Are language models intelligent enough for entrepreneurial work? A language-centered perspective
挑战了关于大语言模型（LLMs）无法支持创业判断的普遍怀疑，基于维特根斯坦和图灵的语言哲学，提出以语言为中心的视角，并通过两项研究证明LLMs能有效创作和优化创业叙事，从而降低创业门槛。
Large Language Models (LLMs) are poised to fundamentally reshape entrepreneurial work, but it remains unclear whether this technology can support judgment-intensive entrepreneurial tasks. Prevailing skepticism holds that LLMs are inherently unreliable for such deep augmentation because, despite their language competence, they do not think. In contrast, we draw on Ludwig Wittgenstein and Alan Turing to advance a language-centered perspective on entrepreneurial work. Wittgenstein demystifies thought as linguistic activity and treats reasoning and understanding as linguistic abilities exercised in thinking. Extending this stance to the domain of machine intelligence, Turing grounds claims about intelligence in testable performances of language use. Together, they enable us to (1) conceptualize LLMs as an epistemic technology whose linguistic competence may suffice for the deep augmentation of entrepreneurship and (2) reorient research from skepticism toward fine-grained Turing tests of entrepreneurial work. We illustrate and support the language-centered perspective through two studies on crafting effective entrepreneurial narratives, a judgment-intensive task. Initially, we document that the LLM competently blends expert rhetorical strategies to create and refine narratives that effectively align with stakeholder needs. We then experimentally demonstrate that, when coupled with stakeholder-guided iterations, LLMs produce measurable improvements in narratives tailored to distinct stakeholder priorities. More broadly, our rethinking of entrepreneurial work through language-centered lenses helps theoretically support bold predictions about what entrepreneurs can accomplish with a nonhuman intelligence that has “only” mastered human language. • Challenges the philosophical underpinnings of skepticism about LLMs in entrepreneurial judgment • Positions LLMs as epistemic technologies for deep augmentation in entrepreneurship • Proposes a research program of fine-grained, task-specific Turing tests for entrepreneurial work • Demonstrates LLMs can craft and iteratively refine effective entrepreneurial narratives across diverse stakeholder priorities • Documents that LLMs help remove a key entry barrier to entrepreneurship