Authors
Abstract
Who will gain and who will lose as AI automates tasks? While much of the discourse focuses on job displacement, we show that job transformation—a shift in the task content of jobs—creates large and heterogeneous earnings effects. We develop a quantitative, task-based model where occupations bundle multiple tasks and workers possessing heterogeneous portfolios of task-specific skills select into occupations by comparative advantage. Automation shifts the relative importance of tasks within each occupation, inducing wage effects that we characterize analytically. To quantify these effects, we measure the task content of jobs using natural language processing, estimate the distribution of task-specific skills, and exploit mappings to prominent automation exposure measures to identify task-specific automation shocks. We apply the framework to analyze automation by large language models (LLMs). Within highly exposed occupations, like office and administrative roles, workers specialized in information-processing tasks leave and suffer wage losses. By contrast, those specialized in customer-facing and coordination tasks stay and experience wage gains as work rebalances toward their strengths. Our findings challenge the common assumption that automation exposure equates to wage losses.