The integration of artificial intelligence (AI) into the workplace is advancing rapidly, necessitating robust metrics to evaluate its tangible impact on the labour market. Existing measures of AI occupational exposure largely focus on AI's theoretical potential to substitute or complement human labour on the basis of technical feasibility, providing limited insight into actual adoption and offering inadequate guidance for policymakers. To address this gap, we introduce the AI Startup Exposure (AISE) index-a novel metric based on occupational descriptions from O*NET and AI applications developed by startups funded by the Y Combinator accelerator. Our findings indicate that while high-skilled professions are theoretically highly exposed according to conventional metrics, they are heterogeneously targeted by startups. Roles involving routine organizational tasks-such as data analysis and office management-display significant exposure, while occupations involving tasks that are less amenable to AI automation due to ethical or high-stakes, more than feasibility, considerations -- such as judges or surgeons -- present lower AISE scores. By focusing on venture-backed AI applications, our approach offers a nuanced perspective on how AI is reshaping the labour market. It challenges the conventional assumption that high-skilled jobs uniformly face high AI risks, highlighting instead the role of today's AI players' societal desirability-driven and market-oriented choices as critical determinants of AI exposure. Contrary to fears of widespread job displacement, our findings suggest that AI adoption will be gradual and shaped by social factors as much as by the technical feasibility of AI applications. This framework provides a dynamic, forward-looking tool for policymakers and stakeholders to monitor AI's evolving impact and navigate the changing labour landscape.

翻译：人工智能（AI）在工作场所的整合正迅速推进，亟需建立稳健的指标以评估其对劳动力市场的实际影响。现有的人工智能职业暴露度测度主要基于技术可行性，聚焦于AI替代或补充人类劳动力的理论潜力，对实际应用情况的洞察有限，难以为政策制定者提供有效指导。为弥补这一不足，我们提出了人工智能初创企业暴露度（AISE）指数——一种基于O*NET职业描述与Y Combinator加速器资助的初创企业所开发AI应用的新型测度指标。研究发现，虽然传统指标显示高技能职业在理论上具有较高的暴露度，但初创企业对其的关注存在异质性。涉及常规组织任务（如数据分析和办公管理）的岗位表现出显著的暴露度，而因伦理或高风险考量（而非技术可行性）较难被AI自动化的职业——如法官或外科医生——则呈现较低的AISE分值。通过聚焦风险资本支持的AI应用，我们的方法为AI如何重塑劳动力市场提供了细致入微的视角。它挑战了高技能岗位普遍面临高AI风险的传统假设，强调当前AI参与者基于社会需求导向和市场驱动的选择是决定AI暴露度的关键因素。与对大规模岗位替代的担忧相反，我们的研究表明AI应用将是渐进式的，其进程不仅受技术可行性影响，同样受社会因素塑造。该框架为政策制定者和利益相关方提供了一个动态、前瞻性的工具，用以监测AI不断演变的影响并应对劳动力市场的持续变革。