The Rapid Growth of AI Foundation Model Usage in Science

2025/11/21 by Trišović, Ana, Fogelson, Alex, Sivaloganathan, Janakan +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · Materials Science · Medicine · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare and Education #Biomedical Text Mining and Ontologies #Digital Libraries (cs.DL) #FOS: Computer and information sciences #Machine Learning in Materials Science

paper · doi:10.48550/arxiv.2511.21739

openalex publication_date 2025/11/21 · openalex created_date 2025/12/03 · openalex updated_date 2026/07/28

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 2013, the median foundation model built was 7.7x larger than the median one adopted in science, by 2024 this had jumped to 26x. 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.

Citations

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Cited by

• Generative Artificial Intelligence in Scientific Research: Individual Benefits, Collective Risks, and a Framework for Responsible Research with AI

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