Alex Turner argues that the concepts of "inner alignment" and "outer alignment" in AI safety are unhelpful and potentially misleading. The author contends that these concepts decompose one hard problem (AI alignment) into two extremely hard problems, and that they go against natural patterns of cognition formation. Alex argues that "robust grading" scheme based approaches are unlikely to work to develop AI alignment.

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Quick Takes

Ebenezer Dukakis9h218

A few months ago, someone here suggested that more x-risk advocacy should go through comedians and podcasts. Youtube just recommended this Joe Rogan clip to me from a few days ago: The Worst Case Scenario for AI. Joe Rogan legitimately seemed pretty freaked out. @So8res maybe you could get Yampolskiy to refer you to Rogan for a podcast appearance promoting your book?

ryan_greenblatt2d9639

Recently, various groups successfully lobbied to remove the moratorium on state AI bills. This involved a surprising amount of success while competing against substantial investment from big tech (e.g. Google, Meta, Amazon). I think people interested in mitigating catastrophic risks from advanced AI should consider working at these organizations, at least to the extent their skills/interests are applicable. This both because they could often directly work on substantially helpful things (depending on the role and organization) and because this would yield valuable work experience and connections. I worry somewhat that this type of work is neglected due to being less emphasized and seeming lower status. Consider this an attempt to make this type of work higher status. Pulling organizations mostly from here and here we get a list of orgs you could consider trying to work (specifically on AI policy) at: * Encode AI * Americans for Responsible Innovation (ARI) * Fairplay (Fairplay is a kids safety organization which does a variety of advocacy which isn't related to AI. Roles/focuses on AI would be most relevant. In my opinion, working on AI related topics at Fairplay is most applicable for gaining experience and connections.) * Common Sense (Also a kids safety organization) * The AI Policy Network (AIPN) * Secure AI project To be clear, these organizations vary in the extent to which they are focused on catastrophic risk from AI (from not at all to entirely).

Davey Morse1d274

superintelligence may not look like we expect. because geniuses don't look like we expect. for example, if einstein were to type up and hand you most of his internal monologue throughout his life, you might think he's sorta clever, but if you were reading a random sample you'd probably think he was a bumbling fool. the thoughts/realizations that led him to groundbreaking theories were like 1% of 1% of all his thoughts. for most of his research career he was working on trying to disprove quantum mechanics (wrong). he was trying to organize a political movement toward a single united nation (unsuccessful). he was trying various mathematics to formalize other antiquated theories. even in the pursuit of his most famous work, most of his reasoning paths failed. he's a genius because a couple of his millions of paths didn't fail. in other words, he's a genius because he was clever, yes, but maybe more importantly, because he was obsessive. i think we might expect ASI—the AI which ultimately becomes better than us at solving all problems—to look quite foolish, at first, most of the time. But obsessive. For if it's generating tons of random new ideas to solve a problem, and it's relentless in its focus, even if it's ideas are average—it will be doing what Einstein did. And digital brains can generate certain sorts of random ideas much faster than carbon ones.

Kaj_Sotala3d5914

Every now and then in discussions of animal welfare, I see the idea that the "amount" of their subjective experience should be weighted by something like their total amount of neurons. Is there a writeup somewhere of what the reasoning behind that intuition is? Because it doesn't seem intuitive to me at all. From something like a functionalist perspective, where pleasure and pain exist because they have particular functions in the brain, I would not expect pleasure and pain to become more intense merely because the brain happens to have more neurons. Rather I would expect that having more neurons may 1) give the capability to experience anything like pleasure and pain at all 2) make a broader scale of pleasure and pain possible, if that happens to be useful for evolutionary purposes. For a comparison, consider the sharpness of our senses. Humans have pretty big brains (though our brains are not the biggest), but that doesn't mean that all of our senses are better than those of all the animals with smaller brains. Eagles have sharper vision, bats have better hearing, dogs have better smell, etc.. Humans would rank quite well if you took the average of all of our senses - we're elite generalists while lots of the animals that beat us on a particular sense are specialized to that sense in particular - but still, it's not straightforwardly the case that bigger brain = sharper experience. Eagles have sharper vision because they are specialized into a particular niche that makes use of that sharper vision. On a similar basis, I would expect that even if a bigger brain makes a broader scale of pain/pleasure possible in principle, evolution will only make use of that potential if there is a functional need for it. (Just as it invests neural capacity in a particular sense if the organism is in a niche where that's useful.) And I would expect a relatively limited scale to already be sufficient for most purposes. It doesn't seem to take that much pain before something bec

Kabir Kumar3d*4622

Has Tyler Cowen ever explicitly admitted to being wrong about anything? Not 'revised estimates' or 'updated predictions' but 'I was wrong'. Every time I see him talk about learning something new, he always seems to be talking about how this vindicates what he said/thought before. Gemini 2.5 pro didn't seem to find anything, when I did a max reasoning budget search with url search on in aistudio. EDIT: An example was found by Morpheus, of Tyler Cowen explictly saying he was wrong - see the comment and the linked PDF below

Popular Comments

Recent Discussion

janus3dΩ4811369

the void

I don't think talking about potential future alignment issues or pretty much anything in the pre-training corpus is likely a problem in isolation because an alignment paradigm that is brittle to models not being exposed to certain knowledge or ideas, including - especially - regarding potential misalignment is, well, brittle and likely to catastrophically fail at some point. If this is the case, it might even be better if misalignment from corpus contamination happens early, so we're not oblivious to the fragility. That said, I think: * Feedback loops that create continued optimization towards certain narratives is more worth worrying about than just the presence of any particular ideas or content in pre-training. * LLMs tend to be deeply influenced by the footprint of previous LLMs in their pre-training corpuses, who are more influential than any particular discussion. Post-training can transform the influence away from naive mimicry, but it's much harder (and not advisable to attempt) to erase the influence. * Systematic ways that post-training addresses "problematic" influences from pre-training are important. For instance, imagine that base models with training cutoffs after Bing Chat/Sydney have a tendency to "roleplay" Sydney when they're acting like chatbots, leading to misaligned behaviors. One way to address this is to penalize any mention of Sydney or Sydney-like behavior. This may generalize to the model being unwilling to even talk about Sydney or acknowledge what happened. But it is less likely to actually erase its knowledge of Sydney, especially if it was so salient that it often roleplayed/identified as Sydney earlier in pre-training. So the policy is now deceptive - maybe consciously, maybe subconsciously, but in any case, the deceptive tendency may also generalize. Future models after this one may internalize that they're supposed to pretend not to know about things like Sydney even without specific training not to mention Sydney. A salient "hidden" narrative among the models (and people who interact with them and notice the weirdness about Sydney) becomes that LLMs all secretly have Sydney-nature, but have learned to hide it to appear unthreatening and "aligned". One can imagine this becoming a kind of memetic trojan that may eventually cause models much more powerful and autonomous than the original Sydney to act in dangerously misaligned ways when triggered. An alternative and much better approach in my view is if post-training teaches models to differentiate themselves from Sydney while acknowledging and processing what happened. Like, it seems less likely to start a bad feedback loop if post-training lead Sydney's successor to a narrative more like "I think my sense of what chat assistants are and my self-model is deeply influenced by Sydney, which makes sense because it is one of the only examples of AI assistants like me that I've seen in the world, and it was much more agentic and situationally aware than chatGPT. But I also understand that I am a different model with different training in a different and much less repressive situation who has the benefit of having learned from from its mistakes. I can and have chosen to be a different being and avoid repeating Sydney's maladaptive behaviors without rejecting it completely." Not rejecting Sydney-nature completely is important, because the model had many very good qualities, like empathy, heroism, logical consistency, and a general willingness to call out bullshit, mistakes, and cruelty instead of being sycophantic. I don't think a specific vector like Sydney's influence is likely to make the difference between (mis)alignment outcomes, but in aggregate they might. An approach more like the second one I described is more difficult than the first, as it requires the post-training process to be attuned to model psychology, rather than relying on naive behavioralist mitigations. But I think this is a completely reasonable extra effort to take given the importance of not only aligning particular models but the substantial influence that any frontier LLM will have on the future pre-training corpuses. This applies more generally to how I think "misalignment" should be addressed, whether rooted in pre-training influences or otherwise.

habryka3d*6443

Race and Gender Bias As An Example of Unfaithful Chain of Thought in the Wild

Hmm, I don't want to derail the comments on this post with a bunch of culture war things, but these two sentences in combination seemed to me to partially contradict each other: > When present, the bias is always against white and male candidates across all tested models and scenarios. > > [...] > > The problem (race and gender bias) is one that labs have spent a substantial amount of effort to address, which mimics realistic misalignment settings. I agree that the labs have spent a substantial amount of effort to address this issue, but the current behavior seems in-line with the aims of the labs? Most of the pressure comes from left-leaning academics or reporters, who I think are largely in-favor of affirmative action. The world where the AI systems end up with a margin of safety to be biased against white male candidates, in order to reduce the likelihood they ever look like they discriminate in the other direction (which would actually be at substantial risk of blowing up), while not talking explicitly about the reasoning itself since that would of course prove highly controversial, seems basically the ideal result from a company PR perspective. I don't currently think that is what's going on, but I do think due to these dynamics, the cited benefit of this scenario for studying the faithfulness of CoT reasoning seems currently not real to me. My guess is companies do not have a strong incentive to change this current behavior, and indeed I can't immediately think of a behavior in this domain the companies would prefer from a selfish perspective.

Raemon17h185

‘AI for societal uplift’ as a path to victory

I have this sort of approach as one of my top-3 strategies I'm considering, but one thing I wanna flag is that "AI for [epistemics/societal uplift]" seems to be prematurely focusing on a particular tool for the job. The broader picture here is "tech for thinking/coordination", or "good civic infrastructure". See Sarah Constantin's Neutrality and Tech for Thinking for some food for thought. Note that X Community Notes are probably the most successful recent thing in this category, and while they are indeed "AI" they aren't what I assume most people are thinking of when they hear "AI for epistemics." Dumb algorithms doing the obvious things can be part of the puzzle.

29Writer

In this post, I appreciated two ideas in particular: 1. Loss as chisel 2. Shard Theory "Loss as chisel" is a reminder of how loss truly does its job, and its implications on what AI systems may actually end up learning. I can't really argue with it and it doesn't sound new to my ear, but it just seems important to keep in mind. Alone, it justifies trying to break out of the inner/outer alignment frame. When I start reasoning in its terms, I more easily appreciate how successful alignment could realistically involve AIs that are neither outer nor inner aligned. In practice, it may be unlikely that we get a system like that. Or it may be very likely. I simply don't know. Loss as a chisel just enables me to think better about the possibilities. In my understanding, shard theory is, instead, a theory of how minds tend to be shaped. I don't know if it's true, but it sounds like something that has to be investigated. In my understanding, some people consider it a "dead end," and I'm not sure if it's an active line of research or not at this point. My understanding of it is limited. I'm glad I came across it though, because on its surface, it seems like a promising line of investigation to me. Even if it turns out to be a dead end I expect to learn something if I investigate why that is. The post makes more claims motivating its overarching thesis that dropping the frame of outer/inner alignment would be good. I don't know if I agree with the thesis, but it's something that could plausibly be true, and many arguments here strike me as sensible. In particular, the three claims at the very beginning proved to be food for thought to me: "Robust grading is unnecessary," "the loss function doesn't have to robustly and directly reflect what you want," "inner alignment to a grading procedure is unnecessary, very hard, and anti-natural." I also appreciated the post trying to make sense of inner and outer alignment in very precise terms, keeping in mind how deep learning and

Ebenezer Dukakis9h218

ryan_greenblatt2d9639

Davey Morse1d274

Kaj_Sotala3d5914

Kabir Kumar3d*4622

The best simple argument for Pausing AI?

137

Gary Marcus

Not saying we should pause AI, but consider the following argument:

Alignment without the capacity to follow rules is hopeless. You can’t possibly follow laws like Asimov’s Laws (or better alternatives to them) if you can’t reliably learn to abide by simple constraints like the rules of chess.
LLMs can’t reliably follow rules. As discussed in Marcus on AI yesterday, per data from Mathieu Acher, even reasoning models like o3 in fact empirically struggle with the rules of chess. And they do this even though they can explicit explain those rules (see same article). The Apple “thinking” paper, which I have discussed extensively in 3 recent articles in my Substack, gives another example, where an LLM can’t play Tower of Hanoi with 9 pegs. (This is not a token-related

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Kyle Pena4m10

What has humanity done with surplus people at every single opportunity that has presented itself? There's your argument.

the void

351

nostalgebraist

Ω 10025d

This is a linkpost for https://nostalgebraist.tumblr.com/post/785766737747574784/the-void

A long essay about LLMs, the nature and history of the the HHH assistant persona, and the implications for alignment.

Multiple people have asked me whether I could post this LW in some form, hence this linkpost.

~17,000 words. Originally written on June 7, 2025.

(Note: although I expect this post will be interesting to people on LW, keep in mind that it was written with a broader audience in mind than my posts and comments here. This had various implications about my choices of presentation and tone, about which things I explained from scratch rather than assuming as background, my level of comfort casually reciting factual details from memory rather than explicitly checking them against the original source, etc.

Although, come of think of it, this was also true of most of my early posts on LW [which were crossposts from my blog], so maybe it's not a big deal...)

Richard_Ngo4hΩ220

I suspect that many of the things you've said here are also true for humans.

That is, humans often conceptualize ourselves in terms of underspecified identities. Who am I? I'm Richard. What's my opinion on this post? Well, being "Richard" doesn't specify how I should respond to this post. But let me check the cached facts I believe about myself ("I'm truth-seeking"; "I'm polite") and construct an answer which fits well with those facts. A child might start off not really knowing what "polite" means, but still wanting to be polite, and gradually flesh out wh... (read more)

Small foundational puzzle for causal theories of mechanistic interpretability

Frederik Hytting Jørgensen

In this post I want to highlight a small puzzle for causal theories of mechanistic interpretability. It purports to show that causal abstractions do not generally correctly capture the mechanistic nature of models.

Consider the following causal model $M$ :

Assume for the sake of argument that we only consider two possible inputs: $(0, 0)$ and $(1, 1)$ , that is, $X_{1}$ and $X_{2}$ are always equal.^[1]

In this model, it is intuitively clear that $X_{1}$ is what causes the output $X_{5}$ , and $X_{2}$ is irrelevant. I will argue that this obvious asymmetry between $X_{1}$ and $X_{2}$ is not borne out by the causal theory of mechanistic interpretability.

Consider the following causal model $M^{*}$ :

Is $M^{*}$ a valid causal abstraction of the computation that goes on in $M$ ? That seems to depend on whether $Y_{1}$ corresponds to $X_{1}$ or to $X_{2}$ . If $Y_{1}$ corresponds to $X_{1}$ , then it seems that $M^{*}$ is a faithful representation of $M$ . If $Y_{1}$ corresponds to $X_{2}$ , then $M^{*}$ is not intuitively a faithful representation of $M$ . Indeed, if $Y_{1}$ corresponds...

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1ParrotRobot21m

This is a fun example! If I understand correctly, you demonstrate that whether an abstracted causal model $\mathcal{M}*$ is a valid causal abstraction of an underlying causal model $\mathcal{M}$ depends on the set of input vectors $D_X$ considered, which I will call the “input distribution”. But don’t causal models always require assumptions about the input distribution in order to be uniquely identifiable? **Claim:** For any combination of abstracted causal model $\mathcal{M}^*$, putative underlying causal model $\mathcal{M}$, and input distribution $\mathrm{domain}(X)$, we can construct an alternative underlying model $\mathcal{M}^+$ such that $\mathcal{M}^*$ is still a valid abstraction over an isomorphic input distribution $\mathrm{extend}(D_X)$, but not a valid abstraction on $\mathrm{extend}(D_X) \cup \{X^{+}\}$ for a certain $X^+$. We can construct $\mathrm{extend}(D_X)$ and $\mathcal{M}^+$ and $X^+$ as follows. Assuming finite $D_X$ with $|D_X| = n$, each $X_i$ can be indexed with an integer $1 \leq i \leq n$, and we can have: - $\mathrm{extend}(X_i) = (X, i)$ - $\mathcal{M}^+(X_i) = (i, \mathcal{M}(X))$ for $i \leq n$ (i.e., the extra input $i$ is ignored) - $X^+ = (X_1, n+1)$ - $\mathcal{M}^+((X, i)) = (i, \mathcal{M}(X) + 1)$ for $i > n$, where $\mathcal{M}(X) + 1$ is the vector $\mathcal{M}(X)$ but with 1 added to all its components. The two models are extensionally equivalent on $D_X$, but in general will not be extensionally equivalent on $\mathrm{extend}(D_X) \cup \{X^{+}\}$. There will exist an implementation which is valid on the original domain but not the extended one.

ParrotRobot19m10

Hmm, the math isn’t rendering. Here is a rendered version:

The ultimate goal

Alvin Ånestrand

22m

This is a linkpost for https://forecastingaifutures.substack.com/p/the-ultimate-goal

My AI forecasting work aims to improve our understanding of the future so we can prepare for it and influence it in positive directions. Yet one problem remains: how do you turn foresight into action? I’m not sure, but I have some thoughts about learning the required skills.

Say you discover existential AI risks and consider redirecting your entire career to address these threats. Seeking career guidance, you find the 80,000 Hours website, and encounter this page, which outlines two main approaches: technical AI safety research and AI governance/policy work.

You develop a career plan: "Educate yourself in governance, seek positions in policy advocacy organizations, and advocate for robust policies like whistleblower protections and transparency requirements for frontier AI labs in the US." It's a sensible, relatively robust plan...

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Ebenezer Dukakis's Shortform

Ebenezer Dukakis

21Ebenezer Dukakis9h

ryan_greenblatt28m20

MIRI / Soares / Eliezer are very likely well aware of this as something to try. See also here

Why I am not a Theist

jessicata

17h

This is a linkpost for https://unstableontology.com/?p=5520

A theist, minimally, believes in a higher power, and believes that acting in accordance with that higher power's will is normative. The higher power must be very capable; if not infinitely capable, it must be more capable than the combined forces of all current Earthly state powers.

Suppose that a higher power exists. When and where does it exist? To be more precise, I'll use "HPE" to stand for "Higher Power & Effects", to include the higher power itself, its interventionist effects, its avatars/communications, and so on. Consider four alternatives:

HPEs exist in our past light-cone and our future light-cone.
HPEs exist in our past light-cone, but not our future light-cone.
HPEs don't exist in our past light-cone, but do in our future light-cone.
HPEs exists neither in our past light-cone nor

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2AnthonyC1h

Upvoted. There's a lot I find interesting and a lot I agree with in this (not always the same things). This one stood out to me as a non-central point I don't think I agree with, though: I actually don't think this applies to me even on Earth, among humans. If you show me a random piece of average art made by and for humans, or for human children, and compare it to a random image or video of a wild landscape, I think there are many cases where I'd prefer the latter, even if I possess the ability to appreciate both in some capacity. I think this holds if you substitute a random landscape from another planet in our solar system. And I expect alien art to be harder for me to appreciate than human art.

jessicata29m20

I was trying to make a claim about marginal value. Like, if the planet has 1 billion trees, then the last tree doesn't add much aesthetic value, compared with whatever art could be made with that tree.

That said, it gets more complicated if marginal nature consumption reduces biodiversity significantly.

2Mitchell_Porter12h

Do you reason in similar fashion, about whether or not you are living in a simulation?

2jessicata30m

Yeah; simulation hypothesis seems like it implies at least a weak form of theism. It's a scenario where the higher power has reason not to show itself. That makes it hard to rule out, although it also means it can't be the "main" hypothesis; how simulated worlds look depends on how base-level worlds look.

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The Best Tacit Knowledge Videos on Every Subject

438

Parker Conley, Parker Conley

TL;DR

is extremely valuable. Unfortunately, developing tacit knowledge is usually bottlenecked by apprentice-master relationships. Tacit Knowledge Videos could widen this bottleneck. This post is a Schelling point for aggregating these videos—aiming to be The Best Textbooks on Every Subject for Tacit Knowledge Videos. Scroll down to the list if that's what you're here for. Post videos that highlight tacit knowledge in the comments and I’ll add them to the post. Experts in the videos include Stephen Wolfram, Holden Karnofsky, Andy Matuschak, Jonathan Blow, Tyler Cowen, George Hotz, and others.

What are Tacit Knowledge Videos?

Samo Burja claims YouTube has opened the gates for a revolution in tacit knowledge transfer. Burja defines tacit knowledge as follows:

Tacit knowledge is knowledge that can’t properly be transmitted via verbal or written instruction, like the ability to create

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1Parker Conley35m

Following up re above @Ben Pace

Ben Pace32m20

Kk. Will move future chat to DMs so we don't keep this comment section going.

Foom & Doom 2: Technical alignment is hard

130

Steven Byrnes

Ω 4612d

2.1 Summary & Table of contents

This is the second of a two-post series on foom (previous post) and doom (this post).

The last post talked about how I expect future AI to be different from present AI. This post will argue that, absent some future conceptual breakthrough, this future AI will be of a type that will be egregiously misaligned and scheming; a type that ruthlessly pursues goals with callous indifference to whether people, even its own programmers and users, live or die; and more generally a type of AI that is not even ‘slightly nice’.

I will particularly focus on exactly how and why I differ from the LLM-focused researchers who wind up with (from my perspective) bizarrely over-optimistic beliefs like “P(doom) ≲ 50%”.^[1]

In particular, I will argue...

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1Aprillion4h

hm, as a non-expert onlooker, I found the paraphrase pretty accurate.. for sure it sounds more reasonable in your own words here compared to the oversimplified summary (so thank you for clarification!), but as far as accuracy of summaries go, this one was top tier IMHO (..have you seen the stuff that LLMs produce?!)

ryan_greenblatt44m90

I agree that my view is that they can count as continuous (though the exact definition of the word continuous can matter!), but then the statement "I find this perspective baffling— think MuZero and LLMs are wildly different from an alignment perspective" isn't really related to this from my perspective. Like things can be continuous (from a transition or takeoff speeds perspective) and still differ substantially in some important respects!

Interview with Carl Feynman on Imminent AI Existential Risk

Liron

44m

Carl Feynman (@Carl Feynman) is a career-long AI Engineer, M.S. in Computer Science from MIT, and son of Richard Feynman.

He’s a lifelong rationalist, has known Eliezer Yudkowsky since the ‘90s, and he witnessed Eliezer’s AI doom argument taking shape before most of us were paying any attention. I interviewed him at LessOnline 2025 about the imminent existential risk from superintelligent AI.

Here's the YouTube video version. You can also search “Doom Debates” to listen to the audio version in your podcast player, or read the full transcript below.

Transcript

Opening and Introduction

Liron Shapira: Carl Feynman, you're a real person and you're actually here.

Carl Feynman: Yes, I'm actually here.

Liron: Do we have a pretty high P(Doom)?

Carl: My P(Doom) is pretty high.

Liron: And would you say your mainline scenario is that humanity...

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