Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: How to PhD, published by eca on the AI Alignment Forum. 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 f...