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Why Better Models Can Create Riskier Systems: Evidence from LLM Agents in Financial Markets

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

Large language models (LLMs) are being deployed at scale in consequential real-world systems, from financial markets to content moderation to hiring. We show that improving individual model capability can degrade rather than improve system-level outcomes. We hypothesize that shared training and architectures can lead more capable LLMs to behave more similarly, creating correlated actions that do not diversify away. We develop a general framework showing how this correlation creates a non-diversifiable risk floor and test its predictions in financial markets using an agent-based simulation with

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