# 1. Introduction

The field of artificial intelligence (AI) has undergone remarkable development in recent years. Most notable has been the takeoff of generative AI since the introduction of ChatGPT 3.5 in November 2022. Investment in the sector has exploded, and the topic of AI has become a focus in debates worldwide (Maslej et al. 2024). While the future of AI is unpredictable (Brynjolfsson et al. 2024), many observers expect that AI will lead to fundamental changes in jobs, skills requirements, and economic structures like those set in motion by the Industrial Revolution and the advent of the digital age (Cazzaniga et al. 2024). The ultimate effects on workers and jobs, however, remain highly uncertain.

Most economic research on the effects of generative AI on workers has focused on high-income countries (HICs). Only a few papers have considered what AI will mean for low- and middleincome countries (LMICs) (Comunale and Manera, 2024, Gmyrek et al. 2024). AI’s overall impacts may differ in LMICs from HICs due to differences in three principal areas: infrastructure, human capital, and economic structure. First, the basic foundations for AI use—consistent access to electricity and affordable internet—are not universally available in some developing countries. In low-income countries, less than half of the people have access to electricity and just over one in four (27 percent) use the internet. Second, the levels of human capital needed to work effectively with AI are much lower in LMICs. Around 70 percent of children in low- and middle-income countries are “learning poor,” meaning that they cannot read and comprehend a simple text by age 10 (World Bank 2022). Third, LMICs typically have a different economic structure, with a large share of workers in agriculture and low-skilled service jobs and fewer in knowledge-intensive sectors and occupations, which may be most affected by AI.

In this paper we analyze how differences in occupational composition may affect AI’s impact on jobs, with a focus on middle- and low-income countries and including the United States for comparison. We advance over the existing nascent literature in a number of ways. First, we apply the AI Occupational Exposure (AIOE) index developed by Felten et al. (2021) to labor force surveys covering a population of 3.5 billion people or around 45 percent of the world’s population. The AIOE relies on the O*NET classification of occupations originally developed for the US labor market. Second, we use detailed microdata from a larger set of low-, lower-middle-, and uppermiddle-income countries than employed in earlier studies like Cazzaniga et al. (2024) and Pizzinelli et al. (2023). The use of harmonized individual-level data for 25 countries across all income groups allows the identification of systematic differences and stylized facts. The dataset includes occupation-level data at the more granular four-digit International Standard Classification of Occupation (ISCO) level. Third, we exploit that microdata to explore how AI exposure varies by different socio-demographic groups in a multivariate framework. This allows us to provide a more differentiated view than earlier studies.

We find large differences in exposure to AI occupations by country income level. We normalize exposure at the individual worker level on a scale that ranges from 0 (no exposure) to 100 (full exposure). Mean exposure across workers is greatest in our high-income country (the United States) at 62. This is followed by upper-middle-income countries with an average exposure level 3