(Goldman Sachs, 2023; McKinsey, 2023). Such projections might be particularly enticing for the LAC region, which has long grappled with a persistent productivity gap in comparison to other areas of the world. While the developing nations in Asia and Europe managed to narrow their productivity gap with the United States between 1990 and 2019, such gap increased for the LAC region during the same period (IMF, 2022). Recent trends also raise concerns, since, despite some country variation (Erumban et al., 2024), the overall productivity growth has been almost zero in LAC ever since the start of the global productivity slowdown of the last 10 years (Dieppe, 2021). Compared to other regions, barriers to innovation and technology adoption have been particularly salient factors limiting productivity growth in LAC.

Could GenAI help unlock this productivity impasse? Recent empirical studies focused on the use of GenAI in particular occupational settings suggest that the positive impacts on productivity can be large. For example, Peng et al. (2023) implemented a controlled experiment among professional programmers and found that access to a GenAI assistant reduced the time to complete programming tasks by 56 percent. Brynjolfsson et al. (2023) find that access to GenAI increases productivity among customer support workers in terms of issues resolved per hour, which is driven mostly by the boost of performance among the novice and low-skill workers. Similarly, Noy and Zhang (2023) find that having access to ChatGPT helps improve the productivity of writing professionals, by increasing the quality of outputs as well as by reducing the amount of time required to produce them, with the benefits being the largest for low-ability workers.

While the results of this literature suggest a promising role for GenAI to boost productivity, in the context of LAC and emerging economies more broadly, there are important reasons to be cautious.

First, there are good chances that such initial macroeconomic projections are too optimistic and based on oversimplified models. As shown by Acemoglu (2024), when the tasks classified by Eloundou et al. (2023) as exposed to GenAI in the context of US-based occupations are linked to their actual impact on GDP, the average task-level savings and the economic viability AI deployment (Svanberg et al., 2024), the estimated impact amounts to a modest 0.71 of additional Total Factor Productivity at the end of a 10 year period. Accounting for hard-to-learn tasks drops this estimate to 0.55 percent of TFP, corresponding to an additional GDP growth due to AI at 0.92 percent over 10 years. In addition, the actual impact on productivity in specific occupations might be largely dependent on how these technologies will be implemented at the workplace. For example, Doellgast et al., (2023) suggest that productivity benefits might be less consequential if the new AI tools are applied mainly for worker control, thereby limiting creativity and the opportunities for larger value added through innovation in products and services. Acemoglu (2024) also demonstrates that the final impact on productivity largely depends on the type of new tasks that will emerge due to adoption, and that some of such new tasks might either not contribute much new economic value or produce outright “public bads” that can be wrongly accounted as part of GDP growth based exclusively on their monetary value.<sup>4</sup> Second, the rate of adoption and exposure to GenAI is likely to be slower in developing countries where fewer workers are using digital technologies than in their richer counterparts. More specifically, two individuals with the same occupation could have very different levels of GenAI exposure if one of them uses a computer or internet at work, while the other one does not. As

> 4 E.g., tasks related to dealing with the increasing complexity and costs of network security or content manipulation.

6