The crux of the problem is that AI experts largely do not see meaningful progress on any axis that makes an AI doomsday scenario plausible, which causes staunch disbelief and ridicule of people who write things like this.
So realistically if we want to resolve this issue, the place to start is not public policy. That's actually part of the problem -- tech policy is bad largely because it is mostly legislated by people who don't understand the technology. We have to start with a discussion about how this progress towards doomsday AI should be measured, and then formulate policy to directly address those scenarios. As long as Altman et al are extrapolating on axes I (for one) don't understand, I honestly think this conversation is unlikely to be productive.
[1] Most notably, a similar hurrah happened when neural networks first became popular. There were some promising early results, which led to much enthusiasm, and then Papert and Minsky published "Perceptron", whose Group Invariance Theorem cast aspersions on whether simple neural networks were really doing what we thought they were. Eventually we found out that many early experiments just had a bad methodology, which very nearly stopped research interest in the field for almost a decade. Now we are in a period of AI hype again, and when you hear people like Andrew Ng get up in front of audiences and say things like "for the first time in my adult life I am hopeful about real AI", you might start to wonder whether there is a lesson here.
EDIT: Friends, I am confused by the downvotes. :) I am happy to have a discussion about this topic!