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Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth

In: The Economics of Artificial Intelligence: An Agenda

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Listed:
  • Ajay Agrawal
  • John McHale
  • Alexander 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.
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  • Ajay Agrawal & John McHale & Alexander Oettl, 2018. "Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 149-174, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:14024
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    References listed on IDEAS

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    3. Gries, Thomas & Naudé, Wim, 2018. "Artificial Intelligence, Jobs, Inequality and Productivity: Does Aggregate Demand Matter?," IZA Discussion Papers 12005, IZA Network @ LISER.
    4. Hu, Xiaohua & Fei, Yulian, 2023. "Does the matching degree between data and human capital affect firm innovation?," Finance Research Letters, Elsevier, vol. 55(PB).
    5. Andrea Borsato & Patrick Llerena, 2026. "The US university-industry link in the R&D of AI: Back to the origins?," Journal of Evolutionary Economics, Springer, vol. 36(1), pages 1-39, April.
    6. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2021. "The Productivity J-Curve: How Intangibles Complement General Purpose Technologies," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(1), pages 333-372, January.
    7. Stefano Bianchini & Moritz Müller & Pierre Pelletier, 2022. "Artificial intelligence in science: An emerging general method of invention," Post-Print hal-03958025, HAL.
    8. Ayoubi, Charles, 2020. "Machine learning in healthcare: Mirage or miracle for breaking the costs dead-lock?," Thesis Commons tc24d, Center for Open Science.
    9. Seth G. Benzell & Erik Brynjolfsson, 2019. "Digital Abundance and Scarce Genius: Implications for Wages, Interest Rates, and Growth," NBER Working Papers 25585, National Bureau of Economic Research, Inc.
    10. Majid Majzoubi & Eric Yanfei Zhao, 2023. "Going beyond optimal distinctiveness: Strategic positioning for gaining an audience composition premium," Strategic Management Journal, Wiley Blackwell, vol. 44(3), pages 737-777, March.
    11. Naudé, Wim, 2022. "The Future Economics of Artificial Intelligence: Mythical Agents, a Singleton and the Dark Forest," IZA Discussion Papers 15713, IZA Network @ LISER.
    12. Joel Klinger & Juan Mateos-Garcia & Konstantinos Stathoulopoulos, 2021. "Deep learning, deep change? Mapping the evolution and geography of a general purpose technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5589-5621, July.
    13. Gersbach, Hans & Komarov, Evgenij & von Maydell, Richard, 2025. "Artificial intelligence as self-learning capital," Economic Modelling, Elsevier, vol. 153(C).
    14. Naudé, Wim, 2023. "Melancholy Hues: The Futility of Green Growth and Degrowth, and the Inevitability of Societal Collapse," IZA Discussion Papers 16139, IZA Network @ LISER.
    15. Georgios A. Tritsaris, 2025. "Occupational Tasks, Automation, and Economic Growth: A Modeling and Simulation Approach," Papers 2512.16261, arXiv.org, revised Dec 2025.
    16. Li, Dongkun & Chen, Yufeng & Miao, Jiafeng, 2022. "Does ICT create a new driving force for manufacturing?—Evidence from Chinese manufacturing firms," Telecommunications Policy, Elsevier, vol. 46(1).
    17. 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).
    18. Bianchini, Stefano & Müller, Moritz & Pelletier, Pierre, 2025. "Drivers and barriers of AI adoption and use in scientific research," Technological Forecasting and Social Change, Elsevier, vol. 220(C).
    19. Chen, Zhongfei & Jiang, Kangqi, 2024. "Digitalization and corporate investment efficiency: Evidence from China," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
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    21. Li, Hao & Wang, Gaowang & Yang, Liyang, 2024. "Data-driven innovation and growth," MPRA Paper 122388, University Library of Munich, Germany.

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    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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