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Quarterly Journal of Economics Vol. 140 No. 2 2025

Generative AI at Work

Erik Brynjolfsson1; Danielle Li2; Lindsey Raymond3

1 Stanford University and National Bureau of Economic Research · 2 Massachusetts Institute of Technology and National Bureau of Economic Research , · 3 Massachusetts Institute of Technology

open access

Abstract

We study the staggered introduction of a generative AI–based conversational assistant using data from 5,172 customer-support agents. Access to AI assistance increases worker productivity, as measured by issues resolved per hour, by 15% on average, with substantial heterogeneity across workers. The effects vary significantly across different agents. Less experienced and lower-skilled workers improve both the speed and quality of their output, while the most experienced and highest-skilled workers see small gains in speed and small declines in quality. We also find evidence that AI assistance facilitates worker learning and improves English fluency, particularly among international agents. While AI systems improve with more training data, we find that the gains from AI adoption are largest for moderately rare problems, where human agents have less baseline experience but the system still has adequate training data. Finally, we provide evidence that AI assistance improves the experience of work along several dimensions: customers are more polite and less likely to ask to speak to a manager.

DOI
10.1093/qje/qjae044
Volume
140
Issue
2
Pages
889-942
Language
en
Sources
crossref bibtex:phds-export.bib openalex

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