Contextual stories

1. The change is already inside the field I have been thinking about AI and research less as a distant forecast and more as a change in the texture of an ordinary working day. A calculation that once took an afternoon can be checked in minutes. A counterexample that might have emerged after several conversations can appear in the first serious exchange with a model. Code can be written, tested, and changed while the mathematical idea is still forming. Literature from a neighboring area can be entered without first learning all of its local shorthand. None of these uses is completely reliable, but together they change what it feels like to start and pursue a research problem. Two recent talks helped sharpen the question for me. Hsin-Yuan (Robert) Huang framed the automation of research as an open problem for researchers themselves: what should remain valuable, and how should scientific practice respond when parts of it can be automated [ 1 ]? Terence Tao’s ICM lecture asked a...