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Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth

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  • Ajay Agrawal
  • John McHale
  • Alex Oettl

Abstract

Innovation is often predicated on discovering useful new combinations of existing knowledge in highly complex knowledge spaces. These needle-in-a-haystack type problems are pervasive in fields like genomics, drug discovery, materials science, and particle physics. We develop a combinatorial-based knowledge production function and embed it in the classic Jones growth model (1995) to explore how breakthroughs in artificial intelligence (AI) that dramatically improve prediction accuracy about which combinations have the highest potential could enhance discovery rates and consequently economic growth. This production function is a generalization (and reinterpretation) of the Romer/Jones knowledge production function. Separate parameters control the extent of individual-researcher knowledge access, the effects of fishing out/complexity, and the ease of forming research teams.

Suggested Citation

  • Ajay Agrawal & John McHale & Alex Oettl, 2018. "Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth," NBER Working Papers 24541, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24541
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    References listed on IDEAS

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    1. Bresnahan, Timothy F. & Trajtenberg, M., 1995. "General purpose technologies 'Engines of growth'?," Journal of Econometrics, Elsevier, vol. 65(1), pages 83-108, January.
    2. Robert J. Gordon, 2016. "The Rise and Fall of American Growth: The U.S. Standard of Living since the Civil War," Economics Books, Princeton University Press, edition 1, number 10544.
    3. Benjamin F. Jones, 2009. "The Burden of Knowledge and the "Death of the Renaissance Man": Is Innovation Getting Harder?," Review of Economic Studies, Oxford University Press, vol. 76(1), pages 283-317.
    4. Nicholas Bloom & Charles I. Jones & John Van Reenen & Michael Webb, 2017. "Are Ideas Getting Harder to Find?," NBER Working Papers 23782, National Bureau of Economic Research, Inc.
    5. Ajay Agrawal & Avi Goldfarb, 2008. "Restructuring Research: Communication Costs and the Democratization of University Innovation," American Economic Review, American Economic Association, vol. 98(4), pages 1578-1590, September.
    6. Adam B. Jaffe & Manuel Trajtenberg & Rebecca Henderson, 1993. "Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations," The Quarterly Journal of Economics, Oxford University Press, vol. 108(3), pages 577-598.
    7. repec:aea:jecper:v:31:y:2017:i:2:p:87-106 is not listed on IDEAS
    8. Agrawal, Ajay & Kapur, Devesh & McHale, John, 2008. "How do spatial and social proximity influence knowledge flows? Evidence from patent data," Journal of Urban Economics, Elsevier, vol. 64(2), pages 258-269, September.
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    Cited by:

    1. J. Klinger & J. Mateos-Garcia & K. Stathoulopoulos, 2018. "Deep learning, deep change? Mapping the development of the Artificial Intelligence General Purpose Technology," Papers 1808.06355, arXiv.org.

    More about this item

    JEL classification:

    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity
    • Z38 - Other Special Topics - - Tourism Economics - - - Policy

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