Contrary to popular belief, the primary edge in HFT comes from exploiting the physical and regulatory structure of markets, not from discovering complex financial patterns. Speed is the main tool used for this structural exploitation, prioritizing infrastructure over algorithmic genius.

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.

As frontier AI models reach a plateau of perceived intelligence, the key differentiator is shifting to user experience. Low-latency, reliable performance is becoming more critical than marginal gains on benchmarks, making speed the next major competitive vector for AI products like ChatGPT.

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.

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.

The key threat from AI isn't just its capability, but the unprecedented speed of its improvement. Unlike past technological shifts that unfolded over decades, AI agent autonomy on complex tasks has grown exponentially in just two years. This rapid acceleration is what financial systems and labor markets are not stress-tested for.

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.

Even if AI accelerates parts of a workflow like coding, overall progress might stall due to Amdahl's Law. The system's speed is limited by its slowest component, meaning human-dependent tasks like strategic thinking could become the new rate-limiting step.