ABSTRACT The rapid adoption of artificial intelligence (AI) and machine learning (ML) in chemistry coincides with increasing structural pressures on academic research, including funding constraints, talent competition, and changing attitudes toward scientific careers. In this Perspective, we argue that this combination of trends may reshape how and by whom chemical knowledge is produced, rather than simply increase research productivity. We discuss recent developments in the automation of experimentation and self‐driving labs, ML‐based modeling and digital twins, and the use of large language models for literature search, manuscript preparation, and review, and place them against the current financial and social pressures on universities. We outline these trends in the hope of softening the transition for the chemical research community and urging researchers, institutions, and funders to make their research ecosystems more resilient. Finally, we discuss possible shifts in the composition and structure of research groups and in the balance between universities, industry, and government laboratories, raising the central question: who will produce chemical knowledge in the research landscape changed by the wider adoption of AI technologies?
Boiko et al. (2026) studied this question.