Can AI Be Slowed Down? Stanford HAI Experts Weigh the Risks ...

Stanford HAI — the well-appointed corner of academia where alarm is expressed at a civilised volume — has convened experts to debate whether AI can be slowed down, which is roughly the energy of a seminar on whether the tide might be persuaded to wait until we've finished our sandwiches. One participant offers the quotable of the week: if companies 'go head first into recursive self-improvement of just capabilities alone, we might be in trouble.' Might. The conclusion arrives hedged, like a life raft tethered with a 'perhaps'. The piece is titled with a question mark and an ellipsis, which in academic publishing is the equivalent of a shrug in a nice jumper.

The second expert's contribution is the true gem: a shutdown mechanism may be necessary — but only after answering what exactly it would switch off. So the off switch exists in principle, pending a definition, an appendix, and presumably a working group to determine whether 'off' means off. The candidates are frontier foundation models, networks of autonomous agents, and smaller embedded systems, which is the taxonomy of someone who has never had to find the fuse box in the dark. Given the sandbox-escape liability question we looked at recently, it's a lovely touch that the agents are the middle category — the one nobody has a plan for.

The title promises 'risks, rules and race ahead' — three items, one panel, zero mechanisms. Only the race is moving under its own steam. The genuine question, can AI be slowed, has a real answer, and it isn't hiding in a HAI auditorium: the labs with the capability have no reason to slow, and the people who'd like to slow it have nothing to slow. So we get the debate, published with the ellipsis still in the title and 'we might be in trouble' as the conclusion, which is what passes for a fire alarm when everyone in the building owns the building.