#大佬观点
Rohan Paul@rohanpaul_aiAI 评分4343
Alexander Embiricos@embiricoAI 评分6363引用Dan Shipper@danshipperCodex is one of the most important untold stories in the history of technology. A year ago, it was a scrappy effort in a quiet corner of @OpenAI. Its cloud product hadn’t found PMF, and the team was starting over from scratch. Less than 12 months later: - Codex swallowed the flagship ChatGPT desktop app from the inside (it literally runs as com.openai.codex on your Mac) - Codex and ChatGPT for Work hit 40M+ weekly users across engineering and knowledge work - Its leader @thsottiaux was elevated to run all of OpenAI’s core products, including ChatGPT, Codex, and the API How did they pull it off? They trained their own dedicated model line, separate from ChatGPT, with researchers and product builders sitting on the same team They built four different products in a single year (cloud → terminal → IDE sidebar → a desktop app with no code editor at all) as every model leap made a new form factor possible They used Codex to build Codex—so a handful of people could prove a new product in weeks instead of arguing for it in meetings I had a front-row seat to the whole arc—from their failed Cloud launch, to OpenAI’s offices the night before GPT-5 dropped, to the day Codex took over ChatGPT. And I spent months interviewing @gdb, @thsottiaux, @embirico, @mia_glaese, @aambrosino, and @edbayes to get inside how it all went down. Here is the full, behind-closed-doors story of the team that remade OpenAI: https://every.to/p/how-codex-happened We’re pushing the boundaries of how stories like this are told. This piece is completely agent-native—designed to be read alongside your agent inside ChatGPT’s in-app browser: - Every interview transcript is included in full: Click any quote and you (or your agent) can see the exact conversation it came from. - Built for your agent to explore: Ask your agent to pull out product lessons for your team, cross-examine the transcripts, or find insights I missed when writing the piece. It’s a story about the future of software—built to be read with an agent: https://every.to/p/how-codex-happened
Big TechnologyAI 评分5454 Rivian CEO RJ Scaringe:机器人技术进展远超外界想象
Rivian CEO RJ Scaringe 接受 Big Technology 采访称,机器人技术的进展速度将远高于世人所知。他于 2025 年创立 Mind Robotics,已融资 10 亿美元,开发双臂双手机器人,用基于 Transformer 的模型让机器人学习可泛化技能,例如擦表面时遇到手机用手指拿起、遇到信用卡用指甲操作。
Ed Zitron 长文 / Where’s Your Ed AtAI 评分5757 Ed Zitron 长文:JOBS Act 如何催生风投估值泡沫并危害散户
Ed Zitron 撰文主张改革甚至废除 2012 年签署的 JOBS Act。文章指出该法案将股东上限提高到 2000 人且员工持股不再计入披露触发条件,使大公司长期保持私有,催生了史上最大的私募估值泡沫。
Sam Altman@samaAI 评分4848引用Nick@nickcammaratainterpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works https://already-clean.goodfire.com/
a16z:News精选AI 评分6060 a16z 图表周报:智能体购物遭电商拦截,消费意愿仍滞后
a16z 图表周报指出智能体商务遇阻,Cloudflare 数据显示智能体与爬虫收到的 403 拒绝响应自夏季起明显增多,约为原先两倍。文章引用 NRF 数据称退货率与退货成本自 2019 年以来分别翻倍和增至三倍,并引用调查指 42% 消费者完全不信任 AI 代购,Similarweb 数据显示 AI 购物多发生在发现与购买之间的中间阶段,而非下单环节。
推荐理由:原文汇集 Cloudflare、Similarweb 等数据,指出智能体购物正遭遇电商封禁与消费者信任不足的双重阻力。
TechCrunch:AIAI 评分4545 Replit CEO:未来工作是「自动驾驶公司」
Replit CEO Amjad Masad 在 TechCrunch Disrupt 上提出「自动驾驶公司」愿景:AI 智能体承担公司大部分基础工作,人类保留判断力与品味。他以 Replit 客服流程为例,工单先由智能体结合 GitHub issues、代码库和 Linear 工单完成初步分诊,人工决定修复后一键部署另一个智能体执行。
ChinaTalkAI 评分5252 ChinaTalk 访谈蚂蚁 Ling 产品负责人 Richard Bian:为什么他认为 AGI 不可避免
ChinaTalk 发布对蚂蚁 Ling 大模型产品运营负责人 Richard Bian 的访谈,他谈到大模型时代类似 15 世纪大航海时代、AGI 不可避免,以及 Hugging Face 事件中开源模型可用于防御的价值。
IT之家·人工智能AI 评分6060 谷歌布林称 Gemini 正迈向递归自我改进(RSI)阶段
谷歌联合创始人谢尔盖·布林在 Gemini 4 Argon 圆桌讨论中表示,AI 已开始参与打造下一代模型,Flash 系列已显现这一趋势,公司正踏上 RSI(递归自我改进)的跑步机。
Ian Johnson 🔬🤖@enjalotAI 评分4040迫不及待想深入研究了! https://already-clean.goodfire.com/
引用Nick@nickcammaratainterpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works https://already-clean.goodfire.com/
Karina@karinanguyenAI 评分4747引用Nick@nickcammaratainterpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works https://already-clean.goodfire.com/
TechCrunch:AIAI 评分5151 Amazon 设备负责人 Panos Panay 畅谈环境计算将减少对 App 的依赖
Amazon 设备、Alexa 和 Leo 高级副总裁 Panos Panay 在 TechCrunch Disrupt 上表示,个人 AI 的未来是环境计算而非 App,新一批设备将靠传感器主动理解用户需求。
Every:最新文章AI 评分7070 Codex 是如何发生的:从失败云产品到 OpenAI 复兴的基石
Every 创始人通过亲历 GPT-5 测试和对 Embiricos、Sottiaux、Glaese 等核心成员的访谈,复盘 Codex 从 2025 年 5 月 Codex Cloud 发布遇冷,到转向本地 CLI 和编辑器扩展、再到 2026 年 2 月桌面版发布并最终在 7 月并入 ChatGPT 的全过程。
The Verge:AIAI 评分7272 Meta 的 Muse 智能体引发隐私争议:你的朋友可能替你交出数据
The Verge 分析 Meta 的 Muse AI 助手上月起登顶 App Store 下载量数百万,其界面以 dark patterns 鼓励用户授权银行账户、密码、聊天记录等大量数据。
O'Reilly RadarAI 评分4646 Honeycomb 联合创始人 Charity Majors 谈 AI 可观测性:非确定性系统需要更多严谨
Honeycomb 联合创始人兼 CTO Charity Majors 在播客中讨论 AI 生成代码加速后如何构建可靠软件,指出可观测性用于回答未知问题、连接变更与结果。
The Verge:AIAI 评分5959 The Verge 访谈 Benn Jordan 谈 Flock 摄像头、隐私与新一批监控反弹
The Verge Decoder 播客访谈研究员兼调查记者 Benn Jordan,谈他调查 Flock Safety 自动车牌识别系统以及在参议院作证的经过。
IT之家·人工智能AI 评分4040 黄仁勋:英伟达在 AI 安全领域必须与中国企业合作
黄仁勋 10 月 15 日接受 CNBC 采访时表示,英伟达"绝对有必要"与中国 AI 企业合作,共同监测前沿技术带来的风险,并称合作应通过"对话"实现,他一直在与中国研究人员交流。美国财政部长贝森特曾向中方提议建立双边 AI 安全风险通报机制与沟通渠道,以应对 AI 智能体失控、网络安全及生物威胁等潜在危机。
a16z:NewsAI 评分5959 Wallet Wars:智能体支付正在展开的四场争夺
Terry Angelos 分析智能体购物要真正完成任务需要获得付款授权,由此展开钱包之战。文章指出 Stripe Link(3 亿用户)、Shop Pay(2.5 亿用户)、PayPal(4 亿以上用户)竞相接入 Muse 和 Instinct 等智能体,但 Stripe 用一次性虚拟卡代为付款会切断用户与商户的关系,丢失积分和权益。
IT之家·人工智能AI 评分4545 黄仁勋盛赞 OpenClaw 推动机器人技术发展,强调权限管控必须到位
英伟达 CEO 黄仁勋在伦敦 Build a Claw 活动上称 OpenClaw 是"首个真正可信的智能体运行框架",认为它像外骨骼的大语言模型,可推动机器人领域发展。他同时强调自主系统必须做好权限管控,并提到英伟达为此开发了 OpenShell 隔离系统,主张默认任何对象都不可信、仅授予最低权限。
lauren@potetoAI 评分4848引用Cheng Lou@_chenglouMy dear friends, I’d like to show you my latest toy project Introducing Handwired: turn your regular JavaScript code into a neural net! - no training - exact outputs vs the JS program - fully differentiable All runnable in a browser, without GPU
Elon Musk@elonmuskAI 评分4444只需让 Grok @Bot 去别处帮你买想要的东西,就能拿到更好的价格。 可购物的网站远不止 Amazon 一家!
引用Nic Cruz Patane@niccruzpataneThis happened when I tried to get my Grok Bot to order stuff for me on Amazon. “Continued access by an unauthorized AI agent violates Amazon's Conditions of Use, to which our customers have agreed.” My bots can no longer access Amazon for orders or anything.
Elon Musk@elonmuskAI 评分4141这很好用。如果 Grok @Bot 在购买时出错或泄露了你的卡信息(目前从未发生过),SpaceX 会全额赔付你。
引用X Freeze@XFreezeGrok @bot booked a flight for me today the entire process was really really easy…..I send a photo of my credit card and it did the entire process…. Tbh, last time I booked, the website was way too confusing…. It’s not an easy website, and Grok Bot did perfectly well and booked a flight for me…. And I got a message for a booking confirmation….going to board it soon
IT之家·人工智能AI 评分4646 比尔·盖茨:AI 比核武器更危险、比核能更有用,与 PC、互联网等创新都不同
比尔·盖茨称 AI 比核武器更危险、可能比核能更有益,认为它更像"进化事件"而非"下一台个人电脑",将其视为 PC 或互联网式的创新会让人完全低估它。他承诺未来两年投入十亿美元,推动 AI 在健康、教育和农业领域落地,并预测 AI 驱动的网络攻击将在 2027 年成为变化最重大的领域之一。
IT之家·人工智能AI 评分4141 Anthropic 国际业务负责人普兰森:开放式 AI 模型风险高于自家模型
Anthropic 国际业务负责人普兰森在都柏林国际人工智能峰会期间称,开放权重 AI 模型比 Anthropic 自家模型风险更高,无法为其安全性作出保证。Anthropic 本周还警告,小型 AI 模型可能被用于驱动新型僵尸网络,在智能烤箱等联网设备间自行复制扩散。普兰森称最新 Claude Opus 5.5 比早期版本便宜 40%。
MIT NewsAI 评分5656 MIT 教授 Eric So 谈新书《The Collision: What AI Does to Us》与 AI 对人类思维的影响
MIT Sloan 教授 Eric So 在 MIT News 访谈中介绍其新书《The Collision: What AI Does to Us》,提出过度依赖 AI 会导致认知萎缩。他引用包括 Anthropic 研究在内的证据称约一半大学生查询是求简单答案的低努力查询,并提出拥抱挣扎、重视没有 AI 的自己、再投资认知盈余、把 AI 当导师而非答案机四条原则。
Gary Marcus:The Road to AI We Can TrustAI 评分5252 Gary Marcus 谈科技股大跌,CoreWeave 单日跌超 15%
Gary Marcus 称科技股前一日遭重挫,其中 CoreWeave 跌幅最大,单日下跌超过 15%。他将其与 CoreWeave 不断增长的债务问题联系起来;所附媒体引述 D.A. Davidson 分析师 Gil Luria 警告,若 AI 支出热潮减弱,即便 CoreWeave 拥有 1040 亿美元收入储备,股东也可能血本无归。
Elon Musk@elonmuskAI 评分4747引用SpaceX Super Intelligence@SpaceXSIIntroducing Grok Voice 3.0, the world’s most intelligent speech-to-speech model. It excels at resolving customer calls, responds in under a second, and costs less than other frontier models.
AI at Meta@AIatMetaAI 评分4545观看与 @finkd、@alexandr_wang 和 @collision 的完整对话: https://x.com/collision/status/2109714250226905248
引用John Collison@collisionCheeky Pint is back! Mark Zuckerberg @finkd and Alexandr Wang @alexandr_wang came to the pub to discuss their new frontier model, Muse Wonder. 00:00:24 Muse’s breakout use cases 00:07:28 The model is the product 00:14:35 Muse Wonder 00:24:56 How frontier models are built 00:48:26 Betting $100B 01:07:21 Agentic commerce 01:19:34 OpenRouter 01:20:06 Beyond the smartphone 01:27:00 Funding 01:34:06 Why AI won’t be winner-take-all 01:40:46 Meta Superintelligence Labs (MSL) 02:08:11 Working for Mark Zuckerberg
Elon Musk@elonmuskAI 评分4242引用Wes@wmorrill3An email from noreply@x.ai landed in my inbox, passed SPF, DKIM, DMARC, and had the blue check Something felt off, so I asked @bot to do a security review. It found a hidden phishing link, explained why every safety check passed, wrote a report to xAI - including proposed fix!
Latent SpaceAI 评分5959 AIE 大会聚焦金融智能体的自主性与安全:OpenAI、Anthropic、Two Sigma 各自给出控制框架
Latent Space 作者 Richard MacManus 总结 AI Engineer 纽约大会上金融智能体安全与自主性的讨论。
Latent SpaceAI 评分4545 Leash Bio 的 Ian Quigley 谈 DEL 数据、Hermes 模型与 Minos 科学推理智能体
Leash Bio 用 DNA 编码库(DEL)为每个分子加数十条条形码,提取干净可复现的蛋白质-小分子结合数据,并训练出仅用自家数据的 Hermes,在公开基准上追平 Boltz。
AI as Normal TechnologyAI 评分5959 Narayanan 与 Unger 提出超越自动化的 AI 人机协作研究议程
Arvind Narayanan 与斯坦福 Digital Economy Lab 经济学家 Gabriel Unger 撰文主张 AI 不应只走全面自动化路线,而应转向人机协作与增强。
The Decoder:AI NewsAI 评分6868 Google DeepMind 工程师称自我改进的 AI 仍很遥远
Google DeepMind 工程师 Haydn Belfield 称自我改进的 AI 仍很遥远,未观察到 AI 驱动的 AI 研究加速。
Timothy B. Lee:Understanding AIAI 评分6868 OpenAI 的信任悖论:安全研究员被裁与 GPT-6 Astra 可监测性下降
Understanding AI 分析 OpenAI 10 月初解雇三名安全部门员工引发的信任争议,三人称被裁与向 METR 等第三方安全组织反映问题有关,OpenAI 则称他们违规处理敏感信息。
Databricks:BlogAI 评分4545 Databricks 谈 AI 智能体为何难以证明 ROI:速度不是正确的生产力指标
Databricks 的 Amber Roberts 指出,企业快速采用 AI 智能体却难以证明 ROI,原因在于采购者与日常使用者往往不是同一批人,目前多数组织正处于试用与筛选有效用例的阶段。她认为"更快"是错误的智能体 AI 生产力指标,生产力应绑定各团队自身的 KPI,并配合质量指标衡量。她还强调,智能体理解业务上下文比单纯执行任务的速度更重要。
The Verge:AIAI 评分5151 The Verge 评测员亲测 Twin Health 数字孪生 100 天:AI 代谢教练改善肝功能指标
The Verge 资深评测员 Victoria Song 亲测 Twin Health 数字孪生代谢管理项目 100 天。该项目需持续输入体成分、血糖、血压等数据,用红橙绿颜色预测食物对血糖的影响;随访化验显示代谢健康评分提高 9 分,甘油三酯下降 70mg/dL,多数肝酶恢复正常。作者也指出持续记录带来追踪疲劳,并在癌症治疗等高风险场景下存在数据隐私与信任挑战。