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The wrong kind of AI? Artificial intelligence and the future of labour demand
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Artificial intelligence (AI) is set to influence every aspect of our lives, not least the way production is organised. AI, as a technology platform, can automate tasks previously performed by labour or create new tasks and activities in which humans can be productively employed. Recent technological change has been biased towards automation, with insufficient focus on creating new tasks where labour can be productively employed. The consequences of this choice have been stagnating labour demand, declining labour share in national income, rising inequality and lowering productivity growth. The current tendency is to develop AI in the direction of further automation, but this might mean missing out on the promise of the ‘right’ kind of AI, with better economic and social outcomes.
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“A study by Acemoglu, Autor, Hazell, and Restrepo (2020) concludes no significant effect yet in the last decade at the occupation or industry level in the US.”
From Artificial intelligence and work: a critical review of recent research from the social sciences (2024) · cites this paper as Acemoglu et al. 2020
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Metadata
- Authors
- Acemoglu, D., & Restrepo, P.
- Affiliations
- MIT, Cambridge, MA, USA
- Boston University (US)
- Venue
- Cambridge Journal of Regions, Economy and Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-12-18
- Volume
- 13
- Issue
- 1
- Pages
- 25-35
- ISSN
- 1752-1378 (ISSN-L)
- 1752-1386
- OpenAlex
- W4230775373
- Cited by
- 634
- References
- 39
- Citations by year
- 2026: 143 · 2025: 137 · 2024: 111 · 2023: 103 · 2022: 76 · 2021: 47 · 2020: 16
- FWCI
- 43.38
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Cambridge Journal of Regions Economy and Society (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- National Science Foundation
- Alfred P. Sloan Foundation
- Smith Richardson Foundation
- Sid W. Richardson Foundation
- Accenture
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic Growth and Productivity
- Keywords
- artificial intelligence, labor demand, automation, technological change, labor share, income inequality, productivity growth, employment, task creation, future of work
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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