Toward understanding the impact of artificial intelligence on labor
Open accessAbstract
Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human–machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.
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Automation facilitated by large language models may disrupt labor markets, posing challenges to employment and exacerbating income inequality globally.
“Additionally, the automation facilitated by large language models may disrupt labor markets, posing challenges to employment and exacerbating income inequality globally (Frank et al. 2019).”
From Fostering AI Literacy in Undergraduates: A ChatGPT Workshop Case Study (2025) · cites this paper as Frank et al. 2019
The Abstract says AI and automation may disrupt labor markets and replace some workers’ work, and it notes concern about employment in advanced and developing economies worldwide. It does not specifically address large language models. It describes automation as occurring amid growing inequality, while “Barrier: Places in Isolation” discusses possible changes in income distribution; neither establishes that this automation would exacerbate income inequality globally. (LLM, conf=0.88)
Experimental — accuracy not yet measuredBusinesses have implemented and deployed AI solutions to automate processes, increase efficiency and reduce costs, in order to gain competitive advantage.
“In order to gain a competitive advantage over their competitors (Raisch & Krakowski, 2021), businesses have implemented and deployed AI solutions to automate their processes, increase efficiency and reduce costs (Frank et al., 2019; Gregory et al., 2020).”
From Toward AI Governance: Identifying Best Practices and Potential Barriers and Outcomes (2023) · cites this paper as Frank et al., 2019
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- Authors
- Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J., Feldman, M., Groh, M., Lobo, J., Moro, E., Wang, D., Youn, H., & Rahwan, I.
- Affiliations
- MIT Media Lab (US)
- Massachusetts Institute of Technology (US)
- Boston University (US)
- National Bureau of Economic Research (US)
- Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139;
- Harvard University (US)
- University of North Carolina at Chapel Hill (US)
- Arizona State University (US)
- Northwestern University (US)
- Max Planck Institute for Human Development (DE)
- Corresponding author
- Rahwan, Iyad
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2019-03-25
- Volume
- 116
- Issue
- 14
- Pages
- 6531-6539
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC6452673
- OpenAlex
- W2924260403
- Cited by
- 713
- References
- 75
- Citations by year
- 2026: 110 · 2025: 129 · 2024: 129 · 2023: 93 · 2022: 91 · 2021: 98 · 2020: 49 · 2019: 11 · 2018: 2
- FWCI
- 77.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (acceptedVersion)
- e-Archivo (Carlos III University of Madrid) (submittedVersion, cc-by-nc-nd)
- bepress Legal Repository (submittedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- UNC Libraries
- Funders
- National Science Foundation (1733545)
- Massachusetts Institute of Technology
- Directorate for Social, Behavioral and Economic Sciences
- 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 › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence, automation, future of work, labor market disruption, technological unemployment, skill substitution, human-machine complementarity, vocational skills, economic inequality, data quality, urban migration, trade policy, decision-making framework
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