Motivated by the rise of artificial intelligence (AI), we set up a quantitative general-equilibrium model of the labor market impact of occupation-specific technical change. The highly tractable model crystallizes how three fundamental forces shape the impact of technical change on the occupational wage distribution: the input substitution elasticity, the final demand elasticity, and the labor supply (reallocation) elasticity. The difference between the former two elasticities determines whether machines and workers are gross complements, while the reallocation elasticity governs the magnitude of the distributional effects. We estimate the reallocation elasticities from group-by-occupation specialization changes, allowing for asymmetric reallocation and associated ripple effects on wages across occupations. After combining these estimates with externally disciplined demand and substitution parameters, as well as AI exposure measures, we shed light on the aggregate and distributional effects of occupation-specific advances in AI: wages in administrative services grow the least, ripple effects on less exposed occupations are substantial, AI modestly compresses the returns to education, and, on average, disproportionately benefits lower-income groups.