This blog was written by Blessing Ajimoti, Vitor Tomaz, Hoda Maged, Mahi Shah, and Abubakar Abdulfatah as part of the Economics of Transformative AI research by BlueDot Impact and Apart Research.

Introduction

Transformative Artificial Intelligence (TAI) promises major productivity gains by automating routine and complex tasks. But it also raises serious concerns about job displacement, especially in developing countries. According to the World Economic Forum’s Future of Jobs 2025 survey, 86% of employers say AI and digital access are the biggest forces reshaping business by 2030.

While discussions about which occupations are most at risk are growing, hard evidence on TAI’s real-time impact on employment is limited. This gap is especially important for developing countries, where limited digital infrastructure, significant market informality, and lower AI adoption present both challenges and opportunities. Policymakers, employers, and researchers need better tools to track how AI is affecting jobs and when to act to prevent risks.

To help close this gap, our research team, supported by BlueDot Impact and Apart Research, developed a new empirical approach. Using the data from Anthropic’s Economic Index: Insights from Claude 3.7 Sonnet released in March 2025, the research built on recent empirical work by Handa and colleagues in their paper Which Economic Tasks Are Performed with AI? Evidence from Millions of Claude Conversations, which analyzed millions of Claude Sonnet 3.7 conversations to study AI’s role across economic tasks. We combined data from millions of AI prompts submitted to Anthropic’s Claude Sonnet 3.7 model with official employment records from Brazil (2021–2024). Then, building on the task-based framework by Acemoglu and Restrepo, we studied whether AI usage for automating or augmenting tasks was associated with changes in net job creation across occupations.

We analyzed four occupation groups according to the share of Claude conversations they had, developing an econometric model that fits the observed trends for two of the groups - the 10 occupations that use Claude the least, and the 10 occupations that use Claude the most for augmentation. The model did not fit the other two groups well, indicating other unidentified dynamics at play. Comparing the trends for the first two groups, we found no statistical evidence that they differ, suggesting that...

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