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Google承认AI模式拖累搜索收入,科技股遭抛售

[AINews] Google admits AI Mode hurts Search revenue; -15% $500B drop worst since 2004 IPO. AI Bear Market coming?

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Google官方确认AI功能直接冲击核心营收,这是AI商业化进程中极具标志性的事件,直接影响行业对AI投入产出比的评估,值得从业者高度关注。

As an engineer-focused newsletter we only very rarely let financial stories through (our last example was Jan 2025), but when this is about the future of Google and Google Search, with a recently departed AI leadership, you cannot not care.

作为一份以工程师为核心的通讯,我们极少刊登财经类故事(上一次是2025年1月),但当话题涉及Google及其搜索业务的未来,以及近期离职的AI领导层时,你无法不关心。

At its heart is the tension between overview pages and underlying pages they summarize. Any good enough summary obviates the need for you reading the source. Google’s Featured Snippets or Knowledge Panels have long had that tension with the publishers that supply that data. But now, Google’s own AI Overviews are potentially affecting traffic with… Google's Search revenue.

其核心在于概览页面与其所总结的底层页面之间的张力。任何足够好的摘要都会消除你阅读源内容的必要。Google的特色摘要(Featured Snippets)或知识面板(Knowledge Panels)长期以来就与提供这些数据的内容发布者存在这种张力。但现在,Google自己的AI概览(AI Overviews)可能会影响流量,进而影响……Google的搜索收入。

Google doesn’t break out AI Mode in sufficient detail that we can make any sort of rigorous analysis, but most people are quoting Philipp Schindler (Chief Business Officer):

Google并未在足够详细的层面披露AI模式(AI Mode)的数据,使我们无法进行任何严谨的分析,但大多数人引用了Philipp Schindler(首席商务官)的话:

“It’s worth noting that the strong growth in AI Mode queries had an impact because of the lower monetization we were seeing in AI Mode.”

"值得注意的是,AI模式查询量的强劲增长产生了影响,因为我们在AI模式中看到的货币化程度较低。"

Together with other bad earnings results it set off a range of steep declines throughout tech:

与其他糟糕的财报结果一起,这引发了科技行业一系列大幅下跌:

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This is against a backdrop of souring tech/AI sentiment in public markets - and with OpenAI raising at $1.25T not $1.4T, not the sort of thing you want in the lead up to one of the biggest tech IPOs of all time.

这是在公共市场科技/AI情绪恶化的背景下发生的——而且OpenAI的估值为1.25万亿美元而非1.4万亿美元,这可不是在所有史上最大科技IPO之一前夕想要看到的情况。

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AI News for 10/29/2026-10/30/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews' website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

2026年10月29日至10月30日的AI新闻。我们检查了12个Reddit子版块、544条Twitter帖子,没有进一步的Discord内容。AINews的网站允许你搜索所有过往期号。提醒一下,AINews现在是Latent Space的一个板块。你可以选择订阅或取消邮件频率!

AI Twitter Recap

AI Twitter回顾

Claude Ultra Drops the 5-Hour Limit as Anthropic Renames Its Max Tiers

Anthropic重命名其Max层级后,Claude Ultra取消了5小时限制

  • Plan rename: Anthropic renamed its Max tiers. Max 5x is now "Max" at $100/month, and Max 20x is now "Ultra" at $200/month. Existing subscribers do not need to take any action (announcement).
  • 5-hour limit removed: Verified Ultra accounts no longer have the 5-hour session limit. The change starts today for most existing subscribers (@claudeai, @bcherny, @alexalbert__).
  • Rollout caveat: Newer accounts keep a temporary limit until they are verified, which Anthropic describes as an anti-fraud measure. Affected users will get an email when the limit is lifted.
  • Unchanged: Weekly limits stay the same.
  • Reaction: The change was widely welcomed, with posts such as "Huge!!!" (@theo) and "Anthropic is on a run" (@kimmonismus).
  • Subscription economics (unverified analysis): @teortaxesTex cites a SemiAnalysis reconstruction of Anthropic's finances.
  • Margins: Anthropic's API margin is put at about 92%, versus about 84% for DeepSeek.
  • Subsidy: Opus on subscriptions is described as heavily subsidized, breaking even only below about 21.3% utilization.
  • Revenue vs compute: A follow-up estimates that subscriptions bring in about 18% of revenue while using more than 50% of compute (thread).
  • Comparison with DeepSeek: The same author argues that a heavily subsidized DeepSeek V4.1-Flash plan ($10 for about $76.5 of API-equivalent usage) is close in price per token to Claude subscriptions (long post).
  • 计划重命名:Anthropic重命名了其Max层级。Max 5x现在称为“Max”,价格为每月100美元;Max 20x现在称为“Ultra”,价格为每月200美元。现有订阅者无需采取任何操作(公告)。
  • 5小时限制已移除:Verified Ultra账户不再有5小时的会话限制。大多数现有订阅者从今日起生效(@claudeai, @bcherny, @alexalbert__)。
  • 推广注意事项:较新的账户在通过验证之前会保留临时限制,Anthropic将其描述为反欺诈措施。当限制解除时,受影响的用户将收到电子邮件通知。
  • 保持不变:每周限制保持不变。
  • 反应:这一变化受到广泛欢迎,出现了诸如“太棒了!!!”(@theo)和“Anthropic正在发力”(@kimmonismus)等帖子。
  • 订阅经济学(未经证实的分析):@teortaxesTex 引用了SemiAnalysis对Anthropic财务状况的重建。
  • 利润率:Anthropic 的 API 利润率约为 92%,而 DeepSeek 约为 84%。
  • 补贴:订阅版 Opus 被描述为重度补贴,仅在利用率低于约 21.3% 时实现盈亏平衡。
  • 收入与算力对比:后续估算显示,订阅收入约占收入的 18%,但消耗了超过 50% 的算力(讨论串)。
  • 与 DeepSeek 的比较:同一作者认为,重度补贴的 DeepSeek V4.1-Flash 计划(约 76.5 美元 API 等效用量仅需 10 美元)在每 token 价格上接近 Claude 订阅(长文)。

Frontier and Open Model Releases

前沿模型与开源模型发布

  • Gemini 4 Carbon (Google DeepMind): The model is ranked #1 in Text Arena, #2 in Agent Arena and #4 in WebDev, and changed the Pareto frontier in all three (Arena).
  • Research quality: Perplexity reports a new state of the art on its WANDR long-form research benchmark. Carbon is coming to Pro and Max users inside Computer "once we have sufficient token capacity" (@AravSrinivas).
  • Genie 4 world model: Google's new world model generates interactive simulations with sound that users can steer in real time (@Google).
  • Availability: It is a research preview for trusted testers. It is coming soon to Google AI Ultra in Project Genie and to Gemini API and AI Studio early-access partners (rollout).
  • Mistral Large 4 ("Le Chonk"): The model has 1T parameters (TRL) and 1M-token context (llama.cpp).
  • Fine-tuning: Axolotl has day-0 LoRA support. Its recommended recipe uses NVFP4 experts with merge-aware LoRA and fits on one 8xB200 node (@winglian).
  • Coding: One third-party coding benchmark ranks it the second-best open-source model (@ThibaultJaigu).
  • NVIDIA Nemotron 3.5 tech report: Super has 120B total and 12B active parameters and handles text, image and video. It scores 51.78% on SWE-bench Pro, beating Nemotron 3 Ultra with about 4.6x fewer active parameters (summary).
  • Lightning variant: A 30B text-only model with 3B active parameters.
  • Serving: Up to 4,856 output tok/s/GPU on GB200 while holding at least 50 tok/s/user.
  • Quantization: NVFP4 recovers 99.62% of median BF16 accuracy.
  • Smaller and specialized models:
  • Microsoft computer-use agent: A 4B hybrid agent that can both click and write code scores 66.0% on OSWorld-Verified, ahead of 27B and 30B agents (@HuggingPapers).
  • Phonic-1: Claims #1 on AA's speech-to-speech index, on τ-Voice and on Sierra's τ³-Voice (@moinnadeem). It was built by a 10-person team with $4M (@davisblalock).
  • mischief 1 (BioComputing Co): An image-to-video model priced from 0.5¢ per second. The company says it is at least 5x faster than its base model and draws on biological-neuron research (launch).
  • Skepticism: Critics say the link between neuron research and the model is unexplained or marketing (@willdepue, @jachiam0).
  • OpenAI Private Intelligence: An enterprise offering that gives businesses stronger data controls when using OpenAI's top models. It is being built with large customers (@OpenAI).
  • Gemini 4 Carbon(Google DeepMind):该模型在 Text Arena 中排名第一,在 Agent Arena 中排名第二,在 WebDev 中排名第四,并在这三个榜单中改变了帕累托前沿(Arena)。
  • 研究质量:Perplexity 报告称其在 WANDR 长篇研究基准测试中取得了新的最先进水平。Carbon 将在拥有足够 token 容量后上线给 Pro 和 Max 用户(@AravSrinivas)。
  • Genie 4 世界模型:Google 的新世界模型可生成带有声音的交互式模拟,用户可实时操控(@Google)。
  • 可用性:目前仅为受信任测试人员的研究预览版。即将在 Project Genie 中面向 Google AI Ultra 用户开放,并早期提供给 Gemini API 和 AI Studio 合作伙伴(发布说明)。
  • Mistral Large 4(“Le Chonk”):该模型拥有 1T 参数(TRL)和 1M token 上下文窗口(llama.cpp)。
  • 微调:Axolotl 已支持首日 LoRA。其推荐配方使用 NVFP4 专家混合架构,结合感知合并的 LoRA,可在单个 8xB200 节点上运行(@winglian)。
  • 编程能力:一项第三方编程基准测试将其评为第二好的开源模型(@ThibaultJaigu)。
  • NVIDIA Nemotron 3.5 技术报告:Super 版本总参数量为 120B,激活参数量为 12B,支持文本、图像和视频处理。在 SWE-bench Pro 上得分 51.78%,以少约 4.6 倍的激活参数量超越 Nemotron 3 Ultra(摘要)。
  • Lightning 变体:一个仅处理文本的 30B 模型,激活参数量为 3B。
  • 服务性能:在 GB200 上每 GPU 输出可达 4,856 tok/s,同时保持至少 50 tok/s/用户的吞吐量。
  • 量化:NVFP4 可恢复 99.62% 的中位 BF16 精度。
  • 更小及专用模型:
  • Microsoft 计算机使用智能体:一个兼具点击和代码编写能力的 4B 混合智能体在 OSWorld-Verified 上得分 66.0%,优于 27B 和 30B 的智能体(@HuggingPapers)。
  • Phonic-1:声称在 AA 的语音到语音索引、τ-Voice 以及 Sierra 的 τ³-Voice 上均排名第一(@moinnadeem)。由 10 人团队耗资 400 万美元打造(@davisblalock)。
  • mischief 1(BioComputing Co):一款图像转视频模型,定价为每秒 0.5¢。该公司表示其速度至少是基础模型的 5 倍,并借鉴了生物神经元研究(发布)。
  • 质疑声:批评者称神经元研究与该模型之间的联系未得到解释或仅为营销手段(@willdepue, @jachiam0)。
  • OpenAI Private Intelligence:一项企业级服务,使企业在使用 OpenAI 顶级模型时拥有更强的数据控制权。该项目正在与大型客户合作构建中(@OpenAI)。

Independent Evals, Benchmarks and Quantization Disputes

独立评估、基准测试与量化争议

  • AA Intelligence Index v5: 60% of the score now comes from private or held-out tests. Scores are Opus 5.5 (max) 58, GPT-6 Astra 57 and GPT-6.1 Sol 55, with Sol costing about one-eighth as much per task (summary).
  • Effort scaling: Opus 5.5 scores 35 at low effort versus 58 at max, at about 10x the cost.
  • AA-Omniscience v1.1: The knowledge and hallucination benchmark was cut from 6,000 to 3,000 questions, with saturated items removed (details).
  • Public set: A new 300-question public set correlates 0.99 with private scores.
  • Leaders: Gemini 4 Carbon leads at Net Score 54, followed by Opus 5.5 at 48 and Fable 5.1 at 46.
  • Other results: Gemini 4 Argon has the lowest hallucination rate at 4%. MiniMax M3.1 Flash fell 22 points (16 → −6).
  • Agent Arena cost data: An analysis of 26K sessions finds that without prompt caching, a task on a top-10 agent would cost 3.6x as much (Arena).
  • Example: A typical Fable 5.1 task costs $7.44 with caching versus $27 without.
  • Cache expiry: A single cache invalidation adds about 5% to a task's bill.
  • GPT-6.1 Terra (Max): It entered Agent Arena at #8 with a 10.6% net improvement at $0.62 median cost per task (Arena).
  • DeepSeek V4.1-Flash-1017 quants: Unsloth released dynamic v3.1 GGUFs that run on 64GB+ RAM (@UnslothAI).
  • Reproducibility dispute: Unsloth says it cannot reproduce GSQ-RCO's numbers. For example, the claimed 100% AIME25 score at 1-bit came out as 91.88% in its runs (@danielhanchen).
  • Reasoning length: Unsloth also reports that lower-bit quants reason somewhat longer than the unquantized model.
  • DeepSeek-Cyber (unconfirmed): Leaked "dreamed" scores circulated, including about 92% on CyberGym and 96.8% on ExploitBench at 3,000 steps. They are not an official release.
  • Third-party data: Per Fireworks, CyberGym pass@3 moved only from 94.82% (0910) to 95.36% (1017). @teortaxesTex argues that near saturation, almost any number is consistent with the same checkpoint.
  • Analyst view: The rumored scores are argued to fit a guardrail-free 1017 rather than a new model (analysis).
  • Usage restriction: The model reportedly cannot be used by Chinese entities sanctioned by the US government (note).
  • AA Intelligence Index v5:得分的 60% 现在来自私有或隔离测试。得分为 Opus 5.5(最高分)58 分,GPT-6 Astra 57 分,GPT-6.1 Sol 55 分,其中 Sol 每项任务的成本约为前者的八分之一(摘要)。
  • 努力缩放:Opus 5.5 在低努力程度下得分为 35,而在最大努力程度下得分为 58,成本约为后者的 10 倍。
  • AA-Omniscience v1.1:知识与幻觉基准测试的题目数量从 6,000 道削减至 3,000 道,并移除了饱和项(详情)。
  • 公开数据集:一个新的 300 题公开集与私有得分的相关系数为 0.99。
  • 领先者:Gemini 4 Carbon 以净得分 54 领先,其次是 Opus 5.5(48 分)和 Fable 5.1(46 分)。
  • 其他结果:Gemini 4 Argon 的幻觉率最低,为 4%。MiniMax M3.1 Flash 下降了 22 分(16 → −6)。
  • Agent Arena 成本数据:对 26K 次会话的分析发现,如果没有提示词缓存,在排名前 10 的代理上执行任务的成本将是原来的 3.6 倍(Arena)。
  • 示例:典型的 Fable 5.1 任务在使用缓存时成本为 $7.44,而不使用缓存时为 $27。
  • 缓存过期:单次缓存失效会使任务账单增加约 5%。
  • GPT-6.1 Terra(Max):它以第 8 名的身份进入 Agent Arena,每项任务的中间成本为 $0.62,净提升幅度为 10.6%(Arena)。
  • DeepSeek V4.1-Flash-1017 量化版本:Unsloth 发布了可在 64GB+ 内存上运行的动态 v3.1 GGUF 格式(@UnslothAI)。
  • 可复现性争议:Unsloth 表示无法复现 GSQ-RCO 的数据。例如,其声称的 1-bit 精度下 AIME25 得分为 100%,但在其运行中结果为 91.88%(@danielhanchen)。
  • 推理长度:Unsloth 还报告称,低位数量化版本的推理过程比未量化的模型稍长。
  • DeepSeek-Cyber(未经确认):流传着泄露的“想象”得分,包括在 CyberGym 上约 92%,在 ExploitBench 上 3,000 步时达 96.8%。这并非官方发布。
  • 第三方数据:据 Fireworks 称,CyberGym 的 pass@3 仅从 94.82%(0910)微升至 95.36%(1017)。@teortaxesTex 认为,在接近饱和的情况下,几乎任何数字都与同一个检查点一致。
  • 分析师观点:传闻中的分数被认为更符合无护栏的1017模型,而非全新模型(分析)。
  • 使用限制:据报道,该模型不得被美国政府制裁的中国实体使用(注)。

Safety, Security and Agent Supply Chain

安全、安全与智能体供应链

  • Automated red-teaming (Anthropic): Thousands of Claude agents were tasked with evading a training-data monitor (@AnthropicAI).
  • 自动化红队测试(Anthropic):数千个Claude智能体被 tasked 于规避训练数据监控器(@AnthropicAI)。

更进一步:量化金融体系

看懂新闻只是起点——沿量化金融路径,把它变成能交付的工程能力

进入量化体系 →