Non-EA interests include chess and TikTok (@benthamite). Formerly @ CEA, METR + a couple now-acquired startups.
Ben_Westđ¸
Karma: 16,046
Your conflict of interest here feels enormous (even if declared) and its hard to read this and not feel like it might be a bid to directly protect your own interests by asking others to not step into your turf here as a lobbyist.
I think you could also read it as him attempting to solve the problem heâs describing.
I would be keen to hear if you think you have any solutions to this birifuction.
Huh, this feels like prime EA territory to me. We need disagreement so that people can engage in key EA activities like âmaking persnickety critiques of footnote #237 on someoneâs 10k word forum post.â
The case for EA feels much weaker to me if we are all confident that X is the best thing to doâthen you should just do X and not worry about cause prio etc.
Iâm sorry you had to go through this, Fran.
Congrats to everyone who worked on this!
Thanks for doing this. This question is marked as required but I think should either be optional or have a ânoneâ option:
To decompose your question into several sub-questions:
Should you defer to price signals for cause prioritization?
My rough sense is that price signals are about as good as the 80th percentile EAâs cause prio, ranked by how much time theyâve spent thinking about cause prioritization.
(This is mostly because most EAs do not think about cause prio very much. I think you could outperform by spending ~1 week thinking about it, for example.)
Should you defer to price signals for choosing between organizations within a given cause?
This mostly seems decent to me. For example, CG struggled to find organizations better than Givewellâs top charities for near-termist, human-centric work.
Notable exceptions here for work which people donât want to fund for non-effectiveness reasons, like politics or adversarial campaigning.
Should you defer to price signals for choosing between roles within an organization?
Yes, I mostly trust organizations to price appropriately, although also I think you can just ask the hiring manager.
Thanks, thatâs helpful. Do you have a sense of where we are on the current S-curve? E.g., if capabilities continue to progress in a straight line through the end of this year, is that evidence that we have found a new S-curve to stack on the current one?
the strength of this tail-wind that has driven much of AI progress since 2020 will halve
I feel confused about this point because I thought the argument you were making implies a non-constant âtailwind.â E.g. for the next generation these factors will be 1â2 as important as before, then the one after that 1â4, and so on. Am I wrong?
Interesting ideas! For Guardian Angels, you say âit would probably be at least a major software projectââmaybe we are imagining different things, but I feel like I have this already.
e.g. I donât need a âheated-email guard pluginâ which catches me in the middle of writing a heated email and redirects me because I donât write my own emails anyway. I would just ask an LLM to write the email and 1) itâs unlikely that the LLM would say something heated and 2) for the kinds of mistakes that LLMs might make, itâs easy enough to put something in the agents.md to ask it to check for these things before finalizing the draft.
(I think software engineering might be ahead of the curve here, where a bunch of tools have explicit guardian angels. E.g. when you tell the LLM âbuild feature Xâ, what actually happens is that agent 1 writes the code, then agent 2 reviews it for bugs, agent 3 reviews it for security vulns, etc.)
Related, from an OAI researcher.
The AI Eval Singularity is Near
AI capabilities seem to be doubling every 4-7 months
Humanityâs ability to measure capabilities is growing much more slowly
This implies an âeval singularityâ: a point at which capabilities grow faster than our ability to measure them
It seems like the singularity is ~here in cybersecurity, CBRN, and AI R&D (supporting quotes below)
Itâs possible that this is temporary, but the people involved seem pretty worried
Appendixâquotes on eval saturation
âFor AI R&D capabilities, we found that Claude Opus 4.6 has saturated most of our
automated evaluations, meaning they no longer provide useful evidence for ruling out ASL-4 level autonomy. We report them for completeness, and we will likely discontinue them going forward. Our determination rests primarily on an internal survey of Anthropic staff, in which 0 of 16 participants believed the model could be made into a drop-in replacement for an entry-level researcher with scaffolding and tooling improvements within three months.ââFor ASL-4 evaluations [of CBRN], our automated benchmarks are now largely saturated and no longer provide meaningful signal for rule-out (though as stated above, this is not indicative of harm; it simply means we can no longer rule out certain capabilities that may be pre-requisities to a model having ASL-4 capabilities).â
It also saturated ~100% of the cyber evaluations
âWe are treating this model as High [for cybersecurity], even though we cannot be certain that it actually has these capabilities, because it meets the requirements of each of our canary thresholds and we therefore cannot rule out the possibility that it is in fact Cyber High.â
Thanks for the article! I think if your definition of âlong termâ is â10 years,â then EAAs actually do often think on this time horizon or longer, but maybe donât do so in the way you think is best. I think approximately all of the conversations about corporate commitments or government policy change that I have been involved in have operated on at least that timeline (sadly, this is how slowly these areas move).
For example, you can see this CEA where @saulius projects out the impacts of broiler campaigns a cool 200 years, and links to estimates from ACE and AIM which use a constant discount rate.
Ah yeah good point, I updated the text.
Iâm excited about this series!
I would be curious what your take is on this blog post from OpenAI, particularly these two graphs:Investment in compute powers leading-edge research and step-change gains in model capability. Stronger models unlock better products and broader adoption of the OpenAI platform. Adoption drives revenue, and revenue funds the next wave of compute and innovation. The cycle compounds.
While their argument is not very precise, I understand them to be saying something like, âSure, itâs true that the costs of both inference and training are increasing exponentially. However, the value delivered by these improvements is also increasing exponentially. So the economics check out.â
A naive interpretation of e.g. the METR graph would disagree: humans are modeled as having a constant hourly wage, so being able to do a task which is 2x as long is precisely 2x as valuable (and therefore canât offset a >2x increase in compute costs). But this seems like an implausible simplification.
Do we have any evidence on how the value of models changes with their capabilities?
EA Animal Welfare Fund almost as big as Coefficient Giving FAW now?
This job ad says they raised >$10M in 2025 and are targeting $20M in 2026. CGâs public Farmed Animal Welfare 2025 grants are ~$35M.
Is this right?
Cool to see the fund grow so much either way.
Hmm. I understood them to be saying they (semi-) voluntarily scaled back before phase 2 was complete, so we canât read that much into the fact that phases 2â3 (where the high quality journalism happens) didnât happen. Maybe I misunderstood?
My read of their phase one plan is that they were intending to get these pretty low quality tabloid stories as a springboard to getting higher quality stuff. Maybe that was a bad plan, but the fact that the bad tabloid articles were in fact bad tabloid articles doesnât seem to discredit that?
Are you asking just about recent graduates, or all graduates?