Karina 谈企业应转型为巨型 RL 环境
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
Karina 提出，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的自我改进 AI，并逐步让 AI 接管公司运营。该观点发布时引用了 Revolut 将约 2,000 名工程师转向自托管 GLM 的帖子。
查看原帖突发：Revolut 将约 2,000 名工程师转向自托管的 GLM 用于编码，并把 Claude Code 和 Codex 列入例外清单。 据负责该公司 AI 运营的 David Abitbol 称：56 块 Nvidia B200，混合成本为每百万 token 30 美分，约为他们此前支付给 Anthropic 和 OpenAI 的十分之一。工程交付周期改善了 70%。 关键在于：当 token 不再被限量配给时，收益才显现出来。工程师们不再把 AI 省着用于难题，而是把它指向一切工作。 前沿模型成为例外，开放权重成为默认。这对每一家即将 IPO 的实验室来说都是定价问题。 h/t @OliverMolander 如果账单下降 90%，你会让你的团队弃用前沿 API 吗？
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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%?
正文 · AI 翻译
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的自我改进 AI，并逐步让 AI 开始运营公司本身