IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2603.17898.html

Workers' Incentives and the Optimal Taxation of AI

Author

Listed:
  • Jakub Growiec
  • Klaus Prettner
  • Maciej Szkr'obka

Abstract

We characterize the optimal tax policy in an economy with human manual and cognitive labor, physical capital, and artificial intelligence (AI). Extending the dynamic taxation setup of Slavik and Yazici (2014), we find that it is optimal to start taxing AI when cognitive workers start to consider switching to manual jobs. This threshold may be crossed once AI becomes sufficiently capable in substituting humans across cognitive tasks.

Suggested Citation

  • Jakub Growiec & Klaus Prettner & Maciej Szkr'obka, 2026. "Workers' Incentives and the Optimal Taxation of AI," Papers 2603.17898, arXiv.org.
    • Handle: RePEc:arx:papers:2603.17898
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2603.17898
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    2. Pawel Gmyrek & Janine Berg & David Bescond, 2025. "Generative AI and Jobs: An Analysis of Potential Effects on Global Employment," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 3, pages 6-30.
    3. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The Skill Content of Recent Technological Change: An Empirical Exploration," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(4), pages 1279-1333.
    4. Minniti, Antonio & Prettner, Klaus & Venturini, Francesco, 2025. "AI innovation and the labor share in European regions," European Economic Review, Elsevier, vol. 177(C).
    5. Slavík, Ctirad & Yazici, Hakki, 2014. "Machines, buildings, and optimal dynamic taxes," Journal of Monetary Economics, Elsevier, vol. 66(C), pages 47-61.
    6. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue nov.
    7. Bloom, David E. & Prettner, Klaus & Saadaoui, Jamel & Veruete, Mario, 2025. "Artificial intelligence and the skill premium," Finance Research Letters, Elsevier, vol. 81(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ikeshita, Kenichiro, 2025. "Effects of automation and human investment on skill premium," Innovation and Green Development, Elsevier, vol. 4(2).
    2. Jiaju Yu & Ye Xu & Qian Li & Changqi Tao, 2026. "Digital transformation and entrepreneurial activity: driving employee wages in China," International Entrepreneurship and Management Journal, Springer, vol. 22(1), pages 1-29, March.
    3. Daniele Angelini, 2023. "Aging Population and Technology Adoption," Working Paper Series of the Department of Economics, University of Konstanz 2023-01, Department of Economics, University of Konstanz.
    4. Kudoh, Noritaka & Miyamoto, Hiroaki, 2025. "Robots, AI, and unemployment," Journal of Economic Dynamics and Control, Elsevier, vol. 174(C).
    5. Hakan Yilmazkuday, 2025. "Artificial intelligence and labor markets: evidence from google trends," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 49(4), pages 1078-1093, December.
    6. Chiara Cimini & David Romero & Roberto Pinto & Sergio Cavalieri, 2023. "Task Classification Framework and Job-Task Analysis Method for Understanding the Impact of Smart and Digital Technologies on the Operators 4.0 Job Profiles," Sustainability, MDPI, vol. 15(5), pages 1-28, February.
    7. Sharon Belenzon & Victor Manuel Bennett & Andrea Patacconi, 2019. "Flexible Production and Entry: Institutional, Technological, and Organizational Determinants," Strategy Science, INFORMS, vol. 4(3), pages 193-216, September.
    8. Gries, Thomas & Naudé, Wim, 2020. "Artificial Intelligence, Income Distribution and Economic Growth," GLO Discussion Paper Series 632, Global Labor Organization (GLO).
    9. David Kunst, 2019. "Deskilling among Manufacturing Production Workers," Tinbergen Institute Discussion Papers 19-050/VI, Tinbergen Institute, revised 30 Dec 2020.
    10. Martin Lábaj & Tomáš Oleš & Gabriel Procházka, 2025. "Impact of robots and artificial intelligence on labor and skill demand: evidence from the UK," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 15(4), pages 953-1001, December.
    11. Lee Ohanian & Musa Orak & Shihan Shen, 2023. "Revisiting Capital-Skill Complementarity, Inequality, and Labor Share," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 479-505, December.
    12. Lu, Jing & Xiao, Qinglan & Wang, Taoxuan, 2023. "Does the digital economy generate a gender dividend for female employment? Evidence from China," Telecommunications Policy, Elsevier, vol. 47(6).
    13. Goos, Maarten & Rademakers, Emilie & Röttger, Ronja, 2021. "Routine-Biased technical change: Individual-Level evidence from a plant closure," Research Policy, Elsevier, vol. 50(7).
    14. Montobbio, Fabio & Staccioli, Jacopo & Virgillito, Maria Enrica & Vivarelli, Marco, 2022. "Robots and the origin of their labour-saving impact," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    15. Andreas Baur & Lisandra Flach & Isabella Gourevich & Florian Unger, 2023. "North-South Trade: The Impact of Robotization," CESifo Working Paper Series 10865, CESifo.
    16. Zhou, Yuwen & Shi, Xin, 2025. "How does digital technology adoption affect corporate employment? Evidence from China," Economic Modelling, Elsevier, vol. 147(C).
    17. Drew Fudenberg & David K Levine, 2026. "Taking the Easy Way Out: AI, Self-Control, and Human Capital Formation," Levine's Working Paper Archive 735347000000000055, David K. Levine.
    18. Thomas Gries & Wim Naudé, 2022. "Modelling artificial intelligence in economics," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 56(1), pages 1-13, December.
    19. Youssef Benzarti & Jarkko Harju, 2021. "Using Payroll Tax Variation to Unpack the Black Box of Firm-Level Production," Journal of the European Economic Association, European Economic Association, vol. 19(5), pages 2737-2764.
    20. He, Chao & Zhou, Zhongsheng & Jian, Fangfang & Qiu, Yuhan, 2025. "Robots and cost of equity: Evidence from China," China Economic Review, Elsevier, vol. 94(PB).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2603.17898. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.