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  • 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

  • Ken Griffin on AI, the Golden Age of Entrepreneurs, and the Taiwan Chip Risk That Would Cut US GDP 8 Percent: Inside the Citadel Founder’s Goldman Sachs Great Investors Interview

    Ken Griffin, founder and CEO of Citadel, sat down with Goldman Sachs’ Raj Mahajan at the firm’s Apex Symposium (recorded June 2, 2026) for this episode of Goldman Sachs Exchanges: Great Investors. It is their third public conversation in seven years, and Griffin is unusually candid: about the Friday he went home “shocked and depressed” over AI, the agentic system inside Citadel that compresses six weeks of PhD-level work into two hours, why a Chinese move on Taiwan would throw the US into a depression within six months, and the one question every hedge fund investor should ask their GP.

    TLDW

    Griffin names his two proudest leadership calls: dragging Citadel back to the office five days a week before it was acceptable (citing Fed research that remote work has hurt young Americans’ employment more than AI has), and Citadel’s pandemic role, from getting the FDA to approve experimental COVID drug trials in 72 hours to shaping the incentive design behind Operation Warp Speed, which he credits with saving roughly half a million American lives. On markets, he explains why the S&P sits at all-time highs despite wars in the Middle East and Europe: US energy insulation, stunning Chinese oil demand destruction, and record corporate earnings. On AI, he distinguishes hype from reality (a dinner of multinational CEOs gave him five stories of “AI transformation,” none of which were actually AI), then describes the internal breakthrough that changed his mind: an agentic system that reads, reproduces, and out-of-sample-tests academic finance papers in 2 to 3 hours instead of 6 to 8 weeks. The consequences: no layoffs at Citadel, but competitive moats across the economy are being filled in at lightning speed, setting up a golden age of entrepreneurship. He covers the compute market (all available compute is utilized all the time; market makers now spend hundreds of millions a year), China’s lead in roughly 67 of 74 critical technologies, the Taiwan scenario in which losing TSMC chips cuts US GDP 8 percent in six months, an energy doctrine built on nuclear, natural gas, and building data centers (with their own generation) in America, his stress-test approach to tail risk (definable, tolerable, still in business), and hedge fund economics: the industry’s cost of capital is roughly risk-free plus 4 percent, which is why Citadel has returned $25 to 30 billion to its LPs.

    Thoughts

    The most useful thing in this conversation is Griffin’s two-sided read on AI, because he refuses to pick a lane. The paper-replication story is the cleanest documented example yet of AI eating not just white-collar work but masters-and-PhD-level work, from the man whose firm profits from that labor. Yet in the same breath he reports zero headcount reduction, because Citadel has more problems to attack than people to attack them. Both things are true at once, and he names the synthesis honestly: the individual firm gets more productive while every firm’s moat gets shallower. Most commentary picks either the doom frame or the productivity frame. Griffin holds both, and his conclusion (a golden age of entrepreneurship, startups running on a few AI systems instead of 30 to 40 employees) is the actionable part.

    His dinner-party anecdote deserves to be a standard reference. Five global CEOs effusing about AI transformation, and every single story was actually machine learning, optimization, or plain digitization. The C-suite cannot tell AI from technology at large, which means a meaningful slice of the “AI is transforming our business” narrative priced into the S&P is really a decade-old digital revolution wearing a new label. That is not a bearish observation, since the earnings are real either way, but it matters for anyone trying to figure out which companies actually have AI leverage and which have rebranded their IT budget.

    The Taiwan section is the starkest risk framing you will hear from someone who runs both a hedge fund and one of the world’s largest market makers. An 8 percent GDP contraction in six months is not a market correction, it is Boeing halting production, new cars stopping, and consumer electronics freezing simultaneously, because TSMC chips are in every high-end product made. What makes his version distinctive is the second-order point: in a Taiwan blockade, he does not expect unified Western sanctions. Europe’s membership on “team USA” is less clear than it was two years ago, and the Middle East will play Switzerland because China buys its oil. Investors should notice that his answer to “how do you hedge this?” is not clever derivatives, it is his stress-test doctrine: know the worst case, size exposures so the loss is definable and tolerable, and stay in business to fight back.

    Finally, the small structural details are where the conversation earns its Great Investors billing. Compute has become a commodity input like jet fuel, fully utilized at all times and allocated purely by willingness to pay, which quietly favors high-margin businesses and squeezes everyone else. Alternative data made the present transparent, so the remaining edge in stock picking is multi-year vision about which companies are building transformative products. And the hedge fund test he closes with is one any allocator can use tomorrow: is your GP in the asset management business or the performance business? Citadel returning $25 to 30 billion to LPs is what the performance answer looks like in practice.

    Key Takeaways

    • Griffin’s proudest leadership call was bringing everyone back to the office five days a week, extremely early and against the culture, because humans are social creatures who learn through apprenticeship and mentorship.
    • He cites a Fed paper on reduced employment among workers under 30: remote work turns out to be a more important factor in diminished opportunities for young Americans than AI.
    • At the start of the pandemic, a hospital-system CEO called Griffin because he could not get FDA approval for drug trials on ventilated COVID patients; Citadel’s team got experimental trials approved in about 72 hours.
    • The key insight behind Operation Warp Speed, which Griffin discussed at length with Jared Kushner, was an incentives fix: the US government paid pharma to manufacture vaccines before FDA results existed, collapsing time-to-market from months to days.
    • By his math, the country spent a few billion dollars on that risk, saved a few trillion dollars of GDP, and saved roughly half a million American lives.
    • The S&P is at all-time highs despite a Middle East war, a still-raging war in Europe, and a potential skirmish over Cuba, because the US is relatively shielded from the energy shock.
    • China’s oil demand elasticity stunned even Citadel’s commodities business, one of the largest in the world; that demand destruction plus episodic oil flows out of the region has kept crude near the low $100s instead of the nearly $200 most models predicted if the straits closed.
    • Citadel has been a huge user of machine learning since TensorFlow arrived roughly a decade ago; the current wave is an acceleration of a digital revolution already underway, not a clean break.
    • At a dinner two years ago, Griffin asked global multinational leaders to share how AI was transforming their businesses: he got four or five great productivity stories and not one actually involved AI. They were machine learning, optimization, and digitization.
    • In the C-suite the nuance between AI and technology at large gets lost, but bigger budgets and CEO enthusiasm are pushing through real projects with real bottom-line impact; US corporate earnings are at all-time highs and multiples have actually come down as a result.
    • The use case that sent Griffin home shocked and depressed: a Citadel team member built an agentic AI system that reads an academic finance paper, reproduces it, verifies the published results, and tests them out of sample in 2 to 3 hours on average.
    • That same replication work previously took a legion of young masters and PhD hires roughly six to eight weeks per paper; Citadel finds a few tradeable ideas a year this way, and a few ideas can be worth a lot of money.
    • The point he stresses: this is not just a white-collar job being automated, it is a master’s or PhD-level job, and AI is now cracking problems (like the 80-year-old math problem OpenAI solved) that seemed beyond its reach two or three years ago.
    • Despite the breakthrough there has been no reduction in headcount at Citadel: the firm has more problems to attack than people, so Griffin takes every productivity gain he can get.
    • The flip side is that competitive moats across corporate America are being filled in at breathtaking speed, which Griffin expects to produce a golden age of entrepreneurial activity.
    • His example: a startup that would traditionally need 30 or 40 employees now runs with just a few AI systems, letting entrepreneurs take on incumbents in ways impossible 5, 10, or 20 years ago.
    • Some workers face genuinely hard transitions (his example is English-to-German translators), and the country needs to figure out how higher education can retrain these people quickly.
    • Stock picking remains a timeless business with a similar skill set, but the market will increasingly reward multi-year vision about which companies are creating transformative products rather than skill at calling quarterly earnings beats.
    • Alternative data (Citadel has access to the credit card spending of millions of Americans) made the here-and-now transparent a decade ago; AI plus bright people now triage the present almost instantly, so relative value accrues to those who can see years ahead.
    • At Citadel Securities, transformer models continue a decade of ML-driven improvement in pricing and risk management, and the same is true at other leading market-making firms.
    • For all intents and purposes, all available compute in the world is utilized all the time; access is decided by who will pay the most, and the per-unit price has risen beyond what anyone reasonably projected two or three years ago.
    • Large market-making firms now spend hundreds of millions of dollars a year on compute; Griffin compares compute inflation to jet fuel and egg prices, a cost that high-margin businesses can bear and low-margin businesses cannot.
    • China leads in roughly 67 or 68 of the 74 or 75 most important technologies in the world, including solar, EV batteries, and multiple quantum fields, and has pulled ahead in published academic papers.
    • The drivers are structural: 1.4 billion people, an extraordinarily strong educational culture, and far more STEM graduates, producing exactly the human talent needed to win in a high-IP world.
    • China is no longer relegated to producing low-margin products designed in America, and Griffin calls that shift a threat to the American way of life; the answer is not tariffs but educating US youth to out-compete, out-innovate, and out-problem-solve.
    • If China takes Taiwan and the US loses access to Taiwanese semiconductors, the rough estimate is US GDP falls 8 percent in six months: a great depression in the blink of an eye, unlike any before.
    • The mechanism is concrete: Boeing stops making planes within six months, most new cars stop being manufactured, consumer electronics production freezes, because TSMC chips are in every high-end product made.
    • There are no winners in a Taiwan escalation: tanking the US economy would have draconian knock-on effects for China given America’s importance as an export market.
    • In a Taiwan blockade Griffin does not expect unified global sanctions against China: where you sit determines your exposure, Europe’s place on team USA is less clear than two years ago, and the oil-exporting Middle East will play Switzerland.
    • On energy, the US must re-embrace nuclear, with small modular reactors a big part of the story: nuclear has effectively no carbon footprint and one of the lowest mortality rates of any energy source ever used (hydro has killed magnitudes more people).
    • He punctures the clean-energy veneer: solar cells are often made in western China by burning coal, with roughly a seven-year energy payback, and carbon fiber wind turbine blades last 20 years then fill landfills because they do not break down. No truly clean solution exists until fusion or broader nuclear.
    • Until then, natural gas is America’s huge asset: decades of cheap supply, and one of the few things that has actually brought down US carbon emissions.
    • Data centers are going to get built somewhere, and Griffin argues it would be inane for America to end up dependent on foreign countries for them; his fix for NIMBY politics is to require data center builders to construct corresponding power generation, tied to the grid for reliability, rather than pushing costs onto consumers.
    • His hedging doctrine for complicated risks: run stress tests, know exactly how much you lose and where in the worst case, and keep exposures sized so the loss is definable, tolerable, and leaves you still in business and able to fight back. You will never hedge every tail event.
    • Hedge fund industry economics: the long-run cost of capital is roughly the risk-free rate plus 4 percent; underperform and capital flows out, outperform and it flows in, and inflows dilute alpha because alpha capacity is finite.
    • Citadel has returned $25 to 30 billion to its limited partners to keep return on equity high: Griffin’s job is to grow annual alpha capacity, and any capital beyond what the portfolio needs goes back to LPs.
    • The alignment test for allocators: the biggest investor in Citadel’s funds is Griffin and his partners, and every LP should ask whether their GP is in the asset management business or the performance business.

    Detailed Summary

    Return to Office and the Cost of Remote Work

    Asked what he is most proud of beyond the numbers, Griffin starts with Citadel’s early, countercultural demand that everyone return to the office five days a week. He frames it as a human capital decision, not a control decision: people learn through apprenticeship, mentors are critical to development, and the underdevelopment of talent from remote work has damaged the broader economy. He points to recent Fed research on falling employment among under-30s: remote work turns out to matter more than AI in diminishing opportunities for young Americans. Citadel not only brought its team back but publicly extolled the virtues of doing so, and Griffin believes history will be on his side.

    72 Hours to FDA Approval and the Warp Speed Incentive Design

    His second point of pride is Citadel’s pandemic chapter. As the first US COVID cases appeared, a former partner running a major New York hospital system called: he could not get FDA approval for experimental drug trials on ventilated patients facing imminent death, and believed only Griffin could make it happen. Citadel’s team, with decades of government experience, got approvals moving in about 72 hours. The second act was Operation Warp Speed, whose core idea Griffin discussed at length with Jared Kushner: pay pharmaceutical companies to manufacture vaccines before FDA results, so a positive result means days to market instead of the standard sequence losing three to six months. No company would spend billions producing vaccines that might be flushed down the sewer, so the US government took the manufacturing risk on unproven efficacy. A few billion dollars spent, a few trillion in GDP saved, and roughly half a million American lives.

    All-Time Highs in a World at War

    Griffin’s market picture is unsentimental: there is a war in the Middle East, a still-raging war in Europe, potential trouble in Cuba, and the peace both men grew up with is off the table. Yet the S&P sits at record highs. His explanation: America is relatively shielded from the war-driven energy crisis. China has curtailed oil demand with an elasticity that stunned even Citadel’s commodity desk, and episodic oil and LNG flows keep leaving the region, holding crude around the low $100s when most estimates had a strait closure producing nearly $200 a barrel. Meanwhile corporate earnings are at all-time highs, enough that multiples have actually compressed over the last 12 months.

    The AI Story CEOs Tell Versus the One That Is True

    Citadel has used machine learning heavily since TensorFlow arrived a decade ago, powering everything from radiology reads to self-driving cars across the economy, so Griffin sees today’s AI wave as an acceleration of an ongoing digital revolution. His favorite corrective: at a dinner with global multinational leaders two years ago, everyone was effusive about AI transforming their businesses, so he asked them to go around the table with specifics. Four or five genuinely impressive productivity stories emerged, and not one involved AI: they were machine learning, optimization, digitization, technology at large. The C-suite blurs the distinction, but the enthusiasm has unlocked bigger technology budgets and real bottom-line projects, which is part of why earnings are at records.

    The Agentic System That Shocked Him

    Then comes the story behind the famous “shocked and depressed” Friday. Citadel employs legions of young masters and PhD graduates to replicate academic finance papers: read the hypothesis, judge the work, reproduce results, and test whether the effect persists out of sample (does buyback activity predict outperformance, for example). Each paper takes six to eight weeks, and the process surfaces a few valuable ideas a year. A colleague built an agentic AI system that does the entire pipeline (read, reproduce, verify, out-of-sample test) in two to three hours on average. Griffin’s emphasis: this is not routine white-collar work, it is master’s and PhD-level work, and paired with OpenAI solving a math problem open for 80 years, it shows AI cracking problems considered out of reach two or three years ago. Notably, Citadel cut zero headcount on the back of the breakthrough; the firm has more problems worth attacking than people to attack them, so every productivity gain gets absorbed.

    Filled-In Moats and a Golden Age of Entrepreneurs

    The macro consequence Griffin draws is double-edged. Hold two thoughts at once: AI is reaching very high-level work in the job market, with some workers (translators, for instance) facing hard transitions that demand fast retraining through higher education. And simultaneously, the competitive moats of corporate America are being filled in at breathtaking rates. That means entrepreneurs can launch businesses at speeds impossible 5, 10, or 20 years ago: he mentions a startup running on a few AI systems where 30 or 40 employees would once have been required. He expects a wave of these stories over the next couple of years as founders use the technology to take on incumbents.

    The Future of the Stock Picker

    Griffin has called stock picking a timeless business, and he still sees a similar skill set for the portfolio manager of the future, with one shift in emphasis. Predicting quarterly earnings beats has gotten far harder over a decade as alternative data (credit card panels covering millions of Americans, telegraphing Starbucks and McDonald’s revenues) made the present transparent. Now bright people plus good AI triage the here-and-now almost instantly. The scarce, rewarded skill becomes vision: identifying which companies are building genuinely transformative products years before the market fully prices it.

    Compute Is the New Jet Fuel

    At Citadel Securities, which holds double-digit market share across equities, futures, and treasuries, transformer models extend a decade of machine learning gains in pricing and risk. The compute market backdrop is what Griffin calls breathtaking: essentially all available compute on Earth is utilized all the time, so access reduces to who will pay the most. Per-unit compute prices exceed what anyone reasonably projected two or three years ago, and large market makers now spend hundreds of millions of dollars annually. He treats it as straightforward input inflation, like jet fuel or eggs: high-margin businesses can bear it, low-margin ones cannot.

    China’s Technology Lead and the Taiwan Equilibrium

    Griffin states the cold reality: China is one of the most innovative, fastest-growing economies in the world, leading in roughly 67 or 68 of the 74 or 75 most important technologies (solar, EV batteries, several quantum fields) and now ahead in published academic papers. The foundation is 1.4 billion people, a culture with an extraordinary emphasis on education, and far more STEM graduates. China is no longer relegated to manufacturing low-margin products designed in America, and Griffin calls that a threat to the American way of life. His prescription is pointed: not tariffs, but educating American youth to out-compete, out-innovate, and out-problem-solve. Taiwan is the painful pressure point with no winner. If China takes Taiwan and the US loses TSMC chips, GDP falls an estimated 8 percent in six months: Boeing stops making planes, most new car production halts, consumer electronics freeze, a great depression in the blink of an eye. China would suffer draconian knock-on effects too. As an investor he thinks about position: sanctions in a Taiwan blockade would not be unified, Europe’s place on team USA is a genuine question mark now, and the oil-exporting Middle East would play Switzerland since China is its biggest customer.

    Energy Realism: Nuclear, Gas, and American Data Centers

    On powering AI, Griffin wants America to lead again in nuclear, with small modular reactors central: no meaningful carbon footprint and one of the lowest mortality rates of any energy source ever deployed (hydro has killed magnitudes more people). He challenges the superficial cleanliness of renewables: solar cells are often made in western China with coal power, requiring about seven years of energy capture to break even against the coal burned making them, and 20-year-old carbon fiber wind turbine blades do not break down and are already filling landfills. Until fusion or expanded nuclear, America’s real asset is natural gas: decades of cheap supply that has actually driven US emissions down. His data center position is blunt: they will get built somewhere, and depending on foreign countries for them would be inane, so build them in America. His answer to NIMBY politics: require data center developers to build corresponding power generation, tied to the grid for reliability, so the cost never lands on the American consumer.

    Tail Risk, Tolerable Losses, and Hedge Fund Alignment

    On hedging complicated risks, Griffin’s method is stress testing: if this happens, how much do we lose and where, and is that loss tolerable? You can never manage a portfolio for every possible tail event, but you can keep exposures sized so the worst case is definable and tolerable, leaving you still in business and positioned to fight back. On industry returns, he pegs the hedge fund cost of capital at roughly the risk-free rate plus 4 percent as the long-run equilibrium: underperformance drains capital, outperformance attracts it, and since recent outperformance keeps pulling money in, growing assets dilute alpha. That is why Citadel has returned $25 to 30 billion to LPs: alpha capacity is finite, Griffin’s job is to grow it, and excess capital goes back to investors to keep return on equity high. The closing advice is an alignment test: Citadel’s biggest investor is Griffin and his partners, and every allocator should ask whether their GP is in the asset management business or the performance business.

    Notable Quotes

    “Turns out that remote working is a more important factor to diminished employment opportunities for young Americans than AI.”

    Ken Griffin, citing Fed research on under-30 employment

    “We spent a few billion dollars as a country. We saved a few trillion dollars in GDP. We saved roughly half a million American lives.”

    Ken Griffin, on Operation Warp Speed’s incentive design

    “I got four or five incredible stories of how companies were achieving meaningful productivity gains. Not one involved AI.”

    Ken Griffin, on his dinner with global multinational CEOs

    “My colleague built an agentic AI system that would read a paper, reproduce it, verify the results that were published in the paper, produce the results out of sample, and do all this work in about on average 2 to three hours.”

    Ken Griffin, on the breakthrough that replaced six to eight weeks of PhD-level work

    “We’re likely to see a golden age of entrepreneur activity. Like entrepreneurs will be able to launch new businesses at breathtaking speeds and will be able to take on incumbents in ways that you just couldn’t do 5, 10, 15, 20 years ago.”

    Ken Griffin, on AI filling in competitive moats

    “All the available compute today is more or less utilized all the time. So the question is who’s willing to pay the most for it?”

    Ken Griffin, on the global compute market

    “The US loses access to Taiwanese semiconductor chips, our GDP falls by 8% in 6 months. Simply put, we go into a great depression in the blink of an eye unlike any we’ve seen before.”

    Ken Griffin, on the Taiwan scenario

    “We better damn well build the data centers in America because they’re going to get built somewhere in the world.”

    Ken Griffin, on energy policy and AI infrastructure

    “Definable, tolerable, still in business, still in a position to fight back from that point.”

    Ken Griffin, summarizing his approach to hedging tail risk

    “Are they in the asset management business or are they in the performance business?”

    Ken Griffin, on the question every hedge fund investor should ask their GP

    Watch the full conversation here: Ken Griffin on Goldman Sachs Exchanges: Great Investors.

    Related Reading

  • Dan Loeb on Building Third Point’s $25 Billion Investment Empire: AI, Activism, Credit, and the FTX Mistake

    Dan Loeb has spent three decades turning a $3 million fund into Third Point, a roughly $25 billion collection of hedge fund, credit, insurance, and venture businesses. In this Invest Like the Best conversation with Patrick O’Shaughnessy, Loeb walks through how he reinvented his strategy from deep value and event-driven trades into quality and thematic investing, why he now believes every serious investor has to be a technology investor, how he reads the AI cycle and the semiconductor melt-up, where activism and corporate governance still pay, and the single mistake that taught him the most. It is a rare, unhurried look at how a famously sharp-elbowed activist actually thinks about markets, businesses, and people.

    TLDW

    Loeb covers an enormous amount of ground: his daily process for staying ahead of the information firehose, Jensen Huang’s AI stack as a mental model, and why Nvidia, Anthropic, and Elon Musk’s companies are the three most consequential firms he tracks. He traces Third Point’s roots in credit and event-driven investing at Jefferies, the influence of Joel Greenblatt’s “You Can Be a Stock Market Genius,” and his later pivot to quality investing shaped by “The Outsiders” and Lawrence Cunningham’s “Quality Investing.” He argues the AI rally is not a dot-com-style valuation bubble because the leaders generate enormous cash, explains why human judgment and structural market quirks still create alpha, and makes the case that AI will never fully run a capital system. He digs into corporate governance and his father’s influence, the Sotheby’s and Sony activism campaigns, the hard reality of activism in Japan, and what investing in Danaher’s operating system taught him. He names FTX as his hardest lesson, breaks down Third Point’s evolution into a 60-percent-credit platform spanning CLOs, structured credit, reinsurance and annuities, describes how he is pushing his analysts to use AI and Claude daily, and closes on kindness and the friend who let him sleep on a couch before he made it.

    Thoughts

    The most striking thing about Loeb is that he treats his own strategy as a thing to be disrupted rather than defended. He built his reputation on Greenblatt-style special situations, spin-offs, demutualizations, and post-reorg equities bought cheap because of forced selling and sandbagged guidance. Most investors who win that way spend the rest of their careers protecting the formula. Loeb instead watched the people who stayed rigid about deep value and low multiples underperform or disappear, and deliberately retrained himself and his team around business quality and thematic conviction. The willingness to abandon a winning identity is the actual edge here, more than any single trade. It is the rare investor who can say his current strategy would not fit cleanly on a PowerPoint deck and treat that as a feature.

    His AI framing deserves attention because it is unfashionably calm. The bear case on AI is usually about valuation, and Loeb dismantles it on the leaders’ own numbers: these are companies investing off their balance sheets, generating enormous cash, trading at multiples that do not resemble 1999. He was short the dot-com bubble, so he is not a permabull cheering from the sidelines. His real point is subtler, that the danger is expectations, not valuations. The semiconductor index ran up 40 percent on genuinely strong fundamentals, but Micron and Nvidia both put up monster quarters and saw their stocks fall because expectations had simply outrun even great results. That gap between fundamentals and price is where he thinks the human investor still earns a living, precisely because quant strategies, CTAs, and risk-managed pods are forced to sell into weakness rather than buy it.

    The governance material is the most quietly radical part of the conversation. Loeb defends shareholder primacy against the Business Roundtable’s softer stakeholder language, but his argument is not the cartoon version where shareholder value means strip-mining a company. It is that boards have one job, accountability for capital allocation and management, and that vague multi-stakeholder mandates become an excuse for directors to avoid the hard work. His read on bad governance is almost always relational: directors who let loyalty to an underperforming CEO override their duty, or who sit on boards for status and income. The Sotheby’s story is the clean illustration, a centuries-old, high-status business run unprofitably because nobody treated it like a business. Loeb’s pattern is to find the gap between claimed status and actual performance and to raise the social cost of coasting.

    What is genuinely new in Loeb’s posture is how he talks about AI inside his own firm. He is not pitching it as a moat or a headcount-reduction story. He frames Claude and AI tools as a way to make each person a more autonomous self-improver, something that gives back whatever you put into it, with some analysts running agents overnight and burning tokens while he personally uses it more for queries. Coming from a 30-year fundamental investor, the absence of defensiveness is the signal. He pairs it with Brad Gerstner’s nod to “Essentialism”: the firehose is now infinite, so the scarce skill is deciding what is actually relevant. That is a more honest answer to the AI question than either doom or hype.

    Finally, the FTX confession is worth sitting with because of how he frames it. He does not retreat into cynicism about venture or crypto. He notes that Sam Bankman-Fried, fraud aside, had a real nose for value, with stakes in Anthropic, Cursor, and Solana that would have made him a top venture investor of the era. The lesson Loeb extracts is procedural, not philosophical: their due diligence now includes checking bank balances, the most basic verification that would have surfaced the problem. It is a useful reminder that even sophisticated capital can skip boring fundamentals when a company is growing fast and the cap table looks good. The discipline is not in having a grand theory of fraud, it is in never skipping the unglamorous checks.

    Key Takeaways

    • Loeb’s macro focus right now collapses to two variables: where oil goes, dictated by war and geopolitics, and what AI does on the spending and infrastructure front and its impact on society and the economy.
    • He argues you can no longer punt on technology and focus on industrials or consumer; tech is a big, growing, compounding part of the economy that affects everything else, so every investor has to become a tech investor.
    • He uses Jensen Huang’s AI stack as a mental model: power and energy at the bottom, then chips and infrastructure, up through large language models, software, and applications.
    • The three most consequential companies he tracks are Nvidia, Anthropic, and Elon Musk’s companies collectively.
    • Third Point’s roots are in credit and event-driven investing, shaped by his time at Jefferies watching investors like David Tepper before he founded Appaloosa, Eric Mindich at Goldman, and firms like Angelo Gordon and Farallon.
    • Joel Greenblatt’s “You Can Be a Stock Market Genius” was his foundational framework: spin-offs, demutualizations, privatizations, and post-reorg equities where a new, illiquid security gets dumped by holders who will not do the work.
    • Spin-off managers often sandbag guidance because their incentive packages get set at the time of the spin-off, creating a predictable gap between conservative numbers and real value.
    • From 1995 to roughly 2013-2015, event-driven special situations were Third Point’s bread and butter; those opportunities still exist, but the real edge now is overlaying them with a business-quality lens.
    • The pivot to quality and thematic investing was influenced most by “The Outsiders” (capital allocation plus great operations) and Lawrence Cunningham’s “Quality Investing” (high-moat, high-return-on-capital businesses to own for years).
    • AI disruption made last year one of the worst for many apparently high-quality companies, as businesses that looked durable rapidly became less so.
    • Loeb sees the AI rally as fundamentally different from the dot-com bubble: the leaders invest off their balance sheets, generate enormous cash, and do not carry the valuation excess of 1999.
    • The danger in semis is expectations, not valuation: Nvidia and Micron posted spectacular quarters yet saw stocks fall because expectations had outrun even great numbers.
    • Structural forces still create alpha for fundamental investors: quants, CTAs, and multi-strategy pods have risk metrics that force selling on the way down, the opposite of what is rational for long-term holders.
    • He believes AI will not fully run a capital system; private equity, restructurings, creditor committees, and high-touch negotiation will always need humans.
    • His interest in governance came from his father, a securities lawyer and corporate governance expert who sat on the boards of Mattel and Williams-Sonoma and pushed ethical sourcing ahead of his time.
    • Loeb defends shareholder primacy, citing Milton Friedman and Warren Buffett, and criticizes the Business Roundtable’s move away from shareholder value as a distraction from the board’s real duty.
    • Bad governance usually comes from directors letting loyalty to a weak CEO override fiduciary duty, lacking the knowledge to do the job, or serving for status and income.
    • Writing is a core activism lever: great writing is clear thinking, and social pressure through writing and PR is one of the most effective ways to move a board, alongside financial and legal levers.
    • The Sotheby’s campaign targeted a high-status, centuries-old business run unprofitably; Third Point bought 9.9 percent, eventually brought in Tad Smith from MSG, who cleaned up operations and technology before the company sold.
    • Third Point increasingly prefers to back great companies with excellent management and cheer them on rather than hunt for mismanaged businesses, because bad management tends to cluster into a morass.
    • Third Point is a collection of businesses; the flagship hedge fund grew from $3 million to about $9 billion and is roughly 30 percent credit, with the broader firm closer to 60 percent credit.
    • The firm spans a roughly $7 billion CLO business, structured and corporate credit, an insurance company, asbestos liabilities, a small private credit unit, and a venture capital arm.
    • The unifying thread is valuing enterprises across early, mid, and mature stages and investing in whichever fulcrum security offers the best risk-reward, from equity to senior debt.
    • Loeb cites buying Twitter’s financing debt near 96-97 cents at a 12 percent yield when most credit investors were scared, and a difficult xAI debt financing, as examples of cross-discipline conviction.
    • He is the portfolio manager only of the hedge fund; the credit, CLO, structured credit, and high-yield businesses have their own PMs and investment committees he does not sit on.
    • The Sony campaign saw Third Point own up to 7 percent and push to separate the conglomerate; management resisted for years before spinning out the semiconductor and financial services businesses.
    • He learned that activism in Japan is hard, but the government often wants reform; he co-wrote a paper with Larry Lindsey and Niall Ferguson urging corporate governance and return on invested capital as a fourth arrow of Abenomics, picked up as a Wall Street Journal editorial.
    • Investing in Danaher was his most instructive experience, teaching him how the Danaher Business System drives continuous improvement (Kaizen) and how the company celebrates rather than shames underperformance because problems are fixable.
    • FTX was his hardest lesson; it looked great and was verifiable on the blockchain, but was not what it appeared, and now Third Point’s diligence includes checking bank balances.
    • He notes that, fraud aside, Sam Bankman-Fried had a strong nose for value with stakes in Anthropic, Cursor, and Solana.
    • Recent mistakes also include shorts where Third Point thought certain info-services businesses would resist AI disruption; he still expects a shakeout with some phoenixes rising from the ashes.
    • He is pushing his whole team to use AI daily, hiring native computer scientists and system integrators, and describes Claude as a tool that makes you autonomous and gives back whatever you put into it.
    • Third Point’s distinctive edge is optimism about AI creating net jobs and the ability to default into credit investing during stressed times, as it did with investment-grade credit in 2020.
    • Credit is hard to copy because it runs on relationships, not electronic trading; that is why Third Point built into CLOs and eyes the roughly $6 trillion structured credit market rather than treating it as tourism.
    • The great analyst has changed: 20 years ago it was someone who could model fast and crack a complex restructuring (Loeb made a career-defining bet on Drexel Burnham claims); today it is a Gavin Baker type who deeply understands an industry, like the analyst who flew to Texas and realized Casey’s General Stores was really a pizza chain.
    • Outside the US, Loeb is more bullish on Korea, Taiwan, and Japan as hunting grounds, finds Europe tough on regulation (though he owns Rolls-Royce and ASML), and finds the Middle East the most vibrant region.
    • What worries him most is not the business but running out of time for family, surfing, and reading; what excites him is incorporating everything relevant about the world and forming relationships with people building interesting things.
    • His closing reflection is on kindness as a top-tier value, and the friend, Carter, who let him sleep on a couch and seeded his early fund, echoing a Palmer Luckey line that money cannot buy friends who believed in you when you had nothing.

    Detailed Summary

    Staying ahead of the firehose and reading the macro

    Loeb opens by admitting he does not have a perfectly organized system for processing the modern flood of information. He checks the news for what is relevant to the economy and to Third Point’s positions, tries not to obsess over minute-to-minute moves, and leans more tactical than strategic. When people ask him about macro, he says the usual government-reported metrics (growth, unemployment, inflation, rates, currencies, gold, crypto) are trumped right now by two things: where oil goes, which depends on war and geopolitics, and what AI does on the spending and infrastructure side and its impact on society and the economy. To understand technology, he leans on Jensen Huang’s framing of the AI stack and talks to smart people regularly, and he watches three companies above all: Nvidia, Anthropic, and Elon Musk’s companies as a group.

    From event-driven roots to quality investing

    Third Point’s DNA comes from Loeb’s time as a credit investor at Jefferies, where he watched some of the best distressed, event-driven, and risk-arbitrage investors operate, from David Tepper to Eric Mindich to firms like Angelo Gordon and Farallon. His first lens was event-driven: spin-offs, demutualizations, privatizations, and post-reorg equities, where a newly created and illiquid security gets dumped by holders who will not do the work, and management sandbags guidance because incentive packages are set at the spin date. He barely thought about moats or returns on capital; he just wanted to buy something genuinely cheap with those characteristics. That was the firm’s bread and butter from 1995 until roughly 2013-2015. Those opportunities still exist, but Loeb describes deliberately evolving toward business quality and thematic investing, influenced by “The Outsiders” on capital allocation and Lawrence Cunningham’s “Quality Investing” on durable, high-return businesses. He organized the team around industry experts rather than generalists. The twist: AI disruption recently turned many apparently high-quality companies into much lower-quality ones, fast.

    The AI cycle, bubbles, and the human edge

    Loeb resists the bubble narrative. He was short the dot-com bubble and remembers the valuation excess; today’s AI leaders, by contrast, invest off their balance sheets and generate enormous cash, so unless you believe the capex yields no return, the earnings and multiples do not look like 1999. The real driver of volatility, he argues, is expectations: the semiconductor index ran up 40 percent on strong fundamentals, but Nvidia and Micron both delivered blowout quarters and still saw their stocks fall because expectations had run too high. That dynamic is exactly where a fundamental investor earns a living, because quants, CTAs, and risk-managed pods are structurally forced to sell into weakness. He also doubts AI will ever fully run a capital system, since private equity, restructurings, creditor committees, and high-touch credit always need humans. He cites “Reminiscences of a Stock Operator” and Ecclesiastes: there is nothing new under the sun, and human nature, with its bubbles, panics, and extremes, does not change.

    Governance, his father, and the duty of boards

    Loeb traces his governance interest to his father, a securities lawyer and corporate-governance expert who served on the boards of Mattel and Williams-Sonoma and championed ethical sourcing before it was common. He calls the American board system beautiful: directors are answerable to shareholders and accountable for strategy and key financial decisions. Governance breaks down when directors lose sight of their fiduciary duty, lack the knowledge or talent diversity to do the job, or prioritize things other than shareholders. He invokes Milton Friedman and Warren Buffett to argue that caring about communities, employees, and conduct is not inconsistent with shareholder value but part of it, and criticizes the Business Roundtable for muddying the board’s core duty. The most common failure he sees is directors letting loyalty to an underperforming CEO override their duty. Most of the time Third Point redirects existing boards without even taking a seat; the extreme proxy fights are the exception.

    Activism, writing, Sotheby’s, and Sony

    Great writing, Loeb says, is clear thinking and organizing your thoughts to get a desired outcome, and it is one of activism’s most effective levers alongside financial and legal pressure. Social pressure through writing and PR can move a board on its own. He sees a pattern in his campaigns: targets that hold themselves out as high status but are not living up to it. Sotheby’s is the clean example, a centuries-old, high-status business run unprofitably, where Third Point bought 9.9 percent, gave the existing CEO a year, then helped install Tad Smith from MSG, who modernized operations and technology before the company was sold. Sony was a two-act campaign in which Third Point owned up to 7 percent and pushed to break up the conglomerate; he recounts sharing the thesis with Andrew Ross Sorkin at the New York Times under embargo, the panic it caused, and how management resisted for years before spinning out the semiconductor and financial services units. The lesson: activism in Japan is genuinely hard, even though the government wanted reform. He co-authored a paper with Larry Lindsey and Niall Ferguson arguing corporate governance and return on invested capital should be a fourth arrow of Abenomics, which ran as a Wall Street Journal editorial.

    The Danaher operating system

    Loeb calls Danaher his most instructive investment. He and his partner persuaded the company to compress its five-day Danaher Business System training into a single day, and he came away with a deep appreciation for how a real operating system drives continuous improvement. The standout lesson was cultural: Danaher holds people individually accountable, but when it finds someone underperforming it celebrates rather than shames, because the problems are addressable and fixable, and it does this relentlessly across operations and working capital. He also points to the diaspora of Danaher executives, including Larry Culp and the leadership at Ingersoll Rand, as evidence of the system’s depth. The investment worked for about four years before COVID-era order surges and inventory swings turned tailwinds into headwinds; Third Point sold and has recently bought back in modestly.

    The structure of Third Point and the fulcrum security

    Third Point is not one fund but a collection of businesses. The flagship hedge fund grew from $3 million to about $9 billion and is roughly 30 percent credit, generically around 110 percent long and 30-40 percent short on the equity side. Across the firm the credit weight is closer to 60 percent, spanning a roughly $7 billion CLO business, several billion in structured and corporate credit, an insurance company, a couple billion in asbestos liabilities, a small new private credit unit, and a venture arm. The unifying thread is valuing enterprises at any stage and investing in whichever fulcrum security (the one with the best risk-reward) makes sense. Loeb illustrates with Credit Suisse’s takeover by UBS, where the holdco paper proved the fulcrum, and with buying Twitter’s resold financing debt near 96-97 cents at a 12 percent yield when other credit investors were scared, plus a difficult xAI debt financing that few credit people wanted. He pushes back on the idea that he sits atop everything: he is the PM only of the hedge fund, while the other businesses have their own PMs and committees he is not on.

    Insurance, the FTX lesson, and recent mistakes

    Loeb started a Bermuda reinsurance company in 2010, backed by himself, Kelso, and Pinebrook, on a barbell thesis of investing the float in Third Point and treasuries to defer taxes and lever capital. The reinsurance side soured, and about three years ago he concluded they had the right idea but the wrong vehicle, that plain-vanilla annuities (which can only invest in credit) would have fit better. Third Point merged the reinsurer into its UK closed-end fund, Third Point Offshore Investors, reincorporated from Guernsey to Cayman, and repurposed it into an insurance company managing private credit, structured credit, whole-loan mortgages, real estate lending, and investment-grade debt. His hardest lesson was FTX: it looked great, was verifiable on the blockchain, and had a strong cap table, but was not what it seemed; diligence now includes checking bank balances. He notes Sam Bankman-Fried, fraud aside, had a great nose for value (Anthropic, Cursor, Solana). Other recent mistakes were shorts where Third Point bet certain info-services businesses would resist AI disruption; he still expects a shakeout with some survivors rising from the ashes.

    AI inside the firm, the analyst of the future, and kindness

    Loeb is pushing his entire team to use AI daily, hiring native computer scientists and system integrators, and describes Claude as a tool that makes you an autonomous self-improver and gives back whatever you put into it, with some analysts running agents overnight while he uses it more for queries. He pairs this with Brad Gerstner’s recommendation of “Essentialism”: you cannot do it all, so you must decide what is most relevant. The great analyst has changed: 20 years ago it was someone who could model fast and crack a complex restructuring, as Loeb did with the Drexel Burnham bankruptcy claims early in his career; today it is a Gavin Baker type who deeply understands an industry and its technology, like the analyst who flew to Texas and realized Casey’s General Stores was really a pizza chain in disguise. On the rest of the world, he is more bullish on Korea, Taiwan, and Japan, finds Europe tough on regulation (while owning Rolls-Royce and ASML), and finds the Middle East the most vibrant region. He closes on what worries and excites him (time with family, surfing, and reading versus the joy of incorporating everything relevant about the world), and on kindness, crediting his friend Carter, who let him sleep on a couch and seeded his early fund, and echoing Palmer Luckey’s line that money cannot buy friends who believed in you when you had nothing.

    Notable Quotes

    “I think you have to be a tech person today. It’s a big and growing and compounding part of the economy. It affects everything else.”

    Dan Loeb, on why no serious investor can punt on technology anymore

    “Hold on to your seats because things are only going to accelerate from here.”

    Dan Loeb, recounting a 2013 Davos warning about technological change he now applies to AI

    “Maybe that’s where the human element comes in, to understand and to be able to make those tough trading decisions when fundamentals are going one way and stock prices are going the other way, and to be able to take the pain of losses in the short run.”

    Dan Loeb, on where a human investor still has an edge over machines

    “It’s very different from the dot-com bubble, which we were short going into. You don’t have the valuation bubble now on those companies that you had back in those days.”

    Dan Loeb, on why he does not see the AI rally as a 1999-style bubble

    “When they found someone that was underperforming, it was celebrated instead of shamed, because look at all these things you’re doing wrong, we can fix those. And they did.”

    Dan Loeb, on the accountability culture he learned from the Danaher Business System

    “I would have to say our investment in FTX. It looked great. The company was growing fast. We could verify it all on the blockchain.”

    Dan Loeb, naming his hardest investment lesson

    “Be kind to people you have no idea how it will ever benefit you. And sometimes it will and sometimes it won’t.”

    Dan Loeb, on elevating kindness in your hierarchy of values

    “The one thing money doesn’t buy you is friends that believed in you when you had nothing.”

    Dan Loeb, quoting Gavin Baker quoting Palmer Luckey, on the friend who seeded his early fund

    Watch the full conversation between Dan Loeb and Patrick O’Shaughnessy here.

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