Summary
Opportunity recognition—the ability to spot good business ideas—is a key part of entrepreneurship. The authors studied how this skill exists inside large language models (LLMs), computer programs that understand and generate human language. They found a specific internal “dial” inside these models that controls how the model judges opportunities. By adjusting this dial, they could steer the model’s decisions about business opportunities, showing that these AI systems hold detailed, steerable representations of entrepreneurial thinking. This insight helps link entrepreneurship theory with AI behavior and opens new ways to understand and guide AI decision-making.
What this means in practice
- •For startup advisors: Use AI tools tuned to recognize and weigh business opportunities to support decision-making during venture formation.
- •For ai system designers: Build interpretable AI models with explicit controls on opportunity recognition to improve transparency and steerability in entrepreneurial applications.
Authors
Christian Fisch, Angela Altmeier, Martin Obschonka, Michal Kosinski, Pin Ni
Abstract
Entrepreneurial cognition is a foundation of entrepreneurship research. Yet the growing involvement of large language models (LLMs) in entrepreneurial work extends the cognition question beyond human actors to systems whose internal representations remain largely unexplored. We introduce artificial entrepreneurial cognition, the functional organisation of entrepreneurship-relevant representations and computations inside artificial intelligence (AI) systems. We bring mechanistic interpretability into entrepreneurship research through representation engineering. Focusing on opportunity recognition (OR), we construct 636 matched OR-present and OR-absent scenario pairs and recover an OR direction in Llama 3.1 8B-Instruct. Rather than infer the construct from outputs, we intervene directly on this direction, steering the model up and down along what we call the opportunity recognition dial, and its opportunity judgments shift with it. To our knowledge, this is the first causal intervention on an internal representation of an entrepreneurship construct inside an LLM. Held-out tests, lexical and topical controls, behavioural ablation, and geometric comparisons show that the direction is recoverable, consequential, and distinct from the opportunity evaluation and exploitation directions, although steering it also shifts judgments about these neighbouring stages. Recovery, signed steering, and geometric separation hold across four additional LLMs spanning different scales and families. These results give the contested distinction between opportunity recognition and evaluation a concrete representational form inside AI systems. More broadly, they establish internal representations as a new object of entrepreneurship inquiry and show how entrepreneurship theory can guide their identification, causal manipulation, and interpretation.