Unlike traditional software that produces identical, auditable results, AI is non-deterministic and often can't explain its reasoning. This poses a major challenge for finance, an industry where processes must be repeatable and transparent to meet regulatory and client expectations for showing work.

High-frequency trading firms are expanding into medium-frequency horizons (days to weeks). They use their sophisticated short-term AI models, which can predict optimal prices within the next hour, to inform the execution strategy for their longer-term positions, creating a cascading effect where intraday precision enhances multi-day trading performance.

Whether AI models truly "reason" or are just sophisticated prediction machines is a philosophical question. From a business perspective, the distinction is irrelevant. The models simulate reasoning and empathy so effectively that the outcome is what matters, not the underlying mechanism.

Hudson River Trading shifted from handcrafted features based on human intuition to training models on raw, internet-scale market data. This emergent approach, similar to how ChatGPT is trained, has entirely overtaken traditional quant methods that relied on simpler techniques like linear regression.

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.

With AI models capably handling implementation, Hudson River Trading is shifting its hiring focus. The firm can now hire "theorists" or "dreamers" who excel at ideation but may lack coding skills. The ability to clearly articulate ideas and prompts to an AI has become a highly valued skill in itself.

The transition from human to machine-driven trading has a specific threshold: one-tenth of a second, the lower limit of human time perception. Once trading speeds crossed this barrier, human decision-making became too slow to compete, necessitating algorithmic control for execution.

The future of financial analysis isn't job replacement but radical augmentation. An analyst's role will shift to managing dozens of AI agents that perform research and modeling around the clock, dramatically increasing the scope and speed of idea generation and validation.

Demanding interpretability from AI trading models is a fallacy because they operate at a superhuman level. An AI predicting a stock's price in one minute is processing data in a way no human can. Expecting a simple, human-like explanation for its decision is unreasonable, much like asking a chess engine to explain its moves in prose.