Why Correct Predictions Are Not Enough: Survival Comes First

Recently, I came across a news headline that immediately caught my attention: "Why Did the 25-Year-Old AI Genius of Wall Street, Leopold Aschenbrenner, Fail?" That headline inspired me to write this blog post because I have seen friends experience similar situations in their own investment journeys. I should also mention that I am not a professional investor. What follows is simply my personal perspective on investing through the lens of mathematics, with significant assistance from ChatGPT.

On July 30, 2026, Leopold Aschenbrenner, the 25-year-old founder of the San Francisco-based hedge fund *Situational Awareness*, sent a letter to his investors. A former OpenAI researcher, Aschenbrenner graduated as the valedictorian of Columbia University in 2021 with a B.A. in economics, mathematics, and statistics. In the letter, he admitted that his fund had lost **67\% of its value in July alone**. For someone who had been celebrated as one of the brightest minds in AI investing, the confession was both shocking and humbling.

To understand why this happened, it is helpful to compare Aschenbrenner with Warren Buffett. Both are extraordinary investors, yet they solve fundamentally different optimization problems. Aschenbrenner seeks to maximize expected return, whereas Buffett seeks to maximize long-term compounded wealth. At first glance, these objectives appear similar, but mathematically they are fundamentally different.

Suppose an investor's wealth evolves according to $W_{t+1}=W_t(1+r_t),$ where $r_t$ denotes the return during period $t$. Most investors naturally try to maximize the expected return, $E[r].$ This way of thinking is perfectly intuitive. If one correctly identifies the most transformative technology of the century, the rational response seems to be maximizing exposure to that opportunity. This was essentially Aschenbrenner's way. His investment thesis was intellectually compelling. Artificial intelligence would require enormous computational resources, HBM supply would remain structurally constrained, data centers would become strategic assets, and companies supplying AI infrastructure would therefore generate exceptional returns.

The industrial thesis may ultimately prove correct. The mathematics of leverage, however, tells a very different story. Suppose an investor employs three times leverage. A decline of only 20% in the underlying assets immediately translates into an approximately 60% loss of equity. If losses continue, the investor may face margin calls and forced liquidation. Mathematically, $W\rightarrow0,$ and once wealth approaches zero, every future investment opportunity becomes irrelevant. Regardless of how accurate the original thesis was, the investment process has effectively ended.

Buffett approaches the same problem from an entirely different perspective. Rather than focusing solely on maximizing $E[r],$his investment philosophy can be interpreted as being much closer to maximizing $E[\log W],$ which is the objective underlying the Kelly Criterion. Buffett has never claimed to follow the Kelly Criterion explicitly, yet many of his investment principles are remarkably consistent with its mathematical intuition.

The logarithmic utility function has an important implication. Wealth compounds multiplicatively rather than additively, making gains and losses fundamentally asymmetric. A gain of 100% doubles wealth, whereas a loss of 50% cuts wealth in half. Recovering from that loss requires another gain of 100%, not 50%. Likewise, an 80% loss requires a subsequent gain of 400% merely to return to the original capital. This asymmetry explains why avoiding catastrophic losses is mathematically far more important than achieving spectacular gains.

The distinction becomes even clearer when viewed through the lens of long-term compounding. Wealth after $n$ investment periods is given by $W_n=W_0\prod_{i=1}^{n}(1+r_i).$ Investment success is therefore governed by a product rather than a sum. One sufficiently large negative event can permanently impair decades of successful investing. Long-term wealth depends not only on earning high returns, but also on remaining in the game long enough for compounding to work.

Viewed from this perspective, Aschenbrenner optimized the magnitude of future gains, whereas Buffett optimized the probability of surviving every market cycle. Conceptually, their objective functions can be written as $\text{Aschenbrenner:}\quad \max E[r]$ and $\text{Buffett:}\quad \max E[\log W].$ Perhaps the most revealing way to summarize Buffett's philosophy is through the following conceptual equation: $\text{Investment Success}=\text{Correct Thesis}\times\text{Position Size}\times\text{Survival Probability}.$

Most investors devote nearly all of their effort to the first term. Buffett devotes attention to the last two. He willingly sacrifices part of the upside if doing so substantially increases the probability of surviving every market cycle. Although this approach may appear conservative over short horizons, over many decades it has produced one of the greatest investment records in financial history.

Aschenbrenner may ultimately be remembered as one of the most insightful analysts of the AI revolution. His understanding of semiconductor supply chains, HBM, and AI infrastructure is correct. Yet financial markets reward not only correct predictions, but also portfolios capable of surviving uncertainty, volatility, and poor timing. Perhaps that is the deepest mathematical distinction between the two investors. Aschenbrenner sought to predict the future. Buffett built a system capable of surviving almost any future.

The lesson extends far beyond these two individuals. In investing, making correct predictions is important, but it is not sufficient. Markets are full of investors who were eventually proven right but failed because they ran out of capital, liquidity, or time before reality caught up with them. Intelligence identifies opportunities, but survival determines whether those opportunities can compound into extraordinary wealth. In the end, mathematics tells us something surprisingly simple: the first rule of compounding is to survive.