Technology

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This paper examines how generative artificial intelligence (GenAI) could affect labor markets globally, with particular attention to the uneven distribution of risks and opportunities between advanced and developing economies. Cross-country differences in occupational structure suggest that developing economies face lower aggregate automation exposure than advanced economies but comparable potential for task augmentation. However, disparities in digital infrastructure create an asymmetry: workers in positions vulnerable to automation typically maintain sufficient internet connectivity to experience displacement effects even in low-income settings, while those who could benefit from GenAI augmentation face substantial digital infrastructure gaps that may prevent them from realizing productivity gains. This finding suggests that developing countries may experience the disruptive effects of GenAI faster than its productivity benefits.

At the same time, conventional occupational exposure measures systematically overestimate the impact of GenAI in developing countries by assuming uniform task content across economies. Using data from skills surveys, the article demonstrates that workers in developing countries perform substantially fewer non-routine analytical tasks—the primary targets of GenAI—even within occupations classified as highly exposed. These findings highlight the importance of adapting GenAI exposure measures to reflect developing countries’ distance from the technology frontier.