Conceptual search: A generative view of entrepreneurial imagination

Research Summary Entrepreneurs cannot pursue ventures they never imagine. This article develops a generative view of entrepreneurial imagination as the construction of an evolving candidate set through generation, provisional evaluation, and revision. Conceptual search represents venture concepts as text, transforms them through interpretable operations, and compares their promise. A large language model prototype demonstrates complementary modes: local refinement lifts a concept from sixteenth to third among 30 ventures under a validated evaluator, while global recombination reaches distant possibilities that refinement makes competitive. The framework shows how search architecture determines what enters, survives, and develops, and how AI may broaden imagination within firms while producing correlated imagination across firms. By making imagination programmable, auditable, and experimentally tractable, conceptual search opens a research program on how entrepreneurial possibilities are constructed. Managerial Summary Entrepreneurs can pursue only the ventures they imagine, and valuable ideas routinely go unbuilt because nobody thinks of them. AI makes imagining cheap: it can draft, critique, and rewrite venture concepts in seconds, so developing ideas becomes a process that can be designed, run, and audited. I demonstrate a system that evolves real venture concepts: step‐by‐step refinement lifted a middling concept to third place among 30 real ventures, as ranked by an AI judge shown to predict fundraising, while recombining distant ideas created novel concepts that refinement made competitive. The entrepreneur's job shifts from generating a few ideas to governing many: choosing what to search, keep, and test in the market. One caution: rivals using the same AI will imagine similar ventures; advantage comes from proprietary problems, data, and evidence.