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Artificial intelligence as a general-purpose technology: an historical perspective

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  • Nicholas Crafts

Abstract

This paper looks at the impact on productivity of general-purpose technologies such as steam, electricity, and ICT. It finds they had big effects but only with a lag which was substantial in the first two cases. The experience of the First Industrial Revolution is explored and it is found that this is not a template for a general-purpose technology having a major adverse effect on workers’ living standards. The essence of that industrial revolution was not rapid productivity growth in the short run, but the ‘invention of a new method of invention’ which increased technological progress in the long run. Since artificial intelligence is potentially a general-purpose technology that raises the productivity of research and development, it may be the basis for a Fourth Industrial Revolution.

Suggested Citation

  • Nicholas Crafts, 2021. "Artificial intelligence as a general-purpose technology: an historical perspective," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 521-536.
    • Handle: RePEc:oup:oxford:v:37:y:2021:i:3:p:521-536.
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    File URL: http://hdl.handle.net/10.1093/oxrep/grab012
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    Citations

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    Cited by:

    1. Tamay Besiroglu & Nicholas Emery-Xu & Neil Thompson, 2022. "Economic impacts of AI-augmented R&D," Papers 2212.08198, arXiv.org, revised Jan 2023.
    2. Benjamin Laufer & Jon Kleinberg & Hoda Heidari, 2023. "Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Models," Papers 2308.04399, arXiv.org, revised Dec 2024.
    3. Kerstin Hotte & Taheya Tarannum & Vilhelm Verendel & Lauren Bennett, 2022. "Measuring artificial intelligence: a systematic assessment and implications for governance," Papers 2204.10304, arXiv.org, revised Dec 2024.
    4. Kim Nguyen & Jonathan Hambur, 2023. "Adoption of Emerging Digital General-purpose Technologies: Determinants and Effects," RBA Research Discussion Papers rdp2023-10, Reserve Bank of Australia.
    5. Xin Du & Hengming Zhang & Yawen Han, 2022. "How Does New Infrastructure Investment Affect Economic Growth Quality? Empirical Evidence from China," Sustainability, MDPI, vol. 14(6), pages 1-30, March.
    6. Zhai, Shaoxuan & Liu, Zhenpeng, 2023. "Artificial intelligence technology innovation and firm productivity: Evidence from China," Finance Research Letters, Elsevier, vol. 58(PB).
    7. Siddharth Madhav Joshi & Anubha Shekhar Sinha, 2023. "Knowledge as practice - How Artificial Intelligence can create new knowledge?," Working papers 550, Indian Institute of Management Kozhikode.
    8. Alexander Cuntz & Carsten Fink & Hansueli Stamm, 2024. "Artificial Intelligence and Intellectual Property : An Economic Perspective," WIPO Economic Research Working Papers 77, World Intellectual Property Organization - Economics and Statistics Division.
    9. Jacques Bughin & Nicolas van Zeebroeck, 2024. "Strategic Renewal and Corporate Return of Digital Transformation," Working Papers TIMES² 2024-071, ULB -- Universite Libre de Bruxelles.
    10. Aivin V. Solatorio & Gabriel Stefanini Vicente & Holly Krambeck & Olivier Dupriez, 2024. "Double Jeopardy and Climate Impact in the Use of Large Language Models: Socio-economic Disparities and Reduced Utility for Non-English Speakers," Papers 2410.10665, arXiv.org.
    11. Manuel Hoffmann & Sam Boysel & Frank Nagle & Sida Peng & Kevin Xu, 2024. "Generative AI and the Nature of Work," CESifo Working Paper Series 11479, CESifo.
    12. Besiroglu, Tamay & Emery-Xu, Nicholas & Thompson, Neil, 2024. "Economic impacts of AI-augmented R&D," Research Policy, Elsevier, vol. 53(7).
    13. Wang, Linhui & Cao, Zhanglu & Dong, Zhiqing, 2023. "Are artificial intelligence dividends evenly distributed between profits and wages? Evidence from the private enterprise survey data in China," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 342-356.
    14. Daniel Souza & Aldo Geuna & Jeff Rodr'iguez, 2024. "How Small is Big Enough? Open Labeled Datasets and the Development of Deep Learning," Papers 2408.10359, arXiv.org.
    15. Agrawal, Ajay & McHale, John & Oettl, Alexander, 2024. "Artificial intelligence and scientific discovery: a model of prioritized search," Research Policy, Elsevier, vol. 53(5).
    16. 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.
    17. Jaehyuk Park, 2024. "Analyzing the direct role of governmental organizations in artificial intelligence innovation," The Journal of Technology Transfer, Springer, vol. 49(2), pages 437-465, April.
    18. Waßenhoven, Anna & Rennings, Michael & Laibach, Natalie & Bröring, Stefanie, 2023. "What constitutes a “Key Enabling Technology” for transition processes: Insights from the bioeconomy's technological landscape," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    19. Inha Oh & Jungho Kim, 2023. "Frontiers and laggards: Which firms benefit from adopting advanced digital technologies?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(2), pages 753-766, March.

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