Microsoft reports AI is more expensive than paying human employees
Microsoft reports AI is more expensive than paying human employees
微软报告称：使用人工智能的成本高于雇佣人类员工
Firms today are pushing employees to use as much AI as possible to squeeze out the technology’s productivity gains. But that pressure is leading to cracks, and those cracks may be irreparable. 如今，各大公司都在推动员工尽可能多地使用人工智能，以榨取该技术带来的生产力红利。然而，这种压力正导致裂痕出现，且这些裂痕可能是无法修复的。
Microsoft has reportedly begun canceling most of its direct Claude Code licenses, according to The Verge, instead moving engineers toward using GitHub Copilot CLI. That comes just six months after the firm first opened up access to Claude Code, encouraging thousands of its developers, project managers, designers, and other employees to experiment with coding. The tech became popular fast. Perhaps too popular. The scale at which employees use it is now prompting the firm to reverse course on a tool its own engineers had come to rely on. 据《The Verge》报道，微软据称已开始取消大部分直接的 Claude Code 许可证，转而引导工程师使用 GitHub Copilot CLI。就在六个月前，微软才刚刚开放 Claude Code 的访问权限，鼓励数千名开发人员、项目经理、设计师和其他员工尝试使用该工具进行编程。这项技术迅速普及，甚至可能普及得过快了。员工们的使用规模现在促使该公司对其工程师已经产生依赖的工具采取了“反向操作”。
Canceling Claude Code licenses won’t affect Microsoft’s Foundry deal, which includes investing up to $5 billion in Anthropic and giving Foundry customers access to Claude models, as well as Anthropic’s $30 billion commitment to purchase Azure compute capacity, according to The Verge. 据《The Verge》报道，取消 Claude Code 许可证不会影响微软的 Foundry 协议。该协议包括向 Anthropic 投资高达 50 亿美元，并允许 Foundry 客户访问 Claude 模型，同时还包括 Anthropic 承诺购买价值 300 亿美元的 Azure 计算能力。
Microsoft isn’t the only company scaling back its internal AI use. Uber’s CTO Praveen Neppalli Naga told The Information in April that the firm had already burnt through its entire 2026 AI coding tools budget in just four months. That comes after the company had actively incentivized adoption through internal leaderboards ranking teams by AI tool usage. 微软并非唯一一家缩减内部人工智能使用的公司。Uber 首席技术官 Praveen Neppalli Naga 在四月份告诉《The Information》，该公司仅用了四个月就耗尽了 2026 年全年的 AI 编程工具预算。此前，该公司曾通过内部排行榜对各团队的 AI 工具使用情况进行排名，以此积极激励员工采用这些工具。
The reports may throw cold water on the bets tech’s biggest firms have placed on the technology. While some cling to the promise of an AI “renaissance” or “revolution,” the cost of adoption is proving a stubborn bottleneck. These developments also suggest that the economics of replacing or augmenting human labor with AI may be more complicated than some early forecasts originally implied. 这些报道可能会给科技巨头们在人工智能技术上的押注泼上一盆冷水。尽管一些人仍坚信人工智能将带来“复兴”或“革命”，但采用成本正成为一个顽固的瓶颈。这些进展也表明，用人工智能替代或增强人类劳动的经济学逻辑，可能比一些早期预测所暗示的要复杂得多。
That echoes what Bryan Catanzaro, vice president of applied deep learning at Nvidia, recently said in an interview with Axios. “For my team, the cost of compute is far beyond the costs of the employees,” he said. Anthropic didn’t immediately respond to Fortune’s request for comment. Microsoft didn’t provide a comment. 这与英伟达应用深度学习副总裁 Bryan Catanzaro 最近在接受 Axios 采访时的言论不谋而合。他说：“对于我的团队来说，计算成本远远超过了员工成本。”Anthropic 没有立即回应《财富》杂志的置评请求。微软未予置评。
An emerging AI paradox: cheaper tokens, bigger bills
一个新兴的人工智能悖论：Token 更便宜，账单却更贵
Uber and Microsoft aren’t the only firms pushing employees to use as much AI as possible. Like at Uber, a Meta employee crafted a leaderboard, fittingly named “Claudeonomics,” after Anthropic’s AI model, to track which workers are using the most AI. Amazon is pushing its employees to “toxenmaxx,” or use as many AI tokens as possible (the basic building blocks of AI compute). But with a token-based pricing system, the work gets more expensive with more use and better efficiency. Uber 和微软并不是唯一推动员工尽可能多地使用人工智能的公司。和 Uber 一样，Meta 的一名员工制作了一个排行榜，并以 Anthropic 的 AI 模型命名为“Claudeonomics”，用来追踪哪些员工使用 AI 最多。亚马逊则在推动员工“tokenmaxx”（即尽可能多地使用 AI token，这是 AI 计算的基本构建块）。然而，在基于 token 的定价系统中，使用量越大、效率越高，工作成本反而变得越昂贵。
Goldman Sachs recently forecasted that agentic AI could drive a 24-fold increase in token consumption by 2030 as consumers and enterprises adopt AI agents, reaching a staggering 120 quadrillion tokens per month. As businesses turn to AI agents to boost productivity, aggregate costs could rise sharply even if the price of each token falls. 高盛最近预测，随着消费者和企业采用 AI 智能体（Agentic AI），到 2030 年，token 的消耗量可能会增加 24 倍，达到每月惊人的 120 千万亿个。随着企业转向使用 AI 智能体来提高生产力，即使每个 token 的价格下降，总成本也可能急剧上升。
But as consumption increases, the cost of individual AI tokens is expected to fall sharply. A recent report from research firm Gartner found that by 2030, inference on a one-trillion-parameter LLM—in simple terms, a highly sophisticated AI model—will cost AI firms nearly 90% less than it did in 2025. 但随着消耗量的增加，单个 AI token 的成本预计将大幅下降。研究公司 Gartner 最近的一份报告发现，到 2030 年，在万亿参数大语言模型（简单来说，就是一种高度复杂的人工智能模型）上进行推理的成本，将比 2025 年降低近 90%。
Even so, Gartner predicted that cheaper tokens won’t translate to cheaper enterprise AI because agentic models require far more tokens per task than standard models, increased consumption can outpace falling unit costs, and AI providers won’t fully pass through lower costs to consumers. In turn, inference costs are likely to push higher. 即便如此，Gartner 预测，更便宜的 token 并不意味着更便宜的企业级人工智能，因为智能体模型在每个任务中所需的 token 远多于标准模型，消耗量的增加可能会超过单位成本的下降，而且人工智能提供商不会将降低的成本完全转嫁给消费者。反过来，推理成本很可能会进一步推高。
“Chief Product Officers (CPOs) should not confuse the deflation of commodity tokens with the democratization of frontier reasoning,” Gartner senior director analyst Will Sommer warned in a statement. Gartner 高级总监分析师 Will Sommer 在一份声明中警告称：“首席产品官（CPO）不应将大宗 token 的通缩与前沿推理能力的普及混为一谈。”
That reality may complicate the grand plans some firms have for deploying AI agents. Nvidia CEO Jensen Huang recently said he thinks 100 AI agents will one day work alongside every employee at his company. Huang is part of a broader wave of CEOs touting an agentic future in which digital workers operate across the enterprise. But if token consumption rises faster than unit costs fall, that future could come with a much heavier bill than executives expect. 这一现实可能会使一些公司部署 AI 智能体的宏伟计划变得复杂。英伟达首席执行官黄仁勋最近表示，他认为未来有一天，每个员工身边都会有 100 个 AI 智能体协同工作。黄仁勋是更广泛的首席执行官浪潮中的一员，他们都在鼓吹一个数字员工在整个企业中运作的“智能体未来”。但如果 token 的消耗速度快于单位成本的下降速度，那么这个未来所带来的账单可能会比高管们预期的要沉重得多。