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  • Lloyd Blankfein on the 3 Sectors Where He Puts His Money Now: Big Tech, Energy, and Financial Services, Day Trading From an iPad, and the Warren Buffett Handshake That Backed Goldman in 2008

    Lloyd Blankfein spent almost 40 years at Goldman Sachs, the last dozen as its chairman and chief executive, and he still trades almost every day from an iPad. In this wide ranging conversation on the My First Million podcast, the former Goldman boss lays out exactly where he is putting his own money right now, why a supportive spouse beats nearly any investment, how Warren Buffett wired five billion dollars into Goldman on a handshake during the 2008 crisis, and why he reads medieval history to stay calm about the present. It is part stock picking, part risk philosophy, and part a frank accounting of money, marriage, and the scars of growing up in the projects.

    TLDW

    Blankfein says he is roughly 98 percent in risky assets, almost all equities, and concentrated in three sectors he knows cold: big tech, energy, and financial services. His personal book leans heavily into single stocks over ETFs, weighted toward the big hyperscalers and a few second tier names, and he trades daily, alone, from an iPad and a phone, using calls and texts as his research network. Yet the advice he gives a normal investor is the boring opposite: a diversified S&P 500 fund like VOO, more risk when you are young because you will outlive your mistakes, the same thing Warren Buffett would tell you. The conversation ranges across the 2008 Buffett investment in Goldman, the cost of trying to legislate risk out of markets, the thin margin between the best and the rest, luck and the myth of the genius, why reputation is the real contract on Wall Street, why a supportive spouse is the highest return asset he knows, the money anxiety he carried out of a Brooklyn housing project, the dignity of a 500 dollar financial aid check, giving with a warm hand versus a cold one, the dangers of gamified investing, the big misses like SpaceX and early cellular, the obituary test a senior partner once gave him, and why reading history keeps the present in proportion.

    Thoughts

    The most useful tension in this interview is the gap between what Blankfein practices and what he preaches. He tells young people to buy a diversified S&P 500 index fund, he holds VOO himself, and he calls the host’s plain 90 percent stocks and 10 percent bonds split sensible. Then he admits his own portfolio is something like 90 percent single stocks that he trades by hand every day. The honest read is that his edge is not a transferable tip. It is a 40 year information network of phone calls and a tolerance for risk that most people neither have nor should want. The replicable lesson is the boring half, not the day trading half.

    The most contrarian idea here is not a stock pick, it is his defense of risk itself. His argument that regulators trying to prevent the hundred year storm also forfeit the 99 normal years of growth in between is a serious claim about the price of safety, and it travels far beyond Wall Street. The same goes for his point that a good risk manager sometimes has to push people to take more risk, not less. The moment after a loss, when everyone goes gunshy, is exactly when the best operators lean back in. That is an uncomfortable thing for a former bank CEO to say out loud, and it is the part of the conversation most worth sitting with.

    The Warren Buffett story is a master class in what actually moves markets, and it is not cash. Goldman did not need the five billion dollars. Blankfein says the money was almost irrelevant because the firm already had money. What it could not manufacture was confidence, and Buffett’s name supplied it. The handshake, the commitment with no paperwork, the line about worrying enough for the both of us, all point to the same thing. At the top, reputation is the collateral. His aside that most trades are never written down because you will never eat lunch in this town again is the same idea wearing street clothes.

    Quietly, the personal finance thread may be the most valuable part for a normal listener. A former Goldman CEO saying that a supportive partner is more game changing than any investment, that a bad marriage is financially worse than being lonely, and that he has not paid a bill in over 40 years because his wife runs the household economy, is a reminder that household stability is itself an asset class. The 500 dollar financial aid check he still remembers half a century later, and his give with your warm hand philosophy, reframe wealth as something measured by how it feels to give and to receive, not just by the size of a pie chart.

    Finally, the history obsession is not a side hobby, it is his risk model. Reading about the black plague, the McCarthy era, and the Vietnam draft is how he keeps the present in proportion. His Mark Twain line, that history does not repeat but it rhymes, is the direct antidote to the in this economy defeatism he and the host both complain about. For an investor, that long view is close to the whole game. It is what lets you hold through the drawdowns that scare everyone else out of the market.

    Key Takeaways

    • Blankfein estimates he is about 98 percent in risky assets, with roughly 95 of those 98 points in equities, and the rest spread thin. He invests in risky assets because, in his words, that is what is fun for him.
    • Within his equities, he is heavily tilted toward single stocks rather than ETFs. He frames it as roughly a quarter to a third in ETFs and the rest in single names, and concedes it could be as lopsided as 90 percent single stocks because picking names is what he enjoys.
    • The three sectors he has concentrated in for years are big tech, energy, and financial services, and he says his outperformance comes from where he focused, not from any special genius.
    • On tech he owns the big hyperscalers, the Googles, Microsofts, and Nvidias of the world, plus a tier just below them, naming Oracle and Larry Ellison as an example of a slightly riskier second tier name. He thinks in categories, not fixed tickers, because he changes positions constantly.
    • He says he has a background in trading energy, which is why energy is a core sleeve, and he knows financial services from the inside after almost 40 years at Goldman, so those are natural areas of edge.
    • He still owns a lot of Goldman Sachs stock, out of affection for the firm he spent his career building.
    • He is bullish on big tech and plans to stay bullish until it stops going up. His foreseeable future, he jokes, lasts until he finishes the conversation and checks the screen again.
    • He trades every single day, alone, with no team. He does it from an iPad and a phone, not a computer, and treats the market like background music rather than a job.
    • His research is human, not algorithmic. He chats and texts with people, then calls them because he is tired of fixing typos, and he reads the New York Post, the Wall Street Journal, the New York Times, the Financial Times, and Bloomberg.
    • The advice he gives ordinary investors is deliberately boring and different from his own behavior: hold a diversified equity portfolio like an S&P 500 fund, with VOO as his own example, and tilt more aggressively when you are young because you have time to outlive mistakes.
    • He notes that broad indexes are already heavily weighted toward tech because of market cap, so a plain index gives meaningful tech exposure, and a tech focused ETF on top can add a disproportionate tilt for believers.
    • He calls the host’s simple 90 percent index and 10 percent bonds allocation sensible, and says this is essentially the same advice Warren Buffett would give a normal person.
    • The older you get, the more conservative you should become, shifting from maximizing gains toward not losing what you have. Young people can afford more risk precisely because they will outlive their errors.
    • During the 2008 financial crisis, Warren Buffett invested about five billion dollars in Goldman through a preferred stock structure, essentially on a phone call and a handshake, with no demand for due diligence.
    • Buffett’s real value was confidence, not capital. Goldman already had money, but it had lost the confidence of the market while peers were failing. Buffett’s name signaled the firm was a good investment being beaten down by circumstances that would reverse.
    • Buffett asked for a verbal commitment that Goldman would not sell shares before he did, and declined to put it in writing. He waved off the worry with the line that five billion dollars going bad would not even be a bad hurricane for Berkshire, an insurer.
    • Most trading is done on reputation, not paper. Blankfein says people buy and sell bonds worth enormous sums without written contracts, relying on probity, because anyone who reneges will never eat lunch in this town again.
    • On risk and regulation, he argues you cannot legislate risk away. Trying to prevent the hundred year storm also forgoes the 99 in between years of growth, and a good risk manager sometimes has to encourage people to take risk, not suppress it.
    • The best traders have resilience. They bounce back, focus on new information rather than the past, and adapt quickly instead of staying gunshy after a loss.
    • The difference between someone who is really good and someone who cannot make it is small. He compares it to a golf tournament won by one stroke with six people tied for second, and notes much of life is winner take all at razor thin margins.
    • Luck matters enormously. He became Goldman CEO partly because his predecessor was nominated to be Treasury Secretary, a reference to Hank Paulson, and the timing of opportunities is often out of your control.
    • He is skeptical of the word genius. He says he can usually see how successful people do what they do, with Elon Musk as a rare exception, and that powerful people are more normal, more insecure, and more flawed than outsiders assume.
    • On democratized investing, he thinks apps that make markets accessible are good in their own terms, but gamifying trading with confetti and high fives can mask real danger for people who can lose more than they can afford.
    • He has missed plenty. He thought SpaceX was overpriced at a 100 billion dollar valuation, now discussed near a trillion and three quarters, and passed on early cellular because he could not imagine why anyone would carry a bulky phone when payphones existed. He says he missed far more than he got.
    • He frames a supportive spouse as more game changing than almost any investment, and warns that a bad marriage, with custody fights and property settlements, is financially and personally worse than being lonely.
    • He has not paid a bill in over 40 years. His wife Laura, a former lawyer he says now chairs Barnard College, runs a bill paying service and manages the household economy. He generates the money, she distributes it.
    • He grew up in an East New York, Brooklyn housing project, the son of a postal worker, and carried money anxiety well into his 30s. He recalls buying a vacation home that cost more than all their savings, with his wife unable to make the math work until they remembered the down payment.
    • A 500 dollar financial aid check, handed to him without shame as a college freshman around 1971, shaped his philosophy on giving. He learned it is not enough to give people what they need, you have to give it in a way that feels dignified.
    • He embraces the give with your warm hand, not your cold hand idea, the notion of giving while alive so you can experience the joy, which connects to the spirit of the book Die With Zero.
    • He admits ambivalence about giving to his kids, the strange feeling of resenting that they have what he provided, and notes the heavy burden carried by children of prominent people who must prove they earned their place.
    • He describes himself as wired for anxiety, inherited from his father, and says looking around corners for what could go wrong actually suited a career in a risky business with a big balance sheet.
    • When he made partner, a senior partner gave him rules of the road, including avoiding misconduct, being conservative on taxes, setting up a charitable foundation, and living so that no more than three of the nine paragraphs in his eventual obituary would be about Goldman. He says he stayed too long to pass that test.
    • He reads history as a discipline, favoring Barbara Tuchman, Robert Caro’s The Power Broker, Ron Chernow, Rick Atkinson, and Stephen Ambrose. His core belief, borrowed from Mark Twain, is that history does not repeat but it rhymes, which is why he would not bet against America.

    Detailed Summary

    The three sectors he actually invests in

    The headline answer to where the former Goldman CEO is putting his money is simple: big tech, energy, and financial services. He says he has been focused on those three areas for a long time, and that his outperformance is a function of where he aimed rather than any unusual investing gift. Energy is natural because he has a background trading it. Financial services is natural because he spent nearly 40 years inside the industry. Tech is where he is most heavily concentrated, and he expects to stay there for good reason, citing the threshold of large changes in technology. He owns the major hyperscalers by category, the Googles, Microsofts, and Nvidias, plus a tier just below, offering Oracle and Larry Ellison as a polite example of a slightly riskier second tier name. He is careful to say he thinks in categories rather than fixed tickers because he changes his positions all the time.

    How the portfolio is really built: single stocks over ETFs

    Asked to describe his portfolio as a pie chart, Blankfein says he is about 98 percent in risky assets, with roughly 95 of those points in equities. He pushes back on the idea that index funds are safe, pointing out that a diversified equity ETF is still equities and still risky, just spread out, and very different from debt or short term money markets. Within his equity sleeve he leans into single stocks, framing it as somewhere between a quarter and a third in ETFs and the rest in individual names, and conceding it might be as extreme as 10 percent ETFs and 90 percent single stocks. The reason is preference, not theory. Picking and trading names is what he likes to do, and he is honest that this is a hobby pursued by a professional, not a model for someone investing for a living.

    How he actually trades: an iPad, a phone, and a network

    He trades every day, by himself, with no team. There is no Bloomberg terminal and no desk of analysts. He uses an iPad and a phone, and admits it takes discipline not to glance at his screen mid conversation. The market, he says, is like music playing in the background while he does other things. His information edge is relational. People text him, he texts back, and then he calls because he is tired of fixing typos with what he calls his fat fingers. He follows general and business news, reads a stack of newspapers starting with the New York Post, and treats companies like little stories, almost like gossip. He even notes, with some delight, that he still watches commercials on Netflix, a small window into a frugality that never fully left him.

    The advice he gives young investors, and what Buffett would say

    For a normal person, his counsel is the opposite of his own behavior. He would hold a diversified portfolio of equities like an S&P 500 fund, naming the SPY and VOO tickers and saying he personally uses VOO. Because of the importance of technology, he might add a tech oriented ETF for extra tilt, while noting the broad index is already tech heavy by market cap. He endorses the host’s plain 90 percent index and 10 percent bonds split as sensible and says it mirrors what Warren Buffett would advise. His one piece of age based guidance is that younger investors should accept more risk through equities, because they have time to recover, while older investors should grow more conservative and focus on not losing what they have rather than maximizing returns.

    The Warren Buffett handshake that backed Goldman in 2008

    The most cinematic story in the conversation is Buffett’s roughly five billion dollar investment in Goldman during the financial crisis, structured as a preferred stock that sits between a loan and equity. Blankfein describes a deal done largely on trust. When he offered to walk Buffett through everything he was worried about, Buffett replied that he knew Lloyd well enough to know he worried enough for the both of them. Buffett also asked, verbally and without writing, for a commitment that Goldman would not sell shares before he did. Blankfein is clear that the cash itself was almost irrelevant, since Goldman had money. What the firm lacked was the confidence of a frightened market, and Buffett’s willingness to invest before things improved supplied exactly that signal. Buffett, he stresses, was acting for his own shareholders, not as a rescuer, which is precisely what made the vote of confidence credible.

    Why you cannot legislate risk out of the system

    Reflecting on the post crisis regulatory push to make sure 2008 never happened again, Blankfein makes a careful argument about the price of safety. Once you are in the business of taking risk, anything can happen, and trying to legislate it away has a hidden cost. You may think you are protecting the world from the hundred year storm, but you also forgo the 99 years of growth in between. He extends this inside the firm too. After a period of big losses, partners had become gunshy and were talking themselves out of every idea. A good risk manager, he argues, sometimes has to promote risk taking rather than repress it, because without risk there is no growth, no entrepreneurship, and no progress. The flip side is real: take risk and there is a meaningful chance you fail and lose other people’s money, which is a terrible outcome. But the alternative, never risking anything, buys comfort at the cost of ever moving forward.

    Small margins, big outcomes, and the role of luck

    Asked what separated the traders who could not outperform from the rest, Blankfein says the gap between the very good and those who cannot make it is surprisingly small. He likens it to a golf tournament decided by a single stroke with six players tied for second, and to acting, where the best performer gets every role and the second best waits tables. Much of life, he says, is winner take all at tiny margins. Luck compounds this. He freely credits fortune for his own rise, noting he became CEO in part because his predecessor was tapped to be Treasury Secretary. He is also skeptical of the genius label. He can usually see how accomplished people do what they do, with Elon Musk a rare exception, and insists the powerful are more normal, more insecure, and more driven by their flaws than outsiders imagine.

    Reputation is the real contract

    A recurring theme is that the financial world runs on reputation more than paperwork. Blankfein notes that most of what traders do is not written down. People buy and sell bonds and other instruments that settle days later, relying on probity rather than signed contracts, because anyone who lies or reneges will never eat lunch in this town again. He references the casual texts between Elon Musk and Larry Ellison around the Twitter acquisition as proof that big does not mean complicated. There are big things that are simple and little things that are complicated. Documentation is good when execution is far off, but when a deal will be performed in two days, dotting every i is often pointless. The point is not that documents do not matter, it is that trust and reputation are the load bearing structure.

    A supportive spouse as the highest return asset

    The conversation turns personal when both men agree that a supportive partner may be the single most game changing factor in a life, more than any investment. Blankfein adds the inverse warning: a bad marriage, with breakups, custody battles, and property settlements, is worse than loneliness. He credits his wife Laura, a former big firm lawyer he says now chairs Barnard College, with handling everything when his career moved the family overseas, from the car to the house to the kids’ schooling, while he took the visible victory laps at work. He has not paid a bill in over 40 years. Laura manages a bill paying service and runs the household finances. As he puts it, he is in charge of generating the money and she is in charge of distributing it. The host contrasts this with his own monthly money meetings with his wife, a discipline he picked up from a personal finance author friend.

    Money scars, the 500 dollar check, and giving with a warm hand

    Blankfein grew up in an East New York housing project, the son of a postal worker who had earlier lost a job, in a household where rent was scarce. He calls himself an urban hick who barely left Brooklyn as a kid. That scarcity left a mark that lasted into his 30s. He tells the story of buying a small beach house that cost more than all their savings, and of his wife driving 30 miles while failing to make the closing math work, until they realized she had forgotten to count the 10 percent down payment. The most resonant memory is a 500 dollar financial aid check handed to him as a freshman around 1971, made out on the spot by a clerk with a generosity of spirit that let him receive it without shame. That experience shaped a lifelong view that giving well means preserving dignity, and he now co chairs a financial aid campaign at his university. It also connects to his embrace of the idea of giving with your warm hand rather than your cold hand, giving while alive so you can feel the joy, the same spirit as the book Die With Zero. He is candid about a strange ambivalence, the way he can resent that his kids enjoy what he himself gave them.

    Robinhood, confetti, and the misses

    On apps like Robinhood, Blankfein takes a balanced view. Democratizing investing and making assets accessible is good in its own terms, and advertising can pull people toward markets they would otherwise ignore. But if you make trading too much like a video game, with confetti and high fives, you can mask the danger and lure people who cannot afford to lose into losing more than they can. He is equally frank about his own misses. He thought SpaceX was overpriced at a 100 billion dollar valuation, a figure now discussed near a trillion and three quarters. He passed on early cellular because he could not imagine why anyone would carry a bulky phone with payphones everywhere. His blunt summary is that he missed far more than he got, and that nobody is great at predicting the future.

    The obituary test, thick skin, and staying too long

    When Blankfein made partner, a senior partner assigned to acculturate new partners gave him rules of the road: avoid anything that would today be called misconduct, be rigorous and conservative on taxes, set up and actually use a charitable foundation, and keep enough balance that, if your obituary runs nine paragraphs, no more than three are about Goldman. Blankfein says he failed that last test by staying too long, even titling his memoir around the firm. He also reflects on having a thick skin, recalling unflattering press and concluding that he could take a punch, a trait not everyone has and one he did not know he possessed until he was tested. He is careful to say this does not make people who cannot take a punch bad, just differently wired.

    Why he reads history: it rhymes

    The final stretch is a love letter to reading history. Blankfein favors Barbara Tuchman, whose A Distant Mirror he has read twice and whose Guns of August he calls fantastic and influential, along with Robert Caro’s The Power Broker on Robert Moses, Ron Chernow’s biographies, Rick Atkinson’s Revolution series, and Stephen Ambrose’s Undaunted Courage. He describes rereading the Robert Moses book after 40 years of trying to get things done and finding his appreciation for the achievements rise, even as the flaws stayed the same, because he had changed. He ties history directly to markets through the Mark Twain line that history does not repeat but it rhymes. Patterns recur, every generation maximizes its own crises and minimizes resolved ones, and reading about the black plague, the McCarthy era, or the Vietnam draft is how he stays calm. His conclusion, echoing a sentiment often attributed to Buffett, is that he would not bet against America, a country he describes as mostly good and able to improve.

    Notable Quotes

    “I invest in risky assets. That’s what’s fun for me.”

    Lloyd Blankfein, describing his own portfolio, which he says is roughly 98 percent risky assets

    “It’s been good to be bullish on big tech, and I’ll stop being bullish on it when it stops going up.”

    Lloyd Blankfein, on why he stays concentrated in technology

    “I’m not at a computer. I don’t have a computer. I have an iPad.”

    Lloyd Blankfein, on how he day trades every day, alone and with no team

    “To me, the market is like music. It’s out there. It’s going on.”

    Lloyd Blankfein, on why trading daily feels like a hobby rather than work

    “Look, $5 billion if it all goes bad, that’s not even a bad hurricane on the East Coast.”

    Warren Buffett to Lloyd Blankfein, waving off the risk of his 2008 investment in Goldman Sachs

    “The difference between somebody who’s really, really good and somebody who can’t make it is not that great.”

    Lloyd Blankfein, on the thin margin between the best and the rest

    “You may think you’re protecting the world from the hundred-year storm, but you’re also going to forego the 99 years of in between when there was growth.”

    Lloyd Blankfein, on the cost of trying to legislate risk out of markets after 2008

    “I’m in charge of generating the money, and she’s in charge of distributing it.”

    Lloyd Blankfein, on his 40-plus-year marriage to Laura and why he has not paid a bill in decades

    “History doesn’t repeat, but to paraphrase Mark Twain, it rhymes.”

    Lloyd Blankfein, on why reading history keeps the present in proportion

    Watch the full conversation with Lloyd Blankfein on the My First Million podcast here.

    Related Reading

    • Lloyd Blankfein (Wikipedia) background on the former Goldman Sachs chairman and CEO whose investing views anchor the conversation.
    • My First Million podcast the show where this interview took place, for the full back catalog of investor and founder conversations.
    • Berkshire Hathaway primary source on Warren Buffett’s company, which made the roughly five billion dollar Goldman investment in 2008.
    • Vanguard S&P 500 ETF (VOO) the diversified index fund Blankfein names as the sensible core holding for a normal investor.
    • Die With Zero by Bill Perkins the book behind the give with your warm hand, not your cold hand philosophy discussed near the end.

  • 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

  • Bill Ackman on Investment Strategy, What the Market Is Missing, and How AI Breaks Businesses

    Bill Ackman, founder and CEO of Pershing Square, joined the All-In Podcast for a conversation about how his investment approach has shifted toward permanent, long-term ownership, why he believes the highest-quality companies are being left behind by a market chasing the new new thing, and how AI is raising the risk of disruption for almost every business. He also lays out his plan to turn Howard Hughes into a Berkshire Hathaway-style compounding machine built on insurance. You can watch the full conversation here. Below is a structured breakdown of the ideas, the stories, and the frameworks he uses to underwrite a business.

    TLDW

    Ackman explains how his philosophy evolved from a smaller, more liquid activist toward concentrated, permanent ownership of durable, non-disruptible businesses, with much of his activism now playing out on X rather than in the boardroom. He tells the origin story of his first big trade, Wendy’s and the Tim Hortons spin-off, and explains why a large long-term shareholder on a board is an antidote to short-term markets. On AI, he argues that this is the greatest era in history to build a company, which means the risk of being disrupted has gone up enormously, and that the market is mispricing high-quality compounders like Microsoft, Meta, and Amazon while crowding into chips, semiconductors, and energy. He works through the SaaS question and why niche software is more at risk than platforms, how he underwrites SpaceX, xAI, OpenAI, Anthropic, and Palantir like late-stage venture bets using a people, opportunity, context, deal framework, and why founder-led companies have an edge in making radical calls. The back half covers his Howard Hughes plan to copy Buffett’s insurance-float model, the role of cost of capital and reflexivity in markets, the meme-stock era, going direct on social media, and the three different ways an investor can put money to work with Pershing Square.

    Thoughts

    The most useful idea in the interview is the way Ackman reframes disruption as the central investing problem of the AI era. His point is that the same forces making this the best time in history to start a company, meaning near-unlimited compute, capital, and talent, also raise the odds that any given incumbent gets disrupted. That reframes the word quality. It is no longer mostly about margins and moats. It becomes about non-disruptibility, which is a much higher bar than most quality investors were using a decade ago, and it is why he says most of his research time now goes into assessing that single risk.

    The what-the-market-is-missing thesis is classic contrarian Ackman. Arguing that Microsoft, Meta, and Amazon are the new old-fashioned, undervalued names while capital piles into semiconductors and energy is a direct echo of 2000, when Berkshire Hathaway bottomed precisely because money was chasing internet stocks. It is worth keeping in mind that he owns all three, so the call is also his book. The durable signal here is the framework, not the specific tickers: capital reliably chases the new new thing, and genuinely high-quality businesses get left behind during those rotations.

    The Howard Hughes plan is the most concrete bet in the conversation. Copying Buffett’s insurance-float playbook, short-term treasuries for policyholder money and equities for the surplus, onto a discounted real-estate holding company is elegant. The hard part is exactly what Ackman flags about insurance as an industry: the best investors go to hedge funds, not insurers, so most insurance companies only ever manage the liability side well. Pershing Square’s edge is that Ackman can both write the business and invest the float, which is the same reason it worked for Buffett. The framing of going from a four billion dollar company to a trillion over fifty years is a statement of intent, not a forecast, and should be read that way.

    Underneath all of it sits cost of capital and reflexivity. His observation that a higher stock price literally makes a company more valuable, because it lowers the cost of capital and creates acquisition currency, is the mechanism behind both Elon Musk’s empire and the meme-stock era he is wary of. Going direct on X is the same lever pointed at himself: communicate the vision, lower your own cost of capital, and make the bet easier for other people to place. It is a coherent worldview in which narrative and balance sheet continuously feed each other, and it explains a lot of his behavior over the last few years.

    Key Takeaways

    • The biggest change in Ackman’s approach over time is an appreciation for business quality, meaning long-term, durable, protected, non-disruptible growth as the most important factor.
    • He says he is as activist as ever, but more of it now happens on X than in the traditional corporate context.
    • His first big investment was Wendy’s, which owned Tim Hortons. The simple thesis was to buy Wendy’s, spin off Tim Hortons, and double the money.
    • Early on no one returned his calls, so he had Steve Schwarzman’s Blackstone write a fairness opinion, filed it publicly, and the company spun off Tim Hortons six weeks later. The CEO later thanked him after being fired with a large exit package.
    • Reputation compounds. Where Pershing Square once had to bang down the door, companies now sometimes tweet a welcome when it buys a stake.
    • A large long-term shareholder on a board is a counterweight to short-term markets, letting management test ideas privately and pursue initiatives that hurt the next few quarters of earnings.
    • Pershing Square owns Microsoft, Meta, and Amazon. Ackman argues you are either invested in AI directly or indirectly, or it is a threat, so you have to understand it.
    • The hardest and most important job for a concentrated investor is judging the risk of disruption, and that risk has risen dramatically.
    • This is the greatest era in history to build a business because of near-unlimited access to compute, capital, and talent, which is exactly why the probability of being disrupted has gone up enormously.
    • Markets bring their eye to the new new thing, currently chips, semiconductors, and energy, while high-quality companies get left behind.
    • He draws an analogy to 2000, when Berkshire Hathaway traded at one of its lowest valuations because everyone chased internet stocks. He sees a similar dynamic around Amazon, Meta, and Microsoft today.
    • On the SaaS question, he worries more about a Salesforce than a platform like Microsoft, because niche software charging high per-seat or per-year prices is most exposed, while low-priced platforms are safer.
    • Any software company today has to be as AI-enabled as possible, or risk losing the monopolistic pricing it once enjoyed.
    • His famous March 2020 CNBC appearance was an attempt to reach President Trump and argue for a short shutdown, paired with the view that stocks were incredibly cheap and worth buying.
    • He describes valuation as a tether on the market: when prices stretch too high they snap back, and when they get too cheap the same rubber band pulls valuations up. Calling that out publicly can trigger a psychological reset.
    • His recent bullish call came because stocks of really high-quality companies had gotten crazy cheap on fundamentals, meaning the present value of the cash they generate.
    • He underwrites high-multiple names like SpaceX as venture investments using a framework from business school: people, opportunity, context, deal.
    • On SpaceX, people and opportunity are one of one, the context is incredible, and Starlink plus near-monopoly low-cost launch make it strategically valuable. The complicated part is the deal, meaning the valuation. He invested via an SPV after Ron Baron’s nudge, and also invested in xAI.
    • He treats OpenAI, Anthropic, and Palantir as late-stage venture bets that have proven they can generate real revenue, and says OpenAI should do a better job communicating how it thinks about its enormous capital commitments.
    • Every CEO in America is asking how to use AI, how it applies to their business, and how it is a threat. It is top of mind and boards open every meeting with it.
    • He has not seen much enterprise AI success yet, citing a McKinsey study that 95 percent of enterprise initiatives fail and the rise of the forward deployed engineer as the hot role bridging promise and ROI. Pershing Square itself uses AI mainly for legal, compliance, and back-office work.
    • Founder-led companies have an advantage because founders have the authority and the economic stake to make radical calls, while the average S&P 500 CEO has a roughly three to four year tenure and is incentivized not to make mistakes.
    • He cites Mark Zuckerberg buying Instagram and WhatsApp as the kind of shocking-at-the-time calls that a founder with a track record can make.
    • Ben Graham’s enduring lesson is that a stock is an interest in a business, not a piece of paper, but Graham mostly invested in liquidations and cash-rich shells, and made most of his money on Geico.
    • Most of Buffett’s value at Berkshire came from owning insurance operations and focusing on the asset side of the balance sheet, not just the liability side.
    • Insurance is hard to copy because top investors do not go to work for insurers. Buffett owned half his company and was a great investor, which is why it worked.
    • Howard Hughes came out of the General Growth bankruptcy and owns master-planned cities like Summerlin, with 26,000 acres in the Las Vegas area, comparable to the Irvine Company that built roughly a hundred billion dollars of wealth for Donald Bren.
    • The plan is to reinvest the cash Howard Hughes generates into insurance, put policyholder float in short-term treasuries and the surplus in common stocks, and build a compounding machine over fifty years, buying it at roughly sixty cents on the dollar.
    • A company must earn a return above its cost of capital for the stock to rise. Elon Musk has kept his companies’ cost of capital extremely low, and a SpaceX IPO near a 1.75 trillion dollar valuation could be one of the lowest cost of equity capital transactions ever.
    • Markets have changed less because of Ackman and more because of figures like Ryan Cohen and GameStop, where a stock can trade well above its value on personality and an army of followers.
    • Higher valuations are reflexive: a rising stock price lowers cost of capital and creates currency to issue stock and acquire businesses, which is part of how Elon built Tesla.
    • There are three ways to invest with Pershing Square: the management company itself (a royalty on compounding assets with no capex), PSUS (a portfolio of best ideas trading at an 18 percent discount), and Howard Hughes (a bet on building the next Berkshire). A dollar invested 22 years ago became roughly 27 to 28 times net of fees.
    • Going direct on X, with 2.2 million followers, lets him communicate his vision and lower the friction for others to back his bets, even as his very long tweets have become a running meme.

    Detailed Summary

    From activist trades to permanent capital

    Ackman frames the evolution of his career as a steady move toward business quality. As a smaller, more liquid investor early on, he did not have to think as long-term. As Pershing Square became a bigger, more concentrated investor, durable growth became the dominant factor in every decision. He insists he is still as activist as ever, but a lot of that energy has shifted to X, where he can argue a position publicly rather than only inside a boardroom. The best investments, he notes, are the ones where you do not need to join the board and do anything at all.

    The Wendy’s and Tim Hortons origin story

    One of Pershing Square’s first investments was Wendy’s, which owned the Canadian coffee and donut chain Tim Hortons. The value of Tim Hortons alone was greater than the entire value of Wendy’s, so the idea was simple: buy Wendy’s, spin off Tim Hortons, and double the money. Ackman bought ten percent of the company and could not get the CEO to return a single call, so he had a contact at Blackstone, with Steve Schwarzman’s sign-off, write a fairness opinion on what Wendy’s would be worth after a spin-off, filed it publicly, and watched the spin-off happen six weeks later. The CEO eventually called back to thank him, having been fired but rewarded with a large exit package. Over the years that scrappy approach gave way to a reputation that now opens doors on its own.

    Why a long-term shareholder on the board matters

    The core problem of being a public company, in Ackman’s telling, is the short-term nature of markets and analysts, when a good business should be run in the context of years and even decades. A large, supportive shareholder on the board gives management a place to test ideas before exposing them to the public and a credible voice willing to back initiatives that hurt earnings for a few quarters. That is the value-add he believes a constructive activist can bring to a mature public company, as opposed to a startup where the best outcome is simply to own a great business and stay out of the way.

    AI and the rising risk of disruption

    For a concentrated, long-term investor, the most challenging task is judging the risk that two people from Stanford in a garage build something that destroys your thesis. Ackman argues that risk has climbed dramatically because this is the greatest era in history to build a company, with near-unlimited access to compute, capital, and talent. The paradox is that the conditions that make building easier also make incumbents more fragile, so the bulk of his research now centers on assessing how disruptible a business really is.

    What the market is missing

    Investors bring their attention to the new new thing, currently chips, semiconductors, and energy, which leaves high-quality companies behind. Ackman compares the moment to 2000, when Berkshire Hathaway traded at one of its lowest valuations ever because capital was chasing internet stocks. He sees an echo today in how Amazon, Meta, and Microsoft are treated as old-fashioned, and he considers them undervalued on fundamentals, where value is the present value of the cash a business generates over its life. His recent bullish call, like his March 2020 appearance, came because stocks of really high-quality companies had simply gotten too cheap.

    The SaaS question and AI-enabled software

    On the so-called SaaS apocalypse, Ackman says it is a company-by-company analysis. He worries more about something like Salesforce than about a low-priced platform. The companies most at risk are those that extracted near-monopolistic profits by charging a high annual price for a niche product, because AI lowers the barrier to replicating that functionality. A platform where the average customer pays a small amount per seat, like Microsoft, is far less exposed. The takeaway for any software company is to become as AI-enabled as it possibly can.

    Underwriting SpaceX, xAI, and the AI labs like venture

    For the highest-multiple private companies, Ackman uses a venture lens and a framework a business school professor taught him: people, opportunity, context, deal. SpaceX scores as one of one on people and opportunity, with an incredible context and a near-monopoly in low-cost launch through Starlink, which makes even Amazon a likely customer. The complicated variable is the deal, meaning the valuation, and he admits he has not done all the math, having invested through an SPV after Ron Baron encouraged him, along with a position in xAI. He treats OpenAI, Anthropic, and Palantir as late-stage venture bets that have proven real revenue, and argues OpenAI in particular should communicate more clearly how it justifies capital commitments that vastly exceed current revenue.

    Founder-led companies and the authority to act

    Ackman agrees that founder-led companies have a structural advantage in a fast-changing environment. The average S&P 500 CEO has a tenure of roughly three to four years, a small economic stake, and an incentive not to make a career-ending mistake. A founder is betting an entire life and reputation, has the authority of a major voting and economic position, and has usually made several hard, contrarian calls that turned out right. He points to Mark Zuckerberg’s acquisitions of Instagram and WhatsApp, which looked shocking at the time, as exactly the kind of decision a founder with a track record can make and a hired manager often cannot.

    Howard Hughes as Berkshire Hathaway 2.0

    Ackman points to a detailed financial history of Berkshire Hathaway showing that the vast majority of Buffett’s value creation came from owning insurance and focusing on the asset side of the balance sheet, not just the liability side. Insurance is hard to replicate because skilled investors join hedge funds rather than insurers, but Buffett owned half his company and was a great investor. Pershing Square is applying the same idea to Howard Hughes, a company created out of the General Growth bankruptcy that owns master-planned cities such as Summerlin, with 26,000 acres around Las Vegas, in the spirit of the Irvine Company that made Donald Bren roughly a hundred billion dollars. The plan is to reinvest the company’s cash into insurance, place policyholder float in short-term treasuries and the surplus in common stocks, avoid issuing stock the way Buffett did, and compound for fifty years, all bought at around sixty cents on the dollar.

    Cost of capital, reflexivity, and going direct

    A company only creates value when it earns above its cost of capital, which is why Howard Hughes, seen as a high-cost-of-capital real-estate business, has long traded at a discount, and why Ackman is repurposing its assets into a higher-returning model. He highlights how reflexive markets are: a higher stock price itself makes a company more valuable by lowering its cost of capital and creating currency to raise money and acquire businesses, a lever Elon Musk used to build Tesla. He attributes real market change less to himself and more to figures like Ryan Cohen and GameStop, where personality and a following can lift a stock far above its value. His own going-direct strategy on X, with 2.2 million followers and famously long posts, is the same mechanism applied to communicating a vision and lowering friction for investors. He closes by laying out three ways to invest with Pershing Square: the management company as a royalty on compounding assets, the PSUS portfolio trading at an 18 percent discount, and Howard Hughes as a bet on building the next Berkshire.

    Notable Quotes

    “The best investments are one where you don’t need to join the board and do anything.”

    Bill Ackman, on the kind of business he most wants to own

    “The probability of your being disrupted has gone up enormously.”

    Bill Ackman, on why assessing disruption risk now dominates his research

    “Valuation is like a tether on the market, right? When it gets too high, it’s like this rubber band that’s stretching and inevitably it bounces back.”

    Bill Ackman, on how prices revert at both extremes

    “People, opportunity, context, deal.”

    Bill Ackman, on the business school framework he uses to underwrite companies like SpaceX

    “Every CEO in America today is like, how do I use AI?”

    Bill Ackman, on AI as the top opportunity and threat in every boardroom

    “A closed mouth gathers no foot.”

    Bill Ackman, quoting the line a friend put next to his name in his high school yearbook

    “The increase in value of the company increases the value of the company, right? Because it lowers the cost of capital, it gives you more flexibility, gives you the ability to issue stock, raise capital, acquire other businesses.”

    Bill Ackman, on the reflexivity between stock price and corporate value

    “The company’s got like a $4 billion market cap and the goal is to build it into a trillion dollar thing over time compounding.”

    Bill Ackman, on his fifty-year plan for Howard Hughes

    Taken together, the conversation is a tour of how Ackman now thinks about quality, disruption, and compounding, and a preview of the Berkshire-style machine he wants to build out of Howard Hughes. Watch the full conversation here.

    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.

    Related Reading

  • Charles Koch and Chase Koch on Koch Industries: 130K Employees, 60 Countries, and a $150B Private Empire Built on Principle-Based Management

    Charles Koch and his son Chase Koch sat down with David Friedberg for a long, candid Forbes/All-In conversation about how a small crude-oil gathering operation in southern Oklahoma became Koch Industries, a privately held company with more than 130,000 employees across 60 countries and revenue that would land it comfortably in the top 25 of the Fortune 500 if it were public. They walked through the founding story, the management principles that drove a 9,000x increase in value since the early 1960s, the failures that almost wiped out the company, and the philanthropic and political work being done through Stand Together. Watch the full conversation on YouTube.

    TLDW

    Charles Koch took over a roughly 300-person family business in 1961 at age 25, fired the bureaucratic president, and built it into one of the most profitable private companies in the world by applying what he calls Principle-Based Management. The core insight is to be capability bounded rather than industry bounded, to run an internal “republic of science” that rewards contribution over credentials, and to treat failure as the price of experimental discovery. Koch grew through both organic capability extension and large acquisitions like Georgia Pacific in 2005 and Molex in 2013, mostly by replacing top-down hierarchies with bottom-up empowerment. The conversation covers the founding by Fred Koch, the near-death failures of the late 1990s “gas to bread spread,” the Pine Bend Minnesota refinery turnaround, the role of Wichita as a competitive advantage, Chase Koch’s path from feed-yard laborer to leader of Koch Disruptive Technologies, the launch of Stand Together as a long-running social-change platform, the rejection of single-party politics, the case against entitlements and occupational licensing, and the principles for using AI as a permissionless empowerment tool rather than a top-down control system. The throughline is Viktor Frankl: more people have the means to live and less meaning to live for, and the remedy is helping every individual find a gift and apply it in a way that creates value for others.

    Key Takeaways

    • Koch Industries today has more than 130,000 employees across 60 countries and has increased in value roughly 9,000 times since Charles took over in the early 1960s, when headcount was about 300.
    • Founded in 1940 by Fred Koch in Wichita, Kansas. The two starting businesses were designing fractionating trays (separating liquids by boiling point) and crude oil gathering in Oklahoma.
    • Charles got three engineering degrees at MIT, worked at Arthur D. Little, and reluctantly came back at 25 only after his father said he would otherwise sell the company. His father gave him full autonomy over every decision except selling.
    • His first move was firing the controlling, memo-driven president and replacing protectionism with three pillars: create value for customers, empower employees, and own end-to-end execution. They built their own plant in Italy instead of stitching together European subcontractors.
    • The defining mental model is “capability bounded, not industry bounded.” You expand into adjacent industries where the capabilities you have already proven (operations, logistics, trading, refining, branding) create more value than incumbents, not because the new industry is in the same SIC code.
    • Wholly owned business platforms today include engineered projects and construction, solar plants, commodity trading and distribution, fertilizers, refined products, chemicals and polymers, glass, forest and consumer products, electrical products (Molex), and management software, plus four distinct investment firms.
    • Koch is explicitly not a Berkshire-style conglomerate of independent silos. Chase frames it as an integrated republic of science, an integrated set of capabilities that share knowledge and people across business lines.
    • “If you are not failing at anything, you are not doing anything new.” Failure is treated as the cost of experimental discovery, but only when the learning value exceeds the cost.
    • The worst failures came from violating the hiring rule. Hire on values first, talent second. People with destructive motivation (power and control over contribution) hide failures and invent successes, and the damage compounds when those people get promoted into leadership.
    • The 1973 trading blowup nearly bankrupted the company. The late 1990s “gas to bread spread” strategy, an attempt to vertically integrate from natural gas through fertilizer to pizza crust, nearly wiped out all of Koch’s earnings. Lesson repeated, then internalized.
    • One acquisition shipped hundreds of millions of dollars in out-of-the-money hog feed contracts that nobody bothered to read before closing. Apply the scientific method: try as hard to disprove your hypothesis as to prove it.
    • Georgia Pacific was acquired in 2005 for roughly $20 billion when Koch was much smaller. They originally tried to buy only the commodity pulp piece so GP could re-rate as a pure consumer-products company at a higher P/E. When legal blockers killed that path, they bought the whole thing.
    • The Georgia Pacific culture change started with sending Joe Moeller in as CEO. He gutted the 51st-floor coat-and-tie executive suite, fired the most bureaucratic managers, moved everyone to working floors, and converted the executive floor into open meeting rooms. Signals like that drive culture more than memos do.
    • The Pine Bend, Minnesota refinery, bought in 1969, was one of the hardest cultural turnarounds. The union strike was violent (rifles fired, switch engines used to ram units), Charles ran it nine months without union labor on his honeymoon, the work rules finally changed, and once empowered, the workforce built its own machine shop, cut spare-part costs, and grew capacity tenfold. It is now one of the best refineries in the country.
    • Molex, bought in 2013, took years to transform. The dominant paradigm was top-line growth rather than bottom-line value creation, partly because it had been public for 30 years and the market rewarded the wrong things. Almost every successful turnaround required swapping in leadership with a bottom-up empowerment paradigm.
    • Sheep-dipping does not work. Pushing 130,000 people through the same seminar will not rewire habits. Coaching one struggling team until it succeeds creates social mimicry. Other teams ask to be next. Demand for Principle-Based Management coaches now exceeds supply inside the company.
    • The talent doctrine is values first, skills second, credentials last. Wichita and the farm-team labor pool are deliberate competitive advantages because farm kids tend to show up contribution-motivated rather than entitlement-motivated.
    • The current Koch CIO, Jared Benson, joined as a contractor striping lines in the parking lot and has no college degree. He learned data science, built the cyber-security capability, and ran circles around credentialed peers.
    • Public-company pressure to IPO was the biggest external threat. Charles refused. Staying private was the only way to keep reinvesting roughly 90 percent of profits, to maintain the capability-bounded model that no analyst would underwrite, and to keep accepting low P/E optics on commodity businesses inside the portfolio.
    • Three things any lasting partnership requires (marriage, business, employment): shared vision, shared values, and complementary capabilities. Miss any one and it does not last.
    • Chase Koch started at age 15 throwing tennis matches to escape practice, got shipped to a feed yard the next morning, shared a single-wide trailer with his boss, shoveled manure, and discovered the “glorious feeling of accomplishment” that his grandfather Fred had written about in his famous letter to the next generation.
    • At one point Chase was promoted to president of Koch Fertilizer, realized after nine months he was a builder and not an optimization operator, walked into his boss’s office, and fired himself. The role went to someone with the right comparative advantage and the business grew faster. Chase went on to launch Koch Disruptive Technologies (KDT).
    • KDT would have been shut down on a normal three-to-four-year venture timeline. Koch kept investing through the losses because of two principles: experimental discovery and creative destruction. They also valued the knowledge inflow about disruptive technologies that might one day eat the core business.
    • Comparative advantage applies to careers. The job of 20,000 plus Koch supervisors is to keep moving people into roles where they can actually contribute. Beating people up in the wrong seat is destructive.
    • Viktor Frankl frames the moral problem of the era: ever more people have the means to live and no meaning to live for. Without meaning, people default to either power or pleasure. Both lead, at scale, to totalitarianism, authoritarianism, or socialism.
    • Charles credits Maslow’s Eupsychian Management, Polanyi’s Personal Knowledge, Hayek’s price-signal work, and Frankl’s logotherapy as the intellectual foundations of Principle-Based Management. The five dimensions: vision, virtue and talents, knowledge processes, decision rights, and incentives.
    • Stand Together, founded in 2003, is a community of close to a thousand business leaders pooling effort on social change rather than working in philanthropic silos. The thesis: every human has a gift and the institutions are putting up barriers (broken schools, broken criminal justice, bad policy, occupational licensing).
    • Education is one of Stand Together’s biggest fronts. Pre-COVID, around 20 percent of families were open to a new model. Post-COVID, it is 70 to 80 percent. They back Alpha School (Joe Liemandt), Khan Academy (Sal Khan), and the VELA Education Fund alongside the Walton family. Roughly 5,000 micro-schools have been seeded.
    • The model for social change mirrors the business model: bet on the person closest to the problem who already shows results. Scott Strode and The Phoenix gym went from a couple of Colorado locations to one million people overcoming addiction, with relapse rates under 10 percent, by combining community and exercise rather than top-down treatment programs.
    • Charles says the biggest mistake of the first 50 years was trying to drive social change through a single political party, first the Libertarians and later just the Republicans. The current rule, from Frederick Douglass, is “I will unite with anybody to do right and with nobody to do wrong.”
    • His policy critique cuts in every direction: occupational licensing locks out newcomers, the treatment of working illegal immigrants is wrong, tariffs undermine division of labor by comparative advantage and raise prices, and entitlements once created are nearly impossible to dismantle.
    • Asked whether capitalism inevitably compounds into monopoly, Charles answers that the fix is removing barriers to others realizing their potential, not capping the winners.
    • On AI: the principle is permissionless innovation. Cost is collapsing, access is widening, and the right use is empowering individuals to learn 1000x faster, not concentrating power.
    • Koch backs Cosmos and other AI efforts that apply market-based management principles. Internally, they launched an AI app called Principal Companion that uses the Socratic method to walk users through problems using the book’s principles, from business to parenting.
    • Writing the new book (Charles’s fifth, Chase’s first) was the most important project Chase has worked on. They went through 27 versions of the stewardship chapter. Charles still corrects Koch leaders who say “the proof is in the pudding” instead of “the proof of the pudding is in the eating.”
    • When asked about legacy, Charles answered in one sentence: he wants the country to more fully live up to the promise in the Declaration of Independence.

    Detailed Summary

    From 300 Employees to 130,000 Across 60 Countries

    Koch Industries was founded in 1940 by Fred Koch in Wichita, Kansas. When Charles took over full-time in 1961, the company had about 300 employees and two main businesses: designing fractionating trays for separating liquids by boiling point, and a crude oil gathering system in Oklahoma. Today the company has more than 130,000 employees in 60 countries and has grown in value roughly 9,000 times over that period. If Koch were public, revenue would put it easily in the top 25 of the Fortune 500. The portfolio spans engineered projects and construction, solar plants, commodity trading and distribution, fertilizers, refined products, chemicals and polymers, glass, forest and consumer products, electrical products through Molex, management software, and four distinct investment vehicles. Roughly 90 percent of profits are reinvested.

    Charles Coming In at 25

    Charles describes himself as a poor engineer who happened to be good at math, science, and theory and bad at making or operating things. After three MIT degrees and a stint at Arthur D. Little doing what he calls “absurd” management consulting at 25, his father called and said the company was struggling and his health was failing. Either Charles came back or it would be sold. He came back. The condition was full autonomy: Charles could run it any way he wanted, the only decision requiring approval was selling. Within a short time he fired the previous president, a top-down memo-writer obsessed with controlling spending, and rewrote the operating philosophy around three things: create value for customers, empower employees, and own the value chain end to end. Instead of farming European fractionating trays out to multiple subcontractors and then re-assembling, Koch built its own plant in Italy.

    Capability Bounded, Not Industry Bounded

    This is the single most important strategic idea in the interview. Conventional advice told Koch to become an integrated oil major because they were in crude oil gathering. Charles rejected that and ran on Hayek and Adam Smith instead: division of labor by comparative advantage. Be in the part of any value chain where you can create more value than anyone else. From crude oil gathering, Koch leveraged operations, logistics, and trading into pipelines, refineries, natural gas, chemicals, fertilizers. Georgia Pacific looked like a non sequitur, wood products, but the underlying capability set transferred, and the acquisition also added branding as a new capability that fed back into the system. Chase calls the result not a Berkshire-style conglomerate of independent businesses but a republic of science: an integrated set of capabilities that share talent, knowledge, and laboratories.

    The Failures That Almost Killed the Company

    Charles spends a long stretch on failures, because he says the strength is in them. The 1973 trading blowup tied to the Middle East war could have bankrupted the company. The late 1990s “gas to bread spread” was an attempt to control the entire chain from natural gas to nitrogen fertilizer to grain to pizza crust. It violated almost every principle in the book at once and wiped out most of Koch Industries earnings for the decade. One acquisition closed before anyone read the hog-feed contracts, and on closing day they discovered hundreds of millions of dollars of out-of-the-money positions. Every failure traced back to two violations: hiring leaders with destructive motivation (power and control instead of contribution), and skipping the scientific method (trying to prove a hypothesis instead of disprove it). Charles says “repetition penetrates even the dullest of minds,” and he had to be punished enough times before the lesson took.

    Georgia Pacific, Molex, and the Pine Bend Refinery

    Three acquisition stories show how Koch transfers culture into businesses ten times larger than the corporate playbook would normally allow. Georgia Pacific in 2005 was a $20 billion bet on a company much larger than Koch at the time. Joe Moeller, sent in as CEO, immediately fired the most bureaucratic managers, gutted the 51st-floor private-elevator executive suite (coat and tie required to visit), moved everyone to working floors, and turned the old executive floor into open meeting rooms. Molex, bought in 2013, had been public for 30 years and ran on top-line growth thinking because that is what the market rewarded. Changing the paradigm to bottom-up empowerment and bottom-line value creation took years and required new leadership. Pine Bend, Minnesota, bought in 1969, was the hardest. The union ran the refinery, ignored work rules, and went on a violent strike when Koch tried to change them, firing rifles and ramming switch engines into units. Charles ran the refinery nine months without union labor (during his honeymoon), eventually got the work rules changed, then spent years rebuilding the culture. The empowered workforce designed and built its own machine shop, cut spare-part costs, and grew capacity tenfold. Pine Bend is now one of the best refineries in the country.

    How Principle-Based Management Actually Diffuses

    Charles is blunt that they tried “sheep dipping” first, hauling everyone through a seminar. It did not work, because changing a habit means rewiring the brain through work at intensity over time, the way a weightlifter has to retrain to become a marathoner. The model that did work was small. Find one team that is struggling, coach them with principles, let them succeed, and the rest of the company asks to be next. Social mimicry replaces top-down rollout. Internally the Principle-Based Management group is now in higher demand than any other function.

    Talent: Values First, Skills Second, Credentials Last

    Koch deliberately stayed in Wichita partly to access a “farm team” labor pool of people who grew up contribution-motivated. Chase tells the story of Jared Benson, who started as a contractor striping lines in the Koch parking lot, taught himself data science, built the company’s cyber-security capability, and is now CIO with no college degree. The lesson runs against the prestige-school default of most large companies. Contribution motivation, not credentials, predicts long-run output, and Charles is willing to “hire slow and stupid” for anyone with bad values so the company can flush them quickly. Aligning incentives matters as much as hiring: reward people on overall long-run contribution to Koch’s future, including the value of what was learned from a failed experiment, not on near-term P&L.

    Why Koch Stayed Private

    Multiple parties pushed hard for an IPO over the decades. Charles refused. Going public would have made the capability-bounded model impossible to communicate to analysts, would have forced a higher payout ratio and broken the reinvestment compounding, and would have introduced the short-termism that wrecks bottom-up empowerment. Buffett gets credit, but Berkshire does not try to integrate its businesses the way Koch does. Asked whether a non-owner public CEO could ever apply the principles, Charles allows it is possible if they can sell a different durable story (as Buffett did), but it is much harder.

    Chase Koch’s Path

    Chase tells two formative stories. The first is being shipped to a feed yard at 15, sharing a single-wide trailer with his boss, shoveling manure for minimum wage, and finding, for the first time, what his grandfather Fred had called “the glorious feeling of accomplishment.” The second is firing himself as president of Koch Fertilizer after nine months because he realized he was a builder, not an operator. The business outgrew where he would have taken it, and he went on to launch Koch Disruptive Technologies, the venture and innovation arm that now feeds technological insight back into every Koch business line. The comparative-advantage principle applied to a career, in public, by the boss’s son.

    Stand Together and Social Change

    Stand Together, founded in 2003, is the Koch family’s social-change platform. It now includes close to a thousand aligned business leaders. The animating belief is that every human has a gift and institutional barriers (broken schools, broken criminal justice, occupational licensing, bad policy) prevent most people from finding and applying it. The Phoenix gym founded by Scott Strode is the canonical Stand Together bet: a person closest to the problem, with results (relapse rates under 10 percent), funded to scale. In seven or eight years it has gone from a couple of Colorado locations to one million people. On education, post-COVID openness to new models jumped from roughly 20 percent of families to 70 to 80 percent. Stand Together backs Alpha School, Khan Academy, and the VELA Education Fund alongside the Walton family, and has helped seed roughly 5,000 micro-schools.

    Politics: The Single-Party Mistake

    Charles says for the first 50 of his 60 years in this work he avoided major-party politics, then concluded the country needed principle-based policies badly enough that engagement was required. The mistake was trying to do it through one party. The Libertarian Party turned into purity tests reminiscent of the early Communist Party. Doing it through Republicans blew up too. The rule going forward is Frederick Douglass’s: unite with anybody to do right and with nobody to do wrong. He is openly critical of both parties on occupational licensing, immigration policy, tariffs, entitlements, and the treatment of working illegal immigrants. He invokes Jefferson on slavery to describe his current mood: “If God is just, I despair for the future of our country.”

    Capitalism, Compounding, and AI

    Asked whether capitalism inevitably ends in monopoly because successful operators compound, Charles flips the framing. The remedy is not to cap the winners, it is to remove the barriers preventing everyone else from realizing their potential. Occupational licensing, immigration restriction on contributors, tariffs that undermine comparative advantage. On AI, Koch’s principle is permissionless innovation: cost is collapsing, access is widening, and the right outcome is individual empowerment and 1000x faster learning, not power concentration. Internally they launched Principal Companion, an AI app built on the principles in the book that uses the Socratic method to walk users through problems rather than handing out answers. Koch backs Cosmos and other AI ventures applying market-based management.

    The Philosophical Spine

    Charles cites four foundational thinkers. Polanyi’s Personal Knowledge gave him the model for how habits encode knowledge in the brain and why retraining is bodily work. Maslow’s Eupsychian Management supplied the empirical link between self-actualization and organizational performance. Hayek supplied the price system and the case against central planning. Frankl supplied the diagnosis: more means to live, less meaning to live for, and in that vacuum people drift to either power or pleasure, both paths to the slippery slope of authoritarianism and socialism. The Principle-Based Management answer is to design the company (and the country) so that everyone can find a gift and apply it to help others succeed.

    Thoughts

    The most useful concept in the conversation, the one worth stealing for any operator regardless of industry, is “capability bounded, not industry bounded.” Most companies define their addressable market by SIC code or competitive set. Koch defines it by the actual transferable skills they have demonstrated: operations, logistics, trading, refining, branding, cyber-security. Each acquisition is a probe to see whether the capability set creates more value than incumbents, and each acquisition that works hands back new capabilities (branding from Georgia Pacific, electronic-components engineering from Molex) that compound the option space. This is the same logic that makes Amazon’s AWS, advertising, and logistics businesses adjacent rather than diversifications. Industry conglomerates collapse. Capability conglomerates do not, because the capabilities reinforce each other.

    The honest treatment of failure is rarer than it sounds. Most CEOs who say “we celebrate failure” mean something performative. Charles’s version has teeth because the failures he names (the 1973 trade, the late 1990s vertical-integration push, the unread hog contracts) were almost terminal, and the lesson he draws is not “fail fast” but a specific causal claim about hiring leaders with destructive motivation. The asymmetry between contribution-motivated and destructively motivated employees, with the latter capable of hiding losses and inventing successes until the damage compounds, is the kind of insight that only comes from forty years of post-mortems. The remedy, hire slow and dumb if values are bad so you can purge fast, is uncomfortable enough to be real advice.

    The case for staying private is also harder than the founder-flex version usually heard from private operators. Charles is not arguing that private is better for everyone. He is arguing that a specific operating model (high reinvestment, cross-business capability sharing, willingness to take long P/E hits on commodity legs, leadership succession over decades) cannot be communicated to public markets without distortion. If you do not run that model, going public is fine. If you do, going public would have killed the system. That distinction is worth holding on to when reading the founder-control discourse in tech, because most “stay private forever” arguments do not actually meet that bar.

    The political reflection is the most surprising part of the conversation, particularly given the public reputation. Charles plainly says the biggest mistake of his life in social change was trying to do it through one party, that the Libertarians collapsed into purity-test factionalism, that the Republican approach failed in similar ways, and that the current operating rule is the one Frederick Douglass actually wrote down. He criticizes the current administration’s treatment of working illegal immigrants and the tariff regime by name. Whether one agrees or disagrees on policy, the willingness to grade your own past work in public, decades after the bets were placed, is rare at this level.

    Finally, the Frankl framing deserves a longer hearing than a podcast can give it. “Ever more people have the means to live and no meaning to live for” is the most economical statement of the malaise running through politics, addiction, education, and labor data right now. Koch’s bet is that the answer is not policy alone but a design problem: build institutions (companies, schools, philanthropies, AI tools) that let each individual find a gift and apply it in a way that creates value for others. That is the through-line connecting Principle-Based Management, Stand Together, the Alpha School partnership, The Phoenix gym, and Principal Companion. Whether it scales is an open question. The fact that one family business has spent 60 years pressure-testing it makes the experiment worth paying attention to.

    Watch the full Charles Koch and Chase Koch conversation on All-In and Forbes.

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

    TLDW

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

    Key Takeaways

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

    Detailed Summary

    From Queens to Wharton: The Accidental Investor

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

    Mujo, Independence of Events, and Why You Cannot Predict

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

    Why Cycles Exist

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

    Where Are We Now?

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

    Drifting Versus Seeing the River

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

    The Bond Decision

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

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

    Distressed Debt and Excess Return

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

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

    Beginning a Career Today: The AI Question

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

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

    Why Indexation Won

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

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

    Second-Level Thinking and Why You Cannot Coach It

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

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

    The $10 Bill That Nobody Has Picked Up

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

    Learning From Andrew, Buffett, and Onion-Skin Manuals

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

    India

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

    History Rhymes, and Final Advice

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

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

    Thoughts

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

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

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

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

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

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

  • Warren Buffett’s Final Thanksgiving Letter: A Historic Farewell from the Oracle of Omaha

    Warren Buffett’s Final Thanksgiving Letter: A Historic Farewell from the Oracle of Omaha

    On November 10, 2025, Berkshire Hathaway released an 8-page document that instantly became one of the most important shareholder letters in the history of American capitalism.

    This is not just another annual report update. This is Warren Buffett’s official retirement announcement at age 95, his last direct message to shareholders, and the clearest blueprint yet for the future of his $1 trillion empire and his remaining $150+ billion fortune.

    In one sweeping move, Buffett converted 1,800 Class A shares into 2.7 million Class B shares and donated them immediately — the largest single-day charitable gift in Berkshire history:

    • 1.5 million B shares → The Susan Thompson Buffett Foundation
    • 400,000 B shares each → The Sherwood Foundation, Howard G. Buffett Foundation, and NoVo Foundation

    That’s over $13 billion at today’s prices, delivered the same day.

    The End of an Era

    In his trademark folksy style, Buffett declares: “I will no longer be writing Berkshire’s annual report or talking endlessly at the annual meeting. As the British would say, I’m ‘going quiet.’ Sort of.”

    He confirms what insiders have known for years: Greg Abel takes over as CEO at year-end 2025. Buffett’s praise is unequivocal: “I can’t think of a CEO, a management consultant, an academic, a member of government — you name it — that I would select over Greg to handle your savings and mine.”

    The Most Personal Letter Ever Written by a Billionaire

    Unlike any previous letter, this one is deeply autobiographical. Buffett recounts:

    • Nearly dying at age 8 from a burst appendix in 1938
    • Fingerprinting Catholic nuns during recovery (and fantasizing about helping J. Edgar Hoover catch a “criminal nun”)
    • Missing Charlie Munger by a whisker — Munger worked at Buffett’s grandfather’s grocery store in 1940; Warren took the same $2-for-10-hours job in 1941
    • Living one block away from Munger, six blocks from future Berkshire legends, and across the street from Coca-Cola president Don Keough — all without knowing it

    His conclusion? “Can it be that there is some magic ingredient in Omaha’s water?”

    Lady Luck, Father Time, and the Acceleration of Giving

    At 95, Buffett is blunt about aging: “Father Time, to the contrary, now finds me more interesting as I age. And he is undefeated.”

    He acknowledges his children (Susie, Howie, and Peter — ages 72, 70, and 67) are entering the zone where “the honeymoon period will not last forever.” To avoid the chaos of post-mortem estate battles, he is accelerating lifetime gifts at warp speed while keeping enough A shares to ease the transition to Greg Abel.

    Most powerful line on wealth and luck:

    “I was born in 1930 healthy, reasonably intelligent, white, male and in America. Wow! Thank you, Lady Luck.”

    Warnings to Corporate America

    Buffett eviscerates CEO pay inflation, dementia in the C-suite, and dynastic wealth. Highlights:

    • CEO pay-disclosure rules “produced envy, not moderation”
    • Boards must fire CEOs who develop dementia — he and Munger failed to act several times
    • Berkshire will never tolerate “look-at-me rich” or dynastic CEOs

    Why This Document Will Be Studied for Centuries

    This letter is the capitalist equivalent of a papal encyclical. It combines:

    • A formal leadership handoff after 60 years
    • The largest ongoing wealth transfer in history
    • A philosophical treatise on luck, aging, kindness, and corporate governance
    • A love letter to Omaha and middle America
    • Buffett’s final ethical will: “Decide what you would like your obituary to say and live the life to deserve it.”

    Business schools will teach this. Biographers will mine it. Investors will quote it for decades.

    Download the full PDF here: Warren Buffett Thanksgiving Letter 2025 (PDF)

    As Buffett signs off:

    “I wish all who read this a very happy Thanksgiving. Yes, even the jerks; it’s never too late to change.”

    The Oracle has spoken — one last time. And the world is listening.

  • The Dhandho Investor: A Low-Risk Path to High Returns

    The Dhandho Investor: A Low-Risk Path to High Returns

    Mohnish Pabrai’s The Dhandho Investor offers a compelling and practical framework for building wealth through low-risk, high-return investments. Inspired by the entrepreneurial spirit of the Patel community and the investment wisdom of Warren Buffett and Charlie Munger, Pabrai distills principles that challenge traditional notions of risk and return. Here’s an in-depth look at the Dhandho philosophy and its application.

    The Dhandho Philosophy

    The Gujarati term “Dhandho” translates to “business” and signifies endeavors that create wealth with minimal risk. Pabrai flips the traditional idea that high returns require high risk. Instead, the Dhandho framework focuses on reducing downside risk while maximizing upside potential. It is a disciplined, pragmatic approach to investing and entrepreneurship.

    Nine Core Principles of the Dhandho Framework

    1. Buy Existing Businesses
      Avoid the risks of startups by acquiring or investing in established businesses with a proven track record and stable cash flows. In public markets, you can own fractions of such businesses without running them yourself.
    2. Invest in Simple, Predictable Businesses
      Simple businesses are easier to understand and analyze. Focus on industries with enduring demand and slow change, such as motels, consumer goods, or basic services.
    3. Target Distressed Businesses or Industries
      Look for businesses experiencing temporary setbacks or industries undergoing downturns. Distressed assets often sell at a significant discount, creating opportunities for outsized returns.
    4. Seek Durable Competitive Advantages (Moats)
      Invest in companies with lasting advantages, such as brand strength, cost leadership, or regulatory barriers. Durable moats ensure that a business can fend off competition and sustain profitability.
    5. Make Few, Big, Infrequent Bets
      Concentrated bets on high-conviction opportunities yield better returns than spreading investments thin. Use tools like the Kelly Criterion to determine optimal bet sizes.
    6. Exploit Arbitrage Opportunities
      Take advantage of price disparities or inefficiencies, such as undervalued stocks, geographic advantages, or business model quirks, to secure low-risk, high-reward outcomes.
    7. Ensure a Margin of Safety
      Purchase assets significantly below their intrinsic value. This cushion protects against downside risk even if things don’t go as planned.
    8. Embrace Low-Risk, High-Uncertainty Investments
      Investments with uncertain outcomes but limited downside risk often offer the best opportunities for substantial returns.
    9. Copy Proven Ideas Instead of Innovating
      Innovation can be risky. Copying successful models and adapting them reduces risk and increases the likelihood of success.

    Case Studies: Dhandho in Action

    The Patel Motel Model

    The Patel community in the U.S. demonstrated the Dhandho mindset by buying distressed motels, cutting costs with family labor, and reinvesting profits. This low-risk, high-return strategy helped them dominate the motel industry.

    Lakshmi Mittal and Steel Arbitrage

    Lakshmi Mittal turned a small steel mill into a global empire by buying distressed mills at steep discounts. His ability to streamline operations and scale created immense value from challenging industries.

    Warren Buffett’s Bet on American Express

    In the 1960s, Buffett invested 40% of his portfolio in American Express during the “salad oil scandal,” when its stock was halved. He recognized that its core business was unaffected and reaped significant returns when the market corrected.

    Richard Branson’s Virgin Empire

    Branson’s ventures, like Virgin Atlantic, exemplify creative arbitrage. By leasing planes and leveraging partnerships, he minimized downside risk while capitalizing on unmet market needs.

    Applying the Dhandho Framework to Investing

    Intrinsic Value and Margin of Safety

    Estimate the intrinsic value of a business using discounted cash flow (DCF) analysis. Only invest when the stock trades at a significant discount to this value, ensuring a margin of safety.

    Finding Opportunities

    Identify distressed businesses or industries through:

    • News and market reports.
    • Value-focused investor filings (e.g., Warren Buffett, Seth Klarman).
    • Resources like Value Investors Club or Joel Greenblatt’s Magic Formula Investing.

    Portfolio Management

    Maintain a concentrated portfolio of a few high-conviction bets. This approach mitigates dilution of returns and allows for meaningful gains when bets succeed.

    Mindset for Dhandho Investing

    1. Think Probabilistically
      Treat investing like betting on favorable odds. Use probabilities to assess risks and returns, ensuring that potential upside far outweighs downside.
    2. Be Patient and Disciplined
      Wait for rare opportunities where the odds are overwhelmingly in your favor. Avoid emotional reactions to market fluctuations.
    3. Focus on Simplicity
      Stick to businesses you can fully understand. Complexity increases the likelihood of mistakes.

    Closing Wisdom: The Dhandho Edge

    The Dhandho framework is a powerful tool for building wealth by minimizing risk while maximizing returns. By focusing on undervalued assets, leveraging durable competitive advantages, and exercising patience and discipline, investors can achieve outsized success. As Pabrai emphasizes, the key lies in embracing simplicity, reducing risk, and acting decisively when opportunities arise.

    The Dhandho Investor offers not just a roadmap for investing but also a philosophy for navigating uncertainty in business and life. Its timeless lessons resonate for anyone seeking to grow wealth sustainably and wisely.

    The Dhandho Investor: A Low-Risk Path to High Returns

    Mohnish Pabrai’s The Dhandho Investor offers a compelling and practical framework for building wealth through low-risk, high-return investments. Inspired by the entrepreneurial spirit of the Patel community and the investment wisdom of Warren Buffett and Charlie Munger, Pabrai distills principles that challenge traditional notions of risk and return. Here’s an in-depth look at the Dhandho philosophy and its application.

    The Dhandho Philosophy

    The Gujarati term “Dhandho” translates to “business” and signifies endeavors that create wealth with minimal risk. Pabrai flips the traditional idea that high returns require high risk. Instead, the Dhandho framework focuses on reducing downside risk while maximizing upside potential. It is a disciplined, pragmatic approach to investing and entrepreneurship.

    Nine Core Principles of the Dhandho Framework

    1. Buy Existing Businesses
      Avoid the risks of startups by acquiring or investing in established businesses with a proven track record and stable cash flows. In public markets, you can own fractions of such businesses without running them yourself.
    2. Invest in Simple, Predictable Businesses
      Simple businesses are easier to understand and analyze. Focus on industries with enduring demand and slow change, such as motels, consumer goods, or basic services.
    3. Target Distressed Businesses or Industries
      Look for businesses experiencing temporary setbacks or industries undergoing downturns. Distressed assets often sell at a significant discount, creating opportunities for outsized returns.
    4. Seek Durable Competitive Advantages (Moats)
      Invest in companies with lasting advantages, such as brand strength, cost leadership, or regulatory barriers. Durable moats ensure that a business can fend off competition and sustain profitability.
    5. Make Few, Big, Infrequent Bets
      Concentrated bets on high-conviction opportunities yield better returns than spreading investments thin. Use tools like the Kelly Criterion to determine optimal bet sizes.
    6. Exploit Arbitrage Opportunities
      Take advantage of price disparities or inefficiencies, such as undervalued stocks, geographic advantages, or business model quirks, to secure low-risk, high-reward outcomes.
    7. Ensure a Margin of Safety
      Purchase assets significantly below their intrinsic value. This cushion protects against downside risk even if things don’t go as planned.
    8. Embrace Low-Risk, High-Uncertainty Investments
      Investments with uncertain outcomes but limited downside risk often offer the best opportunities for substantial returns.
    9. Copy Proven Ideas Instead of Innovating
      Innovation can be risky. Copying successful models and adapting them reduces risk and increases the likelihood of success.

    Case Studies: Dhandho in Action

    The Patel Motel Model

    The Patel community in the U.S. demonstrated the Dhandho mindset by buying distressed motels, cutting costs with family labor, and reinvesting profits. This low-risk, high-return strategy helped them dominate the motel industry.

    Lakshmi Mittal and Steel Arbitrage

    Lakshmi Mittal turned a small steel mill into a global empire by buying distressed mills at steep discounts. His ability to streamline operations and scale created immense value from challenging industries.

    Warren Buffett’s Bet on American Express

    In the 1960s, Buffett invested 40% of his portfolio in American Express during the “salad oil scandal,” when its stock was halved. He recognized that its core business was unaffected and reaped significant returns when the market corrected.

    Richard Branson’s Virgin Empire

    Branson’s ventures, like Virgin Atlantic, exemplify creative arbitrage. By leasing planes and leveraging partnerships, he minimized downside risk while capitalizing on unmet market needs.

    Applying the Dhandho Framework to Investing

    Intrinsic Value and Margin of Safety

    Estimate the intrinsic value of a business using discounted cash flow (DCF) analysis. Only invest when the stock trades at a significant discount to this value, ensuring a margin of safety.

    Finding Opportunities

    Identify distressed businesses or industries through:

    • News and market reports.
    • Value-focused investor filings (e.g., Warren Buffett, Seth Klarman).
    • Resources like Value Investors Club or Joel Greenblatt’s Magic Formula Investing.

    Portfolio Management

    Maintain a concentrated portfolio of a few high-conviction bets. This approach mitigates dilution of returns and allows for meaningful gains when bets succeed.

    Mindset for Dhandho Investing

    1. Think Probabilistically
      Treat investing like betting on favorable odds. Use probabilities to assess risks and returns, ensuring that potential upside far outweighs downside.
    2. Be Patient and Disciplined
      Wait for rare opportunities where the odds are overwhelmingly in your favor. Avoid emotional reactions to market fluctuations.
    3. Focus on Simplicity
      Stick to businesses you can fully understand. Complexity increases the likelihood of mistakes.

    Closing Wisdom: The Dhandho Edge

    The Dhandho framework is a powerful tool for building wealth by minimizing risk while maximizing returns. By focusing on undervalued assets, leveraging durable competitive advantages, and exercising patience and discipline, investors can achieve outsized success. As Pabrai emphasizes, the key lies in embracing simplicity, reducing risk, and acting decisively when opportunities arise.

    The Dhandho Investor offers not just a roadmap for investing but also a philosophy for navigating uncertainty in business and life. Its timeless lessons resonate for anyone seeking to grow wealth sustainably and wisely.

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

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

    Intrinsic Value: The North Star of Investing

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

    Long-Term Orientation: The Antidote to Short-Termism

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

    Tuning Out Market Noise: The Path to Rationality

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

    Margin of Safety: The Investor’s Fortress

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

    Circle of Competence: Knowing Your Limits

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

    Temperament and Discipline: The Investor’s Emotional Rudder

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

    Prioritizing Loss Avoidance: The Foundation of Winning

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

    The Importance of Management: The Human Element

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

    Opportunistic Investing: Seizing the Right Moment

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

    Financial Strength and Conservatism: The Bedrock of Stability

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

    Skepticism of Forecasts: Embracing the Unknown

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

    Value Investing Philosophy: The Time-Tested Path

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

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

  • Warren Buffett and Charlie Munger on Index Funds

    In the world of investing, few names command as much respect as Warren Buffett and Charlie Munger. Their investment philosophy has been a guiding light for many, offering a blend of wisdom, simplicity, and practicality. Central to their approach is the endorsement of index funds, which they regard as a prudent choice for most individual investors. Let’s delve into their perspectives:

    Simplicity and Effectiveness

    Warren Buffett, known for his straightforward approach to investing, has long been an advocate of the simplicity and effectiveness of index funds. His recommendation for most individual investors, especially those who are not investment professionals, is to opt for a low-cost S&P 500 index fund. Buffett’s rationale is rooted in the difficulty of consistently outperforming the market. For the average investor, attempting to beat the market is often a futile endeavor fraught with unnecessary risks and costs.

    Cost Efficiency

    Both Buffett and Munger have been vocal critics of the hefty fees charged by many actively managed funds. They argue that these fees significantly diminish returns, contributing to the often lackluster performance of active funds compared to their benchmarks. In contrast, index funds are known for their low-cost structure, making them a more efficient choice for investors.

    Long-Term Investing

    The investment strategy espoused by Buffett and Munger emphasizes long-term thinking. This philosophy aligns perfectly with the nature of index funds, which are designed to mirror the performance of the broader market over extended periods. Such funds are less susceptible to the short-term volatility that can affect individual stocks, making them suitable for long-term investment strategies.

    Diversification

    A cornerstone of risk management in investing is diversification, and index funds excel in this area. By investing in a broad market index fund, one gains exposure to a diverse array of sectors and companies. This diversification minimizes the risks associated with single-stock investments and offers a more balanced portfolio.

    Passive Management

    Finally, the Buffett-Munger investment ethos criticizes excessive trading and speculation, favoring instead a passive, buy-and-hold approach. Index funds embody this philosophy, as they involve purchasing and holding a diversified portfolio that reflects the market index.

    Wrap Up

    In essence, the advocacy of Warren Buffett and Charlie Munger for index funds is a natural extension of their broader investment philosophy. They champion index funds for their simplicity, cost-efficiency, long-term growth potential, diversification benefits, and passive management style. For the average investor seeking a sensible, low-cost route to market returns, Buffett.