So, MIRI still has a lot of explaining to do, and we're working on it. But allow me a brief reminder that this gap isn't unique to MIRI at all. Arguing for the cost effectiveness of any particular intervention given the overwhelming importance of the far future is extremely complicated, whether it be donating to AMF, doing AI risk strategy, spreading rationality, or something else.

I agree with this. Typically people justify their research on other grounds than this, for instance by identifying an obstacle to progress and showing how their approach might overcome it in a way that previously tried approaches were not able to. My impression is that one reason for doing this is that it is typically much easier to communicate along these lines, because it brings the discourse towards much more familiar technical questions while still correlating well with progress more generally.

Note that under this paradigm, the main thing MIRI needs to do to justify their work is to explain why the Lob obstacle is insufficiently addressed by other approaches (for instance, statistical learning theory). I would actually be very interested in understanding the relationship of statistics to the Lob obstacle, so look forward to any writeup that might exist in the future.