We introduce an equilibrium framework in which firms delegate decision-making to humans or large language models (LLMs) trained on others’ strategic behavior. We model information set by LLMs not from fundamentals, but from equilibrium actions of other agents, introducing a recursive informational structure. Using a beauty contest environment with heterogeneous strategic complementarities, we characterize how endogenous information by LLM arises and how this delegation reshapes strategies.