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。

据报道,Uber 在仅仅四个月内就耗尽了 2026 年的全年 AI 预算,并因此限制了员工的 AI 使用支出。这一进展在 Hacker News 上引发了关于当前 AI 编程工具真实效用和可持续性的激烈讨论。 许多评论者将成本失控归因于“代币最大化”(token-maxxing)——即员工为了证明生产力或在内部排行榜上名列前茅,而不顾实际产出价值,过度使用 AI 代理的文化。另一些人则指出“红皇后效应”(Red Queen effect),即 AI 代理会递归生成新任务,导致代币消耗臃肿且低效。 工程师们指出,虽然 AI 可以在结构良好的代码库上显著提升速度,但在处理复杂且维护不善的“面条代码”时却显得力不从心,往往导致昂贵且循环往复的错误尝试。此外,参与讨论者还强调了企业定价中缺乏成本透明度的问题,基于使用量的计费模式经常导致“账单冲击”。 这场讨论反映了企业情绪的普遍转变:各大公司正从肆意尝试 AI 的时代转向被迫配额限制的阶段,未来的绩效评估可能会将成本效益置于 AI 采用率之上。归根结底,许多参与者认为这是关于 AI 当前投资回报率(ROI)的一次必要且痛苦的“快速失败”式现实核查。
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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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