Number of Donors x Average Donation = Number of Grants x Average Grant
If we lose a big donor, there are four things EA can do:
Increase the number of donors:
Outreach. Community growth. Might be difficult right now for reputation reasons, though fortunately, EA was very quick to denounce SBF.
Maybe lobby the government for cash?
Maybe lobby OpenAI, DeepMind, etc for cash?
Increase average donation:
Get another billionaire donor. Presumably, this is hard because otherwise EA would’ve done it already, but there might be factors that are hidden from me.
80K could begin pushing earning-to-give again. They shifted their recommendations a few years ago to promoting direct-impact careers. This made sense when EA was less funding-constrained.
Get existing donors to ramp up their donations. In the good ol’ days, EA used to be a club for people donating 60% of their income to anti-malaria bednets. Maybe EA will return to that frugal ascetic lifestyle.
Reduce the number of grants:
FTX was funding a number of projects. Some of these were higher priorities than others. Hopefully the high-priority projects retain their funding, whereas low-priority projects are paused.
EA has been engaged in a “hit-or-miss” approach to grant-making. This makes sense when you have more cash than sure-thing ideas. But now we have less cash we should focus on sure-thing ideas.
The problem with the “sure-thing” approach to grant-making is that it biases certain causes (e.g. global health & dev) over others (e.g. x-risk). I think that would be a mistake. Someone needs to think about how to calibrate for this bias.
Here’s a tentative idea: EA needs more prizes and other forms of retrodictive funding. This will shift risk from the grant-maker to the researcher, which might be good because the researcher is more informed about the likelihood of success than the grant-maker.
Reduce average grant:
Maybe EA needs to focus on cheaper projects.
For example, in AI safety there has been a recent shift away from theoretic work (like MIRI’s decision theory) towards experimental work. This experimental work is very expensive because it involves (say) training large language models. This shift should be at least somewhat reversed.
Academics are very cheap! And they often already have funding. EA (especially AI safety) needs to do more outreach to established academics, such as top philosophers, mathematicians, economists, computer scientists, etc.
Get another billionaire donor. Presumably, this is hard because otherwise EA would’ve done it already, but there might be factors that are hidden from me.
It’s a process to recruit billionaires/turn EAs into billionaires, but one estimate was another 3.5 EA billionaires by 2027 (written pre FTX implosion). In the analyses I’ve seen for last dollar cost effectiveness, they have tended to ignore the possibility of EA adding funds over time. Of course we don’t want to run out of money just when we need some big surge. But we could spend a lot of money in the next five years and then reevaluate if we have not recruited significant additional assets. This could make a lot of sense for people with short AI timelines (see here for an interesting model) or for people who are worried about the current nuclear risk. But more generally, by doing more things now, we can show concrete results, which I think would be helpful in recruiting additional funds. I may be biased as I head ALLFED, but I think the optimal course of action for the long-term future is to maintain the funding rate that was occurring in 2022, and likely even increase it.
On the grants side of your formula, there are huge differences in flexibility between different projects. The direct cash transfers of Give Directly can scale up and down very rapidly.
On the donors’ side of your formula, it is not only about size but also volatility and reliability. There are big donors with stable wealth and a track record of regular predictable donations.
In my mind a sensible overall allocation would have at least as much money going to very flexible projects (ex: direct cash transfers) as the amount of money coming from very unpredictable sources (ex: one big donor whose wealth coming from risky assets varies a lot every week). This would capture the high rewards of volatile donors, without putting so much uncertainty to the teams who need some stability over the time.
For sure, this is always under the assumption that all donors, big or small, predictable or volatile, meet a minimum ethical standard in their practices.
EA is constrained by the following formula:
Number of Donors x Average Donation = Number of Grants x Average Grant
If we lose a big donor, there are four things EA can do:
Increase the number of donors:
Outreach. Community growth. Might be difficult right now for reputation reasons, though fortunately, EA was very quick to denounce SBF.
Maybe lobby the government for cash?
Maybe lobby OpenAI, DeepMind, etc for cash?
Increase average donation:
Get another billionaire donor. Presumably, this is hard because otherwise EA would’ve done it already, but there might be factors that are hidden from me.
80K could begin pushing earning-to-give again. They shifted their recommendations a few years ago to promoting direct-impact careers. This made sense when EA was less funding-constrained.
Get existing donors to ramp up their donations. In the good ol’ days, EA used to be a club for people donating 60% of their income to anti-malaria bednets. Maybe EA will return to that frugal ascetic lifestyle.
Reduce the number of grants:
FTX was funding a number of projects. Some of these were higher priorities than others. Hopefully the high-priority projects retain their funding, whereas low-priority projects are paused.
EA has been engaged in a “hit-or-miss” approach to grant-making. This makes sense when you have more cash than sure-thing ideas. But now we have less cash we should focus on sure-thing ideas.
The problem with the “sure-thing” approach to grant-making is that it biases certain causes (e.g. global health & dev) over others (e.g. x-risk). I think that would be a mistake. Someone needs to think about how to calibrate for this bias.
Here’s a tentative idea: EA needs more prizes and other forms of retrodictive funding. This will shift risk from the grant-maker to the researcher, which might be good because the researcher is more informed about the likelihood of success than the grant-maker.
Reduce average grant:
Maybe EA needs to focus on cheaper projects.
For example, in AI safety there has been a recent shift away from theoretic work (like MIRI’s decision theory) towards experimental work. This experimental work is very expensive because it involves (say) training large language models. This shift should be at least somewhat reversed.
Academics are very cheap! And they often already have funding. EA (especially AI safety) needs to do more outreach to established academics, such as top philosophers, mathematicians, economists, computer scientists, etc.
It’s a process to recruit billionaires/turn EAs into billionaires, but one estimate was another 3.5 EA billionaires by 2027 (written pre FTX implosion). In the analyses I’ve seen for last dollar cost effectiveness, they have tended to ignore the possibility of EA adding funds over time. Of course we don’t want to run out of money just when we need some big surge. But we could spend a lot of money in the next five years and then reevaluate if we have not recruited significant additional assets. This could make a lot of sense for people with short AI timelines (see here for an interesting model) or for people who are worried about the current nuclear risk. But more generally, by doing more things now, we can show concrete results, which I think would be helpful in recruiting additional funds. I may be biased as I head ALLFED, but I think the optimal course of action for the long-term future is to maintain the funding rate that was occurring in 2022, and likely even increase it.
On the grants side of your formula, there are huge differences in flexibility between different projects. The direct cash transfers of Give Directly can scale up and down very rapidly.
On the donors’ side of your formula, it is not only about size but also volatility and reliability. There are big donors with stable wealth and a track record of regular predictable donations.
In my mind a sensible overall allocation would have at least as much money going to very flexible projects (ex: direct cash transfers) as the amount of money coming from very unpredictable sources (ex: one big donor whose wealth coming from risky assets varies a lot every week). This would capture the high rewards of volatile donors, without putting so much uncertainty to the teams who need some stability over the time.
For sure, this is always under the assumption that all donors, big or small, predictable or volatile, meet a minimum ethical standard in their practices.