AI 泡沫监测:债务层已经先动了
作者以债券和信用违约互换(CDS)读数观察 AI 基建泡沫,认为债务层比股价更早反映风险。CoreWeave 2032 年到期债券跌至面值约 76 美分,五年期 CDS 利差突破 1000 个基点;甲骨文五年期 CDS 10 月中旬逼近 300 个基点,标普 7 月已将其评级降至 BBB-。
作者以债券和信用违约互换(CDS)读数观察 AI 基建泡沫,认为债务层比股价更早反映风险。CoreWeave 2032 年到期债券跌至面值约 76 美分,五年期 CDS 利差突破 1000 个基点;甲骨文五年期 CDS 10 月中旬逼近 300 个基点,标普 7 月已将其评级降至 BBB-。
Goodfire 研究员 Nick Cammarata 发布 26,000 字博文,称 LLM 的内部机制可能远比此前认为的更容易理解,因而更易监控和控制。他以 Inception v1 中同时响应猫和汽车的神经元为例,认为两者共享 V 形特征,并计划很快公布针对现代 LLM 的技术结果。
图像生成、Sora 和 clawdbot 的炒作 这些都不重要 不过是短期的泡沫
idk what you are all smoking clawdbot is just a passing hype that can be vibe-coded in a week anthropic lost absolutely nothing here except some aura points in the open source community but it's not like they liked anthropic anyway
我从这篇帖子中学到的是,几乎没有人是“AGI-pilled”的,他们也没有认真对待这样一种可能性:数千万到上亿个AI以人类10–100倍的速度全天候工作。
Surveyed Anthropic staff's answers imply 17.1% GDP growth in 2030
Addy Osmani、Morgan Westlee Lunt 和 Qing Wang 在《How the Claude Code team does code review》中提出,"人是否读过代码"只是判断变更风险的粗略替代指标,更该问哪项检查能抓住问题。他们称代码审查已从 PR 末尾的关卡变成持续进行,多数变更在人工查看前已被 Claude 至少审查并修复过一次。
Anthropic 发布报告称,Z.ai 于 8 月发布的开放权重模型 GLM-5.3 可被任何人自由下载和修改,构成重大网络攻击风险。开放模型支持者认为其可本地运行、成本更低、便于学术研究并防止权力集中,而 Artificial Analysis LLM 排行榜前八名目前仍均为闭源模型。
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
People at Anthropic keep telling me they can't coordinate to slow down because of anti-trust, but I've talked to leading lawyers who disagree. Meta spent $2,400,000,000 on lawyers in a single quarter fighting charges that its products hurt kids. How much has Anthropic spent on this anti-trust thing?
Introducing Claude Tag, a new way for teams to work with Claude. In Slack, Claude joins as a team member with access to the channels and tools you choose. Tag Claude in and delegate tasks to it while you focus on other work.
Andrej Karpathy 评价 Claude Fable 5 发布,称其与 Mythos 是同一底层模型但增加了安全防护,基准测试全面 SOTA 且领先明显。
Fable 5 is state-of-the-art on nearly all tested benchmarks, with exceptional performance in software engineering, knowledge work, scientific research, and vision. The longer and more complex the task, the larger Fable 5’s lead over our other models.
推荐理由:Karpathy 以第一手使用体验评价 Claude Fable 5 在长任务和复杂问题上的跃升,并给出对软件需求变化的判断。
The degree to which you are awed by AI is perfectly correlated with how much you use AI to code.