EA - The most important lesson I learned after ten years in EA by Kat Woods

Link to original articleWelcome 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: The most important lesson I learned after ten years in EA, published by Kat Woods on August 3, 2022 on The Effective Altruism Forum. Epistemic status: a rambling, sometimes sentimental, sometimes ranting, sometimes informative, sometimes aesthetic autobiographical reflection on ten years trying to do the most good. Reminder that you can listen to this post on your podcast player within 24 hours of it reaching 25 upvotes using the Nonlinear Library. Ten years ago I first heard about effective altruism, and it was one of the best things that ever happened to me. I was immediately sold. In fact, at the time I had been trying to start a movement of “Scientific World-Changers”, so you can imagine my excitement when I discovered that there was already a movement and it had a much better acronym. Since then it’s been a crazy decade. I’ve started four EA charities in all the major cause areas: global poverty, animal welfare, AI x-risk, and meta. I’ve been so frugal that me and my partner only had one (always damp) towel between the two of us and thought that getting a bed frame for our mattress would cost too much dead baby currency. I’ve updated to being so time-frugal that I’m working on launching a fund to remove financial barriers to productivity for longtermists, such as providing therapy or dishwashers. I’ve been on fire, working long hours trying to save the world. I’ve been burnt out, working long hours trying to set up systems so I don’t overdo it again. And through it all, the EA movement, despite its many flaws, has been such a huge source of meaning, growth, and community in my life. Which is why I wanted to do something on this special anniversary. I asked on Facebook and somebody suggested the most EA way to celebrate ever: writing a post on the EA Forum about my biggest lessons learned. Hence this post. So, what have I learned over ten years of obsessively trying to figure out and implement the question: how do I do the most good? There are a lot of contenders. Here are a few that first came to mind: Fundraising is one of the highest leverage meta-skills you can develop. It makes it so that instead of trying to find a job that does the most good, you can just think of what does the most good, then fundraise for that particular thing. Hiring is perhaps the second highest leverage meta-skill. Imagine you could have any skill in the world. This is what hiring allows you to do. You don’t have to learn how to code or develop decades of experience in a particular political arena. You can just hire people who already have those skills or experiences, and in fact, are probably better than you’d ever have gotten. It’s OK to care about your happiness. And not just because it’ll help you have more impact (which it will). It’s OK to care because you care intrinsically about your happiness. H/t to Spencer’s valuesism for convincing me of this in the end. (Great resources on mental health and EA here and here) The meta the bettah. While it’s important to avoid meta traps and not have insanely long theories of change, generally speaking, meta is where you can get the truly great hits. It’s where you can set up passive impact streams, apply small multipliers to large numbers, and all other sources of juicy utility. It also tends to leave you with skills that cross-apply to a wide range of cause areas, giving you sweet sweet option value. Which brings me to the biggest lesson I’ve learned over all this time: The biggest lesson I learned from my altruistic journey so far: widen your fing confidence intervals You know nothing about doing good in the world. Seriously. To be an EA is to find out, again and again and again, that what you thought was the best thing to do was wrong. You think you know what’s highest impact and you’re almost certainly seriously mistaken. Every single ...