IDEAS home Printed from https://ideas.repec.org/a/eee/moneco/v157y2026ics0304393225001552.html

Artificial intelligence and cognitive inequality

Author

Listed:
  • Puri, Indira
  • Veldkamp, Laura

Abstract

We combine insights from the medical and artificial intelligence (AI) literatures to propose a novel model, which suggests that the expansion of AI may exacerbate cognitive inequality. Information providers maximize profit by tailoring the complexity of content, offering less cognition-enhancing content to less able customers. While individuals with high cognitive abilities may benefit from this increased within-cognitive-group homogeneity, those with lower cognitive abilities – and even children – may suffer adverse effects. Anecdotal data from political discourse and cognitive skills scores are consistent with the model predictions. The findings introduce a new consideration to the debate on financial literacy and AI regulation.

Suggested Citation

  • Puri, Indira & Veldkamp, Laura, 2026. "Artificial intelligence and cognitive inequality," Journal of Monetary Economics, Elsevier, vol. 157(C).
    • Handle: RePEc:eee:moneco:v:157:y:2026:i:c:s0304393225001552
    DOI: 10.1016/j.jmoneco.2025.103884
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304393225001552
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jmoneco.2025.103884?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Indira Puri, 2025. "Simplicity and Risk," Journal of Finance, American Finance Association, vol. 80(2), pages 1029-1080, April.
    2. Bartosz Mackowiak & Mirko Wiederholt, 2009. "Optimal Sticky Prices under Rational Inattention," American Economic Review, American Economic Association, vol. 99(3), pages 769-803, June.
    3. repec:hal:journl:hal-04810695 is not listed on IDEAS
    4. Dirk Bergemann & Alessandro Bonatti, 2019. "The Economics of Social Data: An Introduction," Cowles Foundation Discussion Papers 2171R, Cowles Foundation for Research in Economics, Yale University, revised Sep 2019.
    5. Laura Veldkamp, 2023. "Valuing Data as an Asset," Review of Finance, European Finance Association, vol. 27(5), pages 1545-1562.
    6. Lin William Cong & Danxia Xie & Longtian Zhang, 2021. "Knowledge Accumulation, Privacy, and Growth in a Data Economy," Management Science, INFORMS, vol. 67(10), pages 6480-6492, October.
    7. Maryam Saeedi & Yikang Shen & Ali Shourideh, 2024. "Getting the Agent to Wait," Papers 2407.19127, arXiv.org.
    8. Itay Goldstein & Chester S Spatt & Mao Ye, 2021. "Big Data in Finance," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3213-3225, National Bureau of Economic Research, Inc.
    9. Alessandro Acquisti & Curtis Taylor & Liad Wagman, 2016. "The Economics of Privacy," Journal of Economic Literature, American Economic Association, vol. 54(2), pages 442-492, June.
    10. Charles I. Jones & Christopher Tonetti, 2020. "Nonrivalry and the Economics of Data," American Economic Review, American Economic Association, vol. 110(9), pages 2819-2858, September.
    11. Drew Fudenberg & Indira Puri, 2022. "Simplicity and Probability Weighting in Choice under Risk," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 421-425, May.
    12. Simone Galperti & Aleksandr Levkun & Jacopo Perego, 2024. "The Value of Data Records," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(2), pages 1007-1038.
    13. Andrew Caplin & Mark Dean, 2015. "Revealed Preference, Rational Inattention, and Costly Information Acquisition," American Economic Review, American Economic Association, vol. 105(7), pages 2183-2203, July.
    14. Michael Gerlich, 2025. "AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking," Societies, MDPI, vol. 15(1), pages 1-28, January.
    15. Dirk Bergemann & Jacques Crémer & David Dinielli & Carl-Christian Groh & Paul Heidhues & Maximilian Schäfer & Monika Schnitzer & Fiona Scott Morton & Katja Seim & Michael Sullivan, 2023. "Market design for personal data," Post-Print hal-04213208, HAL.
    16. Goodman, Aaron & Puri, Indira, 2025. "Overvaluing simple bets: Evidence from the options market," Journal of Financial Economics, Elsevier, vol. 172(C).
    17. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    18. Itay Goldstein & Chester S. Spatt & Mao Ye, 2021. "Big Data in Finance," NBER Working Papers 28615, National Bureau of Economic Research, Inc.
    19. David W. Nickerson & Todd Rogers, 2014. "Political Campaigns and Big Data," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 51-74, Spring.
    20. Zhuang Liu & Michael Sockin & Wei Xiong, 2021. "Data Privacy and Temptation," Working Papers 2021-77, Princeton University. Economics Department..
    21. Itay Goldstein & Chester S Spatt & Mao Ye, 2021. "Big Data in Finance [Institutional order handling and broker-affiliated trading venues]," The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3213-3225.
    22. Stefano DellaVigna & Ulrike Malmendier, 2006. "Paying Not to Go to the Gym," American Economic Review, American Economic Association, vol. 96(3), pages 694-719, June.
    23. Dirk Bergemann & Alessandro Bonatti & Tan Gan, 2022. "The economics of social data," RAND Journal of Economics, RAND Corporation, vol. 53(2), pages 263-296, June.
    24. Hemant K. Bhargava & Vidyanand Choudhary, 2008. "Research Note--When Is Versioning Optimal for Information Goods?," Management Science, INFORMS, vol. 54(5), pages 1029-1035, May.
    25. Zhuang Liu & Michael Sockin & Wei Xiong, 2023. "Data Privacy and Algorithmic Inequality," NBER Working Papers 31250, National Bureau of Economic Research, Inc.
    26. Becker, Gary S & Murphy, Kevin M, 1988. "A Theory of Rational Addiction," Journal of Political Economy, University of Chicago Press, vol. 96(4), pages 675-700, August.
    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. Olivier Armantier & Sebastian Doerr & Jon Frost & Andreas Fuster & Kelly Shue, 2024. "Nothing to hide? Gender and age differences in willingness to share data," Swiss Finance Institute Research Paper Series 24-99, Swiss Finance Institute.
    2. Wu, Shan & Zou, Mengqi & Jin, Tongen, 2025. "Data element marketization and corporate investment efficiency: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 104(PA).
    3. Michalski, Lachlan & Low, Rand Kwong Yew, 2024. "Determinants of corporate credit ratings: Does ESG matter?," International Review of Financial Analysis, Elsevier, vol. 94(C).
    4. Brocas, Isabelle & Carrillo, Juan D., 2021. "Value computation and modulation: A neuroeconomic theory of self-control as constrained optimization," Journal of Economic Theory, Elsevier, vol. 198(C).
    5. Bartosz Maćkowiak & Filip Matějka & Mirko Wiederholt, 2023. "Rational Inattention: A Review," Journal of Economic Literature, American Economic Association, vol. 61(1), pages 226-273, March.
    6. Cao, Sean & Jiang, Wei & Wang, Junbo & Yang, Baozhong, 2024. "From Man vs. Machine to Man + Machine: The art and AI of stock analyses," Journal of Financial Economics, Elsevier, vol. 160(C).
    7. Rad, Hossein & Low, Rand Kwong Yew & Miffre, Joëlle & Faff, Robert, 2023. "The commodity risk premium and neural networks," Journal of Empirical Finance, Elsevier, vol. 74(C).
    8. Tommaso Denti & Doron Ravid, 2023. "Robust Predictions in Games with Rational Inattention," Papers 2306.09964, arXiv.org.
    9. Maximilian Schaefer, 2025. "When Should we Expect Non-Decreasing Returns from Data in Prediction Tasks?," Papers 2503.03602, arXiv.org.
    10. Mahsa Samsami & Ralf Wagner, 2021. "Investment Decisions with Endogeneity: A Dirichlet Tree Analysis," JRFM, MDPI, vol. 14(7), pages 1-19, July.
    11. Zhang, Wenkang & Wu, Jing, 2025. "Endogenous growth and data heterogeneity in data economics," Finance Research Letters, Elsevier, vol. 78(C).
    12. Catherine E. Tucker, 2023. "The Economics of Privacy: An Agenda," NBER Chapters, in: The Economics of Privacy, pages 5-20, National Bureau of Economic Research, Inc.
    13. Lin, Yi-Hsuan, 2022. "Stochastic choice and rational inattention," Journal of Economic Theory, Elsevier, vol. 202(C).
    14. Chen, S. & Doerr, S. & Frost, J. & Gambacorta, L. & Shin, H.S., 2023. "The fintech gender gap," Journal of Financial Intermediation, Elsevier, vol. 54(C).
    15. Matysková, Ludmila & Rogers, Brian & Steiner, Jakub & Sun, Keh-Kuan, 2020. "Habits as adaptations: An experimental study," Games and Economic Behavior, Elsevier, vol. 122(C), pages 391-406.
    16. Roc Armenter & Michèle Müller-Itten & Zachary Stangebye, 2020. "Rational Inattention via Ignorance Equivalence," Working Papers 20-24, Federal Reserve Bank of Philadelphia.
    17. Wang, Qin & Wang, Ying, 2025. "What shapes export competitiveness of home country firms? Host country environmental regulations or labor standards," International Review of Financial Analysis, Elsevier, vol. 103(C).
    18. Sofronis Clerides & Pascal Courty, 2017. "Sales, Quantity Surcharge, and Consumer Inattention," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 357-370, May.
    19. Niu, Yuhao & Wang, Sai & Wen, Wen & Li, Sifei, 2023. "Does digital transformation speed up dynamic capital structure adjustment? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    20. Maria Saveria Mavillonio, 2024. "Natural Language Processing Techniques for Long Financial Document," Discussion Papers 2024/317, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    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:eee:moneco:v:157:y:2026:i:c:s0304393225001552. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/505566 .

    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.