As generative artificial intelligence (GenAI) automates coding tasks and expands access to technical resources, this paper examines how GenAI-enabled coding automation, colloquially known as "vibecoding," affects digital entrepreneurial entry and venture performance. We exploit ex-ante variation in ventures' exposure to vibecoding based on the product characteristics of their initial launches and estimate difference-in-differences models around the diffusion of GenAI coding tools. Vibecoding increases first-time launches and shortens time to launch, but economically viable entry rises only where vibecoding augments, rather than fully automates, product development. In these partially exposed product segments, viable entry increases by 11%, driven entirely by ventures founded by individuals with STEM education or work experience, especially those whose most recent employment was outside middle management. Among ventures launched before GenAI became widely accessible, performance gains similarly concentrate among partially exposed ventures with engineering-intensive initial teams. Together, these results suggest that GenAI-enabled coding automation does not eliminate the value of technical expertise. Instead, vibecoding creates the greatest value when it complements internal engineering capabilities, allowing ventures to delegate lower-level coding tasks to GenAI while shifting human effort toward higher-level problem solving and dynamic adaptation.

翻译：随着生成式人工智能（GenAI）自动化编码任务并扩大技术资源的可及性，本文探讨了GenAI驱动的编码自动化（俗称"振动编码"）如何影响数字创业进入与企业绩效。我们基于创业企业最初发布产品的特性，利用其在振动编码暴露程度上的事前差异，并围绕GenAI编码工具的扩散构建双重差分模型进行估计。振动编码增加了首次产品发布的数量并缩短了上市时间，但经济上可行的创业进入仅出现在振动编码增强（而非完全替代）产品开发的领域。在这些部分暴露的产品细分市场中，可行进入率提高了11%，这一增长完全由具有STEM教育背景或工作经验的创业者创立的企业所驱动，尤其是那些最近一次就业岗位不在中层管理岗位的人员。在GenAI广泛应用前成立的创业企业中，绩效提升同样集中于初始团队具有较强工程能力的部分暴露企业。综上，这些结果表明，GenAI驱动的编码自动化并未消除技术专长的价值。相反，振动编码在补充内部工程能力时创造最大价值——它使创业企业能将低阶编码任务委托给GenAI，同时将人力投入转向高阶问题解决与动态适应。