Claude Mythos 关停,让我看清租用 AI 的真正代价

marsbitОпубликовано 2026-06-16Обновлено 2026-06-16

Введение

Claude Mythos 的突然关停,揭示了企业过度依赖外部AI平台的核心风险:当你的核心能力建立在租用的智能之上,生死便不由自己掌控。这不仅关乎成本,更深层的是控制权问题。 租用前沿API如同租房,初期便捷但受制于人。房东可以涨价、改规则甚至让你搬走,正如平台方可以单方面改变服务。Mythos的遭遇正是如此,其业务根基因无法控制的决策而瞬间崩塌。 真正的出路在于“拥有智能”。这意味着企业应以强大的开源模型为起点,用自身独有的数据、工作流程和专业知识对其进行深度定制和训练。经过调优后,这类模型往往能在关键任务上以更低成本达到前沿模型的质量,更重要的是,它成为了企业不可剥离的自有资产。 AI的未来不存在单一的“前沿”。前沿通用模型、深度定制模型、解决狭窄问题的专门模型、以及智能路由集合,共同构成了多元的生态。胜利将属于那些能将智能转化为自身独特竞争优势的公司。 因此,企业的战略重点应从单纯消费AI服务,转向构建和拥有属于自己的、与业务深度绑定的智能能力。这样,产品的根基才不会被轻易抽走。

作者:Lin Qiao

编译:深潮 TechFlow

深潮导读:Mythos 本周被突然关停,直接暴露了一个被大多数创始人忽视的致命风险:当你的核心能力完全依赖别人的平台时,你的生死就不在自己手里。谁真正拥有你产品运行所依赖的智能?

Mythos 本周被关停了。无论你同不同意这个决定,这几乎不是重点。

一家建立在它无法控制的智能之上的公司,突然发现自己暴露在无法影响的决策之下。很多创始人看到这件事,都问了自己同一个问题:我的业务中,哪些部分其实只是在租用?

过去几年,关于开源模型的讨论主要围绕成本。它们真能完成工作吗?如果能,比调用前沿 API 便宜多少?

现在我们有了相当明确的答案。我们与 Ramp、Cursor 和 Harvey 等公司合作,采用同样的基本方法:从一个强大的开源模型开始,针对业务中真正重要的工作进行后训练,并对其与前沿模型进行严格评估。

结果一直让人惊讶。在它们最关心的任务上,经过调优的开源模型能以极低成本达到前沿模型的质量。本周发生的事让一点变得清晰:成本从来不是最重要的问题。

更深层的问题是控制权。谁拥有你的产品所依赖的智能?

最近很多讨论被框定为租用与拥有。这不是完美的类比,但很有用。

租用智能

租用在不出问题之前一直很好用。公寓拎包入住。灯能亮。水管通。有人负责维护。这就是为什么大多数公司从这里开始。

前沿 API 是令人难以置信的产品。它们让创业公司能构建几年前看起来不可能的东西。

但租用有限制。房东可以涨租。他们可以决定你能做什么改动。他们可以改规则。偶尔,因为与你无关的原因,他们会告诉你该搬走了。

你没做错什么。你只是在别人的地盘上运营。这就是为什么 Mythos 的故事触动了这么多人。当你的核心能力完全依赖别人的平台时,你就暴露在你无法控制的决策之下。

大多数时候这不重要。有时候它突然变得很重要。

拥有智能

教训不是公司应该停止使用前沿模型。恰恰相反。前沿实验室构建了非凡的技术。大多数产品应该使用它。我们也在用。在很多方面,前沿模型正在成为基础设施。但基础设施和所有权是两回事。

你可以使用公共基础设施,同时仍然拥有为你的业务创造价值的东西。在 AI 领域,所有权意味着从最先进的开源模型开始,围绕你公司的独特性对其进行塑造。

你的数据。

你的工作流程。

你的领域专业知识。

你的边缘案例。

你的评估标准。

你对"好"的定义。

随着时间推移,模型变得不那么通用,更能反映你公司每天做的工作。这就是价值被创造的地方。

想想一个房子。移动家具很容易。刷墙很容易。但如果你的未来取决于布局本身,最终你会想要移动墙壁的能力。智能也是一样。

当智能属于你时,没人能悄悄把你产品的地基抽走。

这就是我们以这种方式构建 Fireworks 的原因。

训练和推理在同一屋檐下,这样公司可以采用最好的开源模型,围绕对业务最重要的问题对其进行塑造,并在生产中可靠地部署它们。

不只是消费智能。拥有它。

不存在单一前沿

本周一个乐观的结论是,AI 的未来不依赖于某个单一模型的胜利。

不存在单一前沿。有很多前沿。

前沿模型是一种前沿。

基于多年专有公司知识进行后训练的模型是另一种。

更好地解决某个狭窄问题的专门模型是另一种。

将请求映射到一组模型集合的路由器,在许多任务上共同超越任何单个模型,也是一种。

AI 领域最有趣的事情不是某个模型变得更聪明。而是智能正变得越来越可定制。获胜的公司不一定是拥有最大模型的公司。而是那些将智能转化为独特自有资产的公司。

展望未来

当大家这周都在对新闻做出反应时,我们在忙着发布产品——Kimi Moonshot K2.7 Code、MiniMax M3、阿里 Qwen 3.7 Plus。

我期待的未来不是某个模型悄悄吃掉它看到的一切。而是许多团队拥有对他们重要的那部分前沿。

如果 Mythos 关停让你对这种权衡有了不同的思考,我们很乐意聊聊。

Связанные с этим вопросы

Q根据文章,Mythos被关停事件暴露了创业公司面临的一个核心风险是什么?

A文章指出,Mythos被突然关停的事件暴露了创业公司面临的一个致命风险:当你的核心能力完全依赖于别人的平台(如调用前沿AI模型的API)时,你的业务生死就不在自己手中,而是暴露在平台方无法控制的决策之下。

Q文章将使用前沿AI模型API比作什么?这种模式存在哪些潜在问题?

A文章将使用前沿AI模型API比作“租用智能”,类似于租房。这种模式方便快捷,但存在限制:提供方(房东)可以随意涨价、更改规则、限制你能做的改动,甚至因为与你无关的原因要求你离开。这使得依赖于此的业务根基不稳。

Q作者提出的“拥有智能”具体指什么?它与“租用智能”的关键区别在哪里?

A“拥有智能”指的是公司从强大的开源模型出发,利用自身独特的数据、工作流程、领域知识、评估标准和对“好”的定义,对模型进行后训练和塑造,使其成为能够反映并服务于公司核心业务的专有资产。关键在于“控制权”和“所有权”,即智能的根基由公司自己掌握,不会被外部平台抽走。

Q文章对于“AI的前沿”持何种观点?它认为未来的赢家会是哪类公司?

A文章认为不存在单一的“AI前沿”,而是存在多个前沿,包括:前沿闭源模型、基于专有知识后训练的模型、解决特定狭窄问题的专门模型、能智能路由请求的模型集合等。未来的赢家不一定是拥有最大模型的公司,而是那些能够将通用智能定制化、转化为自身独特资产的公司。

Q作者所在的公司Fireworks如何帮助客户实现“拥有智能”?

AFireworks通过将模型训练和推理部署整合在同一平台,帮助客户采用最好的开源模型,并围绕对其业务至关重要的任务进行塑造和优化,然后可靠地部署到生产环境。其目标是让客户不只是消费智能,而是真正拥有和控制它。

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