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.

While AI can optimize answers, Citi's CEO argues it cannot replicate the trust, confidence, and human connection essential for major decisions like transformational M&A. The apprenticeship model of learning through human interaction remains critical for developing judgment.

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.

John Graham, a scientist by training, asserts that investing is not a science. While quantitative models are crucial evidence-based tools, they are just best guesses about an uncertain future. Investing is a "quantitative art" requiring judgment and experience, as market conditions are a living, non-replicable ecosystem.

Despite a long-standing data-science-driven investment thesis, Foresight Capital's founder Jim Tananbaum states that AI tools have not yet objectively led to increased investment returns. The technology is still maturing, highlighting a reality gap between the hype around AI in VC and its current practical impact.

AI's primary impact is not wholesale human replacement but rather collapsing the middle of the value pyramid by automating routine knowledge work. The value of human workers will shift to higher-level judgment and strategic oversight, where AI can structure options and simulate outcomes, but humans retain final say due to liability concerns.

AI tools are automating traditional analytical tasks, diminishing the edge from pure technical skill. The most valuable investors will be those who can apply superior judgment, market structure understanding, and pattern recognition to challenge and interpret AI-generated insights.

Rather than commoditizing alpha, AI tools will initially create more disparity between investors. They empower users with good intuition but limited quantitative skills to test complex ideas efficiently. This makes the quality of one's questions, not just their analytical process, a key differentiator.