The growing influence of industry in AI research
Abstract
Industry is gaining control over the technology’s future
Integrity signals
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Cites
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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?
The compute needed for training generative AI comes with costs that are within reach of none but a handful of larger corporations or government entities.
“The compute needed for training comes with costs that are within reach of none but a handful of larger corporations or government entities [1].”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [1]
Open-washing stands in the way of innovation, because if large corporations can derive benefits from the trappings of open source without doing the requisite work, this sucks up the oxygen in the open source ecosystem, making it less attractive for smaller entities to find funding for truly open projects.
“Open-washing stands in the way of innovation, because if large corporations can derive benefits from the trappings of open source without doing the requisite work, this sucks up the oxygen in the open source ecosystem, making it less attractive for smaller entities to find funding for truly open projects [1].”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [1]
The most prominent AI breakthroughs, particularly large language models, function within an economic system that rewards rapid growth and ever-increasing computational power.
“At present, the most prominent AI breakthroughs, particularly large language models, function within an economic system that rewards rapid growth and ever-increasing computational power [4, 125].”
From From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate (2025) · cites this paper as [4]
Cites
Privacy-preserving deep learning
Shokri, R., & Shmatikov, V. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813687 · Cited by 2,356 · see citing works
Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform
Brynjolfsson, E., Hui, X., & Liu, M. (2019) · Management Science · doi:10.1287/mnsc.2019.3388 · Cited by 408 · see citing works
Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform
Brynjolfsson, E., Hui, X., & Liu, M. (2018) · doi:10.3386/w24917 · Cited by 408
Predictability and Surprise in Large Generative Models
Ganguli, D., Hernandez, D., Lovitt, L., Askell, A., Bai, Y., Chen, A., Conerly, T., Dassarma, N., Drain, D., Elhage, N., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533229 · Cited by 207 · see citing works
The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence
Brynjolfsson, E. (2022) · Daedalus · doi:10.1162/daed_a_01915 · Cited by 198 · see citing works
Do two-year colleges increase overall educational attainment? Evidence from the states
Rouse, C. E. (1998) · Journal of Policy Analysis and Management · doi:10.1002/(sici)1520-6688(199823)17:4 · Cited by 120 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Artificial intelligence and increasing misinformation
Monteith, S., Glenn, T., Geddes, J. R., Whybrow, P. C., Achtyes, E., & Bauer, M. (2024) · The British Journal of Psychiatry · doi:10.1192/bjp.2023.136 · Cited by 208 · see citing works
Liesenfeld, A., Lopez, A., & Dingemanse, M. (2023) · Proceedings of the 5th International Conference on Conversational User Interfaces · doi:10.1145/3571884.3604316 · Cited by 111 · see citing works
Rethinking open source generative AI: open washing and the EU AI Act
Liesenfeld, A., & Dingemanse, M. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3659005 · Cited by 67 · see citing works
Luccioni, A. S., Strubell, E., & Crawford, K. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732007 · Cited by 71 · see citing works
Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation
Eriksson, M., Purificato, E., Noroozian, A., Vinagre, J., Chaslot, G., Gomez, E., & Fernandez-Llorca, D. (2025) · arXiv · doi:10.48550/arxiv.2502.06559 · Cited by 35 · see citing works
Artificial intelligence (AI) as a new trajectory in education
Döğer, M. F., & Göçen, A. (2025) · The Journal of Educational Research · doi:10.1080/00220671.2025.2510391 · Cited by 12 · see citing works
- Authors
- Ahmed, N., Wahed, M., & Thompson, N. C.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Virginia Tech (US)
- Venue
- Science
- Published
- 2023-03-03
- Volume
- 379
- Issue
- 6635
- Pages
- 884-886
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4322763951
- Cited by
- 208
- References
- 6
- Citations by year
- 2026: 62 · 2025: 80 · 2024: 47 · 2023: 19
- FWCI
- 19.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Information Systems and Management › Research, Science, and Academia
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence research, industry influence, technology governance
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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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