#大佬观点

https://x.com/i/article/2107473094155927552

你的下一场会议会有一位记笔记的助手，帮你处理后续跟进。 Meetings 插件会为你记笔记，并根据 ChatGPT 对你的了解以及你们一起完成的工作，在 ChatGPT Space 中保存个性化的摘要和后续步骤。 你可以将笔记设为私密，也可以与团队共享，然后让 ChatGPT 更新项目计划或起草后续跟进内容。 该功能以测试版形式面向 macOS 上 ChatGPT 桌面应用中的 Pro 和 Business 用户提供。Enterprise 版本即将推出。 下载桌面应用，然后在插件目录中搜索“Meetings”。 https://chatgpt.com/download/

OpenAI's granola competitor (the meetings plugin for ChatGPT) kinda flew under the radar in announcements they have this UX where they just put buttons alongside the camera notch on macos and i really like that. it's much more unobtrusive than what wispr and granola are doing

认识一下 Mistral Large 4，又名 Le Chonk。 • 1T 参数，原生多模态。49B 激活。 在综合基准测试中，它是来自美国或欧洲的最佳开放权重模型。 • 在关键工作负载上达到最先进水平，包括网络防御、制造业和金融，并且在视觉定位方面超越了闭源前沿模型。 • 端到端在欧洲打造，并可通过我们自己的 Mistral Cloud 基础设施从欧洲部署。 • 今天即可通过 API 向所有人开放。正与网络安全合作伙伴私下合作。 开放权重版本将于十月底发布。

Power users are driving consumer AI market growth. The top 1% of US consumer AI payers average $903 a month, while flat plans hold the median at $25. One top-1% payer spends as much as roughly 59 bottom-half payers, on average. Median spend has barely moved, while average spend in the top 1% climbed from $504 to $903 a month between January 2025 and August 2026. Model labs are better placed than apps built on their models to capture consumer AI's top 1% of spenders. Those users average $903 a month, up from $504, and most of it buys model usage. A lab pays only its serving cost for that usage, while an app pays API prices. Anthropic earlier said one $200 Max subscriber ran up tens of thousands of dollars in usage, a bill no API-paying app could absorb. Apps that want this tier have to own more of the model, as Cursor began doing with its Composer coding model.

The top 1% of AI spenders now outspend the bottom 50% combined They spend an average of $903/month, while the median customer spends just $25 More charts in our Top 100 Consumer AI Apps breakdown: https://www.a16z.news/p/top-100-consumer-ai-apps-seventh

> Sam Altman does all his prompting on prompting on 'ultra fast' > Boris Cherny, head of Claude Code at Anthropic: “I use Fable for everything" > OpenAI's median researcher used $601 of coding-agent inference a day (about $1.8M a year, r) at API prices, up from under $1, in January 2026.

OpenAI recently published data on its researchers’ coding-agent usage. We took their weekly figures from January to mid-August 2026 and fitted trends to summarize how quickly spending grew. We found that spending has been doubling about once a month.

Today we have set out how we’re building AI to accelerate science and improve people’s lives. Just some examples in the last week or so: - Mapped all 9B possible single letter genetic changes across the human genome with AlphaGenome Atlas and made it openly available to researchers. - Billions of decisions depend on weather predictions so we introduced WeatherNext 3, our most accurate and capable global weather AI model to date. - We published AI & Economy ATLAS, a comprehensive open-access look at how people are using AI globally. - AI has enabled extraordinary advances in language translation. Today our services are available in nearly 300 languages, spoken by 7B people We’re focusing our efforts on four key areas: health, natural disaster and weather resilience, learning, and economic opportunity. https://blog.google/innovation-and-ai/technology/ai/ai-applications-science-people/