At a glance
- Source
- Cazzaniga, Pizzinelli, Rockall and Tavares (2025). Exposure to Artificial Intelligence and Occupational Mobility: A Cross-Country Analysis. Economía, 24(1), 314–339. doi:10.31389/eco.451
- How it was done
- The authors tracked how workers have moved between occupations over their careers, and sorted occupations by how exposed they are to AI and whether AI is likely to help or replace the work. Data analysis. It builds a measure from existing data, such as job and task records.
- Who took part
- Workers’ past moves between occupations, by age and education.
- Where
- Brazil and the United Kingdom.
“AI-driven structural change could most expand opportunities for career progression but also highly disrupt entry into the labor market by removing stepping-stone jobs.”
What it found
- Graduates often move from occupations where AI is likely to replace work to occupations where it is likely to assist. The move usually comes with higher pay.
- This path is most common among young graduates.
- So the same group has the most to gain and the most to lose. If the starting jobs disappear, the path up starts later or not at all.
- The pattern looks similar in the two countries.
When the result holds
The study describes career paths as they were before generative AI. It then asks what would happen if AI reduced demand for the most exposed jobs. That second step is an inference.
Limits
- It shows a risk, not an observed loss of entry-level jobs. Later US studies of payroll and résumé records look at hiring directly: see young workers in AI-exposed jobs and firms that put generative AI into their work.
Practices that use this study
This study supports: