Satya Nadella has issued a shocking warning to companies using AI

萨提亚·纳德拉向使用人工智能的企业发出惊人警告

Of all the debates raging about the potential downsides of AI, there is one worry causing the most hand-wringing among AI enthusiasts in Silicon Valley. Their fear is that the giant AI labs that sell proprietary models are somehow acting like Trojan horses. The concern is that, as startups and enterprises use AI models from labs like OpenAI and Anthropic, the labs gain ever-increasing access to those companies’ most sensitive business information. The model makers can then use that knowledge for themselves, potentially becoming competitors to their own customers. Those issuing such warnings range from VCs like Jason Calacanis to Palantir CEO Alex Karp.

在围绕人工智能潜在负面影响的所有激烈辩论中，有一个担忧让硅谷的人工智能爱好者们最为焦虑。他们担心那些销售专有模型的大型人工智能实验室正扮演着“特洛伊木马”的角色。人们担心，随着初创公司和企业使用 OpenAI 和 Anthropic 等实验室的人工智能模型，这些实验室获得了对其客户最敏感商业信息的日益增长的访问权限。模型制造商随后可以将这些知识据为己有，从而可能成为其自身客户的竞争对手。发出此类警告的人士包括从风险投资人 Jason Calacanis 到 Palantir 首席执行官 Alex Karp 等各界人士。

Now, in a surprising blog post published on Monday, Microsoft CEO Satya Nadella has joined this crowd. Nadella warns that AI users (the “buyers” as he calls them) are paying twice. They knowingly spend for AI token usage but they also, obliviously, hand over valuable data in the process. “You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it!” he writes.

现在，微软首席执行官萨提亚·纳德拉（Satya Nadella）在周一发布的一篇令人惊讶的博客文章中也加入了这一阵营。纳德拉警告称，人工智能用户（他称之为“买家”）正在支付双重代价。他们明知要为人工智能的 Token 使用付费，却在不知不觉中交出了宝贵的数据。“你本质上为智能支付了两次费用，一次是金钱，另一次是更宝贵的东西：为了让智能发挥作用，你必须披露的专有知识。你越想让模型表现得更好，你就必须向它输入越多的知识！”他写道。

Most dangerously, enterprises are literally teaching the models about the nuances of their businesses, he argues. “Models learn from ‘exhaust,’ the prompts people write, the tools agents use, and especially the corrections people make when the model is wrong. Every correction is distilled into institutional know-how,” he writes. This is “the kind of knowledge a competitor could never buy,” and yet enterprises are handing it over.

他认为，最危险的是，企业实际上是在向模型传授其业务的细微差别。“模型从‘废气’中学习，即人们编写的提示词、智能体使用的工具，尤其是当模型出错时人们所做的修正。每一次修正都被提炼成机构的专业知识，”他写道。这是“竞争对手永远无法买到的知识”，然而企业却正在将其拱手相让。

Nadella argues that if AI companies get to freely scrape the internet to train their models, it’s only fair that enterprises get to study — or “distill” — those models in return. “Distillation” is the practice of using a model’s own outputs to learn how it works and to train a new, often cheaper, model based on those insights. In February, Anthropic accused Chinese open source models of sending millions of prompts to Claude as a way to improve their own models, and urged the U.S. government crack down on export controls.

纳德拉认为，如果人工智能公司可以自由地抓取互联网来训练模型，那么企业反过来研究——或“蒸馏”——这些模型也是公平的。“蒸馏”是指利用模型的输出结果来学习其工作原理，并基于这些见解训练一个新的、通常成本更低的模型。今年 2 月，Anthropic 指责中国的开源模型向 Claude 发送了数百万条提示词以改进其自身模型，并敦促美国政府加强出口管制。

Nadella’s point is that model makers can’t have it both ways. It’s hypocritical for them to freely train on the world’s data while restricting others from doing the same to their models. “While the great innovation that comes from model providers having fair use rights to train models on public data is needed, I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation,” Nadella writes. Nadella is particularly concerned when model makers “reserve the right to learn from customer usage and interaction data.”

纳德拉的观点是，模型制造商不能两头占便宜。他们一方面自由地利用全球数据进行训练，另一方面却限制他人对他们的模型进行同样的操作，这是虚伪的。“虽然模型提供商拥有在公共数据上训练模型的合理使用权所带来的巨大创新是必要的，但我认为现状是转过头来对蒸馏施加限制性条款，这很讽刺，”纳德拉写道。纳德拉特别担心模型制造商“保留从客户使用和交互数据中学习的权利”。

Nadella’s solution is the kind of thing the CEO of a giant cloud provider would suggest. He wants companies to “retain ownership” of their data, including prompts, feedback, etc. So he’s urging them to build their own “proprietary learning environments” on the cloud (where their data is likely already stored anyway and, conveniently, could mean Microsoft’s cloud, Azure). He also wants companies to build in what he calls “orchestration layers” — essentially, a way to easily switch between AI models from different providers rather than being locked into one. Tools like AI “gateways” that let companies do exactly this have become increasingly popular.

纳德拉的解决方案正是大型云服务提供商首席执行官会建议的那种方案。他希望企业能够“保留”其数据的所有权，包括提示词、反馈等。因此，他敦促企业在云端构建自己的“专有学习环境”（数据很可能已经存储在那里，而且方便的是，这可能意味着使用微软的云服务 Azure）。他还希望企业构建他所谓的“编排层”——本质上是一种能够轻松在不同提供商的人工智能模型之间切换，而不是被锁定在某一个模型上的方法。像人工智能“网关”这样能让企业实现这一点的工具正变得越来越流行。

While Nadella never uses the words “open source” as the method for retaining ownership, this is an obvious subtext. Yet, there’s another subtext. Large companies, many of which still have some of their own data centers in addition to using the cloud, are already moving to open source models installed on their own premises (“on-prem,” in industry jargon). Idit Levine, founder and CEO of Solo.io — which makes networking and security software that helps enterprises manage AI systems — says she’s seeing exactly this shift play out with her own customers.

虽然纳德拉从未明确使用“开源”一词作为保留所有权的方法，但这显然是其言外之意。然而，还有另一层潜台词。大型企业（其中许多在利用云服务的同时仍保留部分自有数据中心）已经开始转向安装在自有场所（行业术语称为“本地部署”或“on-prem”）的开源模型。Solo.io 的创始人兼首席执行官 Idit Levine 表示，她正在自己的客户身上看到这种转变。该公司生产帮助企业管理人工智能系统的网络和安全软件。

After experimenting with proprietary model makers, they start asking themselves: “Can I take an open source model and run it on-prem? It will do almost 90% of what the big one’s doing. It will cost way less,” she tells TechCrunch. “They understand that, and they can control it.” Solo.io’s technology was selected last year to be the tech powering the Linux Foundation’s Agentgateway project. Her company counts enterprises like T-Mobile, ADP, and SAP as customers. She sees companies increasingly installing on-premise open source models and sees it as the next big wave in enterprise AI use.

在尝试了专有模型制造商之后，他们开始问自己：“我能拿一个开源模型并在本地运行吗？它几乎能完成大模型 90% 的工作，而且成本要低得多，”她告诉 TechCrunch。“他们明白这一点，并且他们可以控制它。”Solo.io 的技术去年被选中，成为 Linux 基金会 Agentgateway 项目的驱动技术。她的公司拥有 T-Mobile、ADP 和 SAP 等企业客户。她看到越来越多的公司在安装本地开源模型，并将其视为企业人工智能应用的下一个大浪潮。

She’s not alone. Vercel (best known as a platform for building and hosting websites, which has recently added AI model-switching tools) and OpenRouter (a company that helps developers route requests across different AI models) are both seeing a surge in traffic to open source models. In fact, open models accounted for 29% of all traffic routed through Vercel’s gateway last month. With the CEO of Microsoft, a company that has invested in both OpenAI and Anthropic, now openly urging enterprises to be wary of using proprietary models, we’ll bet this trend continues to grow. “In consuming intelligence, you are creating intelligence. And what you create should belong to you,” Nadella writes.

她并不孤单。Vercel（以构建和托管网站的平台而闻名，最近增加了人工智能模型切换工具）和 OpenRouter（一家帮助开发者在不同人工智能模型之间路由请求的公司）都看到开源模型的流量激增。事实上，上个月通过 Vercel 网关路由的所有流量中，开源模型占比达到 29%。随着微软（一家同时投资了 OpenAI 和 Anthropic 的公司）的首席执行官现在公开敦促企业警惕使用专有模型，我们敢打赌这一趋势将继续增长。“在消费智能的同时，你也在创造智能。而你所创造的应该属于你自己，”纳德拉写道。