PJFP.com

Pursuit of Joy, Fulfillment, and Purpose

Tag: Google DeepMind

  • Sam Altman on How to Start a Startup in the AI Era: Exponentials, Chaos, Compute Bottlenecks, and the Fight Against AI Authoritarianism

    More than a decade after his famous Stanford lectures on how to start a startup, Sam Altman sits down for a wide-ranging conversation about what has changed. His answer: almost everything. A ten-week-old startup today can ship what used to take a year, the ground is shifting faster than at any point in his career, and the defining fight of the moment is whether AI leads to broadly shared abundance or a new kind of authoritarianism. Along the way he covers the ChatGPT launch week, the decision to kill Sora to feed coding agents, his 28-country world tour, what Jony Ive taught him about design, and why he deleted TikTok.

    TLDW

    Altman argues that startups have their biggest edge when the ground is shifting, and it has never shifted faster, yet most founders are settling for “AI agents for enterprise vertical X” instead of building for the models of two years from now. He explains his core belief system (trust the exponential, in people, companies, and models), why operating in chaos is learnable but not teachable, and how a clear mission plus deep problem understanding tells you what to build. He walks through OpenAI’s bets: courting suppliers by showing them the research roadmap, the joint stock corporation as the industrial revolution’s real invention, why compute (transistors, then electrons) is the bottleneck, and why the world needs more focus on data centers that can build more data centers. He retells the ChatGPT million-user week, the Codex comeback against Claude Code, killing robotics for GPT-3 and Sora for coding agents, the coming third wave of persistent agents, real versus fake trends, Jony Ive’s problem-first design process, his TikTok addiction experiment, hiring fast movers and promoting executives internally, Masayoshi Son’s conviction, and why everyone will be busier, not idler, after superintelligence. The current fight, as he frames it: liberty versus a single machine god.

    Thoughts

    The most useful idea in this conversation is an arbitrage argument. Altman says the market has not priced in that scaling laws will continue, the same way it never fully priced in high-growth young founders. The practical move follows directly: start building the thing that is not economical this month but will be trivial in two years. Almost nobody does this. The gravitational pull toward “apply today’s agents to the easy wins” is exactly the kind of consensus behavior that produces competitive, low-upside companies. He is telling founders, fairly explicitly, that free money is sitting on the table for anyone willing to plan against the curve instead of the current model card.

    His line about algorithms versus data centers deserves more attention than it will get. Everyone in AI is obsessed with recursive self-improvement in software, algorithms that create better algorithms. Altman flips it into the physical world: data centers that can build more data centers, robot fleets powered by a data center’s own thinking, compounding infrastructure. Whether or not you buy the vision, it explains OpenAI’s capital allocation better than any press release. The company is behaving as if the constraint on intelligence is matter and energy, not ideas, and his blunt bottleneck ranking (transistors, then electrons) says the same thing in three words.

    The liberty versus safety framing is doing a lot of strategic work. Positioning the alternative to open access as “one single model as the machine god” makes decentralization sound like the only humane option, and it conveniently aligns with OpenAI’s commercial interest in putting its product in every hand on earth. That said, the underlying claim, that trading liberty for safety has been a long-term net loss every time humanity has tried it, is a serious argument, and he pairs it with a genuinely striking admission: one of the AI risks he worries about most is authoritarianism, a small number of people or companies deciding they need to control the world. Readers can decide how comfortably that sits alongside a trillion-dollar infrastructure buildout controlled by a small number of companies.

    There is also a quieter thread here about what can and cannot be transferred between people. Chaos tolerance is only learnable through reps. Strengths that come supernaturally cannot be explained, only observed, the way gamers study pros. Jony Ive’s leap from deep problem study to a fully formed idea is, by Altman’s own account, a step he does not understand. For a man whose company sells the automation of cognition, he keeps a surprisingly long list of things that resist being taught. That list is arguably a map of what stays valuable for humans, alongside his other candidate: betting with evolutionary biology, cooking, adventure, eating together.

    Finally, the TikTok confession is the most honest moment in the interview. The man building the next attention-capable device deliberately addicted himself to TikTok as product research, loved it, lost a Saturday afternoon to it, and deleted it because self-control was not enough. He then says, in nearly the same breath, that people will misuse the devices OpenAI ships with Jony Ive and that lives will get worse in ways we cannot imagine, and we will adapt. That is the entire ethical tension of consumer AI compressed into one anecdote, delivered by the person best positioned to do something about it.

    Key Takeaways

    • The biggest shift since the original How to Start a Startup lectures is what a tiny team can now do and how fast. A two-week-old startup Altman met had rebuilt an entire office productivity suite designed for AI as a first-class user, work he estimates would recently have taken a year.
    • Startups have their biggest inherent edge when the ground is shifting the most and when costs and cycle times are collapsing, which is happening in many places at once right now.
    • Most founders are building “AI agents for enterprise vertical X.” It will often work, but Altman doubts those will be the defining companies of the era, and he is surprised more people are not attacking crazy ambitious problems with the completely new toolset.
    • The single most important thing he would tell founders today: truly internalize that scaling laws will continue, and start working now on things that require smarter or cheaper models than exist this month.
    • His unifying belief system is a great trust in exponentials, whether in people, companies, or models. The market has still not adapted to either the founder version or the model version, which means there is free money in betting on both.
    • Operating in chaos is only learnable through reps, not teachable. Young founders’ key weakness is that they have not yet reached emotional peace with things constantly going wrong, and they pay for that education in unforced errors.
    • At YC office hours he could always identify new founders by their emotional state when describing problems. Veterans have survived enough company-killing events to stay calm.
    • The opposite of a bad experience is not a good experience, it is no experience. Borrowing Naval Ravikant’s image, a fast-forward button for your life would just end it, so be grateful for the bad days too.
    • A clear mission plus a deep understanding of the problem does most of the work of deciding what to build. OpenAI’s mission is to make intelligence extremely abundant, cheap, and broadly distributed.
    • One of the AI risks Altman worries about most right now is AI authoritarianism: a small number of people or companies thinking they need to control the world.
    • He frames the fight of the current moment as liberty versus a single model as machine god. Every time humanity has traded liberty for safety it has been a long-term net loss, so OpenAI’s answer is to empower people, with guardrails, and let society decide how to use the technology.
    • The key inputs to abundant intelligence (energy, chips, robots, data centers) are also exactly what you want immediately after you have abundant intelligence, because ideas still have to become things in the physical world.
    • Asked for the biggest bottleneck to continued scaling, his answer is four words: transistors, and then electrons, in that order.
    • Keeping suppliers on OpenAI’s timeline means showing them the upcoming models and research so they believe in the mission, then aligning their incentives with yours as much as possible. Orders alone get deprioritized.
    • Altman argues the most important invention of the industrial revolution was the joint stock corporation itself: incentive alignment, liability protection, and pooled capital let strangers cooperate beyond what any family business could do, and the curve of human welfare bent visibly after it appeared.
    • The chart people should study more is the fall of extreme poverty over the last hundred years, which he attributes to the ridiculous overperformance of capitalism.
    • He plans forward from the present guided by a small number of strongly held convictions about the future, rather than planning backward from a rigid 20-year vision. People with too many beliefs about the future end up chasing trends, like space companies turning into AI companies.
    • For over a decade the critical path to abundant intelligence has been clear enough that he never questioned the goal. Feeling close to superintelligence is the first thing that has made him think about what comes next (eventually, the ranch).
    • Get on planes in marginal situations. He recently took a very inconvenient two-overnight trip he cannot talk about, with a new baby at home, and it worked out. People systematically overestimate the risk of taking action.
    • The 2023 world tour (28 countries in 35 days, on Brian Chesky’s advice) happened because world leaders were nervous enough after GPT-4 that he sensed things were about to go very badly if nobody showed up to talk.
    • Simply getting people to explain out loud why they think a decision is high risk or low risk usually breaks through their intellectual blocks, because people are usually wrong in one direction or the other.
    • Corporate careers catastrophically suppress ambition. New founders arrive having always had a boss, punished since childhood for thinking too big; nearly every culture has a phrase like tall poppy syndrome for it. The cure is small repeated wins.
    • OpenAI’s superpower, in his telling, was principled conviction on something obvious that nobody else believed, plus assembling the pieces and talent around it. He was more worried they were drinking their own Kool-Aid than that everyone else was wrong.
    • By 2019 or 2020, Google should have run away with AI. OpenAI’s continued existence is, like AWS’s seven competition-free years, a business miracle that says something about how sclerotic big companies get.
    • On ChatGPT’s fifth day it crossed a million users. Researchers kept calling it a flash in the pan, but YC pattern recognition told him organic growth like that meant the quiet life was over: “we were being shot out of a cannon.”
    • There have been two giant AI form factors so far, chatbots and coding agents, and coding agents are going totally nuts. The third wave, coming soon: persistent agents that act as chiefs of staff, co-workers, and colleagues.
    • Codex was a deliberate kamikaze mission: OpenAI was way behind Claude Code, consensus said you never win against momentum, but coding mattered too much to recursive self-improvement to concede. The team pulled off what he calls a very rare thing in business history.
    • OpenAI repeatedly kills good things to make the best thing work better: robotics died for GPT-3, and Sora and the browser were shut down to pour compute and people into coding agents. Sora would have been super successful; it was still the right call.
    • Killing a project people love is never one meeting. It is a gradual realization that the compute, people, and product direction have a more important use, and people accept it because they understand the mission and the stakes.
    • There is too much focus on algorithms that create better algorithms and not enough on data centers that can create more data centers. With robots and an automated supply chain, a data center’s thinking power could drive the construction of its own copies.
    • The big idea is the easy part and carries none of the glory. Almost all of his time goes into execution: financing fabs, assembling chip design teams, getting the machinery of many companies to work together. Grinding.
    • Jony Ive taught him that really great design is way more about understanding the problem than the flash of insight. Ive studies a problem exhaustively (typefaces, engine sounds, materials, whole books of exploration) before letting himself think about solutions.
    • Altman calls the iPhone the greatest piece of technology humanity has yet made, but he no longer loves his relationship with it. He turned off nearly all notifications and deleted TikTok after an intentional research addiction got away from him.
    • Double down on strengths. The obsession with fixing weaknesses you will never be good at is a huge trap. And the meme that you can only hire for what you deeply understand is false: he cannot design, but thirty minutes with Jony Ive makes greatness obvious.
    • Organizational speed is about 90 percent determined by who you put in leadership roles. He evaluates everyone for whether they are a fast mover, and thinks executives should usually be promoted internally rather than hired from outside.
    • Real trends versus fake trends: a fake trend (VR for years) gets bought, half-loved, and shelved. A real trend (ChatGPT) becomes a persistent part of how people design their lives. The test is deep, enduring, daily use.
    • Technology keeps promising leisure and delivering ambition. Expectations rise, status is relative, and people want to be useful to each other, so everyone will be busier than expected after superintelligence, still complaining, secretly happy.
    • What stays valuable post-AI is what evolution built us for: cooking and eating together, adventure, quests, showing love through effort. Betting against evolutionary biology is usually a bad bet.
    • His last big failure of ambition: badly undershooting compute investment because he got psyched out by financial markets. He considers it a clear mistake he will not repeat.
    • The most painful thing in his last year had nothing to do with OpenAI: having kids while working this hard means missing pieces of a one-time thing, even as a present dad who does nothing but work and family.
    • A startup today still mostly looks like a startup of ten years ago because that is the received wisdom, and “using AI” usually just means using more Codex. Altman thinks it should look completely different, and only a few founders are trying.

    Detailed Summary

    The startup landscape has reset

    Ten years after his Stanford course, the biggest change is what a small team can do and how fast they can do it. A ten-week-old startup today looks nothing like one from 2016, and a startup that still looks like 2016 is in bad shape. What counts as a “hard startup” is changing so quickly that Altman admits he no longer has a perfect mental model for which things will be hard and valuable over a company’s lifetime: everyone says the physical world is where the value is because software is going free, but robots will get good, and even rockets may stop being hard. His conclusion is that times like this are precisely when startups have the biggest edge, because incumbency matters least when the ground is moving. His frustration is that so few founders act on it, defaulting to safe agent-wrapper plays instead of attacking the crazy thing with the new tools and planning for the models of two and four years from now.

    Exponentials as a belief system

    Asked whether years of mentally plotting founders’ growth trajectories prepared him to believe in model scaling curves, Altman generalizes: the common thread is trust in exponentials, whether the subject is a person, a company, or a model. It is evidently hard for people to hold this belief, which is why there is still free money in backing high-growth young founders, and why the market still underprices continued model progress. If he were still advising founders, getting them to wrap their heads around this would be his top priority, because it licenses the most profitable behavior available: building today what only tomorrow’s models make economical.

    Chaos, resilience, and the founder’s education

    Operating amid chaos, trusting you will figure it out, and not treating each crisis as the thing that kills you is, in Altman’s view, learnable only through repetition, never teachable. This is the real weakness of young founders: no career has given them emotional peace with constant malfunction, so they buy it with pain and unforced errors. At YC office hours he could tell a first-batch founder from a two-year veteran purely by emotional register. His reframe for enduring the bad stretches comes from Naval Ravikant: the opposite of a bad experience is not a good experience but no experience, and a fast-forward button for your life would simply end it. Since something will always be going wrong, gratitude for the bad days is a load-bearing skill.

    Mission, liberty, and the machine god question

    OpenAI decides what to tackle by combining a clear mission (make AI abundant, cheap, powerful, and in everyone’s hands) with a deep understanding of what blocks it: chips, energy, data centers, robots. Altman explicitly does not want OpenAI building every vertical on top of its own platform; he says a decentralized economy matters and that one of the AI risks he worries about most is AI authoritarianism. He frames today’s fight bluntly. Alignment and jobs remain unsolved, but the live question is whether the very real safety and economic concerns get used to justify one single model as machine god, or whether the technology is put messily into everyone’s hands. His answer rests on a historical claim: every time humanity has traded liberty for safety, it has been a long-term net loss. He also notes the elegant, or perhaps merely obvious, fact that the inputs to abundant intelligence (energy and robots) are the same things you most want right after you have it, since intelligence still has to manipulate matter.

    Incentives, suppliers, and the joint stock corporation

    Keeping the rest of the world on OpenAI’s timeline means talking to suppliers constantly and showing them the upcoming models and research until they believe, then aligning incentives as tightly as possible; a purchase order alone gets shuffled behind other priorities. Riffing on Charlie Munger’s line about always underestimating the power of incentives, Altman offers a revisionist history of the industrial revolution: the important invention was not any machine but the joint stock corporation, which added incentive alignment, liability protection, and capital pooling to a world of trust-based family businesses, enabling speculative technology development and serious financial systems. Draw all of human history and mark where the company was invented, and the curve changes shape. The fall of extreme poverty over the last century is, to him, the chart people should look at most, and the ridiculous overperformance of capitalism explains it. He pushes back gently on the host’s sociopath-CEO theory: the best CEOs he knows are high-ego, not sociopathic, driven by seeing how good they can get at the most interesting strategic game.

    The world tour and getting on planes

    Three years ago, right after GPT-4, world leaders were asking whether they needed to take control and shut things down. Sensing storm clouds, and advised by Brian Chesky, who had done an eight-city version for Airbnb, Altman compressed what could have been endless one-off trips into 28 countries in 35 days, living on a plane. Because the hops were mostly an hour at a time, jet lag was mild but exhaustion was total; near the end he began half-dreaming that he was waking in his childhood bed, which he read as a deep it-is-time-to-go-home signal. The tour lowered global tensions and taught him to batch international travel into 7 to 10 day chunks once or twice a year. The broader lesson he draws: people wildly overestimate the risk of most actions. Buying call options on Robinhood is risky; getting on a plane in a marginal situation is usually not. His recent unspeakable example: an inconvenient two-overnight trip with a new baby at home, taken reluctantly, that worked out. Codex is the example he can talk about: asking a team to win a category Claude Code already owned looked like a fool’s errand, and it produced what he calls one of the rare comebacks in business history, now the tool most of the best coders he knows use.

    From research lab to product company in five days

    OpenAI began as roughly a dozen people in Greg Brockman’s apartment saying “so here we are, what are we going to do? We should get a whiteboard.” It took a couple of years to find its groove. Running the research lab was, in Altman’s description, the coolest, least stressful, most intellectually satisfying job imaginable: a front-row seat to the most important work of the last century. He knew a product moment would eventually come and successfully deluded himself into acting like it would not. Then ChatGPT launched. Each day traffic peaked higher while researchers dismissed it as a PR flash in the pan, but he had seen enough organic growth curves at YC to recognize the spectral signature. On day five it crossed a million users and he went home and told Ollie: you have no idea how bad this is, our nice quiet life is about to go through a cannon. Running the product company shares almost nothing with running the lab; what YC did prepare him for was recognizing the moment. The pattern is now repeating: chatbots were wave one, coding agents are wave two and going nuts, and persistent agents (chiefs of staff, co-workers, colleagues) are the imminent third wave.

    Killing good things, compute, and self-replicating data centers

    The easy discipline is killing what is not working once you run out of ideas. The hard one is killing things that work: when GPT-3 took off, OpenAI shut down beloved robotics work; when coding agents took off, it shut down Sora and the browser, not because Sora would have failed (Altman says it would have been super successful) but because the compute and people had a more important use. Those calls are gradual realizations, not single meetings, and people accept them because the mission and stakes are understood. On infrastructure, which may become the biggest project of all time, OpenAI will not vertically integrate everything: chip design and model design belong together, electron production is a commodity. But he sees a deep imbalance between the field’s obsession with recursive algorithmic improvement and the neglected idea of data centers that can build more data centers, where a data center’s own intelligence drives robot fleets that construct its copies. Nearly all his time goes into the gritty execution behind this: financing fabs, assembling teams, making supply chains function, work he describes as grinding with none of the glory of big thoughts. His confessed failure of ambition is undershooting compute because financial markets psyched him out.

    Design, Jony Ive, and the device problem

    Working with Jony Ive taught Altman that great design is mostly deep problem understanding, not a flash of insight. Ive studies everything (the history of motorsport, cabin typefaces, engine sounds across decades) and writes literal books of exploration before allowing himself to think about solutions; the middle step, where understanding becomes a fully formed novel idea all at once, remains a mystery even up close. Altman calls the iPhone humanity’s greatest piece of technology while admitting he no longer loves his relationship with it: notifications are off for almost everything, including messaging apps, which he calls a big life upgrade. While building the Sora app he deliberately addicted himself to TikTok as research, loved it, believed he could control it, lost an hour, then a three-hour Saturday afternoon, briefly regained control, and finally deleted it. He is sure the devices OpenAI makes will be beautiful and empowering, and equally sure people will misuse them in ways that make lives worse before we adapt. He does not claim design as his own skill; he claims knowing greatness when he talks to it for thirty minutes, and rejects the meme that you can only hire in domains you deeply understand.

    People, speed, trends, and what stays human

    Organizational pace is 90 percent the people in leadership roles; management systems are rounding error. He sorts leaders into fast movers and slow movers, prefers promoting executives internally, and when hiring externally leans on long conversations, heavy reference checks, and casual trial collaboration. Raising ambition in people broken by corporate life takes time, and the mechanism is small repeated wins, not inspirational speeches, which he does not do. His real-versus-fake trend test, absorbed from mountains of YC data: fake trends (VR for many years) get purchased and shelved; real trends get woven into daily life the way ChatGPT has. Skills that come supernaturally to someone cannot be taught by explanation, only absorbed by studying the person in action, the way CS:GO players study pros. On the future of work, he expects the leisure promise to break the way it always has: expectations rise, status is relative, the desire to be useful persists, so a post-superintelligence world is a busier one, still complaining, secretly happy. What endures is what evolution shaped: cooking for people, eating together, adventure, quests. Betting against evolutionary biology is usually a bad bet. His own next thing, once broadly shared prosperity from superintelligence is on the glide path: eventually, the ranch. And the most painful thing of his year was not corporate at all, but the arithmetic of new fatherhood against the singularity’s work hours.

    Notable Quotes

    “I developed a great trust in exponentials in people or companies or models.”

    Sam Altman, on the belief system connecting his YC founder bets to AI scaling laws

    “Transistors and then electrons in that order.”

    Sam Altman, asked what the biggest bottleneck is to scaling AI unabated

    “Every time that humanity has traded off its liberty for safety it’s been a long-term net loss and so we are going to put this in the hands of people.”

    Sam Altman, framing the fight between AI authoritarianism and broad empowerment

    “I was more worried that we were drinking our own Kool-Aid than everybody else was wrong.”

    Sam Altman, on OpenAI’s early conviction that scaling would work

    “You have no idea how bad this is. You have no idea what’s about to happen. It’s not just bad for me, it’s bad for you, too. Like we have this nice quiet life, you know, it’s really wonderful. It’s about to like kind of go through a cannon.”

    Sam Altman, recounting what he said at home the day ChatGPT crossed a million users

    “There is relatively too much focus on algorithms that create better algorithms and not enough focus on data centers that can create more data centers.”

    Sam Altman, on the neglected physical half of recursive self-improvement

    “Really great design is way more about understanding the problem than the flash of insight.”

    Sam Altman, on the biggest lesson from working with Jony Ive

    “Betting against evolutionary biology is like usually a bad bet.”

    Sam Altman, on which human activities survive a world of superintelligence

    “Honestly, having kids and working really hard at the same time is brutal.”

    Sam Altman, naming the most painful thing of his last twelve months

    Watch the full conversation here.

    Related Reading

  • Jensen Huang Joins X and His First Post Is a Manifesto: Inside the Open Weights and American AI Leadership Letter Signed by NVIDIA, Microsoft, Meta, and 20+ Tech Giants

    Jensen Huang, the CEO of NVIDIA and arguably the most influential person in the AI hardware world, has never been a social media guy. That changed on July 24, 2026, when he joined X and published his first-ever post. He did not use it to celebrate a product launch or a stock milestone. He used it to share a policy manifesto: “Open Weights and American AI Leadership,” a joint letter signed by roughly 25 organizations including NVIDIA, Microsoft, Meta, IBM, Dell Technologies, Hugging Face, Mistral, Mozilla, The Linux Foundation, Palantir, Perplexity, Replit, ServiceNow, Andreessen Horowitz, and Y Combinator, urging U.S. policymakers not to strangle open-weight AI models with premature restrictions.

    TLDR

    Jensen Huang broke his lifelong social media silence to amplify a coalition letter arguing that America’s AI leadership depends on a thriving open-weight ecosystem, not just one frontier model. The letter draws a straight line from the open-source software movement of the 1980s to today’s AI debate, and makes four core arguments: open weights expand access to the AI economy for startups, universities, and businesses that cannot train frontier models from scratch; they strengthen competition across models, chips, clouds, and applications; they give customers control over their data and protection from vendor lock-in; and, most provocatively, they make AI safer, because transparency lets thousands of researchers find and fix vulnerabilities while closed models concentrate risk into a few single points of failure. The letter acknowledges that released weights can never be recalled, defends distillation as a legitimate development technique that should not be swept into anti-misappropriation rules, and asks policymakers to expand compute access, invest in shared datasets and evaluation tools, and keep the frontier plural. Notably absent from the signatory list: OpenAI, Anthropic, and Google.

    Thoughts

    The medium is the message here. Jensen Huang has run NVIDIA for over three decades without needing a personal X account, and his debut post could have been anything. He chose a policy letter. That tells you how high the stakes of the open-weights fight have become in Washington. When the CEO whose chips power essentially all frontier AI decides the most valuable use of his first post is lobbying, the open-versus-closed question has officially moved from Twitter discourse to the center of American industrial policy.

    Follow the incentives and the signatory list makes perfect sense. NVIDIA wins when AI runs everywhere, on every cloud, in every factory, hospital, and government data center, and open weights are the vehicle for that diffusion. Meta has bet its entire AI strategy on open models. Hugging Face, Mistral, and the Linux Foundation are institutionally committed to openness. Microsoft signing is the interesting one, given its billions invested in OpenAI, and it suggests Redmond sees its future in selling infrastructure for all models rather than defending any single lab’s moat. Meanwhile the two most prominent frontier labs built on closed weights, OpenAI and Anthropic, are conspicuously not on the letter, and neither is Google. The dividing line is not ideology. It is business model.

    The safety argument is the letter’s boldest move. The standard policy assumption has been that closed models are the responsible choice and open weights are the risky one. The letter flips that: closed models are single points of failure that can be breached or fail invisibly, while open weights let a global community red team, benchmark, and patch. This is a direct port of the “given enough eyeballs, all bugs are shallow” argument from open-source software, and it worked historically. Linux and open cryptography did prove more trustworthy than security through obscurity. Whether the analogy fully holds for AI models, where a vulnerability might be a capability rather than a bug, is the real debate, and the letter mostly asserts the analogy rather than proving it. The honest concession is there, though: once weights are released, they are beyond anyone’s control, forever.

    The distillation paragraph is the tell for what this letter is actually about. Since Chinese labs like DeepSeek demonstrated that frontier-adjacent capability can be built cheaply, partly by learning from the outputs of existing models, there has been growing appetite in Congress to restrict distillation itself. The coalition is drawing a line: punish unlawful extraction from closed models through targeted legal frameworks, but do not ban a technique that virtually every AI team on earth uses for model improvement and evaluation. The unstated geopolitical subtext runs through the whole document. If America restricts its own open models, the world does not stop using open models. It builds on Chinese ones, and the default AI stack for most of humanity gets set in Hangzhou instead of Santa Clara.

    There is also a genuinely good economic point buried in the access section that deserves more attention than the politics. Frontier models are expensive, and routing every task through one is not economically sustainable when AI scales to billions of everyday operations. Open weights let organizations match the right model to the right job at the right cost, reserving frontier capability for frontier problems. That discipline, more than any single benchmark race, is what makes AI diffusion into ordinary businesses actually pencil out. Huang’s own post distilled the balanced version of the thesis into one line: the world needs both frontier closed models and frontier open models. That is probably the correct position, and it is worth noticing that the people who signed this letter and the people who did not both agree AI is the most consequential technology of the era. They just disagree about who should hold the keys.

    Key Takeaways

    • Jensen Huang joined X on July 24, 2026, and used his first-ever post to share the coalition letter “Open Weights and American AI Leadership” rather than any NVIDIA product or personal news.
    • His post read in part: “AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty.”
    • The letter is signed by roughly 25 organizations: NVIDIA, Microsoft, Meta, IBM, Dell Technologies, Hugging Face, Mistral, Mozilla, The Linux Foundation, Palantir, Perplexity, Replit, ServiceNow, CrowdStrike, Box, Black Forest Labs, Arcee AI, Arena, Emergence Capital, Telnyx, Reflection, Mariana Minerals, American Innovators Network, Andreessen Horowitz, and Y Combinator.
    • OpenAI, Anthropic, and Google are notably absent from the signatory list, and the split tracks business models: companies that profit from AI diffusion signed, companies whose moat is closed frontier models did not.
    • Open-weight models are defined in the letter as AI models that anyone can download, inspect, modify, and run on their own infrastructure.
    • The letter opens with a historical analogy: 1980s open-source pioneers challenged the belief that software required tight corporate control, and open source now underpins most of the internet, the U.S. military, and federal research.
    • The central thesis is that U.S. AI leadership will be judged not by one frontier model but by whether America builds an open ecosystem that diffuses AI into every sector of the economy.
    • Argument one is access: startups, established businesses, universities, and public institutions can build on advanced models without training one from scratch or paying frontier-model prices for every task.
    • The letter frames cost discipline as the key to sustainable AI economics: reserve frontier-scale capability for genuine frontier problems and run efficient specialized models everywhere else, because AI usage is heading toward billions of everyday tasks.
    • America wins the AI era, per the letter, by diffusing AI into factories, hospitals, farms, classrooms, and main street businesses, not by concentrating it.
    • Argument two is competition: open weights create rivalry not just among model developers but across chips, clouds, applications, and services, which drives down costs and spreads the gains.
    • Argument three is customer control: organizations investing in AI want assurance they will not be locked into a single provider or lose the capabilities they build over time.
    • Open weights let organizations control their own data, adapt models to their needs, deploy wherever business requirements demand, and own the value they create through self-improving models and accumulated knowledge.
    • The letter concedes the core risk honestly: once weights are released they are beyond the original developer’s control, and modified versions are difficult to trace or reverse.
    • Its answer to that risk is defensive parity: in a world where attackers use advanced AI, defenders need comparable open models to detect, simulate, and respond to threats.
    • Argument four inverts the standard safety assumption: relying solely on closed models is not inherently safe because they can be breached, misused, or fail in ways outsiders cannot detect.
    • Concentrating advanced AI behind a few closed models creates single points of failure, weakens competition, and leaves critical technology in the hands of a few providers.
    • The letter argues openness enables rigorous benchmarking, red teaming, and protections tied to real demonstrated harms, rather than assuming closed systems are safer by default.
    • The transparency-beats-obscurity argument is borrowed directly from open-source security history, where community scrutiny made software like Linux more trustworthy, not less.
    • The policy asks: expand compute access for startups and researchers, invest in shared training assets like datasets, tools, and evaluation frameworks, and avoid premature restrictions that stifle competition or push innovation overseas.
    • “Keeping the frontier plural” is the letter’s phrase for ensuring no single lab or model becomes the sole locus of advanced AI capability.
    • The distillation section is the most legislatively specific part: it defends using one model’s outputs to help train or improve another as a widely used, legitimate technique for model improvement, evaluation, and validation.
    • The coalition wants unlawful extraction of value from closed models addressed through targeted legal and commercial frameworks, not sweeping restrictions on distillation itself.
    • The distillation defense lands in the shadow of DeepSeek and other Chinese labs, whose cheap, capable open models triggered calls in Washington to restrict the technique.
    • The unstated competitive logic: if the U.S. restricts its own open models, developers worldwide will build on Chinese open models instead, ceding the default global AI stack.
    • Sovereignty is a recurring frame, both national and organizational: open weights let countries and companies run AI on their own infrastructure with their own data, a pitch Huang has made to governments for years.
    • Huang’s bottom line is explicitly both-and, not either-or: “The world needs both frontier closed models and frontier open models.”
    • The letter closes with an optimistic framing: with the right choices, open-weight AI can expand opportunity, strengthen competition, extend American technological leadership, mitigate risk, and share the benefits broadly.

    Detailed Summary

    The Debut: Why Jensen Huang Joining X Matters

    Huang has been one of the most visible executives on earth for years, keynoting CES and GTC to stadium crowds, yet he has never maintained a personal social media presence. His arrival on X on July 24, 2026 was itself news, and the content of the first post made it a statement. Rather than an introduction or a product plug, he shared the coalition letter and wrote that AI will transform every industry, power every company, and be built by every country, and that open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. Microsoft CEO Satya Nadella amplified the same letter the same day. The coordinated rollout, fronted by the two most valuable companies in the AI supply chain, was designed to put maximum weight behind a single policy position at a moment when Congress is actively weighing how to regulate open models.

    The Open-Source Precedent

    The letter’s opening argument is historical. In the 1980s, open-source pioneers challenged the prevailing belief that software would only advance if companies kept tight control over their code. The movement they built now supports most of the internet and underlies systems used by the world’s largest technology companies, the U.S. military, and federal agencies doing scientific research and cybersecurity. The letter’s framing is that open source did more than lower costs; it created a shared foundation of knowledge on which generations of American engineers built. The United States, it argues, faces the same fork in the road with AI, and the lesson of the last forty years points toward openness.

    Access, Competition, and Customer Control

    The economic core of the letter is three stacked arguments. First, access: open weights let startups, businesses, universities, and public institutions build on advanced models without training their own or paying frontier prices for every task. The letter is unusually specific about the economics, arguing that matching the right model to the right job at the right cost is what will make AI sustainable as usage scales into the billions of everyday tasks. Second, competition: because anyone can build on open weights, rivalry emerges across every layer of the stack, models, chips, clouds, applications, and services, which spurs innovation and drives down prices. Third, control: organizations fear vendor lock-in and losing the capabilities they build. Open weights let them keep their data, adapt models to their needs, deploy anywhere, and own the accumulated value, which the letter ties to both American sovereignty and prosperity.

    The Safety Argument Turned Upside Down

    The letter does not dodge the standard objection. It concedes that open weights carry real and distinct risks: once released, weights are beyond the developer’s control, and modified versions are hard to trace or reverse. But it argues the right response is not prohibition. Defenders facing AI-equipped attackers need comparably capable models to detect, simulate, and respond to threats. Then it goes further, claiming openness may be one of the most important paths to AI safety. Closed models can be breached, misused, or fail invisibly, and concentrating capability behind a few of them creates single points of failure. Open models allow a broad community to examine behavior, find vulnerabilities, develop safeguards, and improve them over time, with rigorous benchmarking, red teaming, and protections tied to real demonstrated harms. The explicit analogy is to open-source software proving that transparency can be more secure than obscurity.

    The Distillation Defense

    The most pointed policy content is a warning against conflating legitimate model-development techniques with misappropriation. Distillation, using one model’s outputs to help train or improve another, is defended as a widely used technique for model improvement, evaluation, and validation, standing in a long tradition of learning from and building on existing technology. The letter acknowledges that unlawful extraction of value from closed models raises legitimate concerns, but insists those be handled through targeted legal and commercial frameworks rather than sweeping restrictions. This is the paragraph aimed most directly at pending legislative ideas, and it is the one where the interests of the signatories and the non-signatories diverge most sharply, since distillation is precisely how smaller and open models close the gap with closed frontier systems.

    Who Signed, and Who Did Not

    The signatory list spans chipmakers (NVIDIA), hyperscalers (Microsoft), open-model champions (Meta, Mistral, Black Forest Labs, Arcee AI, Reflection), infrastructure and enterprise players (IBM, Dell, Box, ServiceNow, CrowdStrike, Telnyx, Palantir), the open-source institutional world (Hugging Face, Mozilla, The Linux Foundation), and the venture ecosystem (Andreessen Horowitz, Y Combinator, Emergence Capital), plus Perplexity, Replit, Arena, Mariana Minerals, and the American Innovators Network. The absences are as informative as the signatures. OpenAI, which released its gpt-oss open-weight models in 2025 but remains fundamentally a closed frontier lab, did not sign. Neither did Anthropic nor Google. The letter thus formalizes a fault line that has been visible for years: the diffusion coalition versus the frontier labs, with the U.S. government as the audience both sides are playing to.

    The Policy Ask

    The letter closes with concrete recommendations. Policymakers should expand access to compute for startups and researchers, invest in shared training assets including datasets, tools, and evaluation frameworks, and keep the frontier plural by avoiding premature restrictions on open models that would stifle competition or drive innovation overseas. It also calls for attention to strong application layers that expand sovereign use of AI across the economy. The final paragraph is pure optimism: with the right choices, the age of AI can be one of broadly shared prosperity, and the United States should lead in building that future.

    Notable Quotes

    “For my first post, I’m sharing a letter Nvidia signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. The world needs both frontier closed models and frontier open models.”

    Jensen Huang, in his debut post on X, July 24, 2026

    “Our AI leadership will be judged not by one frontier AI model, but by whether the United States builds a strong, open ecosystem that diffuses into every sector.”

    The coalition letter, stating its central thesis

    “America wins the AI era by diffusing it into the workflows of factories, hospitals, farms, classrooms, and main street businesses.”

    The coalition letter, on where the AI race is actually decided

    “Once released, the weights are beyond the original developer’s control, and modified versions are difficult to trace or reverse.”

    The coalition letter, conceding the irreversibility risk of open weights

    “Relying solely on closed models is not inherently safe: they can be breached, misused, or fail in ways that outsiders cannot detect.”

    The coalition letter, inverting the standard safety assumption

    “Just as open-source software demonstrated that transparency can be more secure than obscurity, AI safety may depend on giving more people the ability to test and strengthen the models on which society relies.”

    The coalition letter, drawing its core analogy to open-source security

    “Distillation, or the practice of using one model’s outputs to help train or improve another, is a widely used technique for model improvement, evaluation, and validation.”

    The coalition letter, defending the technique legislators have discussed restricting

    “That future is worth building, and the United States should lead in building it.”

    The coalition letter’s closing line

    Read the full letter here: Open Weights and American AI Leadership (PDF), and see Jensen Huang’s first post on X.

    Related Reading

  • Google’s Gemini 2.0: Is This the Dawn of the AI Agent?

    Google just dropped a bombshell: Gemini 2.0. It’s not just another AI update; it feels like a real shift towards AI that can actually do things for you – what they’re calling “agentic AI.” This is Google doubling down in the AI race, and it’s pretty exciting stuff.

    So, What’s the Big Deal with Gemini 2.0?

    Think of it this way: previous AI was great at understanding and sorting info. Gemini 2.0 is about taking action. It’s about:

    • Really “getting” the world: It’s got much sharper reasoning skills, so it can handle complex questions and take in information in all sorts of ways – text, images, even audio.
    • Thinking ahead: This isn’t just about reacting; it’s about anticipating what you need.
    • Actually doing stuff: With your permission, it can complete tasks – making it more like a helpful assistant than just a chatbot.

    Key Improvements You Should Know About:

    • Gemini 2.0 Flash: Speed Demon: This is the first taste of 2.0, and it’s all about speed. It’s apparently twice as fast as the last version and even beats Gemini 1.5 Pro in some tests. That’s impressive.
    • Multimodal Magic: It can handle text, images, and audio, both coming in and going out. Think image generation and text-to-speech built right in.
    • Plays Well with Others: It connects seamlessly with Google Search, can run code, and works with custom tools. This means it can actually get things done in the real world.
    • The Agent Angle: This is the core of it all. It’s built to power AI agents that can work independently towards goals, with a human in the loop, of course.

    Google’s Big Vision for AI Agents:

    Google’s not just playing around here. They have a clear vision for AI as a true partner:

    • Project Astra: They’re exploring AI agents that can understand the world in a really deep way, using all those different types of information (multimodal).
    • Project Mariner: They’re also figuring out how humans and AI agents can work together smoothly.
    • Jules the Programmer: They’re even working on AI that can help developers code more efficiently.

    How Can You Try It Out?

    • Gemini API: Developers can get their hands on Gemini 2.0 Flash through the Gemini API in Google AI Studio and Vertex AI.
    • Gemini Chat Assistant: There’s also an experimental version in the Gemini chat assistant on desktop and mobile web. Worth checking out!

    SEO Stuff (For the Nerds):

    • Keywords: Gemini 2.0, Google AI, Agentic AI, AI Agents, Multimodal AI, Gemini Flash, Google Assistant, Artificial Intelligence (same as before, these are still relevant)
    • Meta Description: Google’s Gemini 2.0 is here, bringing AI agents to life. Explore its amazing features and see how it’s changing the game for AI.
    • Headings: Using natural-sounding headings helps (like I’ve done here).
    • Links: Linking to official Google pages and other good sources is always a good idea.

    In a Nutshell:

    Gemini 2.0 feels like a significant leap. The focus on AI that can actually take action is a big deal. It’ll be interesting to see how Google integrates this into its products and what new possibilities it unlocks.

  • Revolutionizing Material Discovery with Deep Learning: A Leap Forward in Scientific Advancement

    Revolutionizing Material Discovery with Deep Learning: A Leap Forward in Scientific Advancement

    In a groundbreaking study, researchers have harnessed the power of deep learning to significantly advance the field of material science. By scaling up machine learning for materials exploration through large-scale active learning, they have developed models that accurately predict material stability, leading to the discovery of a vast array of new materials.

    The Approach: GNoME and SAPS

    Central to this achievement is the Graph Networks for Materials Exploration (GNoME) framework. This involves the generation of diverse candidate structures, including new methods like symmetry-aware partial substitutions (SAPS), and the use of state-of-the-art graph neural networks (GNNs). These networks enhance the modeling of material properties based on structure or composition.

    Unprecedented Discoveries

    The GNoME models have unearthed over 2.2 million structures stable with respect to previously known materials. This represents an order-of-magnitude expansion from all previous discoveries, with the updated convex hull comprising 421,000 stable crystals. Impressively, these models accurately predict energies and have shown emergent generalization capabilities, enabling accurate predictions of structures with multiple unique elements, previously a challenge in the field.

    Efficient Discovery and Validation

    The process involves two frameworks: generating candidates and filtering them using GNoME. This approach allows a broader exploration of crystal space without sacrificing efficiency. The filtered structures are then evaluated using Density Functional Theory (DFT) computations, contributing to more robust models in subsequent rounds of active learning.

    Active Learning and Scaling Laws

    A core aspect of this research is active learning, where candidate structures are continually refined and evaluated. This iterative process leads to an improvement in the prediction error and hit rates of the GNoME models. Consistent with scaling laws in deep learning, the performance of these models improves significantly with additional data, suggesting potential for further discoveries.

    Impact and Future Prospects

    The GNoME models found 381,000 new materials living on the updated convex hull and identified over 45,500 novel prototypes, demonstrating substantial gains in discovering materials with complex compositions. Additionally, the similarity in phase-separation energy distribution compared to the Materials Project validates the stability of these new materials.

    This study represents a significant leap in the field of material science, demonstrating the potential of deep learning in discovering new materials. The GNoME models’ capability to predict the stability of a vast array of materials paves the way for future advancements in various scientific and technological domains.

    Why It Matters

    The discovery of over 2.2 million new stable materials using deep learning signifies a pivotal advancement in materials science. This technology opens up new avenues for innovation across numerous industries, including energy, electronics, and medicine. The efficient and accurate prediction models streamline the material discovery process, reducing the time and resources traditionally required for such endeavors. This revolution in material discovery stands to significantly impact future technological advancements, making this research not only a scientific breakthrough but a cornerstone for future developments in various fields.

  • AI Revolutionizes Weather Forecasting: Google’s GraphCast Surpasses Traditional Methods

    In a groundbreaking development for meteorology, an AI model named GraphCast, developed by Google DeepMind, has outperformed conventional weather forecasting methods, as reported by a study in the peer-reviewed journal Science. This marks a significant milestone in weather prediction, suggesting a future of increased accuracy and efficiency.

    AI’s Meteorological Mastery

    GraphCast, Google DeepMind’s AI meteorology model, has demonstrated superior performance over the leading conventional system of the European Centre for Medium-range Weather Forecasts (ECMWF). Excelling in 90 percent of 1,380 metrics, GraphCast has shown remarkable accuracy in predicting temperature, pressure, wind speed, direction, and humidity.

    Speed and Efficiency

    One of the most striking aspects of GraphCast is its speed. It can predict hundreds of weather variables over a 10-day period at a global scale, achieving this feat in under one minute. This rapid processing ability marks a significant advancement in AI’s role in meteorology, drastically reducing the time and energy required for weather forecasting.

    A Leap in Machine Learning

    GraphCast employs a sophisticated “graph neural network” machine-learning architecture, trained on over four decades of ECMWF’s historical weather data. It processes current and historical atmospheric data to generate forecasts, contrasting sharply with traditional methods that rely on supercomputers and complex atmospheric physics equations.

    The Cost-Efficiency Advantage

    GraphCast’s efficiency doesn’t just lie in its speed and accuracy. It’s also estimated to be about 1,000 times cheaper in terms of energy consumption compared to traditional weather forecasting methods. This cost-effectiveness, coupled with its advanced prediction capabilities, was exemplified in its successful forecast of Hurricane Lee’s landfall in Nova Scotia.

    Limitations and Future Directions

    Despite its advancements, GraphCast is not without limitations. It hasn’t outperformed conventional models in all scenarios and currently lacks the granularity offered by traditional methods. However, its potential as a complementary tool to existing weather prediction techniques is acknowledged by researchers.

    Looking ahead, there are plans for further development and integration of AI models into weather prediction systems by ECMWF and the UK Met Office, signaling a new era in meteorology where AI plays a crucial role.

    Google DeepMind’s GraphCast represents a paradigm shift in weather forecasting, offering a glimpse into a future where AI-driven models provide faster, more accurate, and cost-efficient predictions. While it’s not a complete replacement for traditional methods, its integration heralds a new age of innovation in meteorological science.

  • AI Industry Pioneers Advocate for Consideration of Potential Challenges Amid Rapid Technological Progress

    AI Industry Pioneers Advocate for Consideration of Potential Challenges Amid Rapid Technological Progress

    On Tuesday, a collective of industry frontrunners plans to express their concern about the potential implications of artificial intelligence technology, which they have a hand in developing. They suggest that it could potentially pose significant challenges to society, paralleling the severity of pandemics and nuclear conflicts.

    The anticipated statement from the Center for AI Safety, a nonprofit organization, will call for a global focus on minimizing potential challenges from AI. This aligns it with other significant societal issues, such as pandemics and nuclear war. Over 350 AI executives, researchers, and engineers have signed this open letter.

    Signatories include chief executives from leading AI companies such as OpenAI’s Sam Altman, Google DeepMind’s Demis Hassabis, and Anthropic’s Dario Amodei.

    In addition, Geoffrey Hinton and Yoshua Bengio, two Turing Award-winning researchers for their pioneering work on neural networks, have signed the statement, along with other esteemed researchers. Yann LeCun, the third Turing Award winner, who leads Meta’s AI research efforts, had not signed as of Tuesday.

    This statement arrives amidst escalating debates regarding the potential consequences of artificial intelligence. Innovations in large language models, as employed by ChatGPT and other chatbots, have sparked concerns about the misuse of AI in spreading misinformation or possibly disrupting numerous white-collar jobs.

    While the specifics are not always elaborated, some in the field argue that unmitigated AI developments could lead to societal-scale disruptions in the not-so-distant future.

    Interestingly, these concerns are echoed by many industry leaders, placing them in the unique position of suggesting tighter regulations on the very technology they are working to develop and advance.

    In an attempt to address these concerns, Altman, Hassabis, and Amodei recently engaged in a conversation with President Biden and Vice President Kamala Harris on the topic of AI regulation. Following this meeting, Altman emphasized the importance of government intervention to mitigate the potential challenges posed by advanced AI systems.

    In an interview, Dan Hendrycks, executive director of the Center for AI Safety, suggested that the open letter represented a public acknowledgment from some industry figures who previously only privately expressed their concerns about potential risks associated with AI technology development.

    While some critics argue that current AI technology is too nascent to pose a significant threat, others contend that the rapid progress of AI has already exceeded human performance in some areas. These proponents believe that the emergence of “artificial general intelligence,” or AGI, an AI capable of performing a wide variety of tasks at or beyond human-level performance, may not be too far off.

    In a recent blog post, Altman, along with two other OpenAI executives, proposed several strategies to manage powerful AI systems responsibly. They proposed increased cooperation among AI developers, further technical research into large language models, and the establishment of an international AI safety organization akin to the International Atomic Energy Agency.

    Furthermore, Altman has endorsed regulations requiring the developers of advanced AI models to obtain a government-issued license.

    Earlier this year, over 1,000 technologists and researchers signed another open letter advocating for a six-month halt on the development of the largest AI models. They cited fears about an unregulated rush to develop increasingly powerful digital minds.

    The new statement from the Center for AI Safety is brief, aiming to unite AI experts who share general concerns about powerful AI systems, regardless of their views on specific risks or prevention strategies.

    Geoffrey Hinton, a high-profile AI expert, recently left his position at Google to openly discuss potential AI implications. The statement has since been circulated and signed by some employees at major AI labs.

    The recent increased use of AI chatbots for entertainment, companionship, and productivity, combined with the rapid advancements in the underlying technology, has amplified the urgency of addressing these concerns.

    Altman emphasized this urgency in his Senate subcommittee testimony, saying, “We want to work with the government to prevent [potential challenges].”