As platforms like AlphaSense automate the grunt work of research, the advantage is no longer in finding information. The new "alpha" for investors comes from asking better, more creative questions, identifying cross-industry trends, and being more adept at prompting the AI to uncover non-obvious connections.

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.

As AI models democratize access to information and analysis, traditional data advantages will disappear. The only durable competitive advantage will be an organization's ability to learn and adapt. The speed of the "breakthrough -> implementation -> behavior change" loop will separate winners from losers.

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.

David Kaiser of Methodical Investments posits a contrarian view on AI's market impact. Instead of creating perfect efficiency, he argues AI and the data it processes might actually create more mispricings and inefficiencies. This provides opportunities for disciplined, rules-based strategies that don't constantly adapt to short-term noise.

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.

To compete with superior AI, a human trader should focus on building a portfolio of undervalued assets today. When hyper-intelligent AIs eventually arrive and re-price markets efficiently, they will likely buy the very assets the human trader already holds, validating the initial thesis and accelerating gains.

Amateurs playing basketball compete on a horizontal plane, while NBA pros add a vertical dimension (dunking). Similarly, individual investors cannot beat quantitative funds at their game of speed, data, and leverage. The only path to winning is to change the game's dimensions entirely by focusing on "weird," qualitative factors that algorithms are not built to understand.

In an age where AI can quickly commoditize features, traditional moats like data are weakening. Miro's CEO argues the only sustainable competitive advantage is an organization's speed of learning—its ability to rapidly identify market signals, separate them from noise, and act decisively.