Vercel CEO Guillermo Rauch on the fight to split off models from agents

Vercel 首席执行官 Guillermo Rauch：关于将模型与智能体剥离的博弈

Known for its cloud infrastructure that allows developers to deploy agents without managing servers, Vercel has quietly become one of the most central companies in AI software. The company currently sees 6 million deployments a day, half of them triggered by coding agents, and more than 1 trillion tokens flow through the company’s AI gateway daily. After the company’s ShipNYC conference last week, we sat down with Vercel CEO Guillermo Rauch for his take on this moment in AI, and how platform companies like Vercel end up competing with major labs. Here’s a lightly edited transcript. Vercel 以其云基础设施而闻名，该基础设施允许开发者在无需管理服务器的情况下部署智能体，它已悄然成为 AI 软件领域最核心的公司之一。该公司目前每天处理 600 万次部署，其中一半由编程智能体触发，每天有超过 1 万亿个 Token 流经其 AI 网关。在上周公司举办的 ShipNYC 大会之后，我们采访了 Vercel 首席执行官 Guillermo Rauch，探讨了他对当前 AI 时刻的看法，以及像 Vercel 这样的平台公司最终如何与大型实验室展开竞争。以下是经过轻微编辑的访谈记录。

It feels like there’s a different energy in the community this year, fewer pilot programs and more focus on how to make things work well in practice. I’m sure you’ve seen that a lot with clients, but I’m curious what that journey has looked like within Vercel. 今年社区里的氛围似乎有所不同，试点项目减少了，大家更关注如何让事物在实践中真正发挥作用。我相信你在客户那里也看到了很多这种情况，但我很好奇 Vercel 内部经历了怎样的转变。

Last year was about prototyping. The sky’s the limit, unleash the agents, everyone can build, and so on. We did that, and we learned a lot because we had hundreds of agents organically developed and deployed within the company, and then you started getting into the realities of agents in production, and some of the challenges. The biggest lesson for me was the home-run use cases, the two killer apps of agents. One is the coding agent, of course. That’s driving a lot of the token utilization in the world, but when you produce so much software, you need somewhere to put it. The second killer app of agents is the internal agent that helps you run the company. The challenge there is, how do you securely access data? How do you audit what the agent is doing? How do you get a trail of all of the tool calls and access controls that the agent had to incur in order to get a job done? 去年是原型设计的一年。当时的想法是“天空才是极限”，释放智能体，人人都能构建等等。我们确实这样做了，也学到了很多，因为我们在公司内部自发开发并部署了数百个智能体。随后，你开始接触到智能体在生产环境中的现实情况以及一些挑战。对我来说，最大的教训是那些“全垒打”式的用例，即智能体的两个杀手级应用。一个是编程智能体，这毋庸置疑。它推动了全球大量的 Token 使用量，但当你生产出如此多的软件时，你需要一个地方来托管它们。智能体的第二个杀手级应用是帮助你运营公司的内部智能体。这里的挑战在于：如何安全地访问数据？如何审计智能体正在做什么？如何获取智能体为完成任务而进行的所有工具调用和访问控制的记录？

To solve that, we came up with this framework called Eve, where you can lay out an agents’ instructions and skills in natural language. And another tool is Vercel Sandbox, where you put the agent in a little cage. It can have the freedom still to express its intelligence, but then you can apply policy on what data it can access and what data can leave the sandbox. 为了解决这个问题，我们提出了一个名为 Eve 的框架，你可以用自然语言列出智能体的指令和技能。另一个工具是 Vercel Sandbox，你可以把智能体关进一个小笼子里。它仍然可以自由地发挥其智能，但你可以对其应用策略，规定它能访问哪些数据，以及哪些数据可以离开沙盒。

What sort of problems does that help you avoid? 这能帮你避免哪些问题？

For [the] sandbox, the biggest advantage is data control. A real risk of AI that I always think about is, when you get a coding IDE like Devin or Cursor, if you’re in the wrong setting, they may train on your entire codebase. I remember talking to the president of Airbus about this. You have decades of wealth of very specific C++ code for aerospace engineering. Someone comes in and installs the wrong developer tool and boom, all the code goes out to the cloud for training. 对于沙盒来说，最大的优势是数据控制。我一直在思考 AI 的一个真正风险：当你使用像 Devin 或 Cursor 这样的编程 IDE 时，如果设置不当，它们可能会利用你的整个代码库进行训练。我记得曾与空客（Airbus）的总裁谈过这件事。你们拥有数十年积累的、非常具体的航空工程 C++ 代码财富。如果有人进来安装了错误的开发工具，砰的一声，所有代码都会被发送到云端进行训练。

I’m curious to hear more about that second killer use case. We all know about coding agents, but what does an internal corporate agent look like in practice? 我很好奇想多了解一下第二个杀手级用例。我们都知道编程智能体，但内部企业智能体在实践中是什么样的？

So, there’s a sales rep sitting out there [in Vercel’s office]. She works on install base. Her job is to grow existing accounts. The bottleneck for people like her has not been her creativity, intelligence, ability to build relationships, it’s been data. “I don’t understand what accounts are growing faster. Give me the five accounts that have added the most seats in the last two weeks, so that I can prioritize my work.” She couldn’t ask that question in the past. She needed to wait until a Q1 project for a new sales dashboard completed. We were in that bottleneck for years at Vercel, and it was really frustrating because on the R&D side, we’re the fastest-moving company in the world. But on the sales engine, the Salesforce engineering [side], I was so incompetent. I had never opened Salesforce in my life when I started. Now I feel like I can actually have impact across the entire company, because Eve can be used for our customer-facing agents and can be used to improve productivity. Same technology, it’s just APIs. 比如，外面（在 Vercel 办公室）坐着一位销售代表。她负责存量客户。她的工作是拓展现有账户。像她这样的人，瓶颈不在于创造力、智力或建立关系的能力，而在于数据。“我不了解哪些账户增长得更快。请给我列出过去两周增加席位最多的五个账户，以便我能优先处理我的工作。”过去她无法提出这个问题。她必须等到第一季度的新销售仪表板项目完成。在 Vercel，我们多年来一直处于这种瓶颈中，这真的很令人沮丧，因为在研发方面，我们是世界上行动最快的公司。但在销售引擎，即 Salesforce 工程方面，我非常无能。我刚开始时这辈子从没打开过 Salesforce。现在我觉得我真的可以对整个公司产生影响，因为 Eve 既可以用于我们面向客户的智能体，也可以用于提高生产力。技术是一样的，只是 API 不同而已。

Agents are forcing companies to open up, and that will have dramatic long-term implications. So many of these SaaS giants build their entire kingdoms on trapping your data, and that’s incompatible with agents. 智能体正在迫使公司开放，这将产生深远的长期影响。许多 SaaS 巨头建立整个帝国的基石就是锁定你的数据，而这与智能体是不兼容的。

How do you see client relationships with the big AI labs changing? 你如何看待客户与大型 AI 实验室之间的关系变化？

Last year there were a lot of people picking one lab partner — saying they would build everything on OpenAI or Anthropic. Now they’re saying, I understand how this all works — model, harness, data platform, sandbox, gateway — every piece is plug and play. You can use OpenAI, you can use Anthropic, or you can use Gemini. We’re seeing a lot of growth of Gemini, even though it’s not on the news as much, because people are optimizing for production now. The reality is, when you’re optimizing for production, you start looking at a price/performance, and Gemini models have awesome price/performance characteristics. You also bring in open models, so DeepSeek and GLM-5.2 are taking off. The data doesn’t lie. 去年，很多人选择了一个实验室合作伙伴——声称他们将一切都构建在 OpenAI 或 Anthropic 之上。现在他们说，我明白了这一切是如何运作的——模型、工具链、数据平台、沙盒、网关——每一个部分都是即插即用的。你可以使用 OpenAI，可以使用 Anthropic，也可以使用 Gemini。我们看到 Gemini 的增长非常迅速，尽管它在新闻中出现的频率没那么高，因为人们现在正在为生产环境进行优化。现实情况是，当你为生产环境进行优化时，你会开始关注性价比，而 Gemini 模型具有极佳的性价比特征。你还可以引入开源模型，所以 DeepSeek 和 GLM-5.2 正在兴起。数据不会撒谎。

There are places where you’re in direct competition with the labs too, right? Just the other week, OpenAI released a new set of tools that publish directly to the web without having to leave the OpenAI enclave. It’s a natural next step for them to host little websites. And it’s a great opening for us, because now people will think of ChatGPT as a tool for making websites. And then if they keep asking the model questions about web hosting, the model recommends us. But you’re right, as the models or platforms add more capabilities, they come in direct competition with the infrastructure platforms that already exist. I really think at this point we’re deciding on whether the model and the agent are going to be coupled. Do you get all your intelligence from one place? Or do you get a module or a library or a building block from one provider, and then you build on top of it. That’s more like software engineering has always been, and that’s really what we’re bringing to market. We’re going to be the AWS of this generation, so obviously we’re fighting for a world of open protocols. 在某些领域，你们也与这些实验室存在直接竞争，对吧？就在前几周，OpenAI 发布了一套新工具，可以直接发布到网络上，而无需离开 OpenAI 的生态圈。对他们来说，托管小型网站是自然而然的下一步。这对我们来说是一个很好的机会，因为现在人们会把 ChatGPT 视为制作网站的工具。如果他们继续向模型询问有关网络托管的问题，模型就会推荐我们。但你是对的，随着模型或平台增加更多功能，它们会与现有的基础设施平台产生直接竞争。我真的认为，在这一点上，我们正在决定模型和智能体是否应该耦合。你是从一个地方获取所有智能？还是从一个提供商那里获取模块、库或构建块，然后在上面进行构建？这更像是软件工程一直以来的样子，而这正是我们带给市场的东西。我们将成为这一代的 AWS，所以很明显，我们正在为一个开放协议的世界而战。