[1. Do you think many major insights from longtermist macrostrategy or global priorities research have been found since 2015?]
I think "major insights" is potentially a somewhat loaded framing; it seems to imply that only highly conceptual considerations that change our minds about previously-accepted big picture claims count as significant progress. I think very early on, EA produced a number of somewhat arguments and considerations which felt like "major insights" in that they caused major swings in the consensus of what cause areas to prioritize at a very high level; I think that probably reflected that the question was relatively new and there was low-hanging fruit. I think we shouldn't expect future progress to take the form of "major insights" that wildly swing views about a basic, high-level question as much (although I still think that's possible).
[2. If so, what would you say are some of the main ones?]
Since 2015, I think we've seen good analysis and discussion of AI timelines and takeoff speeds, discussion of specific AI risks that go beyond the classic scenario presented in Superintellilgence, better characterization of multipolar and distributed AI scenarios, some interesting and more quantitative debates on giving now vs giving later and "hinge of history" vs "patient" long-termism, etc. None of these have provided definitive / authoritative answers, but they all feel useful to me as someone trying to prioritize where Open Phil dollars should go.
[3. Do you think the progress has been at a good pace (however you want to interpret that)?]
I'm not sure how to answer this; I think taking into account the expected low-hanging fruit effect, and the relatively low investment in this research, progress has probably been pretty good, but I'm very uncertain about the degree of progress I "should have expected" on priors.
[4. Do you think that this pushes for or against allocating more resources (labour, money, etc.) towards that type of work?]
I think ideally the world as a whole would be investing much more in this type of work than it is now. A lot of the bottleneck to this is that the work is not very well-scoped or broken into tractable sub-problems, which makes it hard for a large number of people to be quickly on-boarded to it.
[5. Do you think that this suggests we should change how we do this work, or emphasise some types of it more?]
Related to the above, I'd love for the work to become better-scoped over time -- this is one thing we prioritize highly at Open Phil.
I also haven't thought much about how much one should typically expect in a random field, how that should increase or decrease for this field in the last 5 years just because of how many people and dollars it got (compared to other fields), or how what was produced in the last 5 years in this field compares to that.
But one thing that strikes me is that longtermist macrostrategy/GPR researchers over the past 5 years have probably had substantially less training and experience than researchers in most academic fields we'd probably compare this to. (I haven't really checked this, but I'd guess it's true.)
So maybe if there was less novel or less major insights from this field than we should typically expect of a field with the same amount of people and dollars, this can be explained by the people having less human capital, rather than by the field being intrinsically harder to make progress on?
(It could also perhaps be explained if the unusual approaches that are decently often taken in this field tend to be less effective - e.g., more generalist/shallow work rather than deeper dives into narrower topics, and more blog post style work.)