Uber 在四个月内花光预算后,限制了员工的 AI 支出。
Uber caps employee AI spending after blowing through budget in four months

原始链接: https://techcrunch.com/2026/06/02/uber-caps-employee-ai-spending-after-blowing-through-budget-in-four-months/

随着人工智能成本飙升,Uber 已对每位员工使用 Claude Code 和 Cursor 等 AI 编程工具设定了每月 1500 美元的消费上限。此前,该公司曾大力鼓励员工使用相关技术,结果仅用了四个月就耗尽了全年的人工智能预算。 员工可以通过内部仪表板监控自己的使用情况,只有获得批准才能申请例外。这一转变反映出 Uber 管理层(包括首席运营官 Andrew Macdonald)日益增长的质疑——即人工智能的使用是否真的能直接转化为生产力的提升或面向消费者的产品改善。 Uber 的这一决定凸显了整个科技行业日益加剧的矛盾:尽管企业在人工智能领域投入巨资,但预期的投资回报大多仍处于理论阶段。随着最初的热情逐渐消退,各公司对“过高”的成本越来越警惕,并开始要求提供更明确的证据,以证明其人工智能支出确实能带来财务收益。

Uber 在短短四个月内耗尽了大部分预算,随后对员工的 AI 支出实施了上限,这一趋势反映出企业对失控的 AI 成本日益感到焦虑。Hacker News 上的讨论指出,随着各公司竞相集成 Anthropic Claude 等工具,“代币最大化”(即在处理每项琐碎任务时都使用高端模型)的习惯已变得难以为继。 评论者指出,尽管由于模型复杂性增加导致代币成本持续上涨,但企业仍难以衡量 AI 应用的实际投资回报率(ROI)。工程师们认为,目前的定价模式缺乏针对企业的可预测订阅上限,这助长了浪费行为。许多人注意到,公司早期吹嘘高 AI 采用率,如今却因预算触顶而不得不转向配给制,这显得颇具讽刺意味。 这场讨论表明科技行业正在产生更广泛的共识:AI 的重心正在转向软件维护成本和架构效率。最终,大家的共识是,除非企业能够有效地将模型能力与任务难度相匹配,否则 AI 支出仍将是一项不稳定且往往低效的企业开支。
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原文

AI is getting expensive, and some companies are cutting back on usage in an attempt to moderate costs. That cohort includes Uber, which recently instituted internal usage caps as a way to cut down on its exorbitant AI spend.

Bloomberg reports that the company has instituted a new rule that places a monthly $1,500 cap per employee and per agentic coding tool, including Anthropic’s Claude Code or Cursor. The usage is trackable via an internal dashboard that each employee has access to, although — in certain cases — the caps can be exceeded with permission, the company says.

The news is perhaps not too surprising, since, in April, the company’s CTO revealed that the ridesharing giant had blown through its entire annual AI budget in a matter of four months. That appears to have occurred after Uber encouraged staff to use AI “as much as possible” and even ranked their internal usage competitively on internal leader boards, The Information previously reported.

Uber’s COO, Andrew Macdonald, also recently cast doubt on AI’s productivity impact, noting during a podcast appearance that “it’s very hard to draw a line” between AI usage and new consumer features.

Uber’s cutback raises a broader issue that the tech industry is currently facing: As enterprises pour money into AI, where exactly is the return on investment? Indeed, AI ROI has so far remained a largely theoretical phenomenon that everybody hopes will eventually materialize — although some companies are obviously getting a little restless while they wait.

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