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10月25日周日
  1. WeChatRSS:投资界

    2026年秋招:大模型公司不再高溢价抢应届生

    2026年秋招中,多家大模型公司的应届生招聘明显收紧,部分双211、Top2背景且有基模实习经历的学生到10月仍未拿到Offer。字节2026年春季ByteIntern招聘计划超7000人,其中研发类超4800人,但Seed不再单独设立Top Seed计划。Anthropic报告显示,其初级技术人员占技术部门新招聘人数比例已从2025年初的27%降至2026年的9%。

  2. Steve Jurvetson

    很喜欢这期与博学家 Naveen Rao 的播客,随后我看到了这段关于我们初次相遇的回忆:https://youtu.be/4UmFfI8CWuA?si=tWZI5zukPmVqZ-x5&t=4053

    引用Harry Stebbings@HarryStebbings

    I think @NaveenGRao is one of the most under-discussed but brilliant entrepreneurs of our time. He is able to consistently see where a market is going and place himself ahead of it. - Founded Nervana and sold to Intel for $400M. - Founded MosaicML and sold to Databricks for $1.3BN. - Raised $450M seed round at $4.5BN valuation for Unconventional. This success has given him license to say things that few people would have the confidence to say. - “Most software investors today are f*****”. - “Agentic coding is overrated and uninvestable”. - “It is completely techno-optimist to assume companies will have their own models and own their own intelligence”. This discussion rallies against some of the conventional beliefs of the valley in a way you have not heard before. My notes from the discussion with Naveen below. (Link in comments). 1. Are Software Investors F**ked? Pure software investors are "kind of f**ked" because SaaS will no longer be where margin is gained. Since around 2010, cloud services let founders spin up products quickly and chase $100M to $200M markets. As applications shift toward the intelligence layer, Naveen does not see software being a huge market in 20 years. Do you agree software investors are f***** in a much more infra/hardware centric landscape @PalmerLuckey @mttgrmm @shaunmmaguire 2. Why Agentic Coding Is Overrated and an Uninvestable Category Agentic coding is overrated as a market because the value moves to the model provider. The harness and the IDE are not very sticky, and people move between tools fluidly. Outside of the model providers, it is a category he would not invest in today. What is the single biggest reason why the coding agent should not sit within the model provider @ScottWu46 @matanSF 3. Is It Completely Techno-Optimist to Assume That Companies Will Own Their Own Intelligence With Their Own Specialized Models? Owning your own intelligence takes scale. A firm like Cooley can invest to make it happen, while a mom-and-pop shop in Nebraska will buy an out-of-the-box service instead. Big players will customize because rivals using AI to supercharge their margins force them to. 4. Where Is the Value in the Robotics Stack? The value in robotics will not sit in the model alone. Naveen invests in companies building robotics models and expects them to end up building end-to-end devices, selling the robot together with a model that does something very well. Where will the most value accrue in the robotics stack? Model? Data? Hardware? @tonyzzhao @joshavata @bercankilic 5. Why Steve Jurvetson Is My Favorite Investor Meeting Of roughly a thousand investor meetings, the one that stood out was with @FutureJurvetson. He geeked out on numeric representations and marked up the papers Naveen sent him like a technical colleague. In 2014, when no one had pitched a hardware startup in 10 years, he was keen to invest because he had been thinking about the space himself. 6. How American Investors Differ From European Investors Founders in the US are given permission to think bigger. In Europe, the investor network is so conservative that a founder with a crazy idea is written off as mad, while in the US the same founder is seen as awesome for thinking big. EF has largely moved focus to the US and SF specifically, would you agree with this @Alicebentinck @matthewclifford? 7. Why the Inference Market Will Be $10 Trillion Within 10 Years AI models alone will be a trillion-dollar market in the next few years, and inference will reach $10 trillion within 10 years. Cutting the cost of delivering a token by one order of magnitude effectively 10Xs the market, since the base of potential uses grows more than 10X, which Naveen ties to Jevons paradox. What does no one see that everyone should see about the inference market @GavinSBaker @lqiao @bernhardsson

10月24日周六
  1. WeChatRSS:智东西

    月之暗面发布 Kimi K3.1 Pro,3 万亿参数并宣布 11 月 4 日前开源

    月之暗面发布旗舰模型 Kimi K3.1 Pro,由 Kimi K3 后训练而来,参数量级 3 万亿,上下文窗口 100 万 token,并宣布 11 月 4 日前完成开源。

    研判 Kimi K3.1 Pro 以 3 万亿参数、100 万 token 上下文和约为 GPT-6 Astra 20% 的刊例价进入市场,可能压低长上下文与编程类任务的调用成本。

10月23日周五
  1. 智东西

    OpenAI 向所有客户开放 Decisions API,响应最快 100 毫秒

    OpenAI 于 10 月 22 日宣布向所有客户正式开放 Decisions API,该接口基于 GPT-6 Luna 调整推理方式,部分场景响应时间由 1 至 3 秒缩短到 100 至 180 毫秒。接口支持文本、图像和结构化数据,每次请求最多处理 400 个问题,为公开测试阶段上限的 2 倍。Datadog、Ramp、Speak 等企业已用其替代部分大模型调用。

10月22日周四
  1. WeChatRSS:智东西

    蚂蚁数科发布决策模型LingDT-Decision-1.0,含4B与27B版本

    蚂蚁数科10月22日发布决策模型LingDT-Decision-1.0,提供4B和27B两个版本,面向客服、风控、金融等产业AI场景,已上线DTMaaS平台并限时免费体验。该模型支持原生多选与按序决策(DAG),直接返回选项、分数或判断结果。蚂蚁数科称,在23项开源评测基准中27B版本综合评分第一,数据标注场景相比DeepSeek-V4-flash Token消耗降低约97%。

  2. 量子位

    中国电信星辰实验室开源意图理解模型Xing4.0-29B-Intent

    中电信人工智能科技有限公司星辰通用人工智能实验室发布并开源意图理解专用模型Xing4.0-29B-Intent,同步开放275.3万条训练语料、模型权重与推理镜像,代码采用Apache 2.0许可。模型将意图识别与参数抽取结合,输出JSON结构化信息,已在电信万号客服和智能家居音箱等产品中应用。实验室自测称其总分58,高于参评18款通用模型的最高49。

  3. Hugging Face Blog

    Dharma-AI 提出 VocabX:专用词表使推理成本降 77%

    Dharma-AI 在 Hugging Face 博客中预告即将发表的 VocabX 论文,提出按 token 成本而非出现频次重建领域专用词表,在基于 Qwen3-4B 的葡萄牙语作文评分器上将实测推理成本降低 77.0%,平均回复从 350 个 token 降至 101 个。

    研判 VocabX 在保持基座模型与训练配方不变的前提下,通过按成本而非频次重建词表,把生产环境作文评分器的实测推理成本降低 77%,说明小模型在单一领域部署时词表是可独立调节的成本杠杆。

10月21日周三
  1. WeChatRSS:智东西

    腾讯升级元宝搜索并通过腾讯云开放 API

    10月20日,腾讯升级元宝搜索,并通过腾讯云向个人开发者和企业提供 API 服务,同时提供 Skill、MCP 和 API 三种接入方式。腾讯称元宝搜索已构建千亿级索引,覆盖 13 个垂直行业、接入 157 个权威信源,日均调用量达数亿次,支撑 WorkBuddy、ima 等 50 多个内部业务。限时免费额度为每月 2000 次,10 月、11 月通过邀请码每月最高 6000 次调用。

  2. WeChatRSS:甲子光年

    蓝耘科技与甲子光年联合发布《2026AI原生基础设施白皮书》

    蓝耘科技与甲子光年智库联合发布《2026AI原生基础设施白皮书》,以蓝耘科技为实践样本,分析其围绕Token Factory、AI Infra OS、Token Ops与Agent应用形成的布局。白皮书提出Token成为智能生产要素,基础设施能力需从算力规模转向Token产效,并选取大模型企业、AI应用企业及行业场景客户案例呈现落地路径。

10月15日周四
  1. Google Research

    Google 发布 Deep Review 智能体文档审阅系统

    Google 宣布公开发布 Deep Review,一个面向复杂文档的智能体审阅系统,可检查逻辑一致性、数学陈述并做事实核查。该系统前身为 Paper Assistant Tool,曾在 STOC 2026、ICML 2026、NeurIPS 2026 和 ICLR 2027 四个会议试点,为超过 16,000 篇论文提供投稿前反馈。

    研判 Deep Review 把长文档审阅拆成分段、按信息密度分配算力、并行核查与全局综合四步,可能降低论文与法律、财务文档的初筛成本,但高层判断仍留给人。

10月14日周三
  1. Google DeepMind

    Google DeepMind 发布 Gemini 4 Argon 前沿模型

    Google DeepMind 发布 Gemini 4 Argon,即日起通过 AI Studio 与 Gemini API 向开发者和企业开放,Google AI Ultra、Pro 订阅者可在 Gemini 应用与搜索 AI Mode 中使用。

    研判 Argon 以 1M 统一输入输出 token 和每百万输入 2 美元、输出 10 美元的定价进入 API 与 Gemini 应用,直接抬高前沿模型的上下文与价格竞争门槛;其自述在 Vals Index 与 ERQA 上居首,若第三方复现则可能改变企业与科研团队的模型选型。

10月1日周四
  1. Google DeepMind

    Google DeepMind 发布 Gemini 4 Argon 前沿模型

    Google DeepMind 宣布推出前沿模型 Gemini 4 Argon,正通过 Fairwind Program 向一批受信任的网络安全防御方开放。该模型输出 token 上限从 64K 提升至 1M,在 DeepSWE v1.1 上得分 77.9%,在 LVBench 上得分 91.7%。

    研判 Gemini 4 Argon 将输出上限提升至 1M tokens,并优先向网络安全防御方开放,显示前沿模型正以分阶段、受控方式进入高敏感企业场景。