Thanks for the really thoughtful reflection!

I think your point is interesting but in a slightly different direction to what I was meaning. I think it is definitely possible that a grantmaker could be more risk-averse than a for-profit investor since they "care" more about the returns, and so failure hurts more.

What I had in mind was more assuming a perfectly "rational" funder who wants to maximise good (for a grantmaker) or profits (for an investor). In those cases, the specific consideration of not wanting to lose all your money will nudge the for profit investor to spread their funding around a bit, rather than just putting everything into the highest ROI opportunity. Over the longterm, a for-profit investor who just puts everything into the highest ROI opportunity will very likely ended up going broke (which is why hedge funds, Warren Buffett etc have many investments even if some are better than others). As soon as that one investment fails, you've lost everything and can't invest again. 

But a rational philanthropist might indeed be fine putting all their funding into just the one best opportunity they have (e.g. GiveWell giving to AMF) since that's the best way to maximise expected value, and the risk of "losing your money on a bad investment" doesn't hit in the same way as it does for a hedge fund, because you were going to lose the money whether the donation was impactful or not i.e. a putting all your money into a failed philanthropic bet doesn't prevent your ability to make future investments but putting all your money into a failed for-profit investment does prevent you from making future investments.

That's not the only consideration, as I note in my post the fact that you can learn from each grant to reduce future risk gives you an incentive to spread your bets a bit, but that's what I meant with the "losing all your money" consideration. 

I hope that makes a bit more sense, otherwise I agree that in practice the psychology of giving might make a failed grant sting more than a failed investment would, I think that's a great point!

Experienced founders were super important-- Prevail Fund's 5 co-founders (listed on the website: prevailfund.org) have all founded large, effective nonprofits before (One Acre Fund, IDinsight, Educate Girls, African Leadership Group). 

I agree we want to be growers vs pickers, which was one of our "utility function" considerations, that we could build a field and grow the absorptive capacity of our grantees over time, rather than just accept their current funding levels and necessarily just fund "the big guys".

This is really great engagement, thank you!

I agree that the ideal would like like cost-effectiveness of each marginal dollar. We sort of tried to account for that through the "probability of success" metric, which was defined as something like "probability that this grant exceeds our funding bar", and considerations like "this funding would really stretch the grantee and could be spend less well than their previous funds" could get included in that, but not in a formal way.

I like the bulleted principles you outlined, that's cool that it syncs up somewhat with the Edge results.

For the Org F funding, I also had that reaction when I re-checked. I think part of it might be that our funding bar was 1 impact unit/$, so even org F was actualy pretty good (and lots of other orgs fell below the bar). I don't know if that changes things for you though, just what jumped out when I read the table again.

Quick note: the Forum does mark posts as AI-generated using Pangram, so users will ideally know before they read the post. 

I agree that if an LLM is just taking a quick take and adding arbitrary length to it, that's bad for the Forum. For me, a challenge is that people use LLMs in a lot of different ways, including (to me!) quite good ways. For example, Rocky and Tom in the below post spoke their detailed thoughts on welfare tech into an LLM and it turned those in a high-quality post: https://forum.effectivealtruism.org/posts/QHgfRkiNFoGyysDpn/beware-silver-bullets-are-we-making-welfare-tech-into-the . In that example, the LLM mostly just made it much easier to turn their ideas into a single post that would have taken a lot longer to write (and therefore might not have been written). 

That's all to say, I'm a bit unsure on whether there's a clear "AI written posts = bad" policy we can implement. The Forum team does sometimes move poor quality posts to Personal Blogs and off the front page, and "AI slop" can definitely count as that, but it's more of a case-by-case thing.

Just some quick thoughts as I think this is important and something the Forum team is working on (I lead the Online team at CEA, which includes the Forum). 

Nice! Though I guess my concern that someone would conclude the program hadn't work, after failing to reject the null, is still there. But if the proposed study goes ahead, I'm sure your quick input could be very helpful!

I really like that you are digging into this! 2 quick questions:

• I could imagine the impact of talent interventions could be quite long tailed (e.g. perhaps 1 person in a cohort of 60 contributes ~half of the impact), might that make your proposed RDD approach understate impact, since it really just tells you the impact on the marginal accepted applicant (rather than the super impressive applicant who was a slam dunk)?
• I agree that the application scores (assume that is indeed a thing) make the RDD approach straightforward, but how would you measure impact post-program, especially for the comparison group that might be hard to follow up with? I'm imagining here that, say, an AIS bootcamp greatly increases someone's AIS knowledge but not in a legible way that would show up in, say, their resume -- developing an assessment tool that captures this impact might be difficult (but also not something I've given serious thought to, and perhaps you have!)

Lastly just a statistical power flag: you link to LawrenceC's piece on variation in application assessments. That variation would suggest we'd need quite a good sample size to allow for precision in the detectable effect, which might be hard. I guess that's just a note to make sure some sample calculation is done once you have clarity on your outcome of interest to make sure you have a path to measuring the kind of effect that would make these programs cost-effective. The worst case scenario would be a study with 100 people looking to detect an impact of, say, 0.05 SD, when you are in fact only powered to detect 0.3 SD and basically doomed to concluding the program doesn't work, when perhaps it does. 

These are intended as genuine questions to see if your ideas could work, if there is indeed a path to a good study here I'd love to see something proposed to, say, the EA Infrastructure Fund, your ideas seem valuable! (Disclaimer, I work at CEA, which contains the EAIF, but I am not involved in EAIF decisions and these are personal takes)

Good catch! I've updated the title for now to be the title of the podcast episode they are promoting (using my Immense Forum Admin Powers)

I love that! Thanks for the example. 

Congrats on the great sign up results! Do you mind giving some more examples of what you did differently? Those could be helpful for other groups to learn from. 

For instance, you noted "For example: you walk past a table and someone goes “Hey! Do you want to join the EA club?” " as an example of what you wouldn't to-- what would you do to get attention when tabling? 

Sounds like you have a super relevant background! Some quick thoughts:

• Try ~replicating some of GiveWell's output, such as a cost-effectiveness model. Not entirely from scratch, that would take forever, but perhaps go through it and test your own ability to explain each step, which might help you identify gaps in your knowledge (e.g. some of the moral weights thinking can be counter-intuitive in my experience)
• Try picking a Charity Entrepreneurship graduate charity and write your own GW-style evaluation. CE stands out as their charities will likely publish a lot of the data you might need, but they also might not have public GW evaluations yet

Both of those might just help you realize which parts of GW's process you don't really "get", so you know what to work on. 

But I have never worked at GW, so take all of this as "one guy's opinion" :)