AI and Labor

The field studies how AI affects which tasks people do, how jobs and labor markets are organized, and how workers’ pay, skills, opportunities, and well-being change. Researchers use task and occupation taxonomies such as O*NET, job postings and workplace data, worker interviews and surveys, economic models, and benchmarks that compare automation with human-AI collaboration. Current evidence points to broad but uneven AI use, with effects depending on occupation, workplace practices, and whether systems assist workers or take over tasks; hiring audits also examine bias and changing evaluation practices. A central tension is that short-term productivity gains may come at the cost of worker expertise, job quality, or future capabilities, while exposure scores and benchmark results alone often cannot show what happens in real workplaces.