Sure, they are more-than-zero helpful. Heck, in a relative sense, they'd be one of the biggest wins in AI safety to date. But alas, reality does not grade on a curve.

One has to bear in mind that the words on that snapshot do not all accurately describe reality in the world where SB1047 passes. "Implement shutdown ability" would not in fact be operationalized in a way which would ensure the ability to shutdown an actually-dangerous AI, because nobody knows how to do that. "Implement reasonable safeguards to prevent societal-scale catastrophes" would in fact be operationalized as checking a few boxes on a form and maybe writing some docs, without changing deployment practices at all, because the rules for the board responsible for overseeing these things made it pretty easy for the labs to capture.

When I discussed the bill with some others at the time, the main takeaway was that the actually-substantive part was just putting any bureaucracy in place at all to track which entities are training models over 10^26 FLOP/$100M. The bill seemed unlikely to do much of anything beyond that.

Even if the bill had been much more substantive, it would still run into the standard problems of AI regulation: we simply do not have a way to reliably tell which models are and are not dangerous, so the choice is to either ban a very large class of models altogether, or allow models which will predictably be dangerous sooner or later. The most commonly proposed substantive proxy is to ban models over a certain size, which would likely slow down timelines by a factor of 2-3 at most, but definitely not slow down timelines by a factor of 10 or more.