搜索“RAG”

months, pricing should continue to deflate in the world of tokens, with margins remaining in the 40-70% range, particularly for frontier models9. Cost per task is the weighted average cost to run one Intelligence Index task, aggregating input, cache hit, cache write, reasoning & answer token prices across the ten evaluations in Artificial Analysis Intelligence Index v4.3.2, weighted by each evaluation’s index weight. Source : Artificial Analysis, retrieved October 2026. All models measured at m

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davon aus, dass sich die Infrastrukturausgaben für 2026 auf rund eine Billion US-Dollar belaufen werden und die für 2027 bis 2030 auf 6 Billionen US-Dollar. Die zu erwartende Nachfrage nach KI-Leistung von Unternehmen und Endkunden werde aber nicht reichen, um bei dieser Investitionshöhe auf alle Ausgaben noch Rendite einzufahren. „Bis 2030 müssten die Betreiber von KI-Rechenzentren kumulierte Umsätze in Höhe von 8,5 Billionen US-Dollar erzielen, um die angekündigten Investitionsausgaben, die Fi

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草原上圈地盖楼。 图源：高德地图 然而，算力饥渴是全球性的。随着大模型训练与推理需求呈指数级爆发，全球范围内的云巨头、科技新贵与主权基金也在极力寻找下一个“乌兰察布”。 那么，全球还有哪些地方，具备类似的组合条件？ 西班牙阿拉贡：欧洲版的最强候选之一，但缺一张牌 在欧洲这片向来以高电价、严环保和漫长行政审批著称的土地上，西班牙东北部广袤而人口稀少的阿拉贡（Aragon）地区，正像极当年的乌兰察布一样，试图上演一场欧洲版的“算力逆袭”。 长期以来，欧洲在AI基础设施建设上远落后于中美。为了打破这一僵局，阿拉贡地方政府选择直接为科技巨头“开绿灯”。他们推出了名为“PIGA”（阿拉贡公共利益项目）的特区审批机制。在传统欧洲国家需要耗费数年才能拿下的环保与建筑许可，在阿拉贡通过“一站式”投资者服务和PIGA绿色通道，被压缩到了12个月以内。 全球已建成数据中心容量（欧洲远远落后于中美两国） 敏捷行政与极速落地的成效是惊人的。亚马逊已宣布在阿拉贡累计投资337亿欧元（约384亿美元）用于云服务和人工智能基础设施，这是其在美国本土之外最大的一笔云与AI基础设施投资。据悉，新设施在大约一年内完成了大

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you can offload some processing to server and re architect what all needs to be stored where I’m not sure I buy this, but the labs are certainly pushing “don’t read the code, leverage signals like “outage, feature request, user behavior change” as STIMULUS to agents to drive change in deployed code” as the future, so it lines up Does that make sense? Are we going back to CVS instead of distributed? Will there be another swing back to distributed 5-10 years after things like Scion become Mainstr

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plosions were allowed to happen just to see how badly things could go wrong." OpenAI was "conducting rather stupid experiments, letting the agents do whatever they wanted and encouraging this behavior." The doomers then loved turning it into something scary. "My God, these agents are self-aware and are trying to find a way to destroy humans." That, too, is science fiction, according to Jordan. The trillion-dollar bet on data centers Jordan considers the massive data center investments "just an e

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he Information and the Wall Street Journal, while Anthropic, per the WSJ and the FT, made $15.5 billion ($4.6 billion in Q1 and $10.9 billion in Q2). We do not have consistent coverage of their underlying costs, but in Q1 (per The Information), OpenAI spent $8.6 billion on training and $3.5 billion on “cost of revenue,” with no reporting on its sales and marketing spend where it allocates the cost of serving free users or discounts. Anthropic said it had hit an annualized run rate (undefined per

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stions I have about you making your own models is what is the constraints you operate in? So if you draw a parallel to language models, you see GPT-OSS 20B is very fit for your average MacBook. Then there’s a larger one that’s fit for a single GPU. Do you work in the constraints of robot, of model on on device, in cloud, a hybrid? And then how does that affect your scaling law of data? A million hours is great, but like do we just scale up parameters, data? Jason Ma [00:17:38]: No, that’s a grea

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stions I have about you making your own models is what is the constraints you operate in? So if you draw a parallel to language models, you see GPT-OSS 20B is very fit for your average MacBook. Then there’s a larger one that’s fit for a single GPU. Do you work in the constraints of robot, of model on on device, in cloud, a hybrid? And then how does that affect your scaling law of data? A million hours is great, but like do we just scale up parameters, data? Jason Ma [00:17:38]: No, that’s a grea

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rity risks and pushing up costs. Now, some businesses that started off fast and furious on AI are pumping the brakes, implementing restrictions where experimentation was once encouraged, bringing back experienced workers and upping training efforts. C-suite leaders say when it comes to AI, there’s a sense that “we’re driving the truck and building it at the same time, and that’s a problem,” said Johnny C Taylor Jr, president and CEO of the Society for Human Resource Management. “If we could all

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July's most reliable AI agent is now just average. With Agent Arena, people delegate real tasks to agents and confirm whether the result met their needs. We call this signal Confirmed Success. On 1 July, Claude Fable 5 was well ahead of every other model, and it only took 12 weeks for the top‑20 average to match it. Kimi K3 beat it within just 3 weeks at 89% lower cost per task, and DeepSeek V4.1 Flash now matches it at 96% lower cost. Meanwhile, the top 5 now get 94% of tasks confirmed done, up

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ated. What Happens When Writing Code Is No Longer the Bottleneck? This transition raises questions that no benchmark can answer by itself: What becomes the developer’s highest-leverage work when agents do most of the coding?; Is software that ships without human code review a mature engineering practice or a risk we do not yet know how to measure?; Do agents democratize software creation, or relocate expertise into specification, evaluation, architecture, and governance?; Who is accountable when

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文 | 穷奇观察 2026 年 10 月 8 日，Anthropic 一天之内推出两项“免费”安排。 第一项叫“关键基础设施防御计划”（Critical Infrastructure Defense Program，CIDP）。前沿 Claude 模型、驻场工程师和威胁研究，交给 11 家创始伙伴：埃森哲、博思艾伦、CrowdStrike、德勤、Dragos、日立、Insane Cyber、Nozomi Networks、Palo Alto Networks、普华永道、罗克韦尔自动化。 这些伙伴服务的对象，是电网、水务、交通网络，以及政府系统背后的运营技术（OT）。 第二项叫 OSS Scanner。免费、自愿接入，用 Anthropic 最强的模型（包括 Claude Mythos）给开源项目做定期安全扫描。 两项归在同一个名字底下：Anthropic Cyber Mission。 把这两件事并排放，有一处别扭：一家卖模型的公司，同时来当开源世界的审计者、关键基础设施的守门人，以及“什么算漏洞”的裁定者。 这篇文章不判它想不想作恶。只判一件事：这几个身份叠在同一家公司身上，结构上哪里

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oth are defensive layers built by fine-tuning—and accessible fine-tuning is what lets defenders everywhere build their own; Give vetted safety researchers white-box access to encourage novel safety work. We understand how shallow refusal safeguards are — bypassable through adversarial token-level optimizationUniversal and Transferable Adversarial Attacks on Aligned Language Models (Zou et al, 2023), removable via a single direction in activation spaceRefusal in Language Models Is Mediated by a S

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r-running tasks. That kind of delegation requires more than increasingly capable models. Related:Google’s workplace agent is out, but questions abound An AI system that drafts a paragraph needs relatively little organizational context. An agent expected to complete work across multiple applications is different. To do that, it may need access to company data, an understanding of business processes, memory of previous work and permission to take actions in enterprise systems. Those requirements h

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G7 (linked to aging) and EGLN1 (a vital cellular oxygen sensor); Reconstructing the brain at nanoscale: In partnership with Harvard’s Lichtman Lab, we reconstructed a human brain fragment at nanoscale resolution and made the data openly available via Neuroglancer. We additionally mapped the brains of model organisms including the fly, larval zebrafish, and songbird. These neural mapping resources enable researchers to advance their study of brain function Accelerating tools for medical solutions

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oth are defensive layers built by fine-tuning—and accessible fine-tuning is what lets defenders everywhere build their own; Give vetted safety researchers white-box access to encourage novel safety work. We understand how shallow refusal safeguards are — bypassable through adversarial token-level optimizationUniversal and Transferable Adversarial Attacks on Aligned Language Models (Zou et al, 2023), removable via a single direction in activation spaceRefusal in Language Models Is Mediated by a S

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and easy to use and scale to billions of people.” User data is stored on a secure VM, what Zuckerberg described as an “isolated linux computer with a browser, CPU, memory, and storage.” The company said that most of the effort in building Muse was related to “careful design and engineering to operate [it] more safely.” Meta’s blog post continued, “Muse can and will still make mistakes, but we expect they’ll be much less frequent and cause much less damage due to the safety systems we’ve built i

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in Germany. All three startups were openly positioning their new models as the ones to pick if you’re concerned about AI sovereignty. Reflection’s new model enjoyed a blitz of coverage across Axios, Reuters, and Fortune, with the company press release explicitly saying it “advances the frontier for the Western open ecosystem.” Beam’s core claim is that it’s even more efficient on inference than Chinese model maker Z.ai’s highly touted GLM 5.2, though that isn’t Z.ai’s latest offering. Absent gov

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overblown and US companies need to speed up development, including by building more data centers, which he insists contrary to polling data will be “very popular.” On top of discouraging industry self-regulation, he’s decided AI needs a rebrand by fiat. Trump declared in a rambling United Nations speech last month that “the use of the word artificial makes intelligence fake” and promised to rename the technology “super intelligence.” He then followed through with an executive order last week, fo

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