This study examines the dynamic interplay between exploration and exploitation and the transformative impact of artificial intelligence (AI) on these dynamics. Rooted in the Carnegie School tradition, specifically the behavioral theory of the firm, exploration-exploitation dynamics describe the shifting balance between pursuing novel knowledge and leveraging existing solutions. We posit that while these two approaches typically establish a rhythmic balance over time, the integration of AI introduces a disruptive force that reshapes established patterns. Leveraging data from 4,864 Grandmaster chess games played at the Tata Steel Tournament (1971–2022), we investigate how AI alters exploration-exploitation dynamics over time. Our findings reveal a critical shift in dynamics, driven by AI’s capacity to enhance individual capabilities while simultaneously homogenizing problem-solving approaches. We identify sequential phases of relative exploration and exploitation, characterized by distinct intensities, durations, and levels. Furthermore, we demonstrate how AI reduces heterogeneity in problem-solving, creating a “new normal” that adjusts the cognitive and strategic foundations of problem-solving. These results extend the relatively nascent research on exploration-exploitation dynamics and highlight AI’s role as both an enabler and a disruptor of patterns. We discuss the theoretical implications for the Carnegie School tradition and offer practical insights for professionals navigating an AI-driven future.
