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Applying AI to Rebuild Middle Class Jobs

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  • David Autor

Abstract

While the utopian vision of the current Information Age was that computerization would flatten economic hierarchies by democratizing information, the opposite has occurred. Information, it turns out, is merely an input into a more consequential economic function, decision-making, which is the province of elite experts. The unique opportunity that AI offers to the labor market is to extend the relevance, reach, and value of human expertise. Because of AI’s capacity to weave information and rules with acquired experience to support decision-making, it can be applied to enable a larger set of workers possessing complementary knowledge to perform some of the higher-stakes decision-making tasks that are currently arrogated to elite experts, e.g., medical care to doctors, document production to lawyers, software coding to computer engineers, and undergraduate education to professors. My thesis is not a forecast but an argument about what is possible: AI, if used well, can assist with restoring the middle-skill, middle-class heart of the US labor market that has been hollowed out by automation and globalization.

Suggested Citation

  • David Autor, 2024. "Applying AI to Rebuild Middle Class Jobs," NBER Working Papers 32140, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:32140
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    Cited by:

    1. Alexander Bick & Adam Blandin & David Deming, 2023. "The Rapid Adoption of Generative AI," On the Economy 98843, Federal Reserve Bank of St. Louis.
    2. Christoph Riedl & Eric Bogert, 2024. "Effects of AI Feedback on Learning, the Skill Gap, and Intellectual Diversity," Papers 2409.18660, arXiv.org.
    3. Enrico Maria Fenoaltea & Dario Mazzilli & Aurelio Patelli & Angelica Sbardella & Andrea Tacchella & Andrea Zaccaria & Marco Trombetti & Luciano Pietronero, 2024. "Follow the money: a startup-based measure of AI exposure across occupations, industries and regions," Papers 2412.04924, arXiv.org, revised Dec 2024.
    4. Findeisen, Sebastian & Dauth, Wolfgang & Schlenker, Oliver, 2024. "Organized Labor Versus Robots? Evidence from Micro Data," CEPR Discussion Papers 19192, C.E.P.R. Discussion Papers.
    5. Minniti, Antonio & Prettner, Klaus & Venturini, Francesco, 2024. "Unslicing the pie: AI innovation and the labor share in European regions," Department of Economics Working Paper Series 369, WU Vienna University of Economics and Business.
    6. Ben Etheridge & Aitor Irastorza-Fadrique & Michael J. Böhm, 2024. "The impact of labour demand shocks when occupational labour supplies are heterogeneous," IFS Working Papers W24/28, Institute for Fiscal Studies.
    7. Flavio Calvino & Chiara Criscuolo & Luca Fontanelli & Lionel Nesta & Elena Verdolini, 2024. "The role of human capital for AI adoption: Evidence from French firms," CEP Discussion Papers dp2055, Centre for Economic Performance, LSE.
    8. Manuel Hoffmann & Sam Boysel & Frank Nagle & Sida Peng & Kevin Xu, 2024. "Generative AI and the Nature of Work," CESifo Working Paper Series 11479, CESifo.

    More about this item

    JEL classification:

    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • J2 - Labor and Demographic Economics - - Demand and Supply of Labor
    • N30 - Economic History - - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy - - - General, International, or Comparative
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology

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