📝 SummarXiv

Abstract

Recent decades have witnessed unprecedented development in Artificial Intelligence (AI) to accelerate scientific discovery. Alongside two recent AI-oriented Nobel prizes, these trends establish the role of AI in science. This advancement raises questions about the potential influences of AI on scientists and science as a whole, and highlights a potential conflict between individual and collective benefits. To evaluate these concerns, we used a highly accurate pretrained language model to identify AI-augmented research, with an F1-score of 0.875 in validation against expert-labeled data. Using a dataset of 41.3 million research papers across the natural science and covering distinct eras of AI, here we show an accelerated adoption of AI among scientists and consistent professional advantages associated with AI use, but a collective narrowing of scientific concerns and a decrease in follow-on scientist engagement. Scientists who engage in AI-augmented research publish 3.02 times more papers, receive 4.84 times more citations, and become research project leaders 1.37 years (15.75%) earlier than those who do not. By contrast, AI research shrinks the collective volume of scientific topics studied by 4.63% and decreases scientist's engagement with one another by 22.00% when they build upon AI-augmented work. In this way, AI adoption in science presents a seeming paradox -- an expansion of individual scientists' impact but a contraction in collective science's reach -- as AI-augmented work moves collectively toward areas richest in data. With reduced follow-on scientific engagement, AI appears to automate established fields rather than explore new ones, highlighting a tension between personal advancement and collective scientific progress.