Artificial Intelligence in Research and Development
Open accessAbstract
How much can AI accelerate progress in different research fields?This paper shows that three features-the share of research tasks AI performs, the productivity of AI at those tasks, and the strength of bottlenecks-are key determinants of AI's implications in any area, from cancer therapeutics to software design.The model maps changes in AI capabilities to research outcomes, quantifies the "marginal returns to intelligence," and shows how AI can shift returns to R&D investment.Concepts like superintelligence, Powerful AI, and Transformative AI are further engaged and disciplined.Finally, the framework sets a measurement agenda linking AI benchmarks to field-specific opportunities for accelerating progress.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Discovery is constrained by multiple frictions, from data collection and experimental throughput to coordination, implementation, and more, many of which LLM involvement does not directly resolve.
“Discovery is constrained by multiple frictions, from data collection and experimental throughput to coordination, implementation, and more, many of which LLM involvement does not directly resolve.”
From The rise of large language models and the direction and impact of US federal research funding (2026) · cites this paper as [38]
The paper lists data access, analysis, and laboratory procedures as distinct research tasks (Section 2, page unavailable), and says that gains in a narrow set of tasks need not overcome broader R&D bottlenecks (Section 5.3, page unavailable). It does not specifically assess LLM involvement or establish that LLMs leave the listed coordination and implementation frictions unresolved. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 16
- Acemoglu, Daron and Pascual Restrepo (2018). "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, 108 (6), 1488-1542.
- Acemoglu, Daron and Pascual Restrepo (2020). "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, vol 128(6), 2188-2244.
- Aghion Philippe, Benjamin F. Jones, Charles I. Jones. (2019). "Artificial Intelligence and Economic Growth" in The Economics of Artificial Intelligence, Ajay Agrawal, Joshua Gans, and Avi Goldfarb (eds.), Universiy of Chicago Press.
- Ahmadpoor, Mohammad and Benjamin F Jones (2019). Decoding team and individ- ual impact in science and invention. Proceedings of the National Academy of Sciences, 116(28), 13885-13890.
- Autor, David, Frank Levy, and Richard J. Murnane (2003). "The Skill Content of Recent Technological Change: An Empirical Exploration," Quarterly Journal of Economics, 118(4), 1297-1333.
- Baumol, William (1967). "Macroeconomics of Unbalanced Growth: The Anatomy of Urban Crisis," The American Economic Review, 57(3), 415-426.
- Bloom, Nicholas, Jones, Charles I., Van Reenen, John, and Webb, Michael. (2020). "Are Ideas Getting Harder to Find?" American Economic Review, 110(4), 1104-1144.
- Chen, Q., Yang, M., Qin, L., Liu, J., Yan, Z., Guan, J., ... and Che, W. (2025). "AI4Research: A Survey of Artificial Intelligence for Scientific Research," arXiv preprint arXiv:2507.01903. · arXiv
- Jones, Benjamin F. (2009). "The Burden of Knowledge and the Death of the Renais- sance Man: Is innovation Getting Harder?" Review of Economic Studies, 76, 283-317.
- Jones, Benjamin F. and Xiaojie Liu (2024). "A Framework for Economic Growth with Capital-Embodied Technical Change," American Economic Review, 114(5), 1448-87.
- Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool et al. (2021) "Highly accurate protein structure prediction with AlphaFold." Nature, 596(7873), 583-589.
- Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G., and Cubuk, E. D. (2023). "Scaling deep learning for materials discovery," Nature, 624(7990), 80-85.
- Segler, M. H., Preuss, M., and Waller, M. P. (2018). "Planning chemical syntheses with deep neural networks and symbolic AI," Nature, 555(7698), 604-610.
- Starace, G., Jaffe, O., Sherburn, D., Aung, J., Chan, J. S., Maksin, L., ... and Pat- wardhan, T. (2025). "PaperBench: Evaluating AI's Ability to Replicate AI Research." arXiv preprint arXiv:2504.01848. · arXiv
- Zeira, Joseph (1998). "Workers, Machines, and Economic Growth," The Quarterly Journal of Economics, Volume 113, Issue 4, November 1998, Pages 1091-1117
- Zhou, Hang-Yu, Yaling Li, Jiaying Li, Jing Meng, and Aiping Wu (2025). "Unleashing the Potential of Artificial Intelligence in Infectious Diseases, National Science Review, 12(3).
Cited by
Sorted by citations
The rise of large language models and the direction and impact of US federal research funding
Qian, Y., Wen, Z., Furnas, A. C., Bai, Y., Shao, E., & Wang, D. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2601439123 · Cited by 5 · see citing works
Automatic corrosion segmentation and classification using image processing and machine learning
Rios, M. P., Ivson, P., & Roehl, D. D. M. (2025) · Journal of Ocean Engineering and Marine Energy · doi:10.1007/s40722-025-00445-2 · Cited by 2 · see citing works
Artificial Intelligence, Alliances, and Innovation1
Lou, B., & Rawley, E. (2026) · MIS Quarterly · doi:10.25300/misq/2026/19132
AI, productivity, and digital capabilities: cross-country evidence from early adoption
Calvino, F., & Fontanelli, L. (2026) · Industrial and Corporate Change · doi:10.1093/icc/dtag043
- Authors
- Jones, B.
- Affiliations
- Northwestern University (US)
- Corresponding author
- Jones, Benjamin
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 2025-10
- OpenAlex
- W4414867103
- Cited by
- 4
- References
- 0
- Citations by year
- 2026: 3 · 2025: 1
- Type
- report
- Language
- en
- Open access
- green
- doi.org
- Locations
- National Bureau of Economic Research (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- Funders
- National Science Foundation (2404035)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic Growth and Productivity
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- artificial intelligence, research and development, AI productivity, research bottlenecks, R&D investment returns, superintelligence, AI benchmarks, cancer therapeutics, software design
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
