PJFP.com

Pursuit of Joy, Fulfillment, and Purpose

Tag: Malcolm Gladwell

  • Howard Marks on AI Investing, Second-Level Thinking, Warren Buffett, and Why Waiting Until You Feel Safe Means the Opportunity Has Passed

    Howard Marks, co-founder of Oaktree Capital and author of the investing memos Warren Buffett says he reads first, returned to the My First Million podcast for a wide-ranging conversation with Shaan Puri and Sam Parr. Marks explains why he rewrote his AI memo after his son pushed back, what AI can and cannot take from professional investors, how Oaktree deployed $450 million a week while the world thought finance was ending, and why the sentence “I’m 100% convinced” is the most dangerous one in markets. Along the way he covers his 39-year partnership with Bruce Karsh, personal stories about Warren Buffett and Charlie Munger, parenting, career choice, and the two books that shaped his thinking.

    TLDW

    Marks updated his AI memo because his VC son Andrew told him too much had changed, and he now sees AI as unprecedented on two axes: autonomy (every prior technology was a tool; AI can be given a job and figure out how to do it) and unpredictability (he never felt the internet was beyond comprehension, but nobody knows the shape of an AI future). He expects AI to “defrock” mediocre active investors the way indexation did, while insight, judgment about people, and decisions with no historical precedent may remain human. He retells the Lehman Brothers moment: Oaktree raised an $11 billion distressed debt fund before the crisis, then invested $7 billion in a single quarter on the logic that if the world melted down nothing would matter, but if it did not and they had failed to invest, they had failed at their jobs. The through-line is acting despite fear: the battle hero is afraid and does it anyway, and if you wait until there is nothing to be afraid of, the opportunity has passed. He closes with the recipe for his partnership with Bruce Karsh (shared values, complementary skills, appreciation), stories about Buffett and Munger, advice to live your life your own way, and book recommendations: A Short History of Financial Euphoria and Fooled by Randomness.

    Thoughts

    The most valuable thing in this conversation is not any single call, it is watching a 79-year-old investor with five decades of pattern recognition publicly change his mind. Marks wrote an AI memo in December, his son told him in February that it was already stale, and he rewrote it entirely. When the host teases him that he sounds “a little seduced,” Marks does not get defensive. He distinguishes between upgrading an opinion on new evidence and getting emotional about an asset. That distinction is the whole game. Most people treat their published positions as identity; Marks treats his as drafts. The irony he would appreciate: the willingness to say “so much has happened, I have to update” is exactly the behavior that made his original reputation, and it is exactly what the “I’m 100% convinced” crowd cannot do.

    His AI framing is sharper than most full-time commentators manage. Every previous technology, from the railroad to the internet, was a tool that made humans faster. AI is the first with autonomy: you give it a job, not instructions. And it is the first innovation he has ever called genuinely unpredictable. Notice what that combination does to his old computer framework. Computers could only read, remember, add, subtract, and compare, a limited list that still beat most people. The question that decides everything, for investing and beyond, is whether AI’s list is limited or unlimited. Marks does not pretend to know, which is precisely why his answer is credible.

    The Lehman story deserves to be studied as decision-making under true uncertainty, not as a war story. There was no data and no historical analogy for the end of the financial system, only supposition. So Oaktree reframed the decision as an asymmetry: if the world melts down and we invest, it does not matter; if the world survives and we did not invest, we failed. That logic is available to anyone. What is not available to most people is the willingness to act on it while feeling terrible, and Marks is emphatic that he felt terrible. He read the same newspapers as everyone else. The lesson is that trepidation is not a signal to wait; it is the price of admission. Confidence is not the tell of a good decision. Structure is.

    The quietest and maybe most transferable idea here is the credibility flywheel. After a fund did well, Oaktree raised a smaller fund next, because great results meant assets had appreciated and the opportunity had shrunk. That is speaking against your own economic interest, repeatedly, for twenty years. The payoff came when they asked for $11 billion before the crisis and investors believed them, because Howard and Bruce do not cry wolf. Most people optimize each individual transaction and wonder why nobody trusts them at the moment trust matters. And it is not a coincidence that his partnership advice (shared values, complementary skills, appreciation), his parenting advice (let your kid be smarter than you, let them make choices), and his fundraising record all reduce to the same move: give up small ego wins now to compound trust for decades.

    Key Takeaways

    • Marks wrote his first AI memo around December 9th, then rewrote it entirely in early February after his son Andrew, a venture capitalist working with AI companies daily, told him too much had changed. Updating on new facts is a feature of good thinking, not a flip-flop.
    • He upgraded his opinion of AI because of qualities he considers unprecedented: it can discuss its own strengths and weaknesses, use humor, and put information in the context of the specific person it is talking to.
    • AI’s first unprecedented quality is autonomy. Every prior technological innovation, from the railroad to computers to the internet, was a tool to increase productivity. Nothing before could be given a job without being told how to do it.
    • AI’s second unprecedented quality is unpredictability. Marks never felt the internet was beyond comprehension or prediction. With AI, he says nobody knows the shape of the future, a feeling he has never had about any prior technology.
    • Indexation exposed that most active equity investors could not do what they claimed and pushed many out of the business. Marks expects AI to “defrock” another group of professionals whose talents are not as great as they purport.
    • His old framework for computers: they could only read, remember, add, subtract, and compare, but they did it with more data, faster, and without arithmetic or emotional mistakes, so the limited list still beat most people. The big question for AI is whether its list is limited or unlimited.
    • A large share of what AI does is knowing history and extrapolating patterns. There will always be events with no history to train on, and some people simply understand the probability distribution of future events better. That may be where human investors survive.
    • Part of Oaktree’s value has been refusing to invest with bad people based on undefinable signals, the “hair on the back of your neck” test. If AI has no hair on its neck, experienced judgment keeps a role.
    • Second-level thinking, the opening chapter of his first book, says that if you do not see anything different from everybody else, you cannot possibly be superior. You need a variant perception, you have to bet on it, and you have to be right.
    • Asked whether second-level thinking can be taught, Marks says probably not. He can teach its importance, but not how to have perceptions that are both at odds with consensus and correct. In basketball you cannot coach height; in investing there is something called insight, and some people have it.
    • He is genuinely unsure whether AGI, defined as AI doing everything a human can do, will arrive. Whether there are things AI will never do “even when it reaches full flower” is one of the central mysteries.
    • Before the 2008 crisis, the largest distressed debt fund in history had been Oaktree’s own $2.5 billion fund from 2002. In 2007-08 they raised $11 billion because they saw distress coming, and kept it on the shelf for deployment when the stuff hit the fan.
    • When Lehman went under in September 2008, there was no data and no prior experience for the end of the financial world, only supposition, borrowing the Harvard epidemiologist’s three bases for decisions: data, analogies to past experience, and supposition.
    • The deployment logic was an asymmetry: if the financial world melts down and we invest, it does not matter; if it does not melt down and we failed to invest, we did not do our job. So they had to invest.
    • Bruce Karsh invested an average of $450 million a week for 15 weeks, roughly $7 billion in a single quarter, buying debt of private-equity-owned companies at prices where Oaktree would break even if the companies were worth a fifth or a fourth of what buyers had paid a few years earlier.
    • They were “absolutely not confident.” Marks argues people who think probabilistically and admit ignorance and uncertainty cannot act without trepidation, and that acting anyway is the job.
    • His memo “Taking the Temperature” reviews the five major macro calls of his career; every one was made with doubt. Markets crash because the news is terrible, and he reads the same terrible news as everyone else, then overcomes it.
    • The battle hero framing: a hero is not someone who is unafraid, but someone who is afraid and does it anyway. If you are running into a hail of bullets without fear, something is wrong with you.
    • The signature line: if you wait until you have nothing to be afraid about, the opportunity has probably passed.
    • Raising $11 billion rested on a reservoir of goodwill built since 1988, a strategy purpose-built for crisis with proven results in 1991 and 2001-02, the pitch that a crisis fund hedges portfolios positioned for prosperity, and the ability to point at specific flaws: the market was failing at its main job of acting as a disciplinarian and saying no to dumb ideas.
    • From the Spy Game movie: when did Noah build the ark? Before the flood. You cannot raise money during a crisis because the news is too terrible, so you build the ark in advance.
    • Oaktree’s contrarian fund sizing built its credibility: after a fund produced great results, the next fund was smaller, because great results meant assets had appreciated and opportunities had shrunk. Most managers raise bigger funds on the back of good numbers. Twenty years of that earned them trust when it counted, and sometimes you have to speak against your own interest.
    • During the 1998 LTCM meltdown, a young portfolio manager told Marks “I think this is it, we’re melting down.” Marks heard him out, then said: now go back to your desk and do your job.
    • He and Bruce Karsh have been partners for 39 years and have never had a fight, partly because neither is a financial maximizer and most fights are about money. They have intellectual disagreements, not fights.
    • The keys to partnership, from his 2002 memo: shared values and complementary skills. One aggressive partner and one timid one, or one ethical partner and one corner-cutter, cannot last.
    • The cowboys-and-chickens story: of the roughly 40 investment banks on the AT&T tombstone ad, almost all eventually disappeared. In bad times the chickens say the cowboys are getting us killed; in good times the cowboys say the chickens are holding us back. Mismatched values kill firms.
    • Complementary skills mean each partner can do things the other cannot, so both are additive. If one partner can do everything, the other is eventually seen as overpaid. Bruce manages the money; Howard goes on the road and does the podcasts. The third element: be appreciative, and thank your lucky stars your partner does the things you do not want to do.
    • On parenting: a Wall Street psychiatrist observed that his patients’ problems were inversely proportional to the support they got from their fathers. Marks finds it terrible how many successful men need to assert superiority over their sons, and says he always let Andrew be smarter than him in some things.
    • When his daughter had to choose between two good schools, he and his wife let her decide, on the logic that neither option was bad and kids need experience making choices, including incorrect ones.
    • His favorite quote, from Christopher Morley: there is only one success, to live your life your own way. You cannot let friends, parents, or society decide what you should do. Find something that plays to your strengths, avoids your weaknesses, and makes you happy, while knowing that in 20 years you will be a different person.
    • By his own account, Marks made his early career decisions unconsciously and haphazardly until about age 49-50, when he left to start Oaktree in 1995. He landed in high yield bonds because a boss called him in 1978 about “a guy named Milken in California,” and if that call had come at lunchtime, someone else would have gotten the career.
    • The Mark Twain rule: it ain’t what you don’t know that gets you into trouble, it’s what you know for certain that just ain’t true. No sentence starting with “I could be wrong, but” ever hurt anyone; the dangerous sentence is “I’m 100% convinced.” If you bet like you are 100% right and it was really 80/20 and the 20 comes up, that is how you get into big trouble.
    • The Buffett relationship began with Enron’s collapse: Oaktree was the largest holder of the debt of off-balance-sheet entity Osprey, Buffett was second largest, and Buffett gave Oaktree his proxy to run the position. Bruce’s masterful restructuring led to a thank-you letter, a lunch in Omaha, and a friendship.
    • Buffett is the reason the first book exists: in 2009 he told Marks “you should write a book, and if you do, I’ll give you a blurb.” Marks had planned to write one in retirement, but you cannot let a note like that sit. The result was The Most Important Thing.
    • What people do not know about Buffett: the depth of his love for Charlie Munger. Buffett’s farewell note described Charlie as the big brother and himself as the little brother, and their relationship was suffused with humor. Marks says the same dynamic describes him and Bruce.
    • Munger’s greatest credited contribution was talking Buffett out of cigar butt investing (picking up discarded companies with three free puffs left) and convincing him to buy great companies at a good price instead of any company at a great price.
    • Buffett and Munger probably had the highest combined IQ of any partnership in history, but different kinds: Munger a classicist, humanist, and man of letters who talked about ideas rather than money; Buffett an incredible computing machine.
    • Book recommendations: A Short History of Financial Euphoria by John Kenneth Galbraith, on the mental weakness that gives rise to booms and busts, and Fooled by Randomness by Nassim Nicholas Taleb, on why in the short run anything can happen, which shapes attitudes toward risk, portfolio construction, and whether a great published track record means skill or luck.

    Detailed Summary

    Changing His Mind on AI

    The conversation opens with the story behind Marks’s updated AI memo. He wrote the first version around December 9th. In early February his son Andrew, a venture capitalist whose portfolio companies use and build AI, told him: “Dad, so much has happened. You have to update the memo.” Marks rewrote it entirely. When the hosts needle him that the sequel sounds “a little seduced,” he pushes back on the framing: he upgraded his opinion because of observable capabilities, including AI’s ability to discuss its own strengths and weaknesses, use humor, and contextualize information to the specific person using it. He identifies two qualities he considers historically unprecedented. First, autonomy: everything from the railroad to the internet was a tool to speed humans up, while AI can be handed a job without being told how to do it, which is also the source of the nagging concern that it may take over. Second, unpredictability: he never once thought the internet was beyond comprehension or prediction, but with AI he says nobody knows the shape of the future.

    What AI Does to Investors

    Asked whether AI will be able to do what he does, Marks reaches for the indexation precedent: index funds revealed that most active equity managers could not do what they claimed, and pushed many out of the business. AI, he says, will “defrock another group of people whose talents are not as great as they purport.” He recalls his old line about computers, which could only read, remember, add, subtract, and compare, yet still beat most people because they did those five things with more data, faster, and without arithmetic or emotional errors. The decisive question for AI is whether its list of capabilities is limited or unlimited, and he admits he does not know. The hosts note that Buffett reading the Moody’s manual page by page is now a task AI does in a heartbeat. What might remain human: events with no history to train on, since so much of AI is pattern recognition over history; superior intuition about the probability distribution of future events; and people judgment, the undefinable signal when the hair on the back of your neck goes up about someone. If AI has no hair on its neck, experienced investors with judgment keep a role.

    Second-Level Thinking and the Limits of Teaching Insight

    Marks retells the origin of his first book: Columbia asked for a sample chapter, he sat down and wrote one he had never consciously thought about, and it became chapter one, on second-level thinking. The idea: if you do not see anything different from everybody else, you cannot possibly be superior. You need a variant perception, a belief that consensus overstates or understates a company’s quality, growth, earning power, or deserved multiple; you must bet on that perception; and you must be right. Can it be taught? He says the answer is more no than yes. He can teach the importance of second-level thinking, but not how to have perceptions that are both contrarian and correct. His analogy: in basketball you cannot coach height, and in investing there is something called insight that some people simply have. Whether AI can have it is, for him, bound up with the AGI question and genuinely unknown.

    Lehman, the $11 Billion Fund, and Investing at the End of the World

    Oaktree’s biggest call illustrates decision-making with no precedent. Before 2007, the largest distressed debt fund in history was Oaktree’s own $2.5 billion 2002 fund. Sensing distress coming, they raised $11 billion in 2007-08 and kept it on the shelf. Then Lehman Brothers failed on September 15, 2008, and people were talking about the end of the world, all financial institutions melting down, everything having to do with money atomizing. Marks cites a Harvard epidemiologist: decisions rest on data, analogies to past experience, and supposition, and at that moment there was no data and no past experience. The reframe that unlocked action: if the financial world melts down and we invest, it does not matter; if it does not melt down and we did not invest, we did not do our job. Bruce Karsh deployed an average of $450 million a week for 15 weeks, about $7 billion in a quarter, buying debt of companies bought by private equity years earlier at prices where Oaktree would break even even if the companies were worth a quarter or a fifth of the buyout price. Quantitatively easy, emotionally brutal: they were, in his words, absolutely not confident.

    Trepidation Is the Price of Admission

    Marks generalizes the feeling in his memo “Taking the Temperature,” which reviews the five major macro calls of his career: all were made with doubt. Markets crash because the news is terrible, and he consumes the same news feeds as everyone else, so the terrible news looks terrible to him too. The difference is overcoming it. People who look at the world probabilistically and admit ignorance and uncertainty cannot act without trepidation, and if you act without any, something may be wrong with you. He recalls the 1998 LTCM and Russian ruble crisis, when a young portfolio manager came to him convinced everything was melting down; Marks heard his concerns and sent him back to his desk to do his job. The battle hero is not unafraid; he is afraid and does it anyway. And the line that anchors the episode: if you wait until you have nothing to be afraid about, the opportunity has probably passed.

    How You Actually Raise $11 Billion

    Pressed on the mechanics of raising the fund, Marks lists the ingredients. Twenty years of managing money well since 1988 created a reservoir of goodwill. The strategy was purpose-built for crisis, with excellent results through the 1991 and 2001-02 downturns. The pitch positioned the fund as a hedge: most investor portfolios are set up for prosperity, so it makes sense to own something that does particularly well when the stuff hits the fan. And Oaktree could point at specific flaws in the environment, chiefly that the market was failing at its main job of acting as a disciplinarian, the job of telling people that a dumb idea does not make sense and will not be funded. When the market stops saying no, dumb ideas get financed, and when they turn out to be dumb, people lose money. He adds the Spy Game line he and his wife love: when did Noah build the ark? Before the flood. You cannot raise money during a crisis because the news is too terrible. Finally, credibility compounding: Oaktree repeatedly raised smaller funds after successful ones, reasoning that great results meant opportunities had shrunk. Two decades of speaking against their own interest meant that when Howard and Bruce said there was a great opportunity, investors believed they meant it.

    39 Years with Bruce Karsh: Shared Values, Complementary Skills, Appreciation

    Marks calls his partnership with Bruce Karsh, 39 years old that month, one of the greatest things in his life after family and close friendships. They have never had a fight, which he attributes partly to neither being a financial maximizer, since most fights are about money. His 2002 memo formula: shared values and complementary skills. Mismatched values, like one cowboy and one chicken, or one ethical partner and one corner-cutter, doom a firm; he illustrates with the AT&T tombstone ad listing roughly 40 investment banks, nearly all of which eventually vanished as the chickens blamed the cowboys in bad times and the cowboys mocked the chickens in good times. Complementary skills mean each partner does what the other cannot: Bruce approached Marks in 1987 with the novel idea of a distressed debt fund, and from the beginning Bruce stayed back managing money while Howard went on the road and, later, on podcasts. The third element is appreciation: thank your lucky stars you have a partner who will do the stuff you do not want to do.

    Parenting Without Asserting Superiority

    Asked how he raised a son he not only loves but enjoys, Marks cites a decades-old Forbes profile of the only psychiatrist with an office on Wall Street, whose patients’ problems were inversely proportional to the support they got from their fathers. He marvels at how many successful men need to prove they are smarter than their sons, and says he always let Andrew be smarter than him in some things while giving full support to whatever his kids wanted to do, provided it was not injurious. When his daughter got into both good Los Angeles schools, he and his wife had a preference but let her choose, reasoning that they could be wrong, neither option was bad, and children need experience making choices, including incorrect ones.

    Live Your Life Your Own Way

    On career choice, Marks confesses he did a terrible job himself: his decisions for his first decades were unconscious and haphazard, and by his own account he did not really make intentional choices until he left to co-found Oaktree in 1995, around age 49. He went to Citibank because of a good summer job, moved from equities to bonds because his equity research was unsuccessful and he was told to get out, and moved to California for sunshine and palm trees. In 1978 the head of the bond department called the fairly idle Marks about “a guy named Milken or something in California” dealing in high yield bonds, and a legendary career resulted from being at his desk when the phone rang, a story straight out of Outliers. His advice to students at Wharton, Harvard, and Columbia is built on his favorite quote, from writer Christopher Morley: there is only one success, to live your life your own way. Find something that plays to your strengths, avoids your weaknesses, and makes you happy, which really means refusing to let friends, society, or parents decide for you, while accepting the hard truth that you will be a different person in 20 years and must choose anyway.

    Humility as Risk Management

    When the hosts remark on his humility, Marks turns it into a risk framework via Mark Twain: it ain’t what you don’t know that gets you into trouble, it’s what you know for certain that just ain’t true. No sentence beginning “I could be wrong, but” or “I don’t know, but” ever got anybody into trouble; the dangerous sentences begin “I’m 100% convinced that.” If you bet as though you are certain and the odds were really 80/20 and the 20 comes up, that is how you get into big trouble. You make the investment because you believe in it, but you must see the other side.

    Buffett and Munger Stories

    The Buffett friendship began in the wreckage of Enron, which did most of its misbehavior through off-balance-sheet entities. Oaktree became the largest holder of the debt of one called Osprey; Warren Buffett was the second largest, gave Oaktree his proxy, and let Bruce run the position, which Bruce restructured masterfully for a big win. Around 2003-04 Buffett wrote Bruce a note saying nice job, and if you find yourself in Omaha, we’ll have lunch; Bruce and Howard promptly found themselves in Omaha. In 2009, Buffett told Marks he should write a book and promised a blurb, which is why The Most Important Thing exists years before the retirement book Marks had planned. What people do not know about Buffett, Marks says, is the depth of his love for Charlie Munger, expressed in Buffett’s farewell note describing Charlie as the big brother and himself as the little brother. Munger’s celebrated contribution was talking Buffett out of cigar butt investing, the practice of picking up discarded companies for three free puffs, and toward great companies at a good price. They probably had the highest combined IQ of any partnership in history, but of different kinds: Munger the classicist and man of letters who preferred talking about ideas over money, Buffett the incredible computing machine.

    Homework from Howard Marks

    His two book recommendations: A Short History of Financial Euphoria by John Kenneth Galbraith, which shaped his objective view of cycles by teaching the mental weakness that gives rise to booms and busts (he was lucky enough to meet Galbraith), and Fooled by Randomness by Nassim Nicholas Taleb, which argues that in the short run anything can happen because of randomness, with consequences for how we think about risk, portfolio construction, and whether a hot track record reflects skill or luck. He notes, with characteristic self-awareness, that his belief in randomness may be his rationale for not being a decisive thinker, and offers his own memos as the “classic comic” version of Taleb. The episode closes with a nod to his January 2021 memo Something of Value, written after three generations of the Marks family spent the pandemic under one roof arguing about value investing with Andrew.

    Notable Quotes

    “If you wait until you have nothing to be afraid about, probably the opportunity has passed.”

    Howard Marks, on why great investments are made with fear intact

    The thesis of the whole conversation, delivered in the cold open and again in the LTCM story.

    “Second level thinking basically says if you don’t see anything different from everybody else, you can’t possibly be superior.”

    Howard Marks, explaining the first chapter of The Most Important Thing

    The variant perception requirement: see it, bet on it, and be right.

    “In basketball there’s a saying, you can’t coach height. And I think there’s something called insight. And I think some people have it.”

    Howard Marks, on why second-level thinking probably cannot be taught

    Also his open question about AI: whether machines can ever have insight.

    “But if we don’t invest and the financial world doesn’t melt down, then we didn’t do our job. So, we have to do it.”

    Howard Marks, on Oaktree’s reasoning the week Lehman Brothers failed

    The asymmetry that justified investing $450 million a week for 15 weeks.

    “A battle hero is not somebody who’s unafraid. It’s somebody who’s afraid but does it anyway.”

    Howard Marks, sending a panicked portfolio manager back to his desk in 1998

    His answer to the LTCM-era fear that everything was melting down.

    “When did Noah build the ark? Before the flood. You got to build the ark before the flood.”

    Howard Marks, quoting the movie Spy Game on raising crisis funds in advance

    Why the $11 billion was raised in 2007-08 and kept on the shelf.

    “No sentence that starts with I could be wrong but or I don’t know but ever got anybody into trouble. The sentences that get people into trouble are I’m 100% convinced that.”

    Howard Marks, channeling Mark Twain on certainty

    His practical definition of humility as a risk-management tool.

    “The key to a successful partnership is shared values and complementary skills.”

    Howard Marks, on 39 years with Bruce Karsh, from his 2002 memo

    Plus the third element he adds now: appreciation for the partner who does what you will not.

    “There is only one success to live your life your own way.”

    Howard Marks, quoting writer Christopher Morley, his favorite line for students

    The advice he gives at Wharton, Harvard, and Columbia, and admits he did not follow until age 49.

    Watch the full conversation with Howard Marks on My First Million here.

    Related Reading

  • 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