Artificial Intelligence and Systemic Risk: A Unified Model of Performative Prediction, Algorithmic Herding, and Cognitive Dependency in Financial Markets

A new unified model demonstrates that AI adoption in financial markets creates systemic risk through three channels: performative prediction, algorithmic herding, and cognitive dependency. Using SEC Form 13F data from 2013-2024, researchers found AI adoption generates superlinear growth in systemic risk and tail-loss amplification of 18-54%.

• →AI adoption in financial markets creates systemic risk through three mutually reinforcing mechanisms that compound as adoption increases.
• →The systemic risk multiplier grows superlinearly with AI penetration due to decreasing market depth and increasing algorithmic correlation.
• →Analysis of 99.5 million SEC filings shows tail-loss amplification of 18-54%, comparable to Basel III countercyclical buffers.
• →Higher AI adoption leads to algorithmic monoculture through a saddle-node bifurcation in adoption dynamics.
• →Each of the three risk channels (performative prediction, algorithmic herding, cognitive dependency) is individually necessary for the systemic risk effect.