approach was developed by Webb (2020). This approach identified the intersection between AI capabilities described in patents and the job descriptions provided by O*NET. Occupations with a higher fraction of tasks that overlap with patented AI capabilities are classified as more exposed to AI. This index provides a patent-based perspective on AI exposure, offering a unique lens on how emerging AI technologies might impact various job roles. Both Felten et al. (2021) and Webb (2020) underscore the capabilities of AI within existing tasks. Separately, Brynjolfsson et al. (2018) evaluate the suitability of specific tasks for machine learning and connect these assessments to O*NET occupations.

# 2.2. AI impacts in the labor market worldwide

The body of work on potential AI-related impacts in the labor market is growing, with a particular focus on the United States.<sup>4</sup> A stylized economic model assumes that AI-related tasks increase the productivity of workers, either substituting or complementing current work. Research has been conducted within firms, at the macroeconomic level, and considering overall effects of AI on workers.

On the firm level, many assume that productivity may increase following the introduction of AI (e.g. Agrawal et al. 2019), viewing it as an additional input to a firm’s production function. However, firms will need time to adapt to the new technology and the impact on productivity may only be observed with a delay (Brynjolfsson et al. 2019). Researchers have linked firm economic performance to AI patent registration (e.g. van Roy et al. 2020, Behrens and Truschke 2020), analyzed online job vacancies (e.g. Acemoglu et al. 2022, Babina et al. 2024), and used a surveybased approach to assess the prevalence of AI usage within firms (Czarnitzki et al. 2023). Svanberg et al. (2024) estimate that firms will introduce AI gradually and at a much slower pace than originally anticipated given the introduction costs firms face. A synthesis of several academic studies estimates the overall impact of AI adoption on annual worker productivity growth within a firm to be between 1.7 and 2.7 percentage points (Hatzius et al. 2023).

There is some evidence that highly skilled workers will be particularly affected by recent AI developments (e.g., Felten et al. 2021, Felten et al. 2023). Some recent studies on generative AI have demonstrated that for a given task, productivity gains are primarily observed in lower-skilled, less experienced workers (Brynjolfsson et al. 2023, Noy and Zhang 2023).

In a study on the United States, Eloundou et al. (2023) used the O*NET database to gauge the impacts of large language models (LLMs) and calculate the potential for AI and automation. The authors conclude that approximately 20 percent of jobs are exposed to LLM in more than 50 percent of their tasks, suggesting that the impact of AI is pervasive and has significant effects on economic activities. A McKinsey study (2024) obtains similar results and estimates that about 27 percent of tasks in European countries and around 30 percent of tasks in the US will be AI-based

> 4 See Comunale and Mandera (2024) for an excellent review of the literature and an analysis of regulations that are currently ongoing.

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