My position is that âdemandâ is a word for âwhat people will pay you for.â
This seems reasonable (at least in an econ/âbusiness context), but I guess really what I was saying in my comment is that your previous comment seemed to me to focus on demand and supply and note that theyâll pretty much always not be in perfect equilibrium, and say âNone of those problems indicate that something is wrongâ, without noting that the thing thatâs wrong is animals suffering, people dying of malaria, the long-term future being at risk, etc.
I think I sort-of agree with your other two points, but I think they seem to constrain the focus to âdemandâ in the sense of âhow much will people pay for people to work on thisâ, and âsupplyâ in the sense of âpeople who are willing and able to work on this if given moneyâ, whereas we could also think about things like what non-monetary factors drive various types of people to be willing to take the money to work on these things.
(Iâm not sure if Iâve expressed myself well here. I basically just have a sense that the framing youâve used isnât clearly highlighting all the key things in a productive way. But Iâm not sure there are actual any interesting, major disagreements here.)
Your previous comment seemed to me to focus on demand and supply and note that theyâll pretty much always not be in perfect equilibrium, and say âNone of those problems indicate that something is wrongâ, without noting that the thing thatâs wrong is animals suffering, people dying of malaria, the long-term future being at risk, etc.
In the context of the EA forum, I donât think itâs necessary to specify that these are problems. To state it another way, there are three conditions that could exist (letâs say in a given year):
Grantmakers run out of money and arenât able to fund all high-quality EA projects.
Grantmakers have extra money, and donât have enough high-quality EA projects to spend it on.
Grantmakers have exactly enough money to fund all high-quality EA projects.
None of these situations indicate that something is wrong with the definition of âhigh quality EA projectâ that grantmakers are using. In situation (1), they are blessed with an abundance of opportunities, and the bottleneck to do even more good is funding. In situation (2), they are blessed with an abundance of cash, and the bottleneck to do even more good is the supply of high-quality projects. In situation (3), they have two bottlenecks, and would need both additional cash and additional projects in order to do more good.
No matter how many problems exist in the world (suffering, death, X-risk), some bottleneck or another will always exist. So the simple fact that grantmakers happen to be in situation (2) does not indicate that they are doing something wrong, or making a mistake. It merely indicates that this is the present bottleneck theyâre facing.
For the rest, Iâd say that thereâs a difference between âwillingness to workâ and âlikelihood of success.â Weâre interested in the reasons for EA project supply inelasticity. Why arenât grantmakers finding high-expected-value projects when they have money to spend?
One possibility is that projects and teams to work on them arenât motivated to do so by the monetary and non-monetary rewards on the table. Perhaps if this were addressed, weâd see an increase in supply.
An alternative possibility is that high-quality ideas/âteams are rare right now, and canât be had at any price grantmakers are willing or able to pay.
I think itâs not especially useful to focus on the division into just those three conditions. In particular, we could also have a situation where vetting is one of the biggest constraints, and even if weâre not in that situation vetting is still a constraintâitâs not just about the number of high-EV projects (with a competent and willing team etc.) and the number of dollars, but also whether the grantmakers can find the high-EV projects and discriminate between them and lower-EV ones.
Relatedly, there could be a problem of grantmakers giving to things that are âactually relatively low EVâ (in a way that couldâve been identified by a grantmaker with more relevant knowledge and more time, or using a better selection process, or something like that).
So the simple fact that grantmakers happen to be in situation (2) does not indicate that they are doing something wrong, or making a mistake.
I think maybe thereâs been some confusion where youâre thinking Iâm saying grantmakers have âtoo high a barâ? Iâm not saying that. (Iâm agnostic on the question, and would expect it differs between grantmakers.)
Yeah, I am worried we may be talking past each other somewhat. My takeaway from the grantmaker quotes from FHI/âOpenPhil was that they donât feel they have room to grow in terms of determining the expected value of the projects theyâre looking at. Very prepared to change my mind on this; Iâm literally just going from the quotes in the context of the post to which they were responding.
Given that assumption (that grantmakers are already doing the best they can at determining EV of projects), then I think my three categories do carve nature at the joints. But if we abandon that assumption and assume that grantmakers could improve their evaluation process, and might discover that theyâve been neglecting to fund some high-EV projects, then that would be a useful thing for them to discover.
Oh, I definitely donât think that grantmakers are already doing the best that could be done at determining the EV of projects. And Iâd be surprised if any EA grantmaker thought that that was the case, and I donât think the above quotes say that. The three quotes you gave are essentially talking about what the biggest bottleneck is, and saying that maybe the biggest bottleneck isnât quite âvettingâ, which is not the same as the claim that thereâd be zero value in increasing or improving vetting capacity.
Also note that one of the three quotes still focuses on a reason why vetting may be inadequate: âas a grantmaker, you often do not have the domain experience, and need to ask domain experts, and sometimes macrostrategy experts⌠Unfortunately, the number of people with final authority is small, their time precious, and they are often very busy with other work.â
I also think that âdoing the best they can at determining EV of projectsâ implies that the question is just whether the grantmakersâ EV assessments are correct. But whatâs often happening is more like they either donât hear about something or (in a sense) they âdonât really make an EV assessmentââbecause a very very quick sort of heuristic/âintuitive check suggested the EV was low or simply that the EV of the project would be hard to assess (such that the EV of the grantmaker looking into it would be low).
I think thereâs ample evidence that these things happen, and itâs obvious that they would happen, given the huge array of projects that could be evaluated, how hard they are to evaluate, and how there are relatively few people doing those evaluations and (as Jan notes in the above quote) there is relatively little domain expertise available to them.
(None of this is intended as an insult to grantmakers. Iâm not saying theyâre âdoing a bad jobâ, but rather simply the very weak and common-sense claim that they arenât already picking only and all the highest EV projects, partly because there arenât enough of the grantmakers to do all the evaluations, partly because some projects donât come to their attention, partly because some projects havenât yet gained sufficient credible signals of their actual EV, etc. Also none of this is saying they should simply âlower their barâ.)
For one of very many data points suggesting that there is room to improve how much money can be spent and what it is spent on, and suggesting that grantmakers agree, hereâs a quote from Luke Muehlhauser from Open Phil regarding their AI governance grantmaking:
Unfortunately, itâs difficult to know which âintermediate goalsâ we could pursue that, if achieved, would clearly increase the odds of eventual good outcomes from transformative AI. Would tighter regulation of AI technologies in the U.S. and Europe meaningfully reduce catastrophic risks, or would it increase them by (e.g.) privileging AI development in states that typically have lower safety standards and a less cooperative approach to technological development? Would broadly accelerating AI development increase the odds of good outcomes from transformative AI, e.g. because faster economic growth leads to more positive-sum political dynamics, or would it increase catastrophic risk, e.g. because it would leave less time to develop, test, and deploy the technical and governance solutions needed to successfully manage transformative AI? For those examples and many others, we are not just uncertain about whether pursuing a particular intermediate goal would turn out to be tractable â we are also uncertain about whether achieving the intermediate goal would be good or bad for society, in the long run. Such âsign uncertaintyâ can dramatically reduce the expected value of pursuing some particular goal,19 often enough for us to not prioritize that goal.20
As such, our AI governance grantmaking tends to focus onâŚ
âŚresearch that may be especially helpful for learning how AI technologies may develop over time, which AI capabilities could have industrial-revolution-scale impact, and which intermediate goals would, if achieved, have a positive impact on transformative AI outcomes, e.g. via our grants to GovAI.
[and various other things]
So this is a case where a sort of âvetting bottleneckâ could be resolved either by more grantmakers, grantmakers with more relevant expertise, or research with grantmaking-relevance. And I think that thatâs clearly the case in probably all EA domains (though note that Iâm not claiming this is the biggest bottleneck in all domains).
This seems reasonable (at least in an econ/âbusiness context), but I guess really what I was saying in my comment is that your previous comment seemed to me to focus on demand and supply and note that theyâll pretty much always not be in perfect equilibrium, and say âNone of those problems indicate that something is wrongâ, without noting that the thing thatâs wrong is animals suffering, people dying of malaria, the long-term future being at risk, etc.
I think I sort-of agree with your other two points, but I think they seem to constrain the focus to âdemandâ in the sense of âhow much will people pay for people to work on thisâ, and âsupplyâ in the sense of âpeople who are willing and able to work on this if given moneyâ, whereas we could also think about things like what non-monetary factors drive various types of people to be willing to take the money to work on these things.
(Iâm not sure if Iâve expressed myself well here. I basically just have a sense that the framing youâve used isnât clearly highlighting all the key things in a productive way. But Iâm not sure there are actual any interesting, major disagreements here.)
In the context of the EA forum, I donât think itâs necessary to specify that these are problems. To state it another way, there are three conditions that could exist (letâs say in a given year):
Grantmakers run out of money and arenât able to fund all high-quality EA projects.
Grantmakers have extra money, and donât have enough high-quality EA projects to spend it on.
Grantmakers have exactly enough money to fund all high-quality EA projects.
None of these situations indicate that something is wrong with the definition of âhigh quality EA projectâ that grantmakers are using. In situation (1), they are blessed with an abundance of opportunities, and the bottleneck to do even more good is funding. In situation (2), they are blessed with an abundance of cash, and the bottleneck to do even more good is the supply of high-quality projects. In situation (3), they have two bottlenecks, and would need both additional cash and additional projects in order to do more good.
No matter how many problems exist in the world (suffering, death, X-risk), some bottleneck or another will always exist. So the simple fact that grantmakers happen to be in situation (2) does not indicate that they are doing something wrong, or making a mistake. It merely indicates that this is the present bottleneck theyâre facing.
For the rest, Iâd say that thereâs a difference between âwillingness to workâ and âlikelihood of success.â Weâre interested in the reasons for EA project supply inelasticity. Why arenât grantmakers finding high-expected-value projects when they have money to spend?
One possibility is that projects and teams to work on them arenât motivated to do so by the monetary and non-monetary rewards on the table. Perhaps if this were addressed, weâd see an increase in supply.
An alternative possibility is that high-quality ideas/âteams are rare right now, and canât be had at any price grantmakers are willing or able to pay.
I think itâs not especially useful to focus on the division into just those three conditions. In particular, we could also have a situation where vetting is one of the biggest constraints, and even if weâre not in that situation vetting is still a constraintâitâs not just about the number of high-EV projects (with a competent and willing team etc.) and the number of dollars, but also whether the grantmakers can find the high-EV projects and discriminate between them and lower-EV ones.
Relatedly, there could be a problem of grantmakers giving to things that are âactually relatively low EVâ (in a way that couldâve been identified by a grantmaker with more relevant knowledge and more time, or using a better selection process, or something like that).
I think maybe thereâs been some confusion where youâre thinking Iâm saying grantmakers have âtoo high a barâ? Iâm not saying that. (Iâm agnostic on the question, and would expect it differs between grantmakers.)
Yeah, I am worried we may be talking past each other somewhat. My takeaway from the grantmaker quotes from FHI/âOpenPhil was that they donât feel they have room to grow in terms of determining the expected value of the projects theyâre looking at. Very prepared to change my mind on this; Iâm literally just going from the quotes in the context of the post to which they were responding.
Given that assumption (that grantmakers are already doing the best they can at determining EV of projects), then I think my three categories do carve nature at the joints. But if we abandon that assumption and assume that grantmakers could improve their evaluation process, and might discover that theyâve been neglecting to fund some high-EV projects, then that would be a useful thing for them to discover.
Oh, I definitely donât think that grantmakers are already doing the best that could be done at determining the EV of projects. And Iâd be surprised if any EA grantmaker thought that that was the case, and I donât think the above quotes say that. The three quotes you gave are essentially talking about what the biggest bottleneck is, and saying that maybe the biggest bottleneck isnât quite âvettingâ, which is not the same as the claim that thereâd be zero value in increasing or improving vetting capacity.
Also note that one of the three quotes still focuses on a reason why vetting may be inadequate: âas a grantmaker, you often do not have the domain experience, and need to ask domain experts, and sometimes macrostrategy experts⌠Unfortunately, the number of people with final authority is small, their time precious, and they are often very busy with other work.â
I also think that âdoing the best they can at determining EV of projectsâ implies that the question is just whether the grantmakersâ EV assessments are correct. But whatâs often happening is more like they either donât hear about something or (in a sense) they âdonât really make an EV assessmentââbecause a very very quick sort of heuristic/âintuitive check suggested the EV was low or simply that the EV of the project would be hard to assess (such that the EV of the grantmaker looking into it would be low).
I think thereâs ample evidence that these things happen, and itâs obvious that they would happen, given the huge array of projects that could be evaluated, how hard they are to evaluate, and how there are relatively few people doing those evaluations and (as Jan notes in the above quote) there is relatively little domain expertise available to them.
(None of this is intended as an insult to grantmakers. Iâm not saying theyâre âdoing a bad jobâ, but rather simply the very weak and common-sense claim that they arenât already picking only and all the highest EV projects, partly because there arenât enough of the grantmakers to do all the evaluations, partly because some projects donât come to their attention, partly because some projects havenât yet gained sufficient credible signals of their actual EV, etc. Also none of this is saying they should simply âlower their barâ.)
For one of very many data points suggesting that there is room to improve how much money can be spent and what it is spent on, and suggesting that grantmakers agree, hereâs a quote from Luke Muehlhauser from Open Phil regarding their AI governance grantmaking:
âŚresearch that may be especially helpful for learning how AI technologies may develop over time, which AI capabilities could have industrial-revolution-scale impact, and which intermediate goals would, if achieved, have a positive impact on transformative AI outcomes, e.g. via our grants to GovAI.
[and various other things]
So this is a case where a sort of âvetting bottleneckâ could be resolved either by more grantmakers, grantmakers with more relevant expertise, or research with grantmaking-relevance. And I think that thatâs clearly the case in probably all EA domains (though note that Iâm not claiming this is the biggest bottleneck in all domains).