# On restraining AI development for the sake of safety

*(Podcast version, read by the author,* [*here*](https://joecarlsmithaudio.buzzsprout.com/2034731/episodes/18869440-on-restraining-ai-development-for-the-sake-of-safety)*, or search for "Joe Carlsmith Audio" on your podcast app.*

*This is the tenth essay in a series I’m calling “*[*How do we solve the alignment problem?*](https://joecarlsmith.com/2025/02/13/how-do-we-solve-the-alignment-problem)*”. I’m hoping that the individual essays can be read fairly well on their own, but see* [*this introduction*](https://joecarlsmith.com/2025/02/13/how-do-we-solve-the-alignment-problem) *for a summary of the essays that have been released thus far, plus a bit more about the series as a whole.*

*I work at Anthropic, but I am here speaking only for myself and not for my employer.)*

1\. Introduction
================

In the [third essay in the series](https://joecarlsmith.com/2025/03/11/paths-and-waystations-in-ai-safety), I distinguished between three key “security factors” for developing advanced AI safely, namely:

*   *Safety progress*: our ability to develop new levels of AI capability safely.
*   *Risk evaluation:* our ability to track and forecast the level of risk that a given sort of AI capability development involves.
*   *Capability restraint*: our ability to steer and restrain AI capability development when doing so is necessary for maintaining safety.

![](data:image/png;base64,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)

  
A lot of my focus in the series has been on safety progress – and to a lesser extent, risk evaluation. In this essay, I want to look at capability restraint, in particular, in more detail.

The basic case for the importance of capability restraint is obvious. Safety progress takes time. If you don’t have enough time, at each stage of AI development, to ensure the AIs that you build in the next stage won’t destroy humanity, then you’ll fail, and humanity will be destroyed. [Automated alignment research](https://joecarlsmith.com/2025/04/30/can-we-safely-automate-alignment-research) helps a lot here, and that’s a core reason I think it’s so important (human alignment researchers are too scarce and slow). But even if AIs are doing most or all of the alignment work, it still matters how much time they have.

Indeed, my sense is that sometimes, arguments about capability restraint fail to grapple directly enough with this basic logic. Of course, opponents of capability restraint can deny that there are any realistic scenarios where, if we proceed forward with AI development, the human species will be killed or disempowered. But to the extent they admit that some such scenarios are realistic, it seems to me, their basic position amounts to something like: “in scenarios where significantly restraining AI development is required in order for humanity as a species to survive, then our plan is to die.” We should try hard to do better than this.[^-McnSgqW9N8E9vgohj-1]

Indeed, as I’ll discuss below, I think the case for idealized forms of capability restraint – and especially, for giving ourselves the *option* to engage in capability restraint if we get stronger evidence that it’s necessary for safety (i.e. “building the brakes”) – is quite strong. That is, I think a wiser and more coordinated civilization would likely be employing quite a lot of capability restraint in building advanced AI, especially as we start to approach transformatively powerful systems – and this despite the potential costs of delaying the benefits of safe superintelligence both to present-day people and to our civilization’s broader existential security.

The hard questions, in my opinion, come down to the feasibility and desirability of various forms of capability restraint in practice, especially in an international context (I’m comparatively optimistic about domestic regulation – though still fairly pessimistic in a more absolute sense). Here, I don’t think we should dismiss the possibility of extraordinary degrees of domestic and international effort aimed at ensuring AI safety. Political will in this respect might change dramatically as AI starts to transform the world, and when there is a lot of political will, there are many precedents for effort on the relevant scale. What’s more, the fact that frontier AI research, training, and inference currently relies so heavily on large amounts of compute, sourced from a very specific supply chain, provides efforts at capability restraint a significant source of leverage.

That said: even in the context of serious political will, efforts at capability restraint face significant barriers. For example:

*   Restraint aimed at *algorithmic* progress is significantly harder than restraint aimed at compute access, because algorithmic research is harder to monitor/verify/prevent. This means that as algorithmic progress continues, whatever compute escapes a given effort at restraint becomes increasingly potent as a source of AI capability, at least given access to frontier algorithms. Depending on various quantitative parameters, this could significantly limit the duration and stability of a given effort at capability restraint, especially in the context of international agreements between nations that don’t trust each other not to race forward in whatever ways the relevant governance regime can’t catch.
*   Also: the design of a given international regime of capability restraint becomes more challenging if you move beyond what I call “red-lighting” (that is, simply halting any further progress) to also incorporate options for ongoing alignment research during the relevant slow-down (“safety progress”) and approval of further steps of capabilities progress once suitable safety has been achieved (“green-lighting”). For example, I think it will be difficult (though perhaps not impossible) to set up multi-lateral international institutions for conducting safety evaluations and approving ongoing forms of AI development without also sharing algorithms and other sensitive IP amongst participating nations. It’s possible that the security situation will be bad enough by default that this won’t matter much, but if not, I worry that it could be an especially hard sell (as well as a force for centralization of AI development). And while it’s possible to simply say “let’s focus on red-lighting for now, and figure out a viable approach to safety progress and greenlighting once the situation is more secure,” if you can’t achieve fully effective global red-lighting for a long period, questions about safety progress and greenlighting might become urgent quite quickly.

What’s more: there are a number of important ways in which efforts at capability restraint can end up net negative – both with respect to safety, and with respect to trade-offs between safety and other civilization-scale AI risks. Here, I am most centrally concerned about ways in which many versions of international capability restraint tend to concentrate power and centralize AI development (especially to the extent they aim to be compatible with safety progress and greenlighting, which I think they probably should be), and about efforts at capability restraint ceding competitive ground to authoritarian regimes unwisely. And there are various other salient concerns as well – e.g., about poorly calibrated or executed efforts at capability restraint leading to backlash or polarization without a corresponding gain in safety; and about building up “overhangs” that end up harming a civilization that hasn’t adapted to built-up AI progress.

Overall, my current view is that despite these uncertainties and trade-offs, we should try hard to put ourselves in a position to steer or restrain AI development when doing so is necessary for safety; that even limited or temporary success in this respect can make a significant positive difference; and that more thoroughgoing success remains, at least, a live possibility. However, I also think that safety-concerned advocates for capability restraint should acknowledge the genuine uncertainties and trade-offs it involves, and remain attentive to the possibility that as we learn more (both about alignment risk and about the broader situation), these trade-offs can tip the balance *against* available options for real-world capability restraint – and this, even, while significant residual misalignment risk remains in play. And while I support significant effort going towards capability restraint, I disagree with advocates who argue that the AI safety community should effectively give up on making technical safety progress, and pivot almost exclusively to promoting capability restraint.[^-McnSgqW9N8E9vgohj-2]To the contrary, and even if you expect alignment to be difficult, serious effort towards actually learning how to make AIs safe (and especially: to make [automated alignment researchers](https://joecarlsmith.com/2025/04/30/can-we-safely-automate-alignment-research) safe) still seems to me extremely worth it.

*Thanks especially to Katja Grace, David Krueger, Thomas Larsen, and Toby Ord for discussion.*

2\. Preliminaries
=================

I’m centrally interested, here, in efforts at capability restraint specifically motivated by concern about the sort of loss of control scenarios that this series is focused on. And one salient way to be skeptical of such efforts is simply to not take risks from loss of control seriously. That is, obviously, if you think it is silly to worry about losing control over superintelligent AI agents in a manner catastrophic for humanity, then you will also think that serious, costly efforts to avoid this outcome are silly as well. But I am not interested, here, in this sort of objection (though: it’s extremely relevant to understanding how different efforts at capability restraint will be received).

Rather, I’m going to assume (per the series as a whole) that loss of control scenarios are worth taking seriously, and thus, that the basic logic I outlined in the introduction applies – i.e., “in some realistic scenarios, if we don’t significantly restrain AI development to provide more time to alignment research, then the entire human species will be killed or disempowered.” The objections to capability restraint I’m interested in have to actually grapple with this basic logic, rather than to ignore or deny it. Indeed, if your main objection to capability restraint is “but existential risks from uncontrolled superintelligence are silly/speculative/extremely unlikely,” then I think it’s good to ask what your attitude towards capability restraint would be if you changed your mind (and also: what it would take for your mind to change).

Of course, it is possible to support efforts at capability restraint out of concern for a whole host of AI-related risks and problems, other than alignment. Some of these – e.g., AI-fueled concentrations of power – I’ll discuss explicitly below. Others – e.g., bioweapons, mass unemployment – I won’t focus on. In general, my guess is that if you factor in these other risks from AI, the case for capability restraint becomes stronger, but the specific dynamics will depend on the risks in play and the form of capability restraint in question. And in some cases (e.g., risks from concentrations of power), I think the sign of certain kinds of capability restraint is quite unobvious.

Regardless, though, my aim here is not to provide a comprehensive analysis of the many factors that might play into a case for or against a given form of capability restraint – and still less, all the possible forms. Rather, I want to focus on what I see as the most important safety-related considerations, and the most salient ways that safety-motivated efforts at capability restraint could end up harmful on net.

3\. AI development isn’t necessarily a prisoner’s dilemma
=========================================================

Before jumping into the details, I want to start with a simple, high-level conceptual point about the game theory of capability restraint. It’s not novel; but I think it’s important to be clear about.[^-McnSgqW9N8E9vgohj-3]

People sometimes think that AI development in the context of existential risk from AI is like a prisoner’s dilemma. In particular, the thought goes: because of the existential risk at stake, two different actors would prefer an outcome where they both go slower to one where they both rush forwards. However, no matter what the other actor does, all actors will likely be incentivized to keep racing forwards regardless, due to some combination of (a) expecting their own AI development to be safer than their competitor’s, and (b) wanting to win the race in the cases where they avoid catastrophe.

This sort of model strongly suggests that creating a credible agreement (or some other mechanism with a similar effect) to avoid racing forward dangerously should be a key goal of AI governance – thereby, in effect, “changing the game.” And broadly speaking, I agree – at least, if creating such an agreement were feasible (more below). But I think this first-pass model of the incentives at stake might make the situation seem harder than it is.

In particular: in an actual prisoner’s dilemma, no matter what the other party does, you have an incentive to defect (and vice versa). So while you both prefer mutual cooperation over mutual defection, still: modulo external mechanisms or fancy decision theories, you get mutual defection regardless.

But AI development isn’t necessarily like that. In particular: *if the other party is going to cooperate, it can easily be the case that you have an incentive to cooperate, too* – not because of some external mechanism or fancy decision theory, but simply because the costs of catastrophe are so high.

Thus, to take a simple model of a race between two countries: suppose you think that if you build ASI and they don’t, there’s a 50% chance of extinction, and a 50% chance of a you-empowered global order, whereas if your competitor builds ASI first, there’s a 60% chance of extinction and a 40% chance of a them-empowered global order. If you prefer a you-empowered global order to a them-empowered one, and you prefer lower chances of extinction overall, then if you assume that your competitor is going to rush forwards, there’s a strong argument for rushing forwards yourself as well – namely, your being first implies *both* a lower risk of extinction and a better non-extinction outcome. (Though: if you think that you’re worse on safety than your competitor, then you have to weigh the extra extinction risk against your preference for a you-empowered global order, and the argument becomes more complicated.)

But what if they *aren’t* going to rush forwards? In that case, in an actual prisoner’s dilemma, the answer would be the same: you should rush forwards regardless. And the same holds for the traditional logic of a basic “arms race,” where regardless of whether your opponent chooses to build more weapons, you have an incentive to keep building (see [Grace (2022)](https://www.lesswrong.com/posts/uFNgRumrDTpBfQGrs/let-s-think-about-slowing-down-ai#The_arms_race) for more).

But this isn’t necessarily the right answer in the context of AI. In particular, in the game above, the key question is what happens if *neither* of you rushes forwards. If we assume that both of you going slower can create meaningful reductions in the eventual risk of extinction, then it can easily be the case that if your opponent *isn’t* going to rush forwards, then *you shouldn’t either* – not because of any external mechanism or fancy decision theory, but simply because it is *worse for you* if, as they say, “anyone builds it,” including yourself.

Thus, suppose that neither of you rushing results in a much lower eventual risk of extinction – say, 10% – and a global order that is somewhere between a you-empowered one and them-empowered one in expected value. Now, if we assume that your opponent *isn’t* rushing forwards, then you rushing forward creates an extra 40% risk of extinction relative to worlds where neither of you rush. And it can easily be the case that whatever improvements to the expected non-extinction global order you get by rushing aren’t worth that cost.

At the least, then, some versions of the game-theoretic logic at stake in AI existential risk can lead to multiple stable equilibria: if they are going slow, you want to go slow; if they are rushing, you want to rush; and in both cases, no one wants to change their move while holding the other’s fixed. This is more similar to a “[stag hunt](https://en.wikipedia.org/wiki/Stag_hunt)” than a traditional prisoner’s dilemma. And while stag hunts still require choosing the beneficial equilibria together, they’re generally a more optimistic scenario.

What’s more, depending on the payouts at stake if you both rush, it can even be the case that there is only *one* rational equilibrium: namely, both of you going slow. This could happen, for example, if your rushing creates *additional* extinction risk, on top of the risk that their rushing creates (e.g., maybe failure of your alignment efforts isn’t fully correlated with failure of theirs), without enough corresponding benefit in the non-extinction outcomes.

Of course, the actual incentives here depend on the more specific parameters, and on the broader set up of the game in question. And of course: very importantly, the relevant “players” may have false beliefs about the expected outcomes of different actions, they may have values that tilt towards more socially-destructive actions overall, and/or they may not behave rationally more generally. Indeed, these sorts of factors can make the situation importantly unlike (and in this case: worse than) a stag hunt or a prisoner’s dilemma in practice, because at least one actor (for example, one who doesn’t think that AI alignment risks are real) may have values or beliefs such that they will rush forward regardless of what they think anyone else is doing (hence: not like a stag hunt), and such that they wouldn’t even want to sign up for a regime in which everyone slows down rather than everyone rushing forward (hence: not like a prisoner’s dilemma).[^-McnSgqW9N8E9vgohj-4]

My point isn’t that the overall game theory here actually favors slowing down even absent mechanisms for enforcing cooperative behavior. Rather, my point is that we shouldn’t *assume* that the game theory here – and in particular, the game theory between some select set of actors, like the US and China – involves the sort of especially destructive incentives at stake in a prisoner’s dilemma/arms race in particular.

I emphasize this, in part, because our background assumptions about the incentives here can matter a lot. In particular: in a stag hunt, path dependence in the equilibria you choose means that it can make a big difference if you assume that your opponent will defect vs. remaining open to mutual cooperation, and prophecies here can be self-fulfilling. That is, if we go around with an attitude like “obviously our opponents will defect, so obviously we will too” – then, perhaps, yes indeed. But to the extent mutual cooperation remains a live possibility, then it can become more likely the more credibly we signal willingness to play our part.

4\. Forms of capability restraint
=================================

Let’s look at some different forms of capability restraint in more detail.

In my essay on [paths and waystations](https://joecarlsmith.com/2025/03/11/paths-and-waystations-in-ai-safety), I offered a number of paradigm examples of capability restraint, namely:

*   caution on the part of individual actors;
*   restrictions on the options for AI development available to a given actor (e.g., via limitations on compute, money, talent, etc);
*   voluntary coordination aimed at safety (e.g., via mutual commitments, and credible means of verifying those commitments);
*   enforcement of pro-safety norms and practices (e.g., by governments);
*   other social incentives and attitudes relevant to decision-making around AI development/deployment (e.g. protests, boycotts, withdrawals of investment, public wariness of AI products, etc).

I expect that all of these have a role to play, and I won’t analyze them each in detail. We can generally distinguish, though, between forms of capability restraint that a single actor is in a position to engage in on their own – what we might call "*individual capability restraint*” – vs. forms of “*collective capability restraint*” that involve multiple actors slowing down as a result of some interaction between them or with some external actor or actors (e.g. a government regulatory apparatus).

4.1 Individual capability restraint
-----------------------------------

Thus, a classic form of individual capability restraint is what we might call “burning a lead.” Here, one actor gains enough breathing room in the AI race that they are able to slow down unilaterally and spend more time on safety, without thereby actively falling behind. Another form is what we might call “dropping out of the race” – that is, slowing down or stopping *even when it means you are going to fall behind*, e.g. because the risks of pressing forward are too high, because you have decided that you yourself destroying humanity is not acceptable even if someone else is going to do it anyways, because you are aiming to marginally reduce pressures to race, or because you are aiming to enact the sort of policy you hope everyone in your position would enact, even if you don’t actually expect them to do so. Here, dropping out of the race is compatible with continuing to provide public goods like safety research. Indeed, it would likely allow you to use more of your resources on safety – though, at the cost of falling behind the frontier.

One key advantage of individual capability restraint is that it doesn’t require similar slow-downs from other actors: a single cautious actor can just do it. And for related reasons, some of the possible costs of capability restraint – for example, tendencies to centralize power over AI development – apply to it less forcefully (the drive towards centralization of power comes most directly from the need to ensure *collective* capability restraint in particular).

However, these advantages are also closely tied to ways in which individual capability restraint can easily end up inadequate to address the sort of race dynamics that make the AI safety problem so challenging. In particular: in the context of burning a lead, you only have as much time as your lead has bought you – and if everyone is racing ahead (even in an effort to get a lead they can then burn), this might not be much. And in the context of dropping out of the race, whichever actors stay *in* the race will continue to pose the sorts of risk you were aiming to avoid (though dropping out in order to provide public-good safety research can help somewhat in this respect).

4.2 Collective capability restraint
-----------------------------------

This is the sort of issue that motivates *collective* capability restraint in particular. Possible variants of this include:

*   Multiple actors in the race cooperate in an effort to slow down together (e.g., voluntary coordination on safety norms).
*   Actor A actively intervenes on actor B in order to slow them down (e.g., export controls, cyber-attacks on datacenters), and then engages in individual capability restraint themselves.[^-McnSgqW9N8E9vgohj-5]
*   Some third-party coordinating actor, not a part of the race themselves, intervenes to slow down multiple other actors in the race (e.g., a domestic government implementing safety-focused regulation, without also nationalizing AI development).

Of course, these variants can blur and combine: for example, multiple actors can cooperate to empower a third-party to verify and enforce norms (e.g., setting up an international body like the International Atomic Energy Agency), and they can even cooperate in order to make it easier (e.g., cooperating to build data-centers in ways that are vulnerable to attack in case cooperation breaks down). And different sorts of capability restraint can apply to different combinations of actors: for example, maybe individual companies within a country slow down as a result of safety-focused domestic regulation, but race dynamics between countries require some other mechanism.

Indeed, my own take is that we should be comparatively optimistic about domestic regulation, in particular, as a mechanism for collective capability restraint between different companies within a given country (and plausibly, also, within close allies of that country). Of course, domestic regulation can go wrong in a lot of ways, and many people (understandably) bring their priors about regulation-in-general to questions about regulation of AI in particular. But while priors here can matter, the stakes of unsafe AI development (i.e., human extinction) also make it an importantly unusual case, and we should look at it on its own terms. Indeed, to my mind, if unregulated AI development would pose meaningful risks to the entire human species, especially in the context of race dynamics wrought partly (though not wholly) of difficulties with suitable coordination, then it seems extremely natural to use domestic regulation to help address the problem. After all, one of the paradigm functions of the state is to help resolve problems that require coordination that private actors won’t engage in by default. And while you can argue that we are over-zealous in the regulation we already do in the context of other industries – e.g. flight, pharmaceuticals, banking, and nuclear power – if a private company is going to take on meaningful risks of killing every living human if their case for safety fails, it seems to me quite reasonable to expect them to make safety cases that are at least as detailed as the ones we currently ask for in the context of e.g. nuclear power or commercial flight, and for it to be illegal to proceed unless a neutral third party certifies that clear safety standards are met. This is vastly more than AI companies currently need to do.

The harder questions, in my opinion, arise at the international level. In particular: absent some kind of capability restraint at the international level, restraint at the domestic level will only go so far (e.g., some other nation will be less restrained) – and the threat of this kind of international competition makes the case for domestic restraint more complicated. What’s more, while we already have an established apparatus – namely, national government – for setting and enforcing regulation at a domestic level, there is no entity that plays an equivalent role on the international stage, nor is it at all clear that the creation of such an entity – an entity akin to a world government, with a state-like monopoly on violence across the world as a whole – would be desirable even if it would significantly lower alignment risks, given the other risks that this kind of centralization of power can pose (more below).

That said, we do have other existing mechanisms for engaging in coordination and regulation at the international level – e.g., treaties (e.g. the Chemical Weapons Convention), international inspection and standard-setting-bodies (the IAEA), norms that some nations decide to enforce on others (e.g. export controls) – together with some emergent dynamics like mutually assured destruction that can play a stabilizing role. All of these seem like they could play a role in an effective regime of international capability restraint; and insofar as the stakes of risks from superintelligence are genuinely unprecedented, it could easily be worth genuinely unprecedented efforts at international coordination to address them.

Regardless: because collective international capability restraint is the hardest case, it will generally be the paradigm case I focus on below.

4.3 Treatment of ongoing AI development
---------------------------------------

I’ll also note one other source of variation in different sorts of capability restraint: namely, their treatment of ongoing AI research and development.

In particular: we can see various regimes of capability restraint as having to craft an approach to all of the following goals simultaneously:

1.  *Red-lighting*: stopping the development of potentially-dangerous levels of AI capability while such levels remain unsafe (e.g., holding off on the next training run or the next deployment).
2.  *Safety progress*: Allowing for ongoing AI research aimed at making those forms and levels of AI capability safe (e.g., doing enough research to become confident that doing that next training run or deployment would be safe).
3.  *Green-lighting*: Allowing for relevant actors to proceed to the next level of AI capability once it is safe to do so (e.g., approving a new training run or deployment).
4.  *Benign applications*: Allowing for less dangerous forms of AI research and development to proceed unencumbered.

The most extreme forms of capability restraint (i.e. “just shut down basically all AI development indefinitely”) focus solely on red-lighting – albeit, perhaps, with some allowing for applications that we can be extremely confident are benign. In doing so, though, they can fail to grapple with some of the trickiest dynamics. In particular, as I’ll discuss below, part of what makes capability restraint difficult is *also* allowing for the sort of AI alignment research that the most paradigmatic form of restraint is supposed to buy time for, and in crafting the sort of apparatus for evaluation and approval that allows for green-lighting when suitable safety is achieved.

Of course, one can treat the trickiness at stake in doing anything other than “shut it all down indefinitely” as a reason to just focus solely on red-lighting everything that could be at all dangerous, without a plan for ongoing safety progress or future greenlighting beyond “we’ll figure that part out once we’ve stabilized the situation” or “when there is widespread scientific and public consensus.” And indeed, if you are suitably pessimistic about alignment research within anything like the current AI paradigm, and/or suitably scared about this research leading to breakthroughs in capabilities, then you can argue that we shouldn’t be doing *any* traditional alignment research with the time bought via red-lighting – rather, either we should be giving up on advanced AI entirely, or we should be pivoting to trying to develop technologies for [enhanced human labor](https://joecarlsmith.com/2025/03/11/paths-and-waystations-in-ai-safety#5-sources-of-labor) that can then help us going forwards (though at some point, even this enhanced human labor may need to start doing alignment research on actual AI systems, at which point the same problems related to safety progress and green-lighting re-arise).[^-McnSgqW9N8E9vgohj-6]I discuss some of these issues in more depth in an appendix below, on what we are using the time bought by capability restraint *for*.

My own take, though, is that efforts to think through viable regimes of capability restraint probably shouldn’t be solely focused on red-lighting. This is partly because, as I’ll discuss below, I think it will likely be difficult to sustain e.g. multi-decade forms of red-lighting in the face of factors like the large number of actors with the potential to develop AI unsafely, the possibility of parties to a restraint coalition pulling out (e.g., due to changes in political leadership), and the difficulty of verifiably restricting algorithmic research. And especially in the context of duration-limitations of this kind, I see allowing for *actual alignment research on actual AI systems* – including, importantly, automated alignment research – as the key goal of capability restraint.[^-McnSgqW9N8E9vgohj-7]What’s more, approaches to capability restraint that don’t come with serious plans for green-lighting (and for allowing benign applications) seem to me less likely to be adopted.[^-McnSgqW9N8E9vgohj-8]That said: it is indeed possible that doing anything other than pure global red-lighting is too complicated, at least initially, but a regime focused purely on red-lighting is still worth it – e.g., because the danger of further capabilities progress is so extreme.

More generally, I think that we may well end up misled if we think of capability restraint centrally in terms of a single “pause” during which we make some large amount of alignment progress, after which, if the “dam breaks,” then the race resumes unaltered. Rather, to my mind, the most reasonable and desirable regimes of capability restraint have a structure much more akin to a “responsible scaling policy,” wherein the relevant mechanisms for restraint continue to apply at each new level of development (we do not assume that the problem gets “solved” once and for all), but where there is also reasonable provision for actually *satisfying* the standards required for greenlighting the next stage – and also, for making ongoing, context-sensitive decisions about the costs and benefits of moving to the next stage (e.g., if the efficacy of a given regime is imperfect and unapproved projects are catching up, the safety standards it makes sense to implement might alter). Of course, the technical landscape could still render the relevant standards difficult to meet. But I don’t think we should ignore the question of what those standards should be, how they should adjust in response to gaps in the efficacy of the restraint regime, how we will make progress in satisfying them, and how their satisfaction should be evaluated and approved – especially given that, as I’ll discuss below, these questions have important implications for the sorts of governance regimes we will need.

That said, in thinking about various approaches to greenlighting and benign applications, I also expect us to be misled by thinking of AI development too much in terms of a one-dimensional notion of “AI capability,” progress along which culminates in superintelligent AI agents of the type at stake in paradigm loss of control scenarios. In particular: the design space for promoting “benign applications” that don’t raise traditional concerns about power-seeking (i.e., narrow AI systems, less agentic AI systems, AI systems with more myopic goals, etc) may be quite large, and it could in principle unlock many of the same benefits that more dangerous systems would also allow for. Thus, for example, [Aguirre (2025)](https://keepthefuturehuman.ai/) advocates for giving up on AIs systems that combine generality, autonomy, and high capability, and focusing on building “tool AI” instead – but he hopes that “tool AI” can do a lot of what we wanted. My own guess is that the trade-offs here will bite harder than strategies like Aguirre’s tend to hope for, and I think advocates of capability restraint should be wary of downplaying some of the potential trade-offs at stake. But I do think it’s important to keep in mind that capability restraint isn’t just about saying “red-light” or “green-light” – it can also be about actively *steering*.[^-McnSgqW9N8E9vgohj-9]

5 Idealized capability restraint
================================

With these different forms of capability restraint in mind, let’s turn to some of the more substantive questions about the feasibility and desirability of safety-focused capability restraint. Here I’m going to first look at the general dynamics at stake in *idealized* forms of capability restraint – i.e., where the restraint in question is fully effective across all relevant actors, where it can be started and stopped via rational decision-making, and where it occurs with minimum negative side effects other than those strictly implied by slowing AI development. Obviously, this degree of idealization is importantly – indeed, maybe dangerously – distant from the practical reality. But even in this idealized context, it can be less clear than you might think when exactly capability restraint would be desirable, and I think the considerations that make it unclear are important to bear in mind.

In particular: even in a fully idealized regime, capability restraint isn’t costless. Rather, it implies delaying whatever unique benefits safe forms of potentially-dangerous AI development would allow for. And if these benefits are significant, then the costs of the delay can matter.

[Bostrom (2026)](https://nickbostrom.com/optimal.pdf) analyzes one version of this dynamic – albeit, one focused solely on the interests of the presently-existing people.[^-McnSgqW9N8E9vgohj-10]Bostrom argues that from this perspective, even idealized regimes of capability restraint generally suggest that only fairly minimal slow-downs are net positive for the quality-adjusted expected lifespans of the people in question – e.g., at maximum, perhaps a slow-down of a few years once you have the most advanced systems you can make safe; but not, for example, a decade or more. Here the basic argument is that if we assume that successful creation of safe superintelligence grants significant extensions and improvements in human life, and given that every year of additional delay involves some background risk of death for existing people, then what matters, to a first approximation, is whether the *reduction* in the risk of death-from-superintelligence you are getting for each unit of delay suitably outweighs the background risk of death you incur during the relevant time.[^-McnSgqW9N8E9vgohj-11]And this conclusion holds even if the initial risk of death-from-superintelligence is very high – e.g., 99%. What matters, roughly, is how fast it *falls*.[^-McnSgqW9N8E9vgohj-12]

Of course, in thinking about risks to the entire future of humanity, we shouldn’t just consider the interests of present-day people (indeed, to ignore future people in this context would seem to me a morally glaring omission). And there are further questions as to whether Bostrom’s depiction of the interests of present-day people is accurate either to their actual or more idealized values.[^-McnSgqW9N8E9vgohj-13]However, some structurally similar considerations apply at the level of human civilization as well. That is, just as individual humans have some background rate of death in our current condition, so too does our civilization have some background rate of existential risk from threats like engineered pandemics, nuclear war, and so on. And just as safe superintelligence could significantly lower the background rate of individual death, plausibly it could significantly lower the background rate of other existential risks as well (though: it could also create new threats as well – and indeed, risks from vectors like synthetic biology are plausibly increasing as we approach more and more advanced AI systems). And on this model, insofar as we think of civilization as focused centrally on making it to a flourishing, post-superintelligence future (an assumption with its own normative baggage), then again what matters is whether the *reduction* in the risk of death-by-superintelligence that our civilization is buying per unit of delay outweighs the additional existential risk it incurs during the relevant period.

Working through either of these models – e.g., Bostrom’s model of the interests of present-day people, or an equivalent model focused on the quality-adjusted lifespan of our civilization as a whole – can get complicated fast. At a high-level, though, my own take is that across a variety of parameters, and especially if, like me, you think that our approach to this issue should be placing a large amount of weight on the interests of future generations, then it seems likely, on simple versions of these models, to be worth at least a few years of delay, and potentially quite a bit longer. Here my basic intuition is that the background rate both of individual death and existential risk aren’t that high, and it seems very easy, if you start out with a moderately high risk of catastrophe from loss of control, for the reduction in catastrophe risk that you’re getting during at least the initial period of delay to outweigh that risk of dying or succumbing to a different existential catastrophe during that time. Thus, for example, if we use an average background death risk of .75% per year for individuals (~60 million deaths per year out of ~8 billion people),[^-McnSgqW9N8E9vgohj-14]and a background non-AI-existential-catastrophe risk of .1% per year (roughly corresponding to a 10% risk over a century without AI[^-McnSgqW9N8E9vgohj-15]), then even if we assume that safe superintelligence totally wipes out both risks, you only need to be getting somewhere between a .1-1% percentage point reduction in misalignment-risk per year in order for continued delay to be worth it. And this seems to me quite easy to achieve – maybe not for decades, but at least for years.[^-McnSgqW9N8E9vgohj-16]

Note, too, that if you can also drive down background risk of individual death or existential risk during the slow-down, e.g. using less-than-superintelligent AI tools, then the argument for slow-down strengthens. Of course, the opposite dynamic also holds: that is, if the world becomes more dangerous during the slow-down, whether to individuals or to civilization as a whole, then the slow-down becomes more costly. For example, plausibly the background risk of biological catastrophe rises significantly as AI democratises relevant forms of capability, and our defenses may not improve fast enough to compensate (this is one argument for pausing *earlier*, if you are going to pause). That said: it also matters how much that rate *reduces* after the invention of safe superintelligence – and while I do think that superintelligence can ultimately help our civilization reach significant levels of existential security, it seems unsurprising to me if the years *after* the creation of safe superintelligence still involve significant dangers of other forms.

I also think we should be wary about various backdrop assumptions that these idealized models can smuggle in, even granted that we’re up for some form of idealization in general. For example:

*   The basic dynamic driving these models tends to rest on values and empirical assumptions that imply that if we knew we weren’t going to make any further progress in alignment, then it would be “worth the gamble” overall to build superintelligence rather than to e.g. pursue the benefits it unlocks via other means – and this, even, if the unavoidable risk of human extinction is very high (e.g., 95%). Here, again, we see some of the ways these models depart from psychological realism – if you are obsessively focused on the benefits of a future with safe superintelligence (whether from a more selfish/person-affecting perspective, or a more impartial one), then you might be willing to take a 95% chance of death/extinction for the sake of a 5% chance of this upside. But for most people, this is very far from their default stance – and this, especially, if that upside (or a significant fraction of it) may be available via other means.
*   Simple versions of idealized models like this don’t always incorporate the possibility of learning more about the situation as you go, and adjusting course accordingly.[^-McnSgqW9N8E9vgohj-17]But this possibility is extremely relevant to what it would look like for a rational actor to engage in capability restraint in this kind of idealized condition – and it obviously matters for more real-world forms of capability restraint as well. That is, especially in an idealized context, a rational actor doesn’t need to commit ahead of time to a given length of capability restraint: rather, it can make decisions as it goes, and as it learns more about the situation. Indeed, if we get suitably idealized about the context we’re considering, then the case for at least preserving the *option* to pause further development becomes over-determined – a rational actor will always want to at least have this *option* available.[^-McnSgqW9N8E9vgohj-18]
*   Relatedly, simple versions of these models tend to focus on a single period of pausing, followed by a resumption of a race forward. As I discussed above, though, I think that the best regimes of capability restraint involve ongoing evaluation, and ongoing possibilities for further restraint, as new levels of capability get developed (plus, potentially, more fine-grained sorts of steering towards forms of development with a better cost-benefit trade-off). And in those contexts, the question isn’t about the costs and benefits of letting-’er-rip, but rather about the costs and benefits of taking-the-next-step, and then re-orienting from there.
*   The shape of various risk curves can matter a lot to whether the basic set-up I used above makes sense. For example, suppose that in each year, you face a background bio x-risk of .1%, but in your first year of alignment research, you’ll only reduce the risk of misalignment by .05%. If you’re only focusing on the marginal cost-benefit at stake in that year, then this could be an argument for pressing forwards. However: suppose that in your *second* year of alignment research, you’d reduce the risk by a full 1%, while only incurring another .1% of background bio risk. This means that if you treat the relevant time horizon of decision-making as two years, rather than one, now it’s worth continuing to pause, because the overall alignment risk reduction over two years outweighs the bio risk you incur during that time.

So: even if we’re playing the game of analyzing idealized models, we need to be careful about what idealizations we’re using.

Overall, and without trying to work through all the possible abstract dynamics here comprehensively, my current best guess is that in a world of idealized coordination and decision-making around loss of control risk – one where fully effective slow-downs could be initiated and then unblocked effectively, rationally, and without significant side effects beyond those implied by the slow-down of the technology itself – then we would indeed see significant (read: multi-year) restraint on the development of artificial superintelligence while we improve our understanding of how to ensure its safety.[^-McnSgqW9N8E9vgohj-19]And I expect this, especially, if we assume that this world places significant weight on the interests of future generations – as I expect a wise and compassionate civilization would. My biggest doubt about this comes from the possibility that the world that would be pausing would itself contain significantly increased background rates of non-AI existential risk that safe superintelligence would significantly alleviate – but my guess is that at least the initial wins from picking low-hanging-fruit alignment research would still dominate.

That said: even while I think these idealized models tend to support capability restraint, I do think the basic dynamic that makes the question even worth analyzing is important to bear in mind. In particular, advocates of capability restraint sometimes talk as though such restraint is worth it even if the existential risk from AI misalignment is quite low – e.g., maybe 1% or less – and without attention to rate of safety *progress* over time. And relatedly, they will sometimes suggest very high absolute standards for lifting a given restraint regime – i.e., some kind of formal proof or extremely thoroughgoing consensus that ongoing AI development will be safe. But even in settings that assume significant idealization, the fact that pauses can incur ongoing background existential risk means that very high absolute standards here can lead us astray: the decision to pause or unpause needs to remain attentive to marginal costs and benefits of ongoing delay.

6 Capability restraint in practice
==================================

Those were a few comments about capability restraint in an idealized world – that is, one where fully effective slow-downs can be initiated and then unblocked effectively, rationally, and without significant side-effects beyond those implied by the slow-down of the technology itself. Obviously, though, we do not live in such a world. And here, in my opinion, is where the most important questions about capability restraint ultimately lie.

6.1 The likelihood of serious effort
------------------------------------

The most salient objection, of course, is just that serious efforts at capability restraint are extremely unlikely, especially at the international level.[^-McnSgqW9N8E9vgohj-20]And indeed, this is one of my biggest concerns about strategies that focus centrally on causing restraint of this kind. But I do think people are often too quick to dismiss efforts focused on capability restraint on these grounds. In particular: I think people sometimes anchor too hard on their perception of the existing political and geopolitical climate, without factoring in the many ways in which the world might change as advanced AI starts to kick off hard.

Some of these potential changes are about political will – e.g., as it becomes less and less reasonable to deny AI’s transformative potential, as more direct empirical evidence of different threat models starts to accumulate, and as AI-related issues start to touch people’s lives more and more directly. Indeed, widespread political will is the first and most central bottleneck to serious capability restraint. I support efforts aimed at informing people around the world of the urgency and peril of the AI situation, and at taking advantage of shifts in the overton window when they occur.

Note, though, that the available set of interventions may also change in important ways as well, as a result of new technologies and levels of productivity that AI labor makes available. Thus, as I discussed in the essay on “[AI for AI safety](https://joecarlsmith.com/2025/03/14/ai-for-ai-safety)”:

*   **Individual caution**: AI-assisted risk evaluation and forecasting can promote individual caution by helping relevant actors understand better what will happen if they engage in a given form of AI development/deployment; and AI advice might be able to help actors make wiser decisions, by their own lights, more generally.
*   **Coordination**: AIs might be able to help significantly with facilitating different forms of coordination – for example, by functioning as negotiators, identifying more viable and mutually-beneficial deals, designing mechanisms for making relevant commitments more credible and verifiable, etc.[^-McnSgqW9N8E9vgohj-21]
*   **Restricted options and enforcement**: AIs might be able to help develop new and/or better technologies (including: AI technologies) for monitoring and enforcement – e.g., on-chip monitoring mechanisms, offensive cyber techniques for shutting down dangerous projects, highly trustworthy and privacy-preserving inspection capacity, etc. They could also help with designing and implementing more effective policies on issues like export controls and domestic regulation. And in the limit of direct military enforcement, obviously AIs could play a role in the relevant militaries.
*   **Broader attitudes and incentives**: AI labor can also play a role in shaping the broader attitudes and incentives that determine how our civilization responds to misalignment risk – e.g., by helping with communication about the risks at stake.

Of course, there are limits to the difference that these sorts of AI applications can make, especially in the face of the psychological, commercial, and geopolitical pressures pushing against capability restraint. Indeed, relative to technical alignment research, capability restraint is an especially *political* problem – one which routes centrally through the interests and decisions of human beings and institutions. So it is plausibly harder to throw large amounts of automated AI labor at, even if you have such labor available. And of course, as with other forms of “AI for AI safety,” any AI labor you use in this respect will need, itself, to be suitably safe. Still, I think AI labor might well play an important role in altering the landscape of realistic forms of capability restraint. And at the least, it’s a source of variance.

Also, though: humans can do big, hard things when we try. [Yudkowsky and Soares (2025)](https://www.amazon.com/Anyone-Builds-Everyone-Dies-Superhuman/dp/0316595640) give the example of World War II. In the face of the threat of totalitarianism, the Allies mobilized 60 to 80 million personnel; deployed hundreds of thousands of planes and tanks; and spent about six trillion in today’s dollars. Their efforts were very imperfect. But ultimately, these efforts were successful – and in the eyes of history, absolutely worth it. If superintelligence risks the death of every living being and the destruction of the entire future, then the stakes are no less. So at the least, levels of effort comparable to those at stake in World War II should be possible for humanity. And this level of effort seems like it could well be enough.

Indeed, as I gestured at above, I think it decently likely (though by no means guaranteed) that we see teeth-y forms of domestic regulation on AI as it becomes more clearly transformative, and that some of this regulation will target loss of control risks quite directly – though whether it’s enough to be genuinely effective is a further question. The bigger question, for me, is what forms of international capability restraint are feasible. Here, [Hendrycks et al (2025)](https://www.nationalsecurity.ai/) express some hope that the threat and reality of different countries sabotaging each other’s frontier AI projects will play an important role in restraining dangerous forms of AI development. At a glance, this kind of regime seems dangerously unstable to me (it’s dependent, for example, on countries being unable to develop effective defenses against these efforts at sabotage), but perhaps it has a role to play, and it has the serious advantage of emerging fairly organically from existing multi-polar dynamics, rather than requiring more top-down efforts at coordination or control.

Very plausibly, though, some more top-down effort would be required for sustained, effective global capability restraint. And we can think of efforts of this form along a spectrum from “imposed by a single actor” (i.e., one country gains enough power to impose the relevant forms of restraint on the rest of the world) to “fully voluntary” (i.e., every country in the world participates voluntarily).[^-McnSgqW9N8E9vgohj-22]And a key question, in particular, is whether the US and China both sign up voluntarily, or whether a coalition involving one needs to exert more coercive pressure on the other.[^-McnSgqW9N8E9vgohj-23]Thus, for example, [Amodei (2024)](https://www.darioamodei.com/essay/machines-of-loving-grace#4-peace-and-governance) describes an “entente” strategy focused on a coalition of democracies – presumably led by the US – that uses a combination of carrots and sticks to get other nations on board with a global regime for governing AI development;[^-McnSgqW9N8E9vgohj-24]whereas [Scher et al (2025)](https://techgov.intelligence.org/research/an-international-agreement-to-prevent-the-premature-creation-of-artificial-superintelligence), for example, propose an international treaty aimed purely at halting further progress, focused on a scenario where both the US and China join cooperatively in the coalition pushing for adoption.

Opinions differ on the feasibility of voluntary coordination between the US and China with respect to this kind of capability restraint. Obviously, the possibility of defection is a huge concern in this kind of context, as are concerns about letting China catch up to the US in the meantime, if the US is still in the lead at the time (I discuss both of these concerns below). And in general, it seems unsurprising to me if this kind of coordination between geopolitical rivals is quite difficult. But especially if we can mitigate concerns about defection, efficacy, and ceding competitive ground (more below), I think it could well be worth trying regardless, and as I discussed above in the context of “stag hunt” dynamics, writing it off as too unlikely can be a self-fulfilling prophecy. Ultimately, misaligned superintelligence does not respect borders. We all face the risks it poses together; and we should give each other a chance to respond wisely together, too.

That said, voluntary coordination is indeed not the only option. And especially in the midst of both carrots and sticks being used by both sides, the meaning of “voluntary” can get a bit muddled – what matters is the full set of incentives at stake.[^-McnSgqW9N8E9vgohj-25]Of course, the bigger the role of “sticks,” or more direct forms of enforcement like cyber or kinetic attacks on projects viewed as suitably dangerous, the greater the risk of conflict – including, potentially, great power conflict of the type that could itself pose significant risks to the entire future, especially as more destructive AI-powered weapons come online. But at the least, we should not equate the likelihood of some kind of international capability restraint with the likelihood of purely voluntary coordination in particular.

6.2 The efficacy of capability restraint
----------------------------------------

Another objection to efforts at capability restraint, closely related to the concern that significant restraint is too unlikely overall, is the concern that the relevant form of restraint won’t be suitably effective. This concern can take various forms. For example, you might think that initial signatories to an international agreement will find it too easy to secretly violate it, or to openly defect from it once doing so becomes suitably convenient. Or you might think that there will always be hold-outs on any such agreement, and that it will be too difficult to enforce the relevant norms on actors who reject them. And this kind of concern can arise in the context of less voluntary approaches to international AI governance as well – e.g., maybe one country trying to use “sticks” to enforce slow-downs in another will predictably fail.

And of course, these concerns can themselves play into predictions about the likelihood that any sort of regime of capability restraint will get set up in the first place. That is, if the regime itself will predictably fail in its purpose, then people aiming to set it up will be in a position to foresee this, and so are more likely not to set it up at all.

And we can also worry about *bungled* efforts at capability restraint – i.e., efforts that might’ve been effective if they were better designed, but which people didn’t think through adequately. I’m generally focused, here, on what options are available even to quite competent efforts at capability restraint, but our estimates of the expected degree of actual competence do matter (e.g., to the sort of backfire risks I’ll discuss below) – and the trickier the problems are to navigate, the higher the standards of competence required.

### 6.2.1 Compute governance

The force of these concerns about efficacy depends on the available mechanisms for making a given approach to capability restraint effective. Many proposals in this respect focus on compute – e.g., on the supply chain at stake in chips development, on the chips and clusters themselves, and/or on the amount of compute used in a given form of AI training. And compute does seem like a notably promising point of focus.[^-McnSgqW9N8E9vgohj-26]For example, the supply chain for cutting-edge AI chips currently depends heavily on a small number of companies – e.g., ASML, TSMC – that build their products using extremely expensive and niche equipment; the chips themselves are relatively specialized; by default AI companies rely on large datacenters that require significant infrastructure and power consumption (though more distributed computing is possible as well); and frontier training runs are themselves quite expensive to conduct.

What’s more, compute governance seems like an area where technical innovations – including those driven by AI labor – could play a significant role in facilitating monitoring and oversight (see, for example, the discussion of geolocation, allow-listed connections, metered inference, speed limits, and attested training in [Aguirre (2025)](https://keepthefuturehuman.ai/)). And approaches familiar from other efforts at arms control – e.g., reporting, inspections – could play a role as well, as could more active dismantling of relevant infrastructure.

That said, in the context of more cooperative forms of compute governance, there remains the question of what happens if cooperation breaks down – e.g., a participant in a given governance regime decides to kick out all the inspectors, flout the relevant norms, rebuild the relevant infrastructure, and so on. For this, the threat of more aggressive forms of enforcement – i.e., cyber or kinetic attacks, with their corresponding risks – may need to play a backstop role in facilitating trust. Indeed, international agreements aimed at capability restraint might even benefit from being crafted in order to actively promote this kind of backstop (e.g., intentionally building approved data-centers in places vulnerable to attack if the agreement breaks down). And of course, the possibility of this kind of enforcement plays an even more central role in less cooperative international compute governance regimes.

### 6.2.2 Algorithmic governance

Compute, though, is only one input to frontier AI development. And here, I think, is where the efficacy of various regimes of capability restraint starts to get especially tricky.

In particular: at present, [frontier AI algorithms improve quite fast](https://epoch.ai/blog/algorithmic-progress-in-language-models). Ho et al (2024) estimate that “the level of compute needed to achieve a given level of performance has halved roughly every 8 months, with a 95% confidence interval of 5 to 14 months.” At this rate, algorithmic progress could allow a rogue actor with only 10% of the compute to end up at parity with a leading actor, if the leading actor is pausing, within a little over two years. And even with only 1% of compute, this rogue actor could reach parity with the frontier within about four and a half years. Of course: rates of algorithmic progress could go down during a pause (and if the pause significantly reduced overall work on AI capabilities and the compute available for experiments, I expect that they would), and the incentives at stake in AI development more broadly could alter as well. But note, also, that automated AI R&D could also speed up algorithmic progress relative to what we’ve seen thus far. What’s more, relative to compute, algorithmic progress is significantly more difficult to monitor and restrict, because it does not rely in the same way on such expensive, niche, and physically-obvious infrastructure (though: the current heavy reliance on compute for experiments and other kinds of algorithmic research does help quite a bit here).

Now one question here is how much difficulties restricting algorithmic progress pose a barrier, even, to the feasibility of effective domestic regulation. Here, though, I am comparatively optimistic. In particular, I think that when a domestic government makes something illegal, and makes a sincere and serious effort to enforce the relevant law, this is generally quite effective in at least providing a very significant deterrent (and note that domestic governments already make various other forms of research illegal – i.e. gain of function research, various forms of chemical and biological weapons research, etc). Yes, the incentives to engage in ongoing, black-market AI development will be stronger than in these cases, and the relevant forms of enforcement may be more difficult. But if the challenge was simply for a committed domestic government to apply fairly effective capability restraint both to compute and to algorithmic progress within their own borders, the problem would seem to me significantly easier.

Unfortunately, though, this isn’t the only challenge. Rather, as I noted above, the hard version of capability restraint tends to occur at the international level, and between adversaries who don’t trust each other. And in this context, capability restraint focused on algorithmic progress generally requires one country to verify and/or enforce restrictions on algorithmic research in a *different* country. That is, maybe the US can become confident that China isn’t building new data centers, and/or that it could strike at such a data center if necessary; but it’s much harder to be confident that there isn’t some secret, state-sanctioned algorithmic effort taking place regardless, using whatever compute has managed to escape US efforts at monitoring, restriction, and so on.[^-McnSgqW9N8E9vgohj-27]And even if major powers involved in a given agreement can build up suitable trust in this respect, it will be ongoingly plausible that various illicit projects throughout the world have slipped through the cracks.

What’s more, if we try to actually imagine the sorts of approaches to monitoring and enforcement that would be required to become highly confident that no such illicit projects are occurring (even within some domestic sphere, let alone in the context of a foreign adversary or all around the world) the required surveillance apparatus (even modulo AI-powered privacy preservation) quickly becomes extremely scary in itself – much scarier, indeed, than the sorts of (already at-least-somewhat scary) mechanisms required for effective compute governance. This exacerbates some of the concerns about AI-powered authoritarianism and centralization of power I discuss below, increasing the costs of actually setting up effective mechanisms for restricting algorithmic progress, even assuming such mechanisms are available.

Now, exactly how much of a problem algorithmic progress poses to various efforts at capability restraint depends a lot on various quantitative factors – e.g. what sort of success at compute-focused governance is possible; exactly how much compute you need for research, training, and inference; how fast algorithmic progress proceeds in the pause regime; how secure you can make various sanctioned forms of algorithmic progress; and how you aim to approach various issues related to green-lighting and ongoing safety research (discussed below). My current guess is that, given the crucial role of huge amounts of compute in the current paradigm of AI development, a large coalition of nations committed to capability restraint would be able to design, verify, and enforce a regime of compute-focused international governance such that participants could be reasonably confident that at least for a few years, and potentially for decently longer, no one is going to build and deploy potentially-world-ending AI capabilities. And this regime could also be designed with the possibility of ongoing algorithmic progress explicitly in mind, such that e.g. compute-related standards become more restrictive over time. So while I do think that difficulties verifying and enforcing restrictions on algorithmic development pose a serious (indeed, potentially fatal) barrier to e.g. decades-long pauses, I think they may well allow for pauses over the course of at least a few years – and these would be the most valuable years for additional alignment research regardless.[^-McnSgqW9N8E9vgohj-28]

However, difficulties posed by algorithmic progress remain one of my biggest uncertainties about the viability of international regimes of capability restraint. And they can get exacerbated by some of the dynamics at stake in trying to set up good mechanisms for ongoing safety research and green-lighting. Let’s turn to those issues now.

### 6.2.3 Greenlighting and safety progress

Above I suggested that approaches to capability restraint probably shouldn’t just focus on what I called “red-lighting” – i.e., mechanisms for stopping AI development. They should also think about mechanisms for allowing ongoing safety progress, for green-lighting further AI development once it’s suitably safe, and ideally for allowing the benefits of more benign applications of AI to accrue to humanity in the meantime. But especially in the context of regimes of capability restraint that aim for international cooperation, I think safety progress and especially green-lighting can become quite difficult if cooperating adversaries aren’t willing to engage in fairly thorough-going information sharing about frontier AI development (information sharing that also then makes ongoing algorithmic progress more likely to leak to illicit projects as well). And I think this could well prove a significant barrier to this sort of cooperation.[^-McnSgqW9N8E9vgohj-29]

Here’s the basic dynamic I have in mind. Consider first the way greenlighting might work in the context of domestic regulation. Here a classic structure is something like: there are multiple competing AI companies within a country like the US. They don’t have to merge. They don’t have to share IP. Rather, they can keep competing in the free market as normal, with the added condition that before engaging in some next-step of potentially dangerous AI development, they need to submit some kind of safety case to a third-party, government regulatory body – a body which is not, itself, a part of the AI race – which then provides approval. This safety case may contain sensitive IP about the company’s methods, but the third party has mechanisms in place for ensuring that relevant employees are suitably neutral, and that they do not leak this information to competitors.

This sort of model is attractive for a number of reasons. First, it has clear precedent in the context of other forms of domestic regulation. Second, it separates the apparatus that *evaluates* the safety of a given form of AI development from the apparatus that *controls* the AI that might be developed, thereby avoiding centralizing power over AI capabilities themselves in the regulatory body itself (more below). And finally, and relatedly, it allows for ongoing forms of reasonably standard market competition, including the development of sensitive IP that competing companies do not share with one another. Obviously, there are lots of issues to work out in implementing a scheme like this effectively in a domestic context, avoiding regulatory capture, and so on. But I think we should be reasonably optimistic about making something work.

Unfortunately, though, this sort of model seems much harder to sustain in the context of international competition between geopolitical adversaries – even if competitors strongly desire to cooperate to set it up. In particular: it seems much harder to staff a third-party evaluation apparatus that countries will trust not to leak their most sensitive IP to foreign adversaries who are also participating. Thus, suppose that the US wants to engage in some sort of next step of new AI development, and it goes to the relevant international body with its safety case. And suppose that evaluating this safety case requires access to tons of sensitive IP about the US project. If some of the people staffing the international body are from China, or might be secretly working on behalf of China, then it seems extremely natural for the US to assume that their algorithms and other secrets will leak immediately. And especially if the relevant participants in the agreement are also assuming that adversary governments are continuing to pursue illicit AI development programs with whatever compute can escape monitoring/enforcement, this means that any algorithmic progress that gets shared with the international body immediately feeds into those illicit projects, further accelerating their development and shortening the duration of viable pause.

You also get some similar issues around safety progress – i.e., sharing safety progress across actors can sometimes leak sensitive IP. But this issue seems potentially more manageable; and in principle, if you had a good third-party regime for gating further AI development on individual actors presenting suitably good safety cases, safety research wouldn’t actually *need* to be fully shared (though obviously, this helps).

Are there ways around the IP-related problems that international greenlighting creates? One way around it is just to share sensitive IP freely – either ending any ongoing international competition in AI development, or letting that competition center on factors other than algorithmic progress. And perhaps, if security is suitably impossible to solve regardless, this isn’t that much of a change. But I expect it to be very difficult for the national security apparatus of participating countries to swallow – and especially, for the country in the lead. That is: this country would need to be up for basically cancelling whatever algorithmic lead they have (rather than just: pausing everyone in place), thereby potentially ceding significant competitive ground including in worlds where cooperation breaks down. And in general, freely sharing the IP at stake in your most powerful national security asset with your adversaries seems like a hard sell.

One could also imagine focusing on regimes for international capability restraint that don’t include any provision for ongoing greenlighting – that is, everyone just stops, *period* – though as I discussed above, I think that failing to make space for greenlighting is a significant policy problem. And perhaps there are options in between full information-sharing and giving up on greenlighting. For example, perhaps fancy forms of AI-assisted verification and evaluation could help; perhaps there are ways to rely on more thoroughly neutral forms of third-party evaluation; and perhaps there are other creative governance or information-sharing arrangements I’m not currently aware of.

Note that this sort of issue is centrally a problem in the context of more cooperative forms of international restraint. That is: if one country or coalition is *imposing* capability restraint on its adversaries, without also trying to include those adversaries in the process of greenlighting their own internal AI development, then you get less of this issue (though: you also get a corresponding increase in the sort of power-centralization I discuss below). But it does seem to me a significant issue for approaches to international capability restraint that aspire to remain genuinely multilateral and cooperative even amongst geopolitical adversaries.

6.3 Ways that capability restraint could end up net negative
------------------------------------------------------------

So far, I’ve been focused on potential ways that capability restraint could either fail to happen, or fail to be suitably effective. I think there are significant problems here, especially in international contexts, but I think serious and effective effort is still a live possibility, especially for buying at least a few extra years.

What’s more, as I discussed in the introduction, alignment-related considerations aren’t the only reasons that capability restraint might be a good idea. To the contrary, unrestrained AI development poses a large number of other risks as well – biological catastrophes, AI-powered authoritarianism, mass civilizational disruption, and so on – which capability restraint could give us more time to prepare for and address.

I won’t attempt to analyze the full range of considerations for and against capability restraint here. But I do want to name a few ways in which it seems plausible, to me, that efforts put towards capability restraint could end up net negative, as I think these are important for advocates of capability restraint to recognize and to bear in mind.

### 6.3.1 Concentrations of power

My most salient concern about capability restraint being net negative comes from ways in which it might concentrate power in dangerous ways – e.g., by pushing towards centralization of AI development.

To be clear, this doesn’t apply to all forms of capability restraint. For example, it isn’t so much of a concern in the context of forms of individual capability restraint at stake in “dropping out of the race,” and it’s mitigated somewhat in the context of “burning a lead” strategies that don’t also focus on using that lead to enforce restraint on other actors (though: the sorts of race-ahead strategies that one might use to try to acquire a lead-to-burn can be power-concentrating regardless).

I’m also not thinking, centrally, about power concentration in the context of most forms of domestic regulation. In particular, as I discussed above, the domestic regulatory entity saying “stop” and “go” doesn’t need to be developing or controlling any AI capabilities themselves. This is similar to the sense in which e.g. the Federal Aviation Administration can certify plane safety without building any planes; the Food and Drug Administration doesn’t need to make food or drugs; and so on. Nor does the presence of these entities imply there must be only one company making planes, food, drugs, etc – and this despite the need for ongoing experimentation and research in pushing plane/food/drug development forward.[^-McnSgqW9N8E9vgohj-30]Of course, you can also worry that the power at stake in being able to say “stop” and “go” to a new technology is worrying in itself. At least at a domestic level, though, this is already baked in – domestic governments regulate new technologies all the time.[^-McnSgqW9N8E9vgohj-31]

However: I do think concerns about concentrations of power start to bite harder once efforts at capability restraint start to involve centralizing AI development into a single project (e.g., a Manhattan Project, “CERN for AI,” a large international coalition that shares all IP, etc), or relying on a single dominant actor (i.e., the US government) developing overwhelming (and potentially AI-powered) military supremacy and using it to enforce the relevant norms. And as I discussed above, I do think there is a lot of momentum in this sort of direction. In particular: while it’s true that the entity “red-lighting” and “green-lighting” ongoing development doesn’t actually *need* to be the one controlling the resulting capabilities, at the international level it becomes much harder to separate them, because there isn’t a neutral third party to do the job.

What’s more, while it’s true that at the international level, governments already engage in things like export controls, coordinated efforts to monitor and restrict development of banned weapons, and strikes (both [cyber](https://en.wikipedia.org/wiki/Stuxnet) and [kinetic](https://en.wikipedia.org/wiki/United_States_strikes_on_Iranian_nuclear_sites)) on nations on a path to building nuclear weapons, still: the sort of international apparatus necessary for suitably restricting and steering frontier AI development may need to be significantly more elaborate and intrusive. At the least, it needs to be strong enough to counteract the extreme commercial and geopolitical incentives pushing in favor of ongoing progress, including amongst great power rivals like the US and China. So I do expect it, by default, to bring us into new territory in terms of the intensity and stakes of the international governance regime in question, and to bring with it correspondingly unprecedented risks of abuse.

Of course, in thinking about these risks, we should also bear in mind the ways in which default forms of AI development themselves bring risks of concentrated power, both in the context of individual AI companies and of governments. And one can also argue that capability restraint is net good *both* from the perspective of safety *and* from the perspective of concerns about concentrations of power – for example, because it gives us more time to prepare for integrating AI into our civilization in a manner that better preserves checks and balances, both domestically and internationally. Indeed, even nations unconvinced of alignment risks would plausibly have reason to participate in various regimes of capability restraint so as to help mitigate risks that one nation in particular makes a break for military supremacy (and/or, risks of great power conflict aimed at preventing this outcome) – not to mention other risks (i.e., bioterrorism) that advanced AI can implicate. And to the extent one is worried about the institutions involved in international capability restraint being abused, one could attempt (perhaps with AI help) to design them in ways that help mitigate concerns in this respect – for example, distributing oversight and governance across multiple independent bodies, including sunset clauses aimed at preventing entrenchment, implementing transparency requirements, and so on.

As I discussed above, evaluating the net effects of different forms of capability restraint on risks from concentration of power is beyond my purpose here. I expect such analysis to get complicated – and I think people whose central interest in capability restraint comes from concerns about alignment should be wary of assuming that capability restraint helps in a similar way in the context of other risks, too.[^-McnSgqW9N8E9vgohj-32]Indeed, for myself, risks of exacerbating problems related to concentrations of power remain one of my central concerns with capability restraint, especially in the context of plans that achieve the relevant forms of restraint via AI-powered forms of military dominance centralized in one country or coalition, even a democratic one.

### 6.3.2 Ceding competitive advantage to authoritarian countries

Another way I think that capability restraint could end up net negative is via democratic countries ceding competitive advantage to authoritarian countries that are then better able to achieve outsized amounts of power using AI – including, in the limit, permanent military and economic dominance. Of course, in principle, not all forms of capability restraint need to do this – you can imagine regimes, for example, that simply freeze everyone in their existing place.[^-McnSgqW9N8E9vgohj-33]But, in practice, and especially in contexts where more democratic countries are in the lead by default, it seems unsurprising if various efforts at capability restraint, both domestic and international, end up costing some or all of this lead. This could happen, for example, if democratic countries regulate domestically in ways that more authoritarian countries do not. Or it could happen if capability restraint at the international level gives authoritarian countries more time to catch up, and/or (per the discussion of green-lighting above) more of the IP necessary to do so.

Indeed, I think some of the discourse about AI safety has generally been too unwilling to acknowledge the possibility of genuine trade-offs in this respect. Candidate reasons for this dismissal include: skepticism that the stakes of permanent authoritarianism could be remotely comparable to those of extinction, and skepticism that there is enough probability of anyone being able to control superintelligence that strategic considerations related to aligned superintelligence are relevant. And I wonder if the discourse has also just absorbed some wariness about allowing for the possibility that the logic at stake in e.g. a race with China might actually matter, given the potential harm that this logic might do. I won’t attempt to analyze prioritization between the goal of preventing permanent authoritarianism and preventing AI misalignment here, but as a first pass, I’m skeptical that either the empirical or normative landscape means that concerns about permanent authoritarianism can be safely ignored or fully deprioritized. And I think we should acknowledge that some forms of safety-focused capability restraint could well implicate trade-offs in this respect.

### 6.3.3 Other concerns

There are also various other ways that safety-focused efforts at capability restraint can do harm. I already discussed one of the most salient – i.e., capability restraint results in more prolonged exposure to backdrop levels of existential risk. But there are others as well:

*   **Exacerbating risks of great power conflict.** Capability restraint might exacerbate risks of great power conflict. Here, again, obviously there is significant risk of this kind of conflict even absent safety-focused efforts at capability restraint. But this doesn’t mean that such efforts won’t make things worse. For example, to the extent that safety-related considerations end up motivating or rationalizing especially drastic forms of international action aimed at shutting down or significantly restricting AI development in other countries, I think this could well be harmful even relative to more baseline forms of economic and military competition.[^-McnSgqW9N8E9vgohj-34]That said, I do think that a lot of the great power conflict risk comes from actions motivated by military and economic competition rather than from actions motivated by concern about alignment, and my overall guess is that capability restraint is net helpful here.
*   **Implementation challenges**. As I discussed above, this post has mostly focused on the options available to quite competent efforts at capability restraint – e.g., roughly, on what it might look like if capability restraint is “done right” (though: with some constraints on realism). Obviously, though, it might just not be done right, in myriad ways – and while some failures in this respect could merely make the effort ineffective and wasting of time/resources, it seems unsurprising if poorly implemented forms of capability restraint end up more actively net negative. For example, maybe the relevant rules are written in ways that don’t actually help with safety, but which differentially disadvantage the most safety-concerned actors. Indeed, I think crafting good rules for green-lighting, in particular, could prove a significant challenge, especially given widespread disagreement about the existing base of evidence re: misalignment, difficulties foreseeing how the technical landscape will evolve, and the need for such rules to be robust to strong pressures towards adversarial optimization. (Though, I also think we have at least some decent sense already of what good safety cases for AI systems might look like – see e.g. my picture [here –](https://joecarlsmith.com/2025/08/18/giving-ais-safe-motivations#) and part of the goal of capability restraint is to provide more time to improve our understanding in this respect.)
*   **Abuse**.  A related concern is that the powers vested in a given restraint regime will be extremely ripe for abuse. This is closely connected to the concerns about concentrations of power I described above, but abuse can take other forms as well – e.g., the actors so empowered using the relevant power to pursue their self interest or their ideological agendas under the guise of safety, without the relevant abuse tipping over into something that looks more like paradigm authoritarianism. 
*   **Maybe alignment is easy, but restraint efforts don’t adjust.** I’ve been writing, centrally, for worlds where the alignment problem is enough of a challenge that additional time for safety research brings with it meaningful reductions in residual risk of loss of control. It’s possible, though, that as more detailed evidence about the technical situation accrues, it becomes clear that alignment isn’t actually that much of an issue, and/or that the benefits of pursuing significant capability restraint in order to lower alignment risk aren’t worth the costs at stake in e.g. increasing risks of concentration of power, abuse, and so on. This is part of the reason it’s so important for efforts at capability restraint to be closely attentive and responsive to the evidence we’re getting about the risk landscape. Real world efforts at capability restraint, though, may not be especially attentive and responsive in this way – indeed, I’m quite concerned that they won’t be.
*   **Other kinds of bad epistemics.** Concerns about failures to adjust course in response to ongoing evidence about alignment are a species of a broader sort of concern about advocacy efforts aimed at capability restraint – namely, that they will harden into ideological agendas that become unmoored from evidence about the object level risks (both, alignment risks and other risks), and the impact of relevant policies on those risks. We know that popular movements can easily go wrong in this way, and I do not expect movements focused on capability restraint to be an exception (though: this downside risk applies to popular movements across the board, and it may be quite hard to avoid at sufficient scale).
*   **Overhangs**. Pauses or slow-downs could lead to build-ups of either algorithmic or hardware progress. If and when the “dam bursts,” this could lead to more rapid and dangerous forms of capabilities progress, with much less time for civilization to recognize and adapt to their implications and risks. Of course, effective and well-timed greenlighting could help here; but you might not have that luxury. 
*   **Polarizing and/or discrediting AI safety efforts.** Efforts aimed at capability restraint – whether through advocacy or actual implementation – could polarize or discredit AI safety concerns in negative ways, especially if (per concerns about implementation, epistemics, and so on above) they are poorly calibrated, executed, or designed.
*   **General civilizational degradation**. Civilization could get worse in various ways during the period of the pause/slow-down, so that when the full transition to superintelligence kicks off again, it occurs under worse conditions. For example, maybe our discourse degrades; maybe the US experiences significant democratic backsliding; maybe the balance of power in the world shifts in a negative direction; and so on. Of course, you’d need a reason to think that the expected trajectory in this respect is negative rather than positive; but one can imagine various reasons for concern. 

This list isn’t exhaustive.[^-McnSgqW9N8E9vgohj-35]I’ll note, though, that there are also a number of common objections to capability restraint that I’m *not* very concerned about. For example:

*   I think that while efforts at capability restraint should aim to minimize the near-term economic costs involved, and to allow for access to the benefits of more benign AI systems, I also think that near-term economic costs (including e.g. slowing down certain kinds of innovation) can easily be worth paying for the sake of meaningful reductions in the chance of human extinction. And these are the sorts of benefits I’ve been contemplating here. 
*   I think it’s relatively rare that people pushing for capability restraint on safety grounds actually have some other, hidden agenda – e.g., a desire to hype their products, a desire to exert some kind of censorship, and so on. Obviously, human psychology is complicated, and human behavior in the context of ideologically-freighted, intense, and politicized issues like AI risk is informed by all sorts of subtle and often-unconscious factors. But to a first approximation, and using our normal standards for a diagnosis of sincerity, I think most people advocating for capability restraint on safety grounds are sincerely concerned. 
*   I am not especially concerned that a pause that started out temporary might calcify into something permanent, and that we might permanently renounce the profound benefits of superintelligence as a result. To my mind, it seems hard enough to get a regime that is effective at all, let alone for a very long period (see, e.g., the concerns about ongoing algorithmic progress described above).

7\. Prioritizing capability restraint relative to other security factors
========================================================================

Finally, there is the obvious and most mundane way that projects aimed at promoting capability restraint could go wrong: namely, they could just be wasted effort. Indeed, my sense is that something in the vicinity of “that will never work” is the first and most central reason that many people concerned about AI safety are nevertheless skeptical of efforts directed at capability restraint, especially of a form that involves active cooperation between geopolitical rivals like the US and China.

For reasons like the ones discussed above, I think many of these dismissals are too quick: political will could change drastically, even purely domestic forms of capability restraint can make an important difference, and real-world efforts at international capability restraint can mix carrots and sticks together in a variety of ways. Still, obviously, there is a serious risk that effort directed at a given form of capability restraint will fail, and that the relevant resources would’ve been better used elsewhere.

This sort of failure is always a risk, though – the real question is the expected value of marginal effort, not the overall probability of success. And here, my current sense is that a wide variety of forms of capability restraint, including some of the most ambitious and cooperative, are live-enough possibilities and valuable-enough if done well that they are worth serious effort.

That said, some advocates of capability restraint go farther than this: they argue that people concerned about AI safety should basically just focus *exclusively* on capability restraint (and especially: on international bans or moratoria), and that they should not do work aimed at actually learning how to align AI systems – e.g., because this is too unlikely to work or help.[^-McnSgqW9N8E9vgohj-37]I think this view is wrong. That is, I think there are a variety of very live scenarios – both with and without substantial capability restraint – in which technical work on alignment makes a meaningful difference to humanity’s prospects, and the idea that we should abandon such work entirely and bet all our cards on an international ban strikes me as importantly misguided.[^-McnSgqW9N8E9vgohj-38]Of course, if you are highly confident that the only scenarios where humanity survives are ones where we implement a multi-decade international ban on advanced AI development, then it makes more sense to focus on promoting those sorts of scenarios in particular. But I don’t think we should be confident in this way – and I hope the discussion in the rest of the series can help explain why.

8\. Conclusion
==============

Overall, then: I think that capability restraint is a very important part of the strategic landscape at stake in solving the alignment problem. I support efforts to promote skillful, effective, and evidence-responsive forms of this restraint, both at the level of individual AI companies, domestic governments, and international relations. And if the alignment problem is hard (as I think it might well be), I expect that some restraint of this form will in fact be required in order for humanity to survive and remain empowered.

However, I also think there are very significant challenges at stake in setting up effective regimes of international capability restraint in particular, especially for more than a few years (here I’m particularly concerned about difficulties restricting algorithmic progress, and about issues with sharing sensitive IP for the purposes of risk evaluation and greenlighting). I also think that advocates of capability restraint should acknowledge the trade-offs at stake in possible costs like concentrating power, ceding competitive ground, and prolonging exposure to (possibly heightened) background levels of existential risk, and that they should stay attentive to the possibility that as we learn more about the situation, these trade-offs (together with the other challenges at stake in actually getting a given form of capability restraint to work) tip the balance of costs and benefits against various forms of capability restraint in practice. And while I think promoting capability restraint is currently worth serious effort, I disagree with advocates who argue it should be the AI safety community’s sole focus.

Appendix 1: What are we using the time for?
===========================================

The point of capability restraint is to buy time. But time for *what?* This appendix offers some more in-depth analysis.

Above I mentioned the most paradigm answer: namely, AI alignment research. And this answer, indeed, is my central focus. Even within this bucket, though, it can matter whether you centrally have in mind research performed by humans, or [research performed substantially or entirely using AIs](https://joecarlsmith.com/2025/04/30/can-we-safely-automate-alignment-research). This matters, in part, because human researchers are extremely scarce and slow, so if you’re relying on *them* to do your alignment research, then for harder versions of the alignment problem, you will likely need to buy a very large amount of time (this is one of the key reasons I think safe automation of alignment research is so important). But also: if you are going to be doing tons of automated alignment research, then your governance regime needs to at least make provision for that sort of application in particular – e.g., by providing for the necessary amount of inference compute, for access to AIs of the relevant level of capability, and so on.

As I mentioned above, though, direct alignment research (whether automated or not) isn’t the only answer for what to do with the time that capability restraint buys. For example, Yudkowsky and Soares (2025) advocate instead for what I previously called a “[pause and enhance strategy](https://joecarlsmith.com/2025/03/11/paths-and-waystations-in-ai-safety#6-waystations-on-the-path),” focused on using the time to [augment human intelligence](https://ifanyonebuildsit.com/13/why-would-making-humans-smarter-help) to a level where the augmented humans can then solve the alignment problem. Yudkowsky and Soares suggest this because they think that current levels of human intelligence aren’t sufficient – you need researchers who are smarter than humanity’s current geniuses.[^-McnSgqW9N8E9vgohj-39]

Obviously, any path in this vein would require grappling with the ethical and political questions at stake in the relevant forms of intelligence augmentation – e.g., about how the smarter humans involved are being treated, and about the implications and consequences for everyone else. And the potential for risks similar to those at stake in AI misalignment (e.g., rogue power-seeking) seems salient as well, especially given that structurally similar “instrumental convergence” arguments apply to humans as well (and as I discussed [earlier in the series](https://joecarlsmith.com/2025/11/12/how-human-like-do-safe-ai-motivations-need-to-be#appendix-1-on-value-fragility), “value fragility” arguments for concern about what AIs do with power extend quite directly to human values-differences as well).

Even if we set those issues aside, though, there’s a further question of how much time you’d need in order to pursue this sort of intelligence augmentation. Here, many relevant forms of intelligent augmentation are what I’ll call “biology-constrained”: that is, they focus on changes or augmentations applied to human biological brains. But in this context, I expect, you’d again need your capability restraint to buy quite a large amount of time – e.g., more than a decade. In particular: you’d need to actually do the research necessary to get the relevant form of biology-constrained intelligence augmentation to work; you’d need to create humans with the relevant augmentations; and *then* you’d need for those humans to perform all the relevant cognitive work you wanted their intelligence enhancement for (e.g., alignment research). AI can help with some aspects of this (e.g., the initial biological research), if you’re up for using it in this way; but even with AI help in creating intelligence-augmented humans, when it comes to the work those *humans* are supposed to do, you still end up bottlenecked by the slowness of biological cognition and the scarcity of (augmented) human labor.

That said: not all forms of human intelligence augmentation are biology-constrained in this way. In particular: earlier in the series, I discussed the possibility of sufficiently high-fidelity human [whole brain emulation](https://en.wikipedia.org/wiki/Mind_uploading) (WBE) or “uploading” – that is, replication in silico of the cognitively-relevant computational structure of the human brain, in a manner that preserves both human cognitive capabilities and human motivations. If you had WBE available, then the relevant emulations would inherit many of the advantages automated alignment researchers have in terms of speed and scale. The key question for timing, though (and again, setting aside all of the other ethical, political, and safety questions above), is how long it would take to make sufficiently high-fidelity human whole brain emulations available.

My sense is that there is some disagreement in the field about exactly how far away we are from this, and there’s been an uptick in optimism in the field in recent years (see e.g. this [2025 report](https://arxiv.org/pdf/2510.15745) for one summary of the current state of the field). My own guess, though, is that despite recent advances (e.g., [studies showing that you might be able to recover some functional behavior from the connectome alone](https://www.nature.com/articles/s41586-024-07939-3)), we are, at present, still quite far away from being able to create working, high-fidelity emulations of human brains that we trust in the same way we trust humans (and far away, too, from having yet grappled with all the ethical, political, and safety issues doing this would involve). If we were going to rely solely on human labor to get to WBE technology, then, I’d expect that this, too, would take quite a while – though, very few people ([Zanichelli et al (2025)](https://arxiv.org/pdf/2510.15745) estimate less than 500) are directly focused on the issue at the moment, so a massive scale-up in human effort could make a major difference.[^-McnSgqW9N8E9vgohj-40]

That said, sufficiently advanced AI labor could speed up the process significantly. Indeed, especially if you are skeptical of automated alignment research, an intense AI-assisted effort to create WBE as fast as possible is one salient way to try to use the time that capability restraint buys. And it’s also possible that the distinction between this path and automated alignment research starts to blur somewhat – for example, if you use some neuroscientific progress (e.g., progress on getting functional behavior from the connectome) to learn how to create somewhat-more-human-like architectures or cognitive dynamics, in ways that help alleviate some concerns about alien-ness in AI cognition and motivations, but without reaching the sort of extremely high-fidelity replication at stake in standard conceptions of “uploading.” Even with lots of AI help, though, by default I expect it to take more time, and to be more bottlenecked on slow, physical-world-constrained biological experimentation, to create whole brain emulations than to create quite capable automated alignment researchers – and I think it’s plausible enough that we can make such researchers safe and effective that I generally think focusing here is a better bet. And note, too, that insofar as a WBE-focused strategy would still require usage of lots of AI labor and compute in doing WBE research itself, and lots of compute to run the emulations once created, the relevant restraint regime would need to accommodate this.

Of course, alignment research and human intelligence augmentation aren’t the only things you can try to do with the time that capability restraint buys. For example, you might want to prepare for the transition to advanced AI more generally (including with respect to issues other than alignment). You could also use the time to try to strengthen the given restraint regime itself. And it’s possible to go into a regime of capability restraint without wanting to assume that *ever* developing advanced AI capabilities is the right choice – e.g., maybe you just want to try to improve our collective epistemology, coordination, and decision-making more generally, and then to decide what to do from there.[^-McnSgqW9N8E9vgohj-41]My own guess, though, is that deciding not to ever develop superintelligence would be a serious mistake; and more importantly, that this decision won’t be an available option, because your efforts at capability restraint, in practice, will be too imperfect. And if the restraint regime is time-limited in a way that requires suitable alignment progress *eventually*, then you need some way of using the time to do the relevant work.

[^-McnSgqW9N8E9vgohj-1]:  

[^-McnSgqW9N8E9vgohj-2]:  

[^-McnSgqW9N8E9vgohj-3]:  

[^-McnSgqW9N8E9vgohj-4]:  

[^-McnSgqW9N8E9vgohj-5]:  

[^-McnSgqW9N8E9vgohj-6]:  

[^-McnSgqW9N8E9vgohj-7]:  

[^-McnSgqW9N8E9vgohj-8]:  

[^-McnSgqW9N8E9vgohj-9]:  

[^-McnSgqW9N8E9vgohj-10]:  

[^-McnSgqW9N8E9vgohj-11]:  

[^-McnSgqW9N8E9vgohj-12]:  

[^-McnSgqW9N8E9vgohj-13]:  

[^-McnSgqW9N8E9vgohj-14]:  

[^-McnSgqW9N8E9vgohj-15]:  

[^-McnSgqW9N8E9vgohj-16]:  

[^-McnSgqW9N8E9vgohj-17]:  

[^-McnSgqW9N8E9vgohj-18]:  

[^-McnSgqW9N8E9vgohj-19]:  

[^-McnSgqW9N8E9vgohj-20]:  

[^-McnSgqW9N8E9vgohj-21]:  

[^-McnSgqW9N8E9vgohj-22]:  

[^-McnSgqW9N8E9vgohj-23]:  

[^-McnSgqW9N8E9vgohj-24]:  

[^-McnSgqW9N8E9vgohj-25]:  

[^-McnSgqW9N8E9vgohj-26]:  

[^-McnSgqW9N8E9vgohj-27]:  

[^-McnSgqW9N8E9vgohj-28]:  

[^-McnSgqW9N8E9vgohj-29]:  

[^-McnSgqW9N8E9vgohj-30]:  

[^-McnSgqW9N8E9vgohj-31]:  

[^-McnSgqW9N8E9vgohj-32]:  

[^-McnSgqW9N8E9vgohj-33]:  

[^-McnSgqW9N8E9vgohj-34]:  

[^-McnSgqW9N8E9vgohj-35]:  

[^-McnSgqW9N8E9vgohj-37]:  

[^-McnSgqW9N8E9vgohj-38]:  

[^-McnSgqW9N8E9vgohj-39]:  

[^-McnSgqW9N8E9vgohj-40]:  

[^-McnSgqW9N8E9vgohj-41]:  

[^-McnSgqW9N8E9vgohj-36]: