People often appeal to Intelligence Explosion/Recursive Self-Improvement as some win-condition for current model developers e.g. Dario argues Recursive Self-Improvement could enshrine the US's lead over China. 

This seems non-obvious to me. For example, suppose OpenAI trains GPT 6 which trains GPT 7 which trains GPT 8. Then a fast follower could take GPT 8 and then use it to train GPT 9. In this case, the fast follower has a lead and has spent far less on R&D (since they didn't have to develop GPT 7 or 8 themselves).  

I guess people are thinking that OpenAI will be able to ban GPT 8 from helping competitors? But has anyone argued for why they would be able to do that (either legally or technically)?

Is the alignment motivation distinct from just using AI to solve general bargaining problems? 

Here is a counterargument: focusing on the places where there is altruistic alpha is 'defecting' against other value systems. See discussion here

Roughly buy that there is more "alpha" in making the future better because most people are not longtermist but most people do want to avoid extinction. 

Good point, but can't this trade occur just through financial markets without involving 1 on 1 trades among EAs? For example, if you have short timelines, you could take out a loan, donate it all to AI Safety. 

Agreed with this. I'm very optimistic about AI solving a lot of incentive problems in science. I don't know if the end case (full audits) as you mention will happen, but I am very confident we will move in a better direction than where we are now. 

I'm working on some software now that will help a bit in this direction! 

Since it seems like a major goal is of the Future Fund is to experiment and gain information on types of philanthropy —how much data collection and causal inference are you doing/plan to do on the grant evaluations? 

Here are some ideas I quickly came up with that might be interesting. 

1. If you decided whether to fund marginal projects by votes or some scoring system—you could later assess what you think is the impact of funding projects by using a regression-discontinuity-design. 
2. You mentioned that there is some randomness in who you used as re-granters. This has some similarities to the random assignment of judges that is frequently used in applied econ.  You could use this to infer if certain features of grantmakers cause better grants (e.g. some grant-makers might tend to believe in providing higher amounts of funding, so you could assess if this more gung-ho attitude leads to better grants, etc.)
3. Explicitly introduce some randomness on if you approve a grant or not. 

In all these cases, you'd need to ex-post assess grant applications a few years later, including the ones you didn't fund on impact. Then these above strategies would let you assess the causal impact of your grants. 

I'd say it's close and depends on the courses you are missing from an econ minor instead of a major. If those classes are 'economics of x' classes (such as media or public finance), then your time is better spent on research. If those classes are still in the core (intermediate micro, macro, econometrics, maybe game theory) I'd probably take those before research. 

Of course, you are right that admissions care a lot about research experience - but it seems the very best candidates have all those classes AND a lot of research experience. 

I would say an ideal candidate is a math-econ double major, also taking a few classes in stats and computer science. All put together, that's quite a few classes, but not an unmanageable amount. 

One case where this doesn't seem to apply is an economics Ph.D. For that, it seems taking very difficult classes and doing very well in them is largely a prerequisite for admissions. I am very grateful I took the most difficult classes and spent a large fraction of my time on schoolwork. 

The caveat here is that research experience is very helpful too (working as an RA).