AI 导读
Karina Nguyen 提出观点：每家公司都应把自己变成一个巨型 RL 环境，构建能从每次交互中学习、并逐步开始自行运营公司的自我改进 AI。其引用内容提到 Revolut 将约 2000 名工程师转向自托管 GLM 编码、把 Claude Code 和 Codex 移入例外清单，用 56 张 Nvidia B200 实现每百万 token 混合成本 30 美分，约为此前 Anthropic 和 OpenAI 费用的十分之一，工程交付周期缩短 70%。
推荐理由
作者提出企业应把自身改造成 RL 环境、让 AI 从每次交互中持续学习的方向性观点，可结合引用的 Revolut 案例对照阅读。
正文
every company should turn itself into a giant RL env, building self-improving AI that learns from every interaction and progressively starts running the company itself
BREAKING: Revolut moved ~2,000 engineers onto self-hosted GLM for coding and pushed Claude Code and Codex to the exception list. Per David Abitbol, who leads AI ops there: 56 Nvidia B200s, a blended 30 cents per million tokens, roughly a tenth of what they paid Anthropic and OpenAI. Engineering lead time improved 70%. Here's the part that matters: the gains showed up when tokens stopped being rationed. Engineers quit saving AI for the hard tickets and pointed it at everything. Frontier models become the exception, open weights become the default. That's a pricing problem for every lab heading to IPO. h/t @OliverMolander Would you move your team off frontier APIs if the bill dropped 90%?在 X 查看被引用的帖子
来源：Karina · x.com