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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.

  • Jensen Huang on Nvidia’s Supply Chain Moat, TPU Competition, China Export Controls, and Why Nvidia Will Not Become a Cloud (Dwarkesh Podcast Summary)

    TLDW (Too Long, Didn’t Watch)

    Jensen Huang sat down with Dwarkesh Patel for over 90 minutes covering Nvidia’s supply chain dominance, the TPU threat, why Nvidia will not become a hyperscaler, whether the US should sell AI chips to China, and why Nvidia does not pursue multiple chip architectures at once. Jensen framed Nvidia’s entire business as transforming “electrons into tokens” and argued that Nvidia’s real moat is not any single technology but the full stack ecosystem it has built over two decades. He was blunt about his regret over not investing in Anthropic and OpenAI earlier, passionate about keeping the American tech stack dominant worldwide, and dismissive of the idea that China’s chip industry can be meaningfully contained through export controls.

    Key Takeaways

    1. Nvidia’s moat is the ecosystem, not the chip. Jensen repeatedly emphasized that Nvidia’s competitive advantage comes from CUDA, its massive installed base, its deep partnerships across the entire supply chain, and the fact that it operates in every cloud. The moat is not a single product but an interlocking system that took 20+ years to build.

    2. Supply chain bottlenecks are temporary, energy bottlenecks are not. Jensen argued that CoWoS packaging, HBM memory, EUV capacity, and logic fabrication bottlenecks can all be resolved in two to three years with the right demand signal. The real constraint on AI scaling is energy policy, which takes far longer to fix.

    3. TPUs and ASICs are not an existential threat to Nvidia. Jensen was emphatic that no competitor has demonstrated better price-performance or performance-per-watt than Nvidia, and challenged TPU and Trainium to prove otherwise on public benchmarks like InferenceMAX and MLPerf. He described Anthropic as a “unique instance, not a trend” for TPU adoption.

    4. Jensen regrets not investing in Anthropic and OpenAI earlier. He admitted he did not deeply internalize how much capital AI labs needed and that traditional VC funding was not sufficient for companies at that scale. He described this as a clear miss, though he said Nvidia was not in a position to make multi-billion dollar investments at the time.

    5. Nvidia will not become a hyperscaler. Jensen’s philosophy is “do as much as needed, as little as possible.” Building cloud infrastructure is something other companies can do, so Nvidia supports neoclouds like CoreWeave, Nebius, and Nscale instead of competing with them. Nvidia invests in ecosystem partners rather than vertically integrating into cloud services.

    6. Jensen is strongly against US chip export controls on China. This was the longest and most heated segment of the interview. Jensen argued that China already has abundant compute, energy, and AI researchers, and that export controls have accelerated China’s domestic chip industry while causing the US to concede the world’s second-largest technology market. He compared the situation to how US telecom policy allowed Huawei to dominate global telecommunications.

    7. AI will cause software tool usage to skyrocket, not collapse. Jensen pushed back on the narrative that AI will commoditize software companies. He argued that agents will use existing tools at massive scale, causing the number of instances of products like Excel, Synopsys Design Compiler, and other enterprise tools to grow exponentially.

    8. Nvidia does not pick winners among AI labs. Jensen explained that Nvidia invests across multiple foundation model companies simultaneously and refuses to favor any single one. He cited his own company’s unlikely survival story as the reason for this humility: Nvidia’s original graphics architecture was “precisely wrong” and would have been counted out by anyone picking winners.

    9. Nvidia added Groq for premium token economics. Nvidia recently acquired Groq and is folding it into the CUDA ecosystem because the market is now segmenting into different token tiers. Some customers will pay premium prices for faster response times even at lower throughput, creating a new segment of the inference market.

    10. Without AI, Nvidia would still be very large. Jensen was clear that accelerated computing, not AI specifically, is the foundational mission of the company. Molecular dynamics, quantum chemistry, computational lithography, data processing, and physics simulation all benefit from GPU acceleration regardless of deep learning.

    Detailed Summary

    Nvidia’s Real Business: Electrons to Tokens

    Jensen opened the conversation by reframing Nvidia’s entire value proposition. When Dwarkesh suggested that Nvidia is fundamentally a software company that sends a GDS2 file to TSMC for manufacturing, Jensen pushed back hard. He described Nvidia’s job as transforming electrons into tokens, with everything in between representing an “incredible journey” of artistry, engineering, science, and invention. He said the transformation is far from deeply understood and the journey is far from over, making commoditization unlikely.

    Jensen described Nvidia as operating a philosophy of doing “as much as necessary and as little as possible.” Whatever Nvidia does not need to do itself, it partners with someone else and makes it part of the broader ecosystem. This is why Nvidia has what Jensen called probably the largest ecosystem of partners in the industry, spanning the full supply chain upstream and downstream, application developers, model makers, and all five layers of the AI stack.

    On the question of whether AI will commoditize software companies, Jensen offered a contrarian take. He argued that agents are going to use software tools at unprecedented scale, meaning the number of instances of products like Excel, Cadence design tools, and Synopsys compilers will skyrocket. Today the bottleneck is the number of human engineers. Tomorrow, those engineers will be supported by swarms of agents exploring design spaces and using the same tools humans use today. Jensen said the reason this has not happened yet is simply that the agents are not good enough at using tools. That will change.

    The Supply Chain Moat

    Dwarkesh pressed Jensen on Nvidia’s reported $100 billion (and potentially $250 billion) in purchase commitments with foundries, memory manufacturers, and packaging companies. The question was whether Nvidia’s real moat for the next few years is simply locking up scarce upstream components so that no competitor can get the memory and logic they need to build alternative accelerators.

    Jensen confirmed this is a significant advantage but framed it differently. He said Nvidia has made enormous explicit and implicit commitments upstream. The implicit commitments matter just as much: Jensen personally meets with CEOs across the supply chain to explain the scale of the coming AI industry, convince them to invest in capacity, and assure them that Nvidia’s downstream demand is large enough to justify that investment. Nvidia’s GTC conference serves this purpose too, bringing the entire ecosystem together so upstream suppliers can see downstream demand and vice versa.

    Jensen described a process of systematically “prefetching bottlenecks” years in advance. CoWoS advanced packaging was a major bottleneck two years ago, but Nvidia swarmed it with repeated doubling of capacity until TSMC recognized it as mainstream computing technology rather than a specialty product. More recently, Nvidia has invested in the silicon photonics ecosystem through partnerships with Lumentum and Coherent, invented new packaging technologies, licensed patents to keep the supply chain open, and even invested in new testing equipment like double-sided probing.

    When Dwarkesh asked about the ultimate physical bottlenecks, Jensen surprised him. The hardest bottleneck to solve is not CoWoS or HBM or EUV machines. It is plumbers and electricians needed to build data centers. Jensen used this as a launching point to criticize “doomers” who discourage people from pursuing careers in software engineering or radiology, arguing that scaring people out of these professions creates the real bottlenecks.

    On EUV and logic scaling specifically, Jensen was optimistic. He said no supply chain bottleneck lasts longer than two to three years. Once you can build one of something, you can build ten, and once you can build ten, you can build a million. The key is a clear demand signal. If TSMC is convinced of the demand, ASML will produce enough EUV machines. Meanwhile, Nvidia continues to improve computing efficiency by 10x to 50x per generation through architecture, algorithms, and system design.

    The TPU Question

    Dwarkesh pushed hard on whether Google’s TPUs represent a real threat, noting that two of the top three AI models (Claude and Gemini) were trained on TPUs. Jensen drew a sharp distinction between what Nvidia builds and what a TPU is. Nvidia builds accelerated computing, which serves molecular dynamics, quantum chromodynamics, data processing, fluid dynamics, particle physics, and AI. A TPU is a tensor processing unit optimized for matrix multiplies. Nvidia’s market reach is far greater than any TPU or ASIC can possibly have.

    Jensen emphasized programmability as Nvidia’s core architectural advantage. If you want to invent a new attention mechanism, build a hybrid SSM model, fuse diffusion and autoregressive techniques, or disaggregate computation in a novel way, you need a generally programmable architecture. The only way to achieve 10x or 100x performance leaps (versus the roughly 25% per year from Moore’s Law) is to fundamentally change the algorithm, and that requires the flexibility CUDA provides.

    On the specific question of whether hyperscalers with huge engineering teams can simply write their own kernels and bypass CUDA, Jensen acknowledged they do write custom kernels but argued that Nvidia’s engineers still routinely deliver 2x to 3x speedups when they optimize a partner’s stack. He described Nvidia’s GPUs as “F1 racers” that anyone can drive at 100 mph, but extracting peak performance requires deep architectural expertise. Nvidia uses AI itself to generate many of its optimized kernels.

    Jensen was particularly blunt about public benchmarks. He pointed to Dylan Patel’s InferenceMAX benchmark and said neither TPU nor Trainium has been willing to demonstrate their claimed performance advantages on it. He said Nvidia’s performance-per-TCO is the best in the world, “bar none,” and challenged anyone to prove otherwise.

    Regarding Anthropic’s multi-gigawatt deal with Broadcom and Google for TPUs, Jensen called it “a unique instance, not a trend.” He said without Anthropic, there would be essentially no TPU growth and no Trainium growth. He traced this back to his own mistake: when Anthropic and OpenAI needed multi-billion dollar investments from their compute suppliers to get off the ground, Nvidia was not in a position to provide that capital. Google and AWS were, and in return, Anthropic committed to using their compute.

    Nvidia’s Investment Strategy and Regrets

    Jensen was unusually candid about his regret over not investing in foundation model companies earlier. He said he did not deeply internalize how different AI labs were from typical startups. A traditional VC would never put $5 to $10 billion into a single AI lab, but that was exactly what companies like OpenAI and Anthropic needed. By the time Jensen understood this, Nvidia was not in a financial or cultural position to make those kinds of investments.

    Now, Nvidia has invested approximately $30 billion in OpenAI and $10 billion in Anthropic. Jensen said he is delighted to support both and considers their existence essential for the world. But he acknowledged that these investments came at much higher valuations than would have been possible years earlier.

    Jensen explained Nvidia’s broader investment philosophy: support everyone, do not pick winners. He invests in one foundation model company, he invests in all of them. This comes from hard-won humility. When Nvidia started, there were 60 3D graphics companies. Nvidia’s original architecture was “precisely wrong” and the company would have been at the top of most lists to fail. Jensen said he has enough humility from that experience to know that you cannot predict which AI company will ultimately succeed.

    Why Nvidia Will Not Become a Hyperscaler

    Dwarkesh pointed out that Nvidia has the cash to build and operate its own cloud infrastructure, bypassing the middleman ecosystem that converts CapEx into OpEx for AI labs. Jensen rejected this path based on his core operating philosophy.

    If Nvidia did not build its computing platform, NVLink, and the CUDA ecosystem, nobody else would have done it. He is “completely certain” of that. These are things Nvidia must do. But the world has lots of clouds. If Nvidia did not build a cloud, someone else would show up. So the answer is to support the ecosystem instead: invest in CoreWeave, Nscale, Nebius, and others to help them exist and scale, rather than competing with them.

    Jensen was clear that Nvidia is not trying to be in the financing business either. When OpenAI needed a $30 billion investment before its IPO, Nvidia stepped up because OpenAI needed it and Nvidia deeply believed in the company. But these are targeted ecosystem investments, not a strategic pivot into cloud services.

    On GPU allocation during shortages, Jensen pushed back on the narrative that Nvidia strategically “fractures” the market by giving allocations to smaller neoclouds. He said the process is straightforward: you forecast demand, you place a purchase order, and it is first in, first out. Nvidia never changes prices based on demand. Jensen said he prefers to be dependable and serve as the foundation of the industry rather than extracting maximum short-term value.

    The China Debate

    The longest and most heated section of the interview was Jensen’s case against US chip export controls on China. This was a genuine debate, with Dwarkesh pushing the national security argument and Jensen pushing back forcefully.

    Jensen’s core argument rested on several pillars. First, China already has abundant compute. They manufacture 60% or more of the world’s mainstream chips, have massive energy infrastructure (including empty data centers with full power), and employ roughly 50% of the world’s AI researchers. The threshold of compute needed to build models like Anthropic’s Mythos has already been reached and exceeded by China’s existing infrastructure.

    Second, export controls have backfired. They accelerated China’s domestic chip industry, forced their AI ecosystem to optimize for internal architectures instead of the American tech stack, and caused the United States to concede the second-largest technology market in the world. Jensen compared this directly to how US telecom policy allowed Huawei to dominate global telecommunications infrastructure.

    Third, Jensen argued that AI is a five-layer stack (energy, chips, computing platform, models, applications) and the US needs to win at every layer. Fixating on one layer (models) at the expense of another layer (chips) is counterproductive. If Chinese open source AI models end up optimized for non-American hardware and that stack gets exported to the global south, the Middle East, Africa, and Southeast Asia, the US will have lost something far more valuable than whatever marginal compute advantage the export controls provided.

    Dwarkesh countered with the Mythos example: Anthropic’s new model found thousands of high-severity zero-day vulnerabilities across every major operating system and browser, including one that had existed in OpenBSD for 27 years. If China had enough compute to train and deploy a model like Mythos at scale before the US could prepare, the cyber-offensive capabilities would be devastating.

    Jensen’s response was direct. Mythos was trained on “fairly mundane capacity” that is already abundantly available in China. The amount of compute is not the bottleneck for that kind of breakthrough. Great computer science is, and China has no shortage of brilliant AI researchers. He pointed to DeepSeek as evidence: most advances in AI come from algorithmic innovation, not raw hardware. If China’s researchers can achieve breakthroughs like DeepSeek with limited hardware, imagine what they could do with more.

    Jensen also argued for dialogue over confrontation. He said it is essential that American and Chinese AI researchers are talking to each other, and that both countries agree on what AI should not be used for. The idea that you can prevent AI risks by cutting off chip sales, when the real advances come from algorithms and computer science, reflects a fundamental misunderstanding of how AI progress works.

    The debate ended without resolution, but Jensen’s final point was sharp: “I’m not talking to somebody who woke up a loser. That loser attitude, that loser premise, makes no sense to me.”

    Why Not Multiple Chip Architectures?

    Near the end of the interview, Dwarkesh asked why Nvidia does not run multiple parallel chip projects with different architectures, like a Cerebras-style wafer-scale design or a Dojo-style huge package, or even one without CUDA.

    Jensen’s answer was simple: “We don’t have a better idea.” Nvidia simulates all of these alternative approaches in its internal simulators and they are provably worse. The company works on exactly the projects it wants to work on. If the workload were to change dramatically (not just the algorithms, but the actual market shape), Nvidia might add other accelerators.

    In fact, Nvidia recently did exactly this by acquiring Groq. The inference market is now segmenting into different tiers. Some customers will pay premium prices for extremely fast response times even if throughput is lower. This creates a new “high ASP token” segment that justifies a different point on the performance curve. But Jensen was clear: if he had more money, he would put it all behind Nvidia’s existing architecture, not diversify into alternatives.

    Nvidia Without AI

    Jensen closed by saying that even if the deep learning revolution had never happened, Nvidia would be “very, very large.” The premise of the company has always been that general-purpose computing cannot scale indefinitely and that domain-specific acceleration is the way forward. Molecular dynamics, seismic processing, image processing, computational lithography, quantum chemistry, and data processing all benefit from GPU acceleration regardless of AI. Jensen said the fundamental promise of accelerated computing has not changed “not even a little bit.”

    Thoughts

    This interview is one of the most revealing Jensen Huang conversations in years, partly because Dwarkesh actually pushes back instead of lobbing softballs. A few things stand out.

    The Anthropic regret is real and significant. Jensen is essentially admitting that Nvidia’s biggest strategic miss of the AI era was not understanding that foundation model companies needed supplier-level capital commitments, not VC funding. The fact that Google and AWS used compute investments to lock in Anthropic’s architecture choices has had downstream consequences that Nvidia is still working to unwind. When Jensen says Anthropic is “a unique instance, not a trend” for TPU adoption, he is simultaneously downplaying the threat and revealing exactly how seriously he takes it.

    The China debate is the highlight. Jensen’s argument is more nuanced than it first appears. He is not saying “sell China everything.” He is saying the current binary approach of near-total restriction has backfired by accelerating China’s domestic chip industry and pushing the Chinese AI ecosystem away from the American tech stack. His comparison to the US telecom industry losing global market share to Huawei is pointed and historically grounded. Whether you agree with his conclusion or not, the framing of AI as a five-layer stack where the US needs to compete at every layer is a useful mental model.

    The “electrons to tokens” framing is Jensen at his best. It is a simple metaphor that captures something genuinely complex about where value is created in the AI supply chain. And his insistence that the transformation is “far from deeply understood” is a subtle way of arguing that Nvidia’s competitive position will be durable because the problem space is not close to being solved.

    The Groq acquisition reveal is interesting for what it signals about the inference market. If Nvidia is creating a separate product tier for premium-priced, low-latency tokens, it suggests the company sees inference economics fragmenting significantly. This aligns with the broader trend of AI becoming an enterprise product where different customers have wildly different willingness to pay based on how they use tokens.

    Finally, Jensen’s refusal to diversify chip architectures is a bold bet. “We simulate it all in our simulator, provably worse” is an incredibly confident statement. History is full of companies that were right until they were not. But Nvidia’s track record of 50x generation-over-generation improvements through co-design across processors, fabric, libraries, and algorithms is hard to argue with. The question is whether the current paradigm of transformer-based models on GPU clusters represents a local or global optimum for AI compute.

  • The AI Revolution Unveiled: Jonathan Ross on Groq, NVIDIA, and the Future of Inference

    TL;DR

    Jonathan Ross, Groq’s CEO, predicts inference will eclipse training in AI’s future, with Groq’s Language Processing Units (LPUs) outpacing NVIDIA’s GPUs in cost and efficiency. He envisions synthetic data breaking scaling limits, a $1.5 billion Saudi revenue deal fueling Groq’s growth, and AI unlocking human potential through prompt engineering, though he warns of an overabundance trap.

    Detailed Summary

    In a captivating 20VC episode with Harry Stebbings, Jonathan Ross, the mastermind behind Groq and Google’s original Tensor Processing Unit (TPU), outlines a transformative vision for AI. Ross asserts that inference—deploying AI models in real-world scenarios—will soon overshadow training, challenging NVIDIA’s GPU stronghold. Groq’s LPUs, engineered for affordable, high-volume inference, deliver over five times the cost efficiency and three times the energy savings of NVIDIA’s training-focused GPUs by avoiding external memory like HBM. He champions synthetic data from advanced models as a breakthrough, dismantling scaling law barriers and redirecting focus to compute, data, and algorithmic bottlenecks.

    Groq’s explosive growth—from 640 chips in early 2024 to over 40,000 by year-end, aiming for 2 million in 2025—is propelled by a $1.5 billion Saudi revenue deal, not a funding round. Partners like Aramco fund the capital expenditure, sharing profits after a set return, liberating Groq from financial limits. Ross targets NVIDIA’s 40% inference revenue as a weak spot, cautions against a data center investment bubble driven by hyperscaler exaggeration, and foresees AI value concentrating among giants via a power law—yet Groq plans to join them by addressing unmet demands. Reflecting on Groq’s near-failure, salvaged by “Grok Bonds,” he dreams of AI enhancing human agency, potentially empowering 1.4 billion Africans through prompt engineering, while urging vigilance against settling for “good enough” in an abundant future.

    The Big Questions Raised—and Answered

    Ross’s insights provoke profound metaphorical questions about AI’s trajectory and humanity’s role. Here’s what the discussion implicitly asks, paired with his responses:

    • What happens when creation becomes so easy it redefines who gets to create?
      • Answer: Ross champions prompt engineering as a revolutionary force, turning speech into a tool that could unleash 1.4 billion African entrepreneurs. By making creation as simple as talking, AI could shift power from tech gatekeepers to the masses, sparking a global wave of innovation.
    • Can an underdog outrun a titan in a scale-driven game?
      • Answer: Groq can outpace NVIDIA, Ross asserts, by targeting inference—a massive, underserved market—rather than battling over training. With no HBM bottlenecks and a scalable Saudi-backed model, Groq’s agility could topple NVIDIA’s inference share, proving size isn’t everything.
    • What’s the human cost when machines replace our effort?
      • Answer: Ross likens LPUs to tireless employees, predicting a shift from labor to compute-driven economics. Yet, he warns of “financial diabetes”—a loss of drive in an AI-abundant world—urging us to preserve agency lest we become passive consumers of convenience.
    • Is the AI gold rush a promise or a pipe dream?
      • Answer: It’s both. Ross foresees billions wasted on overhyped data centers and “AI t-shirts,” but insists the total value created will outstrip losses. The winners, like Groq, will solve real problems, not chase fleeting trends.
    • How do we keep innovation’s spirit alive amid efficiency’s rise?
      • Answer: By prioritizing human agency and delegation—Ross’s “anti-founder mode”—over micromanagement, he says. Groq’s 25 million token-per-second coin aligns teams to innovate, not just optimize, ensuring efficiency amplifies creativity.
    • What’s the price of chasing a future that might not materialize?
      • Answer: Seven years of struggle taught Ross the emotional and financial toll is steep—Groq nearly died—but strategic bets (like inference) pay off when the wave hits. Resilience turns risk into reward.
    • Will AI’s pursuit drown us in wasted ambition?
      • Answer: Partially, yes—Ross cites VC’s “Keynesian Beauty Contest,” where cash floods copycats. But hyperscalers and problem-solvers like Groq will rise above the noise, turning ambition into tangible progress.
    • Can abundance liberate us without trapping us in ease?
      • Answer: Ross fears AI could erode striving, drawing from his boom-bust childhood. Prompt engineering offers liberation—empowering billions—but only if outliers reject “good enough” and push for excellence.

    Jonathan Ross’s vision is a clarion call: AI’s future isn’t just about faster chips or bigger models—it’s about who wields the tools and how they shape us. Groq’s battle with NVIDIA isn’t merely corporate; it’s a referendum on whether innovation can stay human-centric in an age of machine abundance. As Ross puts it, “Your job is to get positioned for the wave”—and he’s riding it, challenging us to paddle alongside or risk being left ashore.