This paper proposes a method to measure potential exposure to generative AI for every task in O*NET, the US occupational database. The result is three nested exposure levels, which can be used for applied work on labour markets. Each task is read in the context of its occupation. We first ask whether AI can replace its output at the quality of a typical worker (Re). If it cannot, we ask whether it can augment the worker enough to halve the time the task takes or to push its quality into the top quartile (Au), and if not, whether it can still support the task with some usable input (Su).<\/p>\n
Three frontier language models rate all 24,087 task-activity pairs of O*NET 31.0 (923 occupations), working from a dated inventory of 33 capability benchmarks, and a Delphi round settles their disagreements. Weighted by time, the average occupation in the catalogue has 37.8% of its working time replaceable, 48.4% augmentable and 79.2% supportable.<\/strong><\/p>\n We also measure three human anchoring factors (relational nexus, organisational responsibility and authorship) that keep a job’s tasks tied to a person doing them, plus the share of working time in which AI helps an expert without replacing them.<\/p>\n","protected":false},"excerpt":{"rendered":" This paper proposes a method to measure potential exposure to generative AI for every task in O*NET, the US occupational database. The result is three nested exposure levels, which can be used for applied work on labour markets. Each task is read in the context of its occupation. We first ask whether AI can replace […]<\/p>\n","protected":false},"featured_media":14645,"template":"","acf":[],"yoast_head":"\n