Ken Griffin is skeptical of AI's role in long-term investing. He argues that since AI models are trained on historical data, they excel at static problems. However, investing requires predicting a future that may not resemble the past—a dynamic, forward-looking task where these models inherently struggle.

Even with vast training data, current AI models are far less sample-efficient than humans. This limits their ability to adapt and learn new skills on the fly. They resemble a perpetual new hire who can access information but lacks the deep, instinctual learning that comes from experience and weight updates.

Historically, investment tech focused on speed. Modern AI, like AlphaGo, offers something new: inhuman intelligence that reveals novel insights and strategies humans miss. For investors, this means moving beyond automation to using AI as a tool for generating genuine alpha through superior inference.

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.

All-AI organizations will struggle to replace human ones until AI masters a wide range of skills. Humans will retain a critical edge in areas like long-horizon strategy and metacognition, allowing human-AI teams to outperform purely AI systems, potentially until around 2040.

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.

AI can quickly find data in financial reports but can't replicate an expert's ability to see crucial connections and second-order effects. This leads investors to a false sense of security, relying on a tool that provides information without the wisdom to interpret it correctly.

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.