What's a good heuristic for knowing if you're being self-sacrificial to the point of negative returns for the thing you're being self-sacrificial for? I'm interested in things people have actually used and found to work in practice, either for themselves or others.
As someone who hires people (I am hiring right now, see bottom of the post), I often get emails asking whether someone should apply for a role I've listed.
If you're considering contacting a hiring manager to ask if you're a good fit for the role, you should just apply.
Why?
Notable exceptions, where you should email:
To be clear, the urge to send an email to check if you should apply is totally normal (I have done it before), and anyone who has done this has nothing to worry about. However, I would strongly recommend applying despite your reservations. You can just do things!
Also, if you want to apply to become Access to Medicines Initiative's Head of Monitoring, Evaluation, and Data, JOB DESCRIPTION AND APPLICATION HERE: https://docs.google.com/document/d/1nqOz2a5bTQs5W361G5gpJ3brLEf6xuG0
You should judge others on a curve, and probably yourself as well.
Judging others on a curve means that folks see reward for doing more than what is typical. This is just good reinforcement-learning theory, since it is easy to get both feedback on rewarded & not-rewarded actions.
I started writing this thinking that you should NOT judge yourself on a curve, in contrast. IE, you know much more about your own circumstances, and so pretending you are average throws away info. For example, if I only gave 5% of my income to charity, that would be above average for the US. But my opportunities are amazing, and so 5% would in reality show low effort.
But actually, I was just using the wrong comparison group! I shouldn't compare myself to a big group of other people. I have a near perfect comparison group in my past self. If I did a better job this week than last week at work, that's a great sign! If my one-rep max for benchpress has gone down over the last month, that's a bad sign!
This all feels obvious in retrospect.
[central europe] We are organising the largest AI-safety march (at least in central europe) to date at Prague, starting at 15:00 on 31st August. We have got parlimentary support as well as very supportive police, allowing us to take the preffered route. We want as many people to come - fell free to spread information about the event outside EA as much as possible, especially if you have friends living near Prague who may come. If you want closer info/visual materials or help us with organisation, you can get in contact with czech PauseAI on whatsapp.
Luma event link
The Saturation View holds that a world of varied joys is better than a world of equal but identical joys. Elliott Thornley objected that no one benefits from this variety, and Will MacAskill answered that...
Excellent job describing this clearly and accessibly. (And teaching me about Saturationism) You portray this well from where I stand on the opposite shore!
I think that truth and variety matter[1] and would prefer to live in a universe with more truth and more variety even at the cost of "loss of perfection" or suffering[2]. Even if I could not tell the difference (unaware of deceptions) or never experienced any positive effects (from variety). I personally value these things, despite them not simplifying to additional positive valence.
Here are some values I have that don't pan out to positive welfare:
1) I think if someone dedicates their life to some endeavor, and they are feeling the rush of emotions as their life's project comes to an end, it would be "bad" if those exact same emotions/experience were via beaming that feeling into their mind. They cared about the project itself, they wanted it to be due to their own effort, they are not doing it for the pleasurable experience portion.
2) I think it is bad if we, with well meaning, erase[3] everyone's memory to replay their perfect happiest possible moment over and over again for the rest of their lifetimes. I think, even if nothing could be a better world of experience for everyone, and no one will ever know it's a lie, that this is a flawed[4] life and a flawed world.
3) Someone who changes the future but doesn't live to see it has, in some sense, a better life than someone who tried and failed, even though their felt experience is identical.
4) I think it would be a shame if many things are never understood or discovered before the universe ends, regardless of how this translates to total happiness experienced.
5) I think an empty universe is bad in comparison to what it could have been, even if no one exists to prefer something else.
You cannot imagine an unwatched world without mentally watching it
Yes, but I think that if you delete the outside view then lots of things are incoherent and worse.
The badness of death disappears. We know that being dead is bad because of the outside view where they could be happily living. Same for unborn people. Accounting for past and future experiences is using an outside view, no one is actively having those experiences. Similarly, if we forcibly rewrite everyone's desires to be something terrible, (like to desire everyone dies horribly, and then we all happily satisfy this!) this only is bad from outside.
The summing view makes arbitrary outside view choices too. Why sum? Why not only care about the single happiest life ever lived? No one experiences the sum, the lives that didn't happen, or the future. You are asking the accountant to look at total welfare achieved out of potential welfare possible, not just total welfare and without judgement on worlds which could have existed instead. There is nothing to evaluate, without an outside view to compare against. Choosing to "add them up" instead of "average them" or "prioritize the worst-off" is a world-shape preference the accountant imposes, not something anyone experiences. You want to say the Totalist's account is fundamental while the Saturationist's account is doing something superficial. But the Totalist is just as arbitrarily choosing how and what to aggregate when he's adding up experiences, and using an outside view to do it. Yes, variety across experiences is an outside view parameter, but that isn't unusual.
Its also a little weird to assume positive welfare "exists" and "can be summed." Maybe when you "sum" it, the redundant identical feelings overlap, occupying the space of one single unique experience, and summing it results in the Saturationist's account. Maybe that's just how adding up felt experiences works.
No one proposes an ugly impersonal value.
No, variety and truth are very much an ugly impersonal values. These could have very high costs and often result in a lot of suffering. All kinds of stories celebrate people who wrecked their lives forcing truth or justice into the world, or chased perfection/uniqueness (even when it doesn't work!), which is strange behavior if these values are just about netting more pleasurable experiences. Their value did NOT evaporate when it had an net-negative-welfare cost. Sometimes we admire it as a life more well-lived, in some non-welfare sense. They had purpose instead of happiness, or something. We often hold these values dear albeit with conflicted uncertainty about what is best and right and matters most when it had such a terrible downside.
We don't celebrate the people that are happiest and heroically help others bury their head in the sand to protect their happiness. Which would indicate that happiness is not the single final value...
Ask A whether she wants to switch worlds: she is indifferent, and she must be, because if she preferred one, the welfare would not be equal. Ask B, same answer, because nothing changes for him.
Turning around and sating that "this must mean the welfare wasn't equal" is hard to respond to. Maybe its high quality welfare without being higher quantity? People do things for for results that they will never personally experience all the time. Its weird to say those values are fake.
I think there are additional fundamental values beyond welfare, like variety and truth. Unfortunately I'm not sure if they can be explained further than "I value them..." so I may have to just describe them and then stop.
Anyway, all this results in this belief that I have:
If we reach the world where everyone has the most perfect life, and it's the same life, I think this world is worse than many non-identical, not-quite-perfect lives. I wouldn't force worse lives on anyone, but I'd volunteer to live a worse life so that the universe contains something different. Normally sharing my different life would benefit others; but even if no one benefited at all and I wasted my perfection, I think I might still want to do it.
I'm open to the possibility that I'm a limited idiot who can't see what the god-minds of the future will want. In my defense, I'm not hopelessly unimaginative. I fully expect that we will want to do currently-crazy-sounding things likes erase ourselves, limit/reshape our minds, replay experiences on loop, merge into amorphous monads, do tedious activities in deep utopia, and tons of other unimaginable things that might seem undesirable, even hideous, to me today...) The god-minds might all agree that tiling the universe with hedonium is obviously correct. Maybe I've just intrinsified some stuff that is a mere side-effect of promoting progress or weeding out traitors or something that will no longer apply in the grand scheme of things. But from my best attempts at imagining the god-minds at the end of the universe, I think they would value variety and truth even at the expense of some lost hedonium.
"cosmically"
I would also not maximize truth and variety at all costs.
assuming they didn't agree to it. I expect we will choose to do this to ourselves quite a lot actually.
Maybe not a bad life compared to today's good lives, etc. It's an alright future. But I think its far worse than many other life-shapes and potential worlds that include variety.
This competition entry has been selected for publication by the Forum team.
The unawareness sequence by Anthony DiGiovanni argues that impartial altruists face a deep challenge when trying to compare the expected value of different strategies. We are not merely uncertain about which outcomes will occur, but many relevant outcomes are too coarsely represented, or not represented at all, in our current thinki...
Thank you. I think that's a fair summary. To be precise, rather than claiming that the maximality rule is generally too sensitive to small changes, my critique is that it's generally too insensitive to small changes, as virtually all updates that would be registered by a graded approach make no difference at all. As a consequence of this insensitivity, maximality implies isolated points of extreme sensitivity where some tiny change can result in a categorical jump. So yes, I think it's too sensitive to small changes in the sense that it packs all its sensitivity into those sharp points where dominance is achieved.
Thank you for these useful comments.
I agree that indeterminacy can still obtain under a graded approach. However, it’s worth noting that the band in which indeterminacy obtains under a graded approach will generally be much narrower than under maximality. So moving from maximality to a graded approach is a big change.

When I try to weigh up the reasons in favor of any A vs. B w.r.t. their total consequences, fully accounting for unawareness, I really don't see why I should consider the "upper endpoint" larger or smaller in absolute value than the "lower endpoint".
I gather we see this differently, but to very briefly explain why it seems warranted to me: Because of the evidence and arguments favoring the most plausible strategies, and the lack of similarly strong evidence and arguments against them. In particular, I don’t think the arguments against the capacity-building strategies I discuss are as strong as those in their favor. Moreover, much of the value of building capacity is the value of being able to act on considerations we aren’t yet aware of. So, in my view, unawareness bears asymmetrically on these strategies rather than neutrally (I realize this latter point is stated very briefly and needs further development).
I would like to see more of an argument that this isn't our situation, perhaps building on what you say in your appendix about low-footprint capacity-building.
Without trying to build an elaborate case here, there is a simple extension that I think further strengthens the case: instead of focusing only on the best-supported strategies, we can specifically compare the seemingly best-supported strategy (say, some form of low-footprint CB) with the strategy that seems worst (I suspect it’s better not to go into details about what that might be).
When comparing two such contrasting strategies, the expected difference plausibly gets beyond the (relatively) narrow band of indeterminacy under some plausible graded approach, considering both the arguments favoring the seemingly better strategy and the arguments opposing the seemingly worse strategy. The reason this helps is that the width of the indeterminacy band doesn’t obviously scale with how contrasting the pair is, whereas the expected difference does. (Again, the expected difference between the two strategies need not be remotely as stark as in the figure below.)

You can have insensitivity to mild sweetening under vague degrees of betterness; it doesn't assume maximality.
I agree that there can be insensitivity to mild sweetening under rules other than maximality. (As noted in more general terms, the blanket objection that rejects “any evidence or proposal we might wager on … assumes the kind of categorical decision rule that collapses a broad set of evidential states into complete and undifferentiated indeterminacy.”)
But interestingly, under a graded approach, even if indeterminacy may in some sense be preserved after a mild sweetening (within the relatively narrow indeterminacy band), we still wouldn’t get complete insensitivity to mild sweetening. In that case, we would have a hazy cloud of vague expected values, and a vague degree of ex ante betterness, but that vague degree would still slightly increase from the mild sweetening, even if the change isn’t large enough to yield a determinately higher degree of betterness for one option. The hazy cloud and degree do move.

A justified wager is not guaranteed under such an approach, as our evidence could in principle be closely balanced, but the bar for finding one is relatively low (since the approach doesn’t collapse a broad set of distinct evidential states into indeterminacy, cf. the figure here). It seems to me we can make such justified wagers given both how mild sweetenings do incrementally move ex ante degrees of betterness and the, in my view, sufficiently weighty arguments and considerations that support the most plausible strategies over the worst ones.
Epistemic status: We are quite confident that Biological AI models (BAIMs) safety requires further work, but uncertain about its scale. The apparent gap may be refuted by one experiment, filled by a few researchers working for a year, or prove to be substantial enough to call for an entire subfield.
Disclaimer: this post has been written with a colleague, that due to her current job can't post out of her own forum account
Wha...
I see. I guess if there's a chance that we can stop genie from being out of box, we should try. You're also correct that tiered access can indeed mitigate issues. And it does seem like general models being as good and 'Bio Mythos' might genuinely not happen. So, exploring tightening access and data makes sense.
All of this requires co-ordinated action though as even one bad actor can significantly turn things for worse. And the fact that covid is fresh in policy makers' minds could help.
My points are more in the case of being or inevitably going to a point where we can 'reasonably freely' edit pathogens and vaccines. If we can do that, making it freer and restricting implementation is generally a better idea. And if possible to do safely, we should aim for that, I think. Because there is a lot of potential of good for everyone: humans, animals, other biological beings and maybe more (like plastic or rust eating bacteria for environment!).
Many members of the Biosecurity and Pandemic Preparedness team at Coefficient Giving are blogging at Defenses in Depth. We're using the blog to share our internal research, both polished and unpolished. Among other things, posts so far have covered:
This post summarizes a new preprint on alternative proteins from the Humane and Sustainable Food Lab: New alternatives, same orders. We investigated 19...
I talk about this paper in light of comparing more modern meat alternatives (Beyond/Impossible/etc.) to traditional vegetarian mains (tofu/curry/etc.), and why the academic literature at least suggests more cause for optimism about the latter than the former, here:
https://regressiontothemeat.substack.com/p/the-more-successful-add-more-veggie
When researching recent projects—impact markets, FundingBench, and AI Safety Funder Bulletin—we kept coming across similar questions that were a bit of a pain to answer. How much money had someone raised, and when? What kind of things was someone fundin...
Chimps, Bonobos, and the Community We Could Be
Robert Sapolsky offers a striking lesson about primate societies. Chimpanzees strive for hierarchy, dominance, and control of resources. Males fight for access to females, bands are brutal to one another, and...
Psychopaths are extraordinary, i.e. out of the ordinary, uncommon, a minority, in a negative sense (lucky there are only few of them around).
I implicitly meant extraordinary in a positive sense, extraordinary achievers of valuable goals.
One is extraordinary in either sense because one was born with uncommon traits and/or because one has spent a lot of effort to develops an uncommon capacity.
Disclaimer: This is a post written by my friend, a fellow community member, that due to her current job she can't post out of her own forum account. If you want to reach out to her, send me a message and I'll make the connection.
tl;dr- While participating in the BlueDot Impact’s Biosecurity course, I was requested to share a deep dive on AI-bio safeguards, so I created...
Thanks for making and sharing this! A friend who is new to EA is curious about AIxBio risk and I think this could be helpful for her.
This week is Cluelessness Critiques Week on the EA Forum.
We'll be publishing eligible entries[1] to the Cluelessness Critiques Essay Competition on the Foru...
Follow-up assuming you'll answer "no", Anthony: same even ex post, right?
Hi Vasco,
Thanks for the question and sorry for the slow reply.
The overall process was to combine two estimates of THL's cost-effectiveness through weighting them in a Guesstimate model, along with some assumptions about the speed-up time of the intervention and the marginal cost-effectiveness relative to the past average cost-effectiveness.
The more detailed steps were:
1. Gather two estimates: THL review by ACE from 2025, wherein cage-free work has an estimated 2 to 44 hens affected per dollar (best guess of 11); THL self-published report from 2025, which reports 2 hens freed per dollar spent between 2015 and 2024.
2. Make assumptions: The THL self-published report was conservative in that it didn't include a counterfactual speed up assumption. So, we assumed that the interventions THL carried out each affected 6 to 15x as many hens than reported on average through speeding up the timelines to hens becoming cage-free. Then, since the marginal cost-effectiveness in 2026 is probably much less than the average cost-effectiveness from 2015-2024, we discounted the resulting THL self-reported estimate by 70%. This is a rough guess.
3. In a Guesstimate model, I combined the two estimates (giving equal weight to each) to get an overall estimate of THL's cost-effectiveness on the margin (caveat below). (The ACE estimate was encoded as a lognormal distribution between 2 and 44 hens/$, which had a mean of 14 which is slightly better than their best-guess, which could be a median or mean, I'm not sure). This produced an estimate of 10 (3.6 to 29) hens/$. The method isn't perfect, since these estimates might not be independent so the variance could be artificially reduced, but for our initial version, we felt it was a good enough estimate (other factors matter more to the CCF).
Upon looking back, I realize that I made a typo in the methodology -- it should be 3.6 to 29 chickens per dollar spent, not 3.6 to 22. So thanks for pointing my attention to this! I've now fixed it.
On the more important question, I agree that the marginal cost-effectiveness is definitely the important question, and I'd like to have this. However, the best estimates we could find for the first version of the CCF were for average cost-effectiveness of past spending. We plan to improve upon these estimates in the future, such as through considering more evidence. If you know of any good marginal cost-effectiveness estimates, or other cost-effectiveness estimates, please do link them below!
Hope this is helpful! Also, I'll look into how to change the settings so you can copy text, I'm not sure why it's like that
Thank you for writing this, Tom and Rocky. I think this is an important caution, and I appreciate the way you are framing it.
As someone directly involved in a welfare-tech-style intervention, I actually agree with much of your core argument. I also strongly believe that good tech interventions can be extremely impactful, partly for some of the reasons you outline: they can sometimes bypass certain barriers that exist for other welfare work, and they can make it possible to help very large numbers of animals relatively quickly. But I also agree that, when looked at from the outside, they can be falsely perceived as silver bullets, so I want to share three insights from SWP about electrical stunning for shrimp.
I’ve also posted a more direct response to “Animal Welfare Has an Evidence Problem,” which you refer to in the post, to clarify some of these points.
First, at SWP, our work on shrimp stunning has been much more complex than simply deploying stunners at scale. The Humane Slaughter Initiative does involve getting electrical stunning equipment used on farms, but in practice, we are doing much more than that: coordinating and funding welfare research and R&D, collecting field data, working with scientists, developing and refining slaughter protocols, iterating on the technology with existing equipment providers, negotiating with industry, engaging corporate stakeholders, building and maintaining producer relationships, training staff, educating farmers… Much of this work is not visible from the surface, but it’s central to making the intervention possible. The tech matters, but it does not operate in a vacuum.
Second, on shrimp stunning specifically, I think you captured something important. SWP entered this space aware that there were uncertainties, and we’ve been proactively trying to reduce them. Some of that work is public, but much of it happens through industry collaborations, field implementation, and producer relationships, which are often necessarily behind closed doors or covered by NDAs. That can make the work less visible from the outside, even though it is central to what we are doing.
This is one reason I think treating our intervention too simplistically, as merely putting stunners on farms, can lead to exactly the kind of disillusionment and backlash you describe.
Third, I feel some of the issues you describe are not limited to tech interventions. Many ambitious attempts to change existing systems seem to have an early phase where progress looks relatively fast, followed by a harder phase where problems become more complex, uncertainty can increase, coordination costs become bigger, and resistance from affected stakeholders becomes stronger. That does not necessarily mean the intervention – tech or otherwise – was a mistake; it can be a natural progression of an ambitious project.
Thanks again for writing this! It’s an important reminder that welfare tech can be genuinely promising and, in some cases, extremely impactful, but it should not be treated as a silver bullet.
I think that this diagnosis is basically on target; it points to something that seems relatively under-resourced, despite some focus by think tanks, and something that the major AIxBio safety groups are not as focused on. I also agree that we don't know if AGI will subsume this, and that it's plausible but uncertain if other bottlenecks matter more, but that's an uncertainty we can't resolve without simply waiting for the outcomes, and so this seems very high value in expectation.
I'm less certain about the object level questions, and don't have strong intuitions - so I think that conditional on not hearing from someone more informed about this that there are additional questions or concerns, or literature you should look at, the best way to figure out whether this is needed, and what the risk is, is to start the work - good luck, and I'd be happy to chat about this more!
EA groups have been and continue to be overpowered.
Running an EA group is among the most impactful things you can do when you are at University.
The other day my previous co-organizer sent me a list of 15 (!) people who were in our EA group who are doing exciting and important GCR work.
In the interest of getting this quick take out, I am not reaching out to everyone to see if I can list their name/quote them but I want to stress the “exciting and important” aspect of this. Some of these people are perhaps within the top 10 people affecting American AI policy from a GCR lens. And they only graduated within the last 5 years - many of whom started in very impactful roles in less than two years(!). People are advising on 100s of millions of dollars, in political office, and founding exciting new orgs in the ecosystem (like, actually awesome orgs!).
And that is only GCR work - a previous member of our group is now CEO of one of the largest EA-affiliated global health orgs and another, a researcher at GiveWell.
When I was a group organizer, I was often stressing about whether I was having an impact and I often hear organizers stressing about whether the payoff will be quick enough. It is true that my group’s outcomes are not the median and that sometimes it does just take many years to pay off - but there definitely are existence proofs of massive impact on a pretty quick timeframe.
I have been involved in EA groups since 2017. Almost a decade now!
And in the meantime I’ve seen so many exciting impact stories coming out from groups all over the world.If you are interested in more of my takes on groups you can check out some of my past talks: Hyping them up and Sharing potential pitfalls
If you are interested in running a group there are many resources available to you!