware, and now its own custom ASIC. Nvidia is not in trouble, demand still vastly outstrips supply, but the era where the largest AI labs were captive single-vendor customers is clearly ending. The most intriguing wildcard is OpenAI’s own line that Jalapeño is “designed with flexibility to work with all LLMs.” That is not how you describe a chip you intend to keep entirely to yourself. It hints, however faintly, at an OpenAI that could one day rent out inference infrastructure the way it now rents models, which would put it in direct competition with the very cloud providers it currently depends on.

    Key Takeaways

    • OpenAI and Broadcom unveiled Jalapeño on Wednesday, June 24, 2026, OpenAI’s first custom AI chip and its first piece of in-house silicon after years focused on models and products.
    • The chip is branded an “Intelligence Processor” and described as the first AI accelerator in a multi-generation compute platform the two companies are building together.
    • Jalapeño is purpose-built for large language model inference, the compute-intensive work of generating responses and serving answers to users, and explicitly not for training.
    • Inference is OpenAI’s recurring cost center: every ChatGPT conversation, coding request, image generation, and agent action relies on it, making it one of the highest ongoing costs in the business.
    • Broadcom President and CEO Hock Tan and President Charlie Kawwas physically delivered the first wafer to OpenAI CEO Sam Altman and President Greg Brockman.
    • OpenAI designed the chip from scratch around its understanding of LLM fundamentals, informed by its roadmap of models, kernels, serving systems, and product needs.
    • Jalapeño is described as a blank-slate design for modern LLM inference, not a general-purpose accelerator adapted from earlier AI workloads.
    • The chip is shaped by the systems OpenAI runs daily across ChatGPT, Codex, the API, and future agentic products, while also being designed to work with current and future LLMs across the industry.
    • The stated performance goal is to combine the throughput of today’s leading AI accelerators with latency closer to the fastest specialized inference systems, suiting it for interactive LLM products at scale.
    • OpenAI frames this as its full-stack advantage: it designs frontier models, builds products on top of them, and now designs the chip architecture, kernels, memory systems, networking, scheduling, and deployment systems underneath.
    • OpenAI claims Jalapeño went from initial design to manufacturing tape-out in just nine months.
    • The companies call it what they believe to be the fastest ASIC development cycle ever achieved in high-performance advanced semiconductors, against a backdrop of typically multi-year timelines.
    • OpenAI used its own AI models to accelerate parts of the chip design and optimization process, which it credits for the speed.
    • OpenAI frames the result as a flywheel: the same models served to users help improve the infrastructure that runs future models, lowering compute cost across the industry.
    • Engineering samples of Jalapeño are already running ML workloads in the lab at production target frequency and power.
    • Among the workloads running on the samples is OpenAI’s GPT-5.3-Codex-Spark model.
    • GPT-5.3-Codex-Spark currently runs on Cerebras hardware, which also specializes in inference, per The Decoder.
    • OpenAI says early testing shows Jalapeño will deliver performance per watt “substantially better” than current state-of-the-art hardware.
    • That performance-per-watt claim is self-reported and lacks independent verification; OpenAI has not said which chips it tested against, on what tasks, or under what conditions.
    • OpenAI says it is still measuring final performance and has promised a detailed technical report in the coming months.
    • The architecture reduces data movement and balances compute, memory, and networking resources to push realized utilization much closer to theoretical peak performance.
    • Jalapeño is an ASIC, which experts say is less flexible than Nvidia’s GPU but less expensive and tailorable to specific AI tasks.
    • Broadcom contributes silicon implementation and networking technologies, including its Tomahawk networking silicon, to bring the platform to large-scale production.
    • Canadian electronics manufacturer Celestica provides board, rack, and system integration expertise and will build the server systems.
    • The chips are manufactured by Taiwan’s TSMC, the world’s leading advanced semiconductor foundry, after OpenAI sent over the design.
    • Both the chips and the Celestica-built server systems will be used only by OpenAI, not sold to outside customers.
    • OpenAI plans to deploy Jalapeño at gigawatt scale by the end of 2026, with expansion in the years ahead, as the first step in a multi-generation plan.
    • Hock Tan said gigawatt-scale data center deployment will happen with Microsoft and other partners beginning in 2026.
    • The Decoder reported Microsoft is expected to buy 40 percent of the chips, with Broadcom reportedly demanding Microsoft guarantee that share to secure the first phase.
    • Broadcom CEO Hock Tan told Reuters that Jalapeño is as good as Nvidia’s Blackwell chips and the TPUs designed by Alphabet’s Google.
    • In October 2025, after 18 months of working together, OpenAI and Broadcom went public with plans to develop and deploy racks of OpenAI-designed chips starting late this year; CNBC framed the unveiling as coming eight months after that deal.
    • The prior OpenAI-Broadcom plan ultimately aimed at 10 gigawatts of custom AI accelerator capacity, with deployments expected between 2026 and 2029.
    • Estimates suggest OpenAI’s broader infrastructure plans could eventually involve around 26 gigawatts of computing capacity across custom chips, Nvidia hardware, and other accelerators.
    • OpenAI has been one of the biggest buyers of Nvidia’s GPUs since kickstarting the generative AI boom in 2022, but explosive demand has pushed it to seek other sources of advanced silicon.
    • Earlier in 2026 OpenAI struck a deal with Amazon Web Services that includes use of AWS Trainium chips, and has also signed agreements with AMD and with Cerebras, which held its IPO in May.
    • The move is widely characterized as OpenAI diversifying away from and reducing dependence on Nvidia while creating an alternative to its GPUs.
    • OpenAI’s stated goals with the chip are to reduce costs, improve energy efficiency, secure long-term computing supply, and gain more control over the infrastructure powering its services.
    • Broadcom shares climbed about 2 percent following the announcement, are up roughly 10 percent year-to-date in 2026, and have multiplied almost sevenfold since the end of 2022.
    • To build in-house chips, Meta, Amazon, and Google have turned to firms like Broadcom and Marvell for design services and IP that are hard to replicate internally; Reuters first reported OpenAI was exploring its own chip in 2023, and sources told Reuters in April 2026 that Anthropic is weighing its own AI chip.
    • Broadcom’s margin on custom AI chips is currently lower than on products like networking switches due to AI-driven high-bandwidth memory demand; Tan said SK Hynix and Samsung Electronics supply Broadcom with memory chips.

    Detailed Summary

    A blank-slate chip built only for inference

    Jalapeño is OpenAI’s first so-called Intelligence Processor, and the company is emphatic that it is not a repurposed general-purpose accelerator. It was designed from a blank slate specifically for modern large language model inference, the job of crunching data to answer a user’s query rather than the separate, bursty work of training a model. OpenAI says it designed the chip from scratch around its own deep understanding of LLM fundamentals, informed by its roadmap of models, kernels, serving systems, and product needs, drawing on the systems it runs every day across ChatGPT, Codex, the API, and future agentic products. The stated objective is to fuse the raw power and throughput of today’s leading AI accelerators with latency closer to the fastest specialized inference systems, which would make Jalapeño particularly well suited to interactive products used at scale. Notably, OpenAI also says the chip is designed with flexibility to work with all LLMs across the industry, not only its own, a claim that sits a little oddly next to its plan to keep the hardware entirely in-house.

    The full-stack flywheel and AI designing its own silicon

    OpenAI is selling Jalapeño as proof of a full-stack advantage. The argument is that because OpenAI now develops frontier models, builds products on top of them, and designs the infrastructure underneath them, including chip architecture, kernels, memory systems, networking, scheduling, deployment systems, and the product experience, every layer can be optimized around the same goal of making its models faster, more reliable, and cheaper. OpenAI describes this as a flywheel: better infrastructure drives compute efficiency, which enables better training and serving, which powers more capable models, which become better products, which drive more usage and revenue, which funds the next generation of infrastructure. The most striking piece of that loop is that OpenAI used its own AI models to accelerate parts of the chip’s design and optimization. The company’s framing is direct: if AI can help engineers design better chips faster, it can lower the cost of compute across the industry. That self-referential loop is the part of the announcement that is genuinely novel rather than a rerun of an existing hyperscaler playbook.

    Nine-month tape-out and the partner stack

    OpenAI claims it took roughly nine months to go from initial design to manufacturing tape-out, and calls this what it believes to be the fastest ASIC development cycle ever achieved in high-performance advanced semiconductors, against an industry norm measured in years. It credits deep software-hardware co-development, Broadcom’s silicon implementation expertise, and the use of its own models to compress the schedule. The work is split across a clear partner stack: OpenAI provides the architecture and AI-specific requirements, Broadcom contributes silicon implementation and networking technology, including its Tomahawk networking silicon, and Celestica handles boards, racks, and system integration, building the actual server systems. Once the design was complete, OpenAI sent it to TSMC in Taiwan, the world’s leading advanced foundry, for manufacturing. Crucially, both the chips and the systems built around them are for OpenAI’s exclusive use; they are not products being sold to outside customers.

    Performance claims that nobody can check yet

    OpenAI says early testing shows Jalapeño will deliver performance per watt substantially better than current state-of-the-art hardware, with an architecture that reduces data movement and balances compute, memory, and networking to push realized utilization much closer to theoretical peak. Hardware program lead Richard Ho said the team optimized around the kernels, memory movement, networking, and serving patterns that matter most for frontier models, and that the chip will execute key workloads close to the hardware’s theoretical limits. He told Reuters it will be performant on what he thinks will be all kinds of future LLM iterations. The important caveat is that none of this is verifiable. OpenAI is still measuring final performance, has not finalized the numbers, and has not disclosed which chips it benchmarked against, on what tasks, or under what conditions, with the technical report only promised in the coming months. As The Decoder put it bluntly, these are self-reported numbers, unverifiable for now, that should not be taken at face value. Broadcom CEO Hock Tan’s separate claim to Reuters that the chip is as good as Nvidia’s Blackwell and Google’s TPUs is similarly an unverified assertion from an interested party.

    Gigawatts, Microsoft’s 40 percent, and who carries the risk

    Jalapeño is the opening move in a much larger infrastructure buildout. Initial deployment is targeted for the end of 2026 at gigawatt scale, expanding over multiple generations. Tan said the gigawatt-scale data centers will come online with Microsoft and other partners beginning in 2026. The deal traces back to October 2025, when, after 18 months of collaboration, OpenAI and Broadcom went public with plans to deploy racks of OpenAI-designed chips, ultimately aiming for 10 gigawatts of custom accelerator capacity with deployments expected between 2026 and 2029. Broader estimates put OpenAI’s total infrastructure ambition at around 26 gigawatts across custom chips, Nvidia hardware, and other accelerators. The detail that cuts through the optimism comes from The Decoder: Microsoft is expected to buy 40 percent of the chips, and Broadcom reportedly demanded that Microsoft guarantee that purchase to secure the first phase. That guarantee shows that the financial risk of this buildout is not OpenAI’s alone; it rests heavily on its largest backer’s balance sheet.

    The Nvidia diversification arc and Broadcom’s windfall

    Jalapeño is the clearest signal yet of OpenAI loosening its dependence on Nvidia. OpenAI has been one of the biggest buyers of Nvidia GPUs since it kickstarted the generative AI boom in 2022, but demand has exploded past what any single vendor can supply. Within 2026 alone, OpenAI has struck a deal with AWS that includes Trainium chips, signed agreements with AMD and with Cerebras, which held its IPO in May, and now rolled out its own ASIC. The pattern mirrors what Meta, Amazon, and Google already did, all of them leaning on firms like Broadcom and Marvell for design IP that is hard to build in-house, and Anthropic is reportedly weighing the same move, per sources who spoke to Reuters in April 2026. Broadcom is the obvious beneficiary, with shares up about 2 percent on the news, up roughly 10 percent in 2026, and up nearly sevenfold since the end of 2022. Even so, Tan noted that the AI-driven surge in high-bandwidth memory demand makes Broadcom’s margin on custom AI chips lower than on products like networking switches, with SK Hynix and Samsung Electronics supplying the memory.

    Notable Quotes

    “The world is moving to a compute-powered economy.”

    Greg Brockman, President and Co-Founder of OpenAI, framing the launch as a broad economic shift

    “Jalapeño is part of our long-term full-stack infrastructure strategy to make compute more abundant, resulting in AI which is faster, more reliable, more affordable for people and businesses, and can be used to solve more important problems. By designing more of the stack ourselves, we can serve more intelligence with greater efficiency and keep pushing advanced AI toward broader access.”

    Greg Brockman, President and Co-Founder of OpenAI, on the full-stack rationale for building its own chip

    “Jalapeño was designed from the ground up for LLM inference using detailed insights from our close collaboration with OpenAI researchers.”

    Richard Ho, who leads OpenAI’s hardware program, describing the chip as purpose-built rather than adapted

    “We optimized the architecture around the kernels, memory movement, networking, and serving patterns that matter most for frontier AI models. Based on early testing, Jalapeño will efficiently execute our most important workloads close to the hardware’s theoretical limits.”

    Richard Ho, who leads OpenAI’s hardware program, on the architecture’s optimization targets and early performance

    “It will be performant on, we think, all kind of future iterations of LLMs.”

    Richard Ho, OpenAI hardware chief, to Reuters on the chip’s forward compatibility with future models

    “Our collaboration with OpenAI represents a fundamental commitment to scaling the physical infrastructure required for the next decade of AI.”

    Hock Tan, President and CEO, Broadcom, on the scale of the infrastructure commitment

    “This is just the beginning of a multi-generation roadmap. By co-developing our industry-leading silicon directly with OpenAI, we are enabling the deployment of gigawatt scale data centers with Microsoft and other partners beginning in 2026.”

    Hock Tan, President and CEO, Broadcom, on the multi-generation plan and 2026 gigawatt-scale deployment with Microsoft

    “The goal is to combine the power and throughput of today’s leading AI accelerators with latency closer to the fastest specialized inference systems, making Jalapeño well suited for interactive LLM products at scale.”

    OpenAI, in the press release, stating the performance objective for the chip

    “These are self-reported numbers that haven’t been finalized. Take them with a grain of salt.”

    Maximilian Schreiner, The Decoder, on the unverified performance-per-watt claim

    Jalapeño is a real chip running real workloads in a lab, but the gap between an engineering sample and a profitable production fleet is exactly where this story will be decided over the next year, and the most important numbers, the performance-per-watt figures that justify the whole effort, remain self-reported and unverified until OpenAI publishes its technical report. Read OpenAI’s full announcement here.

    Related Reading

    • OpenAI, the chip’s designer and the primary source of the announcement and quotes.
    • Broadcom, the co-developer providing silicon implementation and Tomahawk networking.
    • Celestica, which builds the boards, racks, and server systems around the Jalapeño chip.
    • ASIC (application-specific integrated circuit), what Jalapeño is, a custom chip built for one task unlike a general-purpose GPU.
    • Nvidia Blackwell, the Nvidia architecture Broadcom’s CEO claims Jalapeño matches.

  • OpenAI’s Leaked 2025 Financials: $34 Billion in Spending, a $38.5 Billion Net Loss, and a $17 Billion Microsoft Bill Ahead of Its IPO

    Infographic summarizing OpenAI leaked 2025 financials: $13.07B revenue, $34B total costs, $20.92B operating loss, $38.53B net loss, where the $34B went, the $17.2B paid to Microsoft versus $303M paid back, inference costs, and IPO valuation context

    OpenAI’s audited 2025 financials leaked this week, and they are the clearest picture yet of what it actually costs to run the company behind ChatGPT. Independent journalist Ed Zitron first published the documents, and the Financial Times independently confirmed them. The headline: OpenAI spent $34 billion last year, booked $13.07 billion in revenue, and reported a net loss attributable to the company of $38.5 billion. The disclosure lands just days after OpenAI confidentially filed for an IPO that could value it north of $1 trillion.

    TLDR

    OpenAI’s audited 2025 numbers, leaked by Ed Zitron and confirmed by the Financial Times, show revenue tripling to $13.07 billion while total costs reached $34 billion, producing a $20.92 billion operating loss and a $38.53 billion net loss attributable to the company. The much larger net loss is inflated by a one-time $41.55 billion non-cash charge tied to OpenAI’s October 2025 conversion from a nonprofit to a public benefit corporation; strip the non-cash items and the loss is closer to $8 billion. R&D alone was $19.18 billion, cost of revenue (inference) was $7.5 billion, and sales and marketing ballooned to $5.73 billion. OpenAI paid Microsoft $17.2 billion in 2025 while Microsoft paid OpenAI only $303 million, exposing a deep Azure dependency. The company burned $1.60 for every dollar of revenue, down from $2.37 in 2024, and gross margin slipped from roughly 40% to 33% as more capable models consumed more compute per query. The leak arrives as OpenAI files a confidential S-1, targets a listing as early as September 2026 at up to a $1 trillion valuation, and races rival Anthropic, which is more valuable on paper and claims it is already turning an operating profit.

    Thoughts

    The most important thing to understand about these numbers is that there are two loss figures and the press will conflate them. The $38.53 billion net loss is the scary headline, but $41.55 billion of it is a non-cash accounting charge from converting investor convertible interests into equity during the for-profit restructuring. That charge is real on the audited statement and it will show up in the eventual S-1, but it is a one-time artifact of OpenAI’s unusual corporate history, not money that left the building. The number that describes the actual business is the $20.92 billion operating loss. That is the one to watch, and it is still enormous.

    The genuinely encouraging line in the whole release is the loss-per-dollar ratio. In 2024 OpenAI spent $2.37 to generate a dollar of revenue. In 2025 that fell to $1.60. A company that is still losing $1.60 on every dollar is not a healthy business, but a company whose efficiency improved by a third in a single year while tripling its top line is at least pointed in a defensible direction. The bull case for OpenAI lives entirely in the slope of that line. If it keeps improving at that rate, the math eventually crosses over. If it stalls, the valuation is a fantasy.

    The Microsoft relationship is the single most revealing disclosure, and it is wildly asymmetric. OpenAI paid Microsoft $17.2 billion in 2025. Microsoft paid OpenAI $303 million. That is a 56-to-1 ratio, and it reframes the partnership: Microsoft is not really a peer or even just an investor, it is OpenAI’s landlord and primary supplier, collecting rent on every model trained and every query answered. The April 2026 renegotiation that capped revenue-share payments at $38 billion through 2030, down from a projected $135 billion, suddenly looks less like a favor and more like OpenAI desperately trying to lower its single largest cost. The dependency cuts both ways, but right now Microsoft holds the better hand.

    The structural problem hiding inside the cost of revenue line is inference. Training a model is a fixed, one-time cost. Serving it is a recurring cost that scales with every one of ChatGPT’s roughly 800 million weekly users. OpenAI spent $5.02 billion on Azure inference in the first half of 2025 alone, and the more capable its reasoning models get, the more compute each answer burns. That is why gross margin went down even as revenue went up. It is the opposite of how software is supposed to work, where the marginal cost of one more user trends toward zero. OpenAI’s marginal cost is real, large, and growing. The counterargument is that per-token inference costs have been falling roughly tenfold a year, so the unit economics could still flip. That is the entire wager.

    Finally, the timing matters more than the numbers. OpenAI’s confidential S-1 means these audited figures were going to become public regardless, since the SEC requires the full prospectus at least 15 days before a roadshow. What the leak changes is who gets to study them first. Prospective IPO buyers, enterprise customers signing multi-year API contracts, and competitors now have the audited books weeks or months early, and they are reading them against Anthropic, which filed at a higher valuation and claims an operating profit. For a company asking the public markets to underwrite a $1 trillion bet on a monopoly outcome that does not yet exist, losing control of the narrative this early is not a small thing.

    Key Takeaways

    • OpenAI’s audited 2025 financials were first published by independent journalist Ed Zitron and independently confirmed by the Financial Times, the first verified look at the company’s books before its planned IPO.
    • Revenue grew from $3.7 billion in 2024 to $13.07 billion in 2025, more than tripling year over year, making OpenAI one of the fastest-growing businesses in history.
    • By the end of 2025 OpenAI was generating roughly $2 billion in monthly revenue, up from about $1 billion a quarter at the end of 2024.
    • Total costs and expenses hit $34 billion in 2025, up from $12.48 billion in 2024.
    • Research and development was the single largest expense at $19.18 billion, up from $7.81 billion, and exceeded total revenue on its own.
    • Of that R&D spend, $10.59 billion went to Microsoft, almost certainly the GPU compute cost of training frontier models on Azure.
    • Cost of revenue, the expense of serving ChatGPT responses (inference), rose from $2.65 billion to $7.5 billion.
    • Sales and marketing jumped from $1.11 billion to $5.73 billion, a 418% increase.
    • General and administrative costs rose from $907 million to $1.57 billion.
    • The operating loss, the truest measure of day-to-day economics, grew from $8.78 billion to $20.92 billion.
    • The net loss attributable to OpenAI was $38.53 billion, up nearly eightfold from $5.09 billion in 2024.
    • The bulk of that jump was a one-time, non-cash $41.55 billion charge from OpenAI’s October 28, 2025 conversion to a public benefit corporation, reflecting the changing fair value of convertible interests and warrant liabilities.
    • Stripping out the restructuring charge and other non-cash items such as stock-based compensation and Microsoft computing credits, the underlying loss was about $8 billion.
    • Including all factors, gross net loss reached $60.35 billion, lowered to the $38.53 billion attributable figure by removing $21.82 billion attributed to noncontrolling and redeemable noncontrolling interests.
    • OpenAI burned $1.60 for every $1 of revenue in 2025, an improvement from $2.37 in 2024, the clearest data point in the bull case.
    • Measured as a percentage of revenue, the operating loss improved from 237% in 2024 to 160% in 2025.
    • In total, OpenAI paid Microsoft $17.2 billion in 2025: $10.59 billion in R&D fees, $6.047 billion in cost of revenue, $527 million in sales and marketing, and $42 million in G&A.
    • Microsoft paid OpenAI just $303 million in the same year, a 56-to-1 imbalance underscoring OpenAI’s Azure dependency.
    • SoftBank paid OpenAI $867 million in 2025.
    • At year-end OpenAI carried $3.64 billion in outstanding payables to Microsoft, plus tens of millions more in accrued and non-current liabilities.
    • OpenAI spent $5.02 billion on Azure inference in just the first half of 2025; Azure inference from 2024 through Q3 2025 totaled $12.43 billion.
    • ChatGPT serves roughly 800 million weekly users, meaning billions of queries a week, each one burning GPU time at Azure’s pricing of about $6.98 per H100 GPU-hour.
    • Gross margin fell from roughly 40% in 2024 to 33% in 2025, because more capable reasoning models consume more compute per query.
    • Research firm Sacra estimates OpenAI’s inference costs reached $8.4 billion in 2025 and will rise to $14.1 billion in 2026, a 68% increase.
    • At year-end OpenAI held just over $50 billion in assets, with almost half in cash.
    • The April 2026 Microsoft renegotiation ended exclusivity and capped revenue-share payments at $38 billion through 2030, down from a projected $135 billion, potentially saving OpenAI up to $97 billion over five years.
    • OpenAI filed a confidential draft S-1 with the SEC around May 22, 2026 and confirmed it publicly on June 8, naming Goldman Sachs and Morgan Stanley as underwriters.
    • The company is targeting a listing as early as September 2026 at a valuation that could exceed $1 trillion, though Sam Altman has said a public offering “may be a while.”
    • OpenAI raised $122 billion earlier in 2026 at a $730 billion pre-money valuation, putting its post-money value around $852 billion.
    • At an $852 billion valuation, OpenAI trades at roughly 65 times its 2025 revenue.
    • Rival Anthropic also filed IPO paperwork this month after raising $65 billion at a $900-$965 billion valuation, making it more valuable on paper than OpenAI, and says it expects to report an operating profit of $559 million in the June quarter.
    • HSBC analysts estimate OpenAI may need more than $207 billion in additional capital through 2030 even under optimistic projections.
    • OpenAI projects profitability by 2029 or 2030; independent analysts put the more likely date at 2031 or later.
    • Bridgewater partner Greg Jensen reportedly told clients the implied revenue multiples price OpenAI for “a monopoly outcome that does not yet exist.”
    • Zitron separately reported OpenAI had a negative 122% non-GAAP operating margin in Q1 2026 and that ChatGPT growth has stalled, with the company projecting paid ChatGPT Plus subscriptions to fall from 44 million in 2025 toward cheaper tiers in 2026.

    Detailed Summary

    How the leak happened and why it matters now

    The audited documents were obtained and first published by Ed Zitron on his newsletter Where’s Your Ed At, then independently verified by the Financial Times, which reviewed the same materials. That dual sourcing matters: this is not a rumor or a model, it is OpenAI’s actual audited financial statement. The timing is the story. OpenAI filed a confidential draft S-1 with the SEC around May 22, 2026 and confirmed it publicly on June 8. Under SEC rules the full prospectus must be released at least 15 days before an investor roadshow, so the 2025 numbers were going to be public soon regardless. The leak simply moved that disclosure forward, handing prospective investors, enterprise customers, and competitors an early look at the books.

    Revenue tripled, costs grew faster

    OpenAI’s revenue rose from $3.7 billion in 2024 to $13.07 billion in 2025, and monthly revenue reached nearly $2 billion by year-end. By almost any normal standard that is spectacular growth. The problem is that costs grew faster, reaching $34 billion against $12.48 billion the year before. The gap between what OpenAI earns and what it spends has widened every year since its founding, and 2025 is the starkest example yet. Revenue alone was outpaced by research and development as a single line item in both of the last two years.

    Two loss numbers, and why both matter

    There are two figures that get cited interchangeably and should not be. The operating loss of $20.92 billion is what the business spent beyond what it earned from operations: training models, serving ChatGPT, paying engineers, running marketing. The net loss attributable to OpenAI of $38.53 billion is far larger because 2025 was the year OpenAI completed its conversion from a nonprofit to a for-profit public benefit corporation, finalized on October 28, 2025. That restructuring triggered a $41.55 billion non-cash charge reflecting the changing fair value of convertible equity interests and warrant liabilities. Before the conversion, investors held convertible interest rights treated as liabilities under US accounting rules and revalued upward as OpenAI’s valuation climbed, creating the charge. It is not expected to recur. Including all minor items, gross net loss reached $60.35 billion, reduced to the $38.53 billion attributable figure after removing $21.82 billion tied to noncontrolling and redeemable noncontrolling interests, primarily the OpenAI Foundation’s stake. Strip the non-cash noise and the underlying loss was about $8 billion.

    Where the $34 billion went

    The spending breaks into four lines. Research and development was $19.18 billion, the largest category, with $10.59 billion of it flowing to Microsoft for training compute. Cost of revenue, the expense of serving responses to users, was $7.5 billion and captures inference, the compute consumed every time someone prompts ChatGPT or calls the API. Sales and marketing reached $5.73 billion, up 418% year over year, a striking jump for a product that grew largely by word of mouth. General and administrative costs added $1.57 billion. The shape of the spending tells you OpenAI is simultaneously racing to build better models, serve a massive and growing user base, and aggressively defend market share through marketing.

    The Microsoft dependency

    The most striking single disclosure is the scale of the Microsoft relationship. OpenAI paid Microsoft $17.2 billion in 2025: $10.59 billion in R&D fees for model training, $6.047 billion in cost-of-revenue for inference serving, $527 million in sales and marketing, and $42 million in G&A. Microsoft paid OpenAI just $303 million the same year. SoftBank paid OpenAI $867 million. The 56-to-1 ratio between what OpenAI pays Microsoft and what Microsoft pays back makes the structural reality plain: Microsoft is OpenAI’s largest landlord. The dynamic began shifting in April 2026, when the two renegotiated, ending Microsoft’s exclusivity and capping revenue-share payments at $38 billion through 2030, down from a projected $135 billion. That could save OpenAI up to $97 billion over five years, though Microsoft keeps its IP license through 2032 and remains the primary cloud partner.

    Why inference is the core problem

    Training happens once. Serving happens billions of times a day. When OpenAI releases a model it spends months and billions on training compute, a fixed cost that falls away when training ends. Inference is the opposite: every ChatGPT message runs through the model on Azure GPU hardware, consuming electricity and compute to generate a response. With roughly 800 million weekly users, that is billions of queries a week, each burning GPU time at roughly $6.98 per H100 GPU-hour on demand. OpenAI spent $5.02 billion on Azure inference in the first six months of 2025 alone. Sacra estimates full-year inference costs of $8.4 billion in 2025, rising to $14.1 billion in 2026. This is why gross margin fell from about 40% to 33% even as revenue tripled: more capable reasoning models consume far more compute per query, and revenue has not kept pace with the cost growth that capability generates.

    What it means for the IPO and the race with Anthropic

    OpenAI was last valued around $852 billion post-money after raising $122 billion in early 2026, which puts it at roughly 65 times 2025 revenue. It has named Goldman Sachs and Morgan Stanley as underwriters and is targeting a listing as early as September 2026 at up to a $1 trillion valuation, though Altman has hedged that it “may be a while” and that staying private might be the better course. HSBC estimates the company may need more than $207 billion in additional capital through 2030. The race is with Anthropic, which filed paperwork the same month after raising $65 billion at a $900-$965 billion valuation, making it more valuable on paper, and which says it expects a $559 million operating profit in the June quarter. The contrast is sharp: the two leading AI labs heading toward public markets at the same time, one bleeding cash at scale, the other claiming profitability, both asking investors to bet on a future that has not arrived.

    Notable Quotes

    “The financial condition of OpenAI is deeply concerning. $38.53 billion in losses are astronomical, and far higher than most believed it would be. Losses also appear to be mounting year-over-year at a dramatic rate, and I’m not sure how this company finds a way toward any kind of sustainability or profitability.”

    Ed Zitron, the independent journalist who published the leaked audited financials

    “It’s unclear what this means, nor how OpenAI reconciled the removal of $3.74 billion in costs. I will not speculate further.”

    Ed Zitron, on a discrepancy he found in the restated 2024 figures

    “OpenAI’s two biggest expenses are R&D and marketing. Budget cuts there, coupled with an ability to raise prices or win new sources of revenue, could see the company move into the black over time. Cutting R&D would be the most difficult part of that, given that AI companies can only hold onto their customers by generating the best-performing models.”

    Jim Edwards, Fortune, on whether OpenAI has a realistic path to profitability

    “What the audited documents make impossible to argue is that the path to profitability is short, clear, or cheap.”

    TechTimes analysis of the leaked OpenAI financials

    The implied revenue multiples price OpenAI for “a monopoly outcome that does not yet exist.”

    Bridgewater partner Greg Jensen, reportedly telling clients how to read OpenAI’s valuation

    “OpenAI spent $34bn last year as the ChatGPT maker poured money into a race to dominate the fast-growing AI market ahead of a planned stock market listing.”

    George Hammond and Bryce Elder, Financial Times, framing the audited 2025 spend

    Read Ed Zitron’s original reporting with the full breakdown here, and the Financial Times confirmation here.

    Related Reading

    • Ed Zitron, Where’s Your Ed At the primary source that broke the audited 2025 financials with the full line-by-line breakdown.
    • OpenAI (Wikipedia) background on the company’s history, structure, and the nonprofit-to-for-profit conversion that drives the non-cash charge.
    • Inference (Wikipedia) on the recurring compute cost that explains why OpenAI’s gross margin shrinks as usage grows.
    • Anthropic the rival lab that filed IPO paperwork the same month at a higher valuation and claims it is already operating at a profit.
    • SEC on confidential filings context for why OpenAI’s audited numbers were headed for public disclosure regardless of the leak.

  • US Government Orders Anthropic to Suspend Claude Fable 5 and Mythos 5: Inside the Export Control Directive, the Jailbreak Dispute, and What It Means for Frontier AI

    On June 12, 2026, Anthropic published a statement announcing that the US government, citing national security authorities, has issued an export control directive forcing the company to suspend all access to its newest frontier models, Claude Fable 5 and Claude Mythos 5. The order technically targets foreign nationals inside and outside the United States, including Anthropic’s own foreign national employees, but the practical effect is that both models are going dark for every customer worldwide. It is the first publicly known instance of the US government ordering a deployed frontier AI model offline, and Anthropic is complying while openly disputing the basis for the decision.

    TLDR

    The US government delivered an export control directive to Anthropic at 5:21pm ET on June 12, 2026, suspending all access to Fable 5 and Mythos 5 over an alleged jailbreak of Fable 5’s safeguards. Anthropic says the letter contained no specific details, that the only evidence shared was verbal, and that the technique in question amounts to asking the model to read a codebase and fix software flaws, a capability the company says is freely available from other models including OpenAI’s GPT-5.5 and used daily by cyber defenders. Anthropic defends its defense in depth strategy, notes that thousands of hours of red teaming by the US government, the UK AISI, and third parties found no universal jailbreak, and warns that recalling a commercial model over a narrow, non-universal jailbreak would effectively halt all new frontier model deployments if applied industry-wide. Access to all other Anthropic models, including Claude Opus, Sonnet, and Haiku, is unaffected, and the company says it believes the situation is a misunderstanding and is working to restore access, with more details promised within 24 hours.

    Thoughts

    This is a watershed moment regardless of how it resolves. Governments have blocked AI exports before, but ordering a deployed commercial model recalled out from under hundreds of millions of users is a new kind of intervention, closer to a product recall than a trade restriction. The mechanism matters too. Export control authority aimed at foreign nationals, including a company’s own employees, that cascades into a global shutdown is a blunt instrument doing the work of a regulatory regime that does not exist yet. The US has no statutory process for recalling an AI model, so the government reached for the closest tool on the shelf, and the result is a precedent built on improvisation.

    There is real irony in who got hit first. Anthropic has spent years arguing, publicly and in Washington, that governments should have the power to block unsafe AI deployments. Now the company that asked for a referee is the first one whistled, and its complaint is not about the existence of the power but about the process: a letter at 5:21pm with no specifics, verbal evidence only, and no transparent or technically grounded procedure. That distinction is the whole ballgame for AI governance. A power to halt deployments without due process standards is not regulation, it is discretion, and discretion cuts in every direction depending on who holds it.

    The technical dispute underneath is genuinely interesting because it exposes how unsettled the definition of a dangerous jailbreak is. Anthropic’s account of the offending technique, asking the model to read a specific codebase and fix any software flaws, describes something security teams do on purpose every single day. Vulnerability discovery is the canonical dual use capability: the same analysis that lets a defender patch a hole lets an attacker find one. If the bar for recall is that a model can be coaxed into doing competent security analysis, then every capable model on the market fails that bar, which is exactly Anthropic’s point about GPT-5.5. The hard question the directive dodges is not whether Fable 5 can find bugs but whether it provides meaningful uplift beyond what is already freely available, and Anthropic says it does not.

    For builders, the immediate lesson is uncomfortable: model availability is now a political variable, not just an engineering one. Teams that built directly on Fable 5 lost a production dependency overnight through no fault of Anthropic’s infrastructure, their own code, or any terms of service violation. Multi-model fallback strategies, abstraction layers over providers, and graceful degradation paths just moved from nice-to-have to table stakes for anyone running serious workloads on frontier models. The companies that absorbed this outage gracefully are the ones that assumed any single model could vanish.

    The next 24 hours matter more than the directive itself. Anthropic has promised more details, and the government will face pressure to either substantiate a concern that justifies a global recall or quietly walk it back. Either outcome sets the real precedent. If the directive holds on thin evidence, every frontier lab now operates under the threat of arbitrary shutdown. If it collapses under scrutiny, the case for a formal, transparent statutory process for AI deployment decisions, which Anthropic explicitly endorses in its own statement, gets a lot stronger in Congress than it was a week ago.

    Key Takeaways

    • The US government issued an export control directive on June 12, 2026 suspending all access to Claude Fable 5 and Claude Mythos 5, citing national security authorities.
    • The directive formally targets access by any foreign national, inside or outside the United States, including Anthropic’s own foreign national employees.
    • The net effect is that Anthropic must disable Fable 5 and Mythos 5 for all customers worldwide to ensure compliance, not just for foreign users.
    • Access to all other Anthropic models, including the Claude Opus, Sonnet, and Haiku families, is not affected by the order.
    • Anthropic received the directive at 5:21pm ET the same day it published its statement, and says the letter did not provide specific details of the national security concern.
    • Anthropic’s understanding is that the government believes it has become aware of a method of bypassing, or jailbreaking, Fable 5’s safeguards.
    • Anthropic reviewed a demonstration of the specific technique and says it only identified a small number of previously known, minor vulnerabilities.
    • The company says other publicly available models can discover the same vulnerabilities without requiring any bypass at all.
    • Before launch, Fable 5’s safeguards were red-teamed for thousands of hours in total by the US government, the UK AISI, multiple private third-party organizations, and internal teams.
    • No tester has found a universal jailbreak for Fable 5, meaning a method that broadly bypasses safeguards and unlocks a wide range of cyber capabilities.
    • Anthropic openly states that perfect jailbreak resistance does not appear possible for any model provider today, and that every safeguard in the industry is vulnerable to non-universal jailbreaks.
    • Fable 5 was deployed under a defense in depth strategy: make jailbreaks either narrow or very expensive to produce, then combine that with monitoring to quickly detect and shut down successful attacks.
    • Anthropic’s 30-day customer data retention requirement for Fable exists specifically to support jailbreak research and mitigation, a policy the company says carries real costs with customers.
    • Anthropic says it has not received any disclosure of a concerning non-universal jailbreak that led to a harmful result; disclosed potential jailbreaks were benign or provided no Mythos-specific uplift.
    • The only evidence the government has provided is verbal, describing a narrow, non-universal jailbreak that essentially consists of asking the model to read a specific codebase and fix any software flaws.
    • Anthropic reviewed a report it believes is the basis of the directive and validated that the capability level shown is widely available from other models, including OpenAI’s GPT-5.5, and is used every day by cyber defenders.
    • Anthropic is complying with the legal directive while explicitly disagreeing that a narrow potential jailbreak justifies recalling a commercial model deployed to hundreds of millions of people.
    • The company warns that if this recall standard were applied across the industry, it would essentially halt all new model deployments for every frontier model provider.
    • Anthropic supports government power to block unsafe deployments in principle, but only through a statutory process that is transparent, fair, clear, and grounded in technical facts, and says this action meets none of those principles.
    • Anthropic apologized to customers, called the situation a misunderstanding, said it is working to restore access as soon as possible, and promised more details within 24 hours.

    Detailed Summary

    What the directive actually does

    The order arrived as a letter from the US government at 5:21pm ET on June 12, 2026, invoking national security authorities under export control law. On paper it suspends access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, a category that includes some of Anthropic’s own employees. In practice, Anthropic says compliance requires abruptly disabling both models for every customer, since there is no clean way to enforce a nationality-based access boundary across a global product. The letter did not spell out the specific national security concern. Everything else in Anthropic’s statement is the company’s own reconstruction of what prompted the action.

    The jailbreak at the center of the dispute

    Anthropic’s understanding is that the government became aware of a method for bypassing Fable 5’s safeguards. The company reviewed a demonstration of the technique and characterizes the results as a small number of previously known, minor vulnerabilities, all relatively simple, all discoverable by other publicly available models without any jailbreak at all. According to Anthropic, the government’s evidence so far has been entirely verbal, and the technique boils down to asking the model to read a specific codebase and fix any software flaws. The company reviewed a report it believes underlies the directive and validated that the displayed capability is widely available elsewhere, naming OpenAI’s GPT-5.5 directly, and noted that this exact kind of analysis is what defenders use to keep systems safe.

    Anthropic’s defense in depth posture

    The statement restates the safety posture Anthropic laid out at Fable 5’s launch. The safeguards around cybersecurity tasks are strong enough that users have complained they are overly broad. In the weeks before launch, the US government, the UK AISI, multiple private third-party organizations, and internal teams red-teamed the safeguards for thousands of hours combined, and those tests showed Fable’s protections to be substantially more effective than any previously deployed model. No tester found a universal jailbreak. Anthropic is candid that perfect jailbreak resistance is likely impossible for anyone today, which is why the strategy is defense in depth: keep jailbreaks narrow or expensive, monitor aggressively, and shut down attacks fast. The 30-day customer data retention requirement on Fable exists to support that monitoring and mitigation loop. The company says this posture makes Fable’s risks comparable to models already deployed across the industry.

    Complying while disputing the standard

    Anthropic is removing access for all users as legally required, but the statement draws a hard line on the principle. The company disagrees that a narrow potential jailbreak, one that produced no disclosed harmful result, justifies recalling a commercial model serving hundreds of millions of people. Its broader warning is that this standard, applied evenly, would halt all new frontier model deployments industry-wide, since every provider’s safeguards are vulnerable to narrow jailbreaks. Anthropic also turns its own policy position into a critique: the company has publicly supported giving government the ability to block unsafe deployments, but through a statutory process that is transparent, fair, clear, and grounded in technical facts, and it says this action does not adhere to those principles.

    What happens next

    Anthropic closed by apologizing to customers, calling the situation a misunderstanding, and committing to restore access as soon as possible. The company promised to share more details over the next 24 hours, which makes this a developing story. The open questions are whether the government substantiates its concern with written technical evidence, whether the directive survives that scrutiny, and whether this episode accelerates the formal statutory process for AI deployment decisions that Anthropic says should have governed the action in the first place.

    Notable Quotes

    “The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance.”

    Anthropic, on why a directive aimed at foreign nationals becomes a global shutdown

    “We received the directive from the government today at 5:21pm (ET). The letter did not provide specific details of its national security concern.”

    Anthropic, on the abruptness and opacity of the order

    “These vulnerabilities all appear relatively simple, and we have found that other publicly-available models are able to discover them as well without requiring a bypass.”

    Anthropic, on its review of the demonstrated jailbreak technique

    “We suspect that perfect jailbreak resistance is not currently possible for any model provider.”

    Anthropic, restating the position it disclosed at Fable 5’s launch

    “We stand by this defense in depth strategy. It reduces the risks posed by Fable, making them comparable to the risks of existing models already deployed across the industry.”

    Anthropic, defending its layered safeguards approach

    “To date, the government has only given us verbal evidence of a potential narrow, non-universal jailbreak, which essentially consists of asking the model to read a specific codebase and fix any software flaws.”

    Anthropic, describing the technique behind the directive

    “However, we disagree that the finding of a narrow potential jailbreak should be cause for recalling a commercial model deployed to hundreds of millions of people.”

    Anthropic, on complying while contesting the decision

    “If this standard was applied across the industry, we believe it would essentially halt all new model deployments for all frontier model providers.”

    Anthropic, on the industry-wide implications of the recall standard

    “As we have stated publicly, we believe the government should have the ability to block unsafe deployments, as part of a statutory process that is transparent, fair, clear, and grounded in technical facts. This action does not adhere to those principles.”

    Anthropic, on the kind of oversight process it says should have governed the action

    “We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible.”

    Anthropic, closing its statement to customers

    Read the full statement on Anthropic’s site here.

    Related Reading

  • 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.

  • 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.

  • Bill Ackman on Investment Strategy, What the Market Is Missing, and How AI Breaks Businesses

    Bill Ackman, founder and CEO of Pershing Square, joined the All-In Podcast for a conversation about how his investment approach has shifted toward permanent, long-term ownership, why he believes the highest-quality companies are being left behind by a market chasing the new new thing, and how AI is raising the risk of disruption for almost every business. He also lays out his plan to turn Howard Hughes into a Berkshire Hathaway-style compounding machine built on insurance. You can watch the full conversation here. Below is a structured breakdown of the ideas, the stories, and the frameworks he uses to underwrite a business.

    TLDW

    Ackman explains how his philosophy evolved from a smaller, more liquid activist toward concentrated, permanent ownership of durable, non-disruptible businesses, with much of his activism now playing out on X rather than in the boardroom. He tells the origin story of his first big trade, Wendy’s and the Tim Hortons spin-off, and explains why a large long-term shareholder on a board is an antidote to short-term markets. On AI, he argues that this is the greatest era in history to build a company, which means the risk of being disrupted has gone up enormously, and that the market is mispricing high-quality compounders like Microsoft, Meta, and Amazon while crowding into chips, semiconductors, and energy. He works through the SaaS question and why niche software is more at risk than platforms, how he underwrites SpaceX, xAI, OpenAI, Anthropic, and Palantir like late-stage venture bets using a people, opportunity, context, deal framework, and why founder-led companies have an edge in making radical calls. The back half covers his Howard Hughes plan to copy Buffett’s insurance-float model, the role of cost of capital and reflexivity in markets, the meme-stock era, going direct on social media, and the three different ways an investor can put money to work with Pershing Square.

    Thoughts

    The most useful idea in the interview is the way Ackman reframes disruption as the central investing problem of the AI era. His point is that the same forces making this the best time in history to start a company, meaning near-unlimited compute, capital, and talent, also raise the odds that any given incumbent gets disrupted. That reframes the word quality. It is no longer mostly about margins and moats. It becomes about non-disruptibility, which is a much higher bar than most quality investors were using a decade ago, and it is why he says most of his research time now goes into assessing that single risk.

    The what-the-market-is-missing thesis is classic contrarian Ackman. Arguing that Microsoft, Meta, and Amazon are the new old-fashioned, undervalued names while capital piles into semiconductors and energy is a direct echo of 2000, when Berkshire Hathaway bottomed precisely because money was chasing internet stocks. It is worth keeping in mind that he owns all three, so the call is also his book. The durable signal here is the framework, not the specific tickers: capital reliably chases the new new thing, and genuinely high-quality businesses get left behind during those rotations.

    The Howard Hughes plan is the most concrete bet in the conversation. Copying Buffett’s insurance-float playbook, short-term treasuries for policyholder money and equities for the surplus, onto a discounted real-estate holding company is elegant. The hard part is exactly what Ackman flags about insurance as an industry: the best investors go to hedge funds, not insurers, so most insurance companies only ever manage the liability side well. Pershing Square’s edge is that Ackman can both write the business and invest the float, which is the same reason it worked for Buffett. The framing of going from a four billion dollar company to a trillion over fifty years is a statement of intent, not a forecast, and should be read that way.

    Underneath all of it sits cost of capital and reflexivity. His observation that a higher stock price literally makes a company more valuable, because it lowers the cost of capital and creates acquisition currency, is the mechanism behind both Elon Musk’s empire and the meme-stock era he is wary of. Going direct on X is the same lever pointed at himself: communicate the vision, lower your own cost of capital, and make the bet easier for other people to place. It is a coherent worldview in which narrative and balance sheet continuously feed each other, and it explains a lot of his behavior over the last few years.

    Key Takeaways

    • The biggest change in Ackman’s approach over time is an appreciation for business quality, meaning long-term, durable, protected, non-disruptible growth as the most important factor.
    • He says he is as activist as ever, but more of it now happens on X than in the traditional corporate context.
    • His first big investment was Wendy’s, which owned Tim Hortons. The simple thesis was to buy Wendy’s, spin off Tim Hortons, and double the money.
    • Early on no one returned his calls, so he had Steve Schwarzman’s Blackstone write a fairness opinion, filed it publicly, and the company spun off Tim Hortons six weeks later. The CEO later thanked him after being fired with a large exit package.
    • Reputation compounds. Where Pershing Square once had to bang down the door, companies now sometimes tweet a welcome when it buys a stake.
    • A large long-term shareholder on a board is a counterweight to short-term markets, letting management test ideas privately and pursue initiatives that hurt the next few quarters of earnings.
    • Pershing Square owns Microsoft, Meta, and Amazon. Ackman argues you are either invested in AI directly or indirectly, or it is a threat, so you have to understand it.
    • The hardest and most important job for a concentrated investor is judging the risk of disruption, and that risk has risen dramatically.
    • This is the greatest era in history to build a business because of near-unlimited access to compute, capital, and talent, which is exactly why the probability of being disrupted has gone up enormously.
    • Markets bring their eye to the new new thing, currently chips, semiconductors, and energy, while high-quality companies get left behind.
    • He draws an analogy to 2000, when Berkshire Hathaway traded at one of its lowest valuations because everyone chased internet stocks. He sees a similar dynamic around Amazon, Meta, and Microsoft today.
    • On the SaaS question, he worries more about a Salesforce than a platform like Microsoft, because niche software charging high per-seat or per-year prices is most exposed, while low-priced platforms are safer.
    • Any software company today has to be as AI-enabled as possible, or risk losing the monopolistic pricing it once enjoyed.
    • His famous March 2020 CNBC appearance was an attempt to reach President Trump and argue for a short shutdown, paired with the view that stocks were incredibly cheap and worth buying.
    • He describes valuation as a tether on the market: when prices stretch too high they snap back, and when they get too cheap the same rubber band pulls valuations up. Calling that out publicly can trigger a psychological reset.
    • His recent bullish call came because stocks of really high-quality companies had gotten crazy cheap on fundamentals, meaning the present value of the cash they generate.
    • He underwrites high-multiple names like SpaceX as venture investments using a framework from business school: people, opportunity, context, deal.
    • On SpaceX, people and opportunity are one of one, the context is incredible, and Starlink plus near-monopoly low-cost launch make it strategically valuable. The complicated part is the deal, meaning the valuation. He invested via an SPV after Ron Baron’s nudge, and also invested in xAI.
    • He treats OpenAI, Anthropic, and Palantir as late-stage venture bets that have proven they can generate real revenue, and says OpenAI should do a better job communicating how it thinks about its enormous capital commitments.
    • Every CEO in America is asking how to use AI, how it applies to their business, and how it is a threat. It is top of mind and boards open every meeting with it.
    • He has not seen much enterprise AI success yet, citing a McKinsey study that 95 percent of enterprise initiatives fail and the rise of the forward deployed engineer as the hot role bridging promise and ROI. Pershing Square itself uses AI mainly for legal, compliance, and back-office work.
    • Founder-led companies have an advantage because founders have the authority and the economic stake to make radical calls, while the average S&P 500 CEO has a roughly three to four year tenure and is incentivized not to make mistakes.
    • He cites Mark Zuckerberg buying Instagram and WhatsApp as the kind of shocking-at-the-time calls that a founder with a track record can make.
    • Ben Graham’s enduring lesson is that a stock is an interest in a business, not a piece of paper, but Graham mostly invested in liquidations and cash-rich shells, and made most of his money on Geico.
    • Most of Buffett’s value at Berkshire came from owning insurance operations and focusing on the asset side of the balance sheet, not just the liability side.
    • Insurance is hard to copy because top investors do not go to work for insurers. Buffett owned half his company and was a great investor, which is why it worked.
    • Howard Hughes came out of the General Growth bankruptcy and owns master-planned cities like Summerlin, with 26,000 acres in the Las Vegas area, comparable to the Irvine Company that built roughly a hundred billion dollars of wealth for Donald Bren.
    • The plan is to reinvest the cash Howard Hughes generates into insurance, put policyholder float in short-term treasuries and the surplus in common stocks, and build a compounding machine over fifty years, buying it at roughly sixty cents on the dollar.
    • A company must earn a return above its cost of capital for the stock to rise. Elon Musk has kept his companies’ cost of capital extremely low, and a SpaceX IPO near a 1.75 trillion dollar valuation could be one of the lowest cost of equity capital transactions ever.
    • Markets have changed less because of Ackman and more because of figures like Ryan Cohen and GameStop, where a stock can trade well above its value on personality and an army of followers.
    • Higher valuations are reflexive: a rising stock price lowers cost of capital and creates currency to issue stock and acquire businesses, which is part of how Elon built Tesla.
    • There are three ways to invest with Pershing Square: the management company itself (a royalty on compounding assets with no capex), PSUS (a portfolio of best ideas trading at an 18 percent discount), and Howard Hughes (a bet on building the next Berkshire). A dollar invested 22 years ago became roughly 27 to 28 times net of fees.
    • Going direct on X, with 2.2 million followers, lets him communicate his vision and lower the friction for others to back his bets, even as his very long tweets have become a running meme.

    Detailed Summary

    From activist trades to permanent capital

    Ackman frames the evolution of his career as a steady move toward business quality. As a smaller, more liquid investor early on, he did not have to think as long-term. As Pershing Square became a bigger, more concentrated investor, durable growth became the dominant factor in every decision. He insists he is still as activist as ever, but a lot of that energy has shifted to X, where he can argue a position publicly rather than only inside a boardroom. The best investments, he notes, are the ones where you do not need to join the board and do anything at all.

    The Wendy’s and Tim Hortons origin story

    One of Pershing Square’s first investments was Wendy’s, which owned the Canadian coffee and donut chain Tim Hortons. The value of Tim Hortons alone was greater than the entire value of Wendy’s, so the idea was simple: buy Wendy’s, spin off Tim Hortons, and double the money. Ackman bought ten percent of the company and could not get the CEO to return a single call, so he had a contact at Blackstone, with Steve Schwarzman’s sign-off, write a fairness opinion on what Wendy’s would be worth after a spin-off, filed it publicly, and watched the spin-off happen six weeks later. The CEO eventually called back to thank him, having been fired but rewarded with a large exit package. Over the years that scrappy approach gave way to a reputation that now opens doors on its own.

    Why a long-term shareholder on the board matters

    The core problem of being a public company, in Ackman’s telling, is the short-term nature of markets and analysts, when a good business should be run in the context of years and even decades. A large, supportive shareholder on the board gives management a place to test ideas before exposing them to the public and a credible voice willing to back initiatives that hurt earnings for a few quarters. That is the value-add he believes a constructive activist can bring to a mature public company, as opposed to a startup where the best outcome is simply to own a great business and stay out of the way.

    AI and the rising risk of disruption

    For a concentrated, long-term investor, the most challenging task is judging the risk that two people from Stanford in a garage build something that destroys your thesis. Ackman argues that risk has climbed dramatically because this is the greatest era in history to build a company, with near-unlimited access to compute, capital, and talent. The paradox is that the conditions that make building easier also make incumbents more fragile, so the bulk of his research now centers on assessing how disruptible a business really is.

    What the market is missing

    Investors bring their attention to the new new thing, currently chips, semiconductors, and energy, which leaves high-quality companies behind. Ackman compares the moment to 2000, when Berkshire Hathaway traded at one of its lowest valuations ever because capital was chasing internet stocks. He sees an echo today in how Amazon, Meta, and Microsoft are treated as old-fashioned, and he considers them undervalued on fundamentals, where value is the present value of the cash a business generates over its life. His recent bullish call, like his March 2020 appearance, came because stocks of really high-quality companies had simply gotten too cheap.

    The SaaS question and AI-enabled software

    On the so-called SaaS apocalypse, Ackman says it is a company-by-company analysis. He worries more about something like Salesforce than about a low-priced platform. The companies most at risk are those that extracted near-monopolistic profits by charging a high annual price for a niche product, because AI lowers the barrier to replicating that functionality. A platform where the average customer pays a small amount per seat, like Microsoft, is far less exposed. The takeaway for any software company is to become as AI-enabled as it possibly can.

    Underwriting SpaceX, xAI, and the AI labs like venture

    For the highest-multiple private companies, Ackman uses a venture lens and a framework a business school professor taught him: people, opportunity, context, deal. SpaceX scores as one of one on people and opportunity, with an incredible context and a near-monopoly in low-cost launch through Starlink, which makes even Amazon a likely customer. The complicated variable is the deal, meaning the valuation, and he admits he has not done all the math, having invested through an SPV after Ron Baron encouraged him, along with a position in xAI. He treats OpenAI, Anthropic, and Palantir as late-stage venture bets that have proven real revenue, and argues OpenAI in particular should communicate more clearly how it justifies capital commitments that vastly exceed current revenue.

    Founder-led companies and the authority to act

    Ackman agrees that founder-led companies have a structural advantage in a fast-changing environment. The average S&P 500 CEO has a tenure of roughly three to four years, a small economic stake, and an incentive not to make a career-ending mistake. A founder is betting an entire life and reputation, has the authority of a major voting and economic position, and has usually made several hard, contrarian calls that turned out right. He points to Mark Zuckerberg’s acquisitions of Instagram and WhatsApp, which looked shocking at the time, as exactly the kind of decision a founder with a track record can make and a hired manager often cannot.

    Howard Hughes as Berkshire Hathaway 2.0

    Ackman points to a detailed financial history of Berkshire Hathaway showing that the vast majority of Buffett’s value creation came from owning insurance and focusing on the asset side of the balance sheet, not just the liability side. Insurance is hard to replicate because skilled investors join hedge funds rather than insurers, but Buffett owned half his company and was a great investor. Pershing Square is applying the same idea to Howard Hughes, a company created out of the General Growth bankruptcy that owns master-planned cities such as Summerlin, with 26,000 acres around Las Vegas, in the spirit of the Irvine Company that made Donald Bren roughly a hundred billion dollars. The plan is to reinvest the company’s cash into insurance, place policyholder float in short-term treasuries and the surplus in common stocks, avoid issuing stock the way Buffett did, and compound for fifty years, all bought at around sixty cents on the dollar.

    Cost of capital, reflexivity, and going direct

    A company only creates value when it earns above its cost of capital, which is why Howard Hughes, seen as a high-cost-of-capital real-estate business, has long traded at a discount, and why Ackman is repurposing its assets into a higher-returning model. He highlights how reflexive markets are: a higher stock price itself makes a company more valuable by lowering its cost of capital and creating currency to raise money and acquire businesses, a lever Elon Musk used to build Tesla. He attributes real market change less to himself and more to figures like Ryan Cohen and GameStop, where personality and a following can lift a stock far above its value. His own going-direct strategy on X, with 2.2 million followers and famously long posts, is the same mechanism applied to communicating a vision and lowering friction for investors. He closes by laying out three ways to invest with Pershing Square: the management company as a royalty on compounding assets, the PSUS portfolio trading at an 18 percent discount, and Howard Hughes as a bet on building the next Berkshire.

    Notable Quotes

    “The best investments are one where you don’t need to join the board and do anything.”

    Bill Ackman, on the kind of business he most wants to own

    “The probability of your being disrupted has gone up enormously.”

    Bill Ackman, on why assessing disruption risk now dominates his research

    “Valuation is like a tether on the market, right? When it gets too high, it’s like this rubber band that’s stretching and inevitably it bounces back.”

    Bill Ackman, on how prices revert at both extremes

    “People, opportunity, context, deal.”

    Bill Ackman, on the business school framework he uses to underwrite companies like SpaceX

    “Every CEO in America today is like, how do I use AI?”

    Bill Ackman, on AI as the top opportunity and threat in every boardroom

    “A closed mouth gathers no foot.”

    Bill Ackman, quoting the line a friend put next to his name in his high school yearbook

    “The increase in value of the company increases the value of the company, right? Because it lowers the cost of capital, it gives you more flexibility, gives you the ability to issue stock, raise capital, acquire other businesses.”

    Bill Ackman, on the reflexivity between stock price and corporate value

    “The company’s got like a $4 billion market cap and the goal is to build it into a trillion dollar thing over time compounding.”

    Bill Ackman, on his fifty-year plan for Howard Hughes

    Taken together, the conversation is a tour of how Ackman now thinks about quality, disruption, and compounding, and a preview of the Berkshire-style machine he wants to build out of Howard Hughes. Watch the full conversation here.

    Related Reading

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

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

    TLDW

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

    Thoughts

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

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

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

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

    Key Takeaways

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

    Detailed Summary

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

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

    The jagged frontier and the wide spread of adoption

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

    Why the AI labs are buying consultancies

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

    The task versus the job

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

    Jevons paradox and the lump of labour fallacy

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

    The jobs question, Dario, and the enterprise sales cycle

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

    Redefining AGI and superintelligence

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

    Where the value accrues: commodity models and the telecom analogy

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

    Distribution as the moat

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

    Apple Intelligence and the model as the dumb thing underneath

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

    The anti-AI backlash, water, and real harms

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

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

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

    Notable Quotes

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

    Benedict Evans, stating the thesis that frames the whole conversation

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

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

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

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

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

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

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

    Benedict Evans, on why model labs may lack pricing power

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

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

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

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

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

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

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

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

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

    Related Reading

  • Vibe Coding Hardware: Naval, Guillermo Rauch, Blake Scholl, and Max Hodak on AI-Designed Jet Engines, Vertical Integration, China’s Open-Source Bet, and Why Humans Become Verifiers

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

    TLDW

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

    Thoughts

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

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

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

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

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

    Key Takeaways

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

    Detailed Summary

    Turning Hardware Engineering Into Software

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

    Two Engineers, One Jet Engine

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

    The Cataclysm of Enterprise Software

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

    Generating Step Files and PCB Layouts

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

    China’s Open-Source Bet and Hardware Superiority

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

    Frontier Intelligence vs. Cheap Models

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

    Vertical Integration and the Captive MEMS Foundry

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

    Software Still Needs Hands

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

    Lawyers, Paralegals, and the Promotion of Junior Work

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

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

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

    Notable Quotes

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

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

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

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

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

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

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

    Naval Ravikant, on the cataclysm of enterprise software.

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

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

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

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

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

    Guillermo Rauch, on where frontier coding intelligence actually lives.

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

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

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

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

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

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

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

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

    Watch the full conversation on the Naval Podcast here.

    Related Reading

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