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We use advances in natural language processing to construct new measures of workers' task-level exposure to artificial intelligence (AI) and machine learning from 2010 to 2023, capturing variation across firms, occupations, and time.Tasks with higher AI exposure subsequently experience reduced labor demand.To interpret these patterns, we develop a model that separates direct substitution from indirect reallocative effects of labor-saving technologies.Two variables summarize the impact of AI on within-firm labor demand: the mean exposure of an occupation's tasks, which depresses demand, and the concentration of exposure in a few tasks, which offsets losses by enabling workers to reallocate effort.Using an instrument based on historical university hiring networks, we find causal evidence consistent with these predictions.Despite strong substitution at the task level, overall employment effects are modest, as reduced demand in exposed occupations is offset by productivity-driven increases in labor demand at AI-adopting firms.
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