CONCEPT
Geography of AI Disruption
The uneven spatial distribution of AI's labor-market effects across regions, cities, and countries — the extension of Autor's China shock methodology to a disruption that differs from trade in its timing, breadth, and geography.
The geography of AI disruption applies Autor's long-standing concern with spatial variation to the AI transition. Previous research established that technology and trade shocks do not affect all regions equally; specific cities, industries, and communities bear the brunt of adjustment while others benefit disproportionately. AI inherits this pattern but transforms it. Unlike manufacturing automation, which concentrated effects on industrial regions, or the China shock, which concentrated effects on import-exposed communities, AI's exposure map is determined by occupational composition rather than physical industry. Metropolitan areas heavy in legal services, financial analysis, software engineering, and content production face exposure patterns that differ dramatically from regions anchored in manufacturing, resource extraction, or personal services. The geography of AI disruption will redraw the map of economic winners and losers in ways that existing political coalitions are not prepared for.
In The You On AI Field Guide
Autor's China shock research demonstrated that national-average measurements conceal enormous geographic heterogeneity in adjustment costs.