in the near future (Hazan et al. 2024). Hatzius et al. (2023) conclude that generative AI could substitute up to one-quarter of current work, thereby raising US labor productivity growth by approximately 1.5 percentage points over the next ten years.

To date, only a few papers have considered the potential impacts of AI on low- and middle-income countries. The most recent and prominent examples are Pizzinelli et al. (2023) and Cazzaniga et al. (2024). Pizzinelli et al. (2023) expanded the AIOE index with a measure of complementarity and applied it to two high-income countries (the United Kingdom and the United States) and four middle-income countries (Brazil, Colombia, India, and South Africa). The authors identified differences between countries when examining AI occupation exposure due to disparate employment structures. However, when accounting for complementarity, these differences diminished. In all countries, the authors detect a higher occupational exposure to AI for women and highly educated workers, as well as for workers with higher earnings. Cazzaniga et al. (2024) further develop this approach and find that, using ILO employment indicators, nearly 40 percent of workers worldwide are exposed to AI. Additionally, they conclude that AI may exacerbate inequality. Overall, they conclude that the degree of AI exposure varies across economies, with advanced economies reporting a higher prevalence of AI-impacted jobs, reaching up to 60 percent compared to 40 percent in emerging markets and 26 percent in low-income countries.

Gmyrek et al. (2023) employ a distinct methodology, prompting ChatGPT-4 to gauge occupational and task-level exposure to AI. The resulting estimates are then applied to country-level data from 59 countries, comprising eight low-income countries, 24 lower-middle-income countries, 19 upper-middle-income countries, and eight high-income countries. The authors conclude that the effect of AI on labor markets varies across country income groups. Focusing only on workers in Latin America, Gmyrek et al. (2024) further estimate that it is mostly younger, urban-based and better educated workers that are exposed to AI. Workers in high-income countries have the highest exposure to autom a tion and augmentation. Focusing on China as an example of a middle-income country, Lou et al. (2024) demonstrate that the occupational exposure to AI is lower than in the United States. The effects of AI are still pervasive throughout the Chinese economy, but the magnitude of these effects varies by geographic location within China.

Other researchers have used simulations or theoretical frameworks to assess AI’s potential impacts. Acemoglu and Restrepo (2019), for example, provide a framework for AI-related or automation technologies. The framework's core concept is that labor demand for workers in occupations susceptible to automation-induced displacement may decline due to the accrual of productivity gains. Accordingly, the overall effects on job displacement then depend on the workers' ability to move to other occupations and the potential to create new jobs. Depending on what magnitude of the two opposing effects prevails, this could overall result in either more jobs being created (World Economic Forum 2023) or more jobs disappearing (Acemoglu and Johnson 2023).<sup>5</sup> There is also the risk of linking humanoid robots to generative AI appliances, which could further expand the effects of AI on the workforce (Acemoglu and Restrepo 2020). Importantly, certain demographic

> 5 Taking a longer perspective, Autor et al. (2024) analyze technological innovation from 1940 to 2018 and find that more than every second worker today is employed in occupations that did not exist in 1940.

7