Lukas Althoff

Artificial intelligence is transforming the task content of work. Predicting the labor market consequences requires understanding how workers' skills determine productivity across tasks, how workers adapt by changing occupations and acquiring new skills, and how wages adjust in general equilibrium. We introduce a dynamic task-based model in which workers accumulate multidimensional skills that shape their comparative advantage across tasks and, in turn, their occupational choices. We then develop an estimation strategy that recovers (i) the mapping from skills to task-level productivity, (ii) the law of motion for skill accumulation, and (iii) the determinants of occupational choice. We use the quantified model to study generative AI's impact through task augmentation, automation, and simplification. We predict long-run average wage gains of 24 percent and a substantial reduction in wage inequality. The distributional effects arise almost entirely due to task simplification—the degree to which AI reduces the skill level required to perform tasks. We show that AI's labor market effects critically hinge on its technological scope by contrasting generative AI with physically-capable AI robots.