IDEAS home Printed from https://ideas.repec.org/h/nbr/nberch/15301.html
   My bibliography  Save this book chapter

Artificial Intelligence, Competition, and Welfare

In: The Economics of Transformative AI

Author

Listed:
  • Susan Athey
  • Fiona Scott Morton

Abstract

We propose a policy-relevant research agenda examining how market power in up-stream artificial intelligence (AI) affects downstream prices, industry structure, factor returns, and welfare—especially whether labor-displacing AI leaves workers worse off. In our open-economy general equilibrium model, AI is a priced, imported input. Our main model features two nontraded sectors and firms making discrete adoption decisions about technology. Adoption reduces unit costs, displaces some types of workers, and depresses wages for those workers via diminishing returns elsewhere, while leaking AI fees abroad. We identify conditions under which market power in AI leads to a “double harm” for displaced workers, who may experience real wages cuts when AI becomes available at low prices, and then experience further harm from increases in AI prices. Strategic AI pricing reduces welfare by raising downstream marginal costs (via usage fees) and limiting entry and variety (via access fees). We derive an adoption frontier linking feasible usage fees to displaced workers’ outside options, showing that a monopolist typically makes use of both types of fees and prices on the frontier; capping one fee shifts rents to the other. Regulating both fees, alongside policies that absorb displaced labor, can raise national welfare.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Susan Athey & Fiona Scott Morton, 2025. "Artificial Intelligence, Competition, and Welfare," NBER Chapters, in: The Economics of Transformative AI, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberch:15301
    as

    Download full text from publisher

    File URL: http://www.nber.org/chapters/c15301.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Korinek, Anton & Stiglitz, Joseph, 2021. "Artificial Intelligence, Globalization, and Strategies for Economic Development," CEPR Discussion Papers 15772, C.E.P.R. Discussion Papers.
    3. William D. Nordhaus, 2021. "Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(1), pages 299-332, January.
    4. 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.
    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. Alonso, Cristian & Berg, Andrew & Kothari, Siddharth & Papageorgiou, Chris & Rehman, Sidra, 2022. "Will the AI revolution cause a great divergence?," Journal of Monetary Economics, Elsevier, vol. 127(C), pages 18-37.
    2. Caleb Peppiatt, 2024. "The Future of Work: Inequality, Artificial Intelligence, and What Can Be Done About It. A Literature Review," Papers 2408.13300, arXiv.org.
    3. Ana L. Abeliansky & Klaus Prettner & Roman Stoellinger, 2023. "Infection Risk at Work, Automatability, and Employment," Department of Economics Working Papers wuwp352, Vienna University of Economics and Business, Department of Economics.
    4. Anton Korinek & Joseph E. Stiglitz, 2021. "Artificial Intelligence, Globalization, and Strategies for Economic Development," Working Papers Series inetwp146, Institute for New Economic Thinking.
    5. Gregory, Terry & Salomons, Anna & Zierahn, Ulrich, 2016. "Racing With or Against the Machine? Evidence from Europe," VfS Annual Conference 2016 (Augsburg): Demographic Change 145843, Verein für Socialpolitik / German Economic Association.
    6. Guarascio, Dario & Reljic, Jelena & Stöllinger, Roman, 2025. "Diverging paths: AI exposure and employment across European regions," Structural Change and Economic Dynamics, Elsevier, vol. 73(C), pages 11-24.
    7. Philippe Aghion & Benjamin F. Jones & Charles I. Jones, 2018. "Artificial Intelligence and Economic Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 237-282, National Bureau of Economic Research, Inc.
    8. Arntz, Melanie & Gregory, Terry & Zierahn, Ulrich, 2016. "ELS issues in robotics and steps to consider them. Part 1: Robotics and employment. Consequences of robotics and technological change for the structure and level of employment," ZEW Expertises, ZEW - Leibniz Centre for European Economic Research, number 146501.
    9. Xueyuan Gao & Hua Feng, 2023. "AI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity," Sustainability, MDPI, vol. 15(11), pages 1-21, June.
    10. Luca Grilli & Sergio Mariotti & Riccardo Marzano, 2024. "Artificial intelligence and shapeshifting capitalism," Journal of Evolutionary Economics, Springer, vol. 34(2), pages 303-318, April.
    11. Shreya Roy & Sugata Marjit & Bibek Ray Chaudhuri, 2022. "Role of Artificial Intelligence in Intra-Sectoral Wage Inequality in an Open Economy: A Finite Change Approach," CESifo Working Paper Series 9862, CESifo.
    12. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
    13. Manuchehr Irandoust, 2024. "Employment and technology: Creative creation or creative destruction? An asymmetric analysis," Australian Economic Papers, Wiley Blackwell, vol. 63(2), pages 201-219, June.
    14. Cao, Yuanyuan & Chen, Shaojian & Tang, Heyan, 2025. "Robot adoption and firm export: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    15. Wu, Yifan & Yuan, Yiming & Song, Xueyin, 2025. "The impact of AI adoption on R&D productivity: Evidence from Chinese pharmaceutical manufacturing industry," Journal of Asian Economics, Elsevier, vol. 97(C).
    16. Stefan Schweikl & Robert Obermaier, 2020. "Lessons from three decades of IT productivity research: towards a better understanding of IT-induced productivity effects," Management Review Quarterly, Springer, vol. 70(4), pages 461-507, November.
    17. Ajay K. Agrawal & John McHale & Alexander Oettl, 2025. "AI in Science," NBER Chapters, in: Economics of Science, National Bureau of Economic Research, Inc.
    18. Gries, Thomas & Naude, Wim, 2018. "Artificial intelligence, jobs, inequality and productivity: Does aggregate demand matter?," MERIT Working Papers 2018-047, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    19. Samantha A. Sharpe & Cristina M. Martinez-Fernandez, 2021. "The Implications of Green Employment: Making a Just Transition in ASEAN," Sustainability, MDPI, vol. 13(13), pages 1-19, July.
    20. Tommaso AGASISTI & Geraint JOHNES & Marco PACCAGNELLA, 2021. "Tasks, occupations and wages in OECD countries," International Labour Review, International Labour Organization, vol. 160(1), pages 85-112, March.

    More about this item

    JEL classification:

    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
    • L12 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Monopoly; Monopolization Strategies
    • L4 - Industrial Organization - - Antitrust Issues and Policies
    • L40 - Industrial Organization - - Antitrust Issues and Policies - - - General
    • L5 - Industrial Organization - - Regulation and Industrial Policy

    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:nbr:nberch:15301. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    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.