Will AI Make Markets Less Efficient?

In this episode of Goldman Sachs Exchanges, host Allison Nathan and George Lee explore how artificial intelligence—particularly large language models—is transforming investment strategies and market dynamics. Joined by Osman Ali, global co-head of quantitative investment strategies at Goldman Sachs, the conversation delves into how AI enables deeper, faster analysis of vast datasets, including sentiment from management disclosures, public discourse, and sell-side commentary. While AI enhances data-driven decision-making and allows for unprecedented depth in analysis across 15,000 stocks daily, the panel discusses a counterintuitive outcome: rather than making markets more efficient, AI may be increasing inefficiencies through herd behavior and crowding. Osman argues that the democratization of AI tools leads to synchronized investor behavior, pushing prices away from fundamental value and creating new alpha opportunities for those who can model the psychology of markets. Despite automation, the team size at Goldman Sachs remains stable, underscoring the irreplaceable role of human experience and context in investing. The episode concludes with a forward-looking perspective: the future of investing lies not in replacing humans with machines, but in combining data science with seasoned judgment. Key takeaways include: AI is not making markets more efficient but creating new forms of inefficiency through collective behavior; the most valuable edge comes from combining proprietary data, advanced technology, and human insight; investing remains a zero-sum game where only a few can outperform; and future success in finance will require a blend of technical skills and deep contextual understanding. The overall tone is cautiously optimistic, emphasizing opportunity amid disruption.