As my timelines have been shortening, I've been rethinking my priorities. As have many of my colleagues. It occurs to us that there are probably general considerations that should cause us to weight towards short-timelines plans, or long-timelines plans. (Besides, of course, the probability of short and long timelines) For example, if timelines are short then maybe AI safety is more neglected, and therefore higher EV for me to work on, so maybe I should be systematically more inclined to act as if timelines are short.
We are at this point very unsure what the most important considerations are, and how they balance. So I'm polling the hive mind!
(They may spend more on inference compute if doing so would sufficiently increase their revenue. They may train such a more-expensive model just to try it out for a short while, to see whether they're better off using it.)