Musings on Markets is the personal blog of Aswath Damodaran, professor of finance at NYU Stern and widely known as the "Dean of Valuation." Running since 2008, it publishes hands-on intrinsic-value teardowns of headline companies (SpaceX, Tesla, Nvidia) using his narrative-and-numbers DCF framework, plus periodic market-wide reviews.
A finance professor uses the crash of AI-star fund Situational Awareness, which lost two-thirds of its public stock holdings in four weeks and was forced to liquidate, as a warning: markets give and take symmetrically, especially when you borrow to swing bigger. His stance is cautious. He says short-term gains prove little, so check leverage (borrowed money) and fees. He values SpaceX at about $100 a share, but only as one of many bets, not a top conviction. Citadel bought most of the fund's stocks at what looked like fire-sale prices.
Using the four-week collapse of the Situational Awareness fund as a case study, the author warns of "market symmetry": a strategy designed for a certain magnitude of upside can just as easily meet its demise through a comparable downside (stance: [Cautious]).
The author argues that the collapse of Situational Awareness was not a matter of luck but a textbook specimen of "market symmetry": if a strategy targets extraordinary returns, it must accept extraordinary losses as the price. The author initially admits to knowing little about the protagonist, only that he is a 25-year-old prodigy who founded the fund after leaving OpenAI. Leo was born in 2001, enrolled at Columbia University at 15, graduated in 2021, did brief research at Oxford, worked at FTX (Sam Bankman-Fried's fund), then joined OpenAI to work on AI safety. He was fired in 2024 (the leak accusation had unclear motives and he denied it), and published a 167-page paper, Situational Awareness: The Decade Ahead. The Situational Awareness fund was launched in July 2024, betting on AI chips and infrastructure while shorting the software industry that AI would disrupt. It raised billions of dollars from wealthy investors deemed sophisticated, with prominent tech figures providing early capital and Jane Street as a notable later participant. According to Portfolios Lab data, over the interval from August 7, 2025 to the peak-NAV date of June 23, 2026, returns were approximately 367% as of June 19; the author also notes at the outset that returns had approached 450% by late June, and were even higher since inception. Within four weeks in July, public stock holdings shrank by more than two-thirds; from June 19 to July 29, 2026 (the last trading day before liquidation), principal lost more than 43%, while the value of the private holding in Anthropic remained essentially intact, and the public positions fell nearly 67%. The fund was forced into liquidation, and Citadel bought nearly all of its public holdings — in hindsight, at fire-sale prices. The author's original words: "What the market gives easily, it also takes away just as easily." In his view, the real surprise was not losing money in July, but failing to survive that month.
The author does not take sides among "older investors, value investors, and AI skeptics," but instead uses the same story as an entry point for discussing whether investment conviction is truly a virtue. The article notes that the market's interpretation of Leo's meteoric rise and crash resembles a Rorschach test for investment foresight — different people read different conclusions.
| Camp | Lesson Read |
|---|---|
| Older investors | Intelligence is not wisdom; wisdom requires experience, and experience comes with age |
| Value investors | There will be no next Buffett (many had already resented Leo being held up as the next Buffett) |
| AI skeptics | The long-awaited catalyst to puncture the AI boom may have appeared |
The author believes each of these reactions contains a partial truth, and each also overreaches; he does not intend to restate them. What he really wants to ask is: what investment conviction actually is, where it comes from, and what it leads investors to do. He first points out that Leo's core judgment — that AI will defeat the status quo in most industries in a decisive, rapid way — is not unique; other investors and companies pouring capital into AI capex hold the same view. But turning that view into a fund built entirely around buying AI winners, shorting AI losers, and adding debt leverage requires not an ordinary view, but conviction deep enough to act on.
The author defines investment conviction as the degree to which one believes an investment opportunity will generate significant returns relative to its risks, and regards this degree as a spectrum running from "absolute conviction" to "investment mush," whose source lies in your judgment of the gap between the current price and the fair price you perceive. The author's original words: "investment conviction measures the belief that an investment opportunity will generate significant returns, relative its risks." At one end is "absolute conviction" — certainty (or self-styled certainty) that the investment will pay off; at the other end is "investment mush" — a feeling so weak that one would not even state a view, let alone put money in. Most investments fall between the two. The author argues that conviction is treated as a good thing because having no conviction at all leads to inaction, and a portfolio could become entirely or almost entirely cash; but even the strongest advocates of conviction would not argue that one should have certainty about outcomes, because uncertainty is inherently part of investing.
So where does conviction come from? The author suggests that whatever the investment philosophy, the starting point is the same: the market has a price, and in your mind you hold a "fair price" as you see it. Technicians look for the fair price in historical price patterns, fundamental investors look at fundamentals, and traders may rely on an information edge to conclude that the current price is wrong. But for an investment to actually make money, there is a second, often overlooked step: the market must correct — the market price must move toward the fair price, or even arrive there. At the end of the section, the author leaves "what conviction leads investors to do" for the next part, hinting that his skepticism about conviction being a net positive has not yet completed its argument.
The actionable implication of this article: do not validate conviction with spectacular short-term performance; instead, examine leverage, fees, and survival probability at the same time. At the fund's peak, the author already listed three lessons: two-plus years of performance counts in market time only as a shooting star rather than a lighthouse — distinguishing luck from skill takes decades; extraordinary returns require amplification through explicit (borrowing) or implicit (options) leverage; and the fee structure of a 2% management fee plus 20% carried interest — which he calls an abomination — creates nearly insurmountable obstacles for long-term investors and encourages management to take reckless risks. Combined with "market symmetry," investors should realize: whatever upside a strategy is designed to capture, it can die from a downside of the same magnitude. As a third-party academic observer, Damodaran is not a holder of this fund; his judgment is based on public narratives and a valuation/behavioral finance perspective, with a corrective stance toward market hype.
The author argues that investment conviction is not emotion or courage, but a combination of three specific judgments: you believe your valuation is more correct than the market's, the market will correct, and the correction will occur within your holding period.
He elaborates that the first pillar is an assessment of "fair price"—you must believe your valuation is at least closer to correct than the market consensus; the second is that the market will move toward your price; the third is that the correction must occur within the period you plan to hold—a period that can be determined by the investment's own maturity date or by a catalyst you believe will emerge. Based on this definition, the degree of conviction varies with the investment, the market in which it sits, and the investor himself: it differs across asset markets (real estate, equities, fixed income, derivatives), across geographic regions (developed vs. emerging markets), and across industries within equities—for example, the author believes investors typically hold less conviction in technology stocks than in utilities.
The author lists four reasons an investor can be more confident than the market about a mispricing: possession of non-public information, superior information processing, deeper business understanding, and discovery of market pricing errors.
The author points out that true arbitrage—positions that lock in guaranteed profits above the risk-free rate—exists almost exclusively in fixed income or derivatives markets; "arbitrage" in equities is mostly quasi-arbitrage, with risk still present and correction requiring catalysts.
The distinction between a finite maturity and the absence of a defined endgame is central: a mispriced bond—whether viewed on its own or relative to other bonds of the same tenor—has a higher probability of correction than a stock mispriced against its own fundamentals or a paired stock; options and futures also force correction at expiration. Market frictions—including short-selling constraints and limits on imposing control over an investment (such as acquiring and liquidating a company)—can leave obvious errors uncorrected for long periods. The stock market has no maturity date; correction requires catalysts such as corporate earnings disclosures, restructuring (spinoffs/demergers), or high-profile investors (activist positions or short campaigns), and these catalysts are more common in liquid, deep markets. A longer time horizon raises both the probability of correction and conviction. The author says in conclusion: "if you have no sense or faith that the market will correct in your time frame for investing"—meaning: "if you have no sense or faith that the market will correct within your investment time frame." Then even if you are convinced an asset is mispriced, it does not constitute investment conviction.
| Asset Type | Correction Mechanism | Author's Assessment |
|---|---|---|
| Fixed income (bonds) | Has a maturity date; mispricing disappears at maturity | Higher probability of correction |
| Derivatives (options/futures) | Settled at maturity; profits can be locked in against the underlying | Where true arbitrage resides |
| Stocks | No maturity date; requires catalysts | Mostly quasi-arbitrage; risk remains |
The author cites more than fifty years of behavioral finance research, noting that overconfidence is one of the primary drivers of irrational investing, and the most overconfident players often rise to the top of the investment and corporate world.
Two investors facing the same mispricing with the same reasoning can have completely different levels of conviction. The author believes intelligence and educational background play a reinforcing role: the smarter the investor and the more distinguished the educational background (elite schools, certificates, and credentials), the more likely they are to attribute perceived market errors to other participants' lack of intelligence, rather than first examining whether those errors may not be errors at all. Personality also plays a role: low-confidence individuals rely on others for major decisions; high-confidence individuals are willing to make decisions under incomplete information, amid uncertainty and dissent; overconfident individuals have too much confidence and too little self-doubt, and may hold beliefs disconnected from reality. The author writes: "the most overconfident players often rise to the top of the investment and corporate world"—meaning: "the most overconfident players often rise to the top of the investment and corporate world."
For investors, before building conviction, they should actively examine three things: What kind of evidence does my mispricing judgment rest on? Does a genuine market correction mechanism exist? Can my holding period last until the correction occurs? At the same time, beware of the overconfidence bred by a sense of intellectual superiority.
The author deconstructs conviction with a structured framework, in essence reducing "conviction" to testable variables rather than treating it as a natural virtue; readers should note this is a reflection from a valuation scholar's perspective, which may understate the value of execution and decisiveness in investing.
A Successful Track Record Is the Most Dangerous Investment Lesson
Investing gives you almost instantaneous and continuous feedback on wins and losses, but the author argues that the worst investment lessons often come from success, not from losses.
The author begins from the concept of the "track record": investing constantly tells you whether your bets are right or wrong. Success is, of course, better than failure, but "some of the worst investment lessons are learned from that success" — some of the worst investment lessons come precisely from success. Wall Street likes to say "don't mistake luck for skill," but when a bet makes money, especially when the media shapes that success into "the next Buffett" and money floods in, investors quickly forget the saying. The author explicitly notes that he does not list "age" as an additional characteristic, because he does not believe that age equals wisdom or restraint; age usually only muddies and blurs an investment record. He writes: "There is no better bound on over confidence than losing lots of money on what you thought was a sure bet." In other words: "Nothing restrains overconfidence better than losing a lot of money on a bet you thought was a sure thing." Put differently, true restraint comes not from the accumulation of years, but from the memory of being struck hard by the market.
Conviction Determines Position Size and Leverage Use
Conviction does not change the "buy" action itself, but it determines how large a position you take and how much you borrow, thereby amplifying the final outcome.
The author first clarifies an easily confused point: buying Palantir or SpaceX with low conviction is no different, in the "buy" action itself, from buying shares of these two companies with high conviction. The difference in conviction shows up in two decisions made after the purchase. The first is position size — the stronger the conviction in the same investment, the larger the position. The second is financial leverage — the stronger the conviction, the more willing one generally is to borrow money to finance the investment. Together, these three elements constitute the two consequence paths of "concentration" and "leverage." The author expresses neither a bullish nor a bearish view on Palantir or SpaceX themselves; he merely uses them to illustrate that the act of buying is not equivalent to the strength of conviction.
Without Conviction, Buy the Index; with Certainty, Go All In
The author opposes going to extremes on diversification: the higher the certainty, the more you should concentrate; the heavier the doubt, the more you should diversify — even to the point of abandoning active management.
The author frames the diversification debate between two extremes. At one end are efficient-market believers, who advocate holding as many securities as possible across asset classes and within each asset class; the textbook "market portfolio" is to hold all tradable assets in the market in proportion to market capitalization. At the other end are "all-in" investors, who believe that once a significantly undervalued company is found, all or most of one's capital should be put into it rather than diluting the upside with diversification. The author says he is not an absolutist, because the investment approach should depend on individual circumstances. One extreme scenario: if you have full confidence in your valuation judgment of an asset, and the market price will converge to that value within your time horizon, then you should put all your capital into that asset. This is only achievable in "true arbitrage" in bond or derivatives markets — the mispricing can be locked in at maturity and guaranteed to correct. The other extreme scenario: if you are riddled with doubts and have no conviction, you should diversify as much as possible; if there were no transaction costs, you might even hold a small piece of every asset — in the era of index funds and ETFs, this is a very realistic choice. The author writes: "If you have doubts aplenty and no conviction in your investment choices, you should be as diversified as you can get, given transactions costs." That is: "If you have abundant doubts and no conviction in your investment choices, you should, given transaction costs, diversify as much as you can." Furthermore, if an active investor remains in this conviction-less state for a long time, it is best to drop the word "active" and put all of one's capital into index funds. Therefore, for most active investors, the degree of diversification depends on the strength of conviction, which in turn depends on the type of investment object — the younger the company being invested in, the more diversification is needed; the longer the time horizon, the higher the concentration one can bear.
Leverage Magnifies the Upside, but Also Shortens Your Time to Correct
Leverage amplifies both profits and losses, but the author emphasizes that the real danger is that it can force you to liquidate before the market corrects, leaving you no time to turn things around.
Financial leverage is a tool for enhancing returns, but it has always been controversial in investing. In the early days, leverage typically took the form of borrowing money to buy stocks; the wave of defaults and distress triggered by the Great Depression led stock markets to impose restrictions on margin use, but these restrictions varied by investor group — individuals are more constrained than institutions — and by asset class, with real estate generally carrying higher leverage than stocks. After the derivatives market developed, restrictions could be circumvented: buying a naked call option is equivalent to borrowing money to buy the underlying asset, and the more out-of-the-money the option, the higher the implicit leverage. Because leverage amplifies both upside and downside, investors with stronger conviction naturally want to borrow more; if one is very certain about the investment outcome, one can use maximum leverage. The author gives an example: when an option or futures contract is mispriced relative to its underlying asset, an investor can borrow 100% of the capital the investment requires, and at maturity the erroneous price disappears, yielding a pure profit. But leverage has a frequently overlooked side effect: even if you are a good investor with solid conviction, "turbocharging" returns with debt can shorten your time horizon — because when the market moves against you in the short term, you are forced to liquidate those mispriced positions before the price corrects. This "truncation risk" eliminates the possibility of recovering from losses, or even eventually seeing the investment thesis confirmed. The author names no specific companies; this section discusses the general mechanism.
Young Companies Are Priced Less Accurately and Demand More Diversification and Patience
Using the corporate life cycle framework, the author explains that the younger the company, the less precise the pricing, the fewer the corrective catalysts, and the more conviction should be restrained and allocations diversified.
The author uses the corporate life cycle as a lens for understanding investment conviction: as companies move from startup to growth to maturity to decline, their revenue, earnings, and risk all change. The valuation challenge shifts with company age. Young companies have almost no history, their business models are still changing, and almost all of their value comes from the future, making their pricing far less precise than that of mature companies; mature companies have established patterns and a longer financial history, with more of their value coming from investments already completed; declining companies face the possibility of liquidation, and valuation becomes more a matter of estimating liquidation value — that is, how much others would be willing to pay for these assets. Young companies also face a second problem: on top of greater pricing uncertainty, mispricing requires catalysts to correct, and such catalysts are fewer and less decisive in young companies — earnings reports contain little substantive content, business objectives are scattered, and whether or when market errors will be corrected is far more up in the air. This ties back to the earlier discussion: investing in young companies calls for greater diversification, because when the time horizon is insufficient, concentration and leverage make truncation risk deadly; only when the time horizon is long enough can one speak of increasing concentration.
Actionable takeaways for investors: treat a successful track record as an alarm, size positions and use leverage according to the strength of conviction, and calibrate concentration with the corporate life cycle.
Synthesizing the chapter, the author lays out an actionable line of thinking. First, a successful track record is not evidence of "skill" but a breeding ground for overconfidence; after making money, one should proactively check whether one is mistaking luck for skill. Second, conviction is not an adjective but a behavioral variable; it affects outcomes through position size and the use of leverage. One should therefore explicitly map conviction strength to position size and leverage levels — index when there is no conviction, and concentrate bets only with absolute certainty. Third, calibrate concentration with the corporate life cycle: for young companies, "high conviction + high leverage" is inappropriate, because pricing is imprecise and the market is slow to correct errors; mature companies can rely more on valuation anchors; declining companies require shifting to asset liquidation value. Fourth, any investor using leverage must anticipate the truncation risk of "forced early liquidation" — even if the judgment ultimately proves correct, one may be knocked out first. As a scholar of valuation and behavioral finance, the author's overall framework leans toward uncertainty, mean reversion, and risk control; readers should note that this perspective naturally underestimates the tail returns of those who succeed with extreme concentrated bets, but the purpose of this chapter is precisely to counter overconfidence with this risk narrative.
The author argues that tolerance for concentrated positions and leverage cannot be one-size-fits-all: young companies carry substantial valuation noise, so low-conviction investors should spread their bets across multiple names, while mature companies are better suited for concentration and taking on debt. The article opens with a balancing framework: when investing in mature companies, you can tolerate higher portfolio concentration and additional leverage than when investing in young companies; conversely, if an investor spots a mispricing in a young company but is deterred by estimation noise and the uncertainty of market correction, leaving conviction low, then spreading the bets across several such companies can overcome the reluctance to act. Damodaran uses SpaceX as a working example: he once assigned a valuation of roughly $100/share around SpaceX's IPO, and as the market price has pulled back toward that level, he is likely facing an undervalued stock (market price falling below $100). But he concedes that, given the enormous uncertainty visible in his valuation simulations, he could never have enough conviction to make SpaceX the largest or sole position in his portfolio; what he is willing to do is buy it as one of many market bets in the portfolio.
Even when Situational Awareness topped out on June 19, the author believed the fund's pairing of the macro story that "AI wins big in the short term" with maximum leverage was a time bomb—leverage did not amplify success; it shortened the fund's life. The author has no objection to Leo Aschenbrenner's AI story itself: it is a macro-story investment, one on which some have succeeded and others have failed in the past, and with the right timing it can deliver substantial returns; he even admits that Leo's understanding of AI far exceeds his own. Nor does he object to using financial leverage to amplify returns on low-risk investments, but he worries that the (2 & 20) fee structure will tempt managers into excessive borrowing. His exact words were "combining a macro story about AI winning with maximal leverage creates a time bomb"—that is, coupling the macro story that "AI will win" with maximum leverage is building a time bomb. The reasoning is that no matter how well the AI story is told, it must clear multiple hurdles across the commercial economy, politics, and regulation; it is inherently a high-risk bet, and funding it with heavy debt makes no sense. As for the defense that the fund, even after write-downs, is still up substantially since inception, the author counters: it is precisely the fact that leverage cut the fund's life short that shows that with less borrowing, July would have been merely a bad month—the fund would have survived and might still have cashed in on AI's promise.
The author argues that momentum is one of the strongest forces in the market, and Situational Awareness's surge and collapse can be explained at least as much by "momentum plus a leverage accelerator" as by the AI story alone. His exact words were "Momentum is a wild card in every investment strategy, and you ignore it at your own peril"—meaning that momentum is a wild card in every investment strategy, and ignoring it means bearing the risk yourself. Looking at the portfolio composition, the fund's long positions were mainly AI infrastructure beneficiaries selling products and services to hyperscalers and LLM companies, while its shorts were software and other businesses that AI would disrupt—the direction of both sets of bets matched what the market had already priced in at the time, just in a more concentrated form with higher leverage. Market observers attribute the fund's rise and fall entirely to the AI narrative, but the author believes it can just as well be explained as: persistent momentum generated excess returns through June 19, the July market reversal triggered a correction, and leverage was merely the amplifier.
The author argues that the "smart money" myth causes managers to overestimate the precision of their own convictions, leading to excessive concentration and leverage; those delegating capital should prefer "humble money" that acknowledges the role of luck. The smart money narrative persists because it serves both sides' interests at once: smart money attracts more capital on the strength of its reputation, while ordinary investors get hedge funds, insiders, and activists to blame. Most managers never manage to enter this circle in their lifetimes, yet Leo, thanks to his AI insider status and that AI revolution essay, broke in within months—the report linked at the end of the article shows that Jane Street was an early investor, and the fund still held $45 billion in assets at the time of the crash. But the common failing of this circle is that self-perception makes convictions appear more precise than they really are, ultimately leading to overexpansion; the author reminds us that this is not a mistake unique to the young—Long-Term Capital Management (LTCM) is a cautionary tale: John Merriweather had a long and outstanding trading record at Salomon Brothers, backed by two Nobel laureates in economics, and was ultimately undone by borrowing too much money to fund risky trades. The author distinguishes two kinds of money: smart money credits every basis point of excess return to its own investment talent, while humble money frankly admits that no matter how stellar the performance, being in the right place at the right time (luck) had something to do with it. At the close, he takes care to clarify that this is not an attack on Leo or AI, and hopes Leo will return with humility and restraint and adopt a fee structure that gives investors a long-term chance to beat the market—which can be read as an implicit criticism of the (2 & 20) fee arrangement.
The actionable conclusion is that conviction strength must match position structure—spread bets when conviction is low; exercise leverage restraint on any macro narrative; distinguish momentum tailwinds from genuine judgment; and treat "acknowledging luck" as a plus when delegating capital. The author's concrete demonstration is SpaceX: because his conviction was insufficient to support a heavy position, he bought it only as one bet among the portfolio's holdings. On the AI theme itself, he is neither bullish nor bearish; what he opposes is using debt to wager on a macro narrative beset with multiple obstacles. Institutional perspective bias: the author is a researcher within a behavioral finance framework and also holds a potential buy stance on SpaceX; he is clearly conservative on leverage risk and specifically clarifies that he has no intention of talking down AI or Leo—readers should not mistake his caution for a rejection of the AI theme.
| Position | Action | Author's one-line take | Key data |
|---|---|---|---|
| Situational Awareness | Cleared | A typical example of market symmetry: extraordinary returns must be paid for with extraordinary losses | From June 19 to July 29, principal loss exceeded 43%; public holdings fell nearly 67%; shrank by more than two-thirds over four weeks |
| SpaceX | New position | Valuation around $100/share; may be undervalued once the market price pulls back, but bought only as one of a diversified set of bets | Author values it at about $100/share; admits he lacks sufficient conviction to make it the largest or sole position |
| Long-Term Capital Management | Not stated | A classic lesson in how smart money's overconfidence and high leverage brought down the fund | Had two Nobel laureates in economics on board, yet ultimately failed because it borrowed too much capital for risk trades |
| Citadel | New position | In hindsight, bought Situational Awareness's public holdings at a fire-sale price | Bought nearly all of its public holdings |
| Anthropic | Hold for observation | The fund's private holdings remain largely intact in value — the part of the portfolio untouched by the crash | Private holdings largely intact; public positions fell nearly 67% |