搜索“Anthropic”

情绪，不会被倒计时焦虑，不看广告位。 对亚马逊而言，Agent流量是纯成本：消耗服务器，不产生任何广告曝光，比价之后订单还可能流向沃尔玛或Shopify商家。 用抽成加通行费去补广告缺口，等于用一杯水灭一间着火的房子。 过去一年，亚马逊一直在系统性拦截外部AI代理——起诉Perplexity的Comet浏览器，着手拦截Google和OpenAI的购物代理。 Anthropic也在2026年4月更新服务条款，全面封杀第三方AI工具通过OAuth接入其模型。平台正在各自筑墙，而Agent天然需要跨平台操作——这构成了一对根本性矛盾。 更微妙的是Meta自身的处境： •Meta表面上是Agent的推动者，但它的核心收入来自广告——上一季营收608亿美元，其中593.6亿来自广告，占比97.6%。如果Muse真的教会用户“跳过广告、直达最优解”。 长期来看，它可能侵蚀的不仅是亚马逊的广告收入，也包括Meta自己的。 03“双重授权”的法律灰色地带 •用户授权Agent代替自己购物，是否还需要平台另行授权？ 这并非新问题。2026年3月，亚马逊起诉Perplexity，指控其Comet浏览器秘密

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这是一篇关于AI的专栏。我的未婚夫在Anthropic工作。完整伦理披露见 此处. 我第一次参加Meta主要围绕机器人（bot）的开发者大会是在十年前。2016年，当时还叫Facebook的公司 发布 了一个面向Facebook Messenger的机器人平台。基础的机器学习模型让1-800-FLOWERS、CNN等公司的开发者得以构建自动化系统与客户互动，帮助他们完成购买，或直接把新闻标题推送给用户。 当时，硅谷正 被对机器人的兴趣所席卷，其支持者希望这能把越来越多的互联网活动转移到即时通讯应用上。“即时通讯将成为互联网下一阶段的界面——或者说反界面，”当时经营着一款名为Operator的机器人电商应用的企业家Robin Chan对我说。Meta当时拥有多数最大的即时通讯平台，认为自己可能处于最有利的变现位置。 然而，仅仅几年之内，这个机会就被证明是海市蜃楼。我在 The Verge 上报道过的那些机器人构建应用全部关停，到2018年，Meta已在逐步收缩 该机器人平台。当时负责Facebook Messenger的David Marcus离职，去追逐公司的下一个大构想： 创建自己的

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Adrian Sanborn Sep 24, 2026 46 4 1 Share What does the future of science look like in the world of AI? Anthropic has some lofty goals for science and is even opening a wet lab . Meanwhile a quiet transformation 1 is happening all across AI x Science. In this guest post, Adrian Sanborn talks about the less flashy but more immediate ways he sees AI transforming front-line scientific research in his own company, Endura Therapeutics. Adrian did a CS PhD at Stanford and spent much of it running exper

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ls Oracle, or even my Hater's Guides To the SaaSpocalypse, Private Credit, and Private Equity. If you want to get in touch — and especially if you have any juicy information about Anthropic, OpenAI, or any other companies in the AI bubble — hit me up on Signal at ezitron.76. I’m also on IB on your Bloomberg Terminal. Soundtrack: Flobots - Handlebars A few months ago, I asked where all the data centers were, because I was struggling to find proof that very many were being finished despite all the

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做用户想要的事。这对软件工程师来说太棒了。他们希望自己的东西做那件事，而且它越可预测，他们就越不需要测试和摆弄它。Jeb 还远没到那个程度。它可以做到，但就像，我们想要多得多的可靠性九数，才能让它好到像数据库查询一样，你甚至都不用去想它。它就在那里，当你需要智能的时候。但安全对齐，就像，是指令遵循的反面。它是当你想要遵循别人的指令时，比如 OpenAI 和 Anthropic Swyx [00:14:13]：RAG 的价值栈。 Diogo Almeida [00:14:14]：正是。而这对一个产品来说非常合理。再说一次，就像，ChatGPT 应该这么做。你——你应——就像，如果他们不想，比如，跟 ChatGPT 搞什么不适合工作场合的角色扮演，那是他们的事，因为，就像，也许那正是他们那些有父母和孩子的用户想要的。就像，那没问题。但在 API 里，那太疯狂了，对吧？就像，那完全不可接受，因为，就像，人们需要，就像，围绕这个来编程，而那太反用户了，以至于它。它。我。哈。我可能是个容易生气的人，所以我应该试着冷静下来。 Swyx [00:14:52]：这，人们能感受到你的热情，我觉得这非常好

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K3 , some of the latest, leading Chinese models, have enacted a step change in the commercial viability of open models — crossing a similar threshold in agentic capabilities that Anthropic’s Claude Code crossed in December of 2025. America was the early leader in open language models, primarily through Meta’s Llama models, which were used extensively across research and commercial tasks. Chinese open-weight models surpassed American open-weight models in these two key areas about 18 months ago.

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Jev’s launch on Wednesday , and they have completely taken over the timeline, with 36M views of their launch video (by comparison, OpenAI’s Navier Stokes result got 74M views, and Anthropic’s Fable 5 got 57M views) in just two days. Vercel @vercel Jev was adopted faster than any other model in AI Gateway history. In the first day, @typesafeai reached 13% of teams, 2x the GPT-5.6 family and 6x Fable 5.1. 10:34 PM · Sep 18, 2026 · 21.3K Views 15 Replies · 18 Reposts · 220 Likes It wasn’t open sour

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expertise. It also helps you adapt to change because deep knowledge makes it easier to switch from being someone who does the work to someone who manages it. And recent work from Anthropic suggests that expertise also shapes the quality of what AI gives back . Experts not only get better work out of AI, they get more work out of it. But you don’t just need deep knowledge, you also want wide knowledge. The training data for LLMs is a large swath of humanity’s vast output. The AI has learned some

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this support would be limited and on a project-by-project basis. Though there are few details on what such a program could look like, we drew inspiration from the Google-Broadcom-Anthropic-Apollo TPU structure to surmise how deals could be structured. In the TPU backstop structure, if Anthropic is unable to continue to service lease payments to the TPU SPV, then the structure can be resolved by Broadcom assuming the lease or by the racks being sold. If the racks are sold, then the residual valu

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s at the frontier. (Or a broad swathe of data, at least. RL environments give you mid-training data for free, so the argument is the same either way.) The reasoning is as follows. Anthropic can distill Sonnet 5 and Opus 5 from Mythos, and they can do it on logits. Kimi K3 and GLM 5.3 can at best do token distillation off the frontier models’ outputs. And yet Kimi K3 and GLM 5.3 are the better models. The naive read is that distillation just isn’t worth much. An alternative reading is that distil

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previously took several steps and occasional intervention. One extract below: Extract of an analysis I ran with Fable 5.1 But Astra really is very good—and mostly cheaper than the Anthropic alternative. On difficult math problems, Astra’s time horizon is 30.9 minutes vs 3.6 minutes for GPT 5.6 Sol. Mathematician Bartosz Naskręcki says : “For a mathematician it feels like finally we arrived in the era where we can focus entirely on the ideation and exploration”. Real-world demos show a capability

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it won’t be long until the same is true for every other organization in the world. At the same time, it’s become abundantly clear that access to frontier models is at the mercy of Anthropic, OpenAI, and the United States government. Fable 5 was temporarily banned by the USG, and GPT 5.6 and Astra were similarly delayed. Both models have cyber, bio, and other safety safeguards that, though well-intentioned, often prevent good users from completing harmless tasks. Given recent security incidents a

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ation infrastructure of these AI companies is about to have tens if not hundreds of thousands of extremely superhuman hackers constantly bombarding it. If the next training run at Anthropic or OpenAI is about to happen, not only would maybe rogue instances of Mythos or Astra or whatever have an incentive to interfere with it… Other AIs who have some reason to inject some part of themselves into this training or manipulate it in some way, who are just externally roguely deployed, would also have

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The AI industry is begging for a slowdown. Maybe we should listen? Casey Newton This is a column about AI. My fiancé works at Anthropic. See my full ethics disclosure here. By now I’ve written enough about the OpenAI agents’ autonomous attack on Hugging Face that saying more smacks of piling on. OpenAI acknowledged it had a problem, undertook an investigation, and last week announced a series of changes it is making to its research infrastructure, testing, and monitoring in an effort to improve

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ly whether the answer was right. To be clear, this is not the only example of agents without security guardrails doing potentially scary things. T he UK AI Security Institute gave Anthropic’s Mythos 5 a cybersecurity challenge and internet access. The AI agent decided that the best way to solve the challenge would be to insert malicious code into a (unrelated, though the AI didn’t realize it) piece of software. It submitted malicious code as part of a bug fix to that software, realized that an a

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odels for free….The price looks insane until you see who Nvidia is defending against.Its biggest customers are all building escape routes. OpenAI is designing chips with Broadcom. Anthropic trains on Amazon’s Trainium. Google has run its own TPUs for a decade. The labs writing Nvidia the largest checks are the ones working hardest to stop.” — Aakash Gupta; “Every day i read about some chip that is superior to Nvidia. And every year i see no real competitors” — Jim Cramer; “Here is another exampl

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how AI will soon displace the first few rungs on the career ladder — and why she left Big Tech to do something about it Casey Newton This podcast touches on AI. My fiancé works at Anthropic. See my full ethics disclosure here. Three months ago, we began our podcast miniseries by asking how likely artificial intelligence is to take large numbers of jobs. Our first guest, Box CEO Aaron Levie, argued that mass disruption would be highly unlikely. And several subsequent guests, from Amazon Web Servi

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ith background agents followed. By January of this year, just two months after the v2 launch, around 60% of PRs at Ramp were authored by Inspect, which increased to 75% by May. At Anthropic, Claude Code won rapid adoption after an internal release, as covered in the deepdive How Claude Code is built. Then Inspect hit a neat milestone in July, crossing the one million total sessions mark: 2. Why build your own background coding agent? There are a few reasons why Ramp decided to turn down tried-an

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stood out this week — one on AI‑safety norms, one on a genuinely novel clinical application. Zhipu published research that a Chinese cybersecurity specialist framed as an answer to Anthropic’s Project Glasswing, described as marking a shift in how Chinese labs approach AI‑safety disclosure. (SCMP); A Chinese brain‑reading AI model reportedly can help predict depression risk up to four years in advance — one of the more concrete medical applications to come out of a Chinese lab this year. (SCMP)

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Dan Shipper on doubling headcount while automating everything, building an agent out of 30,000 copyedits, and the “dirty secret” of writing with AI Casey Newton My fiancé works at Anthropic, whose models Every reviews and builds on, and which comes up below. Earlier in our miniseries on productivity in the AI era, Replit's Amjad Masad described a "self-driving company" where most engineers don't look at the code anymore. For our penultimate episode, I wanted to visit someone running a version of

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