Science Is Falling Behind the Frontier: Foundation Model Adoption Across Half a Million Papers
cs.DL, cs.AI
Submitted: 2025-11-21
Updated: 2026-10-02
Comments: 22 pages (8 main text), 6 figures, 3 tables. Accepted to the AI for Meta-Science (AI4MetaScience) Workshop at NeurIPS 2026
Code: https://github.com/pydantic/pydantic
License: http://creativecommons.org/licenses/by/4.0/
The gist: We present the first large-scale analysis of AI foundation model usage in science -- not just citations or keywords.
Terminology
Abstract
We present the first large-scale analysis of AI foundation model usage in science -- not just citations or keywords. We find that adoption has grown rapidly, at nearly-exponential rates, with the highest uptake in Linguistics, Computer Science, and Engineering. Vision models are the most used foundation models in science, although language models' share is growing. Open-weight models dominate. As AI builders increase the parameter counts of their models, scientists have followed suit but at a much slower rate: in 2015, the mean foundation model adopted in science was 5.4x larger than the mean model being built; by 2024 that relationship had reversed, with the mean model built 6.9x larger than the mean model adopted. We also present suggestive evidence that scientists' use of these smaller models may be limiting them from getting the full benefits of AI-enabled science, as papers that use larger models appear in higher-impact journals and accrue more citations.
Sources
- The Foundation Model Transparency Index
- On the Opportunities and Risks of Foundation Models
- Physics-Guided Foundation Model for Scientific Discovery: An Application to Aquatic Science
- ChatGPT "contamination": estimating the prevalence of LLMs in the scholarly literature
- Can Foundation Models Wrangle Your Data?
- Artificial Intelligence Tools Expand Scientists' Impact but Contract Science's Focus (Just accepted by Nature, to be online soon)
- Reducing the Barriers to Entry for Foundation Model Training
- The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?
- The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
- The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research
- The Role of Computing Resources in Publishing Foundation Model Research
- Oil & Water? Diffusion of AI Within and Across Scientific Fields
- Quantifying the Benefit of Artificial Intelligence for Scientific Research
- Scientific Discovery in the Age of AI and Supercomputing
- When is a Foundation Model a Foundation Model
- Foundation models in brief: A historical, socio-technical focus
- Machine Learning Model Sizes and the Parameter Gap
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- A Constructive Prediction of the Generalization Error Across Scales
- Scaling Laws for Neural Language Models