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.

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.

Beyond simple quantitative screens, AI can now identify companies fitting complex, qualitative theses. For example, it can find "high-performing businesses with temporary, non-structural hiccups." This requires synthesizing business model quality, recent performance issues, and the nature of those issues—a task previously reliant on serendipity.

AI performs poorly in areas where expertise is based on unwritten 'taste' or intuition rather than documented knowledge. If the correct approach doesn't exist in training data or isn't explicitly provided by human trainers, models will inevitably struggle with that particular problem.

Despite hype in areas like self-driving cars and medical diagnosis, AI has not replaced expert human judgment. Its most successful application is as a powerful assistant that augments human experts, who still make the final, critical decisions. This is a key distinction for scoping AI products.

Despite AI's capabilities, it lacks the full context necessary for nuanced business decisions. The most valuable work happens when people with diverse perspectives convene to solve problems, leveraging a collective understanding that AI cannot access. Technology should augment this, not replace it.

AI can quickly find data in financial reports but can't replicate an expert's ability to see crucial connections and second-order effects. This leads investors to a false sense of security, relying on a tool that provides information without the wisdom to interpret it correctly.

AI accelerates data retrieval, but it creates a dangerous knowledge gap. Junior employees can find facts (e.g., in a financial statement) without the experience-based judgment to understand their deeper connections and second-order consequences for the business.

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.