Are there any other pro athlete aspiring EAs?

Thanks! 1 seems believable to me, at least for EA as it currently presents. 2 seems believable on average but I'd expect a lot of heterogeneity (I personally know athletes who have gone on to be very good researchers). It also seems like donations are pretty accessible to everyone, as you can piggyback on other people's research.

Are there any other pro athlete aspiring EAs?

I personally wouldn't pay that much attention to the particular language people use--it's more highly correlated with their local culture than with abilities or interests. I'd personally be extra excited to talk to someone with a strong track record of handling uncertainty well who had a completely different vocabulary than me, although I'd also expect it to take more effort to get to the payoff.

Are there any other pro athlete aspiring EAs?

This is a bit tangential, but I expect that pro athletes would be able to provide a lot of valuable mentorship to ambitious younger people in EA--my general experience has been that about 30% of the most valuable growth habits I have are imported from sports (and also not commonly found elsewhere). E.g. "The Inner Game of Tennis" was gold and I encourage all my PhD students to read it.

Are there any other pro athlete aspiring EAs?

I didn't downvote, but the analysis seems incorrect to me: most pro athletes are highly intelligent, and in terms of single attributes that predict success in subsequent difficult endeavors I can't think of much better; I'd probably take it over successful startup CEO even. It also seems like the sort of error that's particularly costly to make for reasons of overall social dynamics and biases.

An open letter to Holden from a crybaby

Niceness and honesty are both things that take work, and can be especially hard when trying to achieve both at once. I think it's often possible to achieve both, but this often requires either substantial emotional labor or unusual skill on the part of the person giving feedback. Under realistic constraints on time and opportunity cost, niceness and honesty do trade off against each other.

This isn't an argument to not care about niceness, but I think it's important to realize that there is an actual trade-off. I personally prefer people to err strongly on the honesty side when giving me feedback. In the most blunt cases it can ruin my day but I still prefer overall to get the feedback even then.

Ryan Carey on how to transition from being a software engineer to a research engineer at an AI safety team

Okay, thanks for the clarification. I now see where the list comes from, although I personally am bearish on this type of weighting. For one, it ignores many people who are motivated to make AI beneficial for society but don't happen to frequent certain web forums or communities. Secondly, in my opinion it underrates the benefit of extremely competent peers and overrates the benefit of like-minded peers.

While it's hard to give generic advice, I would advocate for going to the school that is best at the research topic one is interested in pursuing, or where there is otherwise a good fit with a strong PI (though basing on a single PI rather than one's top-2/top-3 can sometimes backfire). If one's interests are not developed enough to have a good sense of topic or PI then I would go with general strength of program.

Ryan Carey on how to transition from being a software engineer to a research engineer at an AI safety team

I'm not sure what the metric for the "good schools" list is but the ranking seemed off to me. Berkeley, Stanford, MIT, CMU, and UW are generally considered the top CS (and ML) schools. Toronto is also top-10 in CS and particularly strong in ML. All of these rankings are of course a bit silly but I still find it hard to justify the given list unless being located in the UK is somehow considered a large bonus.

Ryan Carey on how to transition from being a software engineer to a research engineer at an AI safety team

I intended the document to be broader than a research agenda. For instance I describe many topics that I'm not personally excited about but that other people are and where the excitement seems defensible. I also go into a lot of detail on the reasons that people are interested in different directions. It's not a literature review in the sense that the references are far from exhaustive but I personally don't know of any better resource for learning about what's going on in the field. Of course as the author I'm biased.

The EA Community and Long-Term Future Funds Lack Transparency and Accountability

Given that Nick has a PhD in Philosophy, and that OpenPhil has funded a large amount of academic research, this explanation seems unlikely.

Disclosure: I am working at OpenPhil over the summer. (I don't have any particular private information, both of the above facts are publicly available.)

EDIT: I don't intend to make any statement about whether EA as a whole has an anti-academic bias, just that this particular situation seems unlikely to reflect that.

Comparative advantage in the talent market

If we think of the community as needing one ops person and one research person, the marginal value in each area drops to zero once that role is filled.

Yes, but these effects only show up when the number of jobs is small. In particular: If there are already 99 ops people and we are looking at having 99 vs. 100 ops people, the marginal value isn't going to drop to zero. Going from 99 to 100 ops people means that mission-critical ops tasks will be done slightly better, and that some non-critical tasks will get done that wouldn't have otherwise. Going from 100 to 101 will have a similar effect.

In contrast, in the traditional comparative advantage setting, there remain gains-from-coordination/gains-from-trade even when the total pool of jobs/goods is quite large.

The fact that gains-from-coordination only show up in the small-N regime here, whereas they show up even in the large-N regime traditionally, seems like a crucial difference that makes it inappropriate to apply standard intuition about comparative advantage in the present setting.

If we want to analyze this more from first principles, we could pick one of the standard justifications for considering comparative advantage and I could try to show why it breaks down here. The one I'm most familiar with is the one by David Ricardo (https://en.wikipedia.org/wiki/Comparative_advantage#Ricardo's_example).

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