The End of Algorithmic Trading: Structural Limits of Artificial Intelligence and the Emergence of Adaptive Computing in Financial Markets

Algorithmic trading systems based on artificial intelligence (AI) have dominated financial markets for more than two decades, driving automation, liquidity, and speed across equities, derivatives, and foreign exchange markets. These systems rely primarily on predictive models trained on historical data, continuously refined through retraining and optimization. Despite their success, AI-based trading architectures exhibit fundamental structural limitations when deployed in highly dynamic, adversarial, and non-stationary market environments. Increasing investments in data volume, model complexity, and computational speed have produced diminishing returns, while latency, model staleness, and exploitability persist. This paper argues that these limitations are not implementation flaws but inherent consequences of the predictive paradigm itself. It introduces Adaptive Computing (AC) as a distinct computational approach that emphasizes continuous, real-time state alignment rather than historical prediction. Without disclosing proprietary mechanisms, the paper analyzes why AI-driven trading systems approach structural exhaustion and how AC represents a qualitatively different trajectory for market computation. The implications for high-frequency trading, hedge fund strategies, market stability, and regulatory oversight are examined. The paper positions Adaptive Computing not as an incremental improvement over AI, but as a paradigm shift that redefines how computational systems engage with financial markets.