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The unequal adoption of ChatGPT exacerbates existing inequalities among workers

Author

Listed:
  • Anders Humlum

    (a Microeconomics Unit, Booth School of Business , University of Chicago , Chicago IL , 60637)

  • Emilie Vestergaard

    (b Department of Economics , University of Copenhagen , Copenhagen 1353 , Denmark)

Abstract

We study the adoption of ChatGPT, the icon of Generative AI, using a large-scale survey linked to comprehensive register data in Denmark. Surveying 18,000 workers from 11 exposed occupations, we document that ChatGPT is widespread, especially among younger and less-experienced workers. However, substantial inequalities have emerged. Women are 16 percentage points less likely to have used the tool for work. Furthermore, despite its potential to lift workers with less expertise, users of ChatGPT earned slightly more already before its arrival, even given their lower tenure. Workers see a substantial productivity potential in ChatGPT but are often hindered by employer restrictions and a perceived need for training.

Suggested Citation

  • Anders Humlum & Emilie Vestergaard, 2025. "The unequal adoption of ChatGPT exacerbates existing inequalities among workers," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 122(1), pages 2414972121-, January.
    • Handle: RePEc:nas:journl:v:122:y:2025:p:e2414972121
    DOI: 10.1073/pnas.2414972121
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    Citations

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    Cited by:

    1. Michael Blank & Gregor Schubert & Miao Ben Zhang, 2026. "The Household Impact of Generative AI: Evidence from Internet Browsing Behavior," Papers 2603.03144, arXiv.org.
    2. Fabian Stephany & Ole Teutloff & Angelo Leone, 2026. "AI Skills Improve Job Prospects: Causal Evidence from a Hiring Experiment," Papers 2601.13286, arXiv.org, revised Mar 2026.
    3. Zhou, Yiyong & Liu, Qinghan & Huang, Jihao & Li, Guiquan, 2026. "Creative scar without generative AI: Individual creativity fails to sustain while homogeneity keeps climbing," Technology in Society, Elsevier, vol. 84(C).
    4. Teutloff Ole & Braesemann Fabian, 2025. "Resilienz statt Reskilling: Wie KI den Arbeitsmarkt verändert und wie wir darauf reagieren müssen," Wirtschaftsdienst, Sciendo, vol. 105(10), pages 715-719.
    5. Liu, Laihui & An, Suxia, 2026. "Spillover across borders: Industrial robotics applications in developed countries and deindustrialization in developing countries," Technological Forecasting and Social Change, Elsevier, vol. 223(C).
    6. David Almog & Lucas Lippman & Daniel Martin, 2026. "When an AI Judges Your Work: The Hidden Costs of Algorithmic Assessment," Papers 2603.02076, arXiv.org.
    7. Shaukat, Mahvish Ifrah & Stegmann, Andreas & Toma, Mattie, 2026. "How Do Organizations Learn ? The Diffusion of Scientific Evidence on Generative AI," Policy Research Working Paper Series 11305, The World Bank.
    8. Fabian Stephany & Jedrzej Duszynski, 2026. "Women Worry, Men Adopt: How Gendered Perceptions Shape the Use of Generative AI," Papers 2601.03880, arXiv.org.
    9. Li, Wei & Chang, Yaping & He, Yinghao & Li, Hong, 2025. "Research on fear of artificial intelligence among the elderly: The key of education and the prison of female," Journal of Retailing and Consumer Services, Elsevier, vol. 87(C).

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