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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.

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

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

    TLDW

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

    Key Takeaways

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

    Detailed Summary

    Why Tobi Still Loves the Work After Twenty Years

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

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

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

    Different First, Convergence Second, Failure as Successful Discovery

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

    Why Silicon Valley Lost Its Originality

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

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

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

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

    Tokens, Opex, and the Limits of Spend as Revenue

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

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

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

    There Is No Permanent Underclass in the Shopify Data

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

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

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

    Software Was the Hidden Infrastructure of the Last Thirty Years

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

    Predicting AI Two Years Out, Overhype and Underhype

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

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

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

    Public Company Defense and the Reading List

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

    Thoughts

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

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

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

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

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

    Watch the full conversation here on YouTube.