Abstract

AI exposure measures ask about the work AI is capable of doing. We ask instead where the marginal value of work AI cannot yet do may rise as AI speeds up the rest of the job. To do so, we employ 288 million U.S. job postings mapped line by line to their closest O*NET tasks and GPT-4 feasibility ratings from Eloundou et al. (2024) to construct a measure, “bottleneck pressure,” for each task in each occupation that posts it. Using a simple task-based framework, we derive this pressure at the task level, showing that for an AI-infeasible task it is the exposure of the occupation that posts it, so the same task can face very different bottleneck pressure in different jobs. Aggregated to the occupation level, pressure is the product of exposure and the share of advertised work that is AI-infeasible. We measure both exposure and advertised task composition from postings, where about 90 percent of labeled matched task mentions lie outside the posting occupation’s own O*NET task list. About 53 percent of the mention-weighted variation in bottleneck pressure on AI-infeasible work comes from within tasks, across occupations. Because occupation pressure peaks where advertised work is evenly split between AI-feasible and AI-infeasible tasks, it ranks occupations differently than exposure. As task pressure rises, interpersonal and supervisory work makes up more of the AI-infeasible work carrying it, and physical work less. Reporting bottleneck pressure alongside AI exposure shows both the scope for AI to perform work and where improving the work it cannot do may pay off more as capabilities expand.

Country of study

United States

Research Topic(s)