Despite the wide availability of powerful AI models, a sustainable edge in the zero-sum game of investing comes from a combination of unique, curated data sets, bespoke technology for scale, and the experienced human context to ask the right questions of the models.

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.

Howard Marks believes AI's strength in pattern recognition is also its key limitation in investing. It can extrapolate from historical data but cannot identify true novelty, like a revolutionary business model or a visionary founder like Steve Jobs, where no pre-existing pattern exists. This preserves a role for unique human judgment.

David Kaiser suggests that as AI becomes ubiquitous in investing, a "tiptoes at a parade" problem emerges where no one gains an edge. By intentionally not using AI to constantly evolve his process, he believes his firm can be differentiated. The alpha may lie in the systematic, old-school approach that AI-driven consensus overlooks.

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.

In a market where everyone agrees AI is the future, being a contrarian no longer means betting against it. Instead, the real edge comes from believing in the trend more intensely than others and identifying nuanced, under-appreciated sub-domains like productivity enhancement or the moats created by elite talent.

Ackman argues that the most critical challenge AI poses for long-term investors isn't about AI-powered analysis tools. Instead, it's about how AI exponentially increases the risk of even the most dominant businesses being disrupted, making long-term forecasts much harder.

As AI masters the analysis of financial filings and transcripts, the source of investment alpha may shift to information that is difficult for models to process. Qualitative insights from attending conferences, judging a CEO's character via a handshake, or other forms of scuttlebutt could become increasingly valuable differentiators for human investors.