The forgotten Genius
What John von Neumann Can Teach Us About Risk, Uncertainty, and Edge
The Forgotten Genius Who Could Outsmart Anyone—And Why Investors Should Study Him
Look, I get it. Buffett quotes are everywhere. You can’t open a finance article without tripping over one. And to be clear, Buffett is brilliant. This isn’t some cheap shot at him. But trying to copy the playbook of someone managing hundreds of billions, with a time horizon measured in decades, doesn’t make much sense if your holding period is closer to a few months and you’re working with a couple of million, not the GDP of a small country.
Buffett has said it himself: with smaller sums, he’d invest differently—and get much better returns. I’ve written before about how your time horizon changes which metrics actually matter. Still, people keep clinging to See’s Candies, moats, and the Circle of Competence like they’re God-given. They’re not.
Let me introduce someone who’s inspired me since I was a college student. You won’t find him mentioned much in the investing world: John von Neumann. He’s one of the people I admire most, not just for his intellect, but for how he thought.
Now, before you click away thinking "Who the heck is that?" stick with me. Among mathematicians, mentioning von Neumann is like name-dropping Babe Ruth at a baseball game. The man was a legend.
Most people have never heard of him. And that's a shame, because his story is absolutely fascinating with an heartbreaking end.
If this little introduction piques your curiosity, I'd love for you to share this piece. Von Neumann deserves way more recognition than he gets.
Ready to meet one of history's most remarkable minds?
The first time I came in touch with John von Neumann was when I was an undergraduate student taking my first class in functional analysis. I noticed something weird: this guy's name was attached to half the theorems we were studying. At first, I thought it was just coincidence.
Then his name started popping up everywhere. Quantum mechanics? There's von Neumann's "Mathematical Foundations of Quantum Mechanics"—a book I devoured in one weekend. Even my master's thesis ended up being on topics related to von Neumann's work on operator algebras.
At that time I only knew about his work in pure mathematics and physics. Turns out, while von Neumann was casually revolutionizing the foundations of physics, he was also inventing game theory, creating the Monte Carlo method, laying the groundwork for modern economics and decision making under uncertainty, building the first computers, and helping design the atomic bomb. You know, just typical Tuesday activities.
The anecdotes about von Neumann's intellect are legendary and often intimidating even to his brightest colleagues. The great mathematician George Pólya once confessed: "John von Neumann was the only student I was ever afraid of." Pólya recalled a seminar in Zürich where he mentioned an unproven theorem, saying "it is not proved and it may be difficult." Von Neumann said nothing, but after five minutes raised his hand, went to the blackboard, and wrote down the complete proof.
Edward Teller, his close friend, captured the unsettling experience of interacting with von Neumann: "Von Neumann would carry on a conversation with my 3-year-old son, and the two of them would talk as equals, and I sometimes wondered if he used the same principle when he talked to the rest of us."
What made von Neumann different wasn't just his computational speed—it was his relationship with thinking itself. Edward Teller captured this perfectly: "I have come to suspect that to most people thinking is painful. Some of us are addicted to thinking. Some of us find it a necessity. Johnny enjoyed it. I even have the suspicion that he enjoyed practically nothing else."
But perhaps the most heartbreaking story about von Neumann concerns his final years. In 1955, this man who lived and breathed mathematical thought was diagnosed with cancer that eventually spread to his brain. Edward Teller described visiting him during his decline: "When he was dying of cancer his brain was affected. I visited him frequently and he was trying to do what he always tried to do, and he was trying to argue with me as usual but it was not functioning anymore. And I think he suffered from this loss more than I have seen any human suffer in any circumstance."
For someone who "enjoyed practically nothing else" but thinking, losing his mental faculties was the cruelest possible fate.
From this great mind, we can learn a lot. Here I extract three concrete principles inspired by his mathematical work and apply them to how we analyze and invest in stocks.
Principle #1: Applying Minimax Thinking to Portfolio Construction
Von Neumann's minimax theorem was a cornerstone of game theory. Published in 1928, it proved that in any two-player zero-sum game, there exists an optimal strategy for each player: one minimizes their maximum possible loss while the other maximizes their minimum guaranteed gain. This wasn't just an abstract mathematical curiosity—it was a fundamental insight into strategic thinking under uncertainty.
The genius of von Neumann's theorem lies partly in its counterintuitive interpretation. Hearing "minimize your maximum loss" often evokes a purely defensive mindset, avoiding risk at all costs. However, the minimax solution reveals a far more sophisticated principle: in competitive situations under uncertainty, optimal play isn't about eliminating risk, but about strategically managing it to ensure survival and maintain opportunities.
Consider poker, a game von Neumann analyzed. A player following minimax principles doesn't fold every hand to avoid losing. Instead, they bet and bluff in a calculated way. Even if they lose a specific hand, their strategy ensures they preserve enough capital over the long run to exploit favorable situations when they arise. They aren't minimizing risk per se; they are optimizing their exposure to ensure the worst-case outcome doesn't eliminate their ability to keep playing and capitalize on future advantages.
This core insight – structuring decisions to withstand worst-case scenarios while preserving potential – translates powerfully beyond games into domains like investing, business strategy, and military planning.
Principle #2: Monte Carlo Thinking for Investment Analysis
The Monte Carlo method emerged from a very specific wartime problem. At Los Alamos during the Manhattan Project, physicists needed to understand how neutrons would behave as they traveled through various materials in a nuclear weapon. The mathematical equations were so complex that traditional analytical methods failed completely. Von Neumann and Stanisław Ulam needed to understand neutron behavior—average distances traveled, collision probabilities, energy transfer rates—but had incomplete data and no way to solve the problem deterministically.
Ulam's breakthrough came while he was recovering from illness and playing solitaire. He realized that instead of trying to calculate the exact outcome of each card game mathematically, he could play thousands of games, record the results, and understand the probability distribution of winning. This insight revolutionized how scientists approach problems with inherent uncertainty.
Von Neumann immediately grasped the profound implications and provided the computational implementation. Rather than seeking perfect predictions in complex systems, they could run thousands of random simulations to map out the entire landscape of possible outcomes. The method got its code name "Monte Carlo" from the Monaco casino where Ulam's uncle would gamble.
This represents a fundamental shift in thinking about uncertainty. Traditional mathematical approaches seek exact answers: "What will happen?" Monte Carlo thinking asks: "What might happen, and how likely is each possibility?"
Most investors still think like pre-Monte Carlo physicists. They make point estimates: "This company will grow earnings 15% next year." They're trying to predict the future with false precision, ignoring the inherent uncertainty in complex systems like businesses and markets. Target prices presented as single points ignore outcome distributions.
A Monte Carlo approach to investing acknowledges that we can't predict exactly how a company will perform, but we can map out the range of possibilities and their relative probabilities. Instead of saying "15% growth," we ask: "What's the full distribution of possible growth rates?"
Building Probability Distributions: Instead of predicting "15% growth," create a range of scenarios from worst to best case. Study the company's history, competitors, and industry cycles to estimate realistic probabilities for each outcome. Then calculate expected values across all scenarios. For true Monte Carlo analysis, use thousands of randomized simulations rather than just a few discrete scenarios.
The Monte Carlo mindset forces you to think beyond simple labels like "growth stock" or "value stock" and instead focus on the actual distribution of possible outcomes. Approach investing not by trying to predict the unpredictable, but by systematically understanding uncertainty itself.
Principle #3: Building Self-Improving Information Systems
Von Neumann's work on self-replicating cellular automata revealed a fundamental truth: enduring systems don't merely function; they evolve. They generate the tools to solve tomorrow's problems. For investors, this means designing research processes that compound analytical advantages systematically. The goal isn't static efficiency, it's dynamic capability expansion.
Start by developing asymmetric information channels. Replace digested summaries with direct source immersion. Read SEC filings, noting inconsistencies in management discussion. Analyze patent applications that may reveal insights earlier than Wall Street connects the dots. Monitor supplier inventory shifts or customs data while the market fixates on quarterly reports. This direct access to potentially unfiltered signals provides the raw material for unique insights.
Simultaneously, identify predictive signals others ignore. If consensus tracks quarterly sales, find the metrics that precede them: semiconductor orders predicting smartphone demand, developer activity foreshadowing software adoption, or clinical trial enrollment indicating drug approval likelihood. These leading indicators become your proprietary early-warning system.
Engineer a self-reinforcing feedback loop. Over time, cumulative insights compound into new discovery tools: uncovering new data sources like niche supplier databases discovered during channel checks; generating sharper questions when margin trends diverge from inventory data; refining methods by validating sentiment algorithms against earnings call transcripts. This recursive process transforms today's insights into tomorrow's discovery infrastructure.
The outcome is a living research architecture. Like Von Neumann's theoretical self-replicating models, it generates its own infrastructure for advancement – compounding your edge not through brute-force effort, but through designed evolution. Information advantage becomes self-perpetuating.
The Von Neumann Legacy for Investors
Von Neumann understood that small systematic advantages compound into enormous benefits over time.
The same principle applies to investing. You don't need to find the next Amazon or predict crashes. You need to:
Consistently avoid large losses (minimax)
Make decisions based on probability distributions (Monte Carlo)
Build information systems that improve over time (self-replication)
These won't make you rich overnight, but they'll make you systematically better than most investors. Over time, systematic advantages compound into extraordinary results.