The Rapid Growth of AI Foundation Model Usage in Science

TL;DR

This paper analyzes the rapid growth and adoption patterns of AI foundation models in scientific research, highlighting trends in model types, sizes, and their impact on scientific impact.

Contribution

It provides the first large-scale analysis of AI foundation model usage in science, revealing growth trends, model preferences, and potential effects on research impact.

Findings

Adoption of AI foundation models in science is nearly exponential.

Vision models are most used, but language models' usage is increasing.

Larger models are associated with higher-impact publications.

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.