I looked into this a bit. Unfortunately the quality of evidence in sleep medicine was underwhelming, e.g. on behavioral treatments.
lukeprog
Karma: 905
BTW my “reflections on the 2018 RA recruiting round” post is now up, here.
I agree that if it’s true that “many EAs feel that either they’re working at a top EA org or they’re not contributing much,” then that is much worse than anything about application time cost and urgently needs to be fixed. I’ve never felt that way about EA org work vs. alternatives, so I may have just missed that this is a message many people are getting.
E.g. Scott’s post also says:
Should also acknowledge the possibility that “talent-constrained” means the world needs more clean meat researchers, malaria vaccine scientists, and AI programmers, and not just generic high-qualification people applying to EA organizations. This wasn’t how I understood the term but it would make sense.
…and my reply is “Yes, talent-constrained also means those other things, and it’s a big problem if that was unclear to a noticeable fraction of the community.”
FWIW I suspect there’s also something a bit more subtle going on than overly narrow misunderstandings of “talent-constrained,” e.g. something like Max Daniel’s hypothesis.
Thanks, that all makes sense to me. Will think more about this. Also still curious to hear replies from others here.
Thanks for +1ing the above comment. I’d be keen to hear your reply to this comment, too.
Thanks for sharing. As someone who spends a lot of time trying to fill EA meta/longtermist talent gaps — e.g. by managing Open Phil RA recruiting, helping to match the strongest applicants we don’t hire to other openings, and by working on field-building in AI strategy/policy (e.g. CSET) — hearing stories like yours is unnerving.
What changes to the landscape, or hiring processes, or whatever, do you think would’ve made the most difference in your case?
I’m also curious to hear your reaction to my comment elsewhere about available paths:
There are lots of exciting things for EAs to do besides “apply to one or more of the 20 most competitive jobs at explicitly EA-motivated employers,” including “keep doing what you’re doing and engage with EA as an exciting hobby” and “apply to key positions in top-priority cause areas that are on the 80,000 Hours Job Board but aren’t at one of a handful of explicitly EA-motivated orgs” and “do earn to give for a while while gaining skills and then maybe transition to more direct work later or maybe not,” as well as other paths that are specific to particular priority causes, e.g. for AI strategy & policy I’d be excited to see EAs (a) train up in ML, for later work in either AI safety or AI strategy/policy, (b) follow these paths into a US AI policy career (esp. for US citizens, and esp. now that CSET exists), and (c) train up as a cybersecurity expert (I hope to say more later about why this path should be especially exciting for AI-interested EAs; also the worst that happens is that you’ll be in extremely high demand and highly paid).
Sorry to hear about your long, very difficult experience. I think part of what happened is that it did in fact get a lot harder to get a job at leading EA-motivated employers in the past couple years, but that wasn’t clear to many EAs (including me, to some extent) until very recently, possibly as recently as this very post. So while it’s good news that the EA community has grown such that these particular high-impact jobs can attract talent sufficient for them to be so competitive, it’s unfortunate that this change wasn’t clearer sooner, and posts like this one help with that, albeit not soon enough to help mitigate your own 1.5 years of suffering.
Also, the thing about some people not having runway is true and important, and is a major reason Open Phil pays people to take our remote work tests, and does quite a few things for people who do an in-person RA trial with us (e.g. salary, health benefits, moving costs, severance pay for those not made a subsequent offer). We don’t want to miss out on great people just because they don’t have enough runway/etc. to interact with our process.
FWIW, I found some of your comments about “elite culture” surprising. For context: I grew up in rural Minnesota, then dropped out of counseling psychology undergrad at the University of Minnesota, then worked at a 6-person computer repair shop in Glendale, CA. Only in the past few years have I begun to somewhat regularly interact with many people from e.g. top schools and top tech companies. There are aspects of interacting with such “elites” that I’ve had to learn on the fly and to some degree am still not great at, but in my experience the culture in those circles is still pretty different from the culture at major EA-motivated employers, even though many of the staff at EA-motivated employers are now people who e.g. graduated from schools like Oxford or Harvard. For example, it’s not my experience that people at major EA organizations are as effusively positive as many people in non-EA “elite” circles are. In fact, I would’ve described the culture at the EA organizations I interact with the most in sorta opposite terms, in that it’s hard to get them excited about things. E.g. if you tell one of my Open Phil RA colleagues about a new study in Nature on some topic they care about, a pretty common reaction is to shrug and say “Yeah but who knows if it’s true; most of the time we dig into a top-journal study, it completely falls apart.” Or if you tell people at most EA orgs about a cool-sounding global health or poverty-reduction intervention, they’ll probably say “Could be interesting, but very low chance it’ll end up looking as cost-effective as AMF or even GiveDirectly upon further investigation, so: meh.” Also, EA-motivated employers are generally not as “credentialist,” in my experience, as most “elite” employers (perhaps except for tech companies).
Finally, re: “you never know for sure if it’s not just perfect meritocracy correctly filtering [certain people out].” I can’t speak to your case in particular, but at least w.r.t. Open Phil’s RA recruiting efforts (which I’ve been managing since early 2018), I think I am sure it’s not a perfect meritocracy. We think our application process probably has a high false negative rate (i.e. rejecting people who are actually strong fits, or would be with 3mo of training), and it’s just very difficult to reduce the false negative rate without also greatly increasing the false positive rate. Just to make this more concrete: in our 2018 RA hiring round, if somebody scored really well on our stage-3 work test, we typically thought “Okay, decent chance this person is a good fit,” but when somebody scored medium/low on it, we often threw up our hands and said “No clue if this person is a good fit or not, there are lots of reasons they could’ve scored poorly without actually being a poor fit, I guess we just don’t get to know either way without us and them paying infeasibly huge time costs.” (So why not just improve that aspect of our work tests? We’re trying, e.g. by contracting several “work test testers,” but it’s harder than one might think, at least for such ill-defined “generalist” roles.)
Sounds plausible. E.g. I’m pro “train up as a cybersecurity expert” but I know others have advised against.
Thanks for sharing your comment about personalized invitations, that’s interesting. At Open Phil, almost all our personalized invitations (even to people we already knew well) were only lightly personalized. But perhaps a noticeable fraction of people misperceived that as “high chance you’ll get the job if you apply,” or something. The Open Phil RA hiring committee is discussing this issue now, so thanks for raising it.
Sorry to hear how much misery you’ve experienced. I’m curious to ask a follow-up question, but feel free to ignore if you aren’t comfortable answering.
In particular, I’m wondering whether “make [EA] my career” feels ~identical (to you) to “work at a handful of explicitly EA-motivated employers.” If it does, then maybe the messaging or energy or something in the EA community is pretty far from what I think it should be, which is more like what I said in another comment:
There are lots of exciting things for EAs to do besides “apply to one or more of the 20 most competitive jobs at explicitly EA-motivated employers,” including “keep doing what you’re doing and engage with EA as an exciting hobby” and “apply to key positions in top-priority cause areas that are on the 80,000 Hours Job Board but aren’t at one of a handful of explicitly EA-motivated orgs” and “do earn to give for a while while gaining skills and then maybe transition to more direct work later or maybe not,” as well as other paths that are specific to particular priority causes, e.g. for AI strategy & policy I’d be excited to see EAs (a) train up in ML, for later work in either AI safety or AI strategy/policy, (b) follow these paths into a US AI policy career (esp. for US citizens, and esp. now that CSET exists), and (c) train up as a cybersecurity expert (I hope to say more later about why this path should be especially exciting for AI-interested EAs; also the worst that happens is that you’ll be in extremely high demand and highly paid).
Nearly all of Open Phil’s RA hiring process is focused on assessing someone’s immediate fit for the kind of work we do (via the remote work tests), not (other types of) fit with the team and mission.
Per Buck’s comment, I think identifying software engineering talent is a pretty different problem than identifying e.g. someone who is already a good fit for Open Phil generalist RA roles.
Thanks for mentioning the thing about the conversation notes test. It was simply an oversight to not explicitly say “Please don’t spend more than X hours on this work test,” and I’ve now added such a sentence to our latest draft of those work test instructions. We had explicit time limits for our other two tests.
Not sure I follow the part about how the kind of thing described in the original post makes you “more reluctant to introduce new people into the EA community.” There are lots of exciting things for EAs to do besides “apply to one or more of the 20 most competitive jobs at explicitly EA-motivated employers,” including “keep doing what you’re doing and engage with EA as an exciting hobby” and “apply to key positions in top-priority cause areas that are on the 80,000 Hours Job Board but aren’t at one of a handful of explicitly EA-motivated orgs” and “do earn to give for a while while gaining skills and then maybe transition to more direct work later or maybe not,” as well as other paths that are specific to particular priority causes, e.g. for AI strategy & policy I’d be excited to see EAs (a) train up in ML, for later work in either AI safety or AI strategy/policy, (b) follow these paths into a US AI policy career (esp. for US citizens, and esp. now that CSET exists), and (c) train up as a cybersecurity expert (I hope to say more later about why this path should be especially exciting for AI-interested EAs; also the worst that happens is that you’ll be in extremely high demand and highly paid).
Yeah, this is one reason Open Phil pays people for doing our remote work tests, so that people who don’t happen to have runway/similar can still go through our process. Possibly more EA orgs should do this if they aren’t already.
Oof, 8 weeks of effort to get 0⁄20 positions is pretty brutal. It’s easy to see how that would feel like your “Hey you!…” paragraph. And while I suspect you’re a bit of an outlier in time spent and positions applied for, I also think you’re pointing at something true about the current situation re: job openings at EA-motivated employers, as evidenced by how many upvotes this post has gotten, some of the comments on this page, and the data I’ve got as a result of managing Open Phil’s 2018 recruitment round of Research Analysts, during which we had to say “no” to tons of applicants with quite impressive resumes.
I’ve been writing up some reflections on that recruiting round, which I hope to share soon. One of my takeaways is something like “The base of talent out there is strong, and Open Phil’s current ability to deploy it is weak.” In that way we might be an extreme opposite of Teach for America, and I suspect many other EA-motivated orgs are as well.
Anyway, I plan to say more on these topics when I share my “reflections” post, but in the meantime I just want to say I’m sorry that you spent so much time applying to EA orgs and got no offers. Also, setting the time investment aside, it’s also just emotionally difficult to get an “Unfortunately, we’ve decided…” email, let alone receive 20 of them in a row.
A couple other random notes for now:
- A colleague of mine has heard some EAs — perhaps motivated by considerations like those in this post — saying stuff like “maybe I shouldn’t even try to apply because I don’t want to waste orgs’ time.” In case future potential Open Phil applicants end up reading this comment, let it be known that we don’t think it’s a waste of our time to process applications. If we don’t have the staff capacity to process all the applications we receive, we can always just drop a larger fraction of applicants at each stage. But if someone never applies, we have no opportunity at all to figure out how good a fit they might be. Also, what we’re looking for is pretty unclear (especially to potential applicants), and so e.g. some of our recent hires are people who told us they probably wouldn’t have bothered applying if we hadn’t proactively encouraged them to apply. Of course, an applicant could be worried about whether applying is worth their time, and that’s a different matter.
- I think it would’ve been good to mention that some of these organizations pay applicants for some/all of the time they spend on the application process. (Hopefully Open Phil isn’t the only one?)
Roughly how much time per month/year does each of the fund managers currently expect to spend on investigating, discussing, and deciding on grant opportunities?
Roughly how much time per month/year does each of the fund managers currently expect to spend on investigating, discussing, and deciding on grant opportunities?
I disagree about the strength of evidence for CBT-I effectiveness.
Cases where the scientific knowledge was actually lost and then rediscovered much later provide especially strong evidence w.r.t. the discovery counterfactuals. E.g. Hero’s eolipile or al-Kindi’s development of relative frequency analysis for decoding messages. Probably there are far more cases of this than we realize, because the evidence that someone somewhere once had the knowledge and then lost it has itself been lost; e.g. we could easily have just never rediscovered the Antikythera mechanism.