AI Agents Are Thirsty for Power
AI Agents Are Thirsty for Power
AI 智能体正渴求电力
“What on earth are they building all of these data centers for?” an exasperated friend asked me recently. “他们到底为什么要建这么多数据中心？”一位朋友最近愤愤不平地问我。
They’re not the only one asking: We got several similar questions on our recent data center livestream. It’s a really reasonable thing to wonder about. After all, if AI is already making all these breakthroughs, why are tech companies taking on billions of dollars of debt and constructing some of the biggest power plants in the world to build even more data centers? 不止他一个人有此疑问：在我们最近关于数据中心的直播中，也收到了几个类似的问题。这确实是一个非常合理的疑问。毕竟，如果人工智能已经取得了这么多突破，为什么科技公司还要背负数十亿美元的债务，并建造世界上一些最大的发电厂来建设更多的数据中心呢？
The answer isn’t to help the average user search for recipes or look up places to visit on a vacation; simple chatbot queries are an increasingly outdated way of thinking about how AI works. Now, AI is all about agents—there’s no official definition, but roughly speaking, agents are large language model-based systems designed to make autonomous decisions to execute a task—and the shift towards them is part of what’s driving Silicon Valley’s power buildout. 答案并不是为了帮助普通用户搜索食谱或查找度假地点；简单的聊天机器人查询已经是一种越来越过时的 AI 工作方式。现在，AI 的核心是“智能体”（agents）——虽然没有官方定义，但粗略地说，智能体是基于大语言模型的系统，旨在自主做出决策以执行任务——而向智能体的转变正是推动硅谷电力基础设施建设的原因之一。
“Rather than asking an AI chatbot a simple question and answer, these agents can give themselves hundreds of small prompts based on a user’s original question,” says my colleague Maxwell Zeff, who writes the weekly Model Behavior newsletter. “For example, if someone asked an AI agent to build them a website, it might run for hours to build out features, re-prompting itself dozens of times in the process to build different web pages, menus, and datasets that power the thing.” “这些智能体不再只是回答 AI 聊天机器人的简单问答，而是可以根据用户的原始问题，给自己发送数百个小提示，”我的同事、每周撰写《Model Behavior》通讯的 Maxwell Zeff 说。“例如，如果有人要求 AI 智能体为他们建立一个网站，它可能会运行数小时来构建功能，在此过程中自我提示数十次，以构建支撑该网站的不同网页、菜单和数据集。”
Agents are now at the heart of the frontier labs’ work on AI. They’re doing some astounding—and terrifying—things. Recently, OpenAI announced that a swarm of more than 10,000 agents sending 2.7 million messages had solved a longstanding math problem. (Mathematicians pushed back on the company’s claims.) While this is an outlier—AI labs are highly committed to solving supposedly unsolvable problems, and willing to throw unusual amounts of resources into doing so—all those messages burned through a lot of processing power. That equates to a lot of energy: probably tens of millions of dollars’ worth, Max tells me, though how much exactly is tough to say. 智能体现在处于前沿 AI 实验室工作的核心。它们正在做一些令人震惊——甚至令人恐惧——的事情。最近，OpenAI 宣布，一个由超过 10,000 个智能体组成的集群发送了 270 万条消息，解决了一个长期存在的数学难题。（数学家们对该公司的说法提出了反驳。）虽然这是一个特例——AI 实验室非常致力于解决所谓的“不可解”问题，并愿意为此投入异常庞大的资源——但所有这些消息都消耗了大量的处理能力。这相当于巨大的能源消耗：Max 告诉我，这可能价值数千万美元，尽管具体金额很难说清。
Private AI companies have historically been choosy about what to disclose when it comes to environmental metrics around their products. Many CEOs often point to single queries made by individuals as a measure of resource use. In a recent podcast interview, OpenAI CEO Sam Altman claimed that the water use needed to harvest a single almond amounted to 38,000 ChatGPT queries. (The calculation has been disputed.) 私营 AI 公司在披露其产品环境指标方面历来有所保留。许多首席执行官经常以个人进行的单次查询作为衡量资源使用情况的标准。在最近的一次播客采访中，OpenAI 首席执行官 Sam Altman 声称，收获一颗杏仁所需的用水量相当于 38,000 次 ChatGPT 查询。（这一计算结果已受到质疑。）
“The people that are scarfing down 12 almonds at a time don’t feel like they’re doing something horrible from a water perspective for the most part,” he said. “那些一次吃掉 12 颗杏仁的人，在大多数情况下并不觉得自己在水资源方面做了什么可怕的事情，”他说。
Introducing AI agents, which are much more energy-intensive than simple queries, into the picture makes these calculations a lot more complex. There’s a major dearth of information around the energy use of agents, whose tasks can range from simple jobs to a full day of autonomous coding involving a team of parallel “helper” agents. There’s a massive gulf in power use between these applications—and a potentially limitless expansion as tasks get more complex. 将比简单查询耗能得多的 AI 智能体引入这一图景，使得这些计算变得复杂得多。目前关于智能体能源使用情况的信息严重匮乏，它们的任务范围从简单的工作到涉及一组并行“助手”智能体的全天自主编码不等。这些应用之间的电力使用存在巨大鸿沟，而且随着任务变得越来越复杂，这种消耗可能会无限扩张。
“In other technological growth areas, we’re constrained by how many people are driving a car or streaming Netflix,” says Boris Gamazaychikov, the co-founder and CEO of Sustainable AI, a research and advisory group. “Now, this stuff is kind of decoupled from users—and if you listen to AI leaders, I think that’s what they want. They’re talking about unicorns that have one employee.” “在其他技术增长领域，我们受到驾驶汽车或观看 Netflix 的人数限制，”研究与咨询机构 Sustainable AI 的联合创始人兼首席执行官 Boris Gamazaychikov 说。“现在，这些东西在某种程度上与用户脱钩了——如果你听听 AI 领袖们的言论，我认为这正是他们想要的。他们谈论的是只有一名员工的独角兽企业。”
Well, one human employee. In that imagined world, there could be hundreds or even thousands of AI agents working in the background. I don’t want to debate the odds of that happening, but suffice to say that’s the future AI companies are working toward—and it helps to explain the rush to build data centers. 好吧，是一名人类员工。在那个想象的世界里，后台可能有成百上千甚至数千个 AI 智能体在工作。我不想争论这种情况发生的可能性，但可以说，这就是 AI 公司正在努力实现的未来——这也解释了为什么他们急于建设数据中心。
With little reliable data coming from the companies about their energy use, some AI enthusiasts are trying to do the math themselves. Last month, climate scientist Zeke Hausfather authored a blog post calculating how much energy his own AI use—which leans heavily on agents—consumes. He used a variety of different sources to work out that his average daily Claude session may consume more than the energy needed to power two refrigerators. (Gamazaychikov, whose group will release research later this month with more precise calculations around the environmental footprint of agents running on closed models, noted that Hausfather made a good effort, but that his math was based on somewhat outdated findings. That’s unsurprising, given how little academic work there has been done on this topic and how opaque tech companies are when it comes to disclosing emissions metrics.) 由于公司提供的能源使用数据很少，一些 AI 爱好者正试图自己进行计算。上个月，气候科学家 Zeke Hausfather 写了一篇博客文章，计算了他自己对 AI 的使用（主要依赖智能体）消耗了多少能源。他利用各种不同的来源计算出，他平均每天的 Claude 会话消耗的能量可能超过了维持两台冰箱运行所需的能量。（Gamazaychikov 的团队将于本月晚些时候发布关于运行在闭源模型上的智能体环境足迹的更精确计算研究，他指出 Hausfather 做出了很好的尝试，但他的计算基于一些过时的发现。考虑到目前关于该主题的学术研究很少，且科技公司在披露排放指标方面非常不透明，这并不令人惊讶。）
Hausfather concludes that in the grand scheme of his personal life, his AI use being on par with keeping a few spare fridges running isn’t a world-ending number. But this AI use “also represents a net new source of emissions, at a time when global temperatures are skyrocketing and our emissions reduction goals are increasingly off track,” he writes. And it’s a lot bigger than the fraction-of-an-almond-sized numbers Altman is throwing around as a metric. Hausfather 得出的结论是，在他个人生活的宏观层面，他使用 AI 的能耗相当于维持几台备用冰箱运行，这并不是一个毁灭世界的数字。但他写道，这种 AI 使用“也代表了一个新的净排放源，而此时全球气温正在飙升，我们的减排目标也越来越偏离轨道。”而且，这比 Altman 作为衡量标准所抛出的“几分之一颗杏仁”的数字要大得多。
Hausfather says he uses AI and agentic tools “more than most people,” but that could change soon. Last week, Meta rolled out a personal AI agent that, the company said in a press release, is “built to work for billions of people worldwide.” Dubbed Muse, Meta trumpeted that it will maintain a “dedicated computer in the cloud” for each user that would work even when the user is offline; the company plans to integrate Muse with its AI glasses later this year. It is very possible that in the near future, Meta users toying around with their glasses… Hausfather 说他使用 AI 和智能体工具的频率“比大多数人都要高”，但这很快就会改变。上周，Meta 推出了一个个人 AI 智能体，该公司在新闻稿中称其“旨在为全球数十亿人服务”。Meta 将其命名为 Muse，并大肆宣传它将为每个用户在云端维护一台“专用计算机”，即使在用户离线时也能工作；该公司计划在今年晚些时候将 Muse 与其 AI 眼镜集成。很有可能在不久的将来，摆弄眼镜的 Meta 用户们……