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 with 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 and estimate the distribution of task-specific skills. We construct projections of automation effects due to large language models (LLMs), exploiting a mapping between model tasks and automation exposure measures. 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 occupational automation exposure necessarily implies individual wage losses; and highlight that AI, through job transformation, may be disruptive even absent job displacement.