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Curated and popular this week
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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
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TLDR
When we look across all jobs globally, many of us in the EA community occupy positions that would rank in the 99.9th percentile or higher by our own preferences within jobs that we could plausibly get.[1] Whether you work at an EA-aligned organization, hold a high-impact role elsewhere, or have a well-compensated position which allows you to make significant high effectiveness donations, your job situation is likely extraordinarily fortunate and high impact by global standards. This career conversations week, it's worth reflecting on this and considering how we can make the most of these opportunities.
Intro
I think job choice is one of the great advantages of development. Before the industrial revolution, nearly everyone had to be a hunter-gatherer or a farmer, and they typically didn’t get a choice between those.
Now there is typically some choice in low income countries, and typically a lot of choice in high income countries. This already suggests that having a job in your preferred field puts you in a high percentile of job choice. But for many in the EA community, the situation is even more fortunate.
The Mathematics of Job Preference
If you work at an EA-aligned organization and that is your top preference, you occupy an extraordinarily rare position. There are perhaps a few thousand such positions globally, out of the world's several billion jobs. Simple division suggests this puts you in roughly the 99.9999th percentile of job preference.
Even if you don't work directly for an EA organization but have secured:
* A job allowing significant donations
* A position with direct positive impact aligned with your values
* Work that combines your skills, interests, and preferred location
You likely still occupy a position in the 99.9th percentile or higher of global job preference matching. Even without the impact perspective, if you are working in your preferred field and preferred country, that may put you in the 99.9th percentile of job preference
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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
There is often a clash between "alignment" and "capabilities" with some saying AI labs are pretending to do alignment while doing capabilities and others say they are so closely tied it's impossible to do good alignment research without producing capability gains.
I'm not sure this discussion will be resolved anytime soon. But I think it's often misdirected.
I think often what people are wondering is roughly "is x a good person for doing this research?" Should it count as beneficial EA-flavored research, or is it just you being an employee at a corporate AI lab? The alignment and capabilities discussions often seem secondary to this.
Instead, think we should stick to a different notion: something is "pro-social" (not attached to the term) AI x-risk research if it's research that (1) has a shot of reducing x-risk from AI (rather than increasing it or doing nothing) and (2) is not incentivized enough by factors external to the lab, to pro-social motivation, and to EA (for example: the market, the government, the public, social status in silicon valley, etc.)
Note (1) should include risks that the intervention changes timelines in some negative way, and (2) does not mean the intervention isn't incentivized at all, just that it isn't incentivized enough.
This is actually similar enough to the scale/tractability/neglectedness framework but it (1) incorporates downside risk and (2) doesn't run into the problem of having EAs want to do things "nobody else is doing" (including other EAs). EAs should simply do things that are underincentivized and good.
So, instead of asking things like, "is OpenAI's alignment research real alignment?" ask "how likely is it to reduce x-risk?" and "is it incentivized enough by external factors?" That should be how we assess whether to praise the people there or tell people they should go work there.
Thoughts?
Note: edited "external to EA" to "external to pro-social motivation and to EA"