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

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

    TLDW

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

    Thoughts

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

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

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

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

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

    Key Takeaways

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

    Detailed Summary

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

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

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

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

    Knowing Your Leadership Style and the Challenge Coin

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

    Lessons from Jensen: Killing Politics and Serving the Customer

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

    Reality Quotient, the Dominant Game, and Change Management

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

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

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

    Selling Speed and Intentional Leadership

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

    Grok Bonds: Three Weeks From Zero

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

    Hiring for Negatives, Loss Bias, and Manufactured Discontent

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

    The Optimistic Close: Free Code and Universal Software Literacy

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

    Notable Quotes

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

    Jonathan Ross, on the fundamental shift AI creates

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

    Jonathan Ross, on leading creative teams

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

    Jonathan Ross, on why faster inference produces more capable models

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

    Jonathan Ross, defining reality quotient versus IQ

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

    Jonathan Ross, on the lesson from Turn the Ship Around

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

    Jonathan Ross, on why Grok bonds kept the team together

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

    Jonathan Ross, on inverting his approach to hiring

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

    Jonathan Ross, on the discontent that drives him today

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

    Related Reading

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

  • 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.