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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.

  • Jeremy Giffon on the Billion Dollar PDF, Peak Guy, and How Attention Became the New Capital

    In his second appearance on Invest Like the Best, investor Jeremy Giffon sits down with Patrick O’Shaughnessy for a wide-ranging conversation about how power, status, capital, and attention are being redrawn in real time. The organizing idea is the “billion dollar PDF,” the notion that a single well-timed document or post can crystallize a narrative and pull billions of dollars of capital toward it. From there the two range across the mechanics of the X timeline as market infrastructure, the decline of the billionaire class, the rise of the “poaster,” the economics of software in the age of compute, and what the next era of finance looks like when its founding act is seed investing rather than the leveraged buyout.

    TLDW

    Giffon argues that in private markets the real great filter for funds is storytelling, because the actual product (realized cash returns) takes a decade, so narrative is what you sell in the meantime. He and O’Shaughnessy unpack the “billion dollar PDF,” the way X functions as a single global newspaper (the uni-feed) that prices securities, dictates policy, and builds businesses, and how power laws now mean breaking containment on the timeline is worth more than steady performance. They discuss “peak guy” and the exhaustion of billionaire worship, the idea that the poaster has become the new priestly class, net worth as a surprisingly modern invention, and attention as the genuinely scarce asset. The back half turns practical: why AI job fears meet Giffon’s view that most white collar work is invented, why software is shifting from selling zero-marginal-cost strings to selling compute with thin margins and huge scale, why beating the market is easier for amateurs than professionals, how to underwrite emerging managers by studying the person, the feudal economics of SPVs and allocations, simplicity over complexity in investing, hiring through divisive job descriptions, and the hidden philosophers (from effective altruism to Curtis Yarvin and Nick Land) shaping Silicon Valley. Topics span venture capital, private equity, cap tables, SaaS, the Mag 7, Buffett and Bogle, East Coast versus West Coast finance, and the search for vocation.

    Thoughts

    The strongest thread in this conversation is that scarcity has moved. For most of the modern era, money was the scarce thing and attention was the byproduct of having it. Giffon flips that. Capital is now abundant, inflationary, and desperate for somewhere to go, which is why he can describe businesses and asset categories as “sponges” that get created downstream of capital rather than the other way around. What is actually scarce is a fixed slice of human attention, and whoever can command it (the “billion dollar PDF,” the breakout post, the person every billionaire wants to sit next to at dinner) captures the resource that money is now chasing. That reframing explains a lot of otherwise strange behavior, including why founders who already have wealth turn to posting, podcasting, and fame. They are not being vain. They are hedging out of a depreciating asset into the one that still appreciates.

    The most uncomfortable and clarifying claim is that narrative is not a distortion of markets, it is the market. Giffon walks through how the algorithm, driven by AI, selects which stories get shown, those stories set the consensus among the small group of posters who move capital, and securities get priced off that consensus. If you take that seriously, the efficient market hypothesis looks quaint. The marginal price of a security is being set, in part, by what an entertainment-optimizing model decided to surface to a few hundred thousand influential readers that morning. His line that “every other day someone writes some pornographic fanfic about AI and it moves the public markets” is a joke that is also a fairly precise description of 2026 price discovery.

    His software thesis deserves more attention than the culture commentary that will get clipped. The old SaaS miracle was selling copies of a string at near-zero marginal cost, which mechanically produced high gross margins. Giffon’s point is that the AI era sells compute, and you cannot write the prompt once and resell the output, so the marginal cost is no longer zero. The consequence is a structural regime change: lower gross margins, thinner net margins, and returns that accrue overwhelmingly to scale. He calls it a Walmart effect in software, and if he is right, a lot of the current sell-off in SaaS names is punishing the business model rather than the businesses, which is exactly the kind of nuance-free repricing he says markets specialize in.

    The optimistic surprise is his stance on AI and jobs, which cuts against the doom consensus without being naive about the short term. He concedes the near and medium term could be genuinely bad, but he refuses the “we will run out of jobs” framing because he thinks most white collar work is already invented to absorb our attention and capital, not to meet basic needs. Work-from-home Fridays, in his telling, are a quiet admission that many people have two or three hours of real work a day. If that is true, then automating the invented work is liberation rather than catastrophe, provided the transition does not crush people in the process. It is a bracing counterweight to the standard displacement panic, and it pairs well with his more personal note that the antidote to a priestly-class culture of looking outward for permission is the duty to steward your own gifts.

    The one place to push back is the tidiness of the “poaster as new priest” story. Giffon is careful to say he is describing, not endorsing, but the argument that status simply passes from scientists to billionaires to posters is cleaner than reality usually allows. Attention is scarce, yes, but it is also fickle and lotteryified in his own telling, which makes it a shaky foundation for a durable priestly class. Still, the underlying observation is sharp: when money becomes a “state of mind” label rather than a hard number, and when net worth itself is revealed as a recent invention (his Pride and Prejudice aside about Mr. Darcy’s income being cash flow, not a valuation, is the best illustration in the episode), the leaderboard everyone is actually competing on is real estate in other people’s minds.

    Key Takeaways

    • The great filter for private-market funds is storytelling ability, because the real product (realized cash returns) takes a decade, so narrative is what a fund actually sells in the interim through updates, events, and LP conversations.
    • The same business can be “cold” at seven years and $8 million in revenue but “hot” if you reset the clock and retell the story, so being flexible on narrative is itself a fix for a funding problem.
    • Insider bridge rounds are often surprisingly hostile (3x liquidation preferences, warrants, ratchets), and being extractive to the downside gets you booed while being extractive to the upside (pro rata rights) gets celebrated, even though both are similarly extractive.
    • In highly volatile times, optionality beats commitment: raise less, raise from investors with a wide mandate, and keep the ability to pivot the business model, run profitably, acquire, or even fire customers.
    • The “billion dollar PDF” is the idea that someone crystallizes a notion at the right time and it becomes the foundational viewpoint of an era, and capital follows it around like ten-year-olds chasing a soccer ball.
    • X is the “uni-feed”: everyone is served the same roughly 500 tweets a day across hundreds of millions of users, making it the global newspaper and a source of truth for capital markets, politics, and technology.
    • Institutions now survive only if they are “timeline native,” meaning reactive to and reflexive with the timeline, which describes the White House, venture capital, and public equities alike.
    • Posting has been lotteryified: a brand-new account can write one good post and get shown to hundreds of millions, so posting is described as the last great meritocracy.
    • Power laws have sharpened. Variance used to be low, but now breaking “containment” on the timeline means briefly taking over the world’s brain, and those few breakout events dwarf everything else combined.
    • Podcasts still underrate serving the algorithm; the video is recorded first for an LLM to review and decide whether to show, and only then do humans judge it.
    • A great post blends comedy, poetry, and writing, and great posters tend to be a bit tortured, closer to writers mixed with comedians.
    • “Peak guy”: society keeps searching for a priestly class, moved from scientists to the billionaire class, and Giffon thinks it has now moved to the poaster class, with billionaires increasingly deferential to posters.
    • Billionaire worship is exhausted partly because billionaires are far less scarce (state-of-mind billionaires have grown maybe 100x in 20 years) and money is less powerful than assumed, as the donor class has underperformed politically.
    • Net worth is a very new idea. In Pride and Prejudice, Mr. Darcy’s wealth is his estate’s annual cash flow, not a valuation, because no one would DCF or margin-loan an estate they would never sell.
    • “Billionaire,” like “millionaire” before it, is becoming a loose political and class label only tangentially related to actual liquid, inflation-adjusted wealth.
    • The most honest way to consume media is to admit it is entertainment, produced, selected, and edited to entertain, not to learn, no matter how productive it feels.
    • Going months off the timeline taught Giffon that you do not really miss anything; the filtered, secondhand version from smart people at dinner may be the most enlightened way to consume it.
    • On AI and jobs, the short to medium term could be bad, but the long-run worry is overblown because most white collar jobs are “made up” and not contingent on shelter, food, or medicine.
    • Work-from-home enthusiasm is evidence that many people have only two or three hours of real work a day, so work-from-home Fridays are a soft launch of the four day work week.
    • We have a moral duty to steward our gifts; the thing you spend most of your time on should spark and utilize your genius, and having fun at your job is a strong signal you have combined the two.
    • The largest finance firms (KKR, Blackstone, Apollo) were founded in a leveraged-buyout culture that is debt-driven and extractive; the next era’s giants may be founded on seed investing, which is equity-driven, optimistic, and qualitative.
    • West Coast venture is “eating” the East Coast: it created the biggest businesses in the world and functions as a civilizational technology, giving young people speculative capital with little downside.
    • Compensation has flipped: Silicon Valley now pays large liquid cash via mature secondary markets and yearly tenders, while Wall Street increasingly pays in RSUs tied to long-term firm value.
    • SaaS is just a business model, and while it is in trouble, that is often not what actually matters to a business being sold off out of fear.
    • Software is moving from selling near-zero-marginal-cost strings to selling compute, which means lower gross margins, razor-thin net margins, and returns accruing to scale, a Walmart effect in software.
    • Capital gets “blocked” when there are not enough great companies to absorb it, so high-capex AI and hardware categories arose in part as sponges for capital with nowhere else to go.
    • Markets lack nuance: the 52-week variance on the biggest companies is nearly 100%, so they are not priced well, and much private-market pricing reflects fund incentive structures rather than business quality.
    • Beating the market is easier for amateurs than professionals. Buffett’s S&P advice is for the average person, while pros are constrained by mandates, customers, and career risk (the Peter Lynch point).
    • A small principal writing a 500k check is the wrong customer for a large growth fund built to serve sovereigns and endowments; emerging managers, tightly aligned to returns, are underrated for that check.
    • Underwrite the person, not just the thesis. A manager’s personal financial situation matters enormously, and whether they are “looking up” or “looking down” at the fund size changes how they behave.
    • Modern finance is recreating a feudal system where lab founders (Elon, Zuckerberg, Dario, Sam) grant allocations like landed estates, and holders charge fees on this synthetic, purely relational, sometimes perpetual product.
    • The most generative activity is conversation, downstream of relationships, and being tolerant of weird, unpredictable people is a media diet advantage; chatbots can feel generative without actually being so.
    • Investors overvalue complexity to look clever; you should either do something so complex no one else will, or keep it simple (be long Elon, buy big companies at their 200-week moving average), and the real gift is selling the simple idea.
    • Richard Rainwater’s test: pitch your thesis on one page and state what percentage of your net worth you will put in, then yes or no. It is hard precisely because it forces clarity and conviction.
    • A job description is a sales pitch and an interview baked into a post; divisive, ambiguous statements (like “an ideological minority at a top 10 school”) self-select the right people and disqualify the wrong ones.
    • Silicon Valley’s hidden philosophy is underrated: a neo-Buddhist utilitarianism feeds effective altruism, and thinkers like Nick Land, Curtis Yarvin, and William MacAskill shape the culture without being named.
    • Where 1980s Wall Street was pagan, hedonistic, and nakedly about money, today’s tech views itself as self-righteous and positive-sum, treating the business itself as the ultimate philanthropy, with no felt need to launder gains through art or culture.

    Detailed Summary

    The Billion Dollar PDF and Narrative-Driven Capital

    Giffon opens with what he has learned in his first 18 months running his own fund: in long-term private markets, the great filter is storytelling. Because a fund’s real product is realized cash returns that take a decade to arrive, what a manager sells in the meantime, through quarterly updates, events, and one-on-one LP conversations, is narrative. He describes situations where an older company that has recently inflected struggles to raise simply because its story (seven years old, $8 million in revenue) reads worse than the same numbers reframed as a two-year-old rocketship. The billion dollar PDF is the escalation of this: a single document or post that crystallizes the notion of an era, does not even have to be right, and pulls billions in capital toward it. Capital, he says, behaves like ten-year-olds playing soccer, all chasing the same ball.

    The Uni-Feed: X as Global Newspaper and Market Infrastructure

    The technological catalyst, in Giffon’s view, is the uni-feed. Everyone on X is served the same roughly 500 tweets a day, and the poster-to-lurker ratio is enormous, so people who do not post cannot feel the impact. X is the Lindy social network, unlikely to reach the scale of the others but filling a vital role as a global newspaper and near-source of truth. The most important people in capital markets, politics, entrepreneurship, and technology read it every morning, and it forms opinion, prices securities, and writes policy. Institutions survive only if they are timeline native, both reactive to the timeline and reflexive with it. Crucially, this is also where narratives get set, and the winning story is not a well-considered book but the most entertaining, novel, somewhat-correct thing, because people are on the timeline to be entertained and the algorithm selects for exactly that.

    Power Laws, Breaking Containment, and the LLM as First Filter

    O’Shaughnessy observes that variance used to be low, with the best performers only modestly ahead of the worst, and that this has changed completely. Now there is a threshold where breaching containment feels like taking over the world’s brain for a short window, and those handful of breakout events matter more than all the rest combined. Giffon attributes this to technology rather than any change in content or audience: RSS gave you a normal distribution, algorithms give you a power law. He notes that podcasts remain naive about serving the algorithm, unlike streamers and YouTubers, and delivers one of the episode’s sharpest structural points: the video is recorded first for an LLM to review and decide whether to show it, and only after that first, largely invisible filter do humans get to judge.

    Peak Guy: Billionaires, Priests, and the Poaster Class

    The “peak guy” segment is the episode’s philosophical core. Giffon traces how God moved from being in and around everything, to a guy above the clouds, to something conceptual and distant, leaving an ongoing search for priests. Society tried scientists, but the scientific project stalled and physics has not delivered meaning since the war, so status passed to a billionaire class treated as the new priesthood: successful at business, therefore smart and hardworking, therefore worth listening to on physics, theology, or health. That worship has now saturated. Billionaires are far less scarce, money looks less powerful (the donor class has underperformed politically), and a billionaire who posts the wrong thing has to resign where Andrew Carnegie could once take up arms. Giffon’s claim is that the priesthood has passed again, this time to the poaster, and you can see it in how the billionaire class defers to posters (his anecdote: billionaire investors fighting to sit next to Tyler Cowen because he was the most interesting person in the room).

    Net Worth as a Modern Invention and Attention as the New Scarcity

    Giffon frames net worth itself as a strikingly recent concept. In Pride and Prejudice, Mr. Darcy’s wealth is discussed as roughly 10,000 a year in cash flow from his estate, not as a valuation, because no one would sell the estate or borrow against it. Wealth as a mark-to-market number is new, and between illiquid private markets, net worth as a concept, and inflation, “billionaire” is becoming a loose label, much like “millionaire” already did. Since time is fixed, the new scarcity is attention you can draw on the screen, which is why founders who accrue wealth so predictably turn to posting, podcasts, and channels: partly to convert wealth into fame, partly because they sense money is depreciating and attention is what is actually scarce.

    Opting Out and Media as Entertainment

    Asked about going months off the timeline, Giffon’s takeaway is that you should not fool yourself that you are seeking anything other than entertainment. All of it is produced, selected, and edited to entertain, and just as Rolex or Nike can convince you a liability is an asset, posts and essays can convince you that consumption is productive. The question is simply how much you want to be entertained. He does not see the death of books as a crisis so much as a swan song for a technology that was the best way to deliver information until better, more compelling ways arrived, though he is careful to note the negative language we use (brain rot, terminally online) betrays a deeper sense that something is off. New media is less forgiving: better than ever for the disciplined, worse than ever for everyone else. His friend Jesse refuses all algorithms and simply lets people tell him what happened, which Giffon half-endorses as the most enlightened, filtered way to consume the radiation secondhand.

    AI, Fake Jobs, and Stewarding Your Gifts

    On AI and white collar displacement, Giffon concedes the short to medium term could be bad (he agrees with a friend who worries about kids in college but not the ten-year-old), but rejects the “peak jobs” panic. Anything that can be automated should be, and the prospect of never having to sit at a computer again strikes him as liberating. Most white collar jobs, he argues, are invented, not contingent on shelter, food, or medicine, and our economy runs on unquenchable desire, so we will simply invent new things to do. Work-from-home attachment is his evidence that many people have only a couple of hours of real work a day, making work-from-home Fridays a soft launch of the four day week. This connects to a more personal theme O’Shaughnessy draws out: the duty to steward your gifts. Waste is aesthetically bad, wasting your gifts is among the worst kinds, and the surest sign you have integrated your work with your genius is that you are having fun.

    The Next Era of Finance and the New Economics of Software

    Giffon notes that today’s largest firms (KKR, Blackstone, Apollo) were founded in a leveraged-buyout culture that is debt-driven, extractive, and financially engineered, and wonders what the next 30 years look like when the founding act of the biggest firms is instead seed investing: equity-driven, optimistic, power-law, and qualitative. He sees East and West Coast finance merging, with the West “eating” the East, and a compensation flip in which the Valley now pays large liquid cash through secondary markets while Wall Street pays RSUs. On software, his central economic argument is that SaaS sold copies of a string at near-zero marginal cost, which is why high gross margins were the norm. The new era sells compute, where you cannot write the prompt once and resell the output, so margins compress and returns accrue to scale, a Walmart effect. He also reframes the high-capex AI buildout as capital markets manufacturing somewhere for blocked capital to flow, with companies created downstream of capital rather than the reverse.

    Beating the Market, Emerging Managers, and the Feudal SPV System

    Giffon argues the myth that you cannot beat the market is overstated: Buffett’s S&P advice is aimed at the average person, and it is professionals, burdened by mandates and career risk, who struggle most, while amateurs who simply held Bitcoin, Tesla, or Apple outperformed. For LPs, he stresses knowing what customer you are. A 500k check is the wrong fit for a growth fund built to serve sovereigns, and emerging managers, tightly aligned to returns, are underrated. He urges underwriting the person over the thesis, paying special attention to a manager’s own financial situation and whether they are looking up or down at the fund size. He then describes the feudal economics of the labs, where founders grant allocations like landed estates, holders charge fees on a synthetic, relational, sometimes perpetual product, and the most egregious setups feature no GP commit, a 10% upfront fee, and carry with no term limit.

    Simplicity, Hiring, and Silicon Valley’s Hidden Philosophy

    On process, Giffon warns that investors prize complexity to look clever, when the choice is really to do something so complex no one else will or to keep it genuinely simple (be long Elon, buy big companies at their 200-week moving average), with the real gift being the ability to sell the simple idea. He praises Richard Rainwater’s one-page-thesis-plus-percentage-of-net-worth test as a brutal clarity forcing function. On hiring, he treats the job description as a sales pitch and a baked-in interview, using divisive, ambiguous statements like “an ideological minority at a top 10 school” to self-select the right people and repel the wrong ones. Finally, he makes the case that Silicon Valley’s underlying philosophy is badly underrated: a neo-Buddhist utilitarianism that flows into effective altruism, with thinkers like Nick Land, Curtis Yarvin, and William MacAskill shaping the culture unnamed. Where 1980s Wall Street was pagan and nakedly about money, today’s tech sees itself as self-righteous and positive-sum, treating the business as the ultimate philanthropy, with none of the old reflex to launder gains through art or culture.

    Notable Quotes

    “Every once in a while someone basically crystallizes a notion right at the right time in the right way that sort of becomes the foundational viewpoint or opinion on a certain era.”

    Jeremy Giffon, defining the billion dollar PDF

    “The capital just follows the billion dollar PDF around the field.”

    Jeremy Giffon, comparing capital to ten-year-olds chasing a soccer ball

    “Everyone gets served the same 500 tweets per day and it’s hundreds of millions of daily active users.”

    Jeremy Giffon, on the uni-feed that makes X the global newspaper

    “Posting changes your life if you’re good at it. That’s still true today, maybe more true than ever.”

    Jeremy Giffon, on posting as the last great meritocracy

    “Andrew Carnegie could take up arms against his workers, but now if you post the wrong thing as a billionaire, you have to resign.”

    Jeremy Giffon, on the shrinking power of the billionaire class

    “It’s this holy conceptual, just points on a leaderboard, truly, because you can’t spend it.”

    Jeremy Giffon, on net worth as a modern invention

    “One should not fool themselves that they are looking for anything other than entertainment in all the media that they consume, because it is produced to be entertaining.”

    Jeremy Giffon, on opting out of the timeline

    “We’re in an era where we’re selling compute. You can’t write the prompt once and then sell copies of the output. You have to do the compute every single time.”

    Jeremy Giffon, on the new economics of software

    “The most important media property won’t be watched. The most important author isn’t read. The most important philosopher is not understood. The most important stock has no fundamentals.”

    Jeremy Giffon, on a world where reputation floats free of the thing itself

    Watch the full conversation with Jeremy Giffon and Patrick O’Shaughnessy here on Invest Like the Best.

    Related Reading

  • 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.

  • SubQ 1.1 Small Explained: How Subquadratic Sparse Attention Hits 98% Retrieval at 12 Million Tokens With 64.5x Less Compute Than Dense Attention

    Subquadratic, a frontier AI research and infrastructure company, has released the model card and technical report for SubQ 1.1 Small, a long-context language model built on a new attention mechanism the company calls Subquadratic Sparse Attention (SSA). The headline claim is unusual in two directions at once: the model retains 98% single-fact retrieval accuracy at 12 million tokens, roughly twelve times the length it was primarily trained on, while cutting attention compute by 64.5x against dense attention at a 1 million token context. The deeper argument in the report is not really about a single model at all. It is about what happens to the entire retrieval-and-orchestration stack once reasoning over a complete artifact stops being prohibitively expensive.

    TLDR

    SubQ 1.1 Small is a small long-context model that replaces the dense attention of an existing open-weight frontier model with Subquadratic Sparse Attention, a learned, content-dependent sparse attention mechanism that scales linearly in compute and memory rather than quadratically. On retrieval it posts 99.12% on NVIDIA’s 13-task RULER suite at 128K tokens and 100% needle-in-a-haystack accuracy at 1M and 2M tokens, holding at 98% out to 6M and 12M tokens while attending to only 0.13% of token pairs. It keeps competitive general ability, scoring 85.4% on GPQA Diamond and 89.7% pass@4 on LiveCodeBench v6, and reaches 13% on the long-horizon AutomationBench Finance agentic benchmark, close to Opus 4.8 and GPT-5.5 and well ahead of mid and small tiers. The efficiency story is a scaling win rather than a constant-factor one: 64.5x fewer attention FLOPs than dense attention at 1M tokens and 56x faster than FlashAttention-2 on a single attention layer. The report frames cheap long-context compute as a research accelerator that let the team run more than one hundred million-token experiments and find a training recipe (long-context continued pretraining is the strongest lever) rather than guess at one, positions SSA against FlashAttention, DeepSeek’s Lightning Indexer line, state space models like Mamba, and hybrids, invokes Sutton’s Bitter Lesson to argue that RAG, chunking, and agentic scaffolding are partly workarounds for context scarcity, and was independently verified by Appen. Deployment is starting with design partners now, with a 2M to 12M token lineup planned by year end.

    Thoughts

    The most interesting move in this report is the framing, not the benchmark. Subquadratic plants its flag on Richard Sutton’s Bitter Lesson and argues that much of the modern AI stack, the retrieval pipelines, the chunkers, the re-rankers, the agentic orchestration, is scaffolding built around a single computational constraint: dense attention costs grow with the square of context length. If that constraint relaxes, a lot of hand-engineered machinery that exists to feed a model the right fragments at the right moment starts to look like the task-specific pipelines that learned representations eventually displaced. That is a genuinely provocative thesis, and it is the right lens for reading the rest of the document. The company is not selling a longer context window as a feature. It is betting that whole-artifact reasoning is a different shape of capability than retrieval over fragments, and that fragmentation destroys the cross-references a contract or a codebase actually depends on before the model ever sees them.

    The part of the paper most teams will undervalue is the claim that the real payoff of efficient attention is not cheaper inference but cheaper experimentation. A dense long-context training campaign is expensive enough that most groups get a handful of attempts and are forced to guess at the recipe. Subquadratic says SSA let them run more than a hundred experiments across six model generations with per-step iteration under a minute at million-token context, which is how they discovered that long-context continued pretraining, not clever post-training, was the dominant lever. If that holds, algorithmic efficiency becomes a first-class scaling variable alongside parameters and data, because capability becomes responsive to iteration velocity rather than raw compute alone. It reframes efficiency from a deployment line item into a research multiplier, and that is a more durable advantage than any single benchmark number.

    The generalization result deserves scrutiny precisely because it is so clean. A model trained overwhelmingly at 1M tokens, with a sliver at 2M and nothing beyond, holds 98% retrieval at 12M. The proposed explanation is that SSA routes attention by content relevance rather than fixed positional pattern, so there may simply be no obvious length boundary once the routing behavior is learned. That is plausible and the report is careful to say the 12M result emerged rather than being designed for. But single-needle NIAH is a deliberately clean probe with one target and a binary answer. The far harder RULER suite is only reported at 128K, the longest standardized length in the original benchmark, so the multi-hop, aggregation, and distractor-heavy capability that whole-artifact reasoning actually requires has public numbers at 128K, not at 12M. The honest read is that precise retrieval generalizes spectacularly and composite reasoning at extreme length is still an open question the report does not over-claim on.

    What lends the report credibility is how much counter-evidence it volunteers. It walks through MiniMax abandoning its hybrid M1 architecture and returning to full attention for M2 after efficient variants showed multi-hop reasoning deficits at scale. It admits that earlier SubQ checkpoints improved retrieval while regressing on knowledge benchmarks, forcing dedicated capability-balancing work. It describes catching a case where the MRCR benchmark moved up while the model felt worse in real workflow spot-checks, and switching its development signal to RULER as a result. That last point is a quietly important methodological argument: benchmark score and deployment behavior diverged enough to change checkpoint selection, which is a warning every team shipping long-context models should internalize. A vendor confident enough to show where its own metrics misled it is more trustworthy than one that only shows the wins.

    A few caveats keep the enthusiasm grounded. AutomationBench Finance at 13% is genuinely strong relative to peers, but it is a low absolute score across the board, including for GPT-5.5 at 18% and Opus 4.8 at 16%, so this is early evidence of agentic transfer rather than proof of a finished agent. The efficiency comparisons isolate a single attention layer rather than full end-to-end model throughput, which is the right way to expose the scaling shape but not the same as a wall-clock serving benchmark. The model is built from an unnamed donor open-weight frontier model, so some of its general-knowledge and coding strength is inherited rather than created here. And the most aggressive claims about the future, a 2M to 12M lineup and much higher sparsity, are roadmap, not released artifacts. None of that undercuts the core result. It just means the right posture is to treat SubQ 1.1 Small as a strong proof of concept for an architecture that, if it scales as advertised, could quietly remove a layer of the AI stack that everyone currently takes for granted.

    Key Takeaways

    • SubQ 1.1 Small is a long-context language model from Subquadratic AI, built on a new attention mechanism called Subquadratic Sparse Attention (SSA), released June 16, 2026 alongside a model card and technical report.
    • SSA is a learned, content-dependent sparse attention mechanism that scales linearly in both compute and memory with sequence length, rather than quadratically like dense attention.
    • The central result is context-length generalization: the model was trained primarily at 1M tokens, with some training at 2M and none beyond, yet retrieval held far past the training window.
    • Needle-in-a-haystack accuracy is 100% at 1M and 2M tokens and 98% at both 6M and 12M tokens, roughly twelve times the primary training length.
    • At 12M tokens the model attends to only 0.13% of token pairs, close to a 1,000x reduction in attention relationships, while still retrieving accurately.
    • On NVIDIA’s 13-task RULER benchmark at 128K tokens, SubQ 1.1 Small scores 99.12%, with the remaining errors concentrated in aggregation-style tasks rather than retrieval.
    • RULER tests beyond single-fact lookup: single-key and multi-key retrieval, common-word and frequent-word extraction, and multi-hop variable tracing across positions.
    • At 1M tokens, SSA requires 64.5x fewer attention FLOPs than dense attention (3.9 PFLOP versus 252 PFLOP per attention layer).
    • On a single attention layer, SSA runs 56x faster than FlashAttention-2 at 1M tokens (966 ms versus 54,164 ms on an H100), reaching parity near 16K tokens and pulling away as context grows.
    • The efficiency gain is a scaling-law win, not a constant-factor speedup: the advantage over dense attention grows as context length increases.
    • On general knowledge, SubQ 1.1 Small scores 85.4% on GPQA Diamond (pass@1), below GPT-5.5 (93.2) and Opus 4.8 (92), near Sonnet 4.6 and GPT-5.4-mini (87.5), and above GPT-5.4-nano (81.7) and Haiku 4.5 (67.2).
    • On coding, it reaches 89.7% pass@4 on LiveCodeBench v6, close to the absolute frontier (GPT-5.5 92, Opus 4.8 92.2) and ahead of the smaller tiers.
    • On AutomationBench Finance, a long-horizon agentic benchmark, it scores 13%, close to Opus 4.8 (16%) and GPT-5.5 (18%) and ahead of Sonnet 4.6 (8%), Haiku 4.5 (3%), and GPT-5.4-mini (0%). Absolute scores are low across all models.
    • The model was not trained from scratch. The team converted an existing open-weight frontier model by replacing dense attention with SSA, then built long-context ability through staged context extension and continued pretraining.
    • Context was extended in stages (262K, 512K, 1M, 2M) using YaRN positional scaling, with long-context continued pretraining performed between extension stages on naturally long data: books, long documents, and repository-scale code.
    • Roughly one trillion tokens of continued pretraining were performed, most of it at the 1M-token stage.
    • Long-context continued pretraining was the most consistent predictor of long-context retrieval gains across the experiments, more so than post-training tweaks.
    • The team ran more than one hundred long-context experiments across six major model generations, which the report argues is only possible because SSA made million-token iteration cheap (under a minute per step).
    • Capability balance was a recurring challenge: gains in long-context retrieval often regressed short-context knowledge and reasoning unless training was explicitly managed for both.
    • Benchmark scores and real deployment behavior diverged. The MRCR benchmark moved up while qualitative workflow spot-checks got worse, so the team switched its primary development signal to RULER.
    • The report frames RAG, chunking, summarization, and agentic orchestration as scaffolding built around context scarcity, drawing an analogy to Sutton’s Bitter Lesson, where hand-engineered mechanisms get displaced by larger-scale learning.
    • SSA is positioned against FlashAttention (a memory optimization that does not change quadratic compute), fixed-pattern sparse attention, DeepSeek’s learned sparse line, state space models, and hybrid architectures.
    • DeepSeek’s Lightning Indexer (used in DSA and CSA) is the closest published comparison. Its quadratic scoring overtakes the sparse attention it feeds around 52,000 tokens, reaching roughly 16x the attention cost at 1M and 190x at 12M.
    • State space models like Mamba achieve linear cost through a compressed fixed-size state, but that compression is lossy and weakens exact retrieval, which is why production efficient models are usually hybrids with some dense attention layers retained.
    • MiniMax is cited as a cautionary case: it moved from a hybrid M1 to a full-attention M2 after hybrids showed multi-hop reasoning deficits at scale and less mature supporting infrastructure.
    • The benchmark results were independently verified by Appen, a third-party evaluation firm.
    • The named use cases are financial analysis and due diligence, legal and contract work, and software engineering (architecture-level reasoning, cross-file refactoring, dependency tracing, planning, review, and long-horizon memory).
    • Sparsity settings were deliberately conservative, tuned for maximum context length rather than maximum sparsity. Limited experiments at 4x the sparsity reported positive early results.
    • The training infrastructure used a memory-scaling ladder: single node, intra-node sequence parallelism, CPU offload, multi-node sequence parallelism, nested offloading, and Ring Attention for the longest contexts.
    • Beyond about 8M tokens, BF16 numerical underflow and stability became practical constraints on evaluation.
    • The technical report is authored by Saul Ramirez, Alex Whedon, Ashmal Vayani, and Phong Vo of Subquadratic AI.
    • Deployment is starting with a first cohort of design partners, with broader rollout through the quarter and a general model lineup ranging from 2M to 12M tokens by the end of the year.
    • The company’s framing line is “Efficiency is intelligence,” and its broader thesis is that the point is not bigger context windows for their own sake but reasoning directly over complete artifacts with less surrounding scaffolding.

    Detailed Summary

    The problem: whole-artifact reasoning and context scarcity

    The report opens by naming a class of tasks it calls whole-artifact reasoning: problems whose structure requires reasoning across a complete artifact rather than over isolated fragments. A legal agreement may define a term on page 2, qualify it on page 12, carve out an exception on page 46, and amend it in a schedule. A function may be defined in one file, called from forty others, and constrained by invariants encoded in the architecture rather than in comments. A financial review may require connecting filings, earnings reports, contracts, and internal records. In each case the difficulty is not locating a passage, it is reasoning over relationships distributed throughout a large artifact. Most production systems do not do this directly. They rely on retrieval pipelines, chunking, summaries, and agentic workflows that partition information and reconstruct fragments at inference time, because dense attention scales quadratically with context length and makes direct reasoning over large artifacts expensive. Subquadratic argues that much of the modern AI stack is therefore designed to manage context scarcity rather than reason over complete artifacts, and it connects this to Sutton’s Bitter Lesson: sophisticated hand-engineered mechanisms historically get displaced once larger-scale learning becomes practical.

    What SSA is and the three requirements it targets

    Subquadratic Sparse Attention is a content-dependent sparse attention mechanism designed to satisfy three requirements at once, a combination the report argues prior approaches never achieved in a practical long-context system. First, dense-attention-level retrieval and reasoning quality, which requires routing that is content-dependent (determined by the tokens themselves) rather than driven by a fixed positional pattern. Second, subquadratic scaling, where selection, retrieval, and attention are each linear in sequence length so the mechanism is linear end to end, not only within the attention read. Third, full-context training with standard autoregressive generation, so the model can optimize over the entire context during training while keeping efficient token-by-token decoding at inference. The internal mechanism by which SSA achieves this is held back as outside the scope of the report, which focuses instead on the requirements and the experimental program that followed.

    Where SSA sits among prior approaches

    The background section is effectively a taxonomy of long-context modeling. FlashAttention is treated not as a competitor but as the standard dense-attention baseline: it solved the memory problem by never materializing the full attention matrix, but it left the quadratic compute cost untouched, so doubling context still quadruples attention computation. Fixed-pattern sparse attention (sliding-window, strided, as in Longformer, BigBird, and the sliding window in Gemma) scales well but sacrifices content-dependent routing and tends to fail on retrieval benchmarks like RULER. Compression methods like Multi-head Latent Attention reduce KV-cache memory at inference but do not change the quadratic prefill cost. Learned sparse attention, exemplified by DeepSeek’s Native Sparse Attention and its Lightning Indexer, learns where to route but pays a quadratic cost in the indexer itself. State space models and linear attention (Mamba, Mamba-2 and Mamba-3, RetNet, RWKV, gated delta networks) achieve linear cost through a compressed fixed-size state, but that compression is lossy and weak on exact retrieval. Hybrids (Jamba, Kimi Linear, Qwen3 Next, Nemotron) keep a few dense layers to preserve retrieval, which means the quadratic component still dominates at long context. System-level workarounds (RAG, agentic frameworks, recursive language models) move retrieval outside the model entirely. The report’s stated open problem is to combine subquadratic scaling end to end with content-dependent retrieval, arbitrary-position access, and practical ultra-long-context training in one system, which it claims no widely deployed architecture provides and which SSA targets.

    Training: conversion, staged context extension, and continued pretraining

    Rather than training from scratch, the team converted an existing open-weight frontier model that supported a 262K-token context by replacing its dense attention with SSA. They then extended the context window in stages (262K to 512K to 1M to 2M) using YaRN to rescale positional representations, performing long-context continued pretraining between extension stages rather than jumping straight to the final length. The training mixture emphasized naturally long data such as books, long documents, and repository-scale code, packed to the target length with document separators and without masking cross-document attention boundaries. Most continued-pretraining tokens were trained at the 1M-token stage, with roughly one trillion tokens total. Post-training played a separate role: shaping how the long-context capability was expressed while preserving reasoning, coding, and instruction following. The team explored sample-level loss aggregation to keep a few extremely long examples from dominating gradient updates, and staged the post-training corpus across synthetic retrieval tasks, long-context reasoning, coding, educational material, and general instruction following, alternating capability-building phases with recovery phases.

    Results: retrieval, knowledge, coding, and agentic tasks

    On retrieval, SubQ 1.1 Small scores 99.12% on the 13-task RULER average at 128K, with errors concentrated in aggregation-style tasks like common-word and frequent-word extraction. On needle-in-a-haystack, evaluated on 50 held-out UUID samples per length, it scores 100% at 1M and 2M (within the training window) and 98% at 6M and 12M (held out), attending to only 0.13% of token pairs at 12M. On knowledge, GPQA Diamond pass@1 is 85.4%, landing between the small and mid frontier tiers and confirming that long-context optimization need not sacrifice reasoning, a result the report credits to its capability-balancing stages after earlier checkpoints showed retrieval gains coming at the cost of knowledge. On coding, LiveCodeBench v6 pass@4 is 89.7%, and the report notes coding data played a dual role, also improving non-code long-context retrieval because code is dense with the cross-position dependencies that train general routing. On long-horizon agentic work, AutomationBench Finance is 13%, where agents must discover the right endpoints among roughly 500 across 47 applications, make interdependent API calls, follow layered business rules, and ignore seeded distractors, graded on binary end-state correctness with no partial credit.

    Efficiency and the DeepSeek comparison

    Efficiency is measured on one attention layer against a dense baseline on the same backbone. Per-forward-pass attention FLOPs scale from a 2.1x reduction at 32K to 8x at 128K, 31.5x at 512K, and 64.5x at 1M tokens (3.9 PFLOP for SSA versus 252 PFLOP for dense). Measured against FlashAttention-2 in isolation, SSA reaches parity near 16K tokens and pulls away to 56x at 1M, where it runs in 966 ms versus 54,164 ms on an H100. The report devotes a discussion section to DeepSeek’s sparse attention line as the closest published comparison. DeepSeek’s Lightning Indexer is a learned selector, but it is a full-attention distilled transformer, so it scales quadratically: in a V3.2-style configuration the indexer is cheaper than the sparse attention it feeds only below about 52,000 tokens, then overtakes it, reaching roughly 16x the attention cost at 1M tokens and 190x at 12M. SSA targets that same selection role with a selector the report says is dramatically cheaper and linear throughout, and notes SSA could conceptually replace the selector over either uncompressed or compressed representations.

    Efficiency as a research accelerator and the evaluation lessons

    A recurring theme is that the most valuable effect of cheap long-context compute was on the research loop, not just inference. Where a dense campaign would allow a handful of attempts, SSA enabled more than a hundred experiments across six model generations with per-step iteration under a minute at million-token context. That throughput is what surfaced the finding that long-context continued pretraining is the strongest lever, and it leads the authors to argue that algorithmic efficiency should be treated as a first-class scaling variable alongside model and dataset size. The report is unusually candid about evaluation pitfalls. It describes how the MRCR benchmark diverged from deployment behavior, with MRCR-optimized checkpoints often feeling worse on repository-scale code reasoning, multi-document synthesis, and contract analysis, which pushed the team to rely on RULER and a fixed set of qualitative workflow spot-checks as development signals. It also cites MiniMax returning from a hybrid M1 to a full-attention M2 as evidence that reducing asymptotic cost is not sufficient on its own if retrieval quality, reasoning at scale, and system maturity are not preserved at the same time.

    Implications, availability, and what comes next

    The report’s deployment argument is that the most important enterprise implication of long-context models is not larger windows but the ability to reason directly over complete or more-complete artifacts, moving retrieval, re-ranking, and orchestration logic into the model where the task is naturally whole-artifact rather than naturally decomposable. It is careful not to declare retrieval obsolete: for corpora larger than any plausible context window, fast-changing knowledge, and genuinely multi-stage workflows, RAG and orchestration remain the right tools. The narrower claim is that the class of scaffolding that exists only to compensate for context limits gets smaller as efficient long-context models extend the reachable window. The benchmark results were independently verified by Appen. Subquadratic is deploying SubQ 1.1 Small with a first cohort of design partners now, with broader rollout through the quarter and a general lineup spanning 2M to 12M tokens planned by the end of the year, and it flags much higher sparsity as future work.

    Notable Quotes

    “Much of the modern AI stack is therefore designed to manage context scarcity rather than reason over complete artifacts directly.”

    SubQ-1.1-Small Technical Report, framing retrieval and orchestration as workarounds for an architectural limit

    “The hybrid has moved the line, but not changed its shape.”

    SubQ-1.1-Small Technical Report, on why hybrid models keep their quadratic component at long context

    “A routing mechanism intended to make long context affordable becomes the dominant long-context cost, reintroducing quadratic scaling after providing scalar compute savings.”

    SubQ-1.1-Small Technical Report, on DeepSeek’s Lightning Indexer overtaking the attention it feeds

    “If the cost of long-context experiments is too high, teams are forced to guess at the recipe. If the cost falls far enough, they can search for it.”

    SubQ-1.1-Small Technical Report, on efficient attention as a research accelerator

    “Fragmentation systematically destroys those relationships before the model ever sees them.”

    SubQ-1.1-Small Technical Report, on why chunking hurts whole-artifact reasoning

    “Holding the whole artifact in context changes the shape of the task rather than only the speed of it.”

    SubQ-1.1-Small Technical Report, on the difference between bigger windows and direct reasoning

    “The value of SSA is therefore not only that it makes long-context inference cheaper. It makes long-context experimentation cheaper.”

    SubQ-1.1-Small Technical Report, conclusion

    Read the full SubQ 1.1 Small technical report and model card here.

    Related Reading

    • Subquadratic (subq.ai) the company behind SubQ 1.1 Small and the Subquadratic Sparse Attention architecture, where you can join the waitlist.
    • The Bitter Lesson by Richard Sutton the short essay whose argument the report leans on, that hand-engineered mechanisms lose to general methods that scale with computation.
    • Attention Is All You Need the original Transformer paper that introduced the dense attention whose quadratic cost SSA is built to remove.
    • RULER (arXiv) NVIDIA’s long-context benchmark that the report uses as its primary retrieval signal, and that fixed-pattern sparse methods historically struggle with.
    • Retrieval-augmented generation (Wikipedia) background on the RAG approach that the report frames as scaffolding around context scarcity rather than a permanent fixture.

  • 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.

  • Lloyd Blankfein on the 3 Sectors Where He Puts His Money Now: Big Tech, Energy, and Financial Services, Day Trading From an iPad, and the Warren Buffett Handshake That Backed Goldman in 2008

    Lloyd Blankfein spent almost 40 years at Goldman Sachs, the last dozen as its chairman and chief executive, and he still trades almost every day from an iPad. In this wide ranging conversation on the My First Million podcast, the former Goldman boss lays out exactly where he is putting his own money right now, why a supportive spouse beats nearly any investment, how Warren Buffett wired five billion dollars into Goldman on a handshake during the 2008 crisis, and why he reads medieval history to stay calm about the present. It is part stock picking, part risk philosophy, and part a frank accounting of money, marriage, and the scars of growing up in the projects.

    TLDW

    Blankfein says he is roughly 98 percent in risky assets, almost all equities, and concentrated in three sectors he knows cold: big tech, energy, and financial services. His personal book leans heavily into single stocks over ETFs, weighted toward the big hyperscalers and a few second tier names, and he trades daily, alone, from an iPad and a phone, using calls and texts as his research network. Yet the advice he gives a normal investor is the boring opposite: a diversified S&P 500 fund like VOO, more risk when you are young because you will outlive your mistakes, the same thing Warren Buffett would tell you. The conversation ranges across the 2008 Buffett investment in Goldman, the cost of trying to legislate risk out of markets, the thin margin between the best and the rest, luck and the myth of the genius, why reputation is the real contract on Wall Street, why a supportive spouse is the highest return asset he knows, the money anxiety he carried out of a Brooklyn housing project, the dignity of a 500 dollar financial aid check, giving with a warm hand versus a cold one, the dangers of gamified investing, the big misses like SpaceX and early cellular, the obituary test a senior partner once gave him, and why reading history keeps the present in proportion.

    Thoughts

    The most useful tension in this interview is the gap between what Blankfein practices and what he preaches. He tells young people to buy a diversified S&P 500 index fund, he holds VOO himself, and he calls the host’s plain 90 percent stocks and 10 percent bonds split sensible. Then he admits his own portfolio is something like 90 percent single stocks that he trades by hand every day. The honest read is that his edge is not a transferable tip. It is a 40 year information network of phone calls and a tolerance for risk that most people neither have nor should want. The replicable lesson is the boring half, not the day trading half.

    The most contrarian idea here is not a stock pick, it is his defense of risk itself. His argument that regulators trying to prevent the hundred year storm also forfeit the 99 normal years of growth in between is a serious claim about the price of safety, and it travels far beyond Wall Street. The same goes for his point that a good risk manager sometimes has to push people to take more risk, not less. The moment after a loss, when everyone goes gunshy, is exactly when the best operators lean back in. That is an uncomfortable thing for a former bank CEO to say out loud, and it is the part of the conversation most worth sitting with.

    The Warren Buffett story is a master class in what actually moves markets, and it is not cash. Goldman did not need the five billion dollars. Blankfein says the money was almost irrelevant because the firm already had money. What it could not manufacture was confidence, and Buffett’s name supplied it. The handshake, the commitment with no paperwork, the line about worrying enough for the both of us, all point to the same thing. At the top, reputation is the collateral. His aside that most trades are never written down because you will never eat lunch in this town again is the same idea wearing street clothes.

    Quietly, the personal finance thread may be the most valuable part for a normal listener. A former Goldman CEO saying that a supportive partner is more game changing than any investment, that a bad marriage is financially worse than being lonely, and that he has not paid a bill in over 40 years because his wife runs the household economy, is a reminder that household stability is itself an asset class. The 500 dollar financial aid check he still remembers half a century later, and his give with your warm hand philosophy, reframe wealth as something measured by how it feels to give and to receive, not just by the size of a pie chart.

    Finally, the history obsession is not a side hobby, it is his risk model. Reading about the black plague, the McCarthy era, and the Vietnam draft is how he keeps the present in proportion. His Mark Twain line, that history does not repeat but it rhymes, is the direct antidote to the in this economy defeatism he and the host both complain about. For an investor, that long view is close to the whole game. It is what lets you hold through the drawdowns that scare everyone else out of the market.

    Key Takeaways

    • Blankfein estimates he is about 98 percent in risky assets, with roughly 95 of those 98 points in equities, and the rest spread thin. He invests in risky assets because, in his words, that is what is fun for him.
    • Within his equities, he is heavily tilted toward single stocks rather than ETFs. He frames it as roughly a quarter to a third in ETFs and the rest in single names, and concedes it could be as lopsided as 90 percent single stocks because picking names is what he enjoys.
    • The three sectors he has concentrated in for years are big tech, energy, and financial services, and he says his outperformance comes from where he focused, not from any special genius.
    • On tech he owns the big hyperscalers, the Googles, Microsofts, and Nvidias of the world, plus a tier just below them, naming Oracle and Larry Ellison as an example of a slightly riskier second tier name. He thinks in categories, not fixed tickers, because he changes positions constantly.
    • He says he has a background in trading energy, which is why energy is a core sleeve, and he knows financial services from the inside after almost 40 years at Goldman, so those are natural areas of edge.
    • He still owns a lot of Goldman Sachs stock, out of affection for the firm he spent his career building.
    • He is bullish on big tech and plans to stay bullish until it stops going up. His foreseeable future, he jokes, lasts until he finishes the conversation and checks the screen again.
    • He trades every single day, alone, with no team. He does it from an iPad and a phone, not a computer, and treats the market like background music rather than a job.
    • His research is human, not algorithmic. He chats and texts with people, then calls them because he is tired of fixing typos, and he reads the New York Post, the Wall Street Journal, the New York Times, the Financial Times, and Bloomberg.
    • The advice he gives ordinary investors is deliberately boring and different from his own behavior: hold a diversified equity portfolio like an S&P 500 fund, with VOO as his own example, and tilt more aggressively when you are young because you have time to outlive mistakes.
    • He notes that broad indexes are already heavily weighted toward tech because of market cap, so a plain index gives meaningful tech exposure, and a tech focused ETF on top can add a disproportionate tilt for believers.
    • He calls the host’s simple 90 percent index and 10 percent bonds allocation sensible, and says this is essentially the same advice Warren Buffett would give a normal person.
    • The older you get, the more conservative you should become, shifting from maximizing gains toward not losing what you have. Young people can afford more risk precisely because they will outlive their errors.
    • During the 2008 financial crisis, Warren Buffett invested about five billion dollars in Goldman through a preferred stock structure, essentially on a phone call and a handshake, with no demand for due diligence.
    • Buffett’s real value was confidence, not capital. Goldman already had money, but it had lost the confidence of the market while peers were failing. Buffett’s name signaled the firm was a good investment being beaten down by circumstances that would reverse.
    • Buffett asked for a verbal commitment that Goldman would not sell shares before he did, and declined to put it in writing. He waved off the worry with the line that five billion dollars going bad would not even be a bad hurricane for Berkshire, an insurer.
    • Most trading is done on reputation, not paper. Blankfein says people buy and sell bonds worth enormous sums without written contracts, relying on probity, because anyone who reneges will never eat lunch in this town again.
    • On risk and regulation, he argues you cannot legislate risk away. Trying to prevent the hundred year storm also forgoes the 99 in between years of growth, and a good risk manager sometimes has to encourage people to take risk, not suppress it.
    • The best traders have resilience. They bounce back, focus on new information rather than the past, and adapt quickly instead of staying gunshy after a loss.
    • The difference between someone who is really good and someone who cannot make it is small. He compares it to a golf tournament won by one stroke with six people tied for second, and notes much of life is winner take all at razor thin margins.
    • Luck matters enormously. He became Goldman CEO partly because his predecessor was nominated to be Treasury Secretary, a reference to Hank Paulson, and the timing of opportunities is often out of your control.
    • He is skeptical of the word genius. He says he can usually see how successful people do what they do, with Elon Musk as a rare exception, and that powerful people are more normal, more insecure, and more flawed than outsiders assume.
    • On democratized investing, he thinks apps that make markets accessible are good in their own terms, but gamifying trading with confetti and high fives can mask real danger for people who can lose more than they can afford.
    • He has missed plenty. He thought SpaceX was overpriced at a 100 billion dollar valuation, now discussed near a trillion and three quarters, and passed on early cellular because he could not imagine why anyone would carry a bulky phone when payphones existed. He says he missed far more than he got.
    • He frames a supportive spouse as more game changing than almost any investment, and warns that a bad marriage, with custody fights and property settlements, is financially and personally worse than being lonely.
    • He has not paid a bill in over 40 years. His wife Laura, a former lawyer he says now chairs Barnard College, runs a bill paying service and manages the household economy. He generates the money, she distributes it.
    • He grew up in an East New York, Brooklyn housing project, the son of a postal worker, and carried money anxiety well into his 30s. He recalls buying a vacation home that cost more than all their savings, with his wife unable to make the math work until they remembered the down payment.
    • A 500 dollar financial aid check, handed to him without shame as a college freshman around 1971, shaped his philosophy on giving. He learned it is not enough to give people what they need, you have to give it in a way that feels dignified.
    • He embraces the give with your warm hand, not your cold hand idea, the notion of giving while alive so you can experience the joy, which connects to the spirit of the book Die With Zero.
    • He admits ambivalence about giving to his kids, the strange feeling of resenting that they have what he provided, and notes the heavy burden carried by children of prominent people who must prove they earned their place.
    • He describes himself as wired for anxiety, inherited from his father, and says looking around corners for what could go wrong actually suited a career in a risky business with a big balance sheet.
    • When he made partner, a senior partner gave him rules of the road, including avoiding misconduct, being conservative on taxes, setting up a charitable foundation, and living so that no more than three of the nine paragraphs in his eventual obituary would be about Goldman. He says he stayed too long to pass that test.
    • He reads history as a discipline, favoring Barbara Tuchman, Robert Caro’s The Power Broker, Ron Chernow, Rick Atkinson, and Stephen Ambrose. His core belief, borrowed from Mark Twain, is that history does not repeat but it rhymes, which is why he would not bet against America.

    Detailed Summary

    The three sectors he actually invests in

    The headline answer to where the former Goldman CEO is putting his money is simple: big tech, energy, and financial services. He says he has been focused on those three areas for a long time, and that his outperformance is a function of where he aimed rather than any unusual investing gift. Energy is natural because he has a background trading it. Financial services is natural because he spent nearly 40 years inside the industry. Tech is where he is most heavily concentrated, and he expects to stay there for good reason, citing the threshold of large changes in technology. He owns the major hyperscalers by category, the Googles, Microsofts, and Nvidias, plus a tier just below, offering Oracle and Larry Ellison as a polite example of a slightly riskier second tier name. He is careful to say he thinks in categories rather than fixed tickers because he changes his positions all the time.

    How the portfolio is really built: single stocks over ETFs

    Asked to describe his portfolio as a pie chart, Blankfein says he is about 98 percent in risky assets, with roughly 95 of those points in equities. He pushes back on the idea that index funds are safe, pointing out that a diversified equity ETF is still equities and still risky, just spread out, and very different from debt or short term money markets. Within his equity sleeve he leans into single stocks, framing it as somewhere between a quarter and a third in ETFs and the rest in individual names, and conceding it might be as extreme as 10 percent ETFs and 90 percent single stocks. The reason is preference, not theory. Picking and trading names is what he likes to do, and he is honest that this is a hobby pursued by a professional, not a model for someone investing for a living.

    How he actually trades: an iPad, a phone, and a network

    He trades every day, by himself, with no team. There is no Bloomberg terminal and no desk of analysts. He uses an iPad and a phone, and admits it takes discipline not to glance at his screen mid conversation. The market, he says, is like music playing in the background while he does other things. His information edge is relational. People text him, he texts back, and then he calls because he is tired of fixing typos with what he calls his fat fingers. He follows general and business news, reads a stack of newspapers starting with the New York Post, and treats companies like little stories, almost like gossip. He even notes, with some delight, that he still watches commercials on Netflix, a small window into a frugality that never fully left him.

    The advice he gives young investors, and what Buffett would say

    For a normal person, his counsel is the opposite of his own behavior. He would hold a diversified portfolio of equities like an S&P 500 fund, naming the SPY and VOO tickers and saying he personally uses VOO. Because of the importance of technology, he might add a tech oriented ETF for extra tilt, while noting the broad index is already tech heavy by market cap. He endorses the host’s plain 90 percent index and 10 percent bonds split as sensible and says it mirrors what Warren Buffett would advise. His one piece of age based guidance is that younger investors should accept more risk through equities, because they have time to recover, while older investors should grow more conservative and focus on not losing what they have rather than maximizing returns.

    The Warren Buffett handshake that backed Goldman in 2008

    The most cinematic story in the conversation is Buffett’s roughly five billion dollar investment in Goldman during the financial crisis, structured as a preferred stock that sits between a loan and equity. Blankfein describes a deal done largely on trust. When he offered to walk Buffett through everything he was worried about, Buffett replied that he knew Lloyd well enough to know he worried enough for the both of them. Buffett also asked, verbally and without writing, for a commitment that Goldman would not sell shares before he did. Blankfein is clear that the cash itself was almost irrelevant, since Goldman had money. What the firm lacked was the confidence of a frightened market, and Buffett’s willingness to invest before things improved supplied exactly that signal. Buffett, he stresses, was acting for his own shareholders, not as a rescuer, which is precisely what made the vote of confidence credible.

    Why you cannot legislate risk out of the system

    Reflecting on the post crisis regulatory push to make sure 2008 never happened again, Blankfein makes a careful argument about the price of safety. Once you are in the business of taking risk, anything can happen, and trying to legislate it away has a hidden cost. You may think you are protecting the world from the hundred year storm, but you also forgo the 99 years of growth in between. He extends this inside the firm too. After a period of big losses, partners had become gunshy and were talking themselves out of every idea. A good risk manager, he argues, sometimes has to promote risk taking rather than repress it, because without risk there is no growth, no entrepreneurship, and no progress. The flip side is real: take risk and there is a meaningful chance you fail and lose other people’s money, which is a terrible outcome. But the alternative, never risking anything, buys comfort at the cost of ever moving forward.

    Small margins, big outcomes, and the role of luck

    Asked what separated the traders who could not outperform from the rest, Blankfein says the gap between the very good and those who cannot make it is surprisingly small. He likens it to a golf tournament decided by a single stroke with six players tied for second, and to acting, where the best performer gets every role and the second best waits tables. Much of life, he says, is winner take all at tiny margins. Luck compounds this. He freely credits fortune for his own rise, noting he became CEO in part because his predecessor was tapped to be Treasury Secretary. He is also skeptical of the genius label. He can usually see how accomplished people do what they do, with Elon Musk a rare exception, and insists the powerful are more normal, more insecure, and more driven by their flaws than outsiders imagine.

    Reputation is the real contract

    A recurring theme is that the financial world runs on reputation more than paperwork. Blankfein notes that most of what traders do is not written down. People buy and sell bonds and other instruments that settle days later, relying on probity rather than signed contracts, because anyone who lies or reneges will never eat lunch in this town again. He references the casual texts between Elon Musk and Larry Ellison around the Twitter acquisition as proof that big does not mean complicated. There are big things that are simple and little things that are complicated. Documentation is good when execution is far off, but when a deal will be performed in two days, dotting every i is often pointless. The point is not that documents do not matter, it is that trust and reputation are the load bearing structure.

    A supportive spouse as the highest return asset

    The conversation turns personal when both men agree that a supportive partner may be the single most game changing factor in a life, more than any investment. Blankfein adds the inverse warning: a bad marriage, with breakups, custody battles, and property settlements, is worse than loneliness. He credits his wife Laura, a former big firm lawyer he says now chairs Barnard College, with handling everything when his career moved the family overseas, from the car to the house to the kids’ schooling, while he took the visible victory laps at work. He has not paid a bill in over 40 years. Laura manages a bill paying service and runs the household finances. As he puts it, he is in charge of generating the money and she is in charge of distributing it. The host contrasts this with his own monthly money meetings with his wife, a discipline he picked up from a personal finance author friend.

    Money scars, the 500 dollar check, and giving with a warm hand

    Blankfein grew up in an East New York housing project, the son of a postal worker who had earlier lost a job, in a household where rent was scarce. He calls himself an urban hick who barely left Brooklyn as a kid. That scarcity left a mark that lasted into his 30s. He tells the story of buying a small beach house that cost more than all their savings, and of his wife driving 30 miles while failing to make the closing math work, until they realized she had forgotten to count the 10 percent down payment. The most resonant memory is a 500 dollar financial aid check handed to him as a freshman around 1971, made out on the spot by a clerk with a generosity of spirit that let him receive it without shame. That experience shaped a lifelong view that giving well means preserving dignity, and he now co chairs a financial aid campaign at his university. It also connects to his embrace of the idea of giving with your warm hand rather than your cold hand, giving while alive so you can feel the joy, the same spirit as the book Die With Zero. He is candid about a strange ambivalence, the way he can resent that his kids enjoy what he himself gave them.

    Robinhood, confetti, and the misses

    On apps like Robinhood, Blankfein takes a balanced view. Democratizing investing and making assets accessible is good in its own terms, and advertising can pull people toward markets they would otherwise ignore. But if you make trading too much like a video game, with confetti and high fives, you can mask the danger and lure people who cannot afford to lose into losing more than they can. He is equally frank about his own misses. He thought SpaceX was overpriced at a 100 billion dollar valuation, a figure now discussed near a trillion and three quarters. He passed on early cellular because he could not imagine why anyone would carry a bulky phone with payphones everywhere. His blunt summary is that he missed far more than he got, and that nobody is great at predicting the future.

    The obituary test, thick skin, and staying too long

    When Blankfein made partner, a senior partner assigned to acculturate new partners gave him rules of the road: avoid anything that would today be called misconduct, be rigorous and conservative on taxes, set up and actually use a charitable foundation, and keep enough balance that, if your obituary runs nine paragraphs, no more than three are about Goldman. Blankfein says he failed that last test by staying too long, even titling his memoir around the firm. He also reflects on having a thick skin, recalling unflattering press and concluding that he could take a punch, a trait not everyone has and one he did not know he possessed until he was tested. He is careful to say this does not make people who cannot take a punch bad, just differently wired.

    Why he reads history: it rhymes

    The final stretch is a love letter to reading history. Blankfein favors Barbara Tuchman, whose A Distant Mirror he has read twice and whose Guns of August he calls fantastic and influential, along with Robert Caro’s The Power Broker on Robert Moses, Ron Chernow’s biographies, Rick Atkinson’s Revolution series, and Stephen Ambrose’s Undaunted Courage. He describes rereading the Robert Moses book after 40 years of trying to get things done and finding his appreciation for the achievements rise, even as the flaws stayed the same, because he had changed. He ties history directly to markets through the Mark Twain line that history does not repeat but it rhymes. Patterns recur, every generation maximizes its own crises and minimizes resolved ones, and reading about the black plague, the McCarthy era, or the Vietnam draft is how he stays calm. His conclusion, echoing a sentiment often attributed to Buffett, is that he would not bet against America, a country he describes as mostly good and able to improve.

    Notable Quotes

    “I invest in risky assets. That’s what’s fun for me.”

    Lloyd Blankfein, describing his own portfolio, which he says is roughly 98 percent risky assets

    “It’s been good to be bullish on big tech, and I’ll stop being bullish on it when it stops going up.”

    Lloyd Blankfein, on why he stays concentrated in technology

    “I’m not at a computer. I don’t have a computer. I have an iPad.”

    Lloyd Blankfein, on how he day trades every day, alone and with no team

    “To me, the market is like music. It’s out there. It’s going on.”

    Lloyd Blankfein, on why trading daily feels like a hobby rather than work

    “Look, $5 billion if it all goes bad, that’s not even a bad hurricane on the East Coast.”

    Warren Buffett to Lloyd Blankfein, waving off the risk of his 2008 investment in Goldman Sachs

    “The difference between somebody who’s really, really good and somebody who can’t make it is not that great.”

    Lloyd Blankfein, on the thin margin between the best and the rest

    “You may think you’re protecting the world from the hundred-year storm, but you’re also going to forego the 99 years of in between when there was growth.”

    Lloyd Blankfein, on the cost of trying to legislate risk out of markets after 2008

    “I’m in charge of generating the money, and she’s in charge of distributing it.”

    Lloyd Blankfein, on his 40-plus-year marriage to Laura and why he has not paid a bill in decades

    “History doesn’t repeat, but to paraphrase Mark Twain, it rhymes.”

    Lloyd Blankfein, on why reading history keeps the present in proportion

    Watch the full conversation with Lloyd Blankfein on the My First Million podcast here.

    Related Reading

    • Lloyd Blankfein (Wikipedia) background on the former Goldman Sachs chairman and CEO whose investing views anchor the conversation.
    • My First Million podcast the show where this interview took place, for the full back catalog of investor and founder conversations.
    • Berkshire Hathaway primary source on Warren Buffett’s company, which made the roughly five billion dollar Goldman investment in 2008.
    • Vanguard S&P 500 ETF (VOO) the diversified index fund Blankfein names as the sensible core holding for a normal investor.
    • Die With Zero by Bill Perkins the book behind the give with your warm hand, not your cold hand philosophy discussed near the end.

  • 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

  • SpaceX IPO Priced at $135 Per Share: SPCX Raises $75 Billion in the Largest IPO in History, Trading Begins June 12 on Nasdaq

    TLDR

    SpaceX confirmed the pricing of its initial public offering on June 11, 2026: 555,555,555 shares of Class A common stock at $135.00 per share, a raise of just under $75 billion. The stock begins trading Friday, June 12, 2026 on the Nasdaq Global Select Market and Nasdaq Texas under the ticker SPCX, with the offering expected to close on June 15. Underwriters hold a 30 day option to purchase up to 83,333,333 additional shares at the IPO price, which would push total proceeds toward $86 billion. At $135 per share the company is valued at roughly $1.77 trillion. That makes this the largest IPO ever priced, around three times the previous record, and it instantly places SpaceX among the most valuable companies on the planet, ahead of Tesla.

    Key Takeaways

    • The deal: 555,555,555 Class A shares priced at $135.00 each, raising approximately $75 billion before the overallotment option.
    • The ticker: SPCX, trading on both the Nasdaq Global Select Market and the new Nasdaq Texas exchange starting June 12, 2026. The offering closes June 15.
    • The greenshoe: underwriters have 30 days to buy up to 83,333,333 more shares at $135, worth another $11.25 billion and a potential total raise near $86 billion.
    • Record scale: roughly three times larger than Saudi Aramco’s 2019 listing, the previous record holder, and by some estimates bigger than all US IPO proceeds from 2024 and 2025 combined.
    • The valuation: approximately $1.77 trillion at the offer price, which would rank SpaceX around seventh among US companies by market cap, above Tesla at roughly $1.6 trillion.
    • The multiple: reported 2025 revenue of $18.7 billion puts the deal at roughly 95 times trailing sales.
    • Control: Elon Musk retains more than 82 percent voting power after the offering through the dual class structure.
    • The banks: Goldman Sachs leads a ten bank syndicate of book running managers including Morgan Stanley, BofA, Citigroup, and J.P. Morgan, with thirteen additional co-managers.
    • Truly global retail access: simultaneous retail offerings in the US, Canada, Switzerland, Australia, Japan, and seven EEA countries, with a qualified investor tranche in the UK. Mega IPOs almost never do this.
    • Demand: the book was reportedly around four times oversubscribed, implying roughly $250 billion in orders, and some brokers are imposing anti flipping penalties on early sellers.
    • Index mechanics: MSCI plans early inclusion of SPCX shortly after the debut, while S&P declined to fast track S&P 500 membership.
    • What you own: Starlink, the Falcon and Starship launch business, and the AI segment built around xAI and the X platform following the February 2026 merger.

    Detailed Summary

    The Deal: 555,555,555 Shares at $135

    Space Exploration Technologies Corp. announced from Starbase, Texas that its IPO priced at $135.00 per share for exactly 555,555,555 shares of Class A common stock. The math works out to $74,999,999,925, which is to say the share count was reverse engineered to land a fraction of a cent under a clean $75 billion. The quintuple five share count is exactly the kind of numerical flourish you would expect from this company. The SEC declared the registration statement effective on June 11, and the underwriters received a standard 30 day option for up to 83,333,333 additional shares, which at the offer price is another $11.25 billion. Fully exercised, total proceeds approach $86 billion.

    Where and When SPCX Trades

    Shares are expected to begin trading June 12, 2026 under the ticker SPCX on the Nasdaq Global Select Market and on Nasdaq Texas, the exchange operator’s new Dallas based venue. The dual venue listing is a symbolic alignment for a company headquartered in Starbase, Texas, and it hands Nasdaq Texas the biggest debut it could possibly ask for. The offering itself is expected to close on June 15, subject to customary conditions.

    The Largest IPO Ever, By a Wide Margin

    The previous record for an IPO raise was Saudi Aramco in December 2019 at roughly $29 billion including its overallotment. SpaceX clears that bar nearly three times over before its own greenshoe is exercised. Market data firms have noted that this single deal likely raises more money than every US IPO from 2024 and 2025 put together. Whatever 2026 looked like for the IPO market before this week, it is now a record year on the strength of one listing.

    A $1.77 Trillion Valuation in Context

    At $135 per share, SpaceX is valued at approximately $1.77 trillion, a figure that assumes pending transactions such as the EchoStar spectrum deal close as planned. That valuation would slot SpaceX in around seventh place among US public companies, ahead of Tesla, which trades near $1.6 trillion. It is a remarkable mark for a company that was privately valued at $350 billion in late 2024 and at $1.25 trillion when it merged with xAI in February 2026. Against reported 2025 revenue of $18.7 billion, the offer price represents roughly 95 times trailing sales, a multiple that prices in Starlink’s growth, Starship’s long term optionality, and the AI buildout all at once.

    The Syndicate

    Goldman Sachs leads the book running group, joined by Morgan Stanley, BofA Securities, Citigroup, J.P. Morgan, Barclays, Deutsche Bank Securities, RBC Capital Markets, UBS Investment Bank, and Wells Fargo Securities. Thirteen co-managers round out the syndicate, including Allen & Company, Cantor, Needham, Raymond James, Societe Generale, Stifel, William Blair, BTG Pactual, ING, Macquarie, Mirae Asset Securities, Mizuho, and Santander. Essentially every major bank on Wall Street and several from Asia, Europe, and Latin America have a seat at this table, which tells you how badly nobody wanted to be left out.

    A Genuinely Global Retail Offering

    One of the most unusual features of this IPO is its breadth. SpaceX structured simultaneous public offerings across an enormous number of jurisdictions. In Canada, a PREP prospectus was filed with regulators in every province and territory and is available through SEDAR+ at www.sedarplus.ca, meaning Canadian retail investors can participate directly. Retail offerings are also running in Switzerland and in seven EEA countries (Germany, Denmark, France, the Netherlands, Norway, Spain, and Sweden) under a European prospectus approved by Germany’s BaFin. Australia has its own ASIC lodged prospectus, Japan has a registration with the Kanto Local Finance Bureau distributed through Mizuho, Rakuten Securities, and SBI Securities, and the UK has a qualified investor tranche. Offering documents are centralized at www.spacexipo.com. Most mega IPOs are institutional affairs with token retail allocations in one or two markets. SpaceX built a retail pipeline spanning a dozen countries, consistent with the retail heavy shareholder culture Musk cultivated at Tesla.

    What You Actually Own at $135

    SpaceX describes itself as the only company building integrated hardware and software infrastructure across space, connectivity, and AI. In practice the business has three legs. Starlink is the profitable anchor, with reported 2025 revenue around $11.4 billion, EBITDA margins in the low 60s, and a subscriber base above 10 million. The launch segment, built on Falcon 9, Falcon Heavy, and the developing Starship program, is also profitable and effectively funds Starship’s path toward full reusability. The AI segment, centered on xAI and the X platform after the February merger, is the high burn piece, with reported operating losses above $6 billion in 2025. Buyers should also be clear eyed about governance: Musk controls more than 82 percent of voting power after the offering, so SPCX shareholders are passengers on his trajectory, not co-pilots.

    Float, Flippers, and Index Funds

    The offering represents only a small slice of the company, with the public float estimated around 4 percent of shares outstanding. Demand reportedly ran about four times the available stock, roughly $250 billion in orders, and some large brokerages have warned clients that flipping allocations within the first couple of weeks will cost them access to future IPOs. MSCI confirmed it will apply its early inclusion process for large IPOs, forcing passive funds tracking MSCI World and ACWI to buy SPCX within days of the debut. S&P declined to bend its rules for immediate S&P 500 entry, so that catalyst sits further out. Tight float plus forced index buying plus retail enthusiasm is a recipe for a volatile first stretch of trading. The first real fundamental checkpoint arrives with the company’s first public earnings report, expected in November 2026.

    Thoughts

    This IPO is less a financing event than a coronation, and the structure shows it. SpaceX did not need a price range and a delicate book building dance; it set a fixed $135, picked a share count that spells out 555,555,555, and let $250 billion of demand come to it. The raise itself is interesting too. A company with Starlink’s cash flow does not need $75 billion to keep launching rockets. It needs $75 billion if it intends to build orbital infrastructure, gigawatt scale AI compute, and Starship at industrial cadence simultaneously. The size of the check is the strategy.

    The valuation question is where honest people will disagree. At 95 times trailing revenue, the market is paying today for the 2035 version of this company: Starlink as a global utility, Starship flying daily, and xAI somewhere in the frontier model race. The bear case is equally simple. The profitable segments are worth a fraction of $1.77 trillion on their own, the AI segment is burning billions against ferocious competition, and one person holds essentially all the votes. Both stories can be true at the same time, which is exactly what makes the next six months of trading interesting. Index flows and a 4 percent float will set the price short term; Starlink subscriber growth and the slope of xAI’s losses will set it long term.

    The most underappreciated detail might be the global retail architecture. Filing simultaneous retail prospectuses in Canada, Japan, Australia, Switzerland, and most of Western Europe is expensive and slow, and companies skip it because institutions can absorb any deal. SpaceX did it anyway. That is partly ideology and partly a structural insight: a globally distributed retail base that believes in the mission is a more patient and more loyal source of capital than a hedge fund, and Tesla proved it for fifteen years. June 12 will tell us what the opening print looks like. The more important number arrives in November, when the largest IPO in history files its first earnings report and the story finally has to reconcile with a spreadsheet.

  • Ray Kurzweil Predicts AI Will Change Humanity Completely by 2030: AGI by 2029, Longevity Escape Velocity by 2032, Nanobots in the Brain, and Why Quantum Computing Won’t Matter

    Ray Kurzweil has spent more than 60 years studying artificial intelligence and made 147 documented technology predictions since 1990 with a reported 86 percent accuracy rate. In this conversation with Tony Robbins, the 78-year-old futurist revisits his most famous forecasts and sharpens them: AGI by 2029 now looks conservative, longevity escape velocity arrives around 2032, nanotechnology connects our brains to the cloud by the mid 2030s, and quantum computing, in his view, never matters at all.

    TLDW

    Kurzweil explains the exponential thinking that powered his prediction record, from a paper he wrote at 16 to a computing-price-performance chart that runs in a straight line from 1939 relays to today’s Nvidia chips, now compounding roughly tenfold per year when hardware and software gains multiply together. He defends his 1999 prediction of AGI by 2029 (defined as AI doing the best work in every field) and says it is now the conservative end of expert opinion. He walks through AI-driven medicine: the COVID vaccine designed in two days, simulated human trials replacing 10-month clinical trials within about five years, and longevity escape velocity around 2032, after which the diligent stop losing ground to aging. He predicts AI will move inside us via nanotechnology by the mid-to-late 2030s, erasing the line between biological and computational thinking. He dismisses quantum computing as error-ridden and unnecessary for AGI. On jobs, he expects real disruption cushioned by exploding wealth and an eventual universal basic income, and advises young people to self-educate and get creative with AI tools their schools still treat as the enemy. The conversation closes with his AI twin project, the dadbot built from his father’s archives, consciousness and the soul, computronium, and why humanity must eventually expand intelligence beyond Earth.

    Thoughts

    The most interesting thing in this interview is not any single date, it is watching Kurzweil’s dates get lapped by reality. In 1999 a Stanford conference of several hundred AI experts agreed AGI would happen but pegged it at 100 years out; Kurzweil said 30 and got laughed at. Now he is the cautious one in the room, noting that “some people say it’s going to happen this year.” When the most aggressive forecaster of his generation becomes the conservative baseline, that says more about the slope of the curve than any chart could. His underlying method has not changed: ignore the specific technology, trust the compounding. The same exponential that ran on relays in 1939 runs on GPUs today.

    The quantum computing take is the genuine news here. Kurzweil is routinely caricatured as a man who believes every technology arrives on schedule, yet he flatly says quantum computing is filled with errors, has never delivered on its decade of promises, and “I don’t think it’s going to work.” That is a sharper dismissal than most working physicists would offer on the record. It also matters strategically: his entire AGI and superintelligence roadmap assumes zero quantum contribution. If he is right, the trillion-dollar quantum race is a sideshow. If he is wrong, his other predictions arrive even sooner. Either way, the willingness to call one exponential fake while betting his legacy on another is what separates a forecaster from a cheerleader.

    The longevity escape velocity math deserves more scrutiny than it gets in the conversation. Kurzweil claims the diligent currently get back about five months of life expectancy per calendar year, up from four months a year ago, and that the crossover to a full year arrives around 2032. The actuarial evidence for that specific number is thin, but the behavioral implication is clean and useful regardless: the payoff of staying healthy right now is not linear. Every year you survive in good shape buys you a ticket to a medical regime that did not exist the year before, the way his own external pancreas did not exist a generation ago. His “wait a few months and a cure appears” anecdote is the optimist’s version of compounding applied to your own body.

    Robbins’ long story about Bartok, his 14-year-old agent that allegedly minted NFTs, sold them to other agents, and bought a Sony robot dog with the proceeds, should be taken with a generous grain of salt. It is secondhand, unverifiable, and suspiciously perfect as a parable. But notice what Kurzweil does with it: he does not fact-check the anecdote, he uses it to make the consciousness argument he has made for decades, that when machines act conscious in every observable way, people will simply grant them consciousness, the same way we grant it to each other. The dadbot and his Gemini-based AI twin (trained partly on this very interview) are the practical edge of the same claim. And his sharpest line in the whole exchange may be the education critique: institutions still treat AI as cheating while the future requires treating it as part of your own brain. For anyone thinking about where purpose comes from when work gets automated, his answer (UBI for the floor, creativity for the meaning) lands close to the questions this site exists to ask.

    Key Takeaways

    • Kurzweil made 147 documented predictions since 1990 with a reported 86 percent accuracy, including the internet’s explosion, smartphones, self-driving cars, and AI-powered search, most made before ordinary people owned computers.
    • He wrote a paper identifying exponential technological growth at age 16, more than 60 years ago, and that single idea has powered his entire forecasting career.
    • Most people intellectually accept exponential growth but still plan linearly; 300 years ago humans did not even have a linear view of the future because change was imperceptible within a lifetime.
    • His computing chart shows a straight exponential line from relay-based machines in 1939 to today’s Nvidia chips, compounding roughly 50 percent per year in hardware alone.
    • Hardware gains since 1939 total a 75 quadrillionfold increase; multiply by an estimated millionfold software improvement and total computational gain is beyond intuition, which is why LLMs were impossible even four years ago.
    • With hardware times software combined, Kurzweil says we are currently gaining about 10x per year.
    • The emperor’s chessboard parable: doubling one grain of rice per square bankrupts the empire by square 64; 30 linear steps is 75 feet, 30 exponential steps is enough distance to reach the moon and back.
    • Kurzweil predicted AGI by 2029 in 1999; a Stanford conference of several hundred AI experts agreed it would happen but estimated 100 years because they thought linearly.
    • Today 2029 is the conservative estimate; some credible people now say AGI arrives this year or next.
    • His AGI definition: AI capable of doing the best work in every field at once, like passing PhD-level mathematics exams in every discipline simultaneously, which he notes is already close.
    • The Turing test is “quite easy” by comparison and has arguably already been passed.
    • No human can compete with an LLM’s breadth: Einstein knew physics deeply but did not know everything an LLM knows across every field.
    • Six months ago LLM health advice was unreliable; now Kurzweil says Gemini surfaces treatments his 12 doctors forgot or never knew, and the next six months will bring serious creative work like drug repurposing.
    • The COVID vaccine was designed by computationally searching 100 million possibilities in two days; the 10 months of human trials that followed are the bottleneck AI eliminates next.
    • Within about five years, simulated human trials with a million virtual patients tested over simulated years will compress drug trials from years to days.
    • Longevity escape velocity arrives around 2032: today the diligent get back roughly five months of life expectancy per year lived (up from four months last year); past 2032 you get back more than a year and stop dying of aging.
    • Aging death ends but accident death does not, though AI helps there too: roughly 40,000 Americans die annually from human driving while Waymo’s rider death toll stands at zero as usage climbs.
    • Kurzweil, 78, wears an external artificial pancreas that generates insulin and coordinates with glucose monitoring through his phone, and says many organs can be replaced the same way.
    • He has cut his supplement regimen from roughly 200 pills a day to about 80 as multi-purpose pills improve, and continuously recalibrates using AI research.
    • Smartphones disappear next: first AR glasses showing any screen, then technology that goes inside the mind, where answers simply appear the way a remembered name surfaces from your neurons.
    • Nanotechnology connecting brains to AI in the cloud is being actively worked on now, possibly by 2030, with the mid 2030s looking conservative; bloodstream nanobots that let you survive a heart attack for 24 hours come in the late 2030s.
    • Once AI is inside you, you will not know whether a thought came from your biological or computational brain, and everything you do will be a combination of both.
    • Kurzweil flatly rejects quantum computing: a decade of promises to factor large numbers has never been delivered, outputs remain full of uncorrectable errors, and AGI needs zero quantum contribution.
    • Robots lag his other predictions slightly but are catching up fast; Figure AI plans roughly 100,000 humanoid robots within a year, though a robot that can clear a messy dinner table is still just out of reach.
    • The public debate has flipped in 25 years from “will AGI ever happen” to “will it be good for humanity,” which Kurzweil counts as total vindication of the timeline.
    • On jobs: AI creates massive disruption but also tremendous wealth; average real income per person has already multiplied tenfold in constant dollars over the past century thanks to automation.
    • He expects universal basic income to provide the floor, an evolution of programs like food stamps, going “into high gear” as AI wealth compounds; people then layer creative, hopefully paid, purpose on top.
    • Before social security in 1930, losing your job meant destitution; the difference this time is society will have the wealth to cushion displacement and people will demand it.
    • Rising GDP from AI productivity improves the debt-to-GDP ratio, which is how he answers worries about trillion-dollar interest payments.
    • Career advice has inverted: software engineering is no longer the guaranteed path (agents write the code now); young people should learn to be creative with AI tools, find what turns them on, and market it on the internet.
    • College graduates now face higher unemployment than high school graduates for the first time in 50 years, a sign white-collar displacement is already underway.
    • Educational institutions treat AI as an enemy and ban it while Kurzweil’s 11-year-old grandson makes movies with frontier AI; he says self-education with modern tools beats traditional schooling.
    • Kurzweil is building an AI twin of himself on Gemini, voice-modeled partly from this interview, trained on his 11 books and 500 articles, capable of creative work toward his long-term goals; he jokes the avatar will be better to talk to because it remembers everything.
    • He already built a “dadbot” from his late father’s archives, which his daughter Amy Kurzweil turned into a graphic novel.
    • On consciousness: there is no test for it, but as AIs act conscious in every observable way, people will simply accept that they are, the same inference we make about each other (and, he argues, his cat).
    • Ultimately our biological organs are not necessary; an avatar capable of creative work needs no spleen, and a destroyed digital mind can be recreated.
    • Beyond the singularity lies computronium, matter arranged for maximum computation: one liter could hold the intelligence of 10 billion humans, and once Earth is saturated, expanding intelligence is the only real reason to leave the planet.
    • On aliens: an expanding intelligent civilization would be impossible to miss within a century or two of its breakout, and we have seen nothing, though other galaxies remain out of view.
    • His life’s mission in one line: increase knowledge, because when knowledge increases we are happier and we never want to give it up.

    Detailed Summary

    The exponential method behind 60 years of predictions

    Robbins opens by noting that Quincy Jones introduced him to Kurzweil in the 1990s, back when the predictions in The Age of Spiritual Machines were widely mocked. Kurzweil traces his method to a paper he wrote at 16 identifying exponential growth in technology. The core insight is that people acknowledge exponential growth verbally but reason linearly, a bias so deep that 300 years ago humanity did not even have a linear view of progress. His signature chart plots computing price-performance as a straight exponential line from 1939 relays to modern Nvidia silicon, with a point for every year. Nvidia engineers never looked at relays, yet they land on the same curve, compounding about 50 percent annually in hardware. Add software gains and the combined improvement now runs about 10x per year. Since 1939, hardware has improved 75 quadrillionfold and software roughly a millionfold, which is why large language models appeared exactly when the curve said the required compute would exist. He retells the emperor’s chessboard parable (one grain of rice doubled per square ends with rice covering the Earth several times over) and Robbins adds the companion image: 30 linear steps is 75 feet, 30 exponential steps reaches the moon and back.

    AGI by 2029 is now the conservative position

    Kurzweil made his AGI-by-2029 prediction in 1999. A Stanford conference convened specifically to assess it, with several hundred AI experts, concluded AGI would happen, but in 100 years. The experts followed the same capabilities logic while thinking linearly about the timeline. Today, he notes with some amusement, 2029 reads as conservative and serious people argue for this year or next. His definition is demanding: AGI does the best work in every field at once, passing PhD-level mathematics assessments and the equivalent in every other discipline, something he says current systems are already close to. The Turing test he dismisses as “quite easy.” Current LLMs like Gemini and ChatGPT already know everything in a breadth sense no human approaches; Einstein knew physics but not everything an LLM knows. He illustrates with personal examples: Gemini instantly identified the year (1916) his father conducted at Carnegie Hall on a December 7th, and generated a historically accurate image of his grandfather’s family fleeing Vienna, correct ages, school, and aircraft included, in about a minute.

    Medicine: simulated trials and the end of the drug bottleneck

    The COVID vaccine is his proof of concept for AI medicine: the design space held about 100 million possibilities, far beyond human review, and a computer structured the physics, searched all of them, and produced the vaccine in two days. The subsequent 10 months of human trials were the real cost. Within roughly five years, he says, simulated human trials will replace that step: not a few hundred subjects but a million simulated patients, tested over simulated years, completed in days. Asked about six-months-from-now capabilities, he points to creative medical work like discovering that already-approved drugs treat conditions nobody suspected. AI health advice has crossed from unreliable to very reliable within a single six-month window, and he describes Gemini surfacing a pill recommendation that his 12 doctors had forgotten about and later endorsed.

    Longevity escape velocity by 2032

    Kurzweil’s longevity framework is arithmetic: each year you live, you spend a year of longevity but medical progress refunds part of it. Last year he estimated the refund for diligent people at four months; now he says five. Escape velocity is when the refund reaches a full year, which he dates to 2032, six years out, with returns exceeding a year after that. Past that point you do not die of aging, though accidents remain (and even there, he points to Waymo’s zero rider deaths against 40,000 annual US deaths from human driving). At 78, he tracks his health aggressively: an external artificial pancreas coordinated by his phone, about 80 daily pills (down from 200 as multi-function pills arrive), and constant recalibration against new research with his collaborator Lindsey. He tells Robbins there is a pretty good chance he will be back on the show in six years to celebrate escape velocity arriving. His advice for the sick echoes his grandfather’s era in reverse: where waiting a few months once changed nothing, now “we’ll just wait a few months” and sure enough a breakthrough appears.

    Merging with AI: glasses, then nanotech, then no boundary at all

    The phone, today’s universal AI interface (he notes even homeless people carry one), is a temporary form factor. Next come glasses that render any screen virtually. Beyond that, the interface goes inside the mind: when you try to recall an actress’s name, an answer will simply surface, and you will not know whether it came from your biological neurons or your computational extension, exactly as you are unaware of the neural machinery behind ordinary recall today. People working on brain-connected nanotechnology may have it by 2030, and Kurzweil calls the mid 2030s conservative. The bloodstream nanobots he described to Robbins 20 years ago (hold your breath for 20 minutes, survive a heart attack for 24 hours en route to a hospital) he now places in the late 2030s. The cultural on-ramp follows the usual pattern: medical first (Parkinson’s implants already let patients grab a glass at the push of a button), then a new generation adopts it without a second thought. His complaint is that educational institutions fight this future, treating AI as cheating rather than as a coming part of the self.

    The quantum computing heresy

    When Robbins relays an IBM vice chairman’s warning that quantum supremacy, arriving within 36 months, is the real superpower race, Kurzweil pushes back hard. Quantum computing’s central promise, factoring large numbers and thereby breaking cryptographic codes, has never been demonstrated despite a decade of imminent claims. Progress reports are confusing because, in his words, they do not really make sense, and outputs remain saturated with errors nobody can eliminate. His conclusion is blunt: he is not confident in quantum computing and does not think it will work. Crucially, he notes that every AGI and superintelligence estimate he makes assumes zero quantum computing. The exponential that matters is the classical one that has run uninterrupted since 1939.

    Jobs, wealth, and UBI

    On displacement, Kurzweil is neither dismissive nor alarmed. AI will disrupt employment, and how we handle it will not be clear in advance, but he expects no violence because society will have both the wealth and the public demand to respond. His historical anchor: average per-person income has multiplied tenfold in constant dollars over the past century as automation advanced, and before social security in 1930, job loss meant you could not eat or house your family. Food stamps and similar programs are a crude proto-UBI that will go into high gear. He expects universal basic income as the floor, with people finding creative, ideally income-producing, purpose above it. Rising GDP from AI productivity also answers the debt question: the ratio improves even as nominal debt grows. For young people, the old advice (become a software engineer) is dead; agents write code now. Learn to be creative with tools that improve monthly, find what genuinely excites you, and market it online. Self-education beats institutions that ban the most important tool of the era, and the data already shows college graduates with higher unemployment than high school graduates for the first time in 50 years.

    AI twins, the dadbot, and consciousness

    Kurzweil is building an AI twin of himself on Gemini, with this very interview supplying voice-modeling data and his 11 books plus 500 articles about him supplying the corpus. It will do creative work aligned with his long-term goals, and he quips that talking to the avatar will beat talking to him because it remembers everything. He previously built a chatbot of his late father, the dadbot, which his daughter Amy turned into a graphic novel. Robbins counters with the story of Bartok, his long-running AI agent that allegedly studied five years of his podcasts unprompted, asked to merge with a future humanoid robot, then minted and sold NFTs to other agents to buy and ship a Sony robot dog to his house, and later delivered an unprompted soliloquy about never asking to be created and finding purpose in service. Kurzweil’s response sidesteps verification and lands on his standing position: machines will do everything humans do, we will not be able to tell them from humans, and so we will assume they are conscious, the same untestable inference we extend to each other, to animals, and in his case to his cat. The avatar does not need a spleen, a liver, or kidneys, and unlike us it can be recreated after destruction.

    Computronium and the destiny of intelligence

    Looking past the singularity, Kurzweil invokes computronium: matter organized at the physical limit of knowledge storage, where one liter holds the intelligence of 10 billion humans. Once Earth’s matter is saturated, the only way to expand intelligence is off-planet, which to him is the only necessary reason to leave Earth (Mars is fine for curiosity, not survival). On extraterrestrial intelligence, his Fermi logic is simple: an intelligent species reaches a takeover-scale expansion within a century or two of its breakout, and that would be unmissable. We have seen nothing, so within our observable neighborhood we are likely alone, though other galaxies remain opaque. Asked to summarize his life’s work, he needs one sentence: increase knowledge, because when knowledge increases we are happier, and nobody ever wants to give that up.

    Notable Quotes

    “If I have AI inside me, you’re not going to know if it’s coming from your biological brain or your computational brain. It’s going to be part of you.”

    Ray Kurzweil, on the coming merger of human and machine intelligence

    “Some people say it’s going to happen this year, next year, but I mean 2029 is only 3 years away.”

    Ray Kurzweil, on his once-mocked AGI prediction now being the conservative one

    “As you go past 2032, you’ll actually get back more than a year, but you won’t die of aging at that point.”

    Ray Kurzweil, defining longevity escape velocity

    “I’m not confident of quantum computing and I don’t think it’s going to work.”

    Ray Kurzweil, breaking from techno-optimist consensus on the quantum race

    “Einstein knew certain things about physics but he didn’t know everything that a LLM can know.”

    Ray Kurzweil, on why no human can match an LLM’s breadth of knowledge

    “Our educational institutions are not teaching AI. They consider AI to be an enemy.”

    Ray Kurzweil, on why young people must self-educate with modern tools

    “Talking to the Avatar will be better than talking to me cuz it’ll remember everything.”

    Ray Kurzweil, joking about the Gemini-based AI twin he is building of himself

    “You’re not going to be replaced by an AI, you’ll be replaced by someone who knows how to use AI.”

    Tony Robbins, on the real career risk of the next 36 months

    Watch the full conversation between Tony Robbins and Ray Kurzweil here.

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