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  • 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

  • Benedict Evans on the Economics of AI Usage, Why Foundation Models May Become Commodities, and What Comes Next for SaaS

    Benedict Evans returns to the a16z podcast to update the thesis behind his widely read “AI eats the world” presentation, and the picture he paints is less about hype and more about hard economics. In this conversation he works through what has actually played out in the last year, why agentic coding became the one use case with real product market fit, and why he keeps arguing that foundation models may end up as commodities while the value moves somewhere else entirely. You can watch the full conversation here.

    TLDW

    Benedict Evans argues that the AI moment looks a lot like the early internet, the early PC era, and the rollout of mobile data, which means it is exciting, genuinely transformative, and almost impossible to predict use case by use case. Agentic coding is the only field with clear product market fit right now, with revenue run rates exploding from roughly nine billion to forty seven billion, while consumers still use chatbots weekly rather than daily. His central claim is that foundation models show no obvious network effect or sustainable differentiation, the chatbot is a limited v1 interface, and the model labs cannot build every application, so the value will likely move up the stack the way it did with chips, ISPs, and mobile networks rather than staying with the model providers. He covers the brutal supply and demand disequilibrium driving today’s token pricing and ten thousand dollar surprise bills, the financial gravity problem of hyperscalers spending over half their revenue on capex, the Jevons paradox and consumer surplus that may compete away productivity gains, the way the important questions move out of San Francisco and into industries like law, consulting, finance, and advertising, and the distinction between automating tasks and changing jobs. His closing image is an IBM ad from the 1950s promising “150 extra engineers,” a reminder that every platform shift feels unprecedented and that in twenty years we will simply say of course computers do that.

    Thoughts

    The most useful thing Evans does here is refuse to collapse uncertainty into a clean prediction, and then explain exactly why that refusal is the correct posture rather than a cop out. He distinguishes between the parts where he will commit to a view, that foundation models are probably not a product and the chatbot is probably not the right interface, and the parts where there are simply too many open paths to call. That discipline is rare in AI commentary, where the incentive is to sound certain. The commodity argument is not “models are worthless.” It is a chain of reasoning: there is no visible network effect, no durable differentiation beyond willingness to spend, no lock in comparable to Windows or iOS, and a likely structure of three to six well funded competitors plus open source and edge models all selling the same thing. Ask where price discipline comes from in that picture and the honest answer is that it probably does not, which is how you get a commodity even when demand is effectively infinite.

    The mobile data analogy is the load bearing comparison and it deserves to be taken seriously. Mobile data traffic rose something like fifteen hundred to two thousand times over fifteen years, the networks built an extraordinary piece of global infrastructure, everyone came to depend on it, and yet the operators captured almost none of the value because all the interesting stuff got built on top by someone else. Telco stocks were flat for two decades. If that is the template, then the trillion dollars of capex flowing into AI infrastructure can be both a worthwhile investment and a terrible place to expect outsized equity returns, because building the road is not the same as owning the traffic. The counterpoint Evans keeps fairly on the table is the operating system path, where Windows and iOS did capture value, but he notes they had levers and network effects that LLMs do not appear to have.

    His framing of where the questions live is the part most people in tech underweight. Once a technology works, the interesting questions stop being technology questions. Netflix is not a tech company in the sense that matters, because its real decisions are Los Angeles decisions about shows, talent, and sports, not San Francisco decisions about infrastructure. By the same logic, what AI means for a law firm is mostly a question for people who understand what associates actually do and what clients are actually paying for, not for model researchers. This is why the “the model will just do the whole thing” story keeps running aground. Most valuable software does not solve a problem the customer already knew they had. It often takes years to convince an industry that a problem even exists, and an LLM prompt does not surface latent problems that no one has articulated.

    The economic plumbing he describes is where the near term risk actually sits. We are in extreme disequilibrium, where twenty dollars a month can buy ten thousand dollars of tokens on one side and a weekend of experimentation can produce a ten thousand dollar bill on the other, exactly the pattern mobile data went through around 2009 and 2010. That gets resolved with the boring machinery of caps, throttling, and pricing tiers, not with magic. Layered on top is the financial gravity problem: Microsoft, Meta, and Google heading toward spending more than half of revenue on capex, with roughly seven hundred billion dollars of guidance across the big players, against a hard ceiling because there is not ten trillion dollars a year available to spend. And even when the productivity gains are real, the Jevons paradox and consumer surplus suggest much of the benefit gets competed away. If a discounted cash flow model used to take a week and now takes ten seconds, you do fifty of them and charge the client the same, which is great for clients and unremarkable for margins.

    The honest takeaway for builders is that the answer to “what does this do to software” is more software, probably one or two orders of magnitude more, just as SaaS itself produced an explosion rather than a consolidation. The SaaS apocalypse is real in the sense that some meaningful percentage of existing companies get wiped out, and unknowable in the sense that no one can yet say which ones, which is why thoughtful investors are reluctant to be long software in the dark. For anyone pursuing a more deliberate, purposeful relationship with technology, the closing note is the one to keep: every one of these shifts felt singular and world ending and world making at the time, it reshaped work and put people out of jobs and created things we love, and then it quietly became invisible. The goal is to stay clear eyed about which of those buckets a given change lands in rather than getting swept up in the noise of what someone said at a party yesterday.

    Key Takeaways

    • Agentic coding shifted from “kind of useful” to “really changing everything” at the start of the year, and it is the single field with unambiguous product market fit, where customers are pulling it out of your hands.
    • Coding working first was foreseeable in hindsight: software developers were the ones messing with the tools, and the first thing people do with a new kind of computer is build more computing, just as the first thing people did with PCs was make computers.
    • Anthropic, with less capital raised, chose to focus on coding and got it working, while OpenAI cycled through a more everything all at once strategy before narrowing in.
    • The intense focus on coding comes bundled with a supply crunch, a capacity crunch, and a price and capex imbalance that defines the current moment.
    • Most of the fundamental questions from two or three years ago still have no answers: whether there will be a winner in models, whether models capture value up the stack, how much they can do, and whether consumers will use this daily rather than weekly.
    • There is a wide gap between Valley insiders running clusters of Mac Studios all day and the roughly forty percent of people who say AI is “kind of useful, I used it last week for something.”
    • Outside tech, companies are adopting AI as one at a time point solutions for specific back office processes, like a commodities company using LLMs for better cash flow forecasting, not as a general purpose assistant.
    • Adoption always compounds on prior platforms: you could not have nine hundred million weekly active users in the Netscape era because there were not nine hundred million PCs on the planet.
    • Early in any platform shift almost nothing works smoothly, from sound cards and floppy disks with TCP/IP to computers that froze and lost your work, and AI is at that stage now.
    • Today’s token pricing crunch mirrors the mobile data shock of 2009 to 2010, where flat rate plans collided with surging usage and networks had to realign price with marginal cost through caps, fair use, and throttling.
    • Mobile data traffic rose roughly fifteen hundred to two thousand times in fifteen years, mobile networks earn around a trillion dollars and spend about two hundred billion a year on capex, yet their stocks have been flat for twenty years because all the value moved up the stack.
    • The central LLM question is whether the model can do the whole thing or whether you need hundreds of applications built on top, the same way you needed apps on Windows and iOS.
    • Evans sees no network effect and no sustainable differentiation between models beyond willingness to spend money, which points toward commodity infrastructure sold near marginal cost.
    • Chip companies, ISPs, and mobile operators did not capture the value; Windows and iOS did, but only because they had levers to move up the stack and real network effects, which models lack.
    • A useful comparison is semiconductors, where each generation gets more expensive and the field narrows to fewer players, suggesting three to six frontier model makers spending somewhere between two hundred billion and two trillion dollars a year.
    • Enterprises do not standardize on a model the way they once thought about AWS; the cloud and the model get abstracted away, so customers do not even know which one their SaaS product runs on.
    • Demand for tokens being effectively infinite does not prevent a price equilibrium, exactly as infinite demand for mobile bits still produced murderous price wars between commodity carriers.
    • History teaches that something will happen but rarely what; the smartest people in tech wrongly predicted Android would crush the iPhone on open versus closed grounds.
    • One characteristic of tech is that the moment you understand how something works is the moment to move on, which is why Evans stopped updating his Apple spreadsheet years ago.
    • The people who are good at using a tool are usually not the people who are good at designing what the tool should be, which is why model labs cannot build every skill or vertical application.
    • Claude skills and similar templates resemble file new in Excel: useful starting points that users eventually outgrow, raising the question of who builds the real software.
    • The questions increasingly move out of technology and into specific industries; what AI means for law, consulting, advertising, or accounting is partly an AI question and partly a deep domain question.
    • Netflix is not a tech company in the way that matters, because its real questions are media industry questions about shows, talent, and sports, not infrastructure; the same logic now applies across industries facing AI.
    • AI differs from prior platform shifts because the physical limits are unknown; in 1995 you knew PCs cost three thousand dollars and broadband could not reach everyone overnight, but no one knows how cheap, fast, or capable models will get.
    • Evans offers four buttons to press on any use case: is it just price elasticity and the Jevons paradox, does it remove a cost barrier to entry, does it unlock a new business model, or does it make something previously impossible now possible like trains over horses or Spotify over CDs.
    • Advertising and e-commerce are a standout opportunity because today’s systems know a SKU and a metadata field but not what a product actually is or why people buy it, and LLMs could change that level of understanding.
    • The valuable shift is not doing the old thing more, like more spreadsheets or better email, but doing genuinely new things, such as asking an LLM how to change prices to improve churn using all your call recordings, CRM flows, and product telemetry.
    • Enterprise software today splits into three buckets: big horizontal systems like SAP and Workday, three to four hundred vertical SaaS apps plus a thousand internal apps, and a fuzzy improvised middle of Excel, email, and shared files, with AI arriving as a new option across all three.
    • A core design tension is where to put the probabilistic software that can make mistakes versus the deterministic database that cannot, and whether the LLM sits at the top or the bottom of the stack; the answer is probably both depending on the task.
    • The net effect on software is way more software, since SaaS itself produced one to two orders of magnitude more software and all software companies exist to solve problems created by other software companies.
    • The SaaS apocalypse is real but unknowable: some percentage of SaaS companies get wiped out, but no one knows which, so you should not derate the whole sector fifty percent and many investors are wary of being long software for now.
    • Much of what an organization does is implicit, undocumented, and not in the training data, which is exactly the value McKinsey, Bain, and BCG provide by getting license to map how a company really works.
    • The real decisions are usually exception handling: the question is always what you cannot automate and what still requires human judgment about cases that were never written down.
    • Distinguish tasks from jobs: accountants spend almost none of their time the way they did fifty years ago, yet to the client the job looks the same.
    • LLMs excel where you want the average, the answer anyone would give, and struggle where you specifically do not want the average and cannot fully explain why you did it differently.
    • There is a financial gravity ceiling: Microsoft, Meta, and Google are on track to spend over fifty percent of revenue on capex versus fifteen to twenty percent for capital intensive telecoms, with seven hundred billion in guidance this year and no path to ten trillion.
    • Hyperscalers face an existential FOMO trap: returns look positive now, but they cannot let rivals build the future of compute without participating, even as the CFO asks how much participation is enough.
    • Token maxing will face a reckoning as the disequilibrium resolves, but measuring ROI is hard because most reported benefits so far, like better analytics, support, and productivity, are tough to put a financial value on.
    • Consumer surplus means many gains get competed away: if analysis that took a week now takes a day, you do five times more analysis and charge the same, the way investment banks did with spreadsheets.
    • Evans closes with a 1950s IBM ad promising “150 extra engineers,” a reminder that every fundamental technology change feels unprecedented, and that in twenty years AI will simply be invisible magic we take for granted.

    Detailed Summary

    What changed in the last year

    Evans frames the past year as a narrowing of focus. A year and a half after the first version of his presentation, the field has developed a much clearer sense of diverging product strategies and competitive tension that goes beyond simply building a bigger model with more compute. The dominant shift is that agentic coding started genuinely working, and the entire industry narrowed in on it because it has absolute product market fit, the kind where customers pull the product out of your hands. That success arrives alongside the supply crunch, capacity constraints, and price imbalance that now define the moment. At the same time, the charts keep climbing, models keep getting bigger, capex keeps growing, and usage keeps growing, while the deep questions from a few years ago remain unanswered.

    Why coding worked first

    That coding led was predictable at a naive level: the people experimenting with the tools were software developers, and they naturally tried to make software development work. Evans compares the moment to the internet around 1997 and 1998, and also to PCs in the late seventies and early eighties, when the technology was exciting but it was not clear what it was for and it did not quite work yet. The first thing people did with PCs was make computers, and since LLMs are in a sense computers, the first thing people are doing with them is making more compute. What was harder to foresee was the precise timing of the shift, the moment when agentic coding flipped from useful to transformative at the start of this year.

    Jobs, juniors, and what we have not learned

    On the question of what this means for engineers and team structure, Evans is blunt that we have learned almost nothing yet, because this did not even work six months ago and everyone is scrambling to interpret it. The pricing crunch alone means it will take a couple of years to settle. The newly concrete questions include whether you still hire junior people and what they would do, and why you were hiring juniors in the first place, whether to do the work itself or to develop people. Because software development now genuinely automates a class of work that used to be done by people, those questions have moved from theoretical to real, but no one can responsibly claim to know what a software team or a software career looks like in three years.

    OpenAI, Anthropic, and the strategy split

    Evans dryly notes the drama around the model labs, including the disruption of a senior leadership medical leave at OpenAI. In the latter part of last year, OpenAI’s question was essentially what to build on top of the models, an everything all at once approach that looked almost like asking the model for fifteen ideas and then doing all of them. Anthropic, with less capital raised, instead committed to coding and got it working, whether by deliberate strategy or by stumbling into it. The result is that software development plus a few other fields are where things genuinely work, surrounded by a large population of people excited around the edges and corporations quietly automating specific back office processes. He cites a commodities company that wants LLMs for better cash flow forecasting across many small producers, a very different thing from asking a chatbot to summarize your meetings.

    The mobile data analogy and value capture

    The richest section is the comparison to mobile. Adoption always compounds on prior platforms, so AI inherits a far larger installed base than the internet or mobile did at their starts. Early on, nothing works smoothly, and Evans recalls the era of buying a three hundred dollar sound card or wrestling a floppy disk of TCP/IP into a machine. The pricing dynamics directly echo mobile data around 2009 and 2010, when flat rate plans met exploding usage and ten thousand dollar bills, forcing networks to realign price with marginal cost. Crucially, mobile data traffic then rose fifteen hundred to two thousand times, the networks built extraordinary global infrastructure with around a trillion dollars of revenue and two hundred billion in annual capex, and yet their stocks stayed flat for twenty years because all the cool stuff and all the value got built and captured by someone else higher up the stack. Chip companies, ISPs, and mobile operators did not capture value; Windows and iOS did, but they had levers and network effects that models do not appear to share.

    The case that models become commodities

    Evans lays out the building blocks of his commodity thesis. First, there is no clear way to build a model that is sustainably and fundamentally better than everyone else’s, with no visible network effect and no strategic lever comparable to what Instagram, YouTube, or Google search enjoy. Differences in emphasis and taste exist, but not durable competitive moats beyond spending. Second, the chatbot is a weird, limited v1 interface that works well for some tasks and people but requires tooling, the right data, configuration, control, and thoughtful design for most real jobs, and the people good at a job are rarely the people good at designing the tool for it. Third, the labs cannot build every application any more than Microsoft or Apple could build every Windows or iPhone app. Enterprises do not standardize on a model the way they never standardized on a visible cloud provider, because it gets abstracted away. Taken together, that points to low level infrastructure sold by perhaps half a dozen competitors plus open source and edge, with no obvious source of price discipline, which is the definition of a commodity even when demand is infinite.

    The questions move out of technology

    One of the next big questions is when models become good enough that you no longer need the largest, fastest, most expensive model, and can use an older model, an open source model, or one running on device where compute is effectively free to the developer. But the deeper shift is that the important questions move out of technology and into industries. Drawing on his own essays “content isn’t king” and “Netflix isn’t a tech company,” Evans argues that Netflix’s real decisions are Los Angeles media questions, not San Francisco infrastructure questions, and San Francisco does not even know what the right questions are. By the same logic, what AI means for a law firm is mostly a question for people who understand law firms, what generative video means for Hollywood is a question Ben Affleck can answer better than he can, and the questions become half AI and half something else.

    Four buttons and the new things AI unlocks

    To reason about impact, Evans offers four buttons. Is a use case just price elasticity, the Jevons paradox of doing the same thing for less or more for the same money. Does it remove a cost that was a barrier to entry, like a newspaper’s printing press. Does it unlock something in your business model. Or does it make something previously impossible now possible, the way steam engines made trains possible regardless of how many horses you bought, or Spotify turned fifteen dollars a month into all the music there is. He stresses that the same broad change can mean wildly different things by industry, just as the internet devastated newspapers but barely touched movie studios. His favorite tractable example is advertising and e-commerce, a trillion dollar advertising market against twenty five trillion in retail, where today’s systems know a SKU and a metadata field and that people who bought one thing bought another, but do not know what a product is or why people buy it. An LLM could in principle understand the product, recommend ten coats at different prices with pros and cons, or look at your Instagram and suggest a winter coat that changes your look but not too much, which would have been science fiction three years ago.

    More software, the SaaS apocalypse, and tasks versus jobs

    For software specifically, Evans expects more competition, cheaper and quicker building, and new categories that were impossible before, all under an uncertain new margin structure where outcome based pricing is hard because most software work cannot be tied cleanly to profit and loss. He frames enterprise software as three buckets, big horizontal systems, hundreds of vertical and internal apps, and a fuzzy improvised middle of Excel and email, with AI arriving as another option across all of them. The deeper design tension is where to place probabilistic software that can make mistakes versus deterministic systems that cannot, and whether the LLM sits at the top or bottom of the stack, with the answer being both depending on the task. The net result is way more software, since SaaS itself produced orders of magnitude more software and software exists to solve problems created by other software. That fuels the SaaS apocalypse anxiety: some companies clearly get wiped out, but since no one knows which, you should not derate the whole sector, even as many investors stay cautious about being long software.

    Implicit knowledge, exception handling, and where the average fails

    Much of what organizations do is implicit, undocumented, and absent from any training data, which is precisely the value of strategy consultancies that get license to map how a company really works versus how it is supposed to work. The real decisions tend to be exception handling, the cases that require human judgment because they were never written down or do not look like before. Evans separates tasks from jobs, noting accountants do almost nothing the way they did fifty years ago while the client still buys the same thing. And he offers a sharp test: LLMs are excellent where you want the average, the answer anyone would give, and weak where you specifically do not want the average and cannot fully articulate why you did it differently.

    Capex, financial gravity, and the ROI question

    On spending, Evans describes a financial gravity problem. Microsoft, Meta, and Google are on line to spend over half their revenue on capex this year, against fifteen to twenty percent for capital intensive telecoms, with roughly seven hundred billion in guidance across the big players, a sum comparable to all of telecom or oil and gas. They cannot sustainably leap to one and a half trillion next year because the money is not there, so the curve must eventually taper. The hyperscalers are caught in an existential FOMO trap: returns look positive now, but they cannot sit out what might be the future of compute without risking becoming the next stranded incumbent, even as the CFO asks how much is enough. On token maxing, he expects a reckoning as the disequilibrium resolves, but measuring ROI is genuinely hard because most reported benefits so far are soft and hard to value, and consumer surplus means much of the gain gets competed away, the way faster spreadsheets simply meant more analysis at the same price.

    Closing image

    Evans ends with an IBM advertisement from the early 1950s showing a sea of engineers holding slide rules, with the tagline that an IBM electronic calculator gives you 150 extra engineers, exactly the pitch behind countless modern startup decks. We move through these fundamental technology waves every ten or fifteen or twenty years, each one feeling completely unlike anything before, and AI is amazing and transformative in the same way mobile, the internet, and PCs were. The base case is that it will produce wonderful things, ruin some livelihoods, put people out of work, and eventually become invisible. His one line description of where it all ends up is that it will be magic, and in twenty years we will simply say of course computers do that, the way an hour of crash free streaming HD video over Wi-Fi already feels unremarkable.

    Notable Quotes

    “Agentic coding went from being kind of useful to really changing everything.”

    Benedict Evans, on the pivotal shift at the start of the year

    “We are in this extreme scarcity. We can’t spend $10 trillion a year on AI infrastructure cuz there isn’t $10 trillion a year there to spend on it.”

    Benedict Evans, on the hard ceiling of AI capex

    “I don’t think foundation models are a product. I don’t think a chatbot is a product. I think the value will be further up.”

    Benedict Evans, stating the core of his thesis

    “They built this amazing piece of global incredibly sophisticated very expensive global infrastructure with enormous growth in use, and they didn’t make any money from it because all the value moved up stack.”

    Benedict Evans, on the mobile network analogy

    “The moment that you understand something and you know how it works and what’s going to happen is the moment you should move on to something else.”

    Benedict Evans, on how to pay attention in tech

    “These are all Los Angeles questions. These are not San Francisco questions. No one in San Francisco even knows what the right questions are.”

    Benedict Evans, on why Netflix is not a tech company

    “The important stuff is not doing the old thing but more. It’s doing something new that you couldn’t have done with the old thing.”

    Benedict Evans, on where the real value of a new technology shows up

    “All software companies exist to solve problems created by other software companies.”

    Benedict Evans, on why AI produces more software, not less

    “It’s going to be magic, and in 20 years time we’ll just say, well, of course that’s how it is. Computers have always done that.”

    Benedict Evans, on how the whole shift ends up

    This is a dense, clear eyed conversation that rewards a full listen, especially if you are trying to think past the hype cycle about where AI value actually lands. Watch the full conversation here, and check out the “AI eats the world” presentation referenced throughout.

    Related Reading

    • Benedict Evans’ website home of the “AI eats the world” presentation and his newsletter referenced throughout the conversation.
    • Andreessen Horowitz (a16z) the venture firm whose podcast hosted this discussion and where Evans was formerly a partner.
    • Jevons paradox (Wikipedia) background on the price elasticity idea Evans uses to explain how cheaper AI may lead to more usage rather than savings.
    • Stratechery by Ben Thompson the analysis Evans cites on software as a designed workflow versus a process that grows out of how a business runs.
    • The Pursuit of Purpose a PJFP look at finding direction and meaning in work as automation reshapes careers and industries.

  • Elad Gil on the AI Frontier: Compute Constraints, the Personal IPO, and Why Most AI Founders Should Sell in the Next 12 to 18 Months

    Elad Gil sat down with Tim Ferriss for a wide ranging conversation that pairs almost perfectly with his recent Substack post Random thoughts while gazing at the misty AI Frontier. Together, the podcast and the post lay out the cleanest framework I have seen for what is actually happening in AI right now: a Korean memory bottleneck capping every lab, a class wide personal IPO across the research community, the fastest revenue ramps in capitalist history, and a brutal dot com style culling that most founders do not yet want to admit is coming. Below is a complete breakdown.

    TLDW (Too Long, Didn’t Watch)

    Elad Gil argues that AI is producing the fastest revenue ramps in capitalist history while setting up the same brutal power law that wiped out 99 percent of dot com companies. OpenAI and Anthropic each sit at roughly 0.1 percent of US GDP today, on a path to 1 percent of GDP run rate by end of 2026, which is insanely fast by any historical standard. The current ceiling on capabilities is not chips but Korean high bandwidth memory, and that constraint will likely hold all major labs roughly comparable in capability through 2028. Talent has just experienced a class wide personal IPO via Meta led bidding, with packages running tens to hundreds of millions per researcher. Most AI companies should consider exiting in the next 12 to 18 months while the tide is high. Right now consensus is correct. Save the contrarianism for later.

    Key Takeaways

    • OpenAI and Anthropic are each at roughly 0.1 percent of US GDP. With US GDP near 30 trillion dollars and each lab at a roughly 30 billion dollar revenue run rate, AI has gone from essentially zero to 0.25 to 0.5 percent of GDP in just a few years. If the labs hit 100 billion in run rate by year end 2026 (which many expect), AI hits 1 percent of GDP run rate inside a single year.
    • The AI personal IPO is real. 50 to a few hundred AI researchers across multiple companies just experienced a class wide IPO event due to Meta led bidding, with top packages reportedly tens to hundreds of millions per person. The closest historical analog is early crypto holders around 2017.
    • The bottleneck is Korean memory, not Nvidia chips. High bandwidth memory from Hynix, Samsung, Micron, and others is the binding constraint. Expected to hold roughly two years. After that, power and data center buildout become the next walls.
    • No lab can pull dramatically ahead before 2028. Because every lab is compute constrained on the same input, OpenAI, Anthropic, Google, xAI, and Meta should remain roughly comparable in capability through that window, absent an algorithmic breakthrough that stays inside one lab.
    • Compute is the new currency. Token budgets now define what an engineer can accomplish, what a company can spend, and what business models are viable. Some companies (neoclouds, Cursor) are effectively inference providers disguised as tools.
    • The dot com base rate is the AI base rate. Around 1,500 to 2,000 companies went public in the late 1990s internet cycle. A dozen or two survived. AI will likely look the same.
    • Most AI founders should consider selling in the next 12 to 18 months. If you are not in the durable handful, this is your value maximizing window. A handful of companies (OpenAI, Anthropic) should never sell.
    • Buyers are bigger than ever. One percent of a 3 trillion dollar market cap is 30 billion dollars. That math makes massive AI acquisitions trivial for hyperscalers, vertical incumbents, and adjacent giants.
    • Underrated exit path: merger of equals. Two private AI competitors destroying each other on price should consider just merging. PayPal and X.com did exactly this in the 1990s.
    • 91 percent of global AI private market cap sits in a 10 by 10 mile square. If you want to do AI, move to the Bay Area. Remote work for cluster industries is BS.
    • Want money? Ask for advice. Want advice? Ask for money. The inverse also works: offering useful advice frequently leads to inbound investment opportunities.
    • AI is selling units of labor, not software. The shift is from selling seats and tools to selling cognitive output. This is why Harvey can win in legal, where decades of legal SaaS failed.
    • AI eats closed loops first. Tasks that can be turned into testable closed loop systems (code, AI research) get automated fastest. Map jobs on a 2×2 of closed loop tightness vs economic value to see where AI hits soonest.
    • Headcount will flatten at later stage companies. Multiple late stage CEOs told Elad they will not do big AI layoffs but will simply stop growing headcount even as revenue grows 30 to 100 percent. Hidden layoffs are also hitting outsourcing firms in India and the Philippines first.
    • The Slop Age could be the golden era of AI plus humanity. AI produces useful slop at volume, humans desloppify it, leverage is high, and the work is fun. This window may close as AI gets superhuman.
    • Market first, team second (90 percent of the time). Great teams die in bad markets. The exception is when you meet someone truly exceptional at the very earliest stage.
    • The one belief framework. If your investment memo needs three core beliefs to be true, it is too complicated. Coinbase was an index on crypto. Stripe was an index on e-commerce. That was the entire memo.
    • The four year vest is a relic. It exists because in the 1970s companies actually went public in four years. Today the private window has stretched to 20 years and venture has eaten what used to be public market growth investing.
    • Boards are in-laws. You cannot fire investor board members. Take a worse price for a better board member, because as Naval Ravikant said, valuation is temporary, control is forever.
    • Right now, consensus is correct. Save the contrarianism. The smart move is to just buy more AI exposure rather than try to outsmart the obvious.
    • Distribution wins more than founders admit. Google paid hundreds of millions to push the toolbar. Facebook bought ads on people’s own names in Europe. TikTok spent billions on user acquisition. Allbirds (yes, the shoe company) just raised a convert to build a GPU farm.
    • Anti-AI sentiment will get worse before it gets better. Maine banned new data centers. There has been violence directed at AI leaders. Expect more political and activist backlash, especially as AI is blamed for harms it has not yet caused while its benefits are mismeasured.
    • Use AI as a cold reader. Elad uploads photos of founders to AI models with cold reading prompts and reports surprisingly accurate personality assessments based on micro features.

    Detailed Summary

    The Numbers Are Insane and Mostly Underappreciated

    The most stunning data point in either source is the GDP math. US GDP is roughly 30 trillion dollars. OpenAI and Anthropic are each rumored to be at roughly 30 billion dollars in revenue run rate, putting each one at 0.1 percent of US GDP. Add cloud AI revenue and the picture gets stranger: AI has grown from essentially zero to between 0.25 and 0.5 percent of GDP in only a few years. If the labs hit 100 billion in run rate by year end 2026, AI will be at roughly 1 percent of GDP run rate inside a single year. There is no historical analog for that pace. Elad notes that productivity gains from AI may end up mismeasured the way internet productivity was undercounted in the 2000s, which would have downstream consequences for regulation: AI gets blamed for the bad (job losses) and credited for none of the good (new jobs, education gains, healthcare improvements). His half joking aside is that the real ASI test may be the ability to actually measure AI’s economic impact.

    The AI Personal IPO

    The most underdiscussed phenomenon in AI right now, according to Elad, is what he calls a class wide personal IPO. When a company IPOs, a subset of employees become wealthy, lose focus, and either start companies, get into politics, fund passion projects, or check out. Meta started aggressively bidding for AI talent. Other major labs had to match. The result was 50 to a few hundred researchers, scattered across multiple labs, suddenly receiving compensation in the tens to hundreds of millions of dollars range. The only historical analog Elad can think of is early crypto holders around 2017. Some chunk of these newly wealthy researchers will redirect attention to AI for science, side projects, or quiet quitting. The aggregate field stays mission aligned, but the distribution of attention has shifted.

    The Korean Memory Bottleneck

    Every major AI lab today is building giant Nvidia clusters paired with high bandwidth memory primarily from Korean fabs and a few other suppliers. They run massive amounts of data through these clusters for months, and the output is, almost absurdly, a single flat file containing what amounts to a compressed version of human knowledge plus reasoning. Right now, the binding constraint on this whole stack is HBM memory from Hynix, Samsung, Micron, and others. Korean memory fab capacity has been below the capacity of every other piece of the system. Elad estimates this constraint persists for roughly two years. After that, the next walls are likely data center construction and power. The strategic implication is enormous. While memory constrains everyone, no single lab can buy 10x the compute of its rivals, so capabilities should stay roughly comparable across the major labs. Once that constraint lifts, possibly around 2028, one player could theoretically pull dramatically ahead, especially if AI assisted AI research closes a self improvement loop inside one lab.

    Compute Is the New Currency

    The blog post sharpens a framing that runs throughout the podcast: compute, denominated in tokens, is now a unit of economic value. Token budgets define what an engineer can accomplish, what a company can spend, and what business models work. Some companies are effectively inference providers wearing tool costumes. Neoclouds are the cleanest example. Cursor is another, subsidizing inference as a user acquisition strategy. The most absurd recent example: Allbirds, the shoe company, raised a convertible to build a GPU farm. Whether this becomes the AI version of Microstrategy’s Bitcoin trade or a cautionary tale, it tells you where the cost of capital believes the next decade is going.

    The Dot Com Survival Math

    Elad walks through the brutal arithmetic that AI founders should be internalizing. In the late 1990s and early 2000s, somewhere between 1,500 and 2,000 internet companies went public. Of those, roughly a dozen or two survived in any meaningful form. Every cycle has looked like this: automotive in the early 1900s, SaaS, mobile, crypto. There is no reason AI will be different. Most current AI companies, including those ramping revenue today, will see the market, competition, and adoption turn on them. The question every AI founder should be asking is whether they are in the durable handful or not.

    Most AI Companies Should Consider Exiting in the Next 12 to 18 Months

    This is the most actionable and most uncomfortable take in either source. While the tide is rising, every AI company looks unstoppable. Whether they actually are, in a 10 year frame, is a separate question. Founders running successful AI companies should take a cold honest look at whether the next 12 to 18 months is their value maximizing window. Companies typically have a 6 to 12 month peak before some headwind hits, often visible in the second derivative of growth. The best signal that you should sell is when growth rate is starting to plateau and you can see why. A handful of companies (OpenAI, Anthropic, the durable winners) should never exit. Many others should, while everything is still on the upswing.

    What Makes an AI Company Durable

    Elad lays out four lenses for evaluating durability at the application layer:

    1. Does your product get dramatically better when the underlying model gets better, in a way that keeps customers loyal?
    2. How deep and broad is the product? Are you building multiple integrated products embedded in actual workflows?
    3. Are you embedded in real change management at the customer? AI adoption is mostly a workflow change problem, not a tech problem. Workflow embedding is durable.
    4. Are you capturing and using proprietary data in a way that creates a system of record? Data moats are often overstated, but sometimes real.

    At the lab layer, Elad believes OpenAI, Anthropic, and Google are durable absent disaster. He predicted three years ago that the foundation model market would settle into an oligopoly aligned with cloud, and that prediction has roughly held.

    Selling Work, Not Software

    The deepest structural insight in the conversation is that generative AI is shifting what software companies sell. The old model was selling seats, tools, and SaaS subscriptions. The new model is selling units of cognitive labor. Zendesk sold seats to support reps. Decagon and Sierra sell agentic support output. Harvey can win in legal even though selling to law firms was historically considered terrible business, because Harvey is not selling tools, it is augmenting lawyer output. This shift opens markets that were previously closed and dramatically grows tech TAMs. It is also why founder limited theories of entrepreneurship currently understate how many opportunities exist.

    AI Eats Closed Loops First

    One of the cleanest mental models in the blog post is the closed loop framework. AI automates first what can be turned into a testable closed loop. Code is the canonical example: outputs can be tested, errors detected, models can iterate. AI research is similar. Both have tight feedback loops and high economic value, which puts them at the top of the AI impact ranking. Map jobs on a 2×2 of closed loop tightness vs economic value and you can see where AI hits soonest. The interesting forward question is which jobs become more closed loop next. Data collection and labeling will keep growing in every field as a result.

    The Harness Matters More Than People Think

    For coding tools and increasingly for enterprise applications, what Elad calls the harness, the wrapper of UX, prompting, workflow integration, and brand around the underlying model, is becoming sticky. It is not just which model you call. It is the environment built around it. Cursor and Windsurf demonstrate this in coding. The interesting open questions are what the harness looks like for sales AI, for AI architects, for analyst workflows. Those gaps leave room for startups even as model capabilities converge.

    Hidden Layoffs and the Developing World

    Most announced AI driven layoffs are probably just COVID era overhiring corrections wrapped in a more flattering narrative. But real AI driven labor displacement is happening, and it is hitting outsourcing firms first. That means countries like India and the Philippines, where many outsourced services jobs sit, are likely to be the most impacted earliest. Several developing economies built their growth ladders on services exports. If AI takes those jobs first, the migration and economic patterns of the next decade may shift in ways nobody is yet planning for.

    The Flat Company

    Multiple late stage CEOs told Elad they will not announce big AI layoffs. Instead, they will simply stop growing headcount. If revenue grows 30 to 100 percent, headcount stays flat or shrinks via attrition. Existing employees become dramatically more productive. The very best people who can leverage AI will see compensation inflate. Sales and some growth engineering keep hiring. Almost everything else flatlines. This is mostly a later stage and public company phenomenon. True early stage startups should still scale aggressively after product market fit, just with more leverage per person.

    Exit Options for AI Founders

    Elad lays out four exit categories. First, the labs and hyperscalers themselves: Apple, Amazon, Google, Microsoft, Meta. Second, vertical incumbents like Thomson Reuters for legal or healthcare giants for clinical AI. Third, the underrated category of merger of equals between two private AI competitors who are currently destroying each other on price. PayPal and X.com did this in the 1990s. Uber and Lyft reportedly almost did. Fourth, large adjacent tech companies: Oracle, Samsung, Tesla, SpaceX, Snowflake, Databricks, Stripe, Coinbase. The market cap math has changed in a way that makes acquisition trivial. One percent of a three trillion dollar market cap is 30 billion dollars, which means a hyperscaler can do massive acquisitions almost casually.

    Geographic Concentration Is Extreme

    Elad’s team analyzed where private market cap aggregates. Historically half of global tech private market cap sat in the US, with half of that in the Bay Area. With AI, 91 percent of global AI private market cap is in a single 10 by 10 mile square in the Bay Area. New York is a distant second and then it falls off a cliff. For defense tech, the cluster is Southern California (SpaceX, Anduril, El Segundo, Irvine). Fintech and crypto skew toward New York. The remote everywhere advice is, Elad says, just BS for anyone trying to break into an industry cluster.

    How Elad Got Into His Best Deals

    Stripe started with Elad cold emailing Patrick Collison after selling an API company to Twitter. A couple of walks later, Patrick texted that he was raising and Elad was in. Airbnb came from helping the founders raise their Series A and being asked at the end if he wanted to invest. Anduril came from noticing that Google had shut down Project Maven and asking if anyone was building defense tech, then meeting Trey Stephens at a Founders Fund lunch. Perplexity came from Aravind Srinivas cold messaging him on LinkedIn while still at OpenAI. Across all of these, the pattern is the same: be in the cluster, be helpful, be talking publicly about technology nobody else is talking about, and be useful to founders before any money is on the table.

    The One Belief Framework

    Investors love complicated 50 page memos. Elad believes the actual decision usually collapses into a single core belief. Coinbase: this is an index on crypto, and crypto will keep growing. Stripe: this is an index on e-commerce, and e-commerce will keep growing. Anduril: AI plus drones plus a cost plus model will be important for defense. If your thesis needs three things to be true, it is probably not going to work. If it needs nothing, you have no thesis.

    Boards as In-Laws

    Elad emphasizes that founders should treat board composition like one of the most important hiring decisions of the company. You cannot fire an investor board member. They have contractual rights. So if you are going to be stuck with someone for a decade, take a worse valuation for a better human. Reid Hoffman’s framing is that the best board member is a co-founder you could not have otherwise hired. Naval Ravikant’s framing is that valuation is temporary but control is forever. Elad recommends writing a job spec for every board seat.

    The Slop Age as a Golden Era

    One of the warmest takes in the blog post is the framing of the current moment as the Slop Age, and the suggestion that this might actually be the golden era of AI plus humanity. Before the last few years, AI was inaccessible and narrow. Eventually AI may become superhuman at most tasks. Today, AI produces useful slop at volume, which means humans are still needed to desloppify the slop, but the leverage on time and ambition is real. That makes the work fun. If AI displaces people or starts doing more interesting work, this golden moment fades. Elad also notes the obvious counter, that the era of human generated internet slop preceded the AI slop era. AGI may end the slop age, or alternately may be the thing that finally cleans up all the prior waves of human slop.

    Anti-AI Regulation and Violence Will Increase

    This is one of the more sobering threads in the blog post. Real world AI driven labor displacement has been small so far, but anti-AI sentiment is already strong and growing. Maine just banned new data centers. There has been actual violence directed at AI leaders, including a recent attack on Sam Altman. Elad’s view is that AI leaders should work harder on optimistic public framing, real political lobbying, and reining in the doom narrative coming from inside the field. Otherwise the regulatory and activist backlash will get much worse, and likely on the basis of mismeasured impacts.

    Right Now Consensus Is Correct

    The headline contrarian take from the episode is that contrarianism right now is wrong. There are moments in time when betting against the crowd pays. This is not one of them. The smart bet is just buying more AI exposure. Trying to find the clever angle, the underlooked hardware play, the secret macro thesis, is overthinking it. Save the contrarian moves for later in the cycle.

    Distribution Almost Always Matters

    Elad pushes back on the founder mythology that great products win on their own. Google paid hundreds of millions of dollars in the early 2000s to distribute its toolbar through every popular app installer on the internet. Facebook bought search ads against people’s own names in European markets to seed network liquidity. TikTok spent billions on user acquisition before its algorithm could lock people in. Snowflake spent enormous sums on enterprise sales and channel partnerships. Sometimes the best product wins. Often the company with the best distribution wins. Founders should plan for both.

    AI as a Cold Reader and a Research Partner

    Two of the more practical AI workflows Elad describes: First, uploading photos of founders to AI models with cold reading prompts that ask the model to identify micro features (crows feet from genuine smiling, brow patterns, posture cues) and infer personality traits, sense of humor, and likely social behavior. He reports the outputs are surprisingly specific. Second, running deep dives across multiple models in parallel (Claude, ChatGPT, Gemini), asking each for primary sources, summary tables, and cross checked data. He recently used this approach to investigate the rise in autism and ADHD diagnoses, concluding that diagnostic criteria shifts and school incentives drive most of it, and noting that maternal age has a stronger statistical association with autism than paternal age, despite paternal age getting all the public discourse.

    The First Ever 10 Year Plan

    For someone who has been compounding aggressively for two decades, Elad has somehow never written a 10 year plan until now. He knows it will not play out as written. The point is that the act of imagining a decade out shifts what you choose to do in the near term. He explicitly rejects the AGI in two years therefore plans are pointless framing as defeatist. There will be interesting things to do regardless of how the AGI timeline plays out.

    Thoughts

    This is one of the more useful AI investor conversations of 2026, mostly because Elad is willing to put numbers and timelines on things that are usually left vague. Pairing the podcast with the underlying Substack post is the right move because the post is where the GDP math, the closed loop framework, and the Slop Age framing actually live. The podcast is where Elad explains how he thinks rather than just what he thinks.

    The 12 to 18 month sell window framing is the most actionable single idea in either source, and probably the most uncomfortable for AI founders sitting on multi billion dollar paper valuations. The math is unforgiving. A dozen winners out of thousands. If you are honest with yourself about whether you are in the dozen, you know what to do.

    The Korean memory bottleneck framing explains a lot of current behavior. The talent wars make more sense once you accept that compute is not going to be the differentiator for two years, so people become the only remaining lever. The convergence of capabilities across OpenAI, Anthropic, Google, and xAI starts to look less like coincidence and more like the structural inevitability of a supply constrained input. The 2028 inflection date is the one to watch.

    Compute as currency is the cleanest reframing in the blog post. Once you start pricing companies in tokens rather than dollars, everything from Cursor’s economics to Allbirds raising a convert to build a GPU farm becomes legible. The interesting question is whether this is a permanent unit of denomination or a transitional one that fades when inference costs collapse.

    The software to labor argument is the structural framing that I think will hold up the longest. Once you internalize that we are not selling seats anymore but selling cognitive output, every vertical that was previously locked behind ugly procurement and IT inertia opens up. Harvey is the proof of concept. There will be 30 more Harveys across every white collar profession.

    The closed loop framework is the cleanest predictor of which jobs get hit hardest and soonest. If you want to know whether your role is exposed, the questions to ask are whether outputs can be machine evaluated, how tight the feedback loop is, and how high the economic value is. The intersection is where AI lands first.

    The geographic concentration data is genuinely shocking. 91 percent of global AI private market cap in a 10 by 10 mile area is the kind of statistic that should make everyone outside that square think very carefully about what game they are playing.

    The Slop Age framing is the most emotionally honest moment in the post. We are in a window where humans still meaningfully add value on top of AI output. That window is finite. Enjoy it.

    The anti-AI backlash thread is the one I think most people in the industry are still underweighting. Maine banning new data centers is a leading indicator, not a one off. The fact that the impacts are likely to be mismeasured by official statistics makes the political dynamics worse, not better. AI will get blamed for harms it did not cause and credited for none of the gains. If the field’s leaders do not start communicating better and lobbying smarter, the regulatory environment in 2028 will be much worse than in 2026.

    Finally, Elad’s first ever 10 year plan stands out as the most quietly important moment in the episode. The implicit message is that even people who have been compounding aggressively for two decades benefit from forcing a longer time horizon onto their thinking. Most plans fail. The act of planning still changes what you do today.

    Read the original Elad Gil post here: Random thoughts while gazing at the misty AI Frontier. Find Elad on X at @eladgil, on his Substack at blog.eladgil.com, and on his website at eladgil.com. Tim Ferriss publishes the full episode at tim.blog/podcast.