The Rapid Growth of AI Foundation Model Usage in Science
This research identifies trends and potential limitations in AI adoption for scientists, highlighting a gap between model development and scientific use.
The study analyzed AI foundation model usage in science, finding rapid, nearly-exponential growth with highest adoption in Linguistics, Computer Science, and Engineering, and that papers using larger models tend to appear in higher-impact journals and accrue more citations.
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.