I like this writeup a lot, but I would say to anyone who’s actually reading this should ignore the advice to not go into academia.
If you’re reading this, you’re probably selected (!) to be someone who is atypical and has a decent shot at succeeding in academia. (See also: SSC on ‘reversing all advice you hear’.) i.e.: if you’re someone who’s taking the time out of your day to read this, you’re probably (probably!) similar to “Anita” here.
Ugh. Shrug. That isn’t supposed to be the point of this post. All my comments on this are to alert the reader that I happen to believe this and haven’t tried to stop it from seeping into my writing. It felt disingenuous not to.
But since you raised, I feel like making it clear, if it isn’t already, that I do not recommend reversing this advice. At least if you are considering cause areas/ academic domains that I might know about (see my preamble). I have no idea how applicable this is outside of longtermist technical-leaning work.
If you think you might be an exception to this, feel free to DM me. Exceptions do exist, I just highly doubt you (the reader) are one. THIS DOES NOT MEAN I AM NOT EXCITED ABOUT YOUR IMPACT!! I think there are much better opportunities than becoming a professor out there :)
As I said a lot of smart people disagree with me on this, but here is some of my thinking:
Most people overestimate their chances for the obvious reasons
I’ve advised at least 10 smart, excellent EAs interested in pursuing PhDs and none of them are in “Anita’s” reference class. A first author Nature paper in undergrad is really extremely unique. The only exceptions here are people who are already in early-track faculty positions at good schools, and even then I worry about the counterfactual value. (these are not the people reading this, I imagine)
Having a “good story” for becoming a faculty is a huge part luck. I’ve been interacting with grad students and post docs from top labs at Harvard and MIT since maybe 2015 and for every faculty position people get there are maybe 5 people who are equally or more talented whose research was equally or more compelling in principle; the difference is whether certain parts of their high-risk research panned out in a certain compelling way and whether they were good at “selling it”.
You approximately can’t get directly useful/ things done until you have tenure. I think this should be obvious but some people seem to believe a fairy tale where they are both winning the rat race and doing lots of direct good.
Given the above, academia is a 10-15 year crapshoot. (PhD, postdoc or multiple, 5-ish years as a junior faculty)
It’s not clear to me what you get even after all of this. I think its hard to argue that academia is clearly better than working in a private research org if you want to do direct technology development. This leaves some kind of pulpit/ spokesperson effect. Is this really worth it? Most people who could actually get a tenured faculty position could also write 3 excellent books in the time it takes to do a PhD and post-doc. Are we sure this alternative, as one example among many possible, isn’t a faster way of establishing spokesperson credibility?
Unless you have worked in top labs with EA-minded people, I don’t think it is possible to really understand how bad academic incentives are. You will find yourself justifying the stupidest shit on impact grounds, and/or pursuing projects which directly make the world worse. People who are much better than you will also do this. This just gets worse with time, and needs to be accounted for as a reduction in expected impact when considering an opportunity that only pays off 12 years after steeping in the corrupting juices.
Obviously, academia looks a whole lot worse if you believe lots of things need to happen right now, as opposed to 15 years from now. For my part, I would happily trade work hours 15 years from now for more time now, at a roughly 2:1 premium.
Another risk you are taking, related to the above, is that the field of research you picked has any relevance 15 years from now. Obviously you can change as you go, but switching your “story” around has a big penalty in the academic job market, from what I’ve heard.
If we think we need more professors as a movement, it could be the case that its way more efficient to just reach out to people who already have faculty positions (or are just one step away, in a highly enriched pool). For example, I know of instances where students have influenced their PIs on research directions and goals, in a direction more aligned with longtermist objectives. It might be that targeted outreach and coalition building among academics is just way higher bang for buck. It’s also not clear that we need the most aligned people in faculty positions, rather than people who are allies. Have we ruled this out? Seems like any person considering mortgaging 15 years of their impact might want to spend 1 year testing this hypothesis first.
Putting these random points together, it just feels like a really uphill battle to make academia look good from an impact perspective. I think you need to believe some combination of 1) problems are not urgent 2) academic incentives are actually good (?)/ there is some other side benefit of working toward a faculty position that is really worth having 3) there aren’t many other opportunities for people who could be faculty in a technical domain or 4) we are specifically constrained on something professors have, maybe credible spokespeople, AND there are no more efficient ways to get those resources.
OR you might believe that academia is exciting from a personal fit perspective. I think a lot of people are very motivated by the types of status incentives in academia, which is good I guess if you have trouble finding motivation elsewhere. I’d just want to separate this from the impact story.
My spicy take is that advice to go into academia has arisen through some combination of A) EA being a movement grown out of academia in many ways, B) a lack of better career ideas, C) too much distance from the urgency and concreteness of problems on the ground and D) the same mind destroying publishing and status incentives I have mentioned a number of times here, which lead to a certain kind of self-justification.
So where all this caches out for me is finding it plausible that it is worth preserving some optionality for academia, but being very strategic (as I tried to demonstrate in this post). This includes knowing what you actually are optimizing for, and being willing to leave academic optionality if push comes to shove and there is something better. This is why I wrote the Anita case study this way.
I’m not convinced that academia is generally a bad place to do useful technical work. In the simplest case, you have the choice between working in academia, industry or a non-profit research org. All three have specific incentives and constraints (academia—fit to mainstream academic research taste; industry—commercial viability; non-profit research—funder fit, funding stability and hiring). Among these, academia seems uniquely well-suited to work on big problems with a long (10-20 year) time horizon, while having access to extensive expertise and collaborators (from colleagues in related fields), EA and non-EA funding, and EA and non-EA hires.
For my field of interest (longtermist biorisk), it appears that many of the key past innovations that help e.g. with COVID now come from academic research (e.g. next-generation sequencing, nanopore sequencing, PCR and rapid tests, mRNA vaccines and other platform vaccine tech). My personal tentative guess is that our split should be something like 4 : 4 : 1 between academia, industry and non-profit research (academia to drive long-term fundamental advances, industry/entrepreneurship to translate past basic science advances into defensive products, and non-profit research to do work that can’t be done elsewhere).
Crux 1 is indeed the time horizon—if you think the problem you want to work on will be solved in 20 years/it will be too late, then dropping ‘long-term fundamental advances’ in the portfolio would seem reasonable.
Crux 2 is how much academia constrains the type of work you can do (the ‘bad academic incentives’). I resonate with Adam’s comment here. I can also think of many examples of groundbreaking basic science that looks defensive and gets published very well (e.g. again sequencing innovations, vaccine tech; or, for a recent example, several papers on biocontainment published in Nature and Science).
Thanks Seb. I don’t think I have energy to fully respond here, possibly I’ll make a separate post to give this argument its full due.
One quick point relevant to Crux 2:
“I can also think of many examples of groundbreaking basic science that looks defensive and gets published very well (e.g. again sequencing innovations, vaccine tech; or, for a recent example, several papers on biocontainment published in Nature and Science).”
I think there are many-fold differences in impact/dollar between the tech you build if you are trying to actually solve the problem and the type of probably-good-on-net examples you give here.
Other ways of saying parallels of this point:
Things which are publishable in nature or science are just definitively less neglected, because you are competing against everyone who wants a C/N/S publication
The design space of possible interventions is a superset of, and many times larger than the design space of interventions which also can be published in high impact journals
We find power-laws in cost effectiveness lots of other places, and AFAIK have no counter-evidence here. Given this, even a small orthogonal component between what is incentivized by academia and what is actually good will lead to a large difference in expected impact.
You approximately can’t get directly useful/ things done until you have tenure.
At least in CS, the vast majority of professors at top universities in tenure-track positions do get tenure. The hardest part is getting in. Of course all the junior professors I know work extremely hard, but I wouldn’t characterize it as a publication rat race. This may not be true in other fields and outside the top universities.
The primary impediment to getting things done that I see is professors are also working as administrator and teaching, and that remains a problem post-tenure.
This is interesting and also aligns with my experience depending on exactly what you mean!
If you mean that it seems less difficult to get tenure in CS (thinking especially about deep learning) than the vibe I gave, (which is again speaking about the field I know, bioeng) I buy this strongly. My suspicion is that this is because relative to bioengineering, there is a bunch of competition for top research talent by industrial AI labs. It seems like even the profs who stay in academia also have joint appointment in companies, for the most part. There isn’t an analogous thing in bio? Pharma doesn’t seem very exciting and to my knowledge doesn’t have a bunch of PI-driven basic research roles open. Maybe bigtech-does-bio labs like Calico will change this in the future? IMO this doesn’t change my core point because you will need to change your agenda some, but less than in biology.
If you mean that once you are on the Junior Faculty track in CS, you don’t really need to worry about well-received publications, this is interesting and doesn’t line up with my models. Can you think of any examples which might help illustrate this? I’d be looking for, e.g., recently appointed CS faculty at a good school pursuing a research agenda which gets quite poor reception/ crickets, but this faculty is still given tenure. Possibly there are some examples in AI safety before it was cool? Folks that come to mind mostly had established careers. Another signal would be less of the notorious “tenure switch” where people suddenly change their research direction. I have not verified this, but there is a story told about a Harvard Econ professor who did a bunch of centrist/slightly conservative mathematical econ who switched to left-leaning labor economics after tenure.
If you mean that once you are on the Junior Faculty track in CS, you don’t really need to worry about well-received publications, this is interesting and doesn’t line up with my models. Can you think of any examples which might help illustrate this?
To clarify, I don’t think tenure is guaranteed, more that there’s significant margin of error. I can’t find much good data on this, but this post surveys statistics gathered from a variety of different universities, and finds anywhere between 65% of candidates get tenure (Harvard) to 90% (Cal State, UBC). Informally, my impression is that top schools in CS are the higher end of this: I’d have guessed 80%. Given this, the median person in the role could divert some of their research agenda to less well-received topics and still get tenure. But I don’t think they could work on something that no one in the department or elsewhere cared about.
I’ve not noticed much tenure switch in CS but have never actually studied this, would love to see hard data here. I do think there’s a significant difference in research agendas between junior and senior professors, but it’s more a question of what was in vogue when they were in grad school and shaped their research agenda, than tenured vs non-tenured per se. I do think pre-tenure professors tend to put their students under more publication pressure though.
I don’t see how this is a counterargument. Do you mean to say that, once you are on track to tenure, you can already start doing the high-impact research?
It seems to me that, if this research is too diverged from the academic incentives, then our hypothetical subject may become one of these rare cases of CS tenure-track faculty that does not get tenure.
I bet it is! The example categories I think I had in mind at time of writing would be 1) people in ML academia who want to be doing safety instead doing work that almost entirely accelerates capabilities and 2) people who want to work on reducing biological risk instead publish on tech which is highly dual use or broadly accelerates biotechnology without deferentially accelerating safety technology.
I know this happens because I’ve done it. My most successful publication to date (https://www.nature.com/articles/s41592-019-0598-1) is pretty much entirely capabilities accelerating. I’m still not sure if it was the right call to do this project, but if it is, it will have been a narrow edge revolving on me using the cred I got from this to do something really good later on.
I think even among such selected crowd, Anita would stand out like a bright star. The average top-university PhD student doesn’t end up holding a top faculty job. (This may seem elitist, but it is important: becoming a trainer of mediocre PhD students is likely not more effective than non-profit work). A first-author Nature paper in undergrad (!) is quite rare too.
I like this writeup a lot, but I would say to anyone who’s actually reading this should ignore the advice to not go into academia.
If you’re reading this, you’re probably selected (!) to be someone who is atypical and has a decent shot at succeeding in academia. (See also: SSC on ‘reversing all advice you hear’.) i.e.: if you’re someone who’s taking the time out of your day to read this, you’re probably (probably!) similar to “Anita” here.
Ugh. Shrug. That isn’t supposed to be the point of this post. All my comments on this are to alert the reader that I happen to believe this and haven’t tried to stop it from seeping into my writing. It felt disingenuous not to.
But since you raised, I feel like making it clear, if it isn’t already, that I do not recommend reversing this advice. At least if you are considering cause areas/ academic domains that I might know about (see my preamble). I have no idea how applicable this is outside of longtermist technical-leaning work.
If you think you might be an exception to this, feel free to DM me. Exceptions do exist, I just highly doubt you (the reader) are one. THIS DOES NOT MEAN I AM NOT EXCITED ABOUT YOUR IMPACT!! I think there are much better opportunities than becoming a professor out there :)
As I said a lot of smart people disagree with me on this, but here is some of my thinking:
Most people overestimate their chances for the obvious reasons
I’ve advised at least 10 smart, excellent EAs interested in pursuing PhDs and none of them are in “Anita’s” reference class. A first author Nature paper in undergrad is really extremely unique. The only exceptions here are people who are already in early-track faculty positions at good schools, and even then I worry about the counterfactual value. (these are not the people reading this, I imagine)
Having a “good story” for becoming a faculty is a huge part luck. I’ve been interacting with grad students and post docs from top labs at Harvard and MIT since maybe 2015 and for every faculty position people get there are maybe 5 people who are equally or more talented whose research was equally or more compelling in principle; the difference is whether certain parts of their high-risk research panned out in a certain compelling way and whether they were good at “selling it”.
You approximately can’t get directly useful/ things done until you have tenure. I think this should be obvious but some people seem to believe a fairy tale where they are both winning the rat race and doing lots of direct good.
Given the above, academia is a 10-15 year crapshoot. (PhD, postdoc or multiple, 5-ish years as a junior faculty)
It’s not clear to me what you get even after all of this. I think its hard to argue that academia is clearly better than working in a private research org if you want to do direct technology development. This leaves some kind of pulpit/ spokesperson effect. Is this really worth it? Most people who could actually get a tenured faculty position could also write 3 excellent books in the time it takes to do a PhD and post-doc. Are we sure this alternative, as one example among many possible, isn’t a faster way of establishing spokesperson credibility?
Unless you have worked in top labs with EA-minded people, I don’t think it is possible to really understand how bad academic incentives are. You will find yourself justifying the stupidest shit on impact grounds, and/or pursuing projects which directly make the world worse. People who are much better than you will also do this. This just gets worse with time, and needs to be accounted for as a reduction in expected impact when considering an opportunity that only pays off 12 years after steeping in the corrupting juices.
Obviously, academia looks a whole lot worse if you believe lots of things need to happen right now, as opposed to 15 years from now. For my part, I would happily trade work hours 15 years from now for more time now, at a roughly 2:1 premium.
Another risk you are taking, related to the above, is that the field of research you picked has any relevance 15 years from now. Obviously you can change as you go, but switching your “story” around has a big penalty in the academic job market, from what I’ve heard.
If we think we need more professors as a movement, it could be the case that its way more efficient to just reach out to people who already have faculty positions (or are just one step away, in a highly enriched pool). For example, I know of instances where students have influenced their PIs on research directions and goals, in a direction more aligned with longtermist objectives. It might be that targeted outreach and coalition building among academics is just way higher bang for buck. It’s also not clear that we need the most aligned people in faculty positions, rather than people who are allies. Have we ruled this out? Seems like any person considering mortgaging 15 years of their impact might want to spend 1 year testing this hypothesis first.
Putting these random points together, it just feels like a really uphill battle to make academia look good from an impact perspective. I think you need to believe some combination of 1) problems are not urgent 2) academic incentives are actually good (?)/ there is some other side benefit of working toward a faculty position that is really worth having 3) there aren’t many other opportunities for people who could be faculty in a technical domain or 4) we are specifically constrained on something professors have, maybe credible spokespeople, AND there are no more efficient ways to get those resources.
OR you might believe that academia is exciting from a personal fit perspective. I think a lot of people are very motivated by the types of status incentives in academia, which is good I guess if you have trouble finding motivation elsewhere. I’d just want to separate this from the impact story.
My spicy take is that advice to go into academia has arisen through some combination of A) EA being a movement grown out of academia in many ways, B) a lack of better career ideas, C) too much distance from the urgency and concreteness of problems on the ground and D) the same mind destroying publishing and status incentives I have mentioned a number of times here, which lead to a certain kind of self-justification.
So where all this caches out for me is finding it plausible that it is worth preserving some optionality for academia, but being very strategic (as I tried to demonstrate in this post). This includes knowing what you actually are optimizing for, and being willing to leave academic optionality if push comes to shove and there is something better. This is why I wrote the Anita case study this way.
I’m very happy to be shown where I’m wrong.
I’m not convinced that academia is generally a bad place to do useful technical work. In the simplest case, you have the choice between working in academia, industry or a non-profit research org. All three have specific incentives and constraints (academia—fit to mainstream academic research taste; industry—commercial viability; non-profit research—funder fit, funding stability and hiring). Among these, academia seems uniquely well-suited to work on big problems with a long (10-20 year) time horizon, while having access to extensive expertise and collaborators (from colleagues in related fields), EA and non-EA funding, and EA and non-EA hires.
For my field of interest (longtermist biorisk), it appears that many of the key past innovations that help e.g. with COVID now come from academic research (e.g. next-generation sequencing, nanopore sequencing, PCR and rapid tests, mRNA vaccines and other platform vaccine tech). My personal tentative guess is that our split should be something like 4 : 4 : 1 between academia, industry and non-profit research (academia to drive long-term fundamental advances, industry/entrepreneurship to translate past basic science advances into defensive products, and non-profit research to do work that can’t be done elsewhere).
Crux 1 is indeed the time horizon—if you think the problem you want to work on will be solved in 20 years/it will be too late, then dropping ‘long-term fundamental advances’ in the portfolio would seem reasonable.
Crux 2 is how much academia constrains the type of work you can do (the ‘bad academic incentives’). I resonate with Adam’s comment here. I can also think of many examples of groundbreaking basic science that looks defensive and gets published very well (e.g. again sequencing innovations, vaccine tech; or, for a recent example, several papers on biocontainment published in Nature and Science).
Thanks Seb. I don’t think I have energy to fully respond here, possibly I’ll make a separate post to give this argument its full due.
One quick point relevant to Crux 2: “I can also think of many examples of groundbreaking basic science that looks defensive and gets published very well (e.g. again sequencing innovations, vaccine tech; or, for a recent example, several papers on biocontainment published in Nature and Science).”
I think there are many-fold differences in impact/dollar between the tech you build if you are trying to actually solve the problem and the type of probably-good-on-net examples you give here.
Other ways of saying parallels of this point:
Things which are publishable in nature or science are just definitively less neglected, because you are competing against everyone who wants a C/N/S publication
The design space of possible interventions is a superset of, and many times larger than the design space of interventions which also can be published in high impact journals
We find power-laws in cost effectiveness lots of other places, and AFAIK have no counter-evidence here. Given this, even a small orthogonal component between what is incentivized by academia and what is actually good will lead to a large difference in expected impact.
At least in CS, the vast majority of professors at top universities in tenure-track positions do get tenure. The hardest part is getting in. Of course all the junior professors I know work extremely hard, but I wouldn’t characterize it as a publication rat race. This may not be true in other fields and outside the top universities.
The primary impediment to getting things done that I see is professors are also working as administrator and teaching, and that remains a problem post-tenure.
This is interesting and also aligns with my experience depending on exactly what you mean!
If you mean that it seems less difficult to get tenure in CS (thinking especially about deep learning) than the vibe I gave, (which is again speaking about the field I know, bioeng) I buy this strongly. My suspicion is that this is because relative to bioengineering, there is a bunch of competition for top research talent by industrial AI labs. It seems like even the profs who stay in academia also have joint appointment in companies, for the most part. There isn’t an analogous thing in bio? Pharma doesn’t seem very exciting and to my knowledge doesn’t have a bunch of PI-driven basic research roles open. Maybe bigtech-does-bio labs like Calico will change this in the future? IMO this doesn’t change my core point because you will need to change your agenda some, but less than in biology.
If you mean that once you are on the Junior Faculty track in CS, you don’t really need to worry about well-received publications, this is interesting and doesn’t line up with my models. Can you think of any examples which might help illustrate this? I’d be looking for, e.g., recently appointed CS faculty at a good school pursuing a research agenda which gets quite poor reception/ crickets, but this faculty is still given tenure. Possibly there are some examples in AI safety before it was cool? Folks that come to mind mostly had established careers. Another signal would be less of the notorious “tenure switch” where people suddenly change their research direction. I have not verified this, but there is a story told about a Harvard Econ professor who did a bunch of centrist/slightly conservative mathematical econ who switched to left-leaning labor economics after tenure.
To clarify, I don’t think tenure is guaranteed, more that there’s significant margin of error. I can’t find much good data on this, but this post surveys statistics gathered from a variety of different universities, and finds anywhere between 65% of candidates get tenure (Harvard) to 90% (Cal State, UBC). Informally, my impression is that top schools in CS are the higher end of this: I’d have guessed 80%. Given this, the median person in the role could divert some of their research agenda to less well-received topics and still get tenure. But I don’t think they could work on something that no one in the department or elsewhere cared about.
I’ve not noticed much tenure switch in CS but have never actually studied this, would love to see hard data here. I do think there’s a significant difference in research agendas between junior and senior professors, but it’s more a question of what was in vogue when they were in grad school and shaped their research agenda, than tenured vs non-tenured per se. I do think pre-tenure professors tend to put their students under more publication pressure though.
I don’t see how this is a counterargument. Do you mean to say that, once you are on track to tenure, you can already start doing the high-impact research?
It seems to me that, if this research is too diverged from the academic incentives, then our hypothetical subject may become one of these rare cases of CS tenure-track faculty that does not get tenure.
Could you be a bit more specific about this point? This sounds very field-dependent.
I bet it is! The example categories I think I had in mind at time of writing would be 1) people in ML academia who want to be doing safety instead doing work that almost entirely accelerates capabilities and 2) people who want to work on reducing biological risk instead publish on tech which is highly dual use or broadly accelerates biotechnology without deferentially accelerating safety technology.
I know this happens because I’ve done it. My most successful publication to date (https://www.nature.com/articles/s41592-019-0598-1) is pretty much entirely capabilities accelerating. I’m still not sure if it was the right call to do this project, but if it is, it will have been a narrow edge revolving on me using the cred I got from this to do something really good later on.
I think even among such selected crowd, Anita would stand out like a bright star. The average top-university PhD student doesn’t end up holding a top faculty job. (This may seem elitist, but it is important: becoming a trainer of mediocre PhD students is likely not more effective than non-profit work). A first-author Nature paper in undergrad (!) is quite rare too.