In June 2024, former OpenAI researcher Leopold Aschenbrenner published a 165-page essay arguing that artificial intelligence would require an extraordinary expansion of physical infrastructure.
The future of AI, he argued, would not be built only with better algorithms. It would require enormous quantities of chips, memory, electricity and data centers. He later turned that conviction into Situational Awareness, a hedge fund built around the same thesis.
The strategy was brutally concentrated. The fund bought companies positioned to benefit from the AI infrastructure boom while betting against software businesses that could be disrupted by increasingly capable artificial intelligence.
For a period, the results appeared extraordinary. According to the Financial Times, the fund reportedly returned 439 percent in the first half of 2026 and 1,551 percent since launch. By early July, it was managing approximately $45 billion.
Then the same concentration that created the extraordinary gains became a source of vulnerability. AI infrastructure stocks declined, while the software positions that had been expected to fall moved in the opposite direction.
Leverage amplified the problem. Prime brokers reportedly issued margin calls, forcing Situational Awareness to sell public-market positions to Ken Griffin’s Citadel at prices below their previous market levels. Within days, its assets reportedly fell to roughly $10 billion.
A subsequent letter to clients acknowledged that the fund had failed them. Yet the story did not end with retreat. Barely a week later, the fund was reportedly investing again, placing a $400 million bet.
That decision reveals something important: the underlying thesis and the financial structure supporting it are two different questions. Aschenbrenner may still be correct about the enormous infrastructure requirements of AI.
A correct long-term thesis can nevertheless produce devastating short-term losses when position sizes, leverage and liquidity become misaligned. That distinction extends far beyond hedge funds. Concentration is often the engine of exceptional performance.
Nobody becomes world-class by giving every skill equal attention. Psychologist Anders Ericsson and his colleagues’ research on deliberate practice emphasized sustained, purposeful effort directed toward specific areas of improvement.
Expertise requires repetition, feedback and depth. The person who spends years solving the hardest problems in a narrow field can develop capabilities that a generalist simply cannot reproduce. Career leverage works in much the same way.
If you want to become the person organizations call when a difficult problem appears, you need something unusually valuable to offer. That usually comes from going deeper than everyone else.
An engineer who understands a complex infrastructure system at an exceptional level, a journalist who develops unmatched expertise in a particular market, or a researcher who can connect technical developments with economic consequences creates scarcity around their knowledge.
But concentration has a boundary. The same specialization that accelerates expertise can become dangerous when it turns into total dependence. A career built around one employer, one technology, one customer, one market or one source of income can suffer the same problem as a highly leveraged investment portfolio.
When the central assumption breaks, there may be no protection. The lesson, therefore, is not to choose between concentration and diversification. It is to understand where each belongs. Concentrate your effort when building expertise.
Diversify your exposure when protecting what that expertise has created. Situational Awareness demonstrated both principles in spectacular fashion. Its concentrated thesis generated extraordinary returns, but its leverage and exposure also magnified the consequences when markets moved against it.
The goal is not simply to make a fortune from being right. It is to remain solvent, adaptable and useful long enough to benefit from being right again.






