Trying to Spend $100m+ Well: My Search for the Perfect Grant Sizing Formula

Published on October 8, 2026 5:40 PM GMT Cross-posted from my Substack. Epistemic status: I’ve puzzled over this for a long time but I’m not sure I came to the right answers. I'm sharing this partly because it might help others work on resource allocation, but mostly because some smarter reader might tell me how to do it better. TL;DR: I helped set up a large foundation and we wanted a consistent way to allocate grants We planned to allocate substantially more than our field was able to absorb right now, giving us the opportunity to actively shape our field We outlined key principles and considerations We quickly learned we could not just copy the for-profit world: so what could we do? We could try and optimize perfectly, mathematically, but it gets absurd and impractical Instead, we tried out a bunch of different approaches to finding a consistent grant allocation formula, settling on the one that most “felt” right given our starting principles and constraints I quite liked this process of setting principles and constraints, and then vibing your way to an allocation formula that feels right vs being super mathematical I wanted to share this process as a real example of trying to work out how to allocate funding. It might be useful to people in the same position, or people might have ideas for how we could have done better  x = height of the opposing player When I was about 10, I started to learn algebra in school. Clearly a future EA, I had a brainwave on my way home one day: I can use this new math to become really good at Goldeneye, an N64 shooting game. I got increasingly excited as I walked downhill home, imagining myself briefly pausing the game, whipping out my notebook, firing off some calculations, and then, BAM, quick console controller movements would finish off my nemeses with the precision only numbers could provide.  So I got home, opened my notebook, and wrote “x = height of opposing player”. I paused. I paused longer. "Eugh" I closed my notebook and gave up.  I hadn’t been entirely wrong to try. In principle, there are optimal moves at any given moment that can maximize your chances of winning any game, and those can indeed be identified mathematically. But my 10-year-old brain realized the absurdity of computing all of my actions on paper, while constantly updating the calculations as the game progressed.  But this post isn’t about Nintendo, it’s about doing as much good as possible with limited resources.  Searching for the perfect grant-allocation formula A few years ago I helped set up a private foundation aiming to spend a few hundred million dollars over the coming years on global development. There is a staggering responsibility to spend that money effectively. As we planned our first year (a few $10ms in grants), I thought a lot about how to allocate funding to very different organizations under quite a bit of uncertainty on grantee potential. I didn’t solve it, but I ended up getting to a place that was good enough to begin. Maybe it’s useful, especially if you are working out how to spend sums that your field is not currently ready to absorb. Our core metrics Cost-effectiveness based on historic performance and discounted for evidence quality (i.e. we preferred large scale RCTs to pre/post studies of small pilots) Chance of success defined by the % chance they would exceed our minimum cost-effectiveness bar. Obviously the higher our cost-effectiveness estimate, the higher their chance of success, but this allowed us to factor in other things like “expanding to a new country”, “impressive new CEO” and “need to hire a new, unproven team” that would not be included in the cost-effectiveness estimate, which was based on historic impact assessments These metrics allowed for a neat division of labor. The cost-effectiveness estimate would be fully owned by the research team, while the % success estimates would be owned by the programs team. Setting metrics was the easy part, the hard part was working out how to turn those numbers into a neat “Give Org A $3m this year” output.  We couldn’t just copy from other grantmakers or the for-profit world Hold on, you might say, can’t you just copy the funding allocation model of [one of the famous big EA grant makers]. Surprisingly, at least a few years ago, no org I spoke to had a formula that says how much to allocate to a given opportunity. More common is to see if the opportunity passes your funding bar and fund it if it does, but there is no formula that says “we ideally would give this org $xM”, with the grant sizing generally left to the grantee to request.  Our case was also unique in that we had more money to spend than grantees in our field could currently absorb, and so we were in the position of building our field rather than just allocating to the opportunities that existed in front of us. We had the ability to support the most promising grantees to grow to absorb more, meaning we didn't need to simply "rank everyone by cost-effec