Follow the money: A startup-based measure of AI exposure across occupations, industries, and regions

Abstract

The integration of AI into the workplace is advancing rapidly, necessitating robust metrics to evaluate its tangible impact on the labor market. Existing measures of AI occupational exposure focus primarily on the theoretical potential of AI to substitute or complement human labor based on technical feasibility, offering limited insights into actual adoption. To address this gap, we introduce the AI Startup Exposure (AISE) index, a novel metric based on O*NET occupational descriptions and AI applications developed by venture backed startups worldwide. Our findings indicate that even though white-collar high-skilled occupations are theoretically highly exposed, they are heterogeneously targeted by AI startups. Roles involving routine organizational tasks, such as data analysis and office management, show significant exposure, while occupations involving tasks that are tied to ethical or high-stakes considerations—such as judges or surgeons—present lower AISE scores, despite technical feasibility for automation. Our approach challenges the conventional assumption that high-skilled jobs uniformly face high AI risks, highlighting instead societal desirability 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 the technical feasibility of AI applications. This framework provides a forward-looking tool for policymakers to monitor the evolving impact of AI and navigate a fast changing labor market landscape.