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  • Why the Markets Are Pricing AI Wrong: Gavin Baker on the July 2026 Selloff, GPU Spot Prices, Memory LTAs, and Nvidia’s Credit Wrapper

    Gavin Baker of Atreides Management returned to Invest Like the Best with Patrick O’Shaughnessy days after one of the strangest months the AI trade has ever produced. AI and semiconductor names fell 40 to 60 percent in a straight line while, by Baker’s account, not a single quantitative metric on the ground deteriorated. He spent the week in Silicon Valley hunting for a bearish data point and came back with almost nothing except credit. This conversation is the result: a detailed argument that the market has mispriced the gap between contracted compute and spot compute, that open source is growing the infrastructure pie rather than shrinking it, and that the one risk actually worth fearing is political rather than financial.

    TLDW

    Gavin Baker describes July 2026 as “2022 packed into a single month,” a violent AI and semiconductor drawdown that happened while hyperscaler operating cash flow accelerated from roughly 28 percent growth to 32 percent, or closer to 35 percent adjusting for unusual legal charges. His core claim is that the installed base of GPU compute is locked into long-term contracts priced far below the current spot market, so as those contracts roll off, compute reprices higher, operating cash flow accelerates, and the buildout can be funded internally rather than with the debt that widening credit default swap spreads and a poorly received Meta bond have made look expensive. He walks through each catalyst of the selloff: Meta renting out compute (misread as a capex cut), the open source capability leap from GLM 5.2 and Kimi K3 (misread as deflationary when a token is a token and costs the same flops, watts, and memory to produce), China acquiring a domestic deep ultraviolet lithography machine (real but 25 years behind), and rising real yields (the only genuine negative). He covers the game theory of breaking a memory long-term agreement in a world where market share is set by supply allocations, Nvidia’s new credit wrapper plus revenue share model and why it is misunderstood, the router and fine-tuning stack from Fireworks and Baseten that turns “ChatGPT wrappers” into defensible AI natives, continual learning as the one technical development that could disrupt training demand, SRAM accelerators for disaggregated inference, SpaceX as an underappreciated compute company with orbital ambitions, and his view that regulation, not fundamentals, is the biggest risk because the industry has done a terrible job telling its own story. He also makes an unusual observation about market structure: everyone now feeds news into Claude, and Claude has become a kind of Walter Cronkite for the stock market, collapsing the diversity of interpretation that normally keeps markets stable.

    Thoughts

    The load-bearing claim in this episode is the spread between contracted and spot compute, and to Baker’s credit it is falsifiable in a way most bull cases are not. He is not arguing that AI will be transformative or that demand feels strong. He is arguing something narrow and checkable: hyperscalers and neoclouds signed multi-year GPU contracts in 2024 and 2025 at prices that assumed a gentle decline, prices instead went vertical, and the installed base is therefore systematically under-earning. A startup rented several thousand B200s in the mid two dollars per GPU hour range and expects to pay just under four dollars for an identical cluster seven months later. If that repricing is real and broad, hyperscaler operating cash flow mechanically accelerates and roughly 700 billion dollars of projected credit demand evaporates. If GPU rental prices roll over and stay down for two consecutive quarters, the thesis is dead. That is the number to watch rather than any earnings headline. The caveat he steps past quickly is that the open source mix shift he describes as bullish does not eliminate margin, it relocates it, out of the frontier labs and down into the infrastructure layer. Excellent if you sell GPUs, power, and memory. Considerably more awkward for the labs whose projected cash flows are the reason anyone believes the compute gets paid for at all.

    The Claude as Walter Cronkite observation deserves more attention than it got, where it passed as a joke. Baker is describing a genuine change in market microstructure. Every institutional and retail participant now feeds the same news into roughly the same models, and while those models are probabilistic, they are not producing meaningfully diverse readings of the same headline. He connects this to Michael Mauboussin’s argument that a breakdown in diversity, not leverage alone, is what produces bubbles and crashes. If that is what happened in July, then the Japanese capacitor stock chart he cites, an entire three-year cycle compressed into six weeks before the fundamentals had even arrived, is not a curiosity. It is the signature of a market where thousands of participants share one interpretive engine. That makes drawdowns faster and deeper without making them more informative, which argues for holding through machine-generated narrative cascades rather than trading them.

    The middle of the conversation contains the most consequential business idea in it, and it is one that got almost no coverage during the selloff: memory long-term agreements and Nvidia’s credit wrapper are the same move executed at two different layers of the stack. Both trade near-term upside for durability. The memory companies stopped maximizing spot price and started signing prepaid agreements with floors and ceilings, and the reason those agreements will hold is that the penalty for breaking one has changed category. Apple could renege on memory pricing for years because its volume was overwhelming and it had no equivalent competitor. In a world with four buyers that matter and where AI market share is set by supply allocation rather than product quality, a supplier can answer a broken price agreement by breaking the volume commitment and handing your allocation to a rival, in an industry where oversupply is always followed by undersupply. Nvidia is running the same play one layer up. The credit wrapper with a revenue share above a price floor converts a cyclical one-time chip sale into a royalty on recurring compute revenue, financed on someone else’s balance sheet, which is a materially better business than selling hardware. It also widens the moat, because a startup accelerator pays more at the foundry, pays more for high bandwidth memory, and cannot finance its chips at Nvidia’s rate. Baker is right that this is misunderstood, and it is a strange thing for a stock at a ten-year-low forward multiple to be quietly doing.

    The technical material in the back half reveals an asymmetry worth naming. Baker treats two efficiency developments very differently. Continual learning and sample efficient learning, which several labs believe are close, would collapse the token budget required to produce a capable model, and he handles this by asserting that training asymptotes to a small but nonzero share of compute and that the outcome would be wonderful for the world anyway. SRAM-based accelerators for disaggregated inference, running prefill on one chip, attention on a high-memory chip, and the feed forward network on SRAM, he embraces enthusiastically as a return-on-investment improvement across the installed base. Both are efficiency gains. One is treated as neutral, the other as clearly positive, and Jevons paradox is doing all the work in both directions. That is probably correct given everything we have observed so far, but it is an assumption rather than a finding, and it is the assumption on which the entire “cheaper compute is bullish for compute” framework rests. Worth noting too that the SRAM disaggregation point is genuinely underdiscussed: those chips sit on older nodes and do not compete for leading-edge capacity, so they are additive supply rather than substitute supply.

    The final twenty minutes hold both the largest unpriced upside and the largest unpriced risk, and neither is in consensus estimates. On the upside, only the hyperscalers, CoreWeave, Crusoe, and SpaceX have ever brought more than 500 megawatts online in a single year, and SpaceX has done it fastest and cheapest. When it dumped a large block of compute into the market, the market absorbed it without a blip, which tells you more about demand than any survey. Baker’s sanity check on orbital compute is the sharpest reasoning move in the episode: Benchmark, from entirely outside the Elon ecosystem and without the benefit of internal launch costs, funded StarCloud at a real valuation, so the set of people who would all have to be wrong keeps growing. On the downside, regulation is the risk he names first and it is the one his own framework cannot arbitrage. New York’s data center moratorium is not a fundamentals problem, and no amount of operating cash flow acceleration fixes a permitting ban. His diagnosis is that the industry finds the benefits so obvious that it never learned to explain them, which is how a water usage figure overstated by four orders of magnitude became conventional wisdom. Proposing a foundation that buys World Series ad time is a tell about how far behind he thinks the industry is. Every other risk in this conversation is priced somewhere. That one is not.

    Key Takeaways

    • Baker characterizes July 2026 as “2022 in a month,” with AI names down 40 to 60 percent from their highs in a straight line while underlying fundamentals improved.
    • He spent the week in Silicon Valley explicitly hunting for a negative quantitative metric and found essentially one: third-party data suggesting Anthropic’s growth curve came slightly off trajectory, a data point Anthropic shareholders reportedly dispute.
    • Nvidia was trading at its lowest forward price to earnings multiple in ten years at the time of recording. The only cheaper moments were the DeepSeek shock and Liberation Day, both of which proved to be V-bottoms.
    • A low forward multiple means the market believes these companies are significantly over-earning. Baker’s counter is that they are under-earning because their installed compute is contracted below spot.
    • Combined operating cash flow at Microsoft, Meta, and Amazon accelerated from roughly 28 percent to 32 percent growth, or to about 35 percent after adjusting for an unusual quarter of legal and regulatory charges.
    • Nobody in 2024 or 2025 modeled old GPU prices going vertical in 2026. The bull case assumed a slow decline in rental rates and the bear case assumed a steep one.
    • A concrete example: a well-known startup rented several thousand Blackwell B200s in the mid two dollars per GPU hour range and expects to pay just under four dollars for an identical cluster seven months later, a 50 to 60 percent increase.
    • One inference cloud stated publicly that it plans to pay roughly 100 percent more for Blackwells when its current contract expires.
    • Neoclouds were often forced into below-market long-term contracts because they needed an offtake agreement to finance the GPUs in the first place.
    • Consensus models hyperscalers monetizing Blackwell and Rubin at roughly Ampere rates, two generations behind, producing about 1.3 to 1.4 trillion dollars of hyperscale operating cash flow. Assuming monetization merely at a discount to current Blackwell rates pushes that closer to two trillion and removes roughly 700 billion dollars of credit demand.
    • The credit concerns are real and undeniable: real yields are up, spreads have widened, credit default swap levels for the large buyers have blown out, and a recent Meta bond did not price where a Meta bond should price.
    • Baker’s response is that debt-fueled buildouts demand immediate repayment and unwind violently, which is what happened in the internet buildout, but this buildout is still overwhelmingly funded from operating cash flow.
    • If credit is not available, he argues the existing flops simply become more valuable, which is self-correcting rather than catastrophic.
    • The Meta selloff catalyst was a misread. Meta renting out compute was interpreted as excess capacity and a capex cut. Meta did not cut capex, and the actual motivation appears to have been demonstrating strong internal rates of return on a small slice of capacity ahead of a capital raise.
    • The open source panic was also a misread. Open source taking token share moves margin dollars out of the frontier model layer, but a token still requires the same flops, memory, and watts to produce, so infrastructure demand rises rather than falls.
    • Frontier tokens carry gross margins somewhere in the 80 to 95 percent range. Open source tokens might carry 30 percent. The customer’s savings come almost entirely out of that margin, not out of compute consumption.
    • Baker calls open source “dark matter to the public markets,” growing rapidly through GLM 5.2, Kimi K3, and Nvidia’s Nemotron, but nearly impossible for public investors to measure since it runs through private inference clouds.
    • Jensen Huang being the world’s loudest supporter of open source is itself evidence that open source is good for Nvidia’s business.
    • Enterprises that blow through their AI budget in three months set up a router, which cuts their spend but often increases total GPU hours consumed by shifting volume to cheaper open source tokens.
    • Adoption is happening in staggered waves: AI natives are all in and hiring very few humans, coastal public companies are optimizing, East Coast and non-coastal companies have barely adopted, and Europe is trying to regulate AI before using it.
    • Roughly 500,000 people worldwide use agentic AI, and perhaps half that number use it seriously, yet the world is already in an acute compute shortage. The relevant question is what happens at 100 million or 500 million users.
    • Token spend at the most AI-forward companies now runs 20 to 25 percent of total compensation spend, with individual examples at 30 percent and reports as high as 50 percent, against a roughly 25 trillion dollar global knowledge work market.
    • Founder-controlled companies are not conducting large-scale layoffs, which suggests the cash flow to pay for AI is expected to come from growth rather than from labor substitution.
    • Memory is the dominant variable in token economics. More memory per unit of compute yields more tokens out, which lowers cost per token, which is why demand has shown no negative elasticity to memory pricing.
    • Memory suppliers have shifted from maximizing near-term price to signing long-term agreements with prepayments, floors, and ceilings, trading short-term upside for durability.
    • Breaking a memory long-term agreement is now potentially fatal. With four buyers that matter at scale and market share determined by supply allocation, a supplier can respond by breaking the volume commitment and handing your allocation to a competitor.
    • This is structurally different from the Apple era, when a single dominant buyer could break pricing agreements without consequence.
    • Nvidia’s new model is best described as a credit wrapper with a revenue share triggered when GPU prices exceed a floor. It is not vendor financing, since a third party lends the money, and it could produce a very large cloud-scale royalty business quickly.
    • Baker thinks this model is badly misunderstood, meaningfully increases Nvidia’s revenue per gigawatt, and strengthens its competitive position against startup accelerators that pay more at the foundry, pay more for high bandwidth memory, and cannot finance their chips as cheaply.
    • Nvidia has taken equity stakes across the ecosystem, and Baker’s read is that every time they have not taken a stake it has proven to be a mistake.
    • The scenario that would genuinely frighten him: hyperscaler operating cash flow stops accelerating, forcing the buildout onto debt, or a sustained sharp contraction in GPU rental prices. Nobody he has spoken to says they have too many GPUs.
    • Continual learning and sample efficient learning are the technical developments most likely to disrupt training demand, and several new labs including Safe Superintelligence are focused on them. Baker still thinks training asymptotes to a small share of compute rather than to zero, and that the change would be enormously good for the world regardless.
    • Fireworks launched a product called Nexus that plugs into Claude Code, OpenAI Codex, or Grok in roughly three lines of code, ingests a customer’s data, applies reinforcement learning to a model, and routes queries appropriately.
    • This stack is what converts an alleged “ChatGPT wrapper” into a defensible company. Shifting 30 to 60 percent of token consumption to a customized open model on top of frontier orchestration produces better outcomes at roughly half the cost.
    • Cheap, capable open source models may actually inflate the value of the very best frontier model, since a 160 IQ orchestrator becomes more valuable when it has an army of cheap 120 IQ models to direct.
    • The inference clouds are growing almost as fast as the frontier labs did in their early days while burning very little cash, which is extraordinary by any conventional software metric.
    • China obtaining a domestic deep ultraviolet lithography machine is a genuine phase transition and should not be dismissed, but the technology is roughly 25 years behind extreme ultraviolet, and lithography progress is learning by doing that cannot be teleported through.
    • Baker considers regulation the biggest single risk to AI, citing New York’s data center moratorium as the first of many and describing the current environment as post-factual and post-logical.
    • The public narrative that data centers raise power bills, drain water, and destroy jobs is largely wrong. Behind the meter deals typically lower local electricity prices, and modern community agreements include hospitals, schools, police and fire stations.
    • The widely cited data center water figure originated in a published error overstating usage by roughly 10,000 times, since acknowledged by the author, which Baker likens to the decimal point error that created the myth that spinach is exceptionally high in iron.
    • He argues data centers are among the best things to happen to blue collar wages in his lifetime, with ongoing rather than one-time employment from maintenance, replacement, and upgrade cycles.
    • SRAM-based accelerators built on older nodes and free of high bandwidth memory constraints could substantially improve return on investment by allowing disaggregated inference: prefill on one chip, attention on a high-memory chip, and the feed forward network on SRAM.
    • SpaceX has improved fundamentally since going public, and Baker believes the market does not yet understand it as a compute company. Only the hyperscalers, CoreWeave, Crusoe, and SpaceX have ever brought on more than 500 megawatts of power in a single year, and SpaceX has done it fastest and cheapest.
    • A widely circulated report claims SpaceX intends to bring on eight gigawatts of compute in 18 months. Baker doubts the number but notes that at roughly 50 billion dollars of monetization per gigawatt, even a fraction of it dwarfs the current consensus estimate.
    • When SpaceX dumped a large block of compute into the market, it was absorbed without a blip, which Baker reads as one of the more bullish demand signals of the year.
    • Orbital compute feels more real every day. Benchmark funding StarCloud, from outside the Elon ecosystem and without access to internal launch costs, functions as a useful sanity check on the idea.
    • Dark horse names Baker flags for the next phase: Lip-Bu Tan, Lin Qiao at Fireworks, and Scott Wu at Cognition.

    Detailed Summary

    A Selloff That Contradicted Every Fundamental

    Baker opens by describing July 2026 as 2022 compressed into a single month. AI names fell 40 to 60 percent from their highs in a nearly straight line. What made the month unusual was not the magnitude but the absence of a legible cause. In 2022 the market feared recession, rising rates, and inflation. During the DeepSeek shock and Liberation Day you knew exactly what the market was reacting to. This time the fundamentals moved in the opposite direction from the tape. GPU availability tightened, GPU rental pricing rose, DRAM spot prices rose, and token growth accelerated. Baker asked Patrick, who had also spent the summer in Silicon Valley, whether he had heard a single negative quantitative metric or a single instance of deceleration. The answer was nothing.

    Part of the problem is visibility. Public markets cannot see Anthropic or OpenAI directly, and they cannot see the American open source inference clouds like Fireworks, Baseten, Modal, and Together that monetize inference. Everyone stares at the same chart of semiconductor cash flow rising while hyperscaler free cash flow falls, and that chart omits the private companies entirely. It also omits the repricing dynamic Baker considers the most important fact in the market.

    The Spot Versus Contract Gap

    In 2024 and 2025 every serious forecast assumed GPU rental prices would decline, with the only debate being how fast. Neoclouds locked in long-term contracts partly out of prudence and partly because they needed offtake agreements to finance the hardware at all. The result is a large installed base of contracted compute trading at a steep discount to today’s spot market. Baker’s argument is that as those contracts roll off, compute reprices higher even if spot itself declines from current levels, and that repricing flows directly into hyperscaler operating cash flow.

    The anecdotes are stark. A prominent startup rented several thousand B200s in the mid two dollar per GPU hour range and expects to pay just under four dollars for an identical cluster seven months later. One inference cloud said publicly it plans to pay roughly double for Blackwells at contract renewal. Baker’s read is that hyperscalers are therefore under-earning across the board, which is the exact opposite of what a ten-year-low forward multiple implies the market believes.

    Financing the Buildout and the Credit Question

    Credit is the one bearish input Baker concedes is real. Real yields have risen, spreads have widened, credit default swap levels have blown out across the large buyers, and a recent Meta bond did not price the way a Meta bond should. Sophisticated private capital investors told him this is just banks hedging commitments, but he acknowledges the optics are bad and the facts are undeniable. His concern is the classic capital cycle: debt-financed buildouts demand immediate repayment, so when supply and demand slip out of alignment the unwind is fast and brutal, exactly as it was in the internet buildout.

    The math he ran is the counterweight. Consensus effectively models hyperscalers monetizing Blackwell and Rubin at Ampere rates, two generations behind, producing 1.3 to 1.4 trillion dollars of operating cash flow. Assume instead that they monetize merely at a modest discount to current Blackwell rates and the figure approaches two trillion, taking about 700 billion dollars of credit demand off the table. Better cash flow also improves the credit ratios, which makes debt cheaper if they choose to use it. And if credit disappears entirely, the flops already installed simply become more valuable. Microsoft brought on a large slug of capacity in June that did not even appear in second quarter results.

    How the Month Actually Unfolded

    Baker walks the sequence of catalysts. First, Meta announced it would rent out compute, which the market read as excess capacity and an imminent capex cut. Meta did not cut capex. What Meta appears to have seen was SpaceX selling trading-optimized clusters into the market at an enormous premium to contracted rates, and the plan was likely to demonstrate strong returns on a small slice of capacity before raising equity capital and increasing capex. Shortly afterward Meta released its best model in a long time, overshadowed by a competing release but a clear signal it was not easing off.

    Next came the open source freakout. Kimi K3 arrived, the widely watched token index dipped and flattened, and the two were connected: the index captures mix, and a shift from expensive frontier tokens toward open source tokens looks like weakness even when total compute consumption is rising. Then China’s deep ultraviolet lithography news triggered a broad selloff in semicap equipment. Finally, rising real yields and widening spreads gave the market a genuine reason to worry. Baker’s summary is that with the sole exception of credit, every one of these narratives was factually wrong, and a friend at Fidelity described the winning strategy of the past three years as doing the dumbest, most superficial thing as fast as possible and cycling between them.

    Open Source as Dark Matter

    The most important conceptual argument in the episode is that a token is a token. Regardless of which model produces it, a token consumes the same flops, the same memory, and the same watts. Open source taking share therefore does not reduce compute demand. It transfers margin from the frontier model layer, where gross margins might be 90 percent, to open weights inference at perhaps 30 percent, and the resulting price decline drives elasticity in token volume. Since frontier labs and open source models both run on the same underlying cloud infrastructure at the same compute cost, the effect is to push margin dollars down into the infrastructure layer.

    Baker calls open source dark matter to public markets. It is real, it is accelerating on the back of capability leaps from GLM 5.2 and Kimi K3, Nvidia continues to push Nemotron closer to the frontier, and yet none of it appears in audited financials that public investors can underwrite. He also notes the tell that should have settled the debate: Jensen Huang is the world’s most vocal supporter of open source, which would be an odd position for the largest beneficiary of frontier concentration to hold if open source actually threatened the business. Baker adds a normative point, that a world with only one or two dominant frontier models charging 90 percent margins is not good for humanity, and that many models is the better outcome.

    Routers, Fine-Tuning, and the End of the Wrapper Insult

    The practical mechanism behind the open source surge is the router plus fine-tuning stack. Inference clouds have become genuinely good at supervised fine-tuning and reinforcement learning, so a company can take its proprietary data, customize an open weights model, put it behind a router, and have the router send most queries to that model while escalating to a frontier model for verification or harder work. The result is often slightly better outcomes at half the cost. Fireworks shipped a product called Nexus that connects to Claude Code, OpenAI Codex, or Grok in roughly three lines of code and handles ingestion, reinforcement learning, and routing.

    This changes the durability question for AI natives. Two years ago the criticism was that these companies were thin wrappers with no defensibility. Now a company with domain-specific proprietary data can train on it, own the model serving 30 to 60 percent of its tokens, and get off the frontier lab treadmill it previously had no choice but to accept. Baker points to Cursor, Harvey, and others leaning hard into this. He also raises the counterargument fairly: some believe that once a frontier model achieves recursive self-improvement it will serve every intelligence level more cheaply through distillation, leaving no room for open source. He does not dismiss it, but he thinks the proprietary data held by AI natives and the orchestration value of the single smartest model make the multi-model future more likely. Cheap 120 IQ models arguably make a 160 IQ orchestrator more valuable, not less.

    Where the Money Comes From

    The pushback Baker gets on X is fair: even if hyperscalers are under-earning, where does the customer revenue ultimately come from? Definitionally it must come from faster economic growth through productivity or from labor substitution. He sees labor substitution happening at AI natives, though not through firing. They simply never hire the humans, and gross profit dollars per full-time employee at these companies is vertical compared with prior startup generations. Token spend now runs 20 to 25 percent of total compensation spend at the most aggressive companies, with individual examples at 30 percent and reports as high as 50 percent, against a roughly 25 trillion dollar global knowledge work market.

    The encouraging signal is that founder-controlled companies, the ones most likely to move fast on efficiency, are not conducting large-scale layoffs once you adjust for pandemic-era overhiring. That suggests they see continued opportunity for people plus large token budgets rather than a straight substitution. Data from Cognition, Ramp, and Stripe indicates that companies spending the most on AI are growing meaningfully faster, though Baker acknowledges the skeptics’ point that these datasets do not control for industry.

    The Memory Supply War and LTA Game Theory

    Everything is currently in shortage, and Baker argues the constraint is energizing gigawatts rather than manufacturing. Turbine makers and diesel generator makers are ramping, old aircraft turbines are being stripped and reconditioned for data center power, and regulatory policy is moving favorably. The transition he says he got wrong is the shift, especially in memory, from maximizing short-term pricing to signing long-term agreements with customer prepayments, price floors, and price ceilings.

    The reason those agreements will hold is game theory. Memory is the axis around which everything else revolves, because more memory per unit of compute means more tokens out, which lowers cost per token, which is why demand has shown essentially no negative elasticity. Market share among the four buyers that matter (Amazon with Trainium, Google with TPUs, AMD, and an Nvidia bigger than all of them combined) will be determined for years by supply chain allocation. Break a long-term agreement to chase a lower price in an oversupply year and the supplier can break the volume commitment in return and hand your allocation to a competitor. Since oversupply in this industry is reliably followed by undersupply, that is a decision that can end a franchise. Apple could get away with this historically because its volume was overwhelming and it had no equivalent competitor. That world is gone.

    Nvidia’s New Playbook

    Baker finds Nvidia’s low multiple hard to reconcile with how thoroughly the current environment favors it. If chips need to be financed, nothing on earth is more financeable than an Nvidia GPU. If land and power are the constraint, Nvidia has been playing the matchmaking chess game well. On top of that they have rolled out what Baker describes as a credit wrapper with a revenue share that kicks in when GPU prices sit above a floor. It is not vendor financing, since someone else lends the buyer the money. What it does is give Nvidia a royalty on recurring compute revenue, which could amount to a very large cloud business built entirely out of royalties, while helping bridge the cash flow mismatch between an industry that has gone free cash flow negative and a supplier collecting all the cash.

    Asked what he would do as a memory CEO, Baker says he would do exactly what Nvidia is doing: approach GPU and accelerator buyers, participate in the credit wrapper, perhaps put up cash upfront to make lenders comfortable, and take a cut of ongoing revenue. He expects firms like Blackstone and Apollo are pitching variants of this to the memory companies already. He also thinks the arrangement quietly widens Nvidia’s competitive moat, since startup accelerator companies pay more at the foundry, pay more for high bandwidth memory, and cannot finance their chips at Nvidia’s rate. And he notes that essentially every time Nvidia has declined to take an equity stake in something, it has turned out to be a mistake.

    What Could Break the Thesis

    Pressed for the scenario that would flip him, Baker names two. The first is operating cash flow failing to accelerate, which would force the buildout onto debt and validate the credit bears. That outcome depends largely on whether the combined trajectory of Anthropic, OpenAI, Grok, Cursor, and open source keeps compounding. The second is a sustained sharp contraction in GPU rental prices. The market would react instantly, and it would mean the compute shortage had broken. As of the recording, not a single person he has spoken with says they have too many GPUs.

    The technical wildcard is continual learning and sample efficient learning. Many researchers believe both are close. A human learns effectively on something like 20 billion tokens while frontier models train on 300 trillion, so a model that could be trained on 10 trillion tokens and then learn efficiently in the world would represent a discontinuity in training demand. Baker thinks training will asymptote to a small but nonzero share of compute regardless, and that the development would be extraordinarily good for the world. He also notes Nvidia is deeply involved with essentially all of the labs pursuing it.

    China, Lithography, and Decoupling

    On China’s deep ultraviolet lithography machine, Baker holds both views at once. It is a genuine phase transition, comparable to going from having no propeller plane to having one, because they did not have it before and now allegedly they do. It is also roughly 25 years behind extreme ultraviolet, and lithography is learning by doing, so you cannot teleport through the required cycles. He suspects the market overreacted and that if it ever affects ASML’s order book it will be years out, by which time the market will have forgotten and rediscovered the concern several times.

    He is careful about certainty here. It is very hard for an American to have real clarity on what is happening inside China, the people there are extremely capable and work brutally hard, and they consider this existential for the country. There are unverified reports that an extreme ultraviolet machine was smuggled in, which he treats as noise. His larger point is that decoupling is now self-reinforcing on both sides, it is unfortunate, and neither side is going to stop.

    Regulation, Data Centers, and a Failure of Storytelling

    Asked for the worst thing that could happen to AI, Baker answers regulation without hesitation. New York’s data center moratorium feels like the first of many, and even deep red pro-growth states are telling the industry it is doing a poor job explaining itself. The political narrative among ordinary Americans is that data centers will raise electricity prices, drain water supplies, and eliminate jobs. Baker’s counter is that behind the meter deals generally lower local electricity prices, that community agreements now routinely include hospitals, schools, police stations, and fire stations rather than the old model of buying the fire department new trucks, and that the jobs are ongoing rather than one-time because of continuous maintenance, replacement, and upgrade cycles.

    The water claim is the clearest case of a myth outrunning the correction. An author overstated data center water usage by roughly 10,000 times, has acknowledged the error repeatedly, and the figure still circulates. Patrick offers the parallel of the spinach iron myth, created by a misplaced decimal point in an academic text and still believed 80 years later. Baker’s proposed remedy is blunt: a foundation or political action committee running ads during the Final Four, NFL games, and the World Series explaining what a data center actually does for a community, alongside the story of AI accelerating medical research and improving outcomes for people with serious illness. The people building this find the benefits so obvious that they assume everyone already knows, and they cannot process how divergent their view is from most Americans.

    SRAM Accelerators and Disaggregated Inference

    An underdiscussed development, Baker argues, is what happens when SRAM-based accelerators arrive at scale. These chips are not constrained by high bandwidth memory and are often built on older nodes, so they do not compete for the leading edge capacity that GPUs consume. Inference disaggregates into prefill and decode, and decode splits further into attention and the feed forward network. The holy grail is running prefill on a chip without high bandwidth memory, attention on a high-memory chip, and the feed forward network on SRAM, which nothing beats for that workload. Since workloads keep changing, no single chip can get the ratio of compute to high bandwidth memory to on-die SRAM permanently right, which is precisely the argument for disaggregation. Baker expects this to be strongly positive for the return on investment across the installed base and on new compute.

    SpaceX, Orbital Compute, and Dark Horses

    Baker does not think the market understands SpaceX as a company yet, and he considers it the most important new public company. The fundamentals have improved since the IPO, and the compute story is the part being missed. Only the hyperscalers, CoreWeave, Crusoe, and SpaceX have ever brought more than 500 megawatts of power online in a single year, and SpaceX has done it fastest and cheapest while building clusters customers actually like. When SpaceX dumped a large block of compute into the market, it was absorbed without a blip, which Baker treats as one of the most bullish demand datapoints available. A circulating Substack report claims eight gigawatts within 18 months. He doubts that figure and quotes it only because it is public, but at roughly 50 billion dollars of monetization per gigawatt against a 73 billion dollar consensus estimate, even partial delivery would overwhelm expectations. There is a well-known New York hedge fund short case built on spot compute prices falling 90 percent.

    On orbital compute, Baker says time at Starbase left him thinking it feels more real every day, and the Starship landing reinforced it. His sanity check is that Benchmark, from entirely outside the Elon ecosystem and without the benefit of internal launch costs, chose to fund StarCloud at a real valuation, with SpaceX partnering to provide the Starlink laser technology that orbital compute requires. As he puts it, maybe he is crazy, maybe Elon is crazy, maybe Benchmark is crazy, and maybe the SpaceX engineers are crazy too, but all of that being true simultaneously does not seem probable. Asked for dark horses who could become as consequential as the current giants, he names Lip-Bu Tan, Lin Qiao at Fireworks, and Scott Wu at Cognition. The episode was recorded at Benchmark’s offices, at the table where their dinners are held.

    Notable Quotes

    “I want to be scared. I don’t want to feel like a lunatic watching these stocks get cheaper thinking the expected forward returns are going up.”

    Gavin Baker, on why he spent the week in Silicon Valley hunting for bearish data

    “I would describe July as 2022 in a month.”

    Gavin Baker, characterizing a 40 to 60 percent drawdown in AI names that happened in a straight line

    “Have you heard a single negative quantitative metric about AI? A single instance of deceleration?”

    Gavin Baker to Patrick O’Shaughnessy, framing the central contradiction of the month

    “A token is a token, and you need the exact same amount of compute to make a token. It takes the same amount of flops, the same amount of memory, the same amount of watts.”

    Gavin Baker, on why the open source panic misread infrastructure demand

    “Open source is kind of dark matter to the public markets. It’s hard for public markets to measure it.”

    Gavin Baker, on why the fastest-growing part of inference demand is invisible in audited financials

    “Claude is kind of Walter Cronkite for the stock market and everybody just believes whatever it says. And by the way, it’s really smart, but it’s not always right.”

    Gavin Baker, on the collapse of interpretive diversity among investors

    “Nvidia is actually, as we record this, at its lowest forward PE of the last 10 years.”

    Gavin Baker, noting the only cheaper moments were the DeepSeek shock and Liberation Day, both V-bottoms

    “If you break your LTA and then in the next two or three years for any reason leverage shifts back to the memory guys, you’re out of business.”

    Gavin Baker, on why long-term agreements will hold through the next memory cycle

    “If you need to be able to finance the chips, and you do, nothing’s more financeable than an Nvidia GPU. Nothing.”

    Gavin Baker, on why the current environment favors Nvidia more than its multiple suggests

    “Data centers are in a lot of ways the best thing to happen for blue collar wages in my lifetime.”

    Gavin Baker, on the gap between the political narrative and the local economics

    “A lie could go around the world faster than truth gets out of bed.”

    Gavin Baker, on a data center water usage figure overstated by roughly 10,000 times that still circulates

    “One of Elon’s phrases is we specialize in making the impossible late.”

    Gavin Baker, on why he doubts the eight gigawatt figure without betting against SpaceX

    Watch the full conversation here: Why the Markets Are Pricing AI Wrong with Gavin Baker on Invest Like the Best.

    Related Reading

    • Invest Like the Best on Colossus the show’s home, where the full episode archive and transcripts live.
    • Atreides Management Gavin Baker’s firm and the vantage point behind these compute and semiconductor calls.
    • More Than You Know by Michael Mauboussin, the source of the diversity breakdown framework Baker invokes to explain why markets crash when everyone reasons the same way.
    • High Bandwidth Memory (Wikipedia) background on the memory technology that sits at the center of the long-term agreement game theory.
    • Fireworks AI the inference cloud whose routing and fine-tuning stack Baker credits with making open source models competitive for production workloads.

  • Bill Gurley on Mental Models, Systems Thinking, AI Investing, Stablecoins, and the Future of Venture Capital

    Bill Gurley spent his career at Benchmark backing some of the most consequential marketplaces and network-effect businesses of the internet era, including Uber, and he is one of the few investors who pairs deep Wall Street fundamentals with a real feel for the bleeding edge. In this wide-ranging conversation on Shane Parrish’s The Knowledge Project, he lays out the mental models he keeps returning to, how systems thinking keeps you out of trouble, why the history of your field is a hidden superpower, where AI investing is headed, and how stablecoins and tokenization could quietly rewire finance. It is a masterclass in thinking clearly about complex systems while staying obsessively curious about what is happening on the edge.

    TLDW

    Gurley anchors his thinking in systems thinking and complexity theory, warning that multivariable nonlinear systems produce second and third order consequences that punish anyone who optimizes for a single metric. He argues that mastering both the deep history of your field and its newest edge is wildly differentiating, whether you are interviewing for a marketing job or breaking into venture capital. On AI he is measured: he doubts a single model eats every vertical, sees real moats in workflows and proprietary data, flags that we may be painting in the corners on training data, and explains why Chinese open source models may innovate faster because forced knowledge sharing compounds. He thinks the AI buildout looks overfunded and that circular deals both raise the odds of an eventual correction and delay it. He makes the case that the IPO process is a rigged power grab, that stablecoins and instant payments threaten Visa, Mastercard, and the entire 2 to 3 percent credit card stack, and that proxy advisors like ISS have drifted from shareholder interest into a black-box heist. He closes on the craft of storytelling and writing as thinking, the equal-partnership design of Benchmark, why venture bends toward youth, and what success means now that his dream job is behind him.

    Thoughts

    The most useful idea in this conversation is also the quietest one: most bad decisions are not bad in the moment, they are bad in the second derivative. Gurley’s dating-site story, where lengthening profiles raised engagement in the test and then quietly killed conversion months later, is the whole argument in miniature. A linear model would have shipped that change and called it a win. A systems thinker assumes the variable you optimized is connected to three others you cannot see yet, and waits to find out. That posture, refusing to get deterministic about a single metric, is the difference between a clever experiment and a durable business. It is also the most transferable thing in the episode, because it applies to product changes, hiring, policy, and your own career just as cleanly as it applies to a dating app.

    His pairing of old and new is the second idea worth stealing. Everyone in tech tells you to live on the edge, and Gurley agrees, he keeps five premium AI accounts running so he never misses a release. But he insists the edge is only half of it. Knowing the deep history of your field, the masters of marketing, the forefathers of physics, the classic cartoons that taught animation, is rare enough that it instantly creates contrast and signals genuine passion. The compounding move is to hold both at once. If you understand the legends and you actually get TikTok, you are a power player in a way that someone who only knows one end of the timeline can never be. Most people pick a side. The leverage is in refusing to.

    On AI specifically, Gurley is refreshingly unwilling to pick the consensus lane in either direction. He does not buy that one near-sentient model swallows every vertical, and his reasoning is grounded rather than vibes-based: workflows and proprietary data create real switching costs, which is why he watches the legal AI startups ingesting case law and building new databases rather than assuming everyone reverts to a general chatbot. At the same time he respects the Microsoft pattern of platforms climbing the stack and crushing the apps above them. The honest answer is that it is genuinely up for grabs, and his comfort sitting in that uncertainty is itself a model. The cheap takes are “one model to rule them all” and “it is all wrappers.” Gurley holds both possibilities and keeps testing.

    The systems lens does its best work on China. Rather than moralize, Gurley runs the mechanism: roughly ten open source models, intense domestic competition, and a culture of publishing techniques and weights so every model can learn from, train, and test every other model. His two-farmer metaphor, one market where farmers only trade goods and another where they are forced to share best practices, makes the prediction obvious. Forced knowledge sharing compounds faster than secrecy. The uncomfortable corollary he names is that American startups are quietly forking those open models all over Silicon Valley, and that incumbents may be lobbying for heavy regulation precisely because it pulls up the drawbridge against open source competition. That is the systems thinker’s signature move: follow the incentives to the consequence nobody is saying out loud.

    Finally, the money section is a clinic in spotting rent extraction. The IPO process where bankers pick both the price and the favored buyers, the 2 to 3 percent credit card toll that exists for no defensible reason while the rest of the world built instant bank transfer decades ago, and the proxy advisors who score companies in a black box and then sell you the cure, are all variations on the same pattern: an intermediary that captured a choke point and defends it through regulatory capture rather than value. Gurley’s optimism is that crypto rails, stablecoins, and tokenization may finally route around these tolls the way WeChat Pay and Alipay leapfrogged cards in China. Whether or not you agree on the timeline, the analytical habit is the takeaway. When something costs far more than it should and has for decades, ask who captured the rules, and watch the edge for whoever is about to make those rules irrelevant.

    Key Takeaways

    • Systems thinking means treating the world as multivariable nonlinear systems where one variable flipping can change the entire system’s behavior, the way weather and stock markets do.
    • The real danger is second and third derivative effects, consequences that only show up much later, long after the metric you optimized looked like a win.
    • A dating site lengthened profiles because longer profiles tested as more engaging, then discovered months later it was negative for conversion, the textbook second order trap.
    • Never get too deterministic about a single metric or single variable, and always know what is actually important and what sits on top.
    • Gurley built his foundation on the canon: Peter Lynch’s One Up on Wall Street, A Random Walk Down Wall Street, the Buffett letters, Ben Graham, and Howard Marks.
    • A firm grasp of the financial bedrock is what lets you innovate on top of it, and many Silicon Valley VCs would benefit from understanding finance better.
    • Bill Miller reframed value investing as buying an asset that is underpriced relative to what you think it will be worth in the future, which is how he justified holding Amazon for its network effects.
    • Wall Street is the buyer of the product that venture capitalists create, so even at the two-people-in-a-PowerPoint stage you should ask whether the eventual public market will be excited by it.
    • Trajectory matters more than the starting place, because the trajectory is where the company actually ends up.
    • Knowing the deep history of your field is remarkably differentiating, and tedium while learning it is a signal you are in the wrong lane.
    • John Lasseter served Gurley a ten-course meal where each course was tied to a classic cartoon essential to understanding animation, a display of mastery over the history of the craft.
    • Magnus Carlsen won a trivia contest on the history of chess, and Picasso was a wildly successful realist painter by 14, both proof that the greats master the fundamentals first.
    • Obsessive, constant learning is the trait Gurley sees most in great entrepreneurs, because disruption always happens on a moving edge they need to understand at the top one percentile.
    • The compounding advantage is mastering both the old history and the new edge at once, the way understanding both marketing legends and TikTok would set you apart in any interview.
    • Most people underestimate how much AI can do, so push more of the downstream work into the prompt: identify the top ten, list pros and cons, rank them on one dimension, then another, and add up the numbers too.
    • Gurley uses ChatGPT for project structure and memory, Gemini for restaurant research powered by Google review data, and notes that coders swear by Claude while some prefer Perplexity for finance.
    • He doubts one model dominates everything; verticals like coding already let users swap models, and price optimization will push more swapping over the next few years.
    • Heavy, expensive regulation could ironically create oligopoly, and some players may be quietly begging for regulation because it pulls up the bridge against Chinese open source models.
    • China’s roughly ten open source models compete intensely and share weights and techniques, creating a system that can innovate faster, like farmers forced to share best practices instead of just trading goods.
    • A quiet secret is that startups all over Silicon Valley are forking those Chinese open source models at real volume.
    • Gurley comes down against the idea that one near-sentient model removes the need for vertical models; workflows and proprietary data, like legal startups ingesting all the case law, create durable moats.
    • We may be running out of training data, painting in the corners, which is why one of the most powerful improvements is hiring experts at thousands of dollars an hour to fine-tune the models.
    • Yann LeCun’s view is that the next leap is broader than LLMs, since language-based models hit an asymptote and are weak at math and numbers.
    • AlphaGo’s shocking move proves models can innovate beyond their training, but it lived in a constrained game; the real world has infinite paths a computer cannot exhaustively search.
    • Gurley’s non-consensus view is skepticism of the China vilification mindset, noting the US is only 3 to 5 percent of the global population and wondering how the other 95 percent hears American exceptionalism.
    • The AI buildout looks overfunded: the Magnificent Seven took free cash flow from 50 to 100 billion a year down toward zero by pouring it into capex.
    • The venture community has become more risk-seeking because it now deeply believes in increasing returns and power laws, and the pre-profit losses keep scaling, from Amazon’s 2 to 3 billion to Uber’s 15 billion to far more now.
    • Circular deals, where a cloud provider funds a model company that spends the money right back on its services, inflate growth, which both raises the probability of an eventual correction and extends the time before one hits.
    • Burn rate is a measure of risk; ten years ago a million a month was scary, now companies burn five billion a year and cannot really know their unit economics.
    • Tokenization without financial-disclosure regulation invites speculation and manipulation, which is part of why companies like Stripe stay private and negotiate liquidity prices with trusted investors.
    • The IPO process is unfair because bankers pick both the price and the shareholders; a freshman would simply match supply and demand anonymously in an auction, the way direct listings and ICOs do.
    • Stablecoins threaten the 2 to 3 percent credit card stack; USDC holds dollar-for-dollar Treasuries and rides fast global crypto rails, while US transfers still suffer three-day ACH settlement and 25 dollar wires.
    • The rest of the world built instant transfer long ago, from UK Faster Payments 20 years ago to Argentina’s PIX-style system reaching 60 to 70 percent of transactions, while US bank regulatory capture stalled Fed Now.
    • Visa and Mastercard run roughly 60 percent operating margins as a bank-created duopoly, and China leapfrogged them entirely with WeChat Pay and Alipay QR-code wallets.
    • Moody’s power is being the trusted standard, the watermark, so AI on the back end does not displace it; ISS and proxy advisors, by contrast, score companies in a black box and get paid on both sides.
    • Proxy advisors drifted from shareholder interest into a fraud-and-risk-mitigation mindset, which is why they reflexively opposed the Tesla pay package that only paid out if the stock soared.
    • The rise of passive index funds concentrated voting power in firms that lack time to evaluate votes; it would be healthier if they abstained or voted in proportion to active holders.
    • Storytelling is one of the top founder traits, because founders are recruiting, raising money, and closing customers and partners constantly, selling all the time.
    • Writing is thinking: Bezos’s six-page memo forces you to find the loose ends and tie them up, and a public blog becomes a calling card that magnetizes founders and deal flow.
    • Other founder unfair advantages are product instincts, which fewer than 5 percent of non-product people ever truly learn, and sheer determination, Bezos’s single angel-investing test of whether someone will do it no matter what.
    • Uber had no HBS case study to lean on; its winner-take-all network effects forced mega burn rates with no precedent and no mentor to call, a situation every AI company now faces.
    • Benchmark’s equal partnership, with no king, president, or lead and five equal partners, makes recruiting easy, kills comp politics, and aligns everyone, at the cost of being hard to scale or run new initiatives.
    • Venture bends toward youth because young investors can match founders’ age, master a fresh niche faster, and have the free time to study something 80 hours a week.
    • Gurley defines current success through Arthur Brooks’s From Strength to Strength, hoping to apply his synthesizing and writing skills to bigger societal problems and dent the universe a little.

    Detailed Summary

    Systems Thinking and Second Order Effects

    Gurley opens with the mental model he keeps returning to: systems thinking, shaped by Donella Meadows’s Thinking in Systems and his board seat at the Santa Fe Institute, which studies complexity theory. He describes complex systems as multivariable nonlinear systems that are very hard to predict, capable of behaving one way for a long time until a single variable flips and the whole system behaves differently, like weather or stock markets. The practical payoff is staying out of trouble by anticipating first, second, and third derivative consequences. His clearest example is a large dating site that lengthened user profiles because the test showed more engagement, only to learn many months later that knowing more at that stage was negative for conversion. The lesson is to never get too deterministic about a single metric and to keep the whole system in view, because a change here can ripple to there in ways you only discover much later.

    Learning the Craft of Investing

    Because he started on Wall Street rather than in venture, Gurley absorbed the investing canon first: Peter Lynch’s One Up on Wall Street, A Random Walk Down Wall Street, the Buffett letters, Ben Graham, and Howard Marks, people who spent careers assembling and publishing their thinking. That financial bedrock, he argues, is exactly what lets you innovate on top of it. His friend Michael Mauboussin introduced him to Bill Miller, the Legg Mason manager who beat the S&P for 15 straight years and was Amazon’s largest shareholder for a long stretch. Miller reframed value investing as buying an asset underpriced relative to its future worth, which combined with a belief in network effects justified holding a company that could grow at an unreasonable rate for years. Gurley also frames Wall Street as the buyer of the product venture capitalists create through eventual M&A or IPO, so founders should think early about whether the public market will be excited by what they are building, since trajectory matters more than the starting place.

    Mastering Both the History and the Edge

    Gurley makes an unusually strong case for studying the deep history of your field. He recounts a dinner with Pixar’s John Lasseter, who served a ten-course meal where every course was tied to a classic cartoon he considered essential to understanding animation, and notes that Magnus Carlsen won a chess-history trivia contest and Picasso was a master realist by 14. In a world that skims for the executive summary, walking into a marketing interview with command of the masters of marketing is wildly differentiating and signals genuine passion; if learning that history feels tedious, you are probably in the wrong lane. The counterpart trait he sees in great entrepreneurs is obsessive learning on the moving edge, where disruption actually happens. Gurley keeps five premium AI accounts so he never misses something. The real power player holds both at once, the legends and the newest thing, the way a candidate who knows the marketing greats and truly gets TikTok stands out completely.

    Using AI Well and the Model Wars

    People underestimate how much AI can do, Gurley says, so you should build more of the downstream work into the prompt: instead of asking for the top ten and studying them yourself, ask it to list pros and cons, rank on one dimension, rank again on another, and add up the numbers too. He uses ChatGPT for its project structure and memory, leans on Gemini for restaurant research because it carries Google review data, and notes coders swear by Claude while some prefer Perplexity for finance. On whether one model dominates or models become niche commodities, he points to coding, the largest vertical, where tools like Cursor already let users swap models, and predicts price optimization will drive more swapping. The counterforce is regulation: if it gets expensive and mundane it could create oligopoly, and some players may be quietly begging for it because it pulls up the bridge against Chinese open source models.

    China, Open Source, and the Systems Advantage

    Asked to apply systems thinking to China, Gurley describes roughly ten open source models locked in intense domestic competition, all learning from one another because the ecosystem chose openness, with models able to train and test other models and teams publishing the techniques behind their breakthroughs. His metaphor: two agricultural societies, one where farmers only trade goods at market and another where they are forced to share best practices; the second evolves far faster. The result is a system capable of innovating faster than the more secretive Western approach. The quiet secret he names is that startups all over Silicon Valley are forking those open models at real volume, and a key open question is whether regulation tries to stomp that out. He extends this into a broader non-consensus discomfort with the vilification of China common in Washington and parts of Silicon Valley, observing that the US is only a few percent of the global population.

    AI Investing, Moats, and the Limits of Models

    On how AI changes investing and whether a startup is just a wrapper, Gurley calls it up for grabs but lands on the side of durable verticals. If models become near-sentient, one model does everything; he doubts that, pointing to workflows and data moats, like the several legal AI startups ingesting all the case law and building new databases that customers will not simply swap for a general chatbot. He balances this against the Microsoft pattern of platforms climbing the stack past Lotus 1-2-3 and WordPerfect. He also flags scaling limits: we may be running out of data, painting in the corners, which is why one of the most powerful improvements is paying experts thousands of dollars an hour to fine-tune models, though human knowledge has an edge. He invokes Yann LeCun’s argument that the next leap is broader than language-based LLMs, which hit an asymptote and struggle with math, and the AlphaGo debate, where a shocking innovative move proves creativity within a constrained game but says little about the infinite paths of the real world. He notes AlphaGo and Tesla’s FSD are constrained, non-LLM systems.

    Is the Buildout Overfunded

    Gurley admits he is shocked by the scale of money, noting the Magnificent Seven drove free cash flow from 50 to 100 billion a year down toward zero by spending it all on capex, something he would not have believed five years ago. He traces it to the venture community’s growing conviction in increasing returns and power laws, where proven companies grow far beyond expectations, which makes investors more willing to take risk on the come. The losses before turning cash-flow positive keep scaling, from Amazon’s 2 to 3 billion to Uber’s roughly 15 billion to far larger now. On corrections, he recalls the dot-com crash producing a three to four year nuclear winter before Amazon climbed back, and explains that circular deals, where a cloud provider funds a model company that spends it right back on its services, inflate growth and therefore both raise the probability of a correction and extend the runway before one arrives. Burn rate, he stresses, is a measure of risk, and at five billion a year it is nearly impossible to know your unit economics.

    Tokenization, the IPO Heist, and Going Public

    There is no shortage of capital, so funding is not the bottleneck; the risk with tokenization is that, absent disclosure regulation, it invites speculation and manipulation, as seen in retail-loved names like GameStop and Palantir. Tokenizing a private company like Stripe could create the wild price swings companies stay private to avoid, since private liquidity events let them negotiate a price with trusted investors rather than expose the constantly moving underlying value, and Robinhood’s tokenization plans already drew legal pushback. Gurley reserves his sharpest critique for the IPO process, calling it insanely unfair because bankers pick both the price and the favored shareholders. A freshman computer science and finance student would simply match supply and demand anonymously in an auction, the way an ICO or a direct listing does, but Wall Street will not let go of the greedy power grab and reverted to a controlled oligopoly after direct listings were available.

    Stablecoins Versus the Payment Cartel

    Gurley argues stablecoins could be deeply disruptive to credit cards. Most of the developed world built instant bank-to-bank transfer long ago, from UK Faster Payments 20 years ago to Argentina’s PIX-style system that quickly hit 60 to 70 percent of transactions, while US bank regulatory capture stalled Fed Now and left an ecosystem living under 2 to 2.5 percent card fees. A USDC stablecoin holds dollar-for-dollar US Treasuries and rides proven, fast, global crypto rails, letting anyone move a dollar in seconds for pennies, against the backdrop of three-day ACH settlement and 25 dollar wires. He sees Visa and Mastercard, a bank-created duopoly with roughly 60 percent operating margins, as heavily threatened, and points to China, where WeChat Pay and Alipay built ubiquitous QR-code wallets that leapfrogged the entire card system, all because the government made money transfer easy.

    Moody’s, Proxy Advisors, and Index Funds

    Moody’s power, Gurley explains, comes from being a trusted standard, the watermark, so even AI on the back end does not displace it. Proxy advisors like ISS are a different story: they score companies in a black box, refuse to reveal the criteria, and then get paid by the same companies that want to learn how to score better, which he calls more of a heist than a service. They drifted from a shareholder-interest mandate into a corporate-governance, fraud-mitigation posture obsessed with rules, which is why they reflexively opposed the Tesla pay package that only paid Elon Musk if the stock soared, a deal Gurley says he would sign for every company he has worked with. The rise of passive index funds compounds the problem, concentrating voting power in firms without time to evaluate votes; he would prefer they abstain or vote in proportion to active holders, since closet indexing during the MAG 7 run already distorted active management.

    Storytelling, Writing, and Founder Advantages

    Gurley fell in love with the craft of writing in business school, moving from business books to personal development titles like Dale Carnegie and Seven Habits, then biographies, then long-form narrative nonfiction by Malcolm Gladwell, Michael Lewis, and Jon Krakauer, the New Journalism that reads like fiction. Writing forces clarity: he cites Bezos’s six-page memo as a tool that makes you think through corner cases and tie up loose ends, and notes that codifying his marketplace knowledge and publishing it turned his blog into a calling card that magnetized founders and deal flow. He lists the top founder traits as storytelling, product instincts, understanding the edge, and determination. Storytelling matters because founders are constantly recruiting, fundraising, and closing customers and partners. Product instinct is nearly unteachable, present in well under 5 percent of non-product hires. And determination is Bezos’s single angel-investing test: will this person do it no matter what, come hell or high water.

    Uber, Benchmark, and the Shape of Venture

    The Uber lesson with no HBS case study was that a winner-take-all category with network effects demanded funding ad nauseam, producing burn rates bigger than any public company would dare, with no precedent and no mentor to call, exactly the situation AI companies now face, only with a zero added. Gurley credits Benchmark’s design, an equal partnership with no king, president, or lead and five equal partners, for making it easy to recruit top talent, encouraging senior partners to develop newcomers since everyone shares the upside, and eliminating annual comp politics. The downside is that without a CEO it is hard to scale or run new initiatives, famously captured by the firm settling on a single splash-page website. Founders choose a VC for reputation and network effects, the stamp of approval that carries weight, and young investors can break in because they often match founders’ age and can outwork everyone to master a fresh niche like esports or YouTube, which is why the industry bends toward youth. Asked what success means now, Gurley says his venture career was a dream job he would have done for free, but it is done; inspired by Arthur Brooks’s From Strength to Strength, he wants to apply his synthesizing and writing to bigger societal problems and dent the universe a little.

    Notable Quotes

    “We do live in a world where information is really cut up, but we also live in a world where you can have access to more information than you ever could.”

    Bill Gurley, on why the abundance of knowledge rewards the curious

    “You got to be really conscious of the consequence and not get too deterministic about a single metric or a single variable.”

    Bill Gurley, on the discipline of systems thinking

    “Value just means that the asset is underpriced relative to what you think it will be worth in the future.”

    Bill Gurley, relaying Bill Miller’s reframing of value investing

    “I’ve always thought of Wall Street as the buyer of the product that venture capitalists create.”

    Bill Gurley, on why founders should think about the public market early

    “One society, when the farmers come to market, they just sell each other goods and then they go back. The other society, when the farmers come to market, they’re forced to share best practices. Which one is going to evolve faster?”

    Bill Gurley, on why open source models can out-innovate

    “If you took a freshman computer science student and a freshman finance student and said imagine how a company should go public, they would match supply and demand anonymously like you would in any auction.”

    Bill Gurley, on the rigged IPO process

    “When I meet an entrepreneur, there’s only one thing I ask myself. Is this person gonna do this no matter what? Come hell or high water, they’re doing this.”

    Bill Gurley, quoting Jeff Bezos on his single test for angel investing

    “You’re recruiting employees, you’re recruiting executives, you’re raising money, you’re closing customers, you’re closing partnerships. You’re selling all the damn time.”

    Bill Gurley, on why storytelling is a top founder trait

    “I often said that if we lived in a socialist society and everyone had to work for free, I would still take that job.”

    Bill Gurley, on loving his venture career

    “I would like to see if I can apply those techniques to bigger, broader problems in society and dent the universe a little bit that way.”

    Bill Gurley, on what success looks like in his next chapter

    Watch the full conversation with Bill Gurley on The Knowledge Project here.

    Related Reading

  • 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.