While AI excels at investment analysis, it falls short in final decision-making. Veteran investor Ross Gerber notes that AI can't properly weigh qualitative factors like extreme valuations (P/E ratios) or replicate the intuition gained from decades of market experience, making human oversight essential.

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.

AI excels at learning fixed rules, like in chess or identifying a cat. However, it falters in domains like financial markets or politics where the 'game' is adversarial and multiplayer. Any successful AI strategy is quickly identified and countered, rendering it ineffective.

Expert traders find AI models like LLMs to be "the squarest money out there." Because these models are trained on existing public data and expertise, their outputs merely reflect the prevailing consensus. If that consensus is already beatable by sharps, the AI offers no additional edge and is easily manipulated.

Cliff Asnes explains that integrating machine learning into investment processes involves a crucial trade-off. While AI models can identify complex, non-linear patterns that outperform traditional methods, their inner workings are often uninterpretable, forcing a departure from intuitively understood strategies.

AI models can predict short-term stock prices, defying the efficient market hypothesis. However, the predictions are only marginally better than random, with an accuracy akin to "50.1%". The profitability comes not from magic, but from executing this tiny statistical edge millions of times across the market.

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.

A study found that people given tomorrow's headlines still performed poorly in simulated trading. Their failure wasn't in predicting market direction, but in sizing bets appropriately. Professionals outperform not by having a better crystal ball, but by skillfully modulating investment size based on their level of confidence, even choosing not to bet at all on some days.

In quantitative finance, AI's current strength lies in processing structured data to identify a universe of relevant inputs, like finding correlated stocks for a hedging basket. However, it falls short on optimization, considering real-world constraints like liquidity, or answering abstract strategic questions—tasks that still require human wisdom.