LLMs and the Organization of Scientific Work
The advent of large language models (LLMs) represents a technological revolution for science. Their diffusion may reshape not only how scientists conduct research, but also how they organize their work within teams. Here, we develop a conceptual framework in which LLMs expand individual capabilities across a range of research activities, reducing reliance on complementary human inputs within tasks and facilitating a more specialized division of labor among team members. We further propose that teams' history of collaboration may constrain their ability to reorganize in response to the new technological capabilities. Using CRediT contribution statements from approximately 120,000 PLOS ONE articles published between 2018 and 2025, we investigate changes in the division of labor within scientific teams following the release of ChatGPT in late 2022. We document three main findings. First, participation in scientific tasks falls below its previous trajectory, particularly in conceptualization, execution, and writing. Second, researchers become more specialized, exhibiting narrower task portfolios and less overlap in their contributions with co-authors. Third, these changes are concentrated among newly formed teams, whose members have no prior collaboration ties, whereas established teams, whose members have all collaborated previously, exhibit considerably greater organizational inertia. Our findings suggest that, in the wake of LLM diffusion, scientific work is being reorganized around greater individual autonomy and an increasingly specialized division of labor within teams.