Yeah many of those things seem right to me.

I suspect the crux might be that I don't necessarily think it's a bad thing if "the casual reader of the website doesn't understand that 80k basically works on AGI". E.g. if 80k adds value to someone as they go through the career guide, even if they don' realise that "the organization strongly recommends AI over the rest, or that x-risk gets the lion's share of organizational resources", is there a problem?

I would be concerned if 80k was not adding value. E.g. I can imagine more salesly tactics that look like making a big song and dance about how much the reader needs their advice, without providing any actual guidance until they deliver the final pitch, where the reader is basically given the choice of signing up for 80k's view/service, or looking for some alternative provider/resource that can help them. But I don't think that that's happening here.

I can also imagine being concerned if the service was not transparent until you were actually on the call, and then you received some sort of unsolicited cause prioritisation pitch. But again, I don't think that's what's happening; as discussed, it's pretty transparent on the advising page and cause prio page what they're doing.

Makes sense on (1). I agree that this kind of methodology is not very externally legible and depends heavily on cause prioritisation, sub-cause prioritisation, your view on the most impactful interventions, etc. I think it's worth tracking for internal decision-making even if external stakeholders might not agree with all the ratings and decisions. (The system I came up with for Animal Advocacy Careers' impact evaluation suffered similar issues within animal advocacy.)

For (2), I'm not sure why you don't think 80 do this. E.g. the page on "What are the most pressing world problems?" has the following opening paragraph:

We aim to list issues where each additional person can have the most positive impact. So we focus on problems that others neglect, which are solvable, and which are unusually big in scale, often because they could affect many future generations — such as existential risks. This makes our list different from those you might find elsewhere.

Then the actual ranking is very clear: AI 1, pandemics 2, nuclear war 3, etc. 

And the advising page says quite prominently "We’re most helpful for people who... Are interested in the problems we think are most pressing, which you can read about in our problem profiles." The FAQ on "What are you looking for in the application?" mentions that one criterion is "Are interested in working on our pressing problems".

Of course it would be possible to make it more prominent, but it seems like they've put these things pretty clearly on the front. 

It seems pretty reasonable to me that 80k would want to talk to people who seem promising but don't share all the same cause prio views as them; supporting people to think through cause prio seems like a big way they can add value. So I wouldn't expect them to try to actively deter people who sign up and seem worth advising but, despite the clear labelling on the advising page, don't already share the same cause prio rankings as 80k. You also suggest "when people do apply/email, it's worth making that sort of caveat as well", and that seems in the active deterrence ballpark to me; to the effect of 'hey are you sure you want this call?'

Hey Joel, I'm wondering if you have recommendations on (1) or on the transparency/clarity element of (2)?

(Context being that I think 80k do a good job on these things, and I expect I'm doing a less good job on the equivalents in my own talent search org. Having a sense of what an 'even better' version might look like could help shift my sort of internal/personal overton window of possibilities.)

What do you think are some of the main differences between your guide/advice and 80k's?

I realise that to some extent, merely covering similar ideas with a slightly different framing and emphasis can add value because variations in these things land more or less well with different people.

But I'm wondering about more substantive differences. E.g. this page implies that you either don't endorse longtermism or endorse it less strongly than 80k, and my impression from your content is that you do tend to highlight a broader range of opportunities, including a much more prominent emphasis on global health (and climate change?).

Are then any other differences that jump to mind? E.g. like how Holden Karnofsky's "aptitudes" post was quite a different take to 80k's more 'cause prio first' approach.

(A more provocative framing of this qu: imagine that Probably Good and 80k both have an article on the same topic. Without reading either, if I do endorse longtermism, is there any reason why (or person for who) the Probably Good article is likely to be more useful?)

Thanks!

Tentative recommendation: try to make the episodes more pointedly about useful, impact-relevant topics. You can preserve the chatty vibe and relatively low-effort prep but still cover important topics.

I just listened to most of the Dwarkesh episode and it seemed notably more useful to me! (And similarly fun/interesting?) I think just because of the topics you broached. E.g. Chana has useful takes on loads of impact-relevant topics but you were talking about quizzes and favourite beans. Whereas with Dwarkesh you were chatting about counterfactuals and lessons from history and career exploration and maximising impact through communications.

Initial feedback on the (first?) Episode with Chana: I liked the idea and know Chana has interesting things to say so decided to listen.

Was fun and kind of interesting but felt like I wasn't sure what I was getting out of it.

I felt like wasn't optimising for either 'usefulness' or 'fun/relaxation'. E.g. I didn't feel like I'd learned anything particularly surprising or useful by half way through the episode, and I felt like I was having less fun than I would by watching Netflix or chatting to my partner... So I stopped and went and did those things instead.

To be fair, this is a reason I don't listen to podcasts all that much in general, but since this moved further away from obvious 'usefulness' than a usual 80k podcast, it made it seem less worthwhile.

Low confidence initial Impression though and I'll probably listen to others!

Unfortunately this was quite a while ago at the last org I worked at; I don't have access to the  relevant spreadsheets, email chains etc anymore and my memory is not the best, so I don't expect to be able to add much beyond what I wrote in the comment above. 

I tried doing this a while back. Some things I think I worried about at the time:

(1) disheartening people excessively by sending them scores that seem very low/brutal, especially if you use an unusual scoring methodology (2) causing yourself more time costs than it seems like at first, because (a) you find yourself needing to add caveats or manually hide some info to make it less disheartening to people, (b) people ask you follow-up questions (3) exposing yourself to some sort of unknown legal risk by saying something not-legally-defensible about the candidate or your decision-making.

(1) turned out to be pretty justified I think, e.g. at least one person expressing upset/dissatisfaction at being told this info. (2) definitely happened too, although maybe not all that many hours in the grand scheme of things (3) we didn't get sued but who knows how much we increased the risk by

Thank you!

I understand the reasons for ranking relative to a given cost-effectiveness bar (or by a given cost-effectiveness metric). That provides more information than constraining the ranking to a numerical list so I appreciate that.

Btw, if you had 5-10 mins spare I think it'd be really helpful to add explanation notes to the cells in the top row of the spreadsheet. E.g. I don't know what "MEV" stands for, or what the "cost-effectiveness" or "cause no." columns are referring to. (Currently these things mean that I probably won't share the spreadsheet with people because I'd need to do a lot of explaining or caveating to them, whereas I'd be more likely to share it if it was more self-explanatory.)

Thanks! When you say "median in quality" what's the dataset/category that you're referring to? Is it e.g. the 3 ranked lists I referred to, or something like "anyone who gives this a go privately"?