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: Lessons from my time in Effective Altruism, published by richard_ngo on the effective altruism forum.

I’ll start with an overview of my personal story, and then try to extract more generalisable lessons. I got involved in EA around the end of 2014, when I arrived at Oxford to study Computer Science and Philosophy. I’d heard about EA a few years earlier via posts on Less Wrong, and so already considered myself EA-adjacent. I attended a few EAGx conferences, became friends with a number of EA student group organisers, and eventually steered towards a career in AI safety, starting with a masters in machine learning at Cambridge in 2017-2018.

I think it’s reasonable to say that, throughout that time, I was confidently wrong (or at least unjustifiably confident) about a lot of things. In particular:

I dismissed arguments about systemic change which I now find persuasive, although I don’t remember how - perhaps by conflating systemic change with standard political advocacy, and arguing that it’s better to pull the rope sideways.

I endorsed earning to give without having considered the scenario which actually happened, of EA getting billions of dollars of funding from large donors. (I don’t know if this possibility would have changed my mind, but I think that not considering it meant my earlier belief was unjustified.)

I was overly optimistic about utilitarianism, even though I was aware of a number of compelling objections; I should have been more careful to identify as "utilitarian-ish" rather than rounding off my beliefs to the most convenient label.

When thinking about getting involved in AI safety, I took for granted a number of arguments which I now think are false, without actually analysing any of them well enough to raise red flags in my mind.

After reading about the talent gap in AI safety, I expected that it would be very easy to get into the field - to the extent that I felt disillusioned when given (very reasonable!) advice, e.g. that it would be useful to get a PhD first.

As it turned out, though, I did have a relatively easy path into working on AI safety - after my masters, I did an internship at FHI, and then worked as a research engineer on DeepMind’s safety team for two years. I learned three important lessons during that period. The first was that, although I’d assumed that the field would make much more sense once I was inside it, that didn’t really happen: it felt like there were still many unresolved questions (and some mistakes) in foundational premises of the field. The second was that the job simply wasn’t a good fit for me (for reasons I’ll discuss later on). The third was that I’d been dramatically underrating “soft skills” such as knowing how to make unusual things happen within bureaucracies.

Due to a combination of these factors, I decided to switch career paths. I’m now a PhD student in philosophy of machine learning at Cambridge, working on understanding advanced AI with reference to the evolution of humans. By now I’ve written a lot about AI safety, including a report which I think is the most comprehensive and up-to-date treatment of existential risk from AGI. I expect to continue working in this broad area after finishing my PhD as well, although I may end up focusing on more general forecasting and futurism at some point.

Lessons

I think this has all worked out well for me, despite my mistakes, but often more because of luck (including the luck of having smart and altruistic friends) than my own decisions. So while I’m not sure how much I would change in hindsight, it’s worth asking what would have been valuable to know in worlds where I wasn’t so lucky. Here are five such things.

1. EA is trying to achieve something very difficult.

A lot of my initial attraction towards EA was because it seemed like a slam-dunk case: here’s an obvious i...