The stock market is a 'hyperobject'—a phenomenon too vast and complex to be fully understood through data alone. Top investors navigate it by blending analysis with deep intuition, honed by recognizing patterns from countless low-fidelity signals, similar to ancient Polynesian navigators.

Traditional value metrics are arbitraged away by quants. The new edge lies in unconventional scenarios like stocks with cult followings and assets fueled by zero-day options, similar to how sports strategies evolve to extremes. Fundamental analysis is now just table stakes.

In hyper-competitive fields, the emergence of dominant strategies that seem "insane"—like the Fosbury Flop or AI's aggressive poker bets—signals evolution to the highest level. For investors, this means strategies that appear bizarre may represent the new, optimal approach in a market saturated by traditional thinking, rather than being mere anomalies.

When Garry Kasparov faced IBM's Deep Blue, he used "insane" opening moves to take the computer "out of the book" and away from its programming. Investors can apply this by focusing on situations where historical data is irrelevant, like spinoffs or paradigm shifts like AI's impact on power demand. This forces systematic strategies into uncharted territory where they are weakest.

Most good investors succeed by recognizing patterns (e.g., "SaaS for X"). However, the truly exceptional investors analyze businesses from first principles, understanding their deep, fundamental merits. This allows them to spot outlier opportunities that don't fit any existing mold, which is where the greatest returns are found.

In a world where AI can efficiently predict outcomes based on past data, predictable behavior becomes less valuable. Sam Altman suggests that the ability to generate ideas that are both contrarian—even to one's own patterns—and correct will see its value increase significantly.

As quantitative models and AI dominate traditional strategies, the only remaining source of alpha is in "weird" situations. These are unique, non-replicable events, like the Elon Musk-Twitter saga, that lack historical parallels for machines to model. Investors must shift from finding undervalued assets to identifying structurally strange opportunities where human judgment has an edge.

Institutional investors prefer quantifiable data with historical correlations. They struggle to build teams and models around qualitative, evolving 'conversational data' from social media. This structural inability to act on non-quantifiable signals creates a lasting advantage for observant retail investors.

Advanced AIs, like those in Starcraft, can dominate human experts in controlled scenarios but collapse when faced with a minor surprise. This reveals a critical vulnerability. Human investors can generate alpha by focusing on situations where unforeseen events or "thick tail" risks are likely, as these are the blind spots for purely algorithmic strategies.