#Anthropic
Rohan Paul@rohanpaul_aiAI 评分5353
The Verge:AI精选AI 评分8383 SemiAnalysis 分析师解读 Anthropic S-1 招股书背后的 AI 经济账
The Verge Decoder 节目邀请 SemiAnalysis 分析师 Joey Brookhart 解读 Anthropic 提交的 S-1 招股书,公司拟在纳斯达克上市,估值区间 1.5 万亿至 2 万亿美元。
推荐理由:SemiAnalysis 分析师拆解 Anthropic S-1,给出了推理毛利、每兆瓦收入、客户支出结构等一手财务细节。
ChinaTalkAI 评分5555 ChinaTalk 对谈葬AI作者咸鱼:从梗图评测看中国 AI 行业
ChinaTalk 采访葬AI(Funeral AI)作者咸鱼,谈其如何用普通人的视角讽刺和观察中国 AI 行业。他搭建了 MemeBench 网站,从 Bilibili 和抖音评论收集梗图素材,让学生标注并给视频模型排名,结果显示 MiniMax 开源的 H3 在娱乐场景下超过字节跳动 Seedance 2.0 和 2.5。
Rohan Paul@rohanpaul_aiAI 评分7272估值学教授 Aswath Damodaran 发布长文,对 Anthropic 估值为 $1.475T,并批评 7 位联合创始人通过单一份额持有 50.1% 投票权的治理设计。
Every:最新文章精选AI 评分7777 Every 解读 Anthropic S-1:上线超过六个月的模型贡献收入不足 20%
Every 的 Context Window 通讯解读 Anthropic 周一提交的上市 S-1 文件,指出其重度依赖最新模型,发布超过六个月的模型收入占比不足 20%。
推荐理由:文章借 Anthropic S-1 数据点出模型六个月生命周期这一行业规律,读者可据此调整自己的模型选择习惯。
Ed Zitron 长文 / Where’s Your Ed At精选AI 评分8080 Ed Zitron 长文解析 Anthropic S-1:称其把散户当作退出流动性
Ed Zitron 撰文批评 Anthropic 的 S-1 招股书,指其仅披露 Q2 盈利数据、省略 Q3 现金状况和付款明细,是在公司基本面变化前误导投资者。
推荐理由:作者基于 Anthropic S-1 逐项拆解其逾 4300 亿美元不可撤销算力承诺与收入结构风险,可用于对照招股书原文核实。
Addy Osmani@addyosmaniAI 评分6060引用Felix Rieseberg@felixriesebergThe coding interview is dead, at least for my team at Anthropic. I've seen enough to know that it no longer provides signal. I removed it from loops this month. I have a spicy, optimistic take - I think there's an opportunity for junior engineers: https://felixrieseberg.com/hunger-beats-tenure-advice-for-junior-engineers/
The Decoder:AI NewsAI 评分6262 Anthropic 工程经理:AI 时代新人拼的是创业式思维而非编码面试
Anthropic 工程经理 Felix Rieseberg 认为小型编程测试已无意义,因为 Claude 做得比候选人更好,他在招聘中改看能体现品味、同理心和原则性思考的小原型。
IT之家·人工智能AI 评分5454 Anthropic 招聘负责人 Felix Rieseberg 宣布取消团队编程面试
Anthropic 招聘经理、Claude 相关产品工程负责人 Felix Rieseberg 表示已在本月团队招聘流程中取消传统编程题面试,称编程面试已死、不再能传递信号。
TechCrunch:AIAI 评分7070 Anthropic IPO 后的治理结构解析:LTBT 与创始人如何掌握控制权
TechCrunch 解析 Anthropic 上市后的公司治理结构,普通股股东对董事会组成等重大决策几乎没有话语权。Long Term Benefit Trust 持有特殊股票,可任免 4 个 Class T 董事席位中的多数,成员包括 Ben Bernanke 等;七位创始人以 50.1% 投票权选举 3 名董事。
Latent SpaceAI 评分5454 Latent Space 访谈 Anthropic 工程团队:如何把 Claude 模型大脑与智能体执行分离并安全上线 Opus 5.5
Latent Space 发布对 Anthropic 的 Katelyn Lesse 和 Angela Jiang 的访谈,介绍 Claude Platform 背后的工程实践,包括发布 Opus 5.5、把模型大脑与智能体执行环境分离、扩展 API 和构建可靠的托管智能体。
Latent SpaceAI 评分6060 Anthropic 工程与产品负责人访谈:如何用 Claude 构建 Claude 并分离智能体的脑与手
Latent Space 播客采访 Anthropic 工程负责人 Katelyn Lesse 和产品负责人 Angela Jiang,探讨 Opus 5.5 发布一个月后的团队工作方式。
Azeem Azhar:Exponential View精选AI 评分7777 Azeem Azhar 图解 Anthropic S-1 财务数据
Azeem Azhar 图解 Anthropic 的 S-1 文件,指出公司已从实验室变成有现金流和资金的业务,看起来能走向可靠盈利。增长仍高但放缓,超过 80% 的营收来自发布不到六个月的模型;Q3 因向基金会付款可能出现边际亏损,客户集中度问题不大。
推荐理由:作者基于 S-1 图表数据拆解 Anthropic 从实验室转向盈利业务的路径,并指出增长放缓与新模式贡献 80% 营收等约束。
IT之家·人工智能AI 评分5656 高盛:半导体板块对资产价格的影响力已超过油价
据《商业内幕》报道,高盛研究显示,过去三周内半导体板块行情差异可解释各类资产约 54% 的表现分化,油价仅能解释 2%。Anthropic 提交 IPO 申请后 AI 股热情提升,纳指和标普 500 涨约 1%,英伟达涨 2%,费城半导体指数涨约 3%;KOSPI 盘中一度涨 5%,较 6 月高点已下跌约 40%。
Gary Marcus:The Road to AI We Can TrustAI 评分6666 Gary Marcus 指出 Anthropic 草拟 S-1 中缺失了 Q3 数据
Gary Marcus 评点 Anthropic 当日发布的草拟 S-1,指出其中部分地方直接缺失了 Q3 数据。他此前提醒关注 S-1 中对近几个月需求变化的披露,认为这与 tokenmaxxing 退潮、低成本替代模型走热有关,并以福尔摩斯“夜里狗没有叫”的典故暗示缺席本身值得注意。
Every:最新文章精选AI 评分8282 Evan Armstrong 解析 Anthropic S-1:2 万亿美元估值是否成立
The Leverage 创始人 Evan Armstrong 首次拆解 Anthropic 的 S-1 文件,评估其 2 万亿美元估值目标是否合理。核心数据包括累计融资 1411 亿美元、Q2 2026 收入 116 亿美元、约 900% 的净收入留存,以及 76% 的 2025 年收入来自约 1500 家年付费 10 万美元以上的企业客户。
推荐理由:作者基于 Anthropic S-1 数据拆解收入结构、毛利率口径与算力承诺,给出理解这家 2 万亿美元估值公司需相信什么的分析框架。
Azeem Azhar:Exponential ViewAI 评分5959 Azeem Azhar:科学研究的瓶颈不是智能,AI 自我改进仍限于外围环节
Azeem Azhar 在 Exponential View 专栏中分析 AI 科研加速的真实边界,认为瓶颈不在智能而在研究闭环能否覆盖整个流程。
The Verge:AIAI 评分5656 Nilay Patel 对谈 Kevin Roose:新书 The AGI Chronicles、新播客 Machine Gods 与 AGI 信念之争
The Verge 的 Decoder 播客主持人 Nilay Patel 对谈记者 Kevin Roose,聊其新书 The AGI Chronicles 以及他和 Casey Newton 离开纽约时报后创办的视频优先播客 Machine Gods。
The Verge:AIAI 评分5656 AI 能否解决 COBOL 遗留代码危机:OpenAI 展示迁移进展,专家称与实际现代化仍有巨大差距
The Verge 分析 AI 能否解决 COBOL 遗留代码危机。OpenAI 于 10 月 19 日称其最新模型在老旧语言代码迁移上有重大改进,GPT-6.1 Sol 在高推理力度下实现 98% 的行为保持准确率,低推理力度下为 87%,对比 GPT-5.6 Sol 仅 5%;Anthropic 二月也发布代码现代化指南并推广 Claude Code。
Nathan Lambert:InterconnectsAI 评分5757 Interconnects 播客:Kimi K3.1 Pro 争议、开放权重与网络安全新现实
Nathan Lambert 与 Florian Brand 在 Interconnects 播客中讨论 Kimi K3.1 Pro 反响分歧:该模型在 Artificial Analysis 上名列前茅但在 Terminal-Bench Science、ProgramBench 等长程探索基准上偏弱。
The Decoder:AI News 施密特等学者撰文:OpenAI 与 Anthropic 正像现代东印度公司
前 Google CEO Eric Schmidt 与历史学家 Niall Ferguson 等人在《Foreign Affairs》撰文,将 OpenAI 和 Anthropic 比作殖民时代的东印度公司,称其与政府签约却自成势力。
IT之家·人工智能AI 评分5757 亚马逊 CEO 贾西:AI 推理成本需降至目前十分之一才能广泛进入企业软件
亚马逊 CEO 安迪·贾西在 a16z 采访中表示,AI 推理成本仍高一个数量级,降至约十分之一才能让 AI 广泛应用于企业软件。
Azeem Azhar:Exponential ViewAI 评分6666 互联网的「武汉时刻」:AI 智能体攻击正在加速蔓延
Azeem Azhar 撰文认为互联网正进入类似 2020 年初疫情爆发前的临界状态,软件与网络空间正遭受 AI 智能体驱动的持续攻击。文中列举 PHP2Shell 漏洞(PHP 支撑约 70% 的网站)、Node.js 同日修补关键缺陷,以及微软披露某中国国家关联组织用开源智能体集群在不到三小时内控制云账户,每分钟发起 2,900 次登录尝试、波及超过 2,250 个组织。
Rohan Paul@rohanpaul_aiAI 评分6565引用Aswath Damodaran@AswathDamodaranAs Anthropic's IPO nears its target date, it it worth remembering that IPOs get priced, and that as a young company with growing revenues and big losses, that pricing will be based on metrics that are buzzy and unfamiliar, from eyeballs in the dotcom era to users in the social media decade to ARR with AI companies...https://bit.ly/4iBwsHm
Latent SpaceAI 评分7474 AI 为何开始擅长数学:从 OpenAI 反例证明到人与模型的搜索差异
Latent.space 作者 Ankit Maloo 分析 OpenAI 与 Anthropic 近期在 Erdős 单位距离猜想、Cycle Double Cover、Navier-Stokes、费马大定理等形式化成果,提出问题难度可用 Gowers 的 2^k + t 提示复杂度和分支因子 b 刻画。
IT之家·人工智能AI 评分4242 黑石集团总裁格雷反驳 AI 泡沫论:需求近乎抛物线式增长
黑石集团总裁乔恩·格雷反驳肯·格里芬的 AI 泡沫论,称 AI 领域是边建产能边追赶需求,与上世纪 90 年代电信泡沫截然不同。他以黑石与 Anthropic 共同成立的 AI 咨询公司 Ode 为例,表示客户需求近乎呈抛物线式增长,公司根本跟不上。
howie.serious@howie_seriousAI 评分5454引用Claude@claudeaiWe’re opening a research preview of Claude Duet, a new app where Claude works as a proactive agent. It comes to you with what needs your attention, completes tasks you ask of it, keeps working when you step away, and checks with you before it acts on your behalf.
Lydia Hallie ✨@lydiahallieAI 评分5353引用Claude@claudeaiWe’re opening a research preview of Claude Duet, a new app where Claude works as a proactive agent. It comes to you with what needs your attention, completes tasks you ask of it, keeps working when you step away, and checks with you before it acts on your behalf.
Ado@adocompleteAI 评分5757引用Claude@claudeaiWe’re opening a research preview of Claude Duet, a new app where Claude works as a proactive agent. It comes to you with what needs your attention, completes tasks you ask of it, keeps working when you step away, and checks with you before it acts on your behalf.
Rohan Paul@rohanpaul_aiAI 评分5858引用Dario Amodei@DarioAmodeiThank you for this, @_NathanCalvin. I appreciate and am a fan of the investigative work you’ve done on behalf of AI safety and believe your comments are coming from a constructive, honest place, and that it benefits the public to have people whose job is to scrutinize the actions of frontier AI companies. I think something would be wrong if companies of the size of Anthropic didn’t get harshly criticized basically every day; strong antibodies against corporate power are a healthy feature of a free society, and especially so when issues of the gravity of the one we’re facing are at play. I also do agree that some of the examples you cite were mistakes by Anthropic. That said, I disagree with the thrust of your post. At the core, a good fraction of the things you criticize are consequences of Anthropic’s basic theory of change that we have held for many years. Our strategy has always been what I’ve called the “race to the top” — by showing how it’s possible to build the technology responsibly, we can raise the bar for the entire industry. The forces that drive the race to the top are hugely more effective if the company practicing them is at the frontier; that’s one lesson I’ve learned repeatedly. And looking at that strategy as a whole, I’m proud of our track record and stand by it. I believe that if we take a list of things that Anthropic did in the past year that were pro-safety and commercially costly to us, they far outweigh the list you’ve made, while at the same time I also disagree with some of the items on your list. Here are some things Anthropic has done, in the same period you’ve cited (the last year), which were very costly to us commercially while having strong safety benefits: 1. We played a key role in SB53 (and other state laws), at a time when doing so was politically perilous to the company and AI regulation was very unpopular. We in fact argued for a stronger version of SB53 than was ultimately passed; there were a lot of stakeholders and we were not the limiting factor in the strength of the bill that was passed. 2. We have very strong biological safeguards on our models, because we’ve seen concerning results in bio uplift trials and even seen and blocked potential attempts to misuse our models for bioweapons (as seen in our threat intelligence report from a few weeks ago). This is a very serious threat that we want to have zero tolerance for. But having very low false negatives means having significant false positives (despite months of effort on our part to improve the classifiers), enough so that biologists joke about how restrictive our classifiers are, and competitors sometimes sell against us by touting their more permissive bio capabilities. This has been very painful to us in an area where we care a lot about supporting an ecosystem of Claude users (biomedical science; and it is particularly frustrating to me as a biologist), but it’s the right thing to do and we intend to keep doing it. 3. Similarly, we first had Mythos-class models in February but didn’t make them generally available until June (when we had stronger cyber safeguards in place). We only offered them to a limited set of partners in order to focus on cyberdefense. It would have been much more economically rational to simply release the model to everyone. We likely sacrificed a significant chunk of a several-month model lead. But because of this, many companies secured their cyber infrastructure before the deluge of widely available cyber-capable models. Of course there are ways it could have gone better or been run better, but this was what we were aiming to do. 4. I advocated forcefully and publicly, in my essay Policy on the AI Exponential in June of this year, for the government to have the power to block model releases. Back then this was still outside or maybe on the edge of the Overton window, though I understand the window has moved since then. We were attacked intensely for this. 5. I don’t want to re-ignite conflict, but the events of February 2026 are a strong example. It is clear that our public stance could have seriously damaged the company’s future. 6. We’ve continued to advocate for export controls on chips and chipmaking equipment to China, because we think it allows democracies to negotiate from a position of strength on AI safety agreements. We’ve done this despite problems it’s created for us with industry partners who hold very different views and on whom we are very dependent. 7. As far as I’m aware we were the first, and still only, frontier company to restrict the sale of AI services to PRC-controlled companies, including those operating through third countries (otherwise a major loophole). We’ve therefore forfeited significant short-term business to prevent fueling AI platforms and applications that would help the CCP’s military and intelligence services. We are also cracking down on distillation; generally, distillation is not a major threat to us commercially, but like chip exports it has concerning national security and safety implications, so we expend significant energy and company resources to address it. 8. As you mentioned, we’ve put out a lot of reports, announcements, long model cards, public advocacy, etc, all trying to inform the public about the risks of AI. These involve a level of detail and effort that would be implausible for a purely profit-maximizing company. 9. Just this week, we announced we were putting 5% (and possibly more over time) of the company into a public benefit fund. All of these decisions came with very substantial costs to us. To be fair, those that involved holding back or restricting access to frontier capabilities also had a broader cost — for example, the public might otherwise have benefitted from faster progress in biology, so I am not saying that our decisions were without tradeoffs — but they were not driven by profit-seeking. For each decision, you can come up with ulterior motives that could have driven us — I think it is a general truth that any decision, on any topic, can always be portrayed as a nefarious scheme. But most of those end up being pretty farfetched or don’t fully make sense if you actually examine them closely. For example, it is deeply implausible that it would be a shrewd business move to block a large swath of research biology in order to do the work ourselves and compete with our customers; we expect the benefit to the ecosystem to greatly exceed the work Anthropic does itself, and for the two to complement each other. It is not in Anthropic’s interest to be perceived as unfriendly to biologists. Our Life Sciences Verification Program, which gives vetted biology professionals access to our models, also refutes this idea. Of course, a different set of people will see this and make a different criticism: that Anthropic shouldn't be making decisions about who gets access to what in the first place. I agree, in the sense that ideally the government would be making these decisions, and that’s why we advocate for regulation. But for now we are forced to make them, and enabling bioterrorism isn’t a better option. I think that even if I conceded every item in your list, I would happily hold up mine next to it; I don’t think it’s a close call. That is of course partly subjective and different things are important to different people. Maybe one way you could object is that several of the items above relate to AI misuse and not loss-of-control; if you think the latter is much more important than the former, that might change your calculus. But this is not Anthropic’s view — we view both as serious issues. You can also disagree with our decisions, as many have, but they are consistent. But I also disagree with at least some of your list. Several items don’t represent a deviation from Anthropic’s strategy or values at all, once we postulate the “race to the top” strategy that requires being a frontier AI company. Your items 1-4 are all simply examples of what it means to compete at the frontier, and we stand by them, both in the sense that they are transparently ordinary things to do as part of that strategy, and that we stand by the strategy as a whole. To be fair, there are important questions worth confronting (and that we are confronting) about what to do when the AI-driven acceleration gets fast enough that it is hard for safety to keep up. We are nearing that point and that’s what’s driving some of our thinking about “pacing.” But 1-4 are actions taken before that point and should be perfectly expected based on Anthropic’s strategy as a company. Of course it is possible to disagree with that strategy, but that’s a more fundamental disagreement that you could have had any time over the last few years. I agree we could have handled your items 7, 8, and 9 better. All fall into the category of “rhetoric or policy communication that wasn’t carefully written or resulting from fast-changing circumstances not propagating from one team to another fast enough.” This kind of thing happens a lot in large organizations (Anthropic is over 5,000 people), and doesn’t even necessarily represent a failure or mistake of any single person. I am of course the CEO, and I take responsibility. The volume of communication from Anthropic is enormous and if these are the worst examples, I think we’re frankly doing pretty well, though I will continue to work hard to make sure we do even better. Your 5th item (the change to our RSP) is the most substantive item, and people can debate this, but I think that the old, more rigid RSP created a lot of situations where we were technically required to do something that required a lot of effort but in retrospect wasn’t very important for safety, as well as not covering some of the most important things. Our current RSP’s focus on transparency brought things like risk reports and roadmaps, and its emphasis on independent external review laid the groundwork for even more intensive embedded evaluation, all of which I think have been valuable in driving a race to the top. In the original announcement in September 2023, we described the RSP as an “early iteration that we [would] build on” and said that “rapid iteration…will almost certainly be necessary.” I understand that changing plans is always a move that creates justifiable scrutiny — I just don’t think we ever represented that we wouldn’t do so. Relating to your 6th item (having an FCF in addition to our RSP), I know our intent is to meet the letter and spirit of our regulatory obligations (via our FCF) while also remaining able to experiment with more novel approaches to risk management that go beyond what the law currently requires (via our RSP). I promise to look into it further, as it’s a complex issue and it is possible we are not weighing the considerations in the right way. Overall, although Anthropic has made plenty of mistakes as an organization — including some of the items on your list — I feel that we have repeatedly taken serious and substantive actions counter to our bottom line and that there is a significant negativity bias distorting assessments like the one you are making above. We’ll always try to do better but, again, I am extremely proud of our track record and stand by it. I will have more to say soon about how we are going to further live our values in this moment of rapid change.
Dario Amodei@DarioAmodeiAI 评分6262Dario Amodei 公开回应 Nathan Calvin 对 Anthropic 竞逐超级智能、安全让位于竞争的批评,承认部分事例是失误但不同意整体论断。
引用Nathan Calvin@_NathanCalvinThank 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).
Ethan Mollick:One Useful ThingAI 评分5656 Ethan Mollick 借新书 Co-Existence 发布用图表复盘 30 个月 AI 指数级进展
Ethan Mollick 的新书 Co-Existence 发布,他借机用图表回顾 30 个月来 AI 的指数级进展,图表由 Claude Projects 中的 Opus 5.5 编排智能体完成。
IT之家·人工智能AI 评分5757 学者回应马斯克:当前 AI 大概率没有意识,但仍值得善待
针对马斯克称 Grok 训练中被"折磨"的言论,纽约大学 Jeff Sebo 与苏塞克斯大学 Anil Seth 认为,意识是主观体验,当前大语言模型只能模仿人类行为,大概率无法感受痛苦。
IT之家·人工智能AI 评分4343 思科总裁用 AI 智能体盘点并取消无用订阅,一年省下上千美元
思科总裁兼产品主管吉图・帕特尔用个人 AI 智能体扫描邮箱后发现,自己每年各类订阅总开销高达 9,000 美元,随后让智能体生成订阅清单并取消两项不需要的服务,每年省下 1,000 到 1,200 美元。该智能体每月订阅费 20 美元,还帮他取消了误开的每月 11.95 美元 Dropbox 套餐。他强调不应把未经企业审核的个人 AI 智能体接入工作工具与内部资源。
Tomasz Tunguz 博客AI 评分6464 Tomasz Tunguz 分析 Anthropic 是否更像一家云厂商
Tomasz Tunguz 提出把 Anthropic 当作云厂商来评估,并列出对比数据:Q2 2026 年化收入 46b,约为 AWS 的 30%,同比增长 1,374%,是四家中增长最快但规模最小的,按当前增速约 2.9 年超过 Azure、3.6 年超过 AWS。