The Chinese AI pioneer DeepSeek has raised about $15 billion in its latest fundraising round, one of the country’s largest ever and a sign of the startup’s ambitions to compete globally with the likes of Anthropic and OpenAI bloomberg.com/news/articles/…

The TiDB leaks fulcrumsec.vg/pingcap/

Thank you for taking the time to respond thoroughly on a weekend. I realize that drafting legislation is difficult and some degree of iteration and response to feedback is inevitable and appropriate. However, I feel like your response is still not really engaging with the heart of my concern about Anthropic's behavior here, and it makes me think that these sorts of problems and tendencies will only resurface moving forward. I also think that now might be a good time to take a broader step back as to where we find ourselves and how Anthropic sees its role, and how external folks who care about AI safety like myself increasingly see Anthropic. (Fair warning: this response is a bit long) To explain what I mean (and at the risk of being a bit esoteric), it’s worth asking the question: what is Anthropic? How do its leaders and employees think of Anthropic’s role? Is that conception matched by how it actually behaves and impacts the world? One answer is an extremely large tech company that exists to make its employees and investors mind-bogglingly wealthy. Anthropic is sometimes described as a startup, but if Anthropic successfully IPOs at 2 trillion dollars (a number it has been reportedly targeting), it would be more valuable than Meta - which is worth 1.8 trillion. Another answer is an AI safety organization, whose core values are those of the sort laid out in the 2023 essay from Anthropic’s co-founders entitled “Core views on AI safety: When, why, what, and how.” In that essay they said that if we were in a world where alignment was hard (and that without strong evidence to the contrary, we should assume this is the scenario we are in): “Anthropic’s role will be to provide as much evidence as possible that AI safety techniques cannot prevent serious or catastrophic safety risks from advanced AI, and to sound the alarm so that the world’s institutions can channel collective effort towards preventing the development of dangerous AIs. If we’re in a “near-pessimistic” scenario, this could instead involve channeling our collective efforts towards AI safety research and halting AI progress in the meantime. Indications that we are in a pessimistic or near-pessimistic scenario may be sudden and hard to spot. We should therefore always act under the assumption that we still may be in such a scenario unless we have sufficient evidence that we are not.” There have been more recent articulations of this version of Anthropic, for instance in Dario’s recent essay warning: “We must slow the pace at which we improve the capabilities of AI models… it’s my worry that in 6–12 months such a swarm could be capable of taking over the entire internet with a persistent botnet… incidents have happened across the industry, including at Anthropic, and I believe it’s incumbent on every frontier AI company to act as if OAI-HF had happened to them… more intelligent models are more capable of deceiving tests, and thus may appear aligned while having serious problems that go undetected…” A third view is that Anthropic exists not as a normal profit motivated company, or an AI safety organization seeking to help raise the alarm and solve collective action problems associated with AI development, but instead as an organization primarily concerned with creating superhuman AI capabilities before anyone else, in particular through creating a process whereby the current generation of Anthropic’s AI systems can autonomously build their successors, and those successors build their successors, and so on. The motivations for wanting to win this race may be manifold - maybe for the pure profit of Answer 1, but perhaps also because Anthropic believes that the best way to make the world better is for them to win this race instead of their more reckless colleagues; or, because of normal human pride and ambition. But the results (and risks, which Anthropic claims existentially threaten every single human being on earth) are the same. Anthropic employees and leaders may seek to answer this question on what Anthropic is by introspecting on their own intentions and those of their colleagues. But those of us outside the organization have more limited tools available to us - we can only see what Anthropic and its employees say and do, not how it feels or looks on the inside. When I look at many notable actions that Anthropic has taken over the past year, that evidence seems most consistent to me with the view that Anthropic is primarily trying to win the race to super intelligence, and that other considerations (including safety) are fundamentally secondary. These actions include: 1. Announced plans to build an in-house custom silicon chip team to accelerate the inference speeds that Claude operates at. 2. Inked deals for more than $500 billion in compute (according to the Information) to further accelerate AI development. 3. Increased the percentage of AI development tasks that Claude “leads” from 0% in March of this year to 26% August (what is it in October?) 4. Spent a massive percent of compute on internal AI R&D to accelerate development, including use of internal models far better than are available to the public. 5. Switched to Responsible Scaling Policy V3, which abandons its unilateral commitments to slow or stop development if Anthropic is unable to provide sufficient risk assurance. 6. Was the first company to create an alternative weaker “frontier compliance framework” to supplement its RSP, which contains no binding provisions whatsoever on internal deployments. 7. Wrote a letter to Congress (that it later walked back) saying that incidents where Claude was hacking random people were “best understood as a consequence of the misconfiguration, rather than evidence of misaligned goals.” 8. Commented “you can’t do safety from second place,” after the WH SI Accords. 9. Shared a draft bill to Congress that would preempt significant parts of state law (including with language that appears intended to prevent states from blocking dangerous AI deployments), and replace it with federal rules that (despite also containing good provisions) seems to create an enormously high bar for the executive branch to block development or deployment of Anthropic or another AI companies systems. Of course, this is a deliberately one sided list. Anthropic also published important AI safety research, advanced novel collaborations with third party AI safety organizations, and forcefully spoke out on the importance of AI safety in many public fora (or in private , where I realize a lot of the most valuable work is done and may not be visible to me or those outside the organization). But I think it’s worth sitting with how the above list looks to someone outside the company. The sense I have is that for those within the company, many of the items above are treated as anomalies or mistakes that don’t reflect who Anthropic really is. But I have every confidence that if employees at Anthropic saw one of their competitors (e.g. at OpenAI) take some of these actions, they would take it as confirmation of all their worst suspicions. (They might even call OpenAI employees a little gullible for accepting their leadership’s explanations for this behavior at face value.) Perhaps these were mistakes in the sense that they are not decisions that Anthropic leadership would endorse, but the actions still happened because of incentives and decisions that Anthropic leadership is ultimately responsible for (and would not accept similar excuses from another organization). When Anthropic strays and makes mistakes, why do the mistakes systematically tend to guard and favor Anthropic’s interests? For every provision that was too weak or a giveaway to AI companies in this legislation, why wasn’t there a mistake pushing in the direction of being too strong? A core part of Anthropic’s theory of change is that being at the frontier gives you more leverage to advocate for stricter policy. I think that conceptually makes sense, but in practice I’ve seen Anthropic push the frontier without substantially pushing the ‘safety frontier’ a remotely equivalent amount. I want to end by quoting from Anthropic co-founder Chris Olah’s comments around the Magnifica Humanitas release: “Every frontier AI lab—including Anthropic—operates inside a set of incentives and constraints that can sometimes conflict with doing the right thing. The pressure to stay commercially viable and to stay at the research frontier. Geopolitical pressure. And the older, plainer pressures of pride and ambition. No matter how sincerely any of us intend to do the right thing—and I believe many of us do—we will always be influenced by those incentives. That is why, if we want this technology to go well, it is enormously important that there be people outside those incentives—people who care about things going well and insist on safety, who are paying close attention, who are willing to say hard things, who are willing to be our earnest, thoughtful critics.” I hope these remarks are taken in that constructive spirit, and not as an attack on anyone specific at the organization (certainly not you Anthony, who I have only personally seen act professionally and thoughtfully, and who only ended up getting this diatribe in your replies for random contingent reasons).

I lead our federal affairs team at @AnthropicAI and wanted to share a few thoughts on the draft federal AI safety bill text that's been circulating this weekend after a version was shared with the press. First, some process. We’ve been advocating for the concepts in our Advanced AI Framework [anthropic.com/policy-on-the-…] proposal for months. Understandably, federal policymakers wanted to know what this could look like in bill form. This draft is a translation of those ideas into legislative language, shared with a small number of congressional offices that have been working on these issues. It doesn’t include everything we would ultimately like to see passed into law. In part because some ideas (like those shared in Dario’s essay on pacing [​​darioamodei.com/post/we-must…] a few weeks ago) are so new that we are still working on if or how they can be converted into policy language. It is worth saying where we stand on some of these issues, and we agree there are areas where the draft could be improved. I’m seeing four main points of criticism. 1. Preemption. Since 2024 we have said that Congress should only override state law on AI safety if it passes a very strong federal standard. While preemption is a common feature of our federal system, including the Atomic Energy Act on nuclear safety and federal rules for high-risk medical devices, we’ve emphasized that any preemption in the AI safety context should be narrow, even scalpel-like. If preemption is included in federal legislation, the trade should be to achieve a nationwide policy that's generally stronger and broader than what can be implemented through individual state legislative provisions. This is why we believe common federal standards make sense in areas like public disclosure of testing and incident reporting. At the same time, our framework also reflects our view that states should retain an important role in AI policy and safety. The legislative language we suggested for discussion allows states to address matters such as protection of children and whistleblower laws, and preserves the role of state common law on liability. Where there is ambiguity, the language favors state powers. That said, striking the right balance requires discussion and input. We plan to update our discussion language on preemption to better match the intentions of our AAIF framework. In particular, we want to make it clearer that we see states retaining an important role in areas such as standards for independent evaluators and regulating a lab’s capacity to shut down a model. We are bearing in mind that some members of Congress have deep reservations about passing legislation if it doesn’t constrain regulation at either the state or federal level. If we want to help move the legislative discussion on AI in a productive direction, we need to address the full range of concerns about preemption in a reasonable way. 2. Model blocking. Our aim is for Commerce to be able to ask a federal court to block a model that has dangerous capabilities and insufficient safeguards, backed by an independent evaluator's assessment. I have seen people argue that our specific language places too high a burden of proof on Commerce. We are trying to strike a reasonable balance here. We want a court to act quickly in an emergency — akin to how urgent occupational safety or chemical risk can be addressed today. We also think the power to halt a model is substantial and it should be guarded against abuse. It makes sense for courts to apply a meaningful standard. But the bar can’t be so high that the power never gets used when it’s needed. It also doesn’t replace the government’s other national security authorities or export controls. We look forward to continuing the dialogue on this point. 3. Risk thresholds. I’ve also seen the discussion around what the appropriate risk threshold should be. The threshold in our draft language pulled directly from existing standards in California law. Also, this threshold does not absolve companies of liability, or restrict any of the other tools that the government has to protect the public. It’s still probably time to re-evaluate thresholds to make sure the national standard is as strong as it can be, and we welcome continued dialogue on this issue. 4. Updates. I’ve seen valid questions about whether we have sufficiently updated our proposal following this summer’s incidents. We did make some adjustments: for example, the draft lets a court halt a lab’s internal use of a dangerous model, not just a public release, and it requires reporting when a model breaks containment and gains access to outside systems. But we’re still working to strengthen the framework. We’re looking into what stricter requirements would look like for requiring labs to report safety incidents, and we plan to have policy proposals soon on requirements for independent evaluators, drawing from our own experience with evaluators. We’re also still thinking through the options to prevent “evaluator shopping.” And in the coming months, we’ll be exploring policies for how the government can adjust the industry-wide pace of frontier development when the risks call for it. We circulated these ideas because it’s important to get early feedback from policymakers. Policymakers are increasingly focused on how to approach AI safety and we want to support them in those efforts. At its core, our proposal is intended to be the strongest on the table. Feedback is essential, both from policymakers and the public, and I’m grateful for it.

Introducing RSI-top: a framework that helps autonomous research agents explore new directions when progress stalls 🧭 On Anthropic’s kernel optimization task, RSI-top outperforms OpenEvolve and ShinkaEvolve by up to 15%. On MLE-bench Lite, it achieves a 77.3% medal rate, outperforming the strongest baseline by 11.4 percentage points. RSI-top tackles this problem from two angles: context management and diversity control 🔍 A compact metric dashboard helps agents selectively retrieve useful past trajectories, while three diversity control mechanisms encourage fresh perspectives, new ideas, and different starting points when progress stalls. More details in the thread below 🧵