Anthropic releasing their RSP was an important change in the AI safety landscape. The RSP was likely a substantial catalyst for policies like RSPs—which contain if-then commitments and more generally describe safety procedures—becoming more prominent. In particular, OpenAI now has a beta Preparedness Framework, Google DeepMind has a Frontier Safety Framework but there aren't any concrete publicly-known policies yet, many companies agreed to the Seoul commitments which require making a similar policy, and SB-1047 required safety and security protocols.
However, I think the way Anthropic presented their RSP was misleading in practice (at least misleading to the AI safety community) in that it neither strictly requires pausing nor do I expect Anthropic to pause until they have sufficient safeguards in practice. I discuss why I think pausing until sufficient safeguards are in place is unlikely, at least in timelines as short as Dario's (Dario Amodei is the CEO of Anthropic), in my earlier post.
I also have serious doubts about whether the LTBT will serve as a meaningful check to ensure Anthropic serves the interests of the public. The LTBT has seemingly done very little thus far—appointing only 1 board member despite being able to appoint 3/5 of the board members (a majority) and the LTBT is down to only 3 members. And none of its members have technical expertise related to AI. (The LTBT trustees seem altruistically motivated and seem like they would be thoughtful about questions about how to widely distribute benefits of AI, but this is different from being able to evaluate whether Anthropic is making good decisions with respect to AI safety.)
Additionally, in this article, Anthropic's general counsel Brian Israel seemingly claims that the board probably couldn't fire the CEO (currently Dario) if the board did this despite believing it would greatly reduce profits to shareholders[1]. Almost all of a board's hard power comes from being able to fire the CEO, so if this claim were to be true, that would greatly undermine the ability of the board (and the LTBT which appoints the board) to ensure Anthropic, a public benefit corporation, serves the interests of the public in cases where this conflicts with shareholder interests. In practice, I think this claim which was seemingly made by the general counsel of Anthropic is likely false and, because Anthropic is a public benefit corporation, the board could fire the CEO and win in court even if they openly thought this would massively reduce shareholder value (so long as the board could show they used a reasonable process to consider shareholder interests and decided that the public interest outweighed in this case). Regardless, this article is evidence that the LTBT won't provide a meaningful check on Anthropic in practice.
Misleading communication about the RSP
On the RSP, this post says:
On the one hand, the ASL system implicitly requires us to temporarily pause training of more powerful models if our AI scaling outstrips our ability to comply with the necessary safety procedures.
While I think this exact statement might be technically true, people have sometimes interpreted this quote and similar statements as a claim that Anthropic would pause until their safety measures sufficed for more powerful models. I think Anthropic isn't likely to do this; in particular:
- The RSP leaves open the option of revising it to reduce required countermeasures (so pausing is only required until the policy is changed).
- This implies countermeasures would suffice for ensuring a reasonable level of safety, but given that commitments still haven't been made for ASL-4 (the level at which existential or near-existential risks become plausible) and there aren't clear procedural reasons to expect countermeasures to suffice to ensure a reasonable level of safety, I don't think we should assume this will be the case.
- Protections for ASL-3 are defined vaguely rather than having some sort of credible and independent risk analysis process (in addition to best guess countermeasures) and a requirement to ensure risk is sufficiently low with respect to this process. Perhaps ASL-4 requirements will differ; something more procedural seems particularly plausible as I don't see a route to outlining specific tests in advance for ASL-4.
- As mentioned earlier, I expect that if Anthropic ends up being able to build transformatively capable AI systems (as in, AI systems capable of obsoleting all human cognitive labor), they'll fail to provide assurance of a reasonable level of safety. That said, it's worth noting that insofar as Anthropic is actually a more responsible actor (as I currently tentatively think is the case), then from my perspective this choice is probably overall good—though I wish their communication was less misleading.
Anthropic and Anthropic employees often use similar language to this quote when describing the RSP, potentially contributing to a poor sense of what will happen. My impression is that lots of Anthropic employees just haven't thought about this, and believe that Anthropic will behave much more cautiously than I think is plausible (and more cautiously than I think is prudent given other actors).
Other companies have worse policies and governance
While I focus on Anthropic in this comment, it is worth emphasizing that the policies and governance of other AI companies seem substantially worse. xAI, Meta and DeepSeek have no public safety policies at all, though they have said they will make a policy like this. Google DeepMind has published that they are working on making a frontier safety framework with commitments, but thus far they have just listed potential threat models corresponding to model capabilities and security levels without committing to security for specific capability levels. OpenAI has the beta preparedness framework, but the current security requirements seem inadequate and the required mitigations and assessment process for this is unspecified other than saying that the post-mitigation risk must be medium or below prior to deployment and high or below prior to continued development. I don't expect OpenAI to keep the spirit of this commitment in short timelines. OpenAI, Google DeepMind, xAI, Meta, and DeepSeek all have clearly much worse governance than Anthropic.
What could Anthropic do to address my concerns?
Given these concerns about the RSP and the LTBT, what do I think should happen? First, I'll outline some lower cost measures that seem relatively robust and then I'll outline more expensive measures that don't seem obviously good (at least not obviously good to strongly prioritize) but would be needed to make the situation no longer be problematic.
Lower cost measures:
- Have the leadership clarify its views to Anthropic employees (at least alignment science employees) in terms of questions like: "How likely is Anthropic to achieve an absolutely low (e.g., 0.25%) lifetime level of risk (according to various third-party safety experts) if AIs that obsolete top human experts are created in the next 4 years?", "Will Anthropic aim to have an RSP that would be the policy that a responsible developer would follow in a world with reasonable international safety practices?", "How likely is Anthropic to exit from its RSP commitments if this is needed to be a competitive frontier model developer?".
- Clearly communicate to Anthropic employees (or at least a relevant subset of Anthropic employees) about in what circumstances the board could (and should) fire the CEO due to safety/public interest concerns. Additionally, explain the leadership's policies with respect to cases where the board does fire the CEO—does the leadership of Anthropic commit to not fighting such an action?
- Have an employee liaison to the LTBT who provides the LTBT with more information that isn't filtered through the CEO or current board members. Ensure this employee is quite independent-minded, has expertise on AI safety (and ideally security), and ideally is employed by the LTBT rather than Anthropic.
Unfortunately, these measures aren't straightforwardly independently verifiable based on public knowledge. As far as I know, some of these measures could already be in place.
More expensive measures:
- In the above list, I explain various types of information that should be communicated to employees. Ensure that this information is communicated publicly including in relevant places like in the RSP.
- Ensure the LTBT has 2 additional members with technical expertise in AI safety or minimally in security.
- Ensure the LTBT appoints the board members it can currently appoint and that these board members are independent from the company and have their own well-formed views on AI safety.
- Ensure the LTBT has an independent staff including technical safety experts, security experts, and independent lawyers.
Likely objections and my responses
Here are some relevant objections to my points and my responses:
- Objection: "Sure, but from the perspective of most people, AI is unlikely to be existentially risky soon, so from this perspective it isn't that misleading to think of deviating from safe practices as an edge case." Response: To the extent Anthropic has views, Anthropic has the view that existentially risky AI is reasonably likely to be soon and Dario espouses this view. Further, I think this could be clarified in the text: the RSP could note that these commitments are what a responsible developer would do if we were in a world where being a responsible developer was possible while still being competitive (perhaps due to all relevant companies adopting such policies or due to regulation).
- Objection: "Sure, but if other companies followed a similar policy then the RSP commitments would hold in a relatively straightforward way. It's hardly Anthropic's fault if other companies force it to be more reckless than it would like." Response: This may be true, but it doesn't mean that Anthropic isn't being potentially misleading in their description of the situation. They could instead directly describe the situation in less misleading ways.
- Objection: "Sure, but obviously Anthropic can't accurately represent the situation publicly. That would result in bad PR and substantially undermine their business in other ways. To the extent you think Anthropic is a good actor, you shouldn't be pressuring good actors like them to take actions that will make them differentially less competitive than worse actors." Response: This is pretty fair, but I still think Anthropic could at least avoid making substantially misleading statements and ensure employees are well informed (at least for employees for whom this information is very relevant to their job decision-making). I think it is a good policy to correct misleading statements that result in differentially positive impressions and result in the safety community taking worse actions, because not having such a policy in general would result in more exploitation of the safety community.
The article says: "However, even the board members who are selected by the LTBT owe fiduciary obligations to Anthropic's stockholders, Israel says. This nuance means that the board members appointed by the LTBT could probably not pull off an action as drastic as the one taken by OpenAI's board members last November. It's one of the reasons Israel was so confidently able to say, when asked last Thanksgiving, that what happened at OpenAI could never happen at Anthropic. But it also means that the LTBT ultimately has a limited influence on the company: while it will eventually have the power to select and remove a majority of board members, those members will in practice face similar incentives to the rest of the board." This indicates that the board couldn't fire the CEO if they thought this would greatly reduce profits to shareholders though it is somewhat unclear. ↩︎
3As a general matter, Anthropic has consistently found that working with frontier AI models is an essential ingredient in developing new methods to mitigate the risk of AI.
What are some examples of work that is most largeness-loaded and most risk-preventing? My understanding is that interpretability work doesn't need large models (though I don't know about things like influence functions). I imagine constitutional AI does. Is that the central example or there are other pieces that are further in this direction?
I am surprised to see the Open Philanthropy network taking all of the powerful roles here.
The initial Trustees are:
- Jason Matheny: CEO of the RAND Corporation
- Kanika Bahl: CEO & President of Evidence Action
- Neil Buddy Shah: CEO of the Clinton Health Access Initiative (Chair)
- Paul Christiano: Founder of the Alignment Research Center
- Zach Robinson: Interim CEO of Effective Ventures US
In case it's not apparent:
- Jason Matheny started an org with over $40M of OpenPhil funding (link to the biggest grant)
- Kanika Bahl runs Evidence Action, an org that OpenPhil has donated over $100M to over the past 7 years
- Neil Buddy Shah was the Managing Director at GiveWell for 2.5 years. GiveWell is the organization that OpenPhil grew out of between 2011 and 2014 and continued to maintain close ties with over the decade (e.g. sharing an office for ~5 more years, deferring substantially to GiveWell recommendations for somewhere in the neighborhood of $500M of Global Health grants, etc).
- Paul Christiano is technically the least institutionally connected to OpenPhil. Most commenters here are quite aware of Paul's relationship to Open Philanthropy, but to state some obvious legible things for those who are not: Paul is married to Ajeya Cotra, who leads the AI funding at OpenPhil, has been a technical advisor to OpenPhil in some capacity since 2012 (see him mentioned in the following pages: 1, 2, 3, 4, 5, 6), and Holden Karnofsky has collaborated with / advised Paul's organization ARC (collaboration mentioned here and shown as an advisor here).
- Zach Robinson worked at OpenPhil for 3 years, over half of which was spent as Chief of Staff.
And, as a reminder for those who've forgotten, Holden Karnofsky's wife Daniela Amodei is President of Anthropic (and so Holden is the brother-in-law of Dario Amodei).
Trustees serve one-year terms and future Trustees will be elected by a vote of the Trustees
One year is shockingly short, why such fast turnaround?
And great post I'm excited to see responsible scaling policies becoming a thing!
One year is actually the typical term length for board-style positions, but because members can be re-elected their tenure is often much longer. In this specific case of course it's now up to the trustees!
The model shows early signs of autonomous self-replication ability, as defined by 50% aggregate success rate on the tasks listed in [Appendix on Autonomy Evaluations]
Would you be willing to rephrase this as something like
The model shows early signs of autonomous self-replication ability. Autonomous self-replication ability is defined as 50% aggregate success rate on the capabilities for which we list evaluations in [Appendix on Autonomy Evaluations]
?
The hope here is to avoid something like "well this system doesn't have autonomous self-replication ability/isn't ASL-3, because Anthropic's evals failed to elicit the behaviour. That definitionally means it's not ASL-3", and get a bit more map-territory distinction in.
I'm delighted that Anthropic has formally committed to our responsible scaling policy. We're also sharing more detail about the Long-Term Benefit Trust, which is our attempt to fine-tune our corporate governance to address the unique challenges and long-term opportunities of transformative AI.
Today, we’re publishing our Responsible Scaling Policy (RSP) – a series of technical and organizational protocols that we’re adopting to help us manage the risks of developing increasingly capable AI systems.
As AI models become more capable, we believe that they will create major economic and social value, but will also present increasingly severe risks. Our RSP focuses on catastrophic risks – those where an AI model directly causes large scale devastation. Such risks can come from deliberate misuse of models (for example use by terrorists or state actors to create bioweapons) or from models that cause destruction by acting autonomously in ways contrary to the intent of their designers.
Our RSP defines a framework called AI Safety Levels (ASL) for addressing catastrophic risks, modeled loosely after the US government’s biosafety level (BSL) standards for handling of dangerous biological materials. The basic idea is to require safety, security, and operational standards appropriate to a model’s potential for catastrophic risk, with higher ASL levels requiring increasingly strict demonstrations of safety.
A very abbreviated summary of the ASL system is as follows:
The definition, criteria, and safety measures for each ASL level are described in detail in the main document, but at a high level, ASL-2 measures represent our current safety and security standards and overlap significantly with our recent White House commitments. ASL-3 measures include stricter standards that will require intense research and engineering effort to comply with in time, such as unusually strong security requirements and a commitment not to deploy ASL-3 models if they show any meaningful catastrophic misuse risk under adversarial testing by world-class red-teamers (this is in contrast to merely a commitment to perform red-teaming). Our ASL-4 measures aren’t yet written (our commitment is to write them before we reach ASL-3), but may require methods of assurance that are unsolved research problems today, such as using interpretability methods to demonstrate mechanistically that a model is unlikely to engage in certain catastrophic behaviors.
We have designed the ASL system to strike a balance between effectively targeting catastrophic risk and incentivising beneficial applications and safety progress. On the one hand, the ASL system implicitly requires us to temporarily pause training of more powerful models if our AI scaling outstrips our ability to comply with the necessary safety procedures. But it does so in a way that directly incentivizes us to solve the necessary safety issues as a way to unlock further scaling, and allows us to use the most powerful models from the previous ASL level as a tool for developing safety features for the next level.[1] If adopted as a standard across frontier labs, we hope this might create a “race to the top” dynamic where competitive incentives are directly channeled into solving safety problems.
From a business perspective, we want to be clear that our RSP will not alter current uses of Claude or disrupt availability of our products. Rather, it should be seen as analogous to the pre-market testing and safety feature design conducted in the automotive or aviation industry, where the goal is to rigorously demonstrate the safety of a product before it is released onto the market, which ultimately benefits customers.
Anthropic’s RSP has been formally approved by its board and changes must be approved by the board following consultations with the Long Term Benefit Trust. In the full document we describe a number of procedural safeguards to ensure the integrity of the evaluation process.
However, we want to emphasize that these commitments are our current best guess, and an early iteration that we will build on. The fast pace and many uncertainties of AI as a field imply that, unlike the relatively stable BSL system, rapid iteration and course correction will almost certainly be necessary.
The full document can be read here. We hope that it provides useful inspiration to policymakers, third party nonprofit organizations, and other companies facing similar deployment decisions.
We thank ARC Evals for their key insights and expertise supporting the development of our RSP commitments, particularly regarding evaluations for autonomous capabilities. We found their expertise in AI risk assessment to be instrumental as we designed our evaluation procedures. We also recognize ARC Evals' leadership in originating and spearheading the development of their broader ARC Responsible Scaling Policy framework, which inspired our approach.. [italics in original]
I'm also including a short excerpt from the Long-Term Benefit Trust post, which I suggest reading in full. You may also wish to (re-)read Anthropic's core views on AI safety as background.
The Anthropic Long-Term Benefit Trust (LTBT, or Trust) is an independent body comprising five Trustees with backgrounds and expertise in AI safety, national security, public policy, and social enterprise. The Trust’s arrangements are designed to insulate the Trustees from financial interest in Anthropic and to grant them sufficient independence to balance the interests of the public alongside the interests of Anthropic’s stockholders. ... the Trust will elect a majority of the board within 4 years. ...
The initial Trustees are:
The Anthropic board chose these initial Trustees after a year-long search and interview process to surface individuals who exhibit thoughtfulness, strong character, and a deep understanding of the risks, benefits, and trajectory of AI and its impacts on society. Trustees serve one-year terms and future Trustees will be elected by a vote of the Trustees. We are honored that this founding group of Trustees chose to accept their places on the Trust, and we believe they will provide invaluable insight and judgment.
As a general matter, Anthropic has consistently found that working with frontier AI models is an essential ingredient in developing new methods to mitigate the risk of AI. ↩︎