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

I am writing this to reflect on my experience interning with the Fish Welfare Initiative, and to provide my thoughts on why more students looking to build EA experience should do something similar.  Back in October, I cold-emailed the Fish Welfare Initiative (FWI) with my resume and a short cover letter expressing interest in an unpaid in-person internship in the summer of 2025. I figured I had a better chance of getting an internship by building my own door than competing with hundreds of others to squeeze through an existing door, and the opportunity to travel to India carried strong appeal. Haven, the Executive Director of FWI, set up a call with me that mostly consisted of him listing all the challenges of living in rural India — 110° F temperatures, electricity outages, lack of entertainment… When I didn’t seem deterred, he offered me an internship.  I stayed with FWI for one month. By rotating through the different teams, I completed a wide range of tasks:  * Made ~20 visits to fish farms * Wrote a recommendation on next steps for FWI’s stunning project * Conducted data analysis in Python on the efficacy of the Alliance for Responsible Aquaculture’s corrective actions * Received training in water quality testing methods * Created charts in Tableau for a webinar presentation * Brainstormed and implemented office improvements  I wasn’t able to drive myself around in India, so I rode on the back of a coworker’s motorbike to commute. FWI provided me with my own bedroom in a company-owned flat. Sometimes Haven and I would cook together at the residence, talking for hours over a chopping board and our metal plates about war, family, or effective altruism. Other times I would eat at restaurants or street food booths with my Indian coworkers. Excluding flights, I spent less than $100 USD in total. I covered all costs, including international transportation, through the Summer in South Asia Fellowship, which provides funding for University of Michigan under