Abstract
This paper examines the growing role of AI in scientific discovery. It first surveys the rapid rise of AI capabilities, especially in reasoning, abstraction, planning, and long-horizon task execution, before turning to scientometric evidence of AI’s diffusion across the sciences. It then proposes a typology of AI systems used in research, ranging from specialised scientific AI through scientific AI assistants and agents to hybrid experimental systems that combine computation and physical experimentation. On this basis, it offers a selective overview of recent achievements in mathematics and computer science, physics, chemistry, the life sciences, and the behavioural and social sciences. It argues that, despite these advances, current systems remain constrained by important technical, epistemic, and institutional limitations, and that their growing use introduces both near-term and longer-term risks. The conclusion further suggests that the advancement of AI in science raises broader questions concerning the division of cognitive labour between human researchers and machines.
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