The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Scenarios for the Transition to AGI
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In the citing paper
“An AI that automates all current remote work without overfitting is likely to have many of the same general cognitive skills as humans, allowing it to automate new jobs as they arise [15].”
From Remote Labor Index: Measuring AI Automation of Remote Work (2025) · cites this paper as [15]
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Citations
Cites
The Skill Content of Recent Technological Change: An Empirical Exploration
Autor, D. H., Levy, F., & Murnane, R. J. (2003) · The Quarterly Journal of Economics · doi:10.1162/003355303322552801 · Cited by 6,205
Changes in Relative Wages, 1963-1987: Supply and Demand Factors
Katz, L. F., & Murphy, K. M. (1992) · The Quarterly Journal of Economics · doi:10.2307/2118323 · Cited by 4,793
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023) · arXiv · doi:10.48550/arxiv.2303.10130 · Cited by 2,330
Tasks, automation, and the rise in U.S. wage inequality
Acemoglu, D., & Restrepo, P. (2022) · Econometrica · doi:10.3982/ecta19815 · Cited by 715
Work of the past, work of the future
Autor, D. H. (2019) · AEA Papers and Proceedings · doi:10.1257/pandp.20191110 · Cited by 435
Acemoglu, D., & Restrepo, P. (2016) · doi:10.3386/w22252 · Cited by 406
Compute Trends Across Three Eras of Machine Learning
Sevilla, J., Heim, L., Ho, A., Besiroglu, T., Hobbhahn, M., & Villalobos, P. (2022) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9891914 · Cited by 363
Language Models and Cognitive Automation for Economic Research
Korinek, A. (2023) · doi:10.3386/w30957 · Cited by 129
Self-citation
Research works printed but not linked: 10
- Aghion, P., Jones, B., and Jones, C. (2019). Articial intelligence and economic growth. In Agrawal, A., Gans, J., and Goldfarb, A., editors, The Economics of Articial Intelligence: An Agenda, pages 237290. NBER and University of Chicago Press.
- Besiroglu, T., Emery-Xu, N., and Thompson, N. (2022). Economic impacts of AI-augmented R&D. arXiv:2212.08198. · arXiv
- Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
- Erdil, E. and Besiroglu, T. (2023). Explosive growth from ai automation: A review of the arguments.
- Felten, E. W., Raj, M., and Seamans, R. (2023). How will language modelers like ChatGPT aect occupations and industries? SSRN Working Paper.
- In Alt, F. L. and Rubino, M., editors, Advances in Computers, volume 6, pages 3188. Academic Press.
- Jones, C. I. (1995). R d-based models of economic growth. Journal of Political Economy, 103(4):759784.
- Korinek, A. and Juelfs, M. (2023). Preparing for the (non-existent?) future of work. In Justin Bullock et al., editor, forthcoming, Oxford Handbook of AI Governance. Oxford University Press.
- Moravec, H. (1988). Mind Children: The Future of Robot and Human Intelli- gence. Harvard University Press.
- Zeira, J. (1998). Workers, machines, and economic growth. Quarterly Journal of Economics, 113(4):10911117.
Cited by
Remote Labor Index: Measuring AI Automation of Remote Work
Mazeika, M., Gatti, A., Menghini, C., Sehwag, U. M., Singhal, S., Orlovskiy, Y., Basart, S., Sharma, M., Peskoff, D., Lau, E., et al. (2025) · arXiv · doi:10.48550/arxiv.2510.26787 · Cited by 44
Metadata
- Authors
- Korinek, A., & Suh, D.
- Publisher
- National Bureau of Economic Research
- Published
- 2024-03
- Cited by
- 55
- References
- 8
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
