SORS: The AI frontier: transformative role of foundation models across scientific disciplines

Abstract

This study provides an extensive analysis of artificial intelligence (AI) integration within academic sciences, focusing on the adoption and use of AI foundation models. By manually collecting data on almost 2,000 foundation models—including details such as model size, institution of origin, openness, training data, and software availability—we build a dataset that captures the landscape of AI resources available to researchers. Combined with a corpus of nearly half a million openaccess academic papers from Semantic Scholar that cite these models, our analysis explores how AI is engaged in scholarly work. Using large language models (GPT-4.1), we categorize this engagement into three main applications: developing novel AI technologies, customizing existing models, and employing AI as a routine tool in scientific research. Our findings reveal transformative trends in computational science, including a rapid increase in model complexity and the growing expertise and resources required to use these technologies effectively. We also identify a shift toward industrial dominance in AI development, which could affect the independence of academic research due to industry’s control over talent and resources. Finally, we observe a preference for open-source models among researchers addressing socially significant issues, underscoring the importance of open AI in advancing both scientific and societal goals.