Many thanks to Andrew Snyder-Beattie, Howie Lempel, Alex Norman and Noga Aharony for thoughtful feedback. Mistakes are mine.
Status: Some obviously right stuff. Some spicy takes. In places I'm trying to illustrate a pattern of thinking rather than an explicit recipe. Aimed at a particular audience, YMMV.
These are a few thoughts on how to approach graduate school effectively.
This is not a guide or anything of the sort. Just an attempt to write down a set of considerations I use when thinking about my own grad school, and what seems to be helpful from convos I’ve had with other EA PhD-seekers. I have not tried to make this generally applicable. So some background facts in case you are looking for something else:
- I am a grad student at MIT
- I work on catastrophic risks from biology
- My background is synthetic biology/ bioinformatics/ deep learning
- I have most personal experience with synthetic biology academia
- My favorite theory of change for addressing these risks goes substantially through EAs taking on a lot more object level work— founding organizations, engineering systems, making scientific progress— than I expect is the median view
- I still think policy-ish stuff is important; a substantial part of the reason I’m doing my PhD is to be credible to fancy people types
- I’m not inside-view excited about young longtermist EAs pursuing faculty positions, basically at all. Others I think are reasonable do argue for this, so I’ve tried to include a relevant example.
Some general things
Academic incentives are nefarious and horrible and will poison your brain. This happens to the best people. You can become a status monster unless you know this in your bones and remind yourself of it every day. Recognize it now, and inoculate yourself by knowing what you want before the poison seeps in. If you want to do anything that isn’t optimizing for academic prestige, like spending some of your PhD research time on publishing directly impactful papers or developing directly impactful technologies, or doing these things later in your career, you will need to have a strategy for managing the ways academic incentives push you to waste your brilliance.
This probably involves maintaining and strengthening your EA-adjacent network. This is also broadly important, IMO. If you are doing a PhD for EA career development, remember that 3-7 years is a long time and that you will not just be sacrificing direct impact in that time, but also relationships and EA-specific knowledge and context. Despite the intention to develop career capital, you could come out of a PhD stupider and less useful than you went in if you lose track of what is impactful and are 5 years behind everyone on the best mental models.
A PhD is also in many ways a prolongation of the perpetual childhood instantiated in western education systems. I notice that between people of the same age, one of whom just completed a PhD and the other who has been doing direct work for that duration, the PhD is a little “less grown up” on average (this is a comment about the average and not some claim about strict dominance! I love you, all my PhD bearing friends and colleagues). Most PhDs do not teach you many of the life-lesson-y adult-y things you actually need to be effective. E.g.: taking sole ownership and responsibility for solving a real problem rather than optimizing for fake metrics like impact factor and having your PI to fall back on, leading and managing others, communicating with people who are in a very different place than you, robustness in the face of a wide range of challenges instead of narrow specialization on a few, knowing when something is or isn’t worth your time and developing a palpable urgency, learning how effective organizations work, being held accountable for all aspects of your epistemics instead of domain-specific, etc.
IMO, the above things point to either radically minimizing time spent in a PhD or being exceedingly deliberate with what PhD incentives you conform to vs. actively and persistently push against (or perpendicular to). This means I recommend spending time thinking about what you want out of a degree and crafting your strategy accordingly, where the primary decision points are to which schools you apply, how you reach out to PIs, how you choose to rotate if applicable, how you pick a lab to work in, and how you choose your projects and collaborators. I don’t have time to talk about any of those really, because they are entirely context specific. But I’d encourage you to try and operationalize your high-level goals into tactics around these types of decisions.
In the next section I’ll try to break a PhD down into component “goods”. The hope is that you can think about which of these components mean more to you, in your situation, and which are less important.
Factoring a PhD
PhDs can be good for very different reasons. Know your reasons. A rough factorization:
- Skill: How much do you care about developing a specific technical skillset, or domain-specific knowledge, which is otherwise hard to learn?
- Process: How much do you want to “learn to do research” in a more general sense, in such a way that it is useful for valuable technical EA work? Don’t focus on this if you already feel comfortable with the full research cycle - from novel idea to completed project/ publication if applicable, and can execute this autonomously. Do consider focusing on it if you don’t resonate with the above. Being able to make progress on hard research problems in a generalist-y kind of way is a very in-demand skill. There are certainly many domain-dependent components of this, but I believe there is a transferable component which explains why some people are able to answer questions across different fields.
- Network (of technical peeps): How much do you care about developing a network of other researchers who you can rely on down the line? Relevant for hiring if you plan to found a company, for downstream technical work which depends on tacit knowledge shared throughout your group (as in synthetic biology). Not very relevant if you want to do policy but are doing a technical PhD; in that case it's usually more important to network with other policy people at conferences or fellowships (like ELBI).
- Credential: How much do you care about obtaining a piece of paper that says “P.h.D” and your name? This also includes things like whether your school is name-brand, name-brand awards and fellowships, and other types of honors which are recognized to be prestigious outside a narrow field. Relevant for credentialist industries like old-school pharma and doing anything policy/ advocacy/ public facing.
- Publication: How much do you care about seeming technically impressive to other technical people? Relevant for careers in academia or non-credentialist industries like Software/ AI and for technical EA work.
- Urgency: How much do you care about having an impact sooner rather than later? Could look like picking a lab whose research is directly good and taking a while, or going as fast as you possibly can.
Think about how much each of these statements apply to you, and which you would entirely forgo for which of the others. Your PhD strategy will depend on which of these you prioritize in which amounts. I think different contexts often have optimal strategies which are radically divergent, so it's worth thinking about this first.
One more thing: I’d encourage you to avoid being “greedy”. A type of person I occasionally meet is someone who is doing a PhD to preserve optionality *across everything* and therefore wants a PhD that gives them all of the above things. This is tempting, especially because PhDs are pitched as the optionality-preserving career move. But from what I can see, even if you don’t end up making a particular compromise, it is hugely useful to know what you would give up if push came to shove.
What factors should you prioritize?
I obviously can’t answer this in general, because its all context-dependent. I’ll try to illustrate with two fake examples in longtermist biosecurity (which I know more about). You should consider skipping this section if you already feel like you know what kind of factors you care about.
A simple case
Kevin is a longtermist who thinks he’s a good fit for biosecurity work. He helped lead his undergrad EA chapter and for the past year since graduating with a bioengineering degree has been working at a young EA org doing a mix of research and ops. After this experience, he believes he’s noticed a gap in the community around people who can both run organizations and have the technical know-how to make good strategic decisions. His first choice would be to land a job at an EA org, or if he finds a good opportunity, start an organization himself. His advisors believe he has the skills to be a good candidate for these, but warn him that these types of leadership positions might require interactions with policy makers and other fancy people who would not take him as seriously with only a BA. They also encourage him to find good backup options. Kevin thinks his mix of organizational/ people skills and bioengineering background would also make him a good fit in climbing the policy career ladder, and, putting these two ideas together, decides to do a PhD.
Kevin should straightforwardly focus on Urgency and Credentials. It really doesn’t matter to him exactly what skills he gets. None of the fancy people will look at his publications, and he should care more about building an EA and/or policy network rather than a network of technical people. He doesn’t care about being good at the research process. His opportunity cost is high enough that he shouldn’t do a degree unless it can be done quickly or is especially credential-heavy, e.g. at a name-brand school.
Kevin should prioritize something like 70% Urgency, 30% Credentials. (these numbers are made up)
A more complicated case 
Anita is a longtermist working in biosecurity. Both her and her advisors believe her best shot at having an impact is to become an academic. Her hypothesis is that the community is undersupplied in people who can lead research programs on specific therapeutic countermeasures. She knows that the academic track is extremely competitive, but has had a remarkably successful independent undergraduate research track record in bioinformatics, including a first-author publication in Nature. She plans to use this comparative advantage to try her luck at a young faculty position. If this fails she plans to join an existing lab doing related work and encourage them to work directly on the technology she believes is important.
Both Anita and her previous mentors agree that she is quite comfortable in the research process; having led a project from conception to a Nature publication is ample evidence of this. However, all her previous research was computational, whereas the countermeasure tech is going to require substantial wet lab work.
What factors should Anita care about? Let’s start by ruling some things out. Given Anita’s background she probably doesn’t need to worry much about learning the research Process. She obviously will need to graduate, but cares a lot more about the way technical people would evaluate her work than the types of Credentials salient to e.g. policy folks. With all the other things she cares about doing, she probably can’t also do her degree in 3 years, and should instead compromise on speed even if she feels the Urgency.
Anita needs to think more carefully to decide what her top priorities are. Without Publications, she is SOL on the academia front. However, she also thinks her backup plan is quite good EV, and is concerned about getting caught in some academic niche which isn’t related to the countermeasure tech. She might end up being obligated by academic incentives to continue publishing in some less useful field in order to remain competitive with other would-be faculty. If push came to shove, she would give up an academic career for her second best option if the alternative was working on a useless technology .
On top of this, Anita’s bioinformatic work was only distantly connected to the countermeasure tech, and she has heard that the type of methods required to do the most cutting-edge projects involve a lot of tacit knowledge. She either needs to learn to do this work herself, or develop strong and ongoing connections with wet lab collaborators. This leads her to conclude top priority should be working in a lab which has the specialist Skills and domain expertise she needs to learn. If she ends up being unsuccessful in the wet lab, she plans to double down on bioinformatics and focus on fostering the best Network of collaborators. Only then will she optimize for Publications, taking the bet that her existing publication track record and confidence leading projects from start to high-impact publication can carry her through. It helps in this case that bioinformatics moves a lot faster than wet-lab work, so Anita believes she can push out enough papers to make the academic cut even if she doesn’t develop the “experimental touch”.
Anita should shoot for something like 70% Skill + Network, 30% Publications. (these numbers are made up).
Planning your approach
The hope is that once you know what you care about getting out of your degree, you can make better decisions + plans. If this is indeed possible, it’s obviously also context dependent.
So again, here are a couple very rough sketches to give a sense of how I think about different strategies. They are all made up but are pointing at the kind of thing I could imagine coming up with; some are closer to real strategies/ patterns that seem to work than others. Here I say thing1 + thing2 to mean prioritizing these and being willing to sacrifice all the others:
- Skill + Process: Focus on finding a mentor and group of peers working on the niche thing you care about learning skillset of with a record of doing things that are very solid rather than very flashy. Premium on ability to do a rotation, or equivalent opportunities to interact with multiple labs before locking in. Take as much time as you need in the program. Don’t pick a fancier sounding university over better lab focus, mentor, and peers. Can get info on a mentor and lab vibe by reaching out to lab members through your network or cold-emailing current and former lab members, or even members of other labs at the same institute. In conversations with prospective mentors, ask lots of questions about previous mentoring relationships they have had. Good sign- previous junior mentees have initiated their own projects and gotten first authorship. Bad sign- no mentee driven projects, or younger mentees are never in first author positions.
- Credential + Urgency, fast version: Choose the program on the intersection of “shortest num years required” and “least time investment needed to satisfy class and publishing requirements” and "has a brand name". Go hard satisfying your publication requirement as fast as possible, put in almost no effort into classes if applicable (whatever is required to graduate with no concern for grades or the impression you give to others). Once you finish your pubs, or while you are working on them if they are wall-clocked constrained, spend almost all of your time volunteering for EA projects that seem highest impact.
- Credential + Urgency, slow version: Select a program which might last a long time but has an advisor who is willing to let you entirely do the thing you (almost) would have wanted to do anyway. For example, if you think it would be good for there to be more papers outlining the case against some dual use technology, find an advisor who wants these to exist as well and will make room for you to focus on them. Academia brings many benefits for writing papers like this, most especially credibility. Take as many years as feels like you are still basically doing the best thing directly, then graduate. BE VERY CAREFUL NOT TO GET SUCKED INTO HORRIBLE PUBLISHING INCENTIVES.
- Urgency + Process: Pick a program based on the quality of your research mentor and whether it shares *structural* features with the domain you would like to do work directly in. For example, how paradigmatic vs. confusing and new? How much can you make progress by thinking vs. reading what other people have said in textbooks vs. digging through the most recent publications to find secret nuggets vs. sitting at a bench and pipetting? Typically helpful to seek out EA research projects you can do on the side which are closer to the eventual kind of thing you care about, in order to make an urgent impact and confirm you are learning a research process that works on the real problems. Typically makes sense to shortcut everything besides spending as much time as possible thinking real hard about things as close *in structure* to your eventual goals. Program length does not matter if you keep a keen eye out for compelling opportunities and pre-commit to dropping out if they come by.
- Publication + Urgency: Consider RAing instead of grad school, if you have the opportunity to do so with freedom to operate rather than grunt work. Optimize almost exclusively for compelling publications; for some specific goals these will need to be high-impact publications. Do weak filtering of project ideas to minimize acceleration/ dual use potential but otherwise select only on publishability. Only prioritize Network or Process instrumentally, ie if you need to know lots of tacit experts or need mentorship to learn good research process (if the latter, maybe you should be focusing on that tho!). If you are constrained by not having ideas for what would make a good domain publication, ask other EAs who have published good papers if they are sitting on any publication-worthy ideas that they don’t plan on getting around to. I think you’ll find that some people have more ideas than time and would be happy to share them with you and help you spot how to present the ideas in a way which is compelling for journals.
- Network + Skill: Apply to the lab which has alums that do the coolest stuff, even if the PI is notoriously bad or absentee. Focus on forming tight friendships and working relationships with your lab mates; collaborate extensively with whoever seems coolest. Side projects with cool people are worth it. Probably worth trying to introduce some labmates to EA if the circumstances are right. You get skill by working with the best people rather than self teaching.
I mostly included this example because many smart people think academia is worth pursuing. My inside view says it almost always isn’t. I’ve tried to write this case in such a way that I could imagine myself being persuaded it actually makes sense here, but in practice I still imagine pushing back. I’d probably argue in favor of more direct routes toward working on the tech, such as starting or joining a startup/ org, but do think some aspects of this case make in plausible academia is the right call. ↩︎