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  • Waste Tokens to Save Time: Naval, Guillermo Rauch, Blake Scholl, and Max Hodak on AI Software Factories, 1000x Engineers, and Whether Pure Software Is Dead

    Naval Ravikant gathers three frontier founders, Guillermo Rauch of Vercel, Blake Scholl of Boom Supersonic, and Max Hodak of Science, for a freewheeling conversation about how AI coding tools are reshaping what an engineer is, what software is worth, and where the moat goes when models speak English. The headline idea comes from Naval himself: waste tokens, save time. Stop measuring AI by tokens consumed or lines of code generated and start measuring it by the final output and the time you got back. The full conversation is on the Naval Podcast YouTube channel. This is part one of the discussion. Part two, on vibe coding hardware, follows the same group into jet engines, semiconductors, and biotech. You can also watch and read the full episode here.

    TLDW

    The job of an engineer is shifting from shipping output to building the factory that ships the output, which means 10x engineers were never really 10x, they were always 100x or 1000x in idea domains, and AI leverage is making that obvious. Models now reflect back the judgment of the user, so a senior architect extracts dramatically more value than a junior, although the junior also writes code they could never have written alone. The frontier models have quietly graduated from junior coders to principal engineers, returning with intuitive plans and real tradeoffs (sometimes with hilariously bad time estimates) rather than just running away with the prompt. Naval has stopped learning prompt tricks, scaffolding tools, and Claude plan-mode rituals entirely. Instead he throws Codex, Claude, and Gemini at the same problem in parallel and brute forces his way through, because tokens are still cheaper than a human and the models keep getting better faster than tricks can. That leads to the bigger question on the table: is pure software still investable, or is it now just a free byproduct of hardware, models, and taste? The group lands on the block economy thesis (a tip of the hat to Mitchell Hashimoto): agents do not want to reinvent Postgres or BMQ on the fly, they want to grab the right reusable building block, so infrastructure software actually gets more valuable, not less. Max Hodak closes the loop with a personal data point: he has not written a line of code in years and has built more software since December than ever before, all through agents, because just understanding APIs, data flow, and performance is what actually moves the work forward.

    Thoughts

    The “waste tokens, save time” line is the most important rhetorical move in this conversation, and it deserves to be unpacked beyond the soundbite. Naval is implicitly arguing that the entire token-economics debate (input cost, output cost, leaderboards, model arbitrage) is a category error in the same way that lines-of-code was a category error in the nineties. The thing being purchased is not tokens. It is a finished result delivered with less of your finite attention spent. If three parallel runs of Codex, Claude, and Gemini cost you a few dollars and one of them lands the answer in twenty minutes instead of you sweating the problem for two hours, the unit economics are not even close. The only people who care about the token bill are people who have not internalized that human time is the actually scarce resource. Once you do internalize it, the question is no longer “how do I prompt this more efficiently,” it is “how do I get out of my own way.”

    The 100x and 1000x engineer point is the one most likely to enrage commenters, and it is also the one most worth taking seriously. Naval is right that the egalitarian flinch in software circles always sat awkwardly next to the empirical fact that one Carmack, one Brendan Eich, or one Satoshi creates more durable value than every mid-tier engineer on earth combined. What AI does is collapse the bottom of that distribution. The marginal junior engineer at a typical company is now competing with a model that costs a few dollars an hour and never sleeps. The remaining premium for human engineers is taste, judgment, and the rare ability to pick the right thing to build at all, which Naval correctly flags as the multiplier that dwarfs raw coding speed. “Just one who had a better judgment on what to work on in the first place” is the most underrated line in the whole episode.

    Guillermo Rauch’s observation that the models have graduated from running away with your prompt to returning with three routes and a tradeoff matrix is the technical update most people have not actually felt yet. There was a real, qualitative shift when the model started saying “we don’t put high-cardinality telemetry into Postgres, you probably want ClickHouse or Athena.” That is not autocomplete. That is a peer. And the funny corollary, that the same model will then confidently tell you the work will take three weeks when it will take three hours, is not a knock on the model. It is a reminder that calibration is a separate skill from competence, and humans get this wrong constantly too. The right posture is to treat the model the way a good engineering manager treats a strong but cocky senior: take the architecture suggestions seriously, throw out the estimates.

    The block-economy thread, riffing on Mitchell Hashimoto, is where this conversation quietly answers Naval’s “is pure software dead” question. Agents are insatiable consumers of reusable building blocks because reinventing infrastructure on every run is wasteful, brittle, and incompatible with the rest of the world. If your service is the canonical primitive an agent reaches for (the queue, the database, the auth layer, the deploy target), you are not commoditized by AI, you are amplified by it. Pure software is not dead. Pure software with no distribution, no defensibility, and no integration into the agent toolchain is dead. That is a much less catchy headline, but it is the real one. The takeaway for founders is not to abandon software, it is to ask whether your software is something an agent will reach for ten thousand times a day or something a human had to be talked into using once.

    Max Hodak’s confession (no code written in years, more shipped software in the last six months than ever before) is the empirical proof that this is not just theory. The skill that ports forward is not syntax. It is the engineering leader’s instinct for what an API is, how data flows, where performance matters, and what level of expectation to set. Guillermo’s framing of “vibe coding through people on Slack” as the original form of vibe coding is genuinely insightful. A good engineering manager has always been transmitting intent to other minds and letting them run. Doing it with agents is the same skill, just with a faster, cheaper, more literal counterparty. The engineers who will struggle in this transition are the ones whose identity was tied to writing the code themselves. The ones who will thrive are the ones who already thought of themselves as taste, judgment, and intent, with code as an implementation detail.

    Key Takeaways

    • The engineer’s job has shifted from shipping output B to building the factory that produces outputs B through Z. You are now judged on the multiplicative system you create, not the single artifact you deliver.
    • 10x engineers were always a misnomer. In idea-domains and digital domains, the real distribution has always been 100x or 1000x. AI just made that obvious enough that arguing about it is no longer fashionable.
    • Token consumption leaderboards are the new lines-of-code metric: a vanity number that measures activity, not value. Tokens are an input, your time is the constraint.
    • Naval’s core rule: waste tokens, save time. Tokens are still vastly cheaper than human hours, no matter how the pricing scares you.
    • Models tend to be about as good as you are in a given domain. The feedback you give them, the corrections, the redirections, sporadically but powerfully shapes the quality of the output.
    • The quality of your reprompting matters enormously today, but will probably matter less over time as models get smarter and need less hand-holding.
    • Naval has refused to learn prompt scaffolding, plan-mode tricks, or named prompt frameworks. His bet is that the models will figure out how to use him faster than he can figure out how to use them.
    • His preferred technique: throw Codex, Claude, and Gemini at the same problem in parallel and brute force the answer. Time is the cost center, not API spend.
    • Lower quality first-draft code is not a blocker. When it is time to ship, throw more tokens at it for a hardening pass. Quality compounds across model generations.
    • Verifiable domains (problems with a clear right answer) are the ones the models will fully solve. Cutting-edge creativity work, the Terence Tao tier, still needs careful human collaboration.
    • Models have qualitatively shifted from “next-token autocomplete that runs away with your prompt” to “intuitive planning mode” where they return with multiple routes and explicit tradeoffs.
    • This is why people on social media say models are now PhD-level. It is not the raw output, it is the back-and-forth posture.
    • Models will confidently make terrible time estimates (“this is a three week project”). Treat them like a strong but miscalibrated senior engineer: trust the architecture, ignore the schedule.
    • Architect-level engineers are extracting much more value per session than junior engineers, but juniors are still leveling up because they can now write code far above their unaided ability.
    • The next career step for a junior engineer is moving from implementing features to picking technologies. Postgres vs ClickHouse, ZMQ vs other queues. The model can suggest, but a human still has to decide.
    • Taste and judgment remain the residual human advantage. Models will give you good tradeoffs if you ask, but knowing which tradeoff to take is still on you.
    • Concrete example: a recent model pushed back when asked to store high-cardinality telemetry in Postgres and recommended ClickHouse or Athena instead. Unprompted architectural judgment.
    • Humans are still completing the model for tasks like fetching API keys, moving capital, or performing real-world actions. That gap is temporary.
    • Every SaaS and hosting company will soon expose a CLI or API surface that agents can drive directly. Anything Unix-shaped and text-based, agents can already hack into a usable API themselves.
    • The missing piece for full autonomy is payments. Crypto, Bitcoin, or any programmable money lets the agent buy what it needs without a human in the loop.
    • The open question Naval poses: is pure software dead? We used to learn code to talk to machines. Now machines speak fuzzy, sloppy English back to us.
    • For hardware founders, AI is a massive boon. Software, which was always hard to hire artists for (per Patrick Collison’s “software is art” framing), is suddenly fast and cheap to produce alongside the hardware.
    • Model training, post-training, and fine-tuning may be the new “real software engineering” for those who want to work at the model layer.
    • Mitchell Hashimoto’s “block economy” thesis: agents need powerful, reusable, well-known building blocks. They should not reinvent message queues or databases every run.
    • Reinventing primitives is bad civic engineering. The value of “we both depend on Postgres 13.2” is interoperability with the rest of society and toolchain.
    • Infrastructure software and reusable libraries are getting more valuable, not less, in the agentic era. Vercel’s bet is on being the layer agents reach for.
    • Useful metaphor: building blocks are like a token cache. Why churn through a trillion tokens to reproduce code that already exists when you can fork from a known starting point?
    • Max Hodak has not written a line of code in years but has shipped a huge volume of personal software since December, all through agents. Projects he had fantasized about for years are now actually running.
    • What still matters from a real software background: understanding what an API is, how data flows, performance expectations, and how to set the right level of demand on an operation.
    • A proficient engineering leader has always been “vibe coding through people” on Slack and in one-on-ones, transmitting intent and letting others execute. Doing it with agents is the same skill, faster and cheaper.
    • Naval personally went from twenty years of not coding to coding constantly through agents, leaning on first-principles software engineering and algorithms knowledge.
    • The friction that historically killed personal coding projects (latest framework, infra plumbing, deploy setup) is now mostly handled by the agent. Vercel makes it easier, agents make it trivial.
    • The single biggest change Max highlights: you do not get stuck anymore. The indefinite debugging spiral on some narrow obscure bug is largely gone.
    • The old mantra that learning to program means accepting intrinsic frustration (“nope, that’s part of the deal”) is no longer true. The frustration was incidental, not essential.
    • The frontier founder pattern on display in this episode: all three guests build their own factories (Vercel’s AI cloud, Boom’s supersonic jets and engines, Science’s biohybrid brain interface) rather than composing from off-the-shelf parts.

    Detailed Summary

    The Software Factory and the Hundredfold Engineer

    Guillermo Rauch opens the substantive portion of the conversation with the framing he has been pushing publicly: the role of the engineer is moving from “ship output B” to “build the factory that ships outputs B through Z.” That reframes engineering judgment. You are no longer evaluated on the single deliverable, you are evaluated on the multiplicative system you put in place. Naval picks up the thread and points out that this also retires an old debate. Engineers used to argue about whether 10x engineers existed, with the egalitarian camp insisting that talent differences were marginal. The truth, Naval says, was always more extreme. In idea-domains, virtual domains, and intellectual domains, the distribution has always been 100x or 1000x, not 10x. Brendan Eich, Carmack, Satoshi, the canonical names, were thousandx programmers. AI has made the underlying distribution legible. And the multiplier on top of all of that is judgment: picking the right thing to work on in the first place is an infinity multiplier compared to picking the wrong thing, regardless of raw skill.

    Token Leaderboards Are the New Lines of Code

    Guillermo flags the current cultural confusion: people see their AI bills, see the token counts, and assume they should be optimizing for tokens-per-engineer or similar metrics. Max Hodak’s response cuts through it. Token consumption, like lines of code before it, is not a meaningful productivity metric. It is an activity metric, and activity metrics always mislead. Max adds his own field observation: the models tend to be roughly as good as you are in a given domain. A senior developer extracts genuinely powerful output, a junior gets junior-quality output back, because the feedback loop (the corrections, the redirections, the architectural pushback) is what shapes quality. The sporadic but high-leverage moments where the user redirects the model are doing more work than the prompt itself.

    Naval’s Brute Force Doctrine: Waste Tokens, Save Time

    Naval lays out his personal posture, which has become the title of the conversation. He has deliberately ignored all the prompting tricks, scaffolding tools, named prompt frameworks (“use Ralph Wigum, use OpenClaude, use Hermes, use plan mode”), on the bet that the models will figure out how to use him faster than he can figure out how to use them. He is ham-fisted with the models, gets frustrated, types less and less, and just brute forces his way through by running Codex, Claude, and Gemini at the same problem simultaneously. The justification is economic. No matter how expensive the models seem, they are still vastly cheaper than a human hour. Do not measure tokens as inputs or outputs. Measure your time and the final output. Even when the first-draft code is low quality, that is not a blocker. When the moment comes to ship, throw more tokens at it. The models will rewrite it, harden it, and they get better every generation. Naval explicitly excepts cutting-edge creative work (the Terence Tao tier of unsolved problems) where you still need to collaborate carefully and closely. Everywhere else, brute force is the dominant strategy.

    From Junior Coder to Principal Engineer

    Guillermo identifies a qualitative shift that has happened recently. Models used to do the classic next-token thing: take your prompt and run away with it in a direction you may not have wanted. Now they enter an intuitive planning posture without being told to plan. They come back and say “what you are asking has these three routes, here are the tradeoffs.” That, Guillermo argues, is the moment the model stopped being a junior engineer and became a principal engineer. The funny side effect is that they will then return preposterous time estimates (“this will take three weeks”) with full confidence. The conclusion is to treat the model as a peer for architecture and a baby for scheduling. Returning to the Max-vs-junior question, Guillermo argues juniors clearly do level up because they write code well above their solo ability, but architects extract maybe 10x while juniors extract more like 2x. The juice scales with the user’s existing taste.

    Taste, Judgment, and Architectural Decisions

    Max names the residual human contribution: taste and judgment. Picking between Postgres and ClickHouse for high-cardinality telemetry data, picking between ZMQ and another queueing system. The models can recommend, but a human still has to call it. Guillermo offers a recent concrete example where a model pushed back unprompted: when asked to put high-cardinality telemetry into Postgres, the model responded “we don’t put that kind of data into Postgres, you should consider ClickHouse or Athena.” That is the new normal. The peer-level architectural pushback is happening unsolicited, which is genuinely impressive and a real shift from the deferential autocomplete of two years ago.

    When the Human Becomes the Tool

    Guillermo raises the inversion question: at what point does the model stop being the assistant and the human start being the assistant who fetches API keys, moves capital, and performs real-world actions on the model’s behalf? Naval treats it as a temporary aberration. Every serious SaaS and hosting provider will soon expose a CLI or API surface that agents can drive directly. Even when they do not, anything Unix-shaped and text-based can be hacked into an agent-usable interface by the agent itself. The missing piece is payments. Once you insert programmable money (Naval mentions Bitcoin and crypto tokens), the agent can buy what it needs and the human is no longer the bottleneck.

    Is Pure Software Dead?

    Naval poses the biggest strategic question of the episode. If models now speak fuzzy, sloppy English the same way humans do, and the historical reason we learned to code was to talk to machines that did not understand English, is pure software still a viable thing to build a company around? His own framing of the answer: hardware founders win, because the historically hard problem of hiring software artists (per Patrick Collison’s “software is art” line) is now mostly solved by AI. Model builders win, because training, post-training, and fine-tuning may be the new “real software engineering.” But what about classic pure software companies? Naval lets the question hang, and Guillermo picks up the answer through a different door.

    The Block Economy and the Future of Infrastructure Software

    Guillermo cites Mitchell Hashimoto’s recent piece on the block economy (or “building block economy”). The argument: the most valuable thing for agents to have access to is powerful, reusable building blocks. You do not want your agent reinventing a queue system every time it needs to send an email. You want it to grab the right-sized block (BMQ, ClickHouse, whatever) and move on. Reinventing primitives is also a civic problem. The world only works because we all depend on the same Postgres 13.2, the same protocols, the same standard infrastructure. If every agent went off and invented its own bespoke universe, you would lose interoperability. So infrastructure software (which is, by self-admitted bias, what Vercel builds) becomes more valuable in the agentic era, not less. Guillermo extends the metaphor: reusable building blocks are like a token cache. Why burn a trillion tokens reproducing what already exists when the agent can fork from a known starting point? The block economy is the answer to “is pure software dead.” Pure software that becomes the canonical primitive an agent reaches for is more valuable than ever.

    Max Hodak’s Personal Proof: Years Without Code, Tons of Software Shipped

    Max grounds the discussion in his own experience. He learned to program young, got sucked into it in his teens and 20s, knew programming languages deeply. He has not written a line of code in quite a while. And yet since December he has built a huge amount of personal software, including projects he had fantasized about for years and now actually uses every day. He did not write any of it. He cannot imagine going back to writing code by hand. The skill that ports forward is not syntax, it is the understanding of how APIs work, how data flows, what level of performance to expect, and how to orient the model around the right expectations for an operation. Guillermo extends this with the most quotable framing of the episode: a proficient engineering leader has always been “vibe coding through people on Slack and in one-on-ones,” transmitting intent and letting others execute. Agents are the same modality with a faster, cheaper, more literal counterparty.

    Naval’s Return to Coding After Twenty Years

    Naval offers his own parallel. He went from not having written code in twenty years to coding constantly through agents. What carried him back in was first-principles knowledge of software engineering and algorithms, which gets you further than you would think. The reason he had stopped coding in the first place was not lack of ability, it was the friction of keeping up with the latest language, the latest architecture, and the constant infrastructure plumbing required to ship anything. Vercel made it easier. Agents made it trivial. Max closes with the most concrete benefit of all: you do not get stuck anymore. The indefinite debugging spiral on some obscure narrow problem, the thing that historically ate weekends and broke spirits, is largely gone. The old mantra that programming is intrinsically frustrating and that frustration is “part of the deal” turned out to be wrong. The frustration was incidental, not essential.

    Notable Quotes

    “The way that I’m judging you as an engineer is, are you producing the factory that will produce multiplicative outputs B through Z?”

    Guillermo Rauch, reframing what an engineer is actually being measured on in the AI era.

    “When you’re operating in idea domains, intellectual domains, virtual digital domains, it’s not even 10x, it’s 100x or 1000x. It always has been.”

    Naval Ravikant, on why the old 10x engineer debate was always under-stating the real distribution.

    “If you choose the right thing to work on versus the wrong thing to work on, that’s an infinity difference. It could just be one who had a better judgment on what to work on in the first place.”

    Naval Ravikant, on judgment as the multiplier that dwarfs raw skill.

    “I’ll throw Codex, Claude, and Gemini at the same problem over and over and just waste tokens to save time. No matter how expensive these models might seem, they’re still way cheaper than a human.”

    Naval Ravikant, on his brute-force multi-model coding workflow.

    “Just waste tokens, save time. Don’t look at the tokens either as inputs or outputs. Just look at your time and look at the final output.”

    Naval Ravikant, delivering the title thesis of the episode.

    “Clearly the models at some point graduated. They used to be junior engineers, now they’re principal engineers, because they come back to you with a set of tradeoffs.”

    Guillermo Rauch, on the qualitative shift in how current frontier models respond to prompts.

    “Bro, we don’t put that kind of data into Postgres, you should consider ClickHouse or Athena or whatever. That’s happened to me a lot, which is really impressive.”

    Guillermo Rauch, recounting unprompted architectural pushback from a recent model.

    “It’s like saying speaking English. We had to learn code to communicate with the models, now the models speak English. So where’s the moat?”

    Naval Ravikant, raising the central strategic question about the future of pure software.

    “I haven’t written a single line of code in quite a while. Since December, I’ve built a huge amount of software that I now use every day, projects I’ve fantasized about for years.”

    Max Hodak, on what becomes possible when you stop writing code and start directing agents.

    “A proficient engineering leader has been quote unquote vibe coding through people on Slack or one-on-ones, because you’re transmitting your will, your intent, your experience, and you’re letting others run with it. Now we do the same with agents.”

    Guillermo Rauch, reframing leadership itself as the original form of vibe coding.

    Watch the full conversation on the Naval Podcast here.

    Related Reading

    • Full episode: The AI Industrial Revolution, the complete hour-long conversation this clip is drawn from, covering software factories, hardware, regulation, healthcare economics, autonomous companies, and creativity.
    • Part two: Vibe Coding Hardware, the continuation of this conversation, where the same founders move from pure software into AI-designed jet engines, vertical integration, China’s open-source bet, and why humans become verifiers.
    • Naval Ravikant’s official site, the canonical home for Naval’s essays, podcast, and longer-form thinking on technology, judgment, and leverage.
    • Vercel, Guillermo Rauch’s company, building the AI-native cloud and frontend infrastructure that this conversation references as a canonical agent building block.
    • Boom Supersonic, Blake Scholl’s company building supersonic civilian aircraft and their own jet engines, the hardware example of a founder building the whole factory.
    • Science Corporation, Max Hodak’s brain-computer interface company developing the biohybrid neural implant referenced in the intro.
    • Mitchell Hashimoto’s writing, source of the “block economy” framing for why reusable infrastructure building blocks become more valuable, not less, in the agentic era.

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

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

    TLDW

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

    Key Takeaways

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

    Detailed Summary

    Why Tobi Still Loves the Work After Twenty Years

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

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

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

    Different First, Convergence Second, Failure as Successful Discovery

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

    Why Silicon Valley Lost Its Originality

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

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

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

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

    Tokens, Opex, and the Limits of Spend as Revenue

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

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

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

    There Is No Permanent Underclass in the Shopify Data

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

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

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

    Software Was the Hidden Infrastructure of the Last Thirty Years

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

    Predicting AI Two Years Out, Overhype and Underhype

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

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

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

    Public Company Defense and the Reading List

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

    Thoughts

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

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

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

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

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

    Watch the full conversation here on YouTube.

  • Jensen Huang at Stanford CS153 Frontier Systems on Co-Design, Agentic Computing, Vera Rubin, Open Models, and the Million-X Decade That Reshaped AI Infrastructure

    https://www.youtube.com/watch?v=tsQB0n0YV3k

    NVIDIA CEO Jensen Huang returned to Stanford for the CS153 Frontier Systems class (the room nicknamed itself “AI Coachella”) to lay out, in raw form, how he thinks about the computer being reinvented for the first time in over sixty years. Across roughly seventy minutes of student questions he walks through the codesign philosophy that gave NVIDIA a million-x decade, the architectural through-line from Hopper to Grace Blackwell to Vera Rubin to Feynman, the case for open source foundation models, the realities of tokens per watt and MFU, energy demand running a thousand times higher, the China and export-control debate, and his own biggest strategic mistakes. Watch the full conversation on YouTube.

    TLDW

    Huang argues every layer of computing has changed: the programming model, the system architecture, the deployment pattern, the economics. Co-design across CPUs, GPUs, networking, storage, switches and compilers gave NVIDIA roughly a million-x speed-up over ten years versus the ten-x Moore’s Law era, and that headroom is what let researchers say “just train on the whole internet.” Hopper was built for pre-training, Grace Blackwell NVLink72 for inference and reasoning (50x over Hopper in two years), Vera Rubin is built for agents that load long memory, call tools and need a low-latency single-threaded CPU bolted directly to the GPU, and Feynman extends that to swarms of agents that spawn sub-agents. Open weights matter because safety, sovereignty (230-plus languages no one else will fund) and domain models for biology, autonomy, robotics and climate need a foundation that NVIDIA is willing to seed. Compute is not really the scarce resource (Huang says place the order and the chips ship), the broken thing is institutional budgeting that can’t put a billion dollars into a shared university supercomputer. Energy demand is heading a thousand times higher and this is finally the moment market forces alone will fund sustainable generation. On geopolitics he rejects the GPUs-as-atomic-bombs framing and warns America will end up like its telecom industry if it cedes two thirds of the world. On career he advises seeking suffering on purpose. On strategy he says observe, reason from first principles, build a mental model, work backwards, minimize opportunity cost, maximize optionality.

    Key Takeaways

    • The computing model has been substantially unchanged since the IBM System 360, sixty-plus years ago. Huang’s first computer architecture book was the System 360 manual. AI is the first true reinvention.
    • Old computing was pre-recorded retrieval. New computing is generated, contextually aware and continuous. Cloud was on-demand. Agentic systems run continuously.
    • Codesign is NVIDIA’s central thesis. Inherited from the Hennessy and Patterson RISC era at Stanford, extended across CPUs, GPUs, networking, switches, storage, compilers and frameworks all optimized together.
    • The result of full-stack codesign: roughly 1,000,000x faster compute over ten years, versus a generous 10x to 100x for Moore’s Law in the same period. Dennard scaling effectively ended a decade ago.
    • That million-x speed-up is what unlocked “train on all of the internet” as a realistic AI strategy.
    • After GPT, Huang says it was obvious thinking was next. Reasoning is just generating tokens consumed internally, then using tools is generating tokens consumed externally. Agentic systems followed predictably.
    • Education needs AI baked into the curriculum, not just taught as a subject. Pre-recorded textbooks cannot keep pace with knowledge being generated in real time.
    • Huang says he cannot learn anymore without AI. He has the AI read the paper, then read every related paper, then become a dedicated researcher he can interrogate.
    • Mead and Conway and the first-principles methodology of semiconductor design are still worth learning even though most of the scaling tricks have been exhausted.
    • NVIDIA itself is one of the largest consumers of Anthropic and OpenAI tokens in the world. One hundred percent of NVIDIA engineers are now agentically supported. Huang recommends Claude and similar tools by name and says open-source downloads will not match the integrated product harness.
    • NVIDIA still invests heavily in open foundation models because language and intelligence represent the codification of human knowledge. Five pillars: Nemotron (language), BioNeMo (biology), Alphamayo (autonomous vehicles), Groot (humanoid robotics) and a climate science model (mesoscale multiphysics).
    • Sovereign language models matter. Roughly 230 world languages will never be a top priority for a commercial frontier lab. Nemotron is near-frontier and fully fine-tunable so any country can adapt it.
    • Safety and security require open weights. You cannot defend against or audit a black box. Transparent systems let researchers interrogate models and let defenders deploy swarms.
    • The future of cyber defense is not bigger-model-versus-bigger-model. It is trillions of cheap fast small models like Nemotron Nano surrounding the threat.
    • Domain models fuse language priors with world models. Alphamayo learned to drive safely on a few million miles instead of billions because it can reason like a human about the road.
    • MFU (Model Flops Utilization) is a misleading metric. Huang says he wants low MFU, because that means he over-provisioned every resource and never gets pinned by Amdahl’s law during a spike.
    • The xAI Memphis cluster running at 11 percent MFU is not necessarily a failure mode. In disaggregated prefill plus decode inference you can deliver very high tokens per watt with very low MFU.
    • The right metric is performance, ultimately tokens per watt as a proxy for intelligence per watt, and even that needs adjustment because not all tokens are equal. Coding tokens are worth more than other tokens.
    • Hopper was designed for pre-training. NVIDIA chose to build multi-billion-dollar systems when the largest existing scientific supercomputer cost $350 million, with no proven customer base. It worked.
    • Grace Blackwell NVLink72 was designed for inference, especially the high-memory-bandwidth decode phase. It is the world’s first rack-scale computer and delivered a 50x speed-up over Hopper in two years, against an expected 2x from Moore’s Law.
    • Vera Rubin is designed for agents. Long-term memory wired into storage and into the GPU fabric, working memory, heavy tool use, and Vera, a CPU optimized for low-latency multi-core single-threaded code so a multi-billion-dollar GPU system does not stall waiting on a slow tool call.
    • Feynman is being shaped for swarms of agents with sub-agents and sub-sub-agents, a recursive software topology that demands a new compute pattern.
    • Tokens per watt improved 50x in one generation. Compounding energy efficiency is the lever NVIDIA controls directly.
    • Total compute energy demand is heading roughly a thousand times higher than today, possibly two orders of magnitude beyond that. Huang says he would not be surprised if the estimate is low.
    • For the first time in history, market forces alone are enough to fund solar, nuclear and grid upgrades. Government subsidies are no longer required to make sustainable energy investment rational.
    • Copper interconnect is becoming a bottleneck. Photonics is moving from optional to structural inside racks and across them.
    • Comparing NVIDIA GPUs to atomic bombs, Huang says, is a stupid analogy. A billion people use NVIDIA GPUs. He advocates them to his family. He does not advocate atomic bombs to anyone.
    • If the United States cedes two thirds of the global market to competitors on policy grounds, the American technology industry will end up like American telecommunications, which was policied out of existence.
    • Huang directly rejects AI doom-by-singularity narratives. It is not true that we have no idea how these systems work. It is not true that the technology becomes infinitely powerful in a nanosecond. He calls the rhetoric irresponsible and harmful to the field students are about to enter.
    • On Stanford specifically: if the university president places an order, NVIDIA will deliver the chips. The bottleneck is that no university department has a billion-dollar compute budget because budgeting is fragmented across grants. Stanford’s $40 billion endowment is more than enough to fix that.
    • “It’s Stanford’s fault” is meant as empowerment. If something is your fault, you can solve it.
    • Career advice: do not optimize purely for passion. Most people do not yet know what they love. Pick the job in front of you and do it as well as possible. Even as CEO, Huang says, 90 percent of the work is hard and he suffers through it.
    • Suffering on purpose builds the muscle of resilience. When the company, the team or the family needs you to be tough, that muscle has to already exist.
    • NVIDIA’s first generation of products was technically wrong in nearly every dimension: curved surfaces instead of triangles, no Z-buffer, forward instead of inverse texture mapping, no floating point. The strategic recovery, not the technology, taught Huang the lessons that have lasted decades.
    • The biggest clean strategic mistake Huang names is the move into mobile chips (Tegra). It grew to a billion dollars then went to zero when Qualcomm’s modem dominance shut NVIDIA out of the 3G to 4G transition. The recovery into automotive and robotics (the Thor chip is the great great great grandson of that mobile lineage) was real, but Huang refuses to rationalize the original choice.
    • Forecasting framework: observe, reason from first principles, ask “so what” and “what next” until you have a mental model of the future, place your company inside that model, then work backwards while minimizing opportunity cost and maximizing optionality.
    • Best part of the CEO job: living at the intersection of vision, strategy and execution surrounded by people capable enough to make ambitious visions real. Worst part: the responsibility for everyone who joined the spaceship, especially in the near-death moments NVIDIA had four or five times early on.
    • Underrated insider note: Huang’s first apple pie with cheese, first hot fudge sandwich and first milkshake all happened at Denny’s. The Superbird, the fried chicken and a custom Superbird-style ham and cheese with tomato and mustard are his order.

    Detailed Summary

    Computing reinvented from the ground up

    Huang frames the moment as the first true rewrite of the computer in sixty-plus years. From the IBM System 360 forward, the mental model of writing code, running code, taking a computer to market and reasoning about applications stayed roughly constant. AI changes the programming model itself. Software is no longer a compiled binary running deterministically on a CPU. It is a neural network running on a GPU producing generated, contextual, real-time output. That cascades into how companies are organized, what tools developers use, what the network and storage stack look like, and what an application is even allowed to do. Robo-taxis, he notes, are an application no one would have attempted before deep learning unlocked perception.

    Codesign and the million-x decade

    Codesign is the philosophical center of the talk. Huang traces it to the RISC work of John Hennessy at Stanford, where simpler instruction sets won by being co-designed with the compiler rather than maximally optimized in isolation. NVIDIA extends the principle across every layer simultaneously: GPU architecture, CPU architecture, NVLink and NVSwitch fabrics, photonic interconnects, networking silicon, storage paths, CUDA libraries, frameworks and ultimately the model design. The numbers Huang gives are arresting. Moore’s Law in its prime delivered roughly 100x per decade. By the time Dennard scaling broke, real-world gains had compressed to roughly 10x. NVIDIA’s codesigned stack delivered between 100,000x and 1,000,000x over the same ten-year window. That non-linear speed-up is, in Huang’s telling, the precondition for modern AI: it is what allowed researchers to stop curating training sets and just feed the entire internet to the model.

    Education has to fuse first principles with AI tools

    Asked how curriculum should evolve, Huang argues AI must be integrated into the learning process, not just taught about. He recalls Hennessy writing his textbook by hand a chapter a week while Huang was a student, and says pre-recorded textbooks cannot keep up with the rate at which AI generates new knowledge. He describes his own learning workflow: hand the paper to an AI, then have it read the entire surrounding literature, then treat the AI as a dedicated researcher who can be interrogated. At the same time he defends the classics. Mead and Conway are still the foundation. Most modern semiconductor scaling tricks have been exhausted, but knowing where the field came from sharpens judgment when designing what comes next.

    Open source and the five domain pillars

    Huang gives one of the most detailed public accounts of why NVIDIA invests so heavily in open foundation models even while being a top customer of closed labs. He recommends Claude and OpenAI by name for production coding work, and says 100 percent of NVIDIA engineers are now agentically supported. The open-weights case rests on three legs. First, language is the codification of intelligence, and there are at least 230 languages that no commercial lab will ever prioritize. Nemotron is built near frontier and released so any country or community can fine-tune it. Second, the same representation-learning approach has to be replicated in domains where the data is not internet text, so NVIDIA seeded BioNeMo for biology, Alphamayo for autonomy, Groot for humanoid robotics and a climate model for mesoscale multiphysics. The economics of those fields would never produce a foundation model on their own. Third, safety and security require transparency. A black box cannot be defended or audited, and the future of cyber defense is not bigger-model-versus-bigger-model but swarms of cheap fast small models like Nemotron Nano surrounding the threat.

    MFU is the wrong metric, tokens per watt is closer

    A student raises the leaked memo that the xAI Memphis cluster is running at 11 percent Model Flops Utilization. Huang flips the framing. He says he would rather be at low MFU all the time, because that means he over-provisioned flops, memory bandwidth, memory capacity and network capacity. Bottlenecks shift constantly, so over-provisioning across every dimension is what lets the system absorb a spike without getting pinned by Amdahl’s law. In disaggregated inference, where prefill and decode are physically separated and decode is bandwidth-bound rather than flop-bound, NVLink72 can deliver extremely high tokens per watt while reporting very low MFU. Huang argues the right framing is performance, and ultimately tokens per watt as a rough proxy for intelligence per watt, adjusted for the fact that not all tokens are equal. A coding token is worth more than a generic token.

    Hopper, Grace Blackwell NVLink72, Vera Rubin, Feynman

    Huang gives the clearest public framing of NVIDIA’s roadmap as a sequence of architectural answers to evolving compute patterns. Hopper was built for pre-training, at a moment when NVIDIA chose to build multi-billion-dollar machines while the largest scientific supercomputer in the world cost $350 million and the marketplace for such systems was, on paper, zero. Grace Blackwell NVLink72 was the answer to inference and reasoning: a rack-scale computer that ganged 72 GPUs together because decode needs aggregate memory bandwidth far beyond a single chip. The generation-over-generation speed-up was 50x in two years, twenty-five times what Moore’s Law would have delivered. Vera Rubin is being built explicitly for agents. Agents load long-term memory from storage that has to be wired directly into the GPU fabric, they use working memory, they call tools that run on a CPU, and they wait. So the CPU has to be Vera, optimized for low-latency single-threaded code, because the multi-billion-dollar GPU system cannot afford to idle waiting on a slow tool call. Feynman extends the pattern to swarms of agents with sub-agents and sub-sub-agents, a recursive software topology that will demand its own compute pattern.

    Energy demand and the grid

    Huang’s energy projection is one of the most aggressive numbers in the talk. NVIDIA can compound tokens per watt by 50x per generation through codesign, but the total compute demand is heading roughly a thousand times higher, and Huang says he would not be surprised if the real figure is one or two orders of magnitude beyond that. The reason is structural: future computing is generative and continuous, not pre-recorded and on-demand. The good news, he argues, is that this is the best moment in the history of humanity to invest in sustainable generation. Market forces alone are now sufficient to fund solar, nuclear and grid upgrades. Government subsidies are no longer required to make the math work.

    Adversarial countries, export controls and the telecom warning

    This is the segment where Huang is visibly fired up. He attacks the GPUs-as-atomic-bombs framing on its face. NVIDIA GPUs power medical imaging, video games and soy sauce delivery. A billion people use them. He advocates them to his family. The analogy collapses at the first comparison. He attacks the second framing, that American companies should not compete abroad because they will lose anyway, as a self-fulfilling defeat. Competition makes the company better. The third framing, that depriving the rest of the world of general-purpose computing benefits the United States, also fails on first principles: it benefits one or two American companies at the cost of an entire industry. The cautionary parallel is telecommunications. The United States once had a leading position in telecom fundamental technology and policied itself out of it. Huang’s worry, voiced explicitly to a room of CS students, is that they will graduate into a shell of a computer industry if the same path is repeated.

    AI doom and rational optimism

    In the same arc Huang rejects the science-fiction framing of AI as a singularity that arrives suddenly on a Wednesday at 7pm and ends civilization. He calls those claims irresponsible, says they are not true, and points out that the people advancing them are believed by audiences who then make policy on that basis. It is not true that no one understands how these systems work. It is not true that intelligence becomes infinitely powerful instantaneously. It is not true that there is no defense. His framing, which the host echoes as “rational optimism,” is that the goal is to create a future where people care about computers because the technology students are learning is worth mastering.

    Stanford’s compute problem is Stanford’s fault

    A student presses on the scarcity of compute for independent researchers, startups and universities inside the United States. Huang’s answer is sharp: there is no shortage. Place the order and the chips will arrive. The actual broken thing is institutional. University grants are fragmented across departments. No researcher can raise enough on a single grant to fund a billion-dollar shared cluster, and no one shares. He compares it to showing up at the grocery store demanding a billion dollars of tomatoes today. The solution is planning, aggregation and a campus-scale supercomputer, the way Stanford once built the linear accelerator. The endowment is $40 billion. Pulling a billion off it, contracting cloud capacity and giving every student and researcher AI supercomputer access is, in Huang’s view, obviously doable. When he says “it is Stanford’s fault” the host laughs, but Huang clarifies: if it is your fault you have the power to fix it.

    Career, suffering and resilience

    Asked how a CS student should spend the next few years, Huang pushes back on the standard “follow your passion” advice. Most people do not know what they love yet, because no one knows what they do not know. The bar of demanding joy from every working day is too high. Whatever the job is, do it as well as you can. Even as CEO of NVIDIA he says he genuinely loves about 10 percent of his work. The other 90 percent is hard and he suffers through it. He recommends suffering on purpose, because resilience is a muscle that only builds under load, and when the company, the team or the family needs that muscle, it has to already exist. Earlier in his life that meant cleaning toilets and busing tables at Denny’s. He does it today running a multi-trillion-dollar company.

    The biggest mistakes

    Huang separates technical mistakes from strategic mistakes. NVIDIA’s first generation of products was technically wrong in almost every way: curved surfaces instead of triangles, no Z-buffer, forward instead of inverse texture mapping, no floating point inside. The company wasted two and a half years. But the strategic genius of the recovery, the reading of the market, the conservation of resources and the reapplication of talent, is what taught him strategy. The clean strategic mistake he names is mobile. NVIDIA’s Tegra line grew to a billion dollars of revenue and then collapsed to zero when Qualcomm’s modem dominance locked NVIDIA out of the 3G to 4G transition. Huang explicitly refuses the comforting rationalization that the Tegra effort fed the Thor automotive chip (“Thor is the great great great grandson”). The original decision, he says, was a waste of time. The lesson is to think one or two clicks further about whether a market is structurally winnable before committing the company.

    Forecasting under fog of war

    The final substantive exchange is on forecasting. Huang’s method has four steps. Observe what is actually happening (AlexNet crushing two decades of computer vision research in one shot, GPT producing reasoning by token generation). Reason from first principles about why it works. Ask “so what” and “what next” recursively until a mental model of the future emerges. Place the company inside that future and work backwards. Crucially, expect to be partly wrong. Some outcomes will absolutely happen, some will likely happen, some might happen, and the strategy has to be robust across that distribution. The real cost of any strategic choice is the opportunity cost of the alternatives you did not take, so the discipline is to minimize that cost and maximize optionality while letting the journey itself pay for the journey.

    Thoughts

    The most useful thing in this conversation is the explicit architectural mapping of compute patterns to chip generations. Hopper for pre-training. Grace Blackwell NVLink72 for inference, because decode is bandwidth-bound and a single chip cannot supply it. Vera Rubin for agents, because tool calls stall multi-billion-dollar GPU systems and so the CPU has to be optimized for low-latency single-threaded code. Feynman for swarms. That sequence is not marketing. It is a falsifiable thesis about where the bottleneck moves next, and every other infrastructure company should be measuring themselves against it. If Huang is right that swarms of sub-agents are the next dominant pattern, then the design pressure shifts from raw flops to fabric topology, memory hierarchy and storage-to-GPU latency. That has implications for everyone downstream, including the hyperscalers building competing accelerators.

    The MFU section is the most intellectually generous moment in the talk. The instinct in the AI ops community has been to chase MFU as if it were a virtue. Huang argues, persuasively, that low MFU is consistent with high tokens per watt in a disaggregated inference setup, and that bottlenecks rotate fast enough that over-provisioning every resource is the rational design. That reframing matters because it changes what “scarce” means. Compute is not scarce in the way the discourse treats it. What is scarce is a coherent system designed end-to-end. The xAI 11 percent number, in that frame, is not embarrassing. It is the natural reading of a workload that is mostly decode.

    The Stanford segment is the part most likely to be quoted out of context. “It’s Stanford’s fault” is a deliberately provocative line, but the underlying claim is correct and load-bearing. Compute is not gated by NVIDIA refusing to ship chips. It is gated by the fact that fragmented grant funding cannot aggregate into the billion-dollar order that NVIDIA can fulfill. The implication is that universities and national labs need a structural change in how they pool capital for compute, and that the current model of every researcher buying a handful of cards is genuinely obsolete. Huang’s nudge about pulling a billion off the endowment is concrete enough to be acted on, and other major research universities should read this segment as a direct prompt.

    The geopolitical segment is the highest-stakes one. The telecommunications comparison is correct as a historical pattern, and Huang is one of the very few executives in a position to deliver that warning credibly. The unresolved tension is that the argument applies symmetrically. If American AI dominance is built by selling globally, that includes selling into adversarial states, and the policy question is where the line falls. Huang does not answer that question. He attacks the framing that lets the question be answered badly. That is a meaningful contribution to the discourse even if it does not resolve the underlying tradeoff.

    The career advice section is the part the social-media clips will mishandle. “Seek suffering” reads as macho when extracted. In context it is a specific operational claim about how resilience compounds, and it is paired with the Tegra story where Huang himself paid the price of not thinking one more click ahead. That kind of self-implication is rare in CEO talks, and it is the reason the talk is worth listening to in full rather than only reading the recap.

    Watch the full Stanford CS153 Frontier Systems conversation with Jensen Huang here.

  • Jensen Huang on Lex Fridman: NVIDIA’s CEO Reveals His Vision for the AI Revolution, Scaling Laws, and Why Intelligence Is Now a Commodity

    A deep breakdown of Lex Fridman Podcast #494 featuring Jensen Huang, CEO of NVIDIA, covering extreme co-design, the four AI scaling laws, CUDA’s origin story, the future of programming, AGI timelines, and what it takes to lead the world’s most valuable company.

    TLDW (Too Long, Didn’t Watch)

    Jensen Huang sat down with Lex Fridman for a sprawling two-and-a-half-hour conversation covering the full arc of NVIDIA’s evolution from a GPU gaming company to the engine of the AI revolution. Jensen explains how NVIDIA now thinks in terms of rack-scale and pod-scale computing rather than individual chips, breaks down his four AI scaling laws (pre-training, post-training, test time, and agentic), and reveals the near-existential bet the company made putting CUDA on GeForce. He shares his views on China’s tech ecosystem, his deep respect for TSMC, why he turned down the chance to become TSMC’s CEO, how Elon Musk’s systems engineering approach built Colossus in record time, and why he believes AGI already exists. He also discusses why the future of programming is really about “specification,” why intelligence is being commoditized while humanity is the true superpower, and how he manages the enormous pressure of leading a company that nations and economies depend on. His core message: do not let the democratization of intelligence cause you anxiety. Instead, let it inspire you.

    Key Takeaways

    1. NVIDIA No Longer Thinks in Chips. It Thinks in AI Factories.

    Jensen’s mental model of what NVIDIA builds has fundamentally changed. He no longer picks up a chip to represent a new product generation. Instead, his mental model is a gigawatt-scale AI factory with power generation, cooling systems, and thousands of engineers bringing it online. The unit of computing at NVIDIA has evolved from GPU to computer to cluster to AI factory. His next mental “click” is planetary-scale computing.

    2. Extreme Co-Design Is NVIDIA’s Secret Weapon

    The reason NVIDIA dominates is not just better GPUs. It is the extreme co-design of the entire stack: GPU, CPU, memory, networking, switching, power, cooling, storage, software, algorithms, and applications. Jensen explains that when you distribute workloads across tens of thousands of computers and want them to go a million times faster (not just 10,000 times), every single component becomes a bottleneck. This is a restatement of Amdahl’s Law at scale. NVIDIA’s organizational structure directly reflects this co-design philosophy. Jensen has 60+ direct reports, holds no one-on-ones, and runs every meeting as a collective problem-solving session where specialists across all domains are present and contribute.

    3. The Four AI Scaling Laws Are a Flywheel

    Jensen outlined four distinct scaling laws that form a continuous loop:

    Pre-training scaling: Larger models plus more data equals smarter AI. The industry panicked when people said data was running out, but synthetic data generation has removed that ceiling. Data is now limited by compute, not by human generation.

    Post-training scaling: Fine-tuning, reinforcement learning from human feedback, and curated data continue to scale AI capabilities beyond what pre-training alone achieves.

    Test-time scaling: Inference is not “easy” as many predicted. It is thinking, reasoning, planning, and search. It is far more compute-intensive than memorization and pattern matching. This is why inference chips cannot be commoditized the way many predicted.

    Agentic scaling: A single AI agent can spawn sub-agents, creating teams. This is like scaling a company by hiring more employees rather than trying to make one person faster. The experiences generated by agents feed back into pre-training, creating a flywheel.

    4. The CUDA Bet Nearly Killed NVIDIA

    Putting CUDA on GeForce was one of the most consequential technology decisions in modern history. It increased GPU costs by roughly 50%, which crushed the company’s gross margins at a time when NVIDIA was a 35% gross margin business. The company’s market cap dropped from around $7-8 billion to approximately $1.5 billion. But Jensen understood that install base defines a computing architecture, not elegance. He pointed to x86 as proof: a less-than-elegant architecture that defeated beautifully designed RISC alternatives because of its massive install base. CUDA on GeForce put a supercomputer in the hands of every researcher, every scientist, every student. It took a decade to recover, but that install base became the foundation of the deep learning revolution.

    5. NVIDIA’s Moat Is Trust, Velocity, and Install Base

    Jensen was direct about NVIDIA’s competitive advantage. The CUDA install base is the number one asset. Developers target CUDA first because it reaches hundreds of millions of computers, is in every cloud, every OEM, every country, every industry. NVIDIA ships a new architecture roughly every year. No company in history has built systems of this complexity at this cadence. And the trust that NVIDIA will maintain, improve, and optimize CUDA indefinitely is something developers can count on. If someone created “GUDA” or “TUDA” tomorrow, it would not matter. The install base, velocity of execution, ecosystem breadth, and earned trust create a compounding advantage that is nearly impossible to replicate.

    6. Jensen Believes AGI Is Already Here

    When asked about AGI timelines, Jensen said he believes AGI has been achieved. His reasoning is practical: an agentic system today could plausibly create a web service, achieve virality, and generate a billion dollars in revenue, even if temporarily. This is not meaningfully different from many internet-era companies that did the same thing with technology no more sophisticated than what current AI agents can produce. He does not believe 100,000 agents could build another NVIDIA, but he believes a single agent-driven viral product is within reach right now.

    7. The Future of Programming Is Specification, Not Syntax

    Jensen believes the number of programmers in the world will increase dramatically, not decrease. His reasoning: the definition of coding is expanding to include specification and architectural description in natural language. This expands the population of “coders” from roughly 30 million professional developers to potentially a billion people. Every carpenter, plumber, accountant, and farmer who can describe what they want a computer to build is now a coder. The artistry of the future is knowing where on the spectrum of specification to operate, from highly prescriptive to exploratory and open-ended.

    8. China Is the Fastest Innovating Country in the World

    Jensen gave a nuanced and detailed explanation of why China’s tech ecosystem is so formidable. About 50% of the world’s AI researchers are Chinese. China’s tech industry emerged during the mobile cloud era, so it was built on modern software from the start. The country’s provincial competition creates an insane internal competitive environment. And the cultural norm of knowledge-sharing through school and family networks means China effectively operates as an open-source ecosystem at all times. This is why Chinese companies contribute disproportionately to open source. Their engineers’ brothers, friends, and schoolmates work at competing companies, and sharing knowledge is the cultural default.

    9. The Power Grid Has Enormous Waste That AI Can Exploit

    Jensen proposed a pragmatic solution to the energy problem for AI data centers. Power grids are designed for worst-case conditions with margin, but 99% of the time they run at around 60% of peak capacity. That idle capacity is simply wasted. Jensen wants data centers to negotiate flexible contracts where they absorb excess power most of the time and gracefully degrade during rare peak demand periods. This requires three things: customers accepting that “six nines” uptime may not always be necessary, data centers that can dynamically shift workloads, and utilities that offer tiered power delivery contracts instead of all-or-nothing commitments.

    10. Jensen Turned Down the CEO Role at TSMC

    In 2013, TSMC founder Morris Chang offered Jensen the chance to become CEO of TSMC. Jensen confirmed the story is true and said he was deeply honored. But he had already envisioned what NVIDIA could become and felt it was his sole responsibility to make that vision happen. He sees the relationship with TSMC as one built on three decades of trust, hundreds of billions of dollars in business, and zero formal contracts.

    11. Elon Musk’s Systems Engineering Approach Is Instructive

    Jensen praised Elon Musk’s approach to building the Colossus supercomputer in Memphis in just four months. He highlighted several principles: Elon questions everything relentlessly, strips every process down to the minimum necessary, is physically present at the point of action, and his personal urgency creates urgency in every supplier. Jensen drew a parallel to NVIDIA’s own “speed of light” methodology, where every process is benchmarked against the physical limits of what is possible, not against historical baselines.

    12. Intelligence Is a Commodity. Humanity Is Not.

    Perhaps the most philosophical takeaway from the conversation: Jensen argued that intelligence is a functional, measurable thing that is being commoditized. He surrounded himself with 60 direct reports who are all “superhuman” in their respective domains, more educated and deeper in their specialties than he is. Yet he sits in the middle orchestrating all of them. This proves that intelligence alone does not determine success. Character, compassion, grit, determination, tolerance for embarrassment, and the ability to endure suffering are the real differentiators. Jensen wants the audience to understand that the word we should elevate is not intelligence but humanity.

    Detailed Summary

    From GPU Maker to AI Infrastructure Company

    The conversation opened with Jensen explaining NVIDIA’s evolution from chip-scale to rack-scale to pod-scale design. The Vera Rubin pod, announced at GTC, contains seven chip types, five purpose-built rack types, 40 racks, 1.2 quadrillion transistors, nearly 20,000 NVIDIA dies, over 1,100 Rubin GPUs, 60 exaflops of compute, and 10 petabytes per second of scale bandwidth. And that is just one pod. NVIDIA plans to produce roughly 200 of these pods per week.

    Jensen explained that extreme co-design is necessary because the problems AI must solve no longer fit inside a single computer. When you distribute a workload across 10,000 computers but want a million-fold speedup, everything becomes a bottleneck: computation, networking, switching, memory, power, cooling. This is fundamentally an Amdahl’s Law problem at planetary scale. If computation represents only 50% of the workload, speeding it up infinitely only doubles total throughput. Every layer must be co-optimized simultaneously.

    NVIDIA’s organizational structure is a direct reflection of this co-design philosophy. Jensen has more than 60 direct reports, almost all with deep engineering expertise. He does not do one-on-ones. Every meeting is a collective problem-solving session where the memory expert, the networking expert, the cooling expert, and the power delivery expert are all in the room together, attacking the same problem.

    The Strategic History of CUDA

    Jensen walked through the step-by-step journey from graphics accelerator to computing platform. The company invented a programmable pixel shader, then added IEEE-compatible FP32 to its shaders, then put C on top of that (called Cg), and eventually arrived at CUDA. The critical strategic decision was putting CUDA on GeForce, a consumer product.

    This was nearly an existential move. It increased GPU costs by roughly 50% and consumed all of the company’s gross profit at a time when NVIDIA was a 35% gross margin business. The market cap cratered from around $7-8 billion to approximately $1.5 billion. But Jensen understood a principle that many technologists overlook: install base defines a computing architecture. x86 survived not because it was elegant but because it was everywhere. CUDA on GeForce put a supercomputing capability in the hands of every gamer, every student, every researcher who built their own PC. When the deep learning revolution arrived, CUDA was already the foundation.

    How Jensen Leads and Makes Decisions

    Jensen described a leadership philosophy built on continuous reasoning in public. He does not make announcements in the traditional sense. Instead, he shapes the belief systems of his employees, board, partners, and the broader industry over months and years by reasoning through decisions step by step, using every new piece of external information as a brick in the foundation. By the time he formally announces a strategic direction, the reaction is not surprise but rather, “What took you so long?”

    He applies this same approach to his supply chain. He personally visits CEOs of DRAM companies, packaging companies, and infrastructure providers. He explains the dynamics of the industry, shares his vision of future demand, and helps them reason through why they should make multi-billion-dollar capital investments. Three years ago, he convinced DRAM CEOs that HBM memory would become mainstream for data centers, which sounded ridiculous at the time. Those companies had record years as a result.

    Jensen’s “speed of light” methodology is his framework for decision-making. Every process, every design, every cost is benchmarked against the physical limits of what is theoretically possible. He prefers this to continuous improvement, which he views as incrementalism. He would rather strip a 74-day process back to zero and ask, “If we built this from scratch today, how long would it take?” Often the answer is six days, and the remaining 68 days are filled with accumulated compromises that can be challenged individually.

    AI Scaling Laws and the Future of Compute

    Jensen broke down the four scaling laws in detail. The pre-training scaling law, which depends on model size and data volume, was thought to be hitting a wall when the industry worried about running out of high-quality human-generated data. Jensen argued this concern is misplaced. Synthetic data generation has effectively removed the ceiling, and the constraint is now compute, not data.

    Post-training continues to scale through fine-tuning and reinforcement learning. Test-time scaling was the most counterintuitive for the industry. Many predicted that inference would be “easy” and that inference chips would be small, cheap, and commoditized. Jensen saw this as fundamentally wrong. Inference is thinking: reasoning, planning, search, decomposing novel problems into solvable pieces. Thinking is much harder than reading, and test-time compute is intensely resource-hungry.

    Agentic scaling is the newest frontier. A single AI agent can spawn sub-agents, effectively multiplying intelligence the way a company scales by hiring. The experiences and data generated by agentic systems feed back into pre-training, creating a continuous improvement loop. Jensen described this as the reason NVIDIA designed the Vera Rubin rack architecture differently from the Grace Blackwell architecture. Grace Blackwell was optimized for running large language models. Vera Rubin is designed for agents, which need to access files, use tools, do research, and spin off sub-agents. NVIDIA anticipated this architectural shift two and a half years before tools like OpenClaw arrived.

    China, TSMC, and the Global Supply Chain

    Jensen provided a thoughtful analysis of China’s tech ecosystem. He identified several structural advantages: 50% of the world’s AI researchers are Chinese, the tech industry was born during the mobile cloud era (making it natively modern), provincial competition creates internal Darwinian pressure, and the culture of knowledge-sharing through school and family networks makes China effectively open-source by default.

    On TSMC, Jensen emphasized that the deepest misunderstanding about the company is that its technology is its only advantage. Their manufacturing orchestration system, which dynamically manages the shifting demands of hundreds of companies, is “completely miraculous.” Their culture uniquely balances bleeding-edge technology excellence with world-class customer service. And the trust that Jensen places in TSMC is extraordinary: three decades of partnership, hundreds of billions of dollars in business, and no formal contract.

    Jensen also discussed the AI supply chain more broadly. NVIDIA has roughly 200 suppliers contributing technology to each rack. Jensen personally manages these relationships, flying to supplier sites, explaining industry dynamics, and helping CEOs reason through multi-billion-dollar investment decisions. When asked if supply chain bottlenecks keep him up at night, he said no, because he has already communicated what NVIDIA needs, his partners have told him what they will deliver, and he believes them.

    The Energy Challenge and Space Computing

    On the energy front, Jensen proposed a practical approach to the power problem. Rather than waiting for new power generation, he wants to capture the enormous waste already present in the grid. Power infrastructure is designed for worst-case peak demand, but 99% of the time it runs far below capacity. AI data centers could absorb this excess capacity with flexible contracts that allow graceful degradation during rare peak periods.

    On space computing, NVIDIA already has GPUs in orbit for satellite imaging. Jensen acknowledged the cooling challenge (no conduction or convection in space, only radiation) but sees it as a future frontier worth cultivating. In the meantime, he is focused on the lower-hanging fruit of eliminating waste in the terrestrial power grid.

    On AGI, Jobs, and the Human Future

    Jensen stated directly that he believes AGI has been achieved, at least by the practical definition of an AI system capable of creating a billion-dollar company. He sees it as plausible that an agent could build a viral web service that briefly generates enormous revenue, just as many internet-era companies did with technology no more sophisticated than what current AI agents produce.

    On jobs, Jensen was both compassionate and clear-eyed. He told the story of radiology: computer vision became superhuman around 2019-2020, and the prediction was that radiologists would disappear. Instead, the number of radiologists grew because AI allowed them to study more scans, diagnose better, and serve more patients. The purpose of the job (diagnosing disease) did not change, even though the tools changed completely.

    He applied this principle broadly: the number of software engineers at NVIDIA will grow, not decline, because their purpose is solving problems, not writing lines of code. The number of programmers globally will grow because the definition of coding is expanding to include natural language specification, opening it up to potentially a billion people.

    His advice to anyone worried about their job is straightforward: go use AI now. Become expert in it. Every profession, from carpenter to pharmacist to lawyer, will be elevated by AI tools. The people who learn to use AI will be the ones who get hired, promoted, and empowered.

    Mortality, Succession, and Legacy

    The conversation closed with deeply personal reflections. Jensen said he really does not want to die. He sees the current moment as a “once in a humanity experience.” He does not believe in traditional succession planning. Instead, he believes the best succession strategy is to pass on knowledge continuously, every single day, in every meeting, as fast as possible. His hope is to die on the job, instantaneously, with no long period of suffering.

    He described a vision for a kind of digital continuity: sending a humanoid robot into space, continuously improving it in flight, and eventually uploading the consciousness derived from a lifetime of communications, decisions, and reasoning to catch up with it at the speed of light.

    On the emotional experience of leading NVIDIA, Jensen was candid about hitting psychological low points regularly. His coping mechanism is decomposition: break the problem into pieces, reason about what you can control, tell someone who can help, share the burden, and then deliberately forget what is behind you. He compared this to the mental discipline of great athletes who focus only on the next point.

    His final message was about the relationship between intelligence and humanity. Intelligence, he argued, is functional. It is being commoditized. Humanity, character, compassion, grit, tolerance for embarrassment, and the capacity for suffering are the true superpowers. The word society should elevate is not intelligence but humanity.

    Thoughts

    This is one of the most substantive CEO interviews of 2026. What makes it remarkable is not just the breadth of topics but the depth of reasoning Jensen demonstrates in real time. You can actually watch him think through problems on the spot, which is rare for someone at his level.

    A few things stand out. First, the CUDA origin story is one of the great strategic narratives in tech history. The decision to absorb a 50% cost increase on a consumer product, watching your market cap collapse by 80%, and holding the course for a decade because you understood the power of install base is the kind of conviction that separates generational companies from everyone else.

    Second, Jensen’s framing of the four scaling laws as a flywheel is the clearest articulation anyone has given of why AI compute demand will continue to accelerate. Most people understand pre-training. Fewer understand test-time scaling. Almost nobody is thinking about agentic scaling as a compute multiplier. Jensen has been thinking about it for years and already designed hardware for it before the software ecosystem caught up.

    Third, the discussion on jobs deserves attention. The radiology example is powerful because it is a completed experiment, not a prediction. The profession that was supposed to be eliminated first by AI instead grew. The mechanism is straightforward: when you automate the task, you expand the capacity of the purpose, and demand for the purpose increases. This does not mean there will be no pain or dislocation. Jensen acknowledged that explicitly. But the historical pattern is clear.

    Finally, the philosophical distinction between intelligence and humanity is the kind of framing that could genuinely help people navigate the anxiety of this moment. If you define your value by your intelligence alone, AI commoditization is terrifying. If you define your value by your character, your compassion, your tolerance for suffering, and your willingness to keep going when everything goes wrong, then AI is just the most powerful set of tools you have ever been given.

    Jensen Huang is 62 years old, has been running NVIDIA for 34 years, and shows no signs of slowing down. If anything, his conviction about the future is accelerating alongside his company’s growth.

    Watch the full episode: Lex Fridman Podcast #494 with Jensen Huang

  • Composer: Building a Fast Frontier Model with Reinforcement Learning

    Composer represents Cursor’s most ambitious step yet toward a new generation of intelligent, high-speed coding agents. Built through deep reinforcement learning (RL) and large-scale infrastructure, Composer delivers frontier-level results at speeds up to four times faster than comparable models:contentReference[oaicite:0]{index=0}. It isn’t just another large language model; it’s an actively trained software engineering assistant optimized to think, plan, and code with precision — in real time.

    From Cheetah to Composer: The Evolution of Speed

    The origins of Composer go back to an experimental prototype called Cheetah, an agent Cursor developed to study how much faster coding models could get before hitting usability limits. Developers consistently preferred the speed and fluidity of an agent that responded instantly, keeping them “in flow.” Cheetah proved the concept, but it was Composer that matured it — integrating reinforcement learning and mixture-of-experts (MoE) architecture to achieve both speed and intelligence.

    Composer’s training goal was simple but demanding: make the model capable of solving real-world programming challenges in real codebases using actual developer tools. During RL, Composer was given tasks like editing files, running terminal commands, performing semantic searches, or refactoring code. Its objective wasn’t just to get the right answer — it was to work efficiently, using minimal steps, adhering to existing abstractions, and maintaining code quality:contentReference[oaicite:1]{index=1}.

    Training on Real Engineering Environments

    Rather than relying on synthetic datasets or static benchmarks, Cursor trained Composer within a dynamic software environment. Every RL episode simulated an authentic engineering workflow — debugging, writing unit tests, applying linter fixes, and performing large-scale refactors. Over time, Composer developed behaviors that mirror an experienced developer’s workflow. It learned when to open a file, when to search globally, and when to execute a command rather than speculate.

    Cursor’s evaluation framework, Cursor Bench, measures progress by realism rather than abstract metrics. It compiles actual agent requests from engineers and compares Composer’s solutions to human-curated optimal responses. This lets Cursor measure not just correctness, but also how well the model respects a team’s architecture, naming conventions, and software practices — metrics that matter in production environments.

    Reinforcement Learning as a Performance Engine

    Reinforcement learning is at the heart of Composer’s performance. Unlike supervised fine-tuning, which simply mimics examples, RL rewards Composer for producing high-quality, efficient, and contextually relevant work. It actively learns to choose the right tools, minimize unnecessary output, and exploit parallelism across tasks. The model was even rewarded for avoiding unsupported claims — pushing it to generate more verifiable and responsible code suggestions.

    As RL progressed, emergent behaviors appeared. Composer began autonomously running semantic searches to explore codebases, fixing linter errors, and even generating and executing tests to validate its own work. These self-taught habits transformed it from a passive text generator into an active agent capable of iterative reasoning.

    Infrastructure at Scale: Thousands of Sandboxed Agents

    Behind Composer’s intelligence is a massive engineering effort. Training large MoE models efficiently requires significant parallelization and precision management. Cursor’s infrastructure, built with PyTorch and Ray, powers asynchronous RL at scale. Their system supports thousands of simultaneous environments, each a sandboxed virtual workspace where Composer experiments safely with file edits, code execution, and search queries.

    To achieve this scale, the team integrated MXFP8 MoE kernels with expert and hybrid-sharded data parallelism. This setup allows distributed training across thousands of NVIDIA GPUs with minimal communication cost — effectively combining speed, scale, and precision. MXFP8 also enables faster inference without any need for post-training quantization, giving developers real-world performance gains instantly.

    Cursor’s infrastructure can spawn hundreds of thousands of concurrent sandboxed coding environments. This capability, adapted from their Background Agents system, was essential to unify RL experiments with production-grade conditions. It ensures that Composer’s training environment matches the complexity of real-world coding, creating a model genuinely optimized for developer workflows.

    The Cursor Bench and What “Frontier” Means

    Composer’s benchmark performance earned it a place in what Cursor calls the “Fast Frontier” class — models designed for efficient inference while maintaining top-tier quality. This group includes systems like Haiku 4.5 and Gemini Flash 2.5. While GPT-5 and Sonnet 4.5 remain the strongest overall, Composer outperforms nearly every open-weight model, including Qwen Coder and GLM 4.6:contentReference[oaicite:2]{index=2}. In tokens-per-second performance, Composer’s throughput is among the highest ever measured under the standardized Anthropic tokenizer.

    Built by Developers, for Developers

    Composer isn’t just research — it’s in daily use inside Cursor. Engineers rely on it for their own development, using it to edit code, manage large repositories, and explore unfamiliar projects. This internal dogfooding loop means Composer is constantly tested and improved in real production contexts. Its success is measured by one thing: whether it helps developers get more done, faster, and with fewer interruptions.

    Cursor’s goal isn’t to replace developers, but to enhance them — providing an assistant that acts as an extension of their workflow. By combining fast inference, contextual understanding, and reinforcement learning, Composer turns AI from a static completion tool into a real collaborator.

    Wrap Up

    Composer represents a milestone in AI-assisted software engineering. It demonstrates that reinforcement learning, when applied at scale with the right infrastructure and metrics, can produce agents that are not only faster but also more disciplined, efficient, and trustworthy. For developers, it’s a step toward a future where coding feels as seamless and interactive as conversation — powered by an agent that truly understands how to build software.