A core debate in AI is whether LLMs, which are text prediction engines, can achieve true intelligence. Critics argue they cannot because they lack a model of the real world. This prevents them from making meaningful, context-aware predictions about future events—a limitation that more data alone may not solve.

Professional traders have a simple heuristic: always bet against the consensus in the comment section. "Sharps" keep their valuable insights private, while less-informed traders often broadcast flawed reasoning publicly, making the comments a useful signal for identifying the "dumb money" side of a trade.

Unlike a human expert, an LLM's probability estimates and conclusions can be drastically altered by simple rephrasing or irrelevant suggestions. This instability shows they are too easily "pushed around" and lack the coherent world model necessary for trustworthy, high-stakes decision support.

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's strength in pattern recognition could become its weakness in an adaptive market. Companies and human investors may learn to manipulate AI-driven funds by feeding them historical patterns that signal value, such as initiating dividends during distress to trigger buys, ultimately leading the AI to underperform.

LLMs are technically non-deterministic systems designed to guess the next most probable word, not verify facts like a calculator. This inherent design means they will confidently produce incorrect information, making human verification indispensable for high-stakes business decisions.

Widespread use of similar AI models by average investors will likely lead to herd behavior and crowding in certain securities. This pushes prices away from fundamental value, creating predictable inefficiencies and new alpha opportunities for sophisticated investors who can model these effects.

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.

Contrary to popular belief, generative AI like LLMs may not get significantly more accurate. As statistical engines that predict the next most likely word, they lack true reasoning or an understanding of "accuracy." This fundamental limitation means they will always be prone to making unfixable mistakes.