LLM Search Lifts a Startup Idea From 16th to 3rd Among 30 Ventures
Image: "Reflections on the new Machine Age" by Steve Jurvetson from Los Altos, USA (CC BY 2.0) via Wikimedia Commons.jpg), recoloured.
Can a large language model help entrepreneurs imagine better ventures? Felipe Csaszar (Strategic Entrepreneurship Journal, 2026) proposes "conceptual search" and shows that step-by-step LLM refinement moved one venture concept from sixteenth to third among 30 real ventures, while warning that shared AI tools may make rivals imagine similar ideas.
Making entrepreneurial imagination programmable
Strategy research has long treated search as a choice among known options. Felipe A. Csaszar of the University of Michigan starts one step earlier: entrepreneurs cannot pursue ventures they never imagine. In a new article in the Strategic Entrepreneurship Journal, he models entrepreneurial imagination as the construction of an evolving candidate set, built through three repeated moves: generation, provisional evaluation and revision.
The method, which Csaszar calls conceptual search, represents each venture concept as text. Concepts are then transformed through interpretable operations, such as refining an existing idea or recombining distant ones, and compared on their promise. To show the approach working, the article builds a large language model prototype and scores concepts with an AI evaluator that, according to the paper, was validated as a predictor of fundraising.
According to Csaszar (Strategic Entrepreneurship Journal, 2026), the prototype reveals two complementary modes. Local refinement improved a middling venture concept step by step until it rose from sixteenth to third place among 30 real ventures under the validated evaluator. Global recombination, by contrast, reached distant possibilities that refinement could then make competitive. The broader argument is that the architecture of search, not only the quality of individual ideas, determines which possibilities enter the candidate set, which survive and which get developed.
The paper is explicit about a downside. AI may broaden imagination inside a firm while producing correlated imagination across firms: competitors that use the same models are likely to arrive at similar ventures. Csaszar suggests that advantage then comes from proprietary problems, data and evidence rather than from idea generation itself. Readers should also note that the results come from a prototype demonstration ranked by an AI judge, not from ventures tested in the market, which is the step the author says entrepreneurs must still govern.
Why it matters: For researchers in strategy, entrepreneurship and marketing, the article offers both a tool and a research agenda. Treating ideas as text that can be generated, transformed and scored makes imagination auditable and experimentally tractable, so questions about how new products, ventures or even theories are conceived can be studied with designed search procedures rather than retrospective accounts. The correlated imagination warning is also a testable hypothesis: as more teams use the same models, scholars can measure whether the diversity of proposed ventures, research ideas or product concepts narrows.
Csaszar, F. A. (2026). Conceptual search: A generative view of entrepreneurial imagination. *Strategic Entrepreneurship Journal*. https://doi.org/10.1002/sej.70055. Independent editorial summary of third-party research.
Comments