Thanks Arden. I suspect you don't disagree with the people interviewed for this report all that much then, though ultimately I can only speak for myself. 

One possible disagreement that you and other commenters brought up that which I meant to respond to in my first comment, but forgot: I would not describe 80,000 hours as cause-neutral, as you try to do here and here. This seems to be an empirical disagreement, quoting from second link:

We are cause neutral[1] – we prioritise x-risk reduction because we think it's most pressing, but it’s possible we could learn more that would make us change our priorities.

I don't think that's how it would go. If an individual 80,000 hours member learned things that cause them to downshift their x-risk or AI safety priority, I expect them to leave the org, not for the org to change. Similar observations on hiring. So while all the individuals involved may be cause neutral and open to change in the sense you describe, 80,000 hours itself is not, practically speaking. It's very common for orgs to be more 'sticky' than their constituent employees in this way. 

I appreciate it's a weekend, and you should feel free to take your time to respond to this if indeed you respond at all. Sorry for missing it in the first round. 

Meta note: I wouldn’t normally write a comment like this. I don’t seriously consider 99.99% of charities when making my donations; why single out one? I’m writing anyway because comments so far are not engaging with my perspective, and I hope more detail can help 80,000 hours themselves and others engage better if they wish to do so. As I note at the end, they may quite reasonably not wish to do so.

For background, I was one of the people interviewed for this report, and in 2014-2018 my wife and I were one of 80,000 hours’ largest donors. In recent years it has not made my shortlist of donation options. The report’s characterisation of them - spending a huge amount while not clearly being >0 on the margin - is fairly close to my own view, though clearly I was not the only person to express it. All views expressed below are my own.

I think it is very clear that 80,000 hours have had a tremendous influence on the EA community. I cannot recall anyone stating otherwise, so references to things like the EA survey are not very relevant. But influence is not impact. I commonly hear two views for why this influence may not translate into positive impact:

-80,000 hours prioritises AI well above other cause areas. As a result they commonly push people off paths which are high-impact per other worldviews. So if you disagree with them about AI, you’re going to read things like their case studies and be pretty nonplussed. You’re also likely to have friends who have left very promising career paths because they were told they would do even more good in AI safety. This is my own position.

-80,000 hours is likely more responsible than any other single org for the many EA-influenced people working on AI capabilities. Many of the people who consider AI top priority are negative on this and thus on the org as a whole. This is not my own position, but I mention it because I think it helps explain why (some) people who are very pro-AI may decline to fund.

I suspect this unusual convergence may be why they got singled out; pretty much every meta org has funders skeptical of them for cause prioritisation reasons, but here there are many skeptics in the crowd broadly aligned on prioritisation.

Looping back to my own position, I would offer two ‘fake’ illustrative anecdotes:

Alice read Doing Good Better and was convinced of the merits of donating a moderate fraction of her income to effective charities. Later, she came across 80,000 hours and was convinced by their argument that her career was far more important. However, she found herself unable to take any of the recommended positions. As a result she neither donates nor works in what they would consider a high-impact role; it’s as if neither interaction had ever occurred, except perhaps she feels a bit down about her apparent uselessness.

Bob was having impact in a cause many EAs consider a top priority. But he is epistemically modest, and inclined to defer to the apparent EA consensus- communicated via 80,000 hours - that AI was more important. He switched careers and did find a role with solid - but worse - personal fit. The role is well-paid and engaging day-to-day; Bob sees little reason to reconsider the trade-off, especially since ChatGPT seems to have vindicated 80,000 hours’ prior belief that AI was going to be a big deal. But if pressed he would readily acknowledge that it’s not clear how his work actually improves things. In line with his broad policy on epistemics, he points out the EA leadership is very positive on his approach; who is he to disagree?

Alice and Bob have always been possible problems from my perspective. But in recent years I’ve met far more of them than I did when I was funding 80,000 hours. My circles could certainly be skewed here, but when there’s a lack of good data my approach to such situations is to base my own decisions on my own observations. If my circles are skewed, other people who are seeing very little of Alice and Bob can always choose to fund.

On that last note, I want to reiterate that I cannot think of a single org, meta or otherwise, that does not have its detractors. I suspect there may be some latent belief that an org as central as 80,000 hours has solid support across most EA funders. To the best of my knowledge this is not and has never been the case, for them or for anyone else. I do not think they should aim for that outcome, and I would encourage readers to update ~0 on learning such.

Not the main point of your post, but tax deductibility is a big deal in the UK as well, at least for higher earners; once you earn more than £50k donations are deductible at a rate of at least 40%, i.e. £60 becomes £100.

CEA has now confirmed that Miri was correct to understand their budget - not EVF's budget - as around $30m.

In terms of things that would have helped when I was younger, I'm pretty on board with GWWC's new community strategy,^[1] and Grace's thoughts on why a gap opened up in this space. I was routinely working 60-70 hour weeks at the time, so doing something like an EA fellowship would have been an implausibly large ask and a lot of related things seem vibed in a way I would have found very offputting. My actual starting contact points with the EA community consisted of no-obligation low-effort socials and prior versions of EA Global.

In terms of things now, it's complicated. I suspect anything that prompts people to talk about how much they are giving and/or where is pretty powerful; knowing other traders who were donating 65+% was a real motivation to challenge myself on why I couldn't do the same or at least get closer, and I suspect I've had similar impacts on some others. Obviously, this kind of pressure can go wrong, but when it's mostly self-directed - 'why can't I?' rather than 'why don't you?' - and bouncing around very high-earning circles I think it nets out pretty positive. Seeing people find constructive things to do with their money also helps counter "Funding Overhang" memes. 

Others' mileage may vary on how much these generalise.

1. ^{^}

   Since my wife is involved with the GWWC London group and I have given a lot of money to GWWC since their reboot, I can't really claim to be unbiased here.

Thanks for this, pretty interesting analysis.

Every time I come across an old post in the EA forum I wonder if the karma score is low because people did not get any value from it or if people really liked it and it only got a lower score because fewer people were around to upvote it at that time.

The other thing going on here is that the karma system got an overhaul when forum 2.0 launched in late 2018, giving some users 2x voting power and also introducing strong upvotes. Before that, one vote was one karma. I don't remember exactly when the new system came in, but I'd guess this is the cause of the sharp rise on your graph around December 2018. AFAIK, old votes were never re-weighted, which is why if you go back through comments on old posts you'll see a lot of things with e.g. +13 karma and 13 total votes, a pattern I don't recall ever seeing since. 

Partly as a result, most of the karma old posts have will have been from people going back and upvoting them later once the new system was impemented, e.g. from memory my post from your list was around +10 for most of its life, and has drifted to its current +59 over the past couple of years.

This jumps out to me because I'm pretty sure that post was not a particularly high-engagement post even at the time it was written, but it's the second-highest 2015 post on your list. I think this is because it's been linked back to a fair amount and so can partially benefit from the karma inflation.

(None of which is meant to take away from the work you've done here, just providing some possibly-helpful context.)

So taking a step back for a second, I think the primary point of collaborative written or spoken communication is to take the picture or conceptual map in my head and put it in your head, as accurately as possible. Use of any terms should, in my view, be assessed against whether those terms are likely to create the right picture in a reader's or listener's head. I appreciate this is a somewhat extreme position.

If everytime you use the term heavy-tailed (and it's used a lot - a quick CTRL + F tells me it's in the OP 25 times) I have to guess from context whether you mean the mathematical or commonsense definitions, it's more difficult to parse what you actually mean in any given sentence. If someone is reading and doesn't even know that those definitions substantially differ, they'll probably come away with bad conclusions.

This isn't a hypothetical corner case - I keep seeing people come to bad (or at least unsupported) conclusions in exactly this way, while thinking that their reasoning is mathematically sound and thus nigh-incontrovertible. To quote myself above:

The above, in my opinion, highlights the folly of ever thinking 'well, log-normal distributions are heavy-tailed, and this should be log-normal because things got multiplied together, so the top 1% must be at least a few percent of the overall value'.

If I noticed that use of terms like 'linear growth' or 'exponential growth' were similarly leading to bad conclusions, e.g. by being extrapolated too far beyond the range of data in the sample, I would be similarly opposed to their use. But I don't, so I'm not. 

If I noticed that engineers at firms I have worked for were obsessed with replacing exponential algorithms with polynomial algorithms because they are better in some limit case, but worse in the actual use cases, I would point this out and suggest they stop thinking in those terms. But this hasn't happened, so I haven't ever done so. 

I do notice that use of the term heavy-tailed (as a binary) in EA, especially with reference to the log-normal distribution, is causing people to make claims about how we should expect this to be 'a heavy-tailed distribution' and how important it therefore is to attract the top 1%, and so...you get the idea.

Still, a full taboo is unrealistic and was intended as an aside; closer to 'in my ideal world' or 'this is what I aim for my own writing', rather than a practical suggestion to others. As I said, I think the actual suggestions made in this summary are good - replacing the question 'is this heavy-tailed or not' with 'how heavy-tailed is this' should do the trick- and hope to see them become more widely adopted.