Ability to solve long-horizon tasks correlates with wanting things in the behaviorist sense

Okay, so you know how AI today isn't great at certain... let's say "long-horizon" tasks? Like novel large-scale engineering projects, or writing a long book series with lots of foreshadowing? [...] And you know how the AI doesn't seem to have all that much "want"- or "desire"-like behavior? [...] Well, I claim that these are more-or-less the same fact.

It's pretty unclear if a system that is good at answering the question "Which action would maximize the expected amount of X?" also "wants" X (or anything else) in the behaviorist sense that is relevant to arguments about AI risk. The question is whether if you ask that system "Which action would maximize the expected amount of Y?" whether it will also be wanting the same thing, or whether it will just be using cognitive procedures that are good at figuring out what actions lead to what consequences.

The point seems almost tautological to me, and yet also seems like the correct answer to the people going around saying “LLMs turned out to be not very want-y, when are the people who expected 'agents' going to update?”, so, here we are.

I think that a system may not even be able to "want" things in the behaviorist sense, and this is correlated with being unable to solve long-horizon tasks. So if you think that systems can't want things or solve long horizon tasks at all, then maybe you shouldn't update at all when they don't appear to want things.

But that's not really where we are at---AI systems are able to do an increasingly good job of solving increasingly long-horizon tasks. So it just seems like it should obviously be an update, and the answer to the original question 

Could you give an example of a task you don't think AI systems will be able to do before they are "want"-y? At what point would you update, if ever? What kind of engineering project requires an agent to be want-y to accomplish it? Is it something that individual humans can do? (It feels to me like you will give an example like "go to the moon" and that you will still be writing this kind of post even once AI systems have 10x'd the pace of R&D.) 

(The foreshadowing example doesn't seem very good to me. One way a human or an AI would write a story with foreshadowing is to first decide what will happen, and then write the story and include foreshadowing of the event you've already noted down. Do you think that series of steps is hard? Or that the very idea of taking that approach is hard? Or what?)

Like you, I think that future more powerful AI systems are more likely to want things in the behaviorist sense, but I have a different picture and think that you are overstating the connection between "wanting things" and "ability to solve long horizon tasks" (as well as overstating the overall case). I think a system which gets high reward across a wide variety of contexts is particularly likely to want reward in the behaviorist sense, or to want something which is consistently correlated with reward or for which getting reward is consistently instrumental during training. This seems much closer to a tautology. I think this tendency increases as models get more competent, but that it's not particularly about "ability to solve long-horizon tasks," and we are obviously getting evidence about it each time we train a new language model.

Relatedly: to imagine the AI starting to succeed at those long-horizon tasks without imagining it starting to have more wants/desires (in the "behaviorist sense" expanded upon below) is, I claim, to imagine a contradiction—or at least an extreme surprise. 

This seems like a great spot to make some falsifiable predictions which discriminate your particular theory from the pack. (As it stands, I don't see a reason to buy into this particular chain of reasoning.) 

AIs will increasingly be deployed and tuned for long-term tasks, so we can probably see the results relatively soon. So—do you have any predictions to share? I predict that AIs can indeed do long-context tasks (like writing books with foreshadowing) without having general, cross-situational goal-directedness.^[1] 

I have a more precise prediction:

AIs can write novels with at least 50% winrate against a randomly selected novel from a typical American bookstore, as judged by blinded human raters or LLMs which have at least 70% agreement with human raters on reasonably similar tasks.

Credence: 70%; resolution date: 12/1/2025

Conditional on that, I predict with 85% confidence that it's possible to do this with AIs which are basically as tool-like as GPT-4. I don't know how to operationalize that in a way you'd agree to.

(I also predict that on 12/1/2025, there will be a new defense offered for MIRI-circle views, and a range of people still won't update.)

1. ^{^}

   I expect most of real-world "agency" to be elicited by the scaffolding directly prompting for it (e.g. setting up a plan/critique/execute/summarize-and-postmortem/repeat loop for the LLM), and for that agency to not come from the LLM itself. 

There’s a thing I’m personally confused about that seems related to the OP, though not directly addressed by it.  Maybe it is sufficiently on topic to raise here.

My personal confusion is this:

Some of my (human) goals are pretty stable across time (e.g. I still like calories, and being a normal human temperature, much as I did when newborn).  But a lot of my other “goals” or “wants” form and un-form without any particular “convergent instrumental drives”-style attempts to protect said “goals” from change.

As a bit of an analogy (to how I think I and other humans might approximately act): in a well-functioning idealized economy, an apple pie-making business might form (when it was the case that apple pie would deliver a profit over the inputs of apples plus the labor of those involved plus etc.), and might later fluidly un-form (when it ceased to be profitable), without "make apple pies" or "keep this business afloat" becoming a thing that tries to self-perpetuate in perpetuity.  I think a lot of my desires are like this (I care intrinsically about getting outdoors everyday while there’s profit in it, but the desire doesn’t try to shield itself from change, and it’ll stop if getting outdoors stops having good results.  And this notion of "profit" does not itself seem obviously like a fixed utility function, I think.).

I’m pretty curious about whether the [things kinda like LLMs but with longer planning horizons that we might get as natural extensions of the current paradigm, if the current paradigm extends this way, and/or the AGIs that an AI-accidentally-goes-foom process will summon] will have goals that try to stick around indefinitely, or goals that congeal and later dissolve again into some background process that'll later summon new goals, without summoning something lasting that is fixed-utility-function-shaped.  (It seems to me that idealized economies do not acquire fixed or self-protective goals, and for all I know many AIs might as be like economies in this way.)

(I’m not saying this bears on risk in any particular way.  Temporary goals would still resist most wrenches while they remained active, much as even an idealized apple pie business resists wrenches while it stays profitable.)

I'm not sure if I fall into the bucket of people you'd consider this to be an answer to. I do think there's something [LW · GW] important in the region of LLMs that, by vibes if not explicit statements of contradiction, seems incompletely propagated in the agent-y discourse even though it fits fully within it. I think I at least have a set of intuitions that overlap heavily with some of the people you are trying to answer.

In case it's informative, here's how I'd respond to this:

Well, I claim that these are more-or-less the same fact. It's no surprise that the AI falls down on various long-horizon tasks and that it doesn't seem all that well-modeled as having "wants/desires"; these are two sides of the same coin.

Mostly agreed, with the capability-related asterisk.

Because the way to achieve long-horizon targets in a large, unobserved, surprising world that keeps throwing wrenches into one's plans, is probably to become a robust generalist wrench-remover that keeps stubbornly reorienting towards some particular target no matter what wrench reality throws into its plans.

Agreed in the spirit that I think this was meant, but I'd rephrase this: a robust generalist wrench-remover that keeps stubbornly reorienting towards some particular target will tend to be better at reaching that target than a system that doesn't.

That's subtly different from individual systems having convergent internal reasons for taking the same path. This distinction mostly disappears in some contexts, e.g. selection in evolution, but it is meaningful in others.

If an AI causes some particular outcome across a wide array of starting setups and despite a wide variety of obstacles, then I'll say it "wants" that outcome “in the behaviorist sense”.

I think this frame is reasonable, and I use it.

it's a little hard to imagine that you don't contain some reasonably strong optimization that strategically steers the world into particular states.

Agreed.

that the wanting-like behavior required to pursue a particular training target X, does not need to involve the AI wanting X in particular.

Agreed.

“AIs need to be robustly pursuing some targets to perform well on long-horizon tasks”, but it does not say that those targets have to be the ones that the AI was trained on (or asked for). Indeed, I think the actual behaviorist-goal is very unlikely to be the exact goal the programmers intended, rather than (e.g.) a tangled web of correlates.

Agreed for a large subset of architectures. Any training involving the equivalent of extreme optimization for sparse/distant reward in a high dimensional complex context seems to effectively guarantee this outcome.

 So, maybe don't make those generalized wrench-removers just yet, until we do know how to load proper targets in there.

Agreed, don't make the runaway misaligned optimizer.

I think there remains a disagreement hiding within that last point, though. I think the real update from LLMs is:

1. We have a means of reaching extreme levels of capability without necessarily exhibiting preferences over external world states. You can elicit such preferences, but a random output sequence from the pretrained version of GPT-N (assuming the requisite architectural similarities) has no realistic chance of being a strong optimizer with respect to world states. The model itself remains a strong optimizer, just for something that doesn't route through the world.
2. It's remarkably easy to elicit this form of extreme capability to guide itself. This isn't some incidental detail; it arises from the core process that the model learned to implement.
3. That core process is learned reliably because the training process that yielded it leaves no room for anything else. It's not a sparse/distant reward target; it is a profoundly constraining and informative target.

In other words, a big part of the update for me was in having a real foothold on loading the full complexity of "proper targets."

I don't think what we have so far constitutes a perfect and complete solution, the nice properties could be broken, paradigms could shift and blow up the golden path, it doesn't rule out doom, and so on, but diving deeply into this has made many convergent-doom paths appear dramatically less likely to Late2023!porby compared to Mid2022!porby.

Well, I claim that these are more-or-less the same fact. It's no surprise that the AI falls down on various long-horizon tasks and that it doesn't seem all that well-modeled as having "wants/desires"; these are two sides of the same coin.

It's weird that this sentence immediately follows you talking about AI being able to play chess.  A chess playing AI doesn't "want to win" in the behaviorist sense.  If I flip over the board or swap pieces mid game or simply refuse to move the AI's pieces on it's turn, it's not going to do anything to stop me because it doesn't "want" to win the game.  It doesn't even realize that a game is happening in the real world.  And yet it is able to make excellent long term plans about "how" to win at chess.

Either:
a) A chess playing AI fits into your definition of "want", in which case who cares if AI wants things, this tells us nothing about their real-world behavior.
b) A chess playing AI doesn't "want" to win (my claim) in which case AI can make long term plans without wanting.

Apologies if I'm being naive, but it doesn't seem like an oracle AI[1] is logically or practically impossible, and a good oracle should be able to be able to perform well at long-horizon tasks[2] without "wanting things" in the behaviorist sense, or bending the world in consequentialist ways.

The most obvious exception is if the oracle's own answers are causing people to bend the world in the service of hidden behaviorist goals that the oracle has (e.g. making the world more predictable to reduce future loss), but I don't have strong reasons to believe that this is very likely. 

This is especially the case since at training time, the oracle doesn't have any ability to bend the training dataset to fit its future goals, so I don't see why gradient descent would find cognitive algorithms for "wanting things in the behaviorist sense."

[1] in the sense of being superhuman at prediction for most tasks, not in the sense of being a perfect or near-perfect predictor.

[2] e.g. "Here's the design for a fusion power plant, here's how you acquire the relevant raw materials, here's how you do project management, etc." or "I predict your polio eradication strategy to have the following effects at probability p, and the following unintended side effects that you should be aware of at probability q." 

Okay, so you know how AI today isn't great at certain... let's say "long-horizon" tasks? Like novel large-scale engineering projects, or writing a long book series with lots of foreshadowing?

(Modulo the fact that it can play chess pretty well, which is longer-horizon than some things; this distinction is quantitative rather than qualitative and it’s being eroded, etc.)

And you know how the AI doesn't seem to have all that much "want"- or "desire"-like behavior?

(Modulo, e.g., the fact that it can play chess pretty well, which indicates a certain type of want-like behavior in the behaviorist sense. An AI's ability to win no matter how you move is the same as its ability to reliably steer the game-board into states where you're check-mated, as though it had an internal check-mating “goal” it were trying to achieve. This is again a quantitative gap that’s being eroded.)

I don't think the following is all that relevant to the point you are making in this post, but someone cited this post of yours in relation to the question of whether LLMs are "intelligent" (summarizing the post as "Nate says LLMs aren't intelligent") and then argued against the post as goalpost-moving, so I wanted to discuss that.

It may come as a shock to some, that Abram Demski adamantly defends the following position: GPT4 is AGI. I would be goalpost-moving if I said otherwise. I think the AGI community is goalpost-moving to the extent that it says otherwise. 

I think there is some tendency in the AI Risk community to equate "AGI" with "the sort of AI which kills all the humans unless it is aligned". But "AGI" stands for "artificial general intelligence", not "kills all the humans". I think it makes more sense for the definition of AGI to be up to the community of AI researchers who use the term AGI to distance their work from narrow AI, rather than for it to be up to the AI risk community. And GPT4 is definitely not narrow AI.

I'll argue an even stronger claim: if you come up with a task which can be described and completed entirely in text format (and then evaluated somehow for performance quality), for most such tasks the performance of GPT4 is at or above the performance of a random human. (We can even be nice and only randomly sample humans who speak whichever languages are appropriate to the task; I'll still stand by the claim.) Yes, GPT4 has some weaknesses compared to a random human. But most claims of weaknesses I've heard are in fact contrasting GPT4 to expert humans, not random humans. So my stronger claim is: GPT4 is human-level AGI, maybe not by all possible definitions of the term, but by a very reasonable-seeming definition which 2014 Abram Demski might have been perfectly happy with. To deny this would be goalpost-moving for me; and, I expect, for many.

So (and I don't think this is what you were saying) if GPT4 were being ruled out of "human-level AGI" because it cannot write a coherent set of novels on its own, or do a big engineering project, well, I call shenanigans. Most humans can't do that either.

If you're the sort of thing that skillfully generates and enacts long-term plans, and you're the sort of planner that sticks to its guns and finds a way to succeed in the face of the many obstacles the real world throws your way (rather than giving up or wandering off to chase some new shiny thing every time a new shiny thing comes along), then the way I think about these things, it's a little hard to imagine that you don't contain some reasonably strong optimization that strategically steers the world into particular states.

It seems this post has maybe mixed "generating" with "enacting". Currently, it seems LLMs only attempt the former during prediction. In general terms, predicting a long-horizon-actor's reasoning is implicit in the task of myopically predicting the next thing that actor would do. For a specific example, you could imagine a model predicting the next move of a grandmaster's or stockfish's chess game (or text in an author's book, or an industrial project description, to use your longer-horizon examples).

The first paragraph of /u/paulfchristiano's response might be getting at something similar, but it seems worth saying this directly.^[1]

1. ^{^}

   (This also seems like a basic point, so I wonder if I misunderstood the post.. but it seems like something isomorphic to it is in the top comment, so I'm not sure.)

I want to mention that for Expected Utility Maximization, if we are focused on behavior, any sequence of behavior is an Expected Utility Maximizer, thus it becomes trivial as everything has the property of EUM, and no predictions are possible at all.

This is noted by EJT here, but it really, really matters, because it undermines a lot of coherence arguments for AI risk, and this is a nontrivial issue here.

https://www.lesswrong.com/posts/yCuzmCsE86BTu9PfA/?commentId=Lz3TDLfevjwMJHqat [LW · GW]

https://forum.effectivealtruism.org/posts/ZS9GDsBtWJMDEyFXh/?commentId=GEXEqLDpwaNET5Nnk [EA · GW]

I think you are failing to distinguish between "being able to pursue goals" and "having a goal".

Optimization is a useful subroutine, but that doesn't mean it is useful for it to be the top-level loop. I can decide to pursue arbitrary goals for arbitrary amounts of time, but that doesn't mean that my entire life is in service of some single objective.

Similarly, it seems useful for an AI assistant to try and do the things I ask it to, but that doesn't imply it has some kind of larger master plan.

Strong agree with long-horizon sequential decision-making success being very tied to wantingness.

I kinda want to point at things like the Good and Gooder [LW · GW] Regulator theorems here as theoretical reasons to expect this, besides the analogies you give. But I don't find them entirely satisfactory. I have recently wondered if there's something like a Good Regulator theorem for planner-simulators: a Planner Simulator conjecture something like, 'every (simplest) simulator of a planner contains (something homomorphic to) a planner'. Potential stepping-stone for the agent-like structure problem [LW · GW]. I also have some more specific thoughts about long-horizon and the closed-loop of deliberation for R&D-like tasks. But I've struggled to articulate these, in part because I flinch when it seems too capabilities-laden.

Any tips?

A follow-on inference from the above point is: when the AI leaves training, and it’s tasked with solving bigger and harder long-horizon problems in cases where it has to grow smarter than ever before and develop new tools to solve new problems, and you realize finally that it’s pursuing neither the targets you trained it to pursue nor the targets you asked it to pursue—well, by that point, you've built a generalized obstacle-surmounting engine. You've built a thing that excels at noticing when a wrench has been thrown in its plans, and at understanding the wrench, and at removing the wrench or finding some other way to proceed with its plans.

And when you protest and try to shut it down—well, that's just another obstacle, and you're just another wrench.

This seems right. "you've built a generalized obstacle-surmounting engine." is maybe the the single best distillation of what's hard about AI risk of that length that I've ever read.

But also, I'm having difficulty connecting my experience with LLMs to the abstract claim that corrigibility is anti-natural.

...Actually, never mind, I just thought about it a bit more. You might train your LLM-ish agent to ask for your permission when taking sufficiently big actions. And it will probably continue to do that. But if you also train in some behaviorist goals, it will steer the world in the direction of the satisfaction of the those goals, regardless of the fact that it is "asking your permission", by priming you to answer in particular ways, or choosing strategies that don't require asking for permission, or whatever. "Asking for permission" doesn't really build in corrigibility, that's just a superficial constraint on the planning process.

Why might we see this sort of "wanting" arise in tandem with the ability to solve long-horizon problems and perform long-horizon tasks?

Because these "long-horizon" tasks involve maneuvering the complicated real world into particular tricky outcome-states, despite whatever surprises and unknown-unknowns and obstacles it encounters along the way. Succeeding at such problems just seems pretty likely to involve skill at figuring out what the world is, figuring out how to navigate it, and figuring out how to surmount obstacles and then reorient in some stable direction.

I think maybe I buy it for planning tasks, which entail responding to surprising events that the world throws at you and getting back on track towards a goal. I'm not sure that I buy it for "design" tasks, like designing a rocket ship or a nanofactory. Those tasks seem like they can maybe be solved in one sweep, the way current LLMs (often) answer my question in one single forward pass through the network.

This seems related to Dennett's Intentional Stance https://en.wikipedia.org/wiki/Intentional_stance

Thanks for writing that. I've been trying to taboo "goals" because it creates so much confusion, which this post tries to decrease. In line with this post, I think what matters is how difficult a task is to achieve, and what it takes to achieve it in terms of ability to overcome obstacles.

This makes sense. I think the important part is not the emergence of agency, but that agency is a convergent route to long-term planning. I'm less worried about intention emerging, and more worried about it being built in to improve capabilities like long-term planning through goal directed search. Agency is also key to how humans do self-directed learning, another super useful ability in just about any domain. I just wrote a short post on the usefulness of agency for self-directed learning: Sapience, understanding, and "AGI" [LW · GW]

The LessWrong Review [? · GW] runs every year to select the posts that have most stood the test of time. This post is not yet eligible for review, but will be at the end of 2024. The top fifty or so posts are featured prominently on the site throughout the year.

Hopefully, the review is better than karma at judging enduring value. If we have accurate prediction markets on the review results, maybe we can have better incentives on LessWrong today. Will this post make the top fifty?

What I would also like to add, which is often not addressed and it gives some positive look, is that the "wanting" meaning the objective function of the agent, it's goals, should not necessarily be some certain outcome or certain end-goal on which it will focus totally. It might not be the function over the state of universe but function over how it changes in time. Like velocity vs position. It might prefer some way the world changes or does not change, but not having a certain end-goal (which is also unreachable in long-term in a stable way as universe will die in some sense, everything will be destroyed with P=1 minus very minute epsilon over enough time).

Why positive? Because these things usually need balance and stabilisation in some sense to retain same properties, which means less probability of drastic measures to get a little bit better outcome a little bit sooner. It might cease controll over us, which is bad, but gives lower probability of rapid doom.

Also, looking on current works it seems more likely for me that those property-based goals will be embedded rather than some end-goal like curing cancer or helping humanity. We try to make robust AGI so we don't want to embed certain goals or targets, but rather patterns how to work productively and safely with humans. Those are more meta and about way of how things go/change.

Note it is more like intuition for me than hard argument.

The question is if one can make a thing that is "wanting" in that long-term sense by combining not-wanting LLM model as short-term intelligence engine with some programming-based structure that would refocus it onto it's goals and some memory engine (to remember not only information, buy also goals, plans and ways to do things). I think that the answer is a big YES and we will soon see that in a form of amalgamation of several models and enforced mind structure.

(Modulo, e.g., the fact that it can play chess pretty well, which indicates a certain type of want-like behavior in the behaviorist sense. An AI's ability to win no matter how you move is the same as its ability to reliably steer the game-board into states where you're check-mated, as though it had an internal check-mating “goal” it were trying to achieve. This is again a quantitative gap that’s being eroded.)

I agree with the main point of the post. But I specifically disagree with what I see as an implied assumption of this remark about a "quantitative gap". I think there is a likely assumption that the quantitative gap is such that the ability to play chess better would correlate with being higher in the most relevant quantity.

Something that chooses good chess moves can be seen as "wanting" its side to do well within the chess game context. But that does not imply anything at all outside of that context. If it's going to be turned off if it doesn't do a particular next move, it doesn't have to take that into account. It can just play the best chess move regardless, and ignore the out-of-context info about being shut down.

LLMs aren't trained directly to achieve results in a real-world context. They're trained:

1. to emit outputs that look like the output of entities that achieve results (humans)
2. to emit outputs that humans think are useful, probably typically with the humans not thinking all that deeply about it

To be sure, at least item 1 above would eventually result in selecting outputs to achieve results if taken to the limit of infinite computing power, etc., and in the same limit item 2 would result in humans being mind-controlled.

But both these items naturally better reward the LLM for appearing agentic than for actually being agentic (being agentic = actually choosing outputs based on their effect on the future of the real world). The reward for actually being agentic, up to the point that it is agentic enough to subvert the training regime, is entirely downstream of the reward for appearance of agency. 

Thus, I tend to expect the appearance of agency in LLMs to be Goodharted and discount apparent evidence accordingly.

Other people look at the same evidence and think it might, by contrast, be even more agentic than the apparent evidence due to strategic deception. And to be sure, at some agency level you might get consistent strategic deception to lower the apparent agency level.

But I think more like: at the agency level I've already discounted it down to it really doesn't look likely it would engage in strategic deception to consistently lower its apparent agency level. Yes I'm aware of, e.g., the recent paper that LLMs engage in strategic deception. But they are doing what looks like strategic deception when presented a pretend, text-based scenario. This is fully compatible with them following story-logic like they learned from training.  Just like a chess AI doesn't have to care about anything outside the chess context, the LLM doesn't have to care about anything outside the story-logic context. 

To be sure, story-logic by itself could still be dangerous. Any real-world effect could be obtained by story-logic within a story with intricate enough connections to the real world, and in some circumstances it wouldn't have to be that intricate.  

And in this sense  - the sense that some contexts are bigger and tend to map onto real-world dangerous behaviour better than others - the gap can indeed be quantitative. It's just that it's another dimension of variation in agency than the ability to select best actions in a particular context.

I'm not convinced that LLMs are currently selecting actions to affect the future within a context larger than this story-level context - a large enough domain to have some risk (in particular, I'm concerned with the ability to write code to help make a new/modified AI targeting a larger context) - but one that I think is still likely well short of causing it to choose actions to take over the world (and indeed, well short of being particularly good at solving long-term tasks in general) without it making that new or modified AI first. 

"Want" seems ill-defined in this discussion. To the extent it is defined in the OP, it seems to be "able to pursue long-term goals", at which point tautologies are inevitable. The discussion gives me strong stochastic parrot / "it's just predicting next tokens not really thinking" vibes, where want/think are je ne sais quoi words to describe the human experience and provide comfort (or at least a shorthand explanation) for why LLMs aren't exhibiting advanced human behaviors. I have little doubt many are trying to optimize for long-term planning and that AI systems will exhibit increasingly better long-term planning capabilities over time, but have no confidence whether that will coincide with increases in "want", mainly because I don't know what that means. Just my $0.02, as someone with no technical or linguistics background.

If we are to understand you as arguing for something trivial, then I think it only has trivial consequences. We must add nontrivial assumptions if we want to offer a substantive argument for risk.

Suppose we have a collection of systems of different ability that can all, under some conditions, solve $X$. Let's say an "$X$-wrench" is an event that defeats systems of lower ability but not systems of higher ability (i.e. prevents them from solving $X$).

A system that achieves $X$ with $1-ϵ$ probability must defeat all $X$-wrenches but those with a probability of at most $ϵ$. If the set of events that are $Y$-wrenches but not $X$-wrenches has probability $\delta$, then the system can defeat all $Y$-wrenches but a collection with probability of at most $ϵ+\delta$.

That is, if the challenges involved in achieving $X$ are almost the same as the challenges involved in achieving $Y$, then something good at achieving $X$ is almost as good at achieving $Y$ (granting the somewhat vague assumptions about general capability baked into the definition of wrenches).

However, if $X$ is something that people basically approve of and $Y$ is something people do not approve of, then I do not think the challenges almost overlap. In particular, to do $Y$, with high probability you need to defeat a determined opposition, which is not likely to be necessary if you want $X$. That is: no need to kill everyone with nanotech if your doing what you were supposed to.

In order to sustain the argument for risk, we need to assume that the easiest way to defeat $X$-wrenches is to learn a much more general ability to defeat wrenches than necessary and apply it to solving $X$ and, furthermore, this ability is sufficient to also defeat $Y$-wrenches. This is plausible - we do actually find it helpful to build generally capable systems to solve very difficult problems - but also plausibly false. Even highly capable AI that achieves long-term objectives could end up substantially specialised for those objectives.

As an aside, if the set of $Y$-wrenches includes the gradient updates received during training, then an argument that an $X$-solver generalises to a $Y$-solver may also imply that deceptive alignment is likely (alternatively, proving that $X$-solvers generalise to $Y$-solvers is at least as hard as proving deceptive alignment).