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The AI Penalty: People Reduce Compensation for Workers Who Use AI

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  • Jin Kim
  • Shane Schweitzer
  • David De Cremer
  • Christoph Riedl

Abstract

We investigate whether and why people might adjust compensation for workers who use AI tools. Across 13 studies (N = 4,956), participants consistently lowered compensation for workers who used AI compared to those who did not. This "AI penalty" is robust across different work scenarios and work tasks, worker statuses, forms and timing of compensation, methods of eliciting compensation, and perceptions of output quality. Moreover, the effect emerges in both hypothetical compensation scenarios as well as real monetary compensation of gig workers. We find that perceived effort and perceived agency -- the degree to which an individual serves as the originating source of the core intellectual or creative contribution in a task -- explain decisions to reduce compensation for AI-users. However, the penalty is not inevitable. Workers who strategically retain creative agency over core tasks recover most of the AI penalty, and employment contracts that make compensation reductions impermissible provide structural means of reducing the AI penalty.

Suggested Citation

  • Jin Kim & Shane Schweitzer & David De Cremer & Christoph Riedl, 2025. "The AI Penalty: People Reduce Compensation for Workers Who Use AI," Papers 2501.13228, arXiv.org, revised Mar 2026.
    • Handle: RePEc:arx:papers:2501.13228
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    References listed on IDEAS

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    1. Kyogo Kanazawa & Daiji Kawaguchi & Hitoshi Shigeoka & Yasutora Watanabe, 2026. "AI, Skill, and Productivity: The Case of Taxi Drivers," Management Science, INFORMS, vol. 72(2), pages 1376-1388, February.
    2. Adams-Prassl, Abi & Balgova, Maria & Qian, Matthias, 2020. "Flexible Work Arrangements in Low Wage Jobs: Evidence from Job Vacancy Data," IZA Discussion Papers 13691, IZA Network @ LISER.
    3. Adams, Abigail & Balgova, Maria & Qian, Matthias, 2020. "Flexible Work Arrangements in Low Wage Jobs: Evidence from Job Vacancy Data," CEPR Discussion Papers 15263, Centre for Economic Policy Research.
    4. Chen, Daniel L. & Horton, John J., 2016. "Are Online Labor Markets Spot Markets for Tasks?: A Field Experiment on the Behavioral Response to Wage Cuts," IAST Working Papers 16-37, Institute for Advanced Study in Toulouse (IAST).
    5. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    6. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation, and Work," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 197-236, National Bureau of Economic Research, Inc.
    7. Engberg, Erik & Koch, Michael & Lodefalk, Magnus & Schroeder, Sarah, 2025. "Artificial intelligence, tasks, skills, and wages: Worker-level evidence from Germany," Research Policy, Elsevier, vol. 54(8).
    8. Kahneman, Daniel & Knetsch, Jack L & Thaler, Richard, 1986. "Fairness as a Constraint on Profit Seeking: Entitlements in the Market," American Economic Review, American Economic Association, vol. 76(4), pages 728-741, September.
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