The cause prioritization landscape in EA is changing. Prominent groups have shut down, others have been founded, and everyone’s trying to figure out how to prepare for AI. This is the third in a series of posts critically examining the state of cause prioritization and strategies for moving forward. Executive Summary * An increasingly common argument is that we should prioritize work in AI over work in other cause areas (e.g. farmed animal welfare, reducing nuclear risks) because the impending AI revolution undermines the value of working in those other areas. * We consider three versions of the argument: * Aligned superintelligent AI will solve many of the problems that we currently face in other cause areas. * Misaligned AI will be so disastrous that none of the existing problems will matter because we’ll all be dead or worse. * AI will be so disruptive that our current theories of change will all be obsolete, so the best thing to do is wait, build resources, and reformulate plans until after the AI revolution. * We identify some key cruxes of these arguments, and present reasons to be skeptical of them. A more direct case needs to be made for these cruxes before we rely on them in making important cause prioritization decisions. * Even on short timelines, the AI transition may be a protracted and patchy process, leaving many opportunities to act on longer timelines. * Work in other cause areas will often make essential contributions to the AI transition going well. * Projects that require cultural, social, and legal changes for success, and projects where opposing sides will both benefit from AI, will be more resistant to being solved by AI. * Many of the reasons why AI might undermine projects in other cause areas (e.g. its unpredictable and destabilizing effects) would seem to undermine lots of work on AI as well. * While an impending AI revolution should affect how we approach and prioritize non-AI (and AI) projects, doing this wisel

This is Part 1 of a multi-part series, shared as part of Career Conversations Week. The views expressed here are my own and don't reflect those of my employer. TL;DR: Building an EA-aligned career starting from an LMIC comes with specific challenges that shaped how I think about career planning, especially around constraints: * Everyone has their own "passport"—some structural limitation that affects their career more than their abilities. The key is recognizing these constraints exist for everyone, just in different forms. Reframing these from "unfair barriers" to "data about my specific career path" has helped me a lot. * When pursuing an ideal career path, it's easy to fixate on what should be possible rather than what actually is. But those idealized paths often require circumstances you don't have—whether personal (e.g., visa status, financial safety net) or external (e.g., your dream org hiring, or a stable funding landscape). It might be helpful to view the paths that work within your actual constraints as your only real options, at least for now. * Adversity Quotient matters. When you're working on problems that may take years to show real progress, the ability to stick around when the work is tedious becomes a comparative advantage. Introduction Hi, I'm Rika. I was born and raised in the Philippines and now work on hiring and recruiting at the Centre for Effective Altruism in the UK. This post might be helpful for anyone navigating the gap between ambition and constraint—whether facing visa barriers, repeated setbacks, or a lack of role models from similar backgrounds. Hearing stories from people facing similar constraints helped me feel less alone during difficult times. I hope this does the same for someone else, and that you'll find lessons relevant to your own situation. It's also for those curious about EA career paths from low- and middle-income countries—stories that I feel are rarely shared. I can only speak to my own experience, but I hop

And other ways to make event content more valuable.   I organise and attend a lot of conferences, so the below is correct and need not be caveated based on my experience, but I could be missing some angles here. Also on my substack. When you imagine a session at an event going wrong, you’re probably thinking of the hapless, unlucky speaker. Maybe their slides broke, they forgot their lines, or they tripped on a cable and took the whole stage backdrop down. This happens sometimes, but event organizers usually remember to invest the effort required to prevent this from happening (e.g., checking that the slides work, not leaving cables lying on the stage). But there’s another big way that sessions go wrong that is sorely neglected: wasting everyone’s time, often without people noticing. Let’s give talks a break. They often suck, but event organizers are mostly doing the right things to make them not suck. I’m going to pick on two event formats that (often) suck, why they suck, and how to run more useful content instead. Panels Panels. (very often). suck. Reid Hoffman (and others) have already explained why, but this message has not yet reached a wide enough audience: Because panelists know they'll only have limited time to speak, they tend to focus on clear and simple messages that will resonate with the broadest number of people. The result is that you get one person giving you an overly simplistic take on the subject at hand. And then the process repeats itself multiple times! Instead of going deeper or providing more nuance, the panel format ensures shallowness. Even worse, this shallow discourse manifests as polite groupthink. After all, panelists attend conferences for the same reasons that attendees do – they want to make connections and build relationships. So panels end up heavy on positivity and agreement, and light on the sort of discourse which, through contrasting opinions and debate, could potentially be more illuminating. The worst form of shal