Earlier this year, I was asked what I thought about Leopold Aschenbrenner and I admitted that I knew little about him other than what I had read about him, more in social media, than in the press - that he was a 25-year-old wunderkind who had started at OpenAI but had left to start a hedge fund. That hedge fund, built entirely around a bet that AI would pay off big time and near term, buying companies in the AI orbit and selling short on the businesses (especially software) that AI would disrupt, had been able to raise billions of dollars from well-heeled and presumably sophisticated investors, and had posted eye-popping returns, up almost 450% through late June. Success of that magnitude needs no nitpicking, but it is worth remembering that there is nothing that markets enjoy more than cutting inflated egos and reputations down to size. In this case, the fall from grace was precipitous, and over the course of four weeks, the fund's public equity holdings lost more than two thirds of its value, but was also forced to liquidate, with Citadel buying almost all of its public equity holdings.
The reads on the swift rise and fall of Leo have been fascinating, a Rorschach test of investing priors. For older investors, the lesson was that you can be blessed with intelligence, but that wisdom required experience, which, at least in their saying, conveniently comes with age. For value investors, many of whom chafed at Leo being hailed as the next Buffett, there was vindication that there will never be another Buffett. For AI skeptics, who have long been on the lookout for catalysts that will break AI fever, there was at least a brief moment of hope that this was the catalyst. There is some truth and some overreach in each of these responses, and I don't plan to rehash them. Instead, I would like to use this story to talk about investing conviction, words used mostly in a favorable way, when people talk about success in markets. I am not as convinced that investing conviction is a net plus, but to get to that conclusion, I think we need to look at what it is, where it comes from and what it leads investors to do.
The Story of Situational Awareness
The Situational Awareness story is inextricably tied to the story of Leopold (Leo) Aschenbrenner. The short version of his life story is that he was born in Germany in 2001, and enrolled at Columbia University when he was 15. After graduating with a degree in economics in 2021, doing research briefly at Oxford University and working at Sam Bankman-Fried's FTX fund, Leo joined OpenAI as part of the team working on AI safety. He was fired in 2024 for leaking data on the firm, though his motivations for doing so are murky and he contests the allegation, and he published a long (167 page) paper titled "Situational Awareness: The Decade Ahead", which was not only widely circulated, but also became the blueprint for the fund that he created.
The Situational Awareness fund, founded in July 2024, and initially funded by tech luminaries, quickly took off as its bets on AI chips and infrastructure and against AI-damaged sectors paid off. Its early success allowed it to attract more money, with Jane Street being one of the more prominent names involved., and with the additional capital in play, it expanded its presence. While most of the companies that made the fund's list were publicly traded, it also had a stake that Leo had acquired in Anthropic, still a private business. The numbers posted by the fund made investors notice, as can be seen by its rise From August 7, 2025, to June 23, 2026, its highest mark day:
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| Source: Portfolios Lab |
Measuring returns from August 7, 2025, Situational Awareness was up about 367% through June 19, 2026 and its returns since founding are even more stratospheric. Even at its peak, there were three caveats that any investor with experience in the market would (or should) have brought up. First, in market time, where decades of over performance are needed to separate luck than skill, a fund that has been successful for a little more than two years qualifies more as a shooting star than as a beacon of light. Second, to deliver returns of this magnitude, you not only have to be right in your guesses, but those guesses must be super-charged by adding substantial leverage to your strategy, either explicitly (by borrowing) or implicitly (by using options). Third, the fund followed the classic 2% (of funds under management) and 20 (% of specified upside) fee structure, an abomination that not only creates an almost insurmountable handicap, in the long term, for investors in the fund, but also encourages reckless risk taking on the part of management.
If the rise of the fund was breathtaking, its fall was even more so, and you can see the meltdown in the four weeks of July in the graph below, which looks at the fund performance from June 19, 2026 to July 29, 2026, the last day of trading for the day, before the fund was liquidated:
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| Source: Portfolios Lab |
Note that the loss of principal (of more than 43%), with the Anthropic holding value retaining mostly intact, as a private holding, but with the public investment portion of the portfolio down by almost 67%. I am sure that there will be case studies and forensic analysis of what happened in these weeks, but for me, the lesson is one of market symmetry. What the market gives easily, it also takes away just as easily, and if you put into place strategies that are designed to deliver outsized returns, you have to live with the reality that you can have outsized losses. The surprise, though, for many is not that the fund lost money in July, but that it did not survive the month, and was forced to sell much of its public investment portfolio to Citadel, at prices, that at least in hindsight, look like bargain basement levels.
Conviction: What is it and where does it come from?
How do I get from the Situational Awareness story to a discussion of investment convictions? Simple! Leo's core belief that AI would win the battle with the status quo in most businesses, and that the win would be decisive and quick, was not unique, and not only are there other investors who shared that view, but there are also companies that are investing in AI cap ex, driven by that view. That said, to get from that view to a hedge fund built entirely around buying AI winners and selling AI losers requires that the belief to be deeply set and using debt to magnify those returns suggests strong conviction.
What is investment conviction?
Before we embark on a discussion of investment conviction, it is worthwhile to start with an understanding of what it means. While there a myriad of definitions out there, the general consensus is that investment conviction measures the belief that an investment opportunity will generate significant returns, relative its risks, and with that definition, you can see that conviction is a continuum, rather than an absolute.
At one end of the spectrum, you have absolute conviction, where you know (or think you know) with certainty that an investment will pay off. At the other end of the spectrum is investment mush, where your feelings about an investment paying off are so weak that you are unwilling to even voice that opinion, let alone put money behind it. With most investments, you fall in the middle, with the variations being in the degree of confidence that you have in being right.
As you can see, the elevation of conviction as something to be sought after in investing is because in the complete absence of conviction, you will not act and that paralysis can result in portfolios entirely or almost entirely held as cash. I don't think, though, that even the strongest proponents of conviction as a good quality in investing would make the argument that you should feel certain about the outcome of investments, when uncertainty is part and parcel of investing.
Where does conviction come from?
So, what is it that determines investment conviction or the lack of it? To generate a basis for that discussion, let's go back to basics, and start with an assessment of what has to happen for an investment to be viewed as a money maker. No matter what your investment philosophy, the process starts with a market price for an investment, and an assessment of what you believe is a "fair price" for that same investment. I am being agnostic about how you arrive at the fair price, leaving the door open for chartists, who may find it by looking at past price patterns, fundamentalists, who believe that you can assess fair price, only by looking at the fundamentals of the investment and traders, who may be in possession of information that leads them to believe that the current price is wrong. For the process to deliver winnings, though, there is a second part to the process that often gets less attention, which is that the market has to correct, with the market price moving towards or even to your fair price.
To have conviction in an investment, you therefore need to believe strongly in three aspects of this process:
- That your assessment of "fair" price is right (or at least more right than the market consensus)
- That the market will correct, i.e., that the market price will move to, or towards, your fair price
- That this correction will happen during the time you plan to hold the investment (your time horizon), either because the investment has an expiration date (maturity) or because you feel that there will be a catalyst that causes the correction.
a. Investment Mispricing
If the investment process starts with an assessment of a fair price that is different from the market price, there are at least four reasons why you may be more confident in your assessment of the price of an investment, relative to the market consensus:
- Private information: At the risk of treading on or crossing the line between the legal and the illegal, you may be in possession of information about an investment that is not available (or at least widely enough available to the public to be priced in) that you believe will change its price.
- Information processing: To the extent that private information is rarely available to investors, and even if available, difficult to act on legally, much of active investing is built around collecting and processing information that is available to the market. That private information can range from past prices and trading volume (used by technical analysts) to public filings (the financial data that the company provides, often the basis for fundamental investors) to quasi-public information (analyst forecasts and revisions, hedge fund and mutual fund holdings). If you are using this information to assess a fair price, it is because you believe that you have found patterns in the data that others have not.
- Business understanding: In some cases, your fair price will derive from your belief that you understand the business economics for a company better than other public investors do, with your superior understanding coming either from working in the business or from technical training. This is especially true in complex businesses (like bio pharma) and complicated assets, and likely to be more the case with young companies, where financial history can stand in for business understanding.
- Pricing mistake: There are some investment theses that start with a market mistake, whether it be in pricing an individual asset or a pair of assets. With a pair of assets, often with similar fundamentals, the mispricing manifests with one of the assets being mispriced against the other, and the correction take place when the mispricing disappears. It is at the heart of derivatives trading, where options or futures on a traded asset can be mispriced enough that you can lock in the profits from the pricing mistake, with the guarantee of correction, when the option or futures expire.
b. Market correction
Investment conviction may start with the spotting of a market pricing mistake, but for conviction to build, you need to get a measure of when, why and how the market will correct its mistaket. Here are some of the forces that can cause variations on the market correction dimension:
- Finite maturity versus indeterminate end game: An investment with a finite maturity date comes with a greater likelihood that prices will correct than one without. A bond that is mispriced, by itself, or against other bonds of equal maturity, comes with a greater chance of correction than a stock that is mispriced against its own fundamentals or a paired stock. For the same reasons, if you can lock in a mispriced option against its underlying asset (or stock) or against other options of equal maturity, you are moving the odds in favor of correction. It should come as no surprise that true arbitrage, i.e., positions that can lock in guaranteed profits that exceed the riskfree rate are almost always in the fixed income or derivatives markets and that much of what passes for arbitrage in equities is quasi or pseudo arbitrage, where risk remains in the position.
- Market frictions: Market mispricing can sometimes reflect market inattention or irrational trading, but they more often are the consequence of market frictions, including restrictions on selling short and exerting control over an investment (such as buying and liquidating a company). If that is the case, it is possible that market mistakes, while visible and seeming obvious to everyone involved, may never get corrected, or at least not get corrected until the friction is removed.
- Market liquidity and depth: In equity markets, where there is no date by which mispricing has to disappear, corrections often require catalysts, i.e., events that lead market participants to reassess a market price, and to correct it. Those catalysts can come from corporate disclosures (earnings reports, for instance) by the mispriced company, corporate restructuring (divestitures and spin offs) or high profile investors (activists taking a stake in a under priced company or selling short an overpriced one). All of these catalysts are more likely to be present in liquid and deep markets, where information flows are more frequent and varied.
- Investment time horizon: No matter what the market mistake, it can be argued that having time as an ally, and being able to wait for longer periods, for market corrections, should not only give you a greater chance of gaining from a market correction, but also give you more conviction in your investment, other things being equal.
Is it possible for two investors to find the same market mistake, with the same underlying rationale for the mispricing, and have different degrees of conviction in following through? Absolutely, and while part of the reason is differing time horizons, there are other investor-specific forces that also come into play:
- Intelligence and educational background: This may be a generalization, and if you disagree, you should take exception, but the smarter an investor is, and the more exceptional his or her educational background (the right schools, credentials and certification), the greater the conviction that investor is likely to bring to investments. One reason is that it becomes easier to attribute perceived market mistakes to the lack of intelligence of other market participants than it is to take a closer look at real reasons why they may not be mistakes in the first place.
- Personality: Like conviction, self confidence falls on a spectrum with wide differences across human beings. Some investors measure low on the self confidence scale, and look to others for big decisions. Others measure higher on the self-confidence scale and are willing to make decisions with incomplete information and in the face of uncertainty and disagreement. Still others have so much self confidence and so little self doubt that they risk having convictions that are out of sync with reality. Over five decades of research in behavioral finance has highlighted overconfidence as one of the key drivers of irrational investor behavior, and underscored the reality that not only does this trait vary widely among individuals, but also that the most overconfident players often rise to the top of the investment and corporate world. In the conviction discussion, overconfidence enters the game early and is perhaps the best explainer of why some investors, looking at what they think is a market mistake, feel so much more convinced that they are right than other investors looking at exactly the same mistake.
- Track record: When you invest, you receive almost instantaneous and continuous feedback on whether your investments are paying off, and while investment success is always preferable to failure, it is undeniable that some of the worst investment lessons are learned from that success. Much as Wall Street likes its adages of "not mistaking dumb luck for skill", investors are quick to forget them when an investment bet that they make pays off, especially when their success leads to iconic and admiring profiles ("the next Buffett") and investor capital pouring in.
The Consequences of Conviction
At this point in the post, I don't blame you for wondering why conviction is such a big deal, since buying Palantir or SpaceX with low conviction counts just as much buying shares in these companies with high conviction. Conviction matters in investing because it affects two choices that investors make - the sizing of positions (with more conviction leading to larger positions in the same investment) and the use of financial leverage (with more conviction often translating into a willingness to borrow more money to fund the investment).
Concentration
One of the fundamental questions in investing, and one that evokes strong disagreement, is how much, if at all, investors should spread their bets. The debate is an old one and there are many views that fall between two extremes. At one end is the advice that you get from a believer in efficient markets: be maximally diversified, across asset classes, and within each asset class, across as many assets you can hold. The proverbial “market portfolio” includes every traded asset in the market, held in proportion to its market value. At the other is the “go all in” investor, who believes that if you find a significantly undervalued company, you should put all or most of your money in that company, rather than dilute your upside potential by spreading your bets. The discussion of investment conviction ties into the answer to this question.
I am not an absolutist on this front, because what you do as an investor should reflect your circumstances. At one limit, if you are certain about your assessment of value for an asset and that the market price will adjust to that value within your time horizon, you should put all of your money in that investment. This may seem like an impossible dream, but it it is what you hope to pull off if you find mispricing in the bond or derivatives markets (true arbitrage), where you can lock in the mispricing, with a guaranteed price correction at maturity (of the bond or options). At the other limit, if you have doubts aplenty and no conviction in your investment choices, you should be as diversified as you can get, given transactions costs. If you have no transactions costs, you should own a little piece of everything, a very real choice in a world of index funds and ETFs. After all, you gain nothing by holding back on diversification and your portfolio will be deliver less return per unit of risk taken. If you are active investor who is constantly in this position (of having no conviction), it is best to retire the "active" part of your investment profile and go all in on index funds.
For most active investors then, the question of how much to diversify will depend in large part on how strong or weak their investment conviction is, and that, in turn, depends on what investments these investors are focused upon. In this context, I would argue that the right amount of conviction and by extension concentration (or diversification) will depend upon the types of companies you invest in (with more diversification needed when you invest in younger companies) and your time horizon (with concentration increasing with time horizon)
Leverage
Financial leverage is an instrument that investors can use to enhance returns, but its use in investing has always been controversial. While borrowing money to fund an investment will increase upside, if you are right, it will also magnify downside, and in some cases, precipitate collapse, if you are wrong. In the early year of investing, financial leverage generally took the form of borrowing money to buy riskier investments (stocks), but the wave of default and distress triggered for investors by the great depression led to restrictions on the use of margin in stock markets. However, those restrictions varied across investor groups (with individuals facing more restrictions than institutions) and across asset classes (with more leverage in real estate than in stocks). The growth of derivatives markets opened the door to bypassing these borrowing restrictions, since buying a (naked) call option is equivalent to borrowing the underlying asset with debt, with leverage increasing with how out of the money the call option is.
Since leverage magnifies both upside and downside, it stands to reason that investors with more conviction in their investment, i.e., that it is under priced and that correction is imminent or very likely to happen, will borrow more money than investors with less conviction:
In sum, financial leverage magnifies your core investment judgments. If they are good, leverage will make them look better over time, and if they are bad, they will make them worse. That said, there is a component to the use of leverage that needs to be brought into the picture. Even if you are a good investor, with solid conviction in your investment ideas, using debt to turbocharge your returns can sometimes shrink your time horizon by forcing you to liquidate your mispriced investments before the market corrects its mistakes. That truncation risk eliminates the possibility that you can come back from your losses, and perhaps even have your investment thesis vindicated.
A Corporate Life Cycle Perspective on Conviction, Concentration and Leverage
I use the corporate life cycle construct in almost every aspect of finance, because it not only helps understand corporate and investor behavior, but also provides perspective on why one size (or proposal) does not fit all. The corporate life cycle maps out a firm's evolution from a start-up to a growth company to maturity and eventual decline, and traces out changes in revenues, earnings and risk over the aging process:
Using this framework, you can see that you can be more tolerant of concentrated portfolios, with debt added on, when you invest in mature companies than you should be when investing in younger companies. By the same token, investors who find pricing mistakes in younger companies, but are daunted by the noisiness of their estimates or uncertainty about markets correcting, and thus are low on the conviction scale, can overcome their reluctance to act by spreading their bets across many such companies. As some of you may be aware, I did value SpaceX at the time of its IPO at about $100 a share, and as the price has drifted down towards that price, I may very well be faced with an underpriced stock (where the market price drops below $100). Given the uncertainty that is associated with my estimate, and you can see it in the simulation that I reported in my post, it is unlikely that I would ever have sufficient conviction to make SpaceX the biggest or only investment in my portfolio, but I stand ready to buy the stock as part of a portfolio, where it is one of many bets that I make on markets.
Lessons from Leo
I started this post with the story of Situational Awareness and Leo Aschenbrenner, but I spent most of it talking about investment conviction, what it is, its sources and consequences. I want to end the post by returning to Leo’s story and what we can learn from it as investors.
Lesson 1: Investment actions that are inconsistent with investment conviction risk ruin
I have no issues with Leo's story of AI winning big in the near-term and buying the winners and selling the losers that will result. It is macro story investing, and it has worked for some in the past and failed for others, but if timed right, it can deliver significant returns. In fact, I will concede that Leo knows far more about AI than I ever will and is using that knowledge in constructing his AI story. I also have no bone to pick with investors using financial leverage to supercharge their returns, with low-risk investments, though I remain concerned that the (2 & 20) fee structure may lead them to use too much debt. My concern with Situational Awareness, as a fund, and this would have been true on June 19, even at it peak, is that combining a macro story about AI winning with maximal leverage creates a time bomb. The AI story, no matter how well told, has multiple obstacles to overcome, some related to business economics and some to politics and regulation, and is a risky bet, and it makes little sense to fund it with significant amounts of debt. I know that there are defenders who will point to the fact that the fund, even after its markdown, was up substantially from its inception date, but the fact that leverage cut the fund's life short only strengthens the case that if it had been run with less debt, it would have had a bad month in July, but lived to tell the tale and perhaps even deliver on its AI promise.
Lesson 2: Momentum is a wild card in every investment strategy, and you ignore it at your own peril.
It is a well-established finding in equity markets that momentum is one of the strongest forces moving markets and that it can overwhelm the best planned strategies of most investors. If you look at the composition of Situation Awareness portfolio through much of its rise and fall., the long positions were primarily in companies that benefit from the build-up of Ai architecture, selling their products and services to the hyper scalers and LLM companies, and the short positions were in software and other businesses that would be disrupted by the rise of AI. With both groups, Situational Awareness was taking bets that were in line with what the market was pricing in already, albeit in a more concentrated and leveraged form. While market observers were quick to link both the rise and fall of Situational Awareness to the AI story, you can make just as strong a case that much of that happened at the fund over its brief existence can be explained by momentum, with leverage acting as a super charger; continued momentum generated the outsized return though June 19, and the market reversal in July caused the correction.
Lesson 3: Humble money beats smart money
The legend of smart money persists in markets, where investors who are smarter than the rest of us, with access to information and capital that others do not possess, deliver supersize returns for themselves and those that they invite into their inner circle. That legend serves everyone's interests, since the smart money uses its reputation to attract more capital and the not-so-smart money has someone else (hedge funds, insiders, activists) to blame for investment setbacks. While many money managers aspire to be part of the smart money group, most never make it into that rarefied circle, and those that do often have to pay their dues over long periods. Leo Aschenbrenner, in contrast, broke into the group in just a few months, perhaps helped by his pedigree as an AI insider and with an assist from his post on the coming AI revolution. The problem with smart money is that its self-regard makes its susceptible to attributing more precision to its own convictions, than merited by the circumstances, and that, in turn, results in over reach (portfolios that are much too concentrated or levered). Situational Awareness clearly overused leverage, and while some will attribute that to the youth and inexperience of its lead manager, it is worth remembering Long Term Capital Management, where John Merriweather, after a long and distinguished trading tenure at Salomon Brothers, aided by two Nobel Prize winners in economics, brought the fund to its knees by borrowing too much on risky trades. In a post from years ago, I drew a contrast between smart money and humble money, with the former including investors who attribute every basis point of excess return earned to their investing brilliance, and the latter open about the fact that their performance, no matter how stellar, has as much to do with being in the right place at the right time (being lucky) as it has to do with skill. Investors looking for someone to manage their money are likely to do much better with the latter than the former.
I hope that you don't view this as a hit piece on Leo or AI, since that was not my intent. In fact, I hope that Leo persists and perhaps even comes back as a fund manager, since he strikes me as an original thinker who is willing to take a stand, both scarce qualities among active fund managers. I also hope that he has learned some lessons, especially on humility and restraint, for his next go around, and that he adopts a fee structure that gives his investors a chance of beating the market in the long term.
YouTube Video
- Situational Awareness: The Decade Ahead (Leo's post on AI)
- The 24-year old AI Wiz who counts Jane Street as an investor (The Wall Street Journal in early June, prior to blow-up)
- Inside the Meltdown of a Wunderkind's AI Hedge Fund (New York Times)
- His Wedding Guests were arriving - Just as his $45 billion fund was falling apart (The Wall Street Journal)
- What does the humbling of Leopold Aschenbrenner mean for the AI bubble? (The NewYorker)
