Artificial Intelligence and the Labor Market∗

We utilize recent advances in natural language processing to develop novel measures of workers’ task-level exposure to artificial intelligence (AI) and machine learning technologies from 2010 to 2023, capturing variation across firms and over time. We show that tasks exposed to AI subsequently experience lower labor demand. Employing a model that distinguishes between direct and indirect productivity effects of labor-saving technologies, we identify two variables that summarize the impact of AI on within-firm labor demand: an occupations mean task exposure to AI, and the degree to which this mean exposure is concentrated in a small number of tasks. Higher mean exposure reduces labor demand, whereas more concentrated exposures plays an offsetting role as it allows workers to reallocate their effort to non-displaced tasks. Leveraging exogenous variation in AI adoption linked to firms’ pre-existing hiring practices, we find empirical support for these predictions. Overall, we observe relatively modest net employment effects due to countervailing forces: reduced demand in AI-exposed occupations is offset by productivity-driven employment increases across all occupations at AI-adopting firms.