Yeah, I think this is one of the ways that velocity is really helpful. I’d probably add one caveat specific to research on LLMs, which is that, since the field/capabilities are moving so quickly, there’s much, much more low-hanging fruit in empirical research than almost any other field of research. This means that, for LLM research specifically, you should rarely be in a swamp, because that means that you’ve probably run through the low-hanging fruit on that problem/approach, and there’s other low-hanging in other areas that you probably want to be picking instead.
(High velocity is great for both picking low-hanging fruit and for getting through swamps when you really need to solve a particular problem, so it’s useful to have either way)
Yeah, I think this is one of the ways that velocity is really helpful. I’d probably add one caveat specific to research on LLMs, which is that, since the field/capabilities are moving so quickly, there’s much, much more low-hanging fruit in empirical research than almost any other field of research. This means that, for LLM research specifically, you should rarely be in a swamp, because that means that you’ve probably run through the low-hanging fruit on that problem/approach, and there’s other low-hanging in other areas that you probably want to be picking instead.
(High velocity is great for both picking low-hanging fruit and for getting through swamps when you really need to solve a particular problem, so it’s useful to have either way)