Entrepreneurial theorizing with generative artificial intelligence

Abstract Research Summary Whether large language model (LLM) assistance changes how entrepreneurs construct theories remains unclear. On the one hand, LLMs may help entrepreneurs build more developed causal structures and surface diverse information that supports the formulation of distinctive theories. On the other hand, LLMs may steer causal structures toward conventional forms and discourage the counterintuitive reasoning needed to develop distinctive theories. In a within‐subject experiment with 199 participants theorizing both with and without LLM assistance, we find no significant average treatment effect on either structural or content properties. However, this null average effect masks a redistribution effect, where LLM assistance supplemented theorizing when unaided theories were less developed or distinctive but displaced participants' own theorizing when their theories were already more developed or distinctive. Managerial Summary Large language models (LLMs) can help entrepreneurs develop business ideas by surfacing knowledge across domains and sharpening causal logic. At the same time, LLMs may also push reasoning toward conventional patterns and away from the counterintuitive ideas that are a source of breakthrough opportunities. In a study of 199 participants theorizing with and without LLM assistance, we find that LLMs supported the development of causal structure and content distinctiveness when unaided theories were less developed or distinctive, whereas they had the opposite effect when those theories were already more developed or distinctive. This suggests that while LLMs can scaffold entrepreneurs still developing their theories, greater caution is warranted for those whose theories are already more developed or distinctive, as assistance may diminish those properties.