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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

  • Howard Marks on Why Most Investors Lose, the AI Bubble, India, and the Hunt for the $10 Bill Nobody Picked Up

    TLDW

    Howard Marks, co-founder of Oaktree Capital and the author of the memos every serious investor reads first, sat down with Nikhil Kamath for a wide-ranging conversation on his 50+ year career, the philosophy of Mujo (the inevitability of change), why he chose bonds over stocks, the difference between drifting down the river and seeing it, where we sit in the current cycle, AI as both threat and opportunity, why active management lost to indexation, and why the only way to outperform in a world full of smart, motivated, computer-literate competitors is “superior insight.” His core message: investing is a puzzle that cannot be solved by formula, and the only edge that lasts is being more right than the other person, more often, with the discipline to stay calm when everyone else is panicking or partying.

    Key Takeaways

    • Mujo is the operating system. Marks took Japanese literature at Wharton and walked away with one idea that shaped his whole career: change is inevitable, unpredictable, and uncontrollable. You cannot predict the future, but you can prepare for it.
    • Cycles are excesses and corrections, not ups and downs. The S&P 500 has averaged about 10% per year for 100 years, but it is almost never between 8% and 12% in any given year. The norm is not the average. Greed and fear push the pendulum past equilibrium every time.
    • The recovery is two years older. When asked where we are in the cycle, Marks notes the bull market continued from April 2024 through January 2026, so by definition we are deeper into the cycle, with a recovery distorted by the unique man-made COVID recession.
    • Drifting versus seeing the river. Marks describes the first 35 years of his career (roughly age 14 to 49) as drifting. Starting Oaktree in 1995 was the first truly intentional decision he made. Entrepreneurship forced proactivity on him.
    • Why bonds over equities. The contractual, predictable nature of debt suited his conservative temperament (his parents were adults during the Depression). He was not voluntarily moved to bonds in 1978; a boss reassigned him just in time for the birth of the high-yield bond market.
    • Distressed debt is the bigger story. Bruce Karsh joined in 1987 and has run roughly $70 billion in distressed debt since 1988, with profits well over 90% of the total profit and loss.
    • Excess return is getting paid more than the risk warrants. If the market thinks a borrower has a 5% default probability and you correctly conclude it is 2%, you collect interest priced for 5% risk while taking 2% risk. That gap is the alpha.
    • Oaktree’s default rate is about a third of the market. Over 40 years, roughly 3.6% to 3.7% of high-yield bonds default each year. Oaktree’s rate is roughly one-third of that, achieved through process discipline, institutional memory, and analysts who stay analysts for life.
    • If you are starting a career today, understand AI. Marks says the investor who will make the most money over the next 10 years is the one who best understands AI and its capabilities, whether they bet for or against it.
    • AI is excellent at pattern matching, but cannot create new patterns. Can AI pick the Amazon out of five business plans? The Steve Jobs out of five CEOs? Marks bets no. Most humans cannot either, which means there is still a role for exceptional people.
    • Indexation won because active management lost. Passive did not become dominant because it is brilliant. It dominated because most active managers failed and charged high fees for the privilege.
    • Bad times create openings for active managers, but most cannot take them. Panic drives prices down, but the same panic prevents most investors from buying. Wally Deemer: when the time comes to buy, you will not want to.
    • The job is simple but not easy. Find the best managers, the best companies, the best ideas. Charlie Munger told Marks: anyone who thinks it is easy is stupid.
    • Where is the $10 bill nobody picked up? Marks thinks it is around AI, but only for those with insight above the average. If you are average and you crowd into AI, you get average results in a bull case and worse in a bear case.
    • Quantitative information about the present cannot produce alpha. Andrew Marks (howards son) pointed this out to his father during the COVID lockdown. Everyone has the same data. Outperformance has to come from somewhere else.
    • Buffett’s edge was reading Moody’s Manuals when nobody else would. The pre-internet research process favored those willing to do tedious work alone. The format of the edge changes; the fact that edge requires doing what others will not, does not.
    • You cannot coach height. Marks can tell you that second-level thinking, contrarian insight, and the ability to evolve at 80 are essential. He cannot tell you how to acquire any of them.
    • India: Marks declines to opine. He has deployed roughly $4 billion in India but refuses to claim expertise on the Indian stock market or recommend a sector.
    • History rhymes. Marks credits Mark Twain. The lessons that repeat are lessons of human nature, which changes incredibly slowly.
    • Investing is a puzzle, not dentistry. Quoting Taleb, Marks observes that engineers and dentists succeed by repeating the right answer. Investors face a problem with no certain solution. If you need to be right every time, do not become an investor.

    Detailed Summary

    From Queens to Wharton: The Accidental Investor

    Howard Marks grew up in Queens, New York, in a middle-class family. Neither of his parents went to college, but his father was an intelligent accountant. Marks discovered accounting in high school, fell in love with its orderliness, and chose Wharton because he was told it was the best undergraduate business school in America. Wharton required a literature class in a foreign country and a non-business minor. For reasons he no longer remembers, Marks chose Japanese studies, then took Japanese civilization and Japanese art. He calls it the most important academic decision of his life because of one concept he encountered: Mujo.

    Mujo, Independence of Events, and Why You Cannot Predict

    Mujo, the turning of the wheel of the law, teaches that change is inevitable, unpredictable, and uncontrollable, and that humans must accommodate it rather than try to control it. Marks pairs this with his deep belief in the independence of events: ten heads in a row do not change the odds on flip eleven. Roughly 20 years ago he wrote a memo titled “You Can’t Predict. You Can Prepare.” A portfolio cannot be optimized for both extreme upside and extreme downside, but it can be built to perform respectably across many possible futures, if you suboptimize for the middle of the probability distribution.

    Why Cycles Exist

    If GDP averages 2% growth, why is it never simply 2%? Marks’s answer is excesses and corrections. Optimism leads producers to overbuild and consumers to overspend, growth runs above trend, then satiation and oversupply pull it back below trend. The S&P 500 averages 10% per year over a century, but the return in any given year is almost never between 8% and 12%. The norm is not the average because human beings are not average; they are alternately greedy and fearful.

    Where Are We Now?

    Two years ago Marks told the Norwegian Sovereign Wealth Fund’s Nicolai Tangen that we were near the middle of the cycle. Two years later, the bull market in stocks continued through January 2026, so by simple math the recovery is older. The COVID recession was a man-made anomaly: one quarter of negative growth followed by the best quarter in history, triggered by a deliberate global shutdown rather than by accumulated excess. That distorts every traditional cycle metric.

    Drifting Versus Seeing the River

    One of the most personal moments in the conversation is Marks’s confession that he drifted for the first 35 years of his career. He did not pick his career, his first job, or his transition from equities to bonds in any deliberate way. Other people pushed him; he said yes. The first proactive decision of his life was co-founding Oaktree in 1995 at age 49, and even that came largely because his wife and his partner Bruce Karsh pushed him into it. Once he had to lead, he had to be intentional. Leadership cannot be passive.

    The Bond Decision

    Marks did not choose bonds; bonds chose him. In May 1978 his boss at Citibank moved him to the bond department to start a convertible fund. Three months later another phone call asked him to figure out something called high-yield bonds being run by a guy in California named Milken. Marks said yes both times. He arrived at the front of the line for high-yield in 1978 and has been there for 48 years.

    The conservative temperament fit. Marks’s parents were adults during the Depression, so he grew up hearing “don’t put all your eggs in one basket” and “save for a rainy day.” Bonds offered contractual, predictable returns. The phrase “junk bonds” was a bias that made the asset class cheaply available to anyone willing to do the analytical work.

    Distressed Debt and Excess Return

    When Bruce Karsh joined in 1987, Oaktree launched what Marks believes was the first distressed debt fund from a mainstream institution. Karsh has managed about $70 billion since 1988 with well over 90% of the total being profit. The core skill is predicting default probability better than the market. If consensus prices a borrower at a 5% default risk and you correctly assess 2%, the interest you receive is overpaid relative to actual risk. Marks calls this “excess return” and credits Mike Milken with the foundational insight: lend to borrowers others will not, demand interest beyond what compensates you, and the math works.

    Over 40 years, roughly 3.6% to 3.7% of high-yield bonds default annually on average. Oaktree’s default rate has been roughly one-third of that. Marks credits institutional culture (analysts who stay analysts for life), psychological stability in volatile periods, and a process that forces every analyst to ask the same eight questions of every company every time. In equity research, you can buy a stock for great management without examining the product, or for a great product without examining the management. In Oaktree’s bond process, you cover every base every time.

    Beginning a Career Today: The AI Question

    Asked what he would do today, Marks says the front of the line is AI. The investor who will succeed most over the next decade is the one who best understands AI, whether they bet for or against it. He notes that he was shocked by his own experience using Claude, but adds that he has not fired a single person and does not intend to.

    His view: AI excels at extracting patterns from history and applying them with discipline and without psychological wobble. But investing also requires creating new patterns. Can AI sit with five business plans and identify the future Amazon? Can it sit with five CEOs and pick Steve Jobs? Marks bets not. Then he adds the killer line: most humans cannot either. Which means the role for exceptional humans survives, but the bar gets higher.

    Why Indexation Won

    When Marks went to graduate school at the University of Chicago in 1968, his professor pointed out that most mutual funds underperformed the S&P after fees. Index funds did not exist yet; Jack Bogle launched the first one in 1974. Today, most equity mutual fund capital is passive. Marks’s controversial take: indexation did not win because it is great. It won because active management was so bad and so expensive. Even at equal fees, if active decisions are inferior, passive wins.

    Bad times create openings for active managers because panic drives prices down, but the same panic prevents most people from buying. Marks quotes the old trader Wally Deemer: when the time comes to buy, you will not want to. The advantage of an AI nudge that says “this is one of those moments, get your ass in gear and buy something” might genuinely add value, because it removes the emotion.

    Second-Level Thinking and Why You Cannot Coach It

    Marks’s first book, The Most Important Thing, has 21 chapters, each titled “The Most Important Thing Is…” Each one is different because so many things matter. The chapter on second-level thinking came to him spontaneously while writing a sample chapter for Columbia University Press. The argument is simple: if you think like everyone else, you act like everyone else, and you get the same results. To outperform, you must deviate from the herd and be more right than the herd. Different is not enough. Different and better is the bar.

    Can AI become a contrarian thinker? You can prompt Claude to give you only non-consensus answers, but the catch is that consensus is often close to right because the people building consensus are intelligent, educated, computer-literate, and motivated. Forcing non-consensus often forces wrong. The real edge is being non-consensus AND correct, which is a much narrower target.

    The $10 Bill That Nobody Has Picked Up

    Marks references the joke about the efficient market hypothesis: there is no $10 bill on the sidewalk because if there were, somebody would have already picked it up. He then concedes that the bill is probably around AI today, but only for those whose insight rises above the average. If you are average and you crowd into AI, you go along with the tide if it works and get crushed if it does not. Quoting Garrison Keillor’s Lake Wobegon, “where all the children are above average,” Marks notes that the math does not allow it. Most investors will not be above average, and acknowledging that is the first step toward becoming one of the few who are.

    Learning From Andrew, Buffett, and Onion-Skin Manuals

    Marks lived with his son Andrew during COVID and wrote a memo about it called “Something of Value” in January 2021. Andrew’s most important contribution was a near-revelation: readily available quantitative information about the present cannot be the source of investment alpha because everyone has it. Buffett’s edge in the 1950s was reading Moody’s Manuals (giant books printed on onion-skin paper with tiny type and zero narrative) when nobody else would. The medium changes; the principle that edge requires doing what others will not, does not.

    India

    Kamath asks Marks directly about India. Marks has deployed roughly $4 billion there but politely declines to claim any expertise on the Indian stock market or recommend a sector. He cautions Kamath about taking advice from people who do not know what they are talking about, and includes himself in that category on the question of India. The honesty is striking and is itself an investment lesson.

    History Rhymes, and Final Advice

    Marks reads Andrew Ross Sorkin’s 1929 and references it in an upcoming memo on private credit. He likes Mark Twain’s reputed line that history does not repeat but it rhymes, and Napoleon’s line that history is written by the winners of tomorrow. The lessons that rhyme are lessons of human nature, which evolves incredibly slowly. Fight or flight from the watering hole still drives behavior in financial markets.

    His final advice: investing is a puzzle, not engineering. A civil engineer calculates steel and concrete, builds the bridge, and the bridge stands. Every time. A dentist fills the cavity correctly and it stays filled. Every time. If you need that kind of reliability in your work, become a dentist. Investing is the act of positioning capital for a future that cannot be predicted accurately. You will be wrong sometimes. If something in your makeup cannot tolerate being wrong sometimes, do not become an investor. The puzzle has no final solution, which is exactly what makes it endlessly interesting.

    Thoughts

    The most useful thing Marks does in this conversation is admit, repeatedly and without ego, what he does not know. He does not know whether AI models differ in real intelligence. He does not know which sector in India to bet on. He does not know how to teach second-level thinking. He drifted for 35 years and only began making intentional decisions at 49. This honesty is the inverse of every guru selling certainty, and it is the actual content of the lesson he is trying to convey: epistemic humility is the precondition for superior insight, because you cannot acquire what you already think you have.

    The deepest insight in the conversation might be the one Andrew Marks (Howard’s son) gave his father during COVID: readily available quantitative information about the present cannot produce alpha because everyone has it. This is devastating in the AI era. If everyone is asking the same large language model the same question, the answers converge, and convergence is consensus, and consensus does not pay. The arms race for proprietary data, novel framings, and unconventional questions is the only thing that can break the convergence.

    Marks’s framing of cycles as excesses and corrections rather than ups and downs is genuinely useful. It reframes volatility from something to fear into something to expect, and reframes the question from “where are we going?” to “how far past trend have we already gone?” The 8 to 12 percent observation about the S&P (that the average return is almost never the actual return) is the kind of fact that should be taught in every introductory finance class but is almost never mentioned.

    The most contrarian claim in the conversation is the one about indexation: that it won because active was bad, not because passive is great. This is a useful inversion. Most defenders of passive investing argue from efficient market theory; Marks argues from the empirical failure of active managers. The implication is that if you can find the small population of active managers who genuinely outperform, the indexation argument falls apart for that subset. Most cannot. The hardest job in investing is the meta-job of identifying the few who can.

    The exchange about AI as a contrarian engine is one of the most clarifying short discussions of AI’s investment limits I have read. Different from consensus is easy. Different and better is the actual goal. Forcing different gets you wrong more often than right because consensus, built by smart, motivated, educated competitors, is usually close to correct. This is why “use AI to find non-consensus ideas” is a worse strategy than it sounds.

    Finally, the Buffett-Moody’s-Manual story is the most quietly profound moment in the interview. The edge in 1955 was the willingness to read tiny type on onion-skin paper alone in an office in Omaha when no one else would. The edge in 2026 is whatever the modern equivalent of that is, and the only honest answer is: nobody knows yet, which is precisely why finding it is worth so much money.

  • Converging on Investment Philosophy: Marks and Buffett’s Shared Wisdom

    In the world of investing, few figures command as much respect as Howard Marks and Warren Buffett. While their individual styles and approaches may differ, a careful analysis of their writings reveals a remarkable convergence of key investment principles. This exploration of the shared wisdom found in Marks’ memos and Buffett’s letters offers a roadmap for navigating the complexities of the market.

    Intrinsic Value: The North Star of Investing

    Both Marks and Buffett unequivocally stress the importance of intrinsic value as the bedrock of investment decisions. Intrinsic value, they argue, is the true worth of a business, determined by the present value of its future cash flows. This principle serves as a guiding light, leading investors toward assets that are genuinely undervalued and shielding them from the capriciousness of market sentiment.

    Long-Term Orientation: The Antidote to Short-Termism

    In a world often fixated on short-term gains and quarterly earnings, Marks and Buffett champion the virtues of long-term thinking. They recognize that true value creation is a gradual process, and succumbing to the allure of quick profits can lead to devastating consequences. By maintaining an unwavering focus on the long-term potential of their investments, they navigate through market turbulence and emerge stronger.

    Tuning Out Market Noise: The Path to Rationality

    The daily fluctuations of the market can be a source of anxiety for many investors. However, Marks and Buffett counsel against being swayed by the noise. They posit that short-term price movements are often fueled by irrational exuberance or fear, and astute investors should concentrate on the underlying value of their holdings, not the fleeting whims of the ticker tape.

    Margin of Safety: The Investor’s Fortress

    The concept of margin of safety is deeply embedded in both Marks’ and Buffett’s investment strategies. It entails acquiring assets at a substantial discount to their intrinsic value, creating a buffer against potential losses. This approach not only safeguards against downside risk but also amplifies the potential for extraordinary gains when the market eventually aligns with the investment’s true worth.

    Circle of Competence: Knowing Your Limits

    Both investors underscore the importance of operating within one’s circle of competence. This means investing in businesses and industries that you genuinely comprehend, acknowledging the boundaries of your knowledge. By adhering to this principle, Marks and Buffett sidestep costly errors and seize upon opportunities that others may miss due to a lack of understanding.

    Temperament and Discipline: The Investor’s Emotional Rudder

    Successful investing transcends mere intellect; it necessitates the cultivation of the right temperament and discipline. Marks and Buffett emphasize the significance of remaining patient, rational, and emotionally composed amidst market volatility. By eschewing impulsive decisions fueled by fear or greed, they maintain a steady course and make judicious choices that endure.

    Prioritizing Loss Avoidance: The Foundation of Winning

    While the pursuit of gains is a natural inclination for investors, Marks and Buffett prioritize the avoidance of losses. They understand that by safeguarding capital and mitigating downside risk, the winning investments will naturally reveal themselves over time. This prudent approach ensures that their portfolios are resilient and capable of withstanding market downturns.

    The Importance of Management: The Human Element

    Both investors acknowledge that the caliber of a company’s management team is a pivotal factor in its long-term success. They seek out companies helmed by competent, ethical, and shareholder-oriented leaders who are dedicated to creating value for their investors. By investing in companies with robust leadership, Marks and Buffett align themselves with the paragons of the business world.

    Opportunistic Investing: Seizing the Right Moment

    Marks and Buffett are opportunistic investors, perpetually vigilant for undervalued assets and market dislocations. They exercise patience, waiting for the right opportunities to emerge, rather than succumbing to the allure of fleeting trends. When the market presents them with a bargain, they act decisively and with unwavering conviction.

    Financial Strength and Conservatism: The Bedrock of Stability

    Both investors stress the importance of maintaining financial strength and eschewing excessive debt. They believe that a conservative approach is paramount for long-term survival and prosperity in the unpredictable world of investing. By prioritizing financial stability, they fortify their portfolios against unforeseen challenges.

    Skepticism of Forecasts: Embracing the Unknown

    Marks and Buffett share a healthy skepticism towards macroeconomic forecasts and market predictions. They acknowledge the inherent uncertainty of the future and the limitations of human foresight. Instead of relying on speculative prognostications, they concentrate on what is knowable and controllable, such as the intrinsic value of their investments and the quality of the businesses they own.

    Value Investing Philosophy: The Time-Tested Path

    Both Marks and Buffett are ardent proponents of the value investing philosophy, which entails acquiring assets at a discount to their intrinsic value. This approach, championed by Benjamin Graham and refined by Buffett, has consistently proven to be a reliable path to enduring investment success. By adhering to this philosophy, they consistently unearth and acquire undervalued assets poised to deliver superior returns over time.

    If you want to know where Marks and Buffett diverge on investment philosophy read this.

  • Mastering the Art of Value Investing: A Look into the Strategies of Stan Druckenmiller, Howard Marks, and Bill Gurley

    Mastering the Art of Value Investing: A Look into the Strategies of Stan Druckenmiller, Howard Marks, and Bill Gurley

    Value investing is a strategy that involves buying undervalued stocks or assets with the expectation that their value will increase over time. This approach to investing has been popularized and mastered by a select few in the financial industry, including Stan Druckenmiller, Howard Marks, and Bill Gurley. Each of these individuals have a long history of experience in the financial industry and are known for their expertise in value investing. This article will take a closer look at their investment strategies and what makes them great investors.

    Stan Druckenmiller is a hedge fund manager and the founder of Duquesne Capital. He is considered one of the most successful hedge fund managers of all time, having produced consistent returns for his investors over several decades. Druckenmiller’s investment strategy is based on value investing and he is known for his ability to identify undervalued stocks. He is also known for his ability to adapt his investment strategy to changing market conditions. Druckenmiller has been quoted as saying, “I am a value investor, but I don’t have a long-term time horizon. I am a short-term value investor.”

    Howard Marks is the founder and co-chairman of Oaktree Capital Management, a leading investment management firm. He is also the author of the bestselling book “The Most Important Thing: Uncommon Sense for the Thoughtful Investor.” Marks’ investment strategy is also based on value investing and he is known for his ability to identify undervalued assets. He is also known for his ability to make contrarian investments, which are investments that go against the trend. Marks has been quoted as saying, “The key to successful investing is to have a clear understanding of what you’re trying to achieve and to be patient in the pursuit of your goals.”

    Bill Gurley is a venture capitalist and general partner at Benchmark Capital. He is known for his investments in technology companies such as Uber, Zillow, and GrubHub. Gurley’s investment strategy is also based on value investing, with a focus on identifying undervalued assets in the technology sector. He is known for his ability to identify and invest in disruptive technologies that have the potential to change the way we live and work. Gurley has been quoted as saying, “Value investing is not about buying cheap stocks. It’s about buying stocks that are undervalued relative to their growth prospects.”

    Stan Druckenmiller, Howard Marks, and Bill Gurley are all successful investors and financial industry leaders who have mastered the art of value investing. Their investment strategies are based on identifying undervalued stocks and assets, and they are known for their ability to adapt to changing market conditions. They are also known for their ability to make contrarian investments and for their expertise in identifying disruptive technologies. Their insights and knowledge have had a major impact on the financial world and they continue to be respected for their contributions to the field of investing.