I’m not sure how using Lightcone Commons increases grantmaking-related conflicts of interest compared with the normal LTFF process, but I agree that conflicts of interest are very common in grantmaking.
calebp
Karma: 6,078
I really dislike how, after 5 minutes of reading the post, I’m struggling to tell whether:
The post is from METR (pretty sure it’s not, but it uses the METR tag, brands itself as a METR thing, and METR has a similarly named internal project)
This post is satire
Whether a human was actually substantively involved in writing this post
Being able to filter out AI-generated content from the front page would be great, but I don’t love that people like @Thomas Kwa🔹 will be somewhat obligated to respond to random slop about METR.
I could see that being good. I think I’d still prefer the filter-out-under-n karma option because I understand what’s actually going on under the hood. I expect I’d prefer it to the current front page.
It doesn’t appeal to me because I want higher info density, and this reduces it—I could see others liking it .
It’s not obvious to me that you can’t just hide low karma posts as an option—some options:
1. All first-time posts are reviewed by the forum team, who can upvote things and put them above the threshold
2. Make the low karma/low user karma post hiding disabled by default
3. Try out an option for a bit and remove it if it’s not working well
When I posted, the front page felt “spammy”, with lots of generic, low-quality posts, presumably from first-time posters. I now rarely click on anything below 15 karma, but that was >50% of my frontpage.
Is there a way for me to filter posts below say 15 karma from my frontage? I couldn’t easily find it on mobile.
Nitpick but I think it’s unlikely that biological humans will completely disappear and I’m an AI safety person. The sota literature is ofc Pantheon season 2 where a few humans choose not to be uploaded.
My impression is that Pangram has very low false positive rates and unclear-to-me false negative rates—so I’d suggest using it to rule things in as AI-generated, but not strongly rule them out.
A little related discussion here https://x.com/caleb_parikh/status/2035434186417262863
Oh lol, I haven’t written a post in a while :’). I think it would have been much more obvious if I had seen it for the first time in a blank editor, which is presumably how much people see it for the first time.
Also, minor, but I’m not sure that the first sentence in the flag is grammatically correct, which led me to think I might need to add a disclosure to be compliant with your policy, as it was flagged, even if I haven’t used any LLMs for editing/writing this post.
@EA Forum Team
I made a small edit and saw that my post was flagged for LM usage. The post (to me) doesn’t sound very LM-generated—but I do find it more difficult to assess my own writing than other people’s, so I ran it through pangram which seems to think it’s 100% human generated.
From quickly skimming your LLM usage policy, it looks like you’re also using Pangram, which is a bit confusing. Maybe you have a bug, or are using a different version to me? Or am I using Pangram incorrectly or suboptimally?
Ah yeah, that all makes sense.
I’m sorry that you’re much happier for distancing yourself from EA—but I’m glad that you’re much happier now!
> If I didn’t have a pretty thorough view formed over years, base rate might also be the first thing I want to understand. I guess I’m just personally well past that
I relate to feeling this way about other topics. Normally, I just roll my eyes and don’t really engage with online discussions, but for fwiw I appreciate you giving your thoughts here—it was, at the very least, helpful for me.
I like this comment; the events analogy, in particular, shifted some of my beliefs and gave me a useful frame I had overlooked before. Apologies if you’ve already covered this in other comments.
My impression is that, if asked, many people would say that EA is significantly worse than other spaces. There’s some amount of trying to figure out which other spaces are reasonable to compare to, which allows people to agree with some versions and disagree with others, but on a vibes level, I feel like people think EA is significantly worse. [1] Idk if this is useful, but unlike (most?) other commenters, I do think Nathan is responding to a real sentiment.
Re “why do we even care about a baseline?” Ime, when harassment gets brought up (e.g. on the forum), people say that there were basic/common/cheap/expected mitigations that would have prevented the incident. If it turns out that most other orgs/communities, in fact, don’t have these measures in place or are similarly bad at mitigating problems, then imo it’s harder for proponents of change to argue that “EA” “should”[2] solve the problem. [3]
I liked the events analogy—one reason why I don’t care that much about baselines for events (particiularly ones I have little connection to) is that most events are just … pretty bad—or at least bad at achieving goals that are analogous to mine, and if I expected a similar level of success to them, I’d spend my time doing other stuff, but I do care a lot about the performance of events that seem particularly great and where my events stack up compared to those (maybe a baseline for “competent according to me” events) - idk if there are transparent groups out there that are doing a really great job in this area and would meet the standards of forum commenters but if there are I would be curious to hear about them.- ^
As an aside, talking about this topic does seem pretty cursed, and I don’t think there’s much upside socially to being on the “EA is no worse than others” side or even the “idk man, it’s pretty confusing how bad EA is on this dimension” side.
- ^
To be clear, I mean “should” in more of a deontological sense than a consequentialist one. I do care about outcomes in the world, but I just don’t think it’s possible to have an online discussion about the consequentialist case that couldn’t ~only engage with one side.
- ^
It might even point to the problem being unusually difficult to solve (otherwise, why haven’t the other well-intentioned communities solved it?).
- ^
Hmm, I think if smart EA/Rat types get “corrupted” in general, they’ll present as thoughtful people with reasons that are hard to dismiss quickly when questioned by EAs. I get the vague sense that your evidence bar for “corruption” is going to be too high to be useful in most worlds where there’s a lot of corruption.
(that’s not to say that EAs/Rats/etc. who join labs/start wildly profitable companies speeding up AI progress have been “corrupted”—I just think if they were, it would present pretty similarly to how it has done and it’s hard to get lots of easy to share evidence)
Interesting, is sports betting plausibly as bad as tobacco/alcohol in low-income countries?
Like, I think sports betting is plausibly one of the “worst businesses” for the US, comparable to alcohol/tobacco—but my impression is that the EAs that care about tobacco/alcohol don’t care very much about interventions in high-income countries relative to low-income countries.
Whilst I strongly disagree with the claim at the object level, many other non-forecasting AI safety interventions work with labs in some way, so even if this were true, the relative penalty applied to AIS forecasting work would be fairly low.
I think this post significantly overstates its conclusion and is plausibly poorly calibrated on the relative value of forecasting.
My main “directional” issues with the post as it’s currently written:
I think it overstates the amount of funding devoted to forecasting on a “worldview” basis.
Most forecasting funding is (iiuc) not going to neartermist causes or particularly fungible with neartermist causes, so pointing to a bunch of neartermist causes to justify better funding options seems irrelevant.
From my perspective, it seems like:
Within Animal welfare fungible money, very little goes into forecasting e.g. less than $2M per year
Tbh—I would probably prefer that more money went into some kinds of forecasting on the margin. For example, I think that people are generally too bullish on clean meat, and Linch/Open Phil’s work investigating the difficulty of clean meat has plausibly resulted in better allocation of millions of dollars because there are, in fact, good alternatives (like cage-free campaigns).
Within Longtermist/AI fungible money, maybe $10M/year goes into forecasting, which seems pretty reasonable to me but i think to get to 10M you need to be including projects that seems very promising to me for different reasons to mainstream forecasting infrastructure e.g. AI 2027, METR.
I think the strongest version of the argument would be attacking AI evals but I’m unsure about whether those are in-scope for this post—my impression is that evals are useful for forecasting capabilities are a pretty great bet relative to other funding opportunities within the AI space.
So the argument actually seems to be “longtermist funding is not as cost-effective as neartermist funding” which is not totally unreasonable, but clearly needs to engage with the long/neartermist worldview (e.g. moral size of the future) as opposed to just engaging with tangible short-term impact indicators.
I’m less convinced than the OP that funders in particular are overrating forecasting—I just don’t see much effort going into forecasting grantmaking compared to ~every other grantmaking area.
My impression is that a lot of forecasting dollars are funded by organisations that are incentivised to use the money well (e.g. AI companies paying FRI to produce forecasts around safety and capability evaluations for safety planning). I see that others have weighed in on this already so not planning to elaborate on this more.
I agree with some of the post’s vibes and think it’s pointing at real cultural traits of rationalist communities. Though tbh, I think OP is too bearish on the usefulness of betting/making falsifiable predictions for people in EA-spaces. I suspect that OP seen lots of people getting very distracted by futarchy/manifold etc. (and I do think this is a risk), but culturally I think EA should be pretty into “betting/making falsifiable predictions” and that cluster of epistemic traits AND I think forecasting infrastructure has a meaningful effect on this. E.g In two office spaces (out of three that I’ve spent substantial time in), I think Manifold/prediction markets have very clearly made the communities more forecasting-y, and this has had tangible effects on people’s research/choice of projects—this is probably the most explicit example, though most changes are harder to hyperlink.
Given that you are criticising the epistemics of EAs taking AGI very seriously, I think it’s reasonable to hold this post to a higher epistemic standard than a typical EA forum post. Apologies if this comes across as combative—I spent some time trying to tone it down with Claude and struggled to get something that wasn’t just hedged/weak sauce. I am excited about more discussion of the capabilities of AI systems on the EA forum and would like more people to write up their takes on the current situation.
…...I think you are applying more rigour to the bullish case than the bearish one. For example, you say:
[Mythos not providing a substantive improvement in cybersec capabilities] is further highlighted by the fact that an independent analysis was able to find many of the same vulnerabilities using much smaller open-source models.
I think this is misleading for a few reasons:
AISLE is not an “independent” entity—their whole business depends on Mythos and frontier models not being as big a deal as harnesses
That analysis does not “find” many of the same vulns—they were presented to the LLMs selectively
They don’t give a false positive rate, so it’s not clear that the LLMs classifications have much validity
On the claim that Anthropic talks about risks from their own models primarily to create hype: I find this hard to square with the evidence. Talking about how your B2B product might be extremely dangerous, or publishing lengthy documents critically assessing your own product and admitting to errors that would be difficult to identify independently (e.g. accidentally training against the CoT), is not a common marketing tactic. It feels like your model implies that companies should only release materials optimised for short-term interests, which doesn’t predict the real differences in how AI companies approach releases.
Benchmarks are interpreted uncritically
The benchmark contamination arguments are worth engaging with in principle, but I’m not sure they’re doing much work in practice—I don’t think many people in EA are actually updating heavily on raw benchmark scores right now. METR, arguably EA’s favourite benchmarking org, has been pretty vocal about their own benchmarks being saturated, so I think the community is reasonably aware of these limitations already.
Negative results are ignored
I’m genuinely uncertain what you want Anthropic and other AI companies to do here. Do you think “genuine intelligence” is easy to measure and well-defined? The more concrete concepts being used as proxies—coding ability, economic value generated, uplift—seem defensible on their own terms rather than as misleading substitutes for something more fundamental.
On “fundamental limits of LLMs” more broadly: these arguments have been made confidently by prominent researchers since the advent of LLMs and have not had a great track record. That doesn’t make them wrong, but it’s worth noting.
.....
I think this post would be much stronger if it applied its standards more symmetrically. It would also help to have a more concrete conclusion. The current takeaway is essentially “further research is needed”, which is a claim you can make about most areas of research (so much so that it’s been banned from multiple journals), but I don’t have a great sense of what research would actually convince you that the “AI hype” is reasonable.
Having an AI that doesn’t willingly participate in coups doesn’t imply that you need to specify all of the AI’s values in advance, or that it will be incorrigible in a broad (and x-risk increasing sense).
I think that the people preventing AI-assisted coups are imagining pretty corrigible AIs (in the sense that Claude right now is very corrigible); they just won’t want to do coups (in a similar sense to Claude not wanting to help with bioweapons research), and this just seems pretty workable.
I don’t really understand this post; I suspect the LLM has made it a little confused.
You say:
> When we mistake an early-stage research or field-building problem for a last-mile delivery problem, we set ourselves up for disappointment.
Cultivated meat was, for years, implicitly treated as if it was close to a last-mile problem: a few years from cost parity, just needing scale and distribution. It wasn’t. Something similar could happen with welfare tech.
Isn’t most of the focus already on development?
> Leafleting was perhaps the original example. In the early-to-mid 2010s, it was often treated as one of the most effective things people could do for animals, based largely on evidence that later looked much weaker than initially believed. The story was wonderfully legible: distribute leaflets, convert people to vegetarianism, reduce animal suffering. Simple, measurable, scalable. However, the reality was messier. The foundational studies were methodologically flawed (e.g., self-reporting on diets leading to social-desirability bias) and long-term effects were unreliably extrapolated. When these flaws were found, there was a rapid downward adjustment, and now leafleting has effectively been fully removed from EA animal work.
I don’t see why this was named as a “bubble”, my impression is that this was basically good. People thought leafletting was good; people tried leafletting; it wasn’t very good; people stopped doing it. That seems very functional to me.
My impression is that bubbles are bad because they are ex-post indications of overcorrections (on entry or exit or both) that have a bunch of harmful externalities—but I think one needs to make an actual argument at the object level for why there could be an overcorrection towards welfare tech rn (e.g. maybe you think that too much funding is going into it too quickly, but are you sure that money would actually be counterfactually spent on other animal stuff? Or maybe you think that people are moving away from other interventions to work on this stuff, but the main people I know doing AW tech dev are unsurprisingly technical people and weren’t working on other AW interventions before that, so there’s not much of a tradeoff there).