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Explosive growth from AI automation: A review of the arguments

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  • Ege Erdil
  • Tamay Besiroglu

Abstract

We examine whether substantial AI automation could accelerate global economic growth by about an order of magnitude, akin to the economic growth effects of the Industrial Revolution. We identify three primary drivers for such growth: 1) the scalability of an AI "labor force" restoring a regime of increasing returns to scale, 2) the rapid expansion of an AI labor force, and 3) a massive increase in output from rapid automation occurring over a brief period of time. Against this backdrop, we evaluate nine counterarguments, including regulatory hurdles, production bottlenecks, alignment issues, and the pace of automation. We tentatively assess these arguments, finding most are unlikely deciders. We conclude that explosive growth seems plausible with AI capable of broadly substituting for human labor, but high confidence in this claim seems currently unwarranted. Key questions remain about the intensity of regulatory responses to AI, physical bottlenecks in production, the economic value of superhuman abilities, and the rate at which AI automation could occur.

Suggested Citation

  • Ege Erdil & Tamay Besiroglu, 2023. "Explosive growth from AI automation: A review of the arguments," Papers 2309.11690, arXiv.org, revised Jul 2024.
    • Handle: RePEc:arx:papers:2309.11690
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    1. Robert York & Paul Atkinson, 1997. "The Reliability of Quarterly National Accounts in Seven Major Countries: A User's Perspective," OECD Economics Department Working Papers 171, OECD Publishing.
    2. Herzer Dierk, 2022. "Semi-endogenous Versus Schumpeterian Growth Models: A Critical Review of the Literature and New Evidence," Review of Economics, De Gruyter, vol. 73(1), pages 1-55, April.
    3. Brynjolfsson, Erik & Collis, Avinash & Diewert, W. Erwin & Eggers, Felix & Fox, Kevin J., 2019. "GDP-B: Accounting for the Value of New and Free Goods in the Digital Economy," OSF Preprints sptfu, Center for Open Science.
    4. N. Gregory Mankiw & Matthew D. Shapiro, 1986. "News or Noise? An Analysis of GNP Revisions," NBER Working Papers 1939, National Bureau of Economic Research, Inc.
    5. Jeremy, David J., 1977. "Damming the Flood: British Government Efforts to Check the Outflow of Technicians and Machinery, 1780–1843," Business History Review, Cambridge University Press, vol. 51(1), pages 1-34, April.
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    Cited by:

    1. Anton Korinek & Donghyun Suh, 2024. "Scenarios for the Transition to AGI," NBER Working Papers 32255, National Bureau of Economic Research, Inc.
    2. Almeida, Derick & Naudé, Wim & Sequeira, Tiago Neves, 2024. "Artificial Intelligence and the Discovery of New Ideas: Is an Economic Growth Explosion Imminent?," IZA Discussion Papers 16766, Institute of Labor Economics (IZA).
    3. 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.

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