Task-specific technical change and comparative advantage

Artificial intelligence is changing which tasks workers do and how they do them. Predicting its labor market consequences requires understanding how technical change affects workers’ 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 and, in turn, their occupational choices. We then develop an estimation strategy that recovers (i) the mapping from skills to task-specific 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 via augmentation, automation, and a third and new channel - simplification - which captures how technologies change the skills needed to perform tasks. Our key finding is that AI substantially reduces wage inequality while raising average wages by 21 percent. AI’s equalizing effect is fully driven by simplification, enabling workers across skill levels to compete for the same jobs. We show that the model’s predictions line up with recent labor market data.