When Artificial Intelligence Does Strategy: Learning, Good Times, Lock-in, and Human-Driven Strategic Renewal

What happens in industries where firms delegate strategy choices to private artificial intelligence (AI) agents? Would markets spiral into hypercompetition or settle into a comfortable status quo? We develop a formal model in which AI agents consider large business-model catalogs, predict performance, select, and learn from realized outcomes. Our representation accommodates existing AI paradigms, allowing for substantial increases in scale and computational capacity. We show that market dynamics converge to a self-confirming equilibrium; along the realized path, AI agents become well calibrated, and their choices become subjectively optimal—even though objectively superior business models may remain unexplored. This convergence can indeed sustain high profits. However, it also produces strategic lock-in; novel business-model implementations become rare long before catalogs are exhausted. This creates a distinct role for humans. A single episode of human-driven frame expansion—introducing a genuinely new business model to a catalog—can disrupt the AI-induced equilibrium and initiate strategic renewal. Yet, the ability to do so does not imply that it will be done. When the prevailing equilibrium is sufficiently lucrative, managers rationally refrain from triggering renewed learning. Our results clarify where humans still matter in AI-enabled strategy: deciding when to change the frame and not merely optimizing within it.