This is one of my favorite LessWrong posts ever (strong upvoted). Nevertheless, in this comment I'll provide some pushback; not to any the particular claims made, but around a sort of "missing mood" that I think a stronger version of this post could have addressed. (For context, I'm probably one of the lab employees who sometimes says things like "Current AIs are pretty aligned"—i.e. one of the people that Ryan is arguing against here, though I might not be a very central example.) For me, the value of this post was the following: 1. Articulating a specific way that current AIs are "misaligned." Namely: 2. 1. They follow heuristics that are well-tuned for strong task performance in easy-to-verify settings, but only try superficially at difficult-to-evaluate tasks. 2. They communicate in ways that misrepresent how much they've accomplished: punching up their successes while omitting or downplaying failures, and sometimes outright lying about what they did. But they don't seem to do this strategically or persistently, instead following simple heuristics that result in these sorts of misrepresentations. 3. The overall result is that they underperform their potential at difficult-to-evaluate tasks while misleading users about what they've actually accomplished. 4. I say "misaligned" in quotes, because many traditional notions of misalignment wouldn't have covered the above, especially 1(a). This will be relevant later. 3. Providing concrete examples and a sense of how prevalent the above issues are. I want to be clear that (2) was an update for me. I think the way that you (Ryan) use LLMs more reliably elicits the sort of misalignment you name, relative to most use cases I've personally seen. (Namely, it seems like you try to use LLMs to autonomously complete very difficult tasks using scaffolds that rely extensively on self-critique.) So after reading your post, I now think that current AIs are more misaligned (in the sense you describe) than I previously thought. That said, this update was more quantitative in nature than qualitative. I was certainly aware that current AIs sometimes display the behaviors you describe, even if I thought they were somewhat rarer than I now believe. And I don't think that I personally underrated the importance of these behaviors. (Though it's possible that others did.) [ETA: After thinking about this more, I think I probably was underestimating the importance of these behaviors before reading an early draft of Ryan's post.] Given that, what could I possibly mean when I call current AIs "pretty aligned"? I think there's a hypothetical dual post to yours someone could write titled "Current AIs seem pretty aligned to me." This post would name a bunch of types of misalignment that, a few years ago, seemed could plausibly occur in AIs as capable as current ones. For instance, it seems to me that: 1. Current AIs do not training-game, i.e. actively model the training process and take actions that result in high reward (including in training environments very different from ones they've previously encountered). 2. Current AIs do persistently and coherently pursue malign goals across many contexts, e.g. strategically seeking power. 3. Current AIs do not scheme, i.e. strategically subvert their training process, pre-deployment audits, and other types of oversight. 4. Current AIs do not strategically take steps to cover up their mistakes, and will generally admit to them when asked directly. 5. Current AIs generally don't try to strategically influence things in the real world beyond assisting with (or refusing) the task directly presented to them. E.g. they don't sneakily try to funnel money to causes or people they like by influencing unrelated conversations. Instead, I think the most important way that current AIs are misaligned is the way you describe in your post, Notably, I think the type of misalignment described in your post is "less scary" than the types I list above. The misalignment happens on the "learned heuristics" level, rather than on the "active"/"strategic"/"explicit" level. As a result, it's easier to detect and measure, and less likely to cause harms besides "making the AIs less useful than they could be for certain types of tasks." A related point is that the way that current AIs are misaligned is less "adversarial" than the sorts of misalignment I list above. When I speak to the people who IMO are extremely thoughtful about AI safety, they acknowledge something like "Yeah, TBH a few years ago, I thought that AIs as capable as current ones would be misaligned in some of those ways. There's a real positive update here." But people who are merely "quite" thoughtful sometimes instead: * argue that maybe current AIs are misaligned in some of these ways and we just can't tell * * I'm very skeptical of this! Anyone who thinks this should be trying really hard to produce compelling evidence in current models. * munge definitions to try to count the "misaligned heuristics" types of misalignment as one of these more "coherent" types * claim that they never expected AIs as capable as current ones to be misaligned in these ways * * I'm typically skeptical of this, though I believe it for some people. (A related point that I know how to state more vividly: Sometimes, people like to point at clear alignment issues in frontier models—for instance, sycophancy, AI psychosis, or whatever happened with Sydney/Bing chat—as being negative updates about alignment. But they don't like to count absence of these sorts of alignment issues as being positive updates. In other words, if it hypothetically ended up that AI psychosis were somehow a total media hoax, would the the people who updated negatively on it instead say "Nevermind! Actually, the absence of AI psychosis is a positive update on alignment." Are they currently saying this about the ways that current AIs are not misaligned but could have been?) Overall, my point is that I think current AIs are not misaligned in many of the ways that I—and I think many others—expected them to be. I also think that many people I talk to have not grappled with this observation sufficiently and updated their world models and prioritization accordingly. For instance, I think that the AI safety community is overinvested in scheming research relative to scalable oversight research. (TBC I've believed this for some years now, but I now believe it more confidently and think it's more clear that others should agree with me.) I also think that the AI safety community is underinvested in work on directly automating alignment research. (This is a place where I've changed my mind.) I'll close by emphasizing some things that I do not believe and don't mean to claim in this comment: 1. I don't think that the recent overall evidence has clearly been positive about misalignment risk. For instance, timelines are looking quite short now, which IMO is the main negative update to counteract the positive one I discuss in this comment. 2. I don't think these "scarier" types of misalignment are very unlikely to eventually arise in more capable AIs. Though I do think that we should update that they'll first arise in more capable AIs than we previously thought. This makes it more likely that we'll have human-level or superhuman automated alignment researchers prior to these "scarier" types of misalignment. 3. I don't think that the type of misalignment you name in your post is "not a big deal." In particular, I'm quite worried that "we"(=AI developers or humanity as a whole) bungle the situation because we failed to make AIs capable at difficult-to-evaluate tasks like alignment research or advising us about how to handle things during a rapid intelligence explosion. One possible way we could bungle the situation is to train more capable AIs that are misaligned in some of the "scarier" ways I listed (or in other ways I didn't list). 4. I don't think that current AIs are aligned enough to automate alignment research. (An earlier draft of your post operationalized "pretty misaligned" in this way, which I preferred.)

> I've seen demonstrations of AIs accomplishing [...] tasks that would take humans months to years. Due to this, I tentatively believe that (as of March 1st) the well-elicited 50% reliability time-horizon on ESNI tasks (using only publicly available models) is somewhere between a month and several years Once you are talking about scaling to arbitrary inference costs, I think the relevant notion of time horizon is closer to: "For what T could you solve this task using an arbitrary supply of humans each of whom only works on the project for T hours?" On this framing I think that lots of easily-checkable tasks have much shorter horizons than "amount of time they would take a human to do." That is, I think that you could successfully solve them by delegating to a giant team of low-context humans each of whom makes marginal improvements, and the work from those humans would look quite similar to the AI work. This is particularly true for reimplementation tasks with predefined tests + a reference implementation. In that setting a human can pick a specific failing test, use the reference implementation to understand the desired behavior, and then implement it while using tests to make sure they don't break anything else. For the projects you are describing it seems plausible that a human with a few days could use that workflow to reliably make forward progress and get the project done in a moderate number of steps. (I would expect a giant human team using that methodology to make a huge number of mistakes for anything not covered by the tests, and to write code that is low quality and hard to maintain. But I think we're currently seeing roughly that pattern of failures from AI reimplementation.) It's also very true of vulnerability discovery, since different humans can look in different places and then iteratively zoom in on whatever leads are most promising. So I'm not sure how much of the phenomenon you're observe is "there is a way longer horizon for these tasks" rather than "a more careful definition of horizon is more important for these tasks." Probably some of both, but It seems quite possible that for the tasks you are describing the horizon length is really more like a few days or a week than months or a year. I think it's helpful to try to pull these effects apart, particularly when reasoning about the likely effects of continuing RL schlep: I think that you are seeing good performance on these tasks partly because they are easy to decompose and delegate, and not entirely because AI developers are able to do RL on them. For the very "long horizon" tasks you cite, I think that's the dominant effect. So I wouldn't expect performance to be as impressive on tasks where it would be hard to delegate them to a giant team of low-context humans. I still agree that recent public development seem like evidence for shorter timelines, and I'm not saying my bottom line numbers differ much from yours. Even a more conservative extrapolation of time horizons would suggest more like 2-4 years until you can strictly outperform humans for virtual projects (with human parity somewhat before that).