I now think principles-first EA is more important than I previously thought because it helps prevent effectiveness drift. My anecdotal evidence from AI Safety and especially biosecurity gives me the impression that without constant anchoring to EA and especially comparisons to the clear ToCs and tractability for e.g. AMF, it is easy to lose focus on the high demands of choosing x-risk as a cause area over others. I previously placed less value on having strong links between EA and the various cause areas but now think I should update to thinking strong, continuous engagement with EA is important to keep one's focus on each intervention's cause prioritization assumptions that make it comparable to e.g. AMF or cage free chicken campaigns. This is not to say that causes such as AI Safety and biosec are not important, but that unless constantly tied back to EA cause prioritization, there is risk of drifting away from what made the cause look good in the first place. An example from biosecurity is the very easy slippage away from human extinction scenarios to ones where nearly everone dies (the difference being a crux as it is the potentially enormous future one is saving, not the people living at the time of the catastrophe). That said, it is completely fine and commendable that there is AI Safety and biosecurity work that does not target existential threats, but for EAs such changes in the nature of the work means they should consider changing their career. I think this is also important for newcomers to EA: For those of us who were around when we discussed whether x-risks demanded attention we might take concepts such as Pascal's Mugging as obvious, but for newcomers it is important to engage with such concepts. Another observation I have made is that in animal welfare and global health one is constantly reminded by metrics of suffering alleviated per dollar, but such recurring reminders are lacking in x-risk focused cause areas.

Over the past 12 years, I almost always avoided applying for any jobs in effective altruism – though they did often seem like dream jobs – because: 1. I was afraid I might not be the best candidate, and if, by chance, I replace the best candidate, my work would not only be a waste but outright harmful. I'm the sort of person who's afraid to drive a car for fear of hurting someone, and the funding allocation can affect the lives of billions or trillions of beings, so any mistakes I could make would vastly outstrip any harm I could do with a car if I tried. 2. That the best candidate might not make up for that harm in some other job that they do instead because they might be more socially motivated than me and not fall back on earning to give if they don't find a charity job but rather value drift and do some mainstream stuff – in the worst case AI capabilities. 3. That I can survive many years of earning to give without value-drifting because I have managed to do that in the past (a USP I should capitalize on because the counterfactual of the money that I earn at a random company is very low impact, so I can generate great counterfactual impact rather than a bit of marginal impact that I'd get at a charity). 4. That applying for a job, being considered the best candidate, and then not living up to the expectations would feel deeply humiliating – I'd feel like fraud, feel guilty for the harm I've caused, feel ashamed of having betrayed all the people at the organization, feel like I can never live it down or risk running into any of them ever again at conferences and such. 5. That sometimes they end up hiring a world-renowned researcher like Carl Shulman, and then I'd feel ashamed of even having considered applying because just the thought of it already feels hubristic. The upshot for me was: 1. To apply to places that have the funding and management capacity to hire everyone above some bar. 2. To time the boom and bust cycles in EA and go into grantmaking a

It is common in EA circles to compare deaths counts from some systemic problem to deaths from war. The implication is often that "actually war isn't that bad if you just look at the numbers". The latest being Bentham's Bulldog in an otherwise good article on Nestlé's harmful practices with baby formula (he doesn't say anything about war not being that bad though). I wish that this would stop because deaths aren't the only thing that matters. Below follow a number of claims that are based on my personal impression, not actual sources.  Injury and disability. Generally much more common than death, and the ratio of injury:deaths varies a lot per problem. (Admittedly, baby formula in poor countries seems to have a high disability burden) Trauma and grief. All deaths are grieved. But violent deaths tend to create a lot of trauma in the people around them, including from fleeing/displacement and separation of families and social ties. Economic harm. This is the big one. It sounds cold, but I think the emotional response people have to war footage is actually quite appropriate because of the economic harm. War creates enormous economic harm through the destruction of infrastructure, the displacement of people, and the prevention of (foreign) investment and productive activity. People lose their jobs, become refugees, don't build productive skills, stop education, businesses don't get started, etc etc.  Cultural effects & institutions. I suspect that war reduces the likelihood of tolerant, liberal democratic cultural norms and institutions developing. Instead, I'd expect vengeful, and extractive systems to become more likely. Overall, I don't think deaths are a good proxy for the total harm of war when compared to other causes.

Some notes on OpenAI disproving the Erdős unit distance conjecture (from a non-mathematician): * First, this is big. A notorious math conjecture being disproved by AI would be sci-fi 10 years ago. In my layman's read, this is plausibly the most prominent math result in the last 12 months -- AI, centaur, human or whatever. * Second, it rebutted an Erdős conjecture, and I found it curious that the first clear math breakthrough goes against consensus. There are a few potential reads to this: a) this seems to go against the LLMs-are-sycophantic-machine claims; b) even if LLMs are that sycophantic, exploring different intellectual paths is so cheap to them that sycophancy doesn't quite matter as much; c) it may mean that AI is sycophantic at the user-level but not at the literature-level, which actually may be great for finding novel solutions,  but is also the very thing that enables e.g. AI psychosis. * Third, it's hard to wrap my head around having an intelligence that is probably at the level of a very promising Terence Tao graduate student -- but not Tao-level yet. It allows exploring many hypotheses/conjectures/counter-examples/constructions that go against intuitive human ~quick evaluation/priors of what is promising, simply because they can be so exhaustive in their exploration. It’s the country part of a “country of geniuses in a datacenter” * Fourth, the solution combines insights/techniques from different fields. It pulled off an answer that used algebraic number theory to solve a combinatorial geometry problem. Mathematicians seem to think how it did it may unlock more. In a world where specialization is deemed necessary structurally/institutionally, AIs have a special advantage even with "mere" cross-field interpolation [tbc, in this case there seems to be substantial extrapolation in my layman's read]. Also, the constraint here may not be human intelligence per se. Surely we don't have a current Riemann-level mathematician partly because of bottlenecks

My personal messy thoughts on some of the things EA Netherlands (and maybe other community building orgs?) should be doing in the near future (building on our recent post). Sharing to get input. Please tell me what you think I'm getting wrong.  Some Tenets  Community building, not talent placement. EAN is primarily a community-building organisation. Our theory of change is to grow community capital — the community's capacity for future impact, which we take to be roughly (sum of individual career capital) × coordination ability. This is the stock we're building. Members deploy it over time through three medium-term outcomes: career switches into impact-relevant roles, significant donations to effective organisations, and community organising or advocacy for EA-aligned causes, solutions, and tools. (Organising and advocacy are partly deployments and partly investments back into coordination ability — meta-work, worth flagging as such.)  Over a longer horizon, those flows aggregate into population-level outcomes: shifts in awareness and perception of EA and EA-style thinking, and eventually norm and institutional change.  Career switches and donations in any given year are how we tell whether the stock is being built well; they're not what we should be optimising for. This is the difference from talent placement. Placement orgs — BlueDot for AI safety upskilling, AIM for entrepreneurship, MATS for AI safety research — convert specific kinds of career capital into specific roles on roughly annual cycles. That's a different job, and it's done well elsewhere. EAN's contribution is to grow the underlying stock: more members, more career capital per member, more coordination ability across them. What makes this worthwhile is what the approach produces over long horizons: engagement spread across many roles rather than concentrated in one bet, ecosystem infrastructure that no single placement focus would build, values that persist when people switch careers, and the c