image: Bar plot of average sectoral AI Startup Exposure (AISE) for industries in the US economy, where blue and green indicate lower to intermediate exposure, yellow to red indicate high exposure.
Credit: Fenoaltea et al.
A study of funded AI startups provides a glimpse of which jobs may be most affected by AI. As AI tools are embraced by industry after industry, the impacts of these tools on jobs remain unclear. Previous analyses have focused on the theoretical capabilities of LLMs, but social factors are also likely play a role in shaping what aspects of work see AI integration—or full automation. Enrico Maria Fenoaltea and colleagues validated a version of Meta’s Llama3 LLM, which they used to cross reference products developed by AI startups backed by the venture capital firm Y Combinator with descriptions of essential tasks for various jobs drawn from the O*NET occupational database. Because AI products that have attracted significant funding are seen by investors as economically viable and socially appealing, these products are more likely to become marketplace realities than other theoretical uses for AI.
The resulting Occupational AI Startup Exposure (AISE) index seeks to capture the potential near-future AI exposure of occupations. “Exposure” could include AI complementing or substituting for human labor in performing a job. Occupations with high AI exposure include office clerks, data scientists, computer and information systems managers, and market research analysts and marketing specialists. Occupations with low AI exposure include those primarily composed of manual tasks, such as athletes, chefs, and construction workers. Compared with indices based on the theoretical abilities of AI, the AISE predicts lower exposure for occupations requiring high levels of responsibility and ethical decision-making and occupations requiring a master’s degree or higher and significant experience.
While LLMs could theoretically perform many of the tasks completed by high school teachers, judges, or marriage counselors, people may be reluctant to trust AI with roles that require social skills, judgment, or ethically charged decision-making. According to the authors, rather than hitting the entire economy as an indiscriminate technological wave, AI will gradually spread into the economy, its path shaped by social factors as much as by the technical feasibility of AI applications.
Journal
PNAS Nexus
Article Title
Follow the money: A startup-based measure of AI exposure across occupations, industries, and regions
Article Publication Date
23-Jun-2026