Which Jobs Are Most Exposed to AI

Researchers measure exposure by breaking occupations into tasks and asking which tasks a technology could do or speed up. How they do this has changed over time. An influential 2013 study by Carl Frey and Michael Osborne estimated that about 47 percent of US employment was in occupations at high risk of computerization. Critics pointed out that it scored whole occupations, even though jobs are bundles of tasks. Later OECD work that used task-level data found a much smaller share of jobs that could be fully automated. In 2023, Tyna Eloundou, Sam Manning, Pamela Mishkin and Daniel Rock published "GPTs are GPTs." They rated US O*NET tasks using both human annotators and GPT-4. Their estimate was that roughly 80 percent of US workers are in occupations where at least 10 percent of tasks are exposed, and around 19 percent are in occupations where at least half are. In a reversal of earlier automation waves, which mostly hit routine manual work, higher-wage and more educated occupations tended to be more exposed. A 2023 International Labour Organization study found clerical support work to be the most exposed group worldwide and concluded that augmentation was more likely than full automation for most occupations. In 2024 the IMF estimated that about 40 percent of global employment is exposed, with a higher share in advanced economies. Exposure is not the same as replacement. Whether jobs are lost depends on adoption costs, regulation, customer preferences, and how demand responds when output gets cheaper. A frequently cited historical example is that US bank teller employment did not collapse after ATMs spread, because banks opened more branches and tellers took on different work. The most common misconception is that exposure scores predict layoffs. They are estimates of what is technically feasible, not forecasts.