Thanks, very interesting!
I agree the examples you gave could be done by a recent graduate. (Though my guess is the community building stuff would benefit from some kinds of additional experience that has trained relevant project management and people skills.)
I suspect our impressions differ in two ways:
1. My guess is I consider the activities you mentioned less valuable than you do. Probably the difference is largest for programming at MIRI and smallest for Hubinger-style AI safety research. (This would probably be a bigger discussion.)
2. Independent of this, my guess would be that EA does have a decent number of unidentified people who would be about as good as people you’ve identified. E.g., I can think of ~5 people off the top of my head of whom I think they might be great at one of the things you listed, and if I had your view on their value I’d probably think they should stop doing what they’re doing now and switch to trying one of these things. And I suspect if I thought hard about it, I could come up with 5-10 more people—and then there is the large number of people neither of us has any information about.
Two other thoughts I had in response:
It might be quite relevant if “great people” refers only to talent or also to beliefs and values/preferences. E.g. my guess is that there are several people who could be great at functional programming who either don’t want to work for MIRI, or don’t believe that this would be valuable. (This includes e.g. myself.) If to count as “great person” you need to have the right beliefs and preferences, I think your claim that “EA needs more great people” becomes stronger. But I think the practical implications would differ from the “greatness is only about talent” version, which is the one I had in mind in the OP.
One way to make the question more precise: At the margin, is it more valuable (a) to try to add high-potential people to the pool of EAs or (b) change the environment (e.g. coordination, incentives, …) to increase the expected value of activities by people in the current pool. With this operationalization, I might actually agree that the highest-value activities of type (a) are better than the ones of type (b), at least if the goal is finding programmers for MIRI and maybe for community building. (I’d still think that this would be because, while there are sufficiently talented people in EA, they don’t want to do this, and it’s hard to change beliefs/preferences and easier to get new smart people excited about EA. - Not because the community literally doesn’t have anyone with a sufficient level of innate talent. Of course, this probably wasn’t the claim the person I originally talked to was making.)
(The following summary [not by me] might be helpful to some readers not familiar with the book:
I almost never read the EA Facebook group. But I tend to generally dislike Facebook, and there simply is no Facebook group I regularly use. I think I joined the EA Facebook group in early 2016, though it’s possible that it was a few months earlier or later. (In fact, I didn’t have a Facebook account previously. I only created one because a lot of EA communication seemed to happen via Facebook, which I found somewhat annoying.) Based on my very infrequent visits, I don’t have a sense that it changed significantly. But I’m not sure if I would have noticed.
[On https://www.technologyreview.com/s/615181/ai-openai-moonshot-elon-musk-sam-altman-greg-brockman-messy-secretive-reality/ ]
(Most of the following doesn’t apply in cases where someone is acting in bad faith and is determined to screw you over. And in fact I’ve seen the opposing failure mode of people assuming good faith for too long. But I don’t think this is a case of bad faith.)
I’ve seen some EAs react pretty negatively or angrily to that piece. (Tbc, I’ve also seen different reactions.) Some have described the article as a “hit piece”.
I don’t think it qualifies as a hit piece. More like a piece that’s independent/pseudo-neutral/ambiguous and tried to stick to dry facts/observations but in some places provides a distorted picture by failing to be charitable / arguably missing the point / being one-sided and selective in the observation it reports.
I still think that reporting like this is net good, and that the world would be better if there was more of it at the margin, even if it has flaws similarly severe to that one. (Tbc, I think there would have been a plausibly realistic/achievable version of that article that would have been better, and that there is fair criticism one can direct at it.)
To put it bluntly, I don’t believe that having even maximally well-intentioned and intelligent people at key institutions is sufficient for achieving a good outcome for the world. I find it extremely hard to have faith in a setup that doesn’t involve a legible system/structure with things like division of labor, checks and balances, procedural guarantees, healthy competition, and independent scrutiny of key actors. I don’t know if the ideal system for providing such outside scrutiny will look even remotely like today’s press, but currently it’s one of the few things in this vein that we have for nonprofits, and Karen Hao’s article is an (albeit flawed) example of it.
Whether this specific article was net good or not seems pretty debatable. I definitely see reasons to think it’ll have bad consequences, e.g. it might crowd out better reporting, might provide bad incentives by punishing orgs for trying to do good things, … I’m less wedded to a prediction of this specific article’s impact than to the broader frame for interpreting and reacting to it.
I find something about the very negative reactions I’ve seen worrying. I of course cannot know what they were motivated by, but some seemed like I would expect someone to react who’s personally hurt because they judge a situation as being misunderstood, feels like they need to defend themself, or like they need to rally to protect their family. I can relate to misunderstandings being a painful experience, and have sympathy for it. But I also think that if you’re OpenAI, or “the EA community”, or anyone aiming to change the world, then misunderstandings are part of the game, and that any misunderstanding involves at least two sides. The reactions I’d like to see would try to understand what has happened and engage constructively with how to productively manage the many communication and other challenges involved in trying to do something that’s good for everyone without being able to fully explain your plans to most people. (An operationalization: If you think this article was bad, I think that ideally the hypothesis “it would be good it we had better reporting” would enter your mind as readily as the hypothesis “it would be good if OpenAI’s comms team and leadership had done a better job”.)
[Is longtermism bottlenecked by “great people”?]
Someone very influential in EA recently claimed in conversation with me that there are many tasks X such that (i) we currently don’t have anyone in the EA community who can do X, (ii) the bottleneck for this isn’t credentials or experience or knowledge but person-internal talent, and (iii) it would be very valuable (specifically from a longtermist point of view) if we could do X. And that therefore what we most need in EA are more “great people”.
I find this extremely dubious. (In fact, it seems so crazy to me that it seems more likely than not that I significantly misunderstood the person who I think made these claims.) The first claim is of course vacuously true if, for X, we choose some ~impossible task such as “experience a utility-monster amount of pleasure” or “come up with a blueprint for how to build safe AGI that is convincing to benign actors able to execute it”. But of course more great people don’t help with solving impossible tasks.
Given the size and talent distribution of the EA community my guess is that for most apparent X, the issue either is that (a) X is ~impossible, or (b) there are people in EA who could do X, but the relevant actors cannot identify them, or (c) acquiring the ability to do X is costly (e.g. perhaps you need time to acquire domain-specific expertise), even for maximally talented “great people”, and the relevant actors either are unable to help pay that cost (e.g. by training people themselves, or giving them the resources to allow them to get training elsewhere) or make a mistake by not doing so.
My best guess for the genesis of the “we need more great people” perspective: Suppose I talk a lot to people at an organization that thinks there’s a decent chance we’ll develop transformative AI soon but it will go badly, and that as a consequence tries to grow as fast as possible to pursue various ambitious activities which they think reduces that risk. If these activities are scalable projects with short feedback loops on some intermediate metrics (e.g. running some super-large-scale machine learning experiments), then I expect I would hear a lot of claims like “we really need someone who can do X”. I think it’s just a general property of a certain kind of fast-growing organization that’s doing practical things in the world that everything constantly seems like it’s on fire. But I would also expect that, if I poked a bit at these claims, it would usually turn out that X is something like “contribute to this software project at the pace and quality level of our best engineers, w/o requiring any management time” or “convince some investors to give us much more money, but w/o anyone spending any time transferring relevant knowledge”. If you see that things break because X isn’t done, even though something like X seems doable in principle (perhaps you see others do it), it’s tempting to think that what you need is more “great people” who can do X. After all, people generally are the sort of stuff that does things, and maybe you’ve actually seen some people do X. But it still doesn’t follow that in your situation “great people” are the bottleneck …
Curious if anyone has examples of tasks X for which the original claims seem in fact true. That’s probably the easiest way to convince me that I’m wrong.
Thank you for sharing your reaction!
Would be interested to hear if the authors have though through this.
I haven’t, but it’s possible that my coauthors have. I generally agree that it might be worthwhile to think along the lines you suggested.
Thanks for sharing your reaction! There is some chance that I’ll write up these and maybe other thoughts on AI strategy/governance over the coming months, but it depends a lot on my other commitments. My current guess is that it’s maybe only 15% likely that I’ll think this is the best use of my time within the next 6 months.
That sounds great! I find the arguments for giving (potentially much) later intriguing and underappreciated. (If I had to allocate a large amount of money myself, I’m not sure what I’d end up doing. But overall it seems good to me if there is at least the option to invest.) I’d be very excited for such a fund to exist—partly because I expect that setting it up and running it will provide a bunch of information on empirical questions relevant for deciding whether investing into such a fund beats giving now.
[Some of my tentative and uncertain views on AI governance, and different ways of having impact in that area. Excerpts, not in order, from things I wrote in a recent email discussion, so not a coherent text.]
1. In scenarios where OpenAI, DeepMind etc. become key actors because they develop TAI capabilities, our theory of impact will rely on a combination of affecting (a) ‘structure’ and (b) ‘content’. By (a) I roughly mean how the relevant decision-making mechanisms look like irrespective of the specific goals and resources of the actors the mechanism consists of; e.g., whether some key AI lab is a nonprofit or a publicly traded company; who would decide by what rules/voting scheme how Windfall profits would be redistributed; etc. By (b) I mean something like how much the CEO of a key firm, or their advisors, care about the long-term future. -- I can see why relying mostly on (b) is attractive, e.g. it’s arguably more tractable; however, some EA thinking (mostly from the Bay Area / the rationalist community to be honest) strikes me as focusing on (b) for reasons that seem ahistoric or otherwise dubious to me. So I don’t feel convinced that what I perceive to be a very stark focus on (b) is warranted. I think that figuring out if there are viable strategies that rely more on (a) is better done from within institutions that have no ties with key TAI actors, and also might be best done my people that don’t quite match the profile of the typical new EA that got excited about Superintelligence or HPMOR. Overall, I think that making more academic research in broadly “policy relevant” fields happen would be a decent strategy if one ultimately wanted to increase the amount of thinking on type-(a) theories of impact.
2. What’s the theory of impact if TAI happens in more than 20 years? More than 50 years? I think it’s not obvious whether it’s worth spending any current resources on influencing such scenarios (I think they are more likely but we have much less leverage). However, if we wanted to do this, then I think it’s worth bearing in mind that academia is one of few institutions (in a broad sense) that has a strong track record of enabling cumulative intellectual progress over long time scales. I roughly think that, in a modal scenario, no-one in 50 years is going to remember anything that was discussed on the EA Forum or LessWrong, or within the OpenAI policy team, today (except people currently involved); but if AI/TAI was still (or again) a hot topic then, I think it’s likely that academic scholars will read academic papers by Dafoe, his students, the students of his students etc. Similarly, based on track records I think that the norms and structure of academia are much better equipped than EA to enable intellectual progress that is more incremental and distributed (as opposed to progress that happens by way of ‘at least one crisp insight per step’; e.g. the Astronomical Waste argument would count as one crisp insight); so if we needed such progress, it might make sense to seed broadly useful academic research now.
My view is closer to “~all that matters will be in the specifics, and most of the intuitions and methods for dealing with the specifics are either sort of hard-wired or more generic/have different origins than having thought about race models specifically”. A crux here might be that I expect most of the tasks involved in dealing with the policy issues that would come up if we got TAI within the next 10-20 years to be sufficiently similar to garden-variety tasks involved in familiar policy areas that as a first pass: (i) if theoretical academic research was useful, we’d see more stories of the kind “CEO X / politician Y’s success was due to idea Z developed through theoretical academic research”, and (ii) prior policy/applied strategy experience is the background most useful for TAI policy, with usefulness increasing with the overlap in content and relevant actors; e.g.: working with the OpenAI policy team on pre-TAI issues > working within Facebook on a strategy for how to prevent the government to split up the firm in case a left-wing Democrat wins > business strategy for a tobacco company in the US > business strategy for a company outside of the US that faces little government regulation > academic game theory modeling. That’s probably too pessimistic about the academic path, and of course it’ll depend a lot on the specifics (you could start in academia to then get into Facebook etc.), but you get the idea.
Overall, the only somewhat open question for me is whether ideally we’d have (A) ~only people working quite directly with key actors or (B) a mix of people working with key actors and more independent ones e.g. in academia. It seems quite clear to me that the optimal allocation will contain a significant share of people working with key actors [...]
If there is a disagreement, I’d guess it’s located in the following two points:
(1a) How big are countervailing downsides from working directly with, or at institutions having close ties with, key actors? Here I’m mostly concerned about incentives distorting the content of research and strategic advice. I think the question is broadly similar to: If you’re concerned about the impacts of the British rule on India in the 1800s, is it best to work within the colonial administration? If you want to figure out how to govern externalities from burning fossil fuels, is it best to work in the fossil fuel industry? I think the cliche left-wing answer to these questions is too confident in “no” and is overlooking important upsides, but I’m concerned that some standard EA answers in the AI case are too confident in “yes” and are overlooking risks. Note that I’m most concerned about kind of “benign” or “epistemic” failure modes: I think it’s reasonably easy to tell people with broadly good intentions apart from sadists or even personal-wealth maximizers (at least in principle—if this will get implemented is another question); I think it’s much harder to spot cases like key people incorrectly believing that it’s best if they keep as much control for themselves/their company as possible because after all they are the ones with both good intentions and an epistemic advantage (note that all of this really applies to a colonial administration with little modification, though here in cases such as the “Congo Free State” even the track record of “telling personal-wealth maximizers apart from people with humanitarian intentions” maybe isn’t great—also NB I’m not saying that this argument would necessarily be unsound; i.e. I think that in some situations these people would be correct).
(1b) To what extent to we need (a) novel insights as opposed to (b) an application of known insights or common-sense principles? E.g., I’ve heard claims that the sale of telecommunication licenses by governments is an example where post-1950 research-level economics work in auction theory has had considerable real-world impact, and AFAICT this kind of auction theory strikes me as reasonably abstract and in little need of having worked with either governments or telecommunication firms. Supposing this is true (I haven’t really looked into this), how many opportunities of this kind are there in AI governance? I think the case for (A) is much stronger if we need little to no (a), as I think the upsides from trust networks etc. are mostly (though not exclusively) useful for (b). FWIW, my private view actually is that we probably need very little of (a), but I also feel like I have a poor grasp of this, and I think it will ultimately come down to what high-level heuristics to use in such a situation.
[Some of my high-level views on AI risk.]
[I wrote this for an application a couple of weeks ago, but thought I might as well dump it here in case someone was interested in my views. / It might sometimes be useful to be able to link to this.]
[In this post I generally state what I think before updating on other people’s views – i.e., what’s sometimes known as ‘impressions’ as opposed to ‘beliefs.’]
Transformative AI (TAI) – the prospect of AI having impacts at least as consequential as the Industrial Revolution – would plausibly (~40%) be our best lever for influencing the long-term future if it happened this century, which I consider to be unlikely (~20%) but worth betting on.
The value of TAI depends not just on the technological options available to individual actors, but also on the incentives governing the strategic interdependence between actors. Policy could affect both the amount and quality of technical safety research and the ‘rules of the game’ under which interactions between actors will play out.
Why I’m interested in TAI as a lever to improve the long-run future
I expect my perspective to be typical of someone who has become interested in TAI through their engagement with the effective altruism (EA) community. In particular,
My overarching interest is to make the lives of as many moral patients as possible to go as well as possible, no matter where or when they live; and
I think that in the world we find ourselves in – it could have been otherwise –, this goal entails strong longtermism, i.e. the claim that “the primary determinant of the value of our actions today is how those actions affect the very long-term future.”
Less standard but not highly unusual (within EA) high-level views I hold more tentatively:
The indirect long-run impacts of our actions are extremely hard to predict and don’t ‘cancel out’ in expectation. In other words, I think that what Greaves (2016) calls complex cluelessness is a pervasive problem. In particular, evidence that an action will have desirable effects in the short term generally is not a decisive reason to believe that this action would be net positive overall, and neither will we be able to establish the latter through any other means.
Increasing the relative influence of longtermist actors is one of the very few strategies we have good reasons to consider net positive. Shaping TAI is a particularly high-leverage instance of this strategy, where the main mechanism is reaping an ‘epistemic rent’ from having anticipated TAI earlier than other actors. I take this line of support to be significantly more robust than any particular story on how TAI might pose a global catastrophic risk including even broad operationalizations of the ‘value alignment problem.’
My empirical views on TAI
I think the strongest reasons to expect TAI this century are relatively outside-view-based (I talk about this century just because I expect that later developments are harder to predictably influence, not because I think a century is particularly meaningful time horizon or because I think TAI would be less important later):
We’ve been able to automate an increasing number of tasks (with increasing performance and falling cost), and I’m not aware of a convincing argument for why we should be highly confident that this trend will stop short of full automation – i.e., AI systems being able to do all tasks more economically efficiently than humans –, despite moderate scientific and economic incentives to find and publish one.
Independent types of weak evidence such as trend extrapolation and expert surveys suggest we might achieve full automation this century.
Incorporating full automation into macroeconomic growth models predicts – at least under some assumptions – a sustained higher rate of economic growth (e.g. Hanson 2001, Nordhaus 2015, Aghion et al. 2017), which arguably was the main driver of the welfare-relevant effects of the Industrial Revolution.
Accelerating growth this century is consistent with extrapolating historic growth rates, e.g. Hanson (2000).
I think there are several reasons to be skeptical, but that the above succeeds in establishing a somewhat robust case for TAI this century not being wildly implausible.
My impression is that I’m less confident than the typical longtermist EA in various claims around TAI, such as:
Uninterrupted technological progress would eventually result in TAI;
TAI will happen this century;
we can currently anticipate any specific way of positively shaping the impacts of TAI;
if the above three points were true then shaping TAI would be the most cost-effective way of improving the long-term future.
My guess is this is due to different priors, and due to frequently having found extant specific arguments for TAI-related claims (including by staff at FHI and Open Phil) less convincing than I would have predicted. I still think that work on TAI is among the few best shots for current longtermists.
What’s the right narrative about global poverty and progress? Link dump of a recent debate.
The two opposing views are:
(a) “New optimism:”  This is broadly the view that, over the last couple of hundred years, the world has been getting significantly better, and that’s great.  In particular, extreme poverty has declined dramatically, and most other welfare-relevant indicators have improved a lot. Often, these effects are largely attributed to economic growth.
Proponents in this debate were originally Bill Gates, Steven Pinker, and Max Roser. But my loose impression is that the view is shared much more widely.
In particular, it seems to be the orthodox view in EA; cf. e.g. Muehlhauser listing one of Pinker’s books in his My worldview in 5 books post, saying that “Almost everything has gotten dramatically better for humans over the past few centuries, likely substantially due to the spread and application of reason, science, and humanism.”
(b) Hickel’s critique: Anthropologist Jason Hickel has criticized new optimism on two grounds:
1. Hickel has questioned the validity of some of the core data used by new optimists, claiming e.g. that “real data on poverty has only been collected since 1981. Anything before that is extremely sketchy, and to go back as far as 1820 is meaningless.”
2. Hickel prefers to look at different indicators than the new optimists. For example, he has argued for different operationalizations of extreme poverty or inequality.
Link dump (not necessarily comprehensive)
If you only read two things, I’d recommend (1) Hasell’s and Roser’s article explaining where the data on historic poverty comes from and (2) the take by economic historian Branko Milanovic.
By Hickel (i.e. against “new optimism”):
By “new optimists”:
Joe Hasell and Max Roser: https://ourworldindata.org/extreme-history-methods
Steven Pinker: https://whyevolutionistrue.wordpress.com/2019/01/31/is-the-world-really-getting-poorer-a-response-to-that-claim-by-steve-pinker/
Commentary by others:
Branko Milanovic (a leading economic historian): https://www.globalpolicyjournal.com/blog/11/02/2019/global-poverty-over-long-term-legitimate-issues
Dylan Matthews: https://www.vox.com/future-perfect/2019/2/12/18215534/bill-gates-global-poverty-chart
LW user ErickBall: https://www.lesswrong.com/posts/eTMgL7Cx8TsA9nedn/is-the-world-getting-better-a-brief-summary-of-recent-debate
I’m largely unpersuaded by Hickel’s charge that historic poverty data is invalid. Sure, it’s way less good than contemporary data. But based on Hasell’s and Roser’s article, my impression is that the data is better than I would have thought, and its orthodox analysis and interpretation more sophisticated than I would have thought. I would be surprised if access to better data would qualitatively change the “new optimist” conclusion.
I think there is room for debate over which indicators to use, and that Hickel makes some interesting points here. I find it regrettable that the debate around this seems so adversarial.
Still, my sense is that there is an important, true, and widely underappreciated (particularly by people on the left, including my past self) core of the “new optimist” story. I’d expect looking at other indicators could qualify that story, or make it less simplistic, point to important exceptions etc. - but I’d probably consider a choice of indicators that painted an overall pessimistic picture as quite misleading and missing something important.
On the other hand, I would quite strongly want to resist the conclusion that everything in this debate is totally settled, and that the new optimists are clearly right about everything, in the same way in which orthodox climate science is right about climate change being anthropogenic, or orthodox medicine is right about homeopathy not being better than placebo. But I think the key uncertainties are not in historic poverty data, but in our understanding of wellbeing and its relationship to environmental factors. Some examples of why I think it’s more complicated
The Easterlin paradox
The unintuitive relationship between (i) subjective well-being in the sense of the momentary affective valence of our experience on one hand and (ii) reported life satisfaction. See e.g. Kahneman’s work on the “experiencing self” vs. “remembering self”.
On many views, the total value of the world is very sensitive to population ethics, which is notoriously counterintuitive. In particular, on many plausible views, the development of the total welfare of the world’s human population is dominated by its increasing population size.
Another key uncertainty is the implications of some of the discussed historic trends for the value of the world going forward, about which I think we’re largely clueless. For example, what are the effects of changing inequality on the long-term future?
 It’s not clear to me if “new optimism” is actually new. I’m using Hickel’s label just because it’s short and it’s being used in this debate anyway, not to endorse Hickel’s views or make any other claim.
 There is an obvious problem with new optimism, which is that it’s anthropocentric. In fact, on many plausible views, the total axiological value of the world at any time in the recent past may be dominated by the aggregate wellbeing of nonhuman animals; even more counterintuitively, it may well be dominated by things like the change in the total population size of invertebrates. But this debate is about human wellbeing, so I’ll ignore this problem.
Thanks for posting this! I’d really like to see more organizations evaluate their impact, and publish about their analysis.
Just a quick note: You mention that I indicated I “found [y]our work on nuclear weapons somewhat useful”. This is correct. I’d like to note that the main reason why I don’t find it very useful simply is that I currently don’t anticipate to work on nuclear security personally, or to make any decisions that depend on my understanding of nuclear security. In general, how “useful” people find your work is a mix of their focus and the quality of your work (which in this case AFAICT is very high, though I haven’t reviewed it in detail), which might make it hard to interpret the results.
Regarding your “outside view” point: I agree with what you say here, but think it cannot directly undermine my original “outside view” argument. These clarifications may explain why:
My original outside view argument appealed to the process by which certain global health interventions such as distributing bednets have been selected rather than their content. The argument is not “global health is a different area from economic growth, therefore a health intervention is unlikely to be optimal for accelerating growth”; instead it is “an intervention that has been selected to be optimal according to some goal X is unlikely to also be optimal according to a different goal Y”.
In particular, if GiveWell had tried to identify those interventions that best accelerate growth, I think my argument would be moot (no matter what interventions they had come up with, in particular in the hypothetical case where distributing bednets had been the result of their investigation).
In general, I think that selecting an intervention that’s optimal for furthering some goal needs to pay attention to all of importance, tractability, and neglectedness. I agree that it would be bad to exclusively rely on the heuristics “just focus on the most important long-term outcome/risk” when selecting longtermist interventions, just as it would be bad to just rely on the heuristics “work on fighting whatever disease has the largest disease burden globally” when selecting global health interventions. But I think these would just be bad ways to select interventions, which seems orthogonal to the question when an intervention selected for X will also be optimal for Y. (In particular, I don’t think that my original outside view argument commits me to the conclusion that in the domain of AI safety it’s best to directly solve the largest or most long-term problem, whatever that is. I think it does recommend to deliberately select an intervention optimized for reducing AI risk, but this selection process should also take into account feedback loops and all the other considerations you raised.)
The main way I can see to undermine this argument would be to argue that a certain pair of goals X and Y is related in such a way that interventions optimal for X are also optimal for Y (e.g., X and Y are positively correlated, though this in itself wouldn’t be sufficient). For example, in this case, such an argument could be of the type “our best macroeconomic models predict that improving health in currently poor countries would have a permanent rate effect on growth, and empirically it seems likely that the potential for sustained increases in the growth rate is largest in currently poor countries” (I’m not saying this claim is true, just that I would want to see something like this).
The “inside view” point is that Christiano’s estimate only takes into account the “price of a life saved”. But in truth GiveWell’s recommendations for bednets or deworming are to a large measure driven by their belief, backed by some empirical evidence, that children who grow up free of worms or malaria become adults who can lead more productive lives. This may lead to better returns than what his calculations suggest. (Micronutrient supplementation may also be quite efficient in this respect.)
I think this is a fair point. Specifically, I agree that GiveWell’s recommendations are only partly (in the case of bednets) or not at all (in the case of deworming) based on literally averting deaths. I haven’t looked at Paul Christiano’s post in sufficient detail to say for sure, but I agree it’s plausible that this way of using “price of a life saved” calculations might effectively ignore other benefits, thus underestimating the benefits of bednet-like interventions compared to GiveWell’s analysis.
I would need to think about this more to form a considered view, but my guess is this wouldn’t change my mind on my tentative belief that global health interventions selected for their short-term (say, anything within the next 20 years) benefits aren’t optimal growth interventions. This is largely because I think the dialectical situation looks roughly like this:
The “beware suspicious convergence” argument implies that it’s unlikely (though not impossible) that health interventions selected for maximizing certain short-term benefits are also optimal for accelerating long-run growth. The burden of proof is thus with the view that they are optimal growth interventions.
In addition, some back-of-the-envelope calculations suggest the same conclusion as the first bullet point.
You’ve pointed out a potential problem with the second bullet point. I think it’s plausible to likely that this significantly to totally removes the force of the second bullet point. But even if the conclusion of the calculations were completely turned on their head, I don’t think they would by themselves succeed in defeating the first bullet point.
As I said in another comment, one relevant complication seems to be that risk and growth interact. In particular, the interaction might be such that speeding up growth could actually have negative value. This has been debated for a long time, and I don’t think the answer is obvious. It might something we’re clueless about.
(See Paul Christiano’s How useful is “progress”? for an ingenious argument for why either
(a) “People are so badly mistaken (or their values so misaligned with mine) that they systematically do harm when they intend to do good, or”
(b) “Other (particularly self-interested) activities are harmful on average.”
Conditional on (b) we might worry that speeding up growth would work via increasing the amount or efficiency of various self-interested activities, and thus would be harmful.
I’m not sure if I buy the argument, though. It is based on “approximat[ing] the changes that occur each day as morally neutral on net”. But on longer timescales it seems that we should be highly uncertain about the value of changes. It thus seems concerning to me to look at a unit of time for which the magnitude of change is unintuitively small, round it to zero, and extrapolate from this to a large-scale conclusion.)
You say that:
I will [...] focus instead on a handful of simple model cases. [...] These models will be very simple. In my opinion, nothing of value is being lost by proceeding in this way.
I agree in the sense that I think your simple models succeed in isolating an important consideration that wouldn’t itself be qualitatively altered by looking at a more complex model.
However, I do think (without implying that this contradicts anything you have said in the OP) that there are other crucial premises for the argument concluding that reducing existential risk is the best strategy for most EAs. I’d like to highlight three, without implying that this list is comprehensive.
One important question is how growth and risk interact. Specifically, it seems that we face existential risks of two different types: (a) ‘exogenous’ risks with the property that their probability per wall-clock time doesn’t depend on what we do (perhaps a freak physics disaster such as vacuum decay); and (b) ‘endogenous’ risks due to our activities (e.g. AI risk). The probability of such endogenous risks might correlate with proxies such as economic growth or technological progress, or more specific kinds of these trends. As an additional complication, the distinction between exogenous and endogenous risks may not be clear-cut, and arguably is itself endogenous to the level of progress—for example, an asteroid strike could be an existential risk today but not for an intergalactic civilization. Regarding growth, we might thus think that we face a tradeoff where faster growth would on one hand reduce risk by allowing us to more quickly reach thresholds that would make us invulnerable to some risks, but on the other hand might exacerbate endogenous risks that increase with the rate of growth. (A crude model for why there might be risks of the latter kind: perhaps ‘wisdom’ increases at fixed linear speed, and perhaps the amount of risk posed by a new technology decreases with wisdom.)
I think “received wisdom” is roughly that most risk is endogenous, and that more fine-grained differential intellectual or technological progress aimed at specifically reducing such endogenous risk (e.g. working on AI safety rather then generically increasing technological progress) is therefore higher-value than shortening the window of time during which we’re exposed to some exogenous risks.
See for example Paul Christiano, On Progress and Prosperity
A somewhat different lense is to ask how growth will affect the willingness of impatient actors—i.e., those that discount future resources at a higher rate than longtermists—to spend resources on existential risk reduction. This is part of what Leopold Aschenbrenner has examined in his paper on Existential Risk and Economic Growth.
More generally, the value of existential risk reduction today depends on the distribution of existential risk over time, including into the very long-run future, and on whether todays effort would have permanent effects on that distribution. This distribution might in turn depend on the rate of growth, e.g. for the reasons mentioned in the previous point. For an excellent discussion, see Tom Sittler’s paper on The expected value of the long-term future. In particular, the standard argument for existential risk reduction requires the assumption that we will eventually reach a state with much lower total risk than today.
A somewhat related issue is the distribution of opportunities to improve the long-term future over time. Specifically, will there be more efficient longtermist interventions in, say, 50 years? If yes, this would be another reason to favor growth over reducing risk now. Though more specifically it would favor growth, not of the economy as a whole, but of the pool of resources dedicated to improving the long-term future—for example, through ‘EA community building’ or investing to give later. Relatedly, the observation that longtermists are unusually patient (i.e. discount future resources at a lower rate) is both a reason to invest now and give later, when longtermists control a larger share of the pie—and a consideration increasing the value of “ensuring that the future proceeds without disruptions”, potentially by using resources now to reduce existential risk. For more, see e.g.:
Toby Ord, The timing of labour aimed at reducing existential risk
Owen Cotton-Barratt, Allocating risk mitigation across time
Will MacAskill, Are we living at the most influential time in history?
Phil Trammel, Philanthropic timing and the Hinge of History
You describe the view you’re examining as:
cause areas related to existential risk reduction, such as AI safety, should be virtually infinitely preferred to other cause areas such as global poverty
You then proceed by discussing considerations that are somewhat specific to the specific types of interventions you’re comparing—i.e., reducing extinction risk versus speeding up growth.
You might be interested in another type of argument questioning this view. These arguments attack the “virtually infinitely” part of the view, in a way that’s agnostic about the interventions being compared. For such arguments, see e.g.:
Brian Tomasik, Why Charities Usually Don’t Differ Astronomically in Expected Cost-Effectiveness
Tobias Baumann, Uncertainty smooths out differences in impact
Thank you, I think this is an excellent post!
I also sympathize with your confusion. - FWIW, I think that a fair amount of uncertainty and confusion about the issues you’ve raised here is the epistemically adequate state to be in. (I’m less sure whether we can reliably reduce our uncertainty and confusion through more ‘research’.) I tentatively think that the “received longtermist EA wisdom” is broadly correct—i.e. roughly that the most good we can do usually (for most people in most situations) is by reducing specific existential risks (AI, bio, …) -, but I think that
(i) this is not at all obvious or settled, and involves judgment calls on my part which I could only partly make explicit and justify; and
(ii) the optimal allocation of ‘longtermist talent’ will have some fraction of people examining whether this “received wisdom” is actually correct, and will also have some distribution across existential risk reduction, what you call growth interventions, and other plausible interventions aimed at improving the long-term future (e.g. “moral circle expansion”) - for basically the “switching cost” and related reasons you mention [ETA: see also sc. 2.4 of GPI’s research agenda].
One thing in your post I might want to question is that, outside of your more abstract discussion, you phrase the question as whether, e.g., “AI safety should be virtually infinitely preferred to other cause areas such as global poverty”. I’m worried that this is somewhat misleading because I think most of your discussion rather concerns the question whether, to improve the long-term future, it’s more valuable to (a) speed up growth or to (b) reduce the risk of growth stopping. I think AI safety is a good example of a type-(b) intervention, but that most global poverty interventions likely aren’t a good example of a type-(a) intervention. This is because I would find it surprising if an intervention that has been selected to maximize some measure of short-term impact also turned out to be optimal for speeding up growth in the long-run. (Of course, this is a defeatable consideration, and I acknowledge that there might be economic arguments that suggest that accelerating growth in currently poor countries might be particularly promising to increase overall growth.) In other words, I think that the optimal “growth intervention” Alice would want to consider probably isn’t, say, donating to distribute bednets; I don’t have a considered view on what it would be instead, but I think it might be something like: doing research in a particularly dynamic field that might drive technological advances; or advocating changes in R&D or macroeconomic policy. (For some related back-of-the-envelope calculations, see Paul Christiano’s post on What is the return to giving?; they suggest “that good traditional philanthropic opportunities have a return of around 10 and the best available opportunities probably have returns of 100-1000, with most of the heavy hitters being research projects that contribute to long term tech progress and possibly political advocacy”, but of course there is a lot of room for error here. See also this post for how maximally increasing technological progress might look like.)
Lastly, here are some resources on the “increase growth vs. reduce risk” question, which you might be interested in if you haven’t seen them:
Paul Christiano’s post on (literal) Astronomical waste, where he considers the permanent loss of value from delayed growth due to cosmological processes (expansion, stars burning down, …). In particular, he also mentions the possibility that “there is a small probability that the goodness of the future scales exponentially with the available resources”, though he ultimately says he favors roughly what you called the plateau view.
In an 80,000 Hours podcast, economist Tyler Cowen argues that “our overwhelming priorities should be maximising economic growth and making civilization more stable”.
For considerations about how to deal with uncertainty over how much utility will grow as a function of resources, see GPI’s research agenda, in particular the last bullet point of section 1.4. (This one deals with the possibility of infinite utilities, which raises somewhat similar meta-normative issues. I thought I remembered that they also discuss the literal point you raised—i.e. what if utility will in the long-run grow exponentially? -, but wasn’t able to find it.)
I might follow up in additional comments with some pointers to issues related to the one you discuss in the OP.
Very interesting, thank you!
Potential typo: In the following paragraph, I think “likely yes” should probably read “very probably yes”.
Although incomplete, there is direct evidence that individuals of these taxa exhibit features which, according to expert agreement, seem to be necessary –although not sufficient– for consciousness (Bateson, 1991; Broom, 2013; EFSA, 2005; Elwood, 2011; Fiorito, 1986; Sneddon et al., 2014; Sneddon, 2017) (see the first criterion of the ‘likely yes’ category);
(Super minor: It also seems slightly inconsistent that the section title “Very Probably Yes” is capitalized, while others aren’t.)