Uber 首席运营官表示,现在越来越难以证明在“代币最大化”上投入资金的合理性。
Uber’s COO says it’s getting harder to justify money spent on tokenmaxxing

原始链接: https://www.businessinsider.com/uber-coo-andrew-macdonald-ai-token-spending-harder-justify-2026-5

Uber 运营总监 Andrew Macdonald 对公司在人工智能方面的高额支出及其投资回报率提出了质疑,并指出高昂的 Token(代币)消耗与高价值消费者功能的交付之间缺乏明确的关联。 此前,Uber 首席技术官曾坦言公司已提前耗尽了 2026 年的人工智能预算,这引发了业界的广泛关注。Macdonald 表示,人工智能使用量的增加需要进行艰难的权衡,例如放缓员工人数的增长。Uber 首席执行官 Dara Khosrowshahi 近期也证实,公司正在通过限制招聘来抵消这些不断上升的人工智能成本。 Uber 并非唯一一家重新评估其战略的公司,其他企业也在抵制“为了用 AI 而用 AI”(tokenmaxxing)。例如,多邻国(Duolingo)近期已停止将人工智能使用情况纳入绩效考核,此前有员工担心这会导致强制且不必要的应用,而非优先考虑实际成果。 最后,Macdonald 警告称,尽管人工智能的使用对个人员工而言似乎无需成本,但公司却承担着巨大的财务负担;如果不能提供明确的生产力提升证据,这种负担将变得越来越难以辩解。

近期在 Hacker News 上的讨论凸显了对“代币最大化”(tokenmaxxing)——即企业将大语言模型(LLM)的代币消耗量作为衡量工程师生产力主要指标这一趋势——日益增长的抵触情绪。 Uber 首席运营官近日暗示了方向的转变,指出更高的代币使用量并不一定与更好的用户功能或有意义的产出挂钩。评论者们普遍嘲讽了这种做法,将其比作通过 AWS 账单或代码行数来评估工程师,并批评领导层忽视了“古德哈特定律”(Goodhart’s Law)——即当一个指标变成目标时,它就不再是一个好的衡量指标。 此次讨论的主要观点包括: * **激励错位:** 工程师们反映,为了达到配额或避免显得生产力不足,他们会在毫无意义的提示词上“浪费”代币。 * **“万灵丹”谬误:** 许多人认为,企业将人工智能视为“万能精灵”而非工具,导致了不可持续的资金消耗和糟糕的集成效果。 * **可持续性担忧:** 人们对人工智能的长期投资回报率普遍持怀疑态度,许多人认为,一旦风险投资补贴枯竭,企业将难以证明目前的支出水平是合理的。 总而言之,人们的共识是:高效的人工智能使用需要战略意图,而非仅仅追求原始用量。
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原文

A top Uber exec said AI is not giving the company bang for its buck.

In a Rapid Response interview released on Saturday, Uber's operations chief, Andrew Macdonald, said it was becoming harder to justify AI costs within the company.

He said that Uber CTO Praveen Neppalli Naga went viral after telling The Information in an April interview that Uber had already blown through its Claude Code budget for 2026.

The comment led to what he described as a "head-exploding moment," sparking discussions about AI token consumption within the company and the trade-offs it creates, such as on head count.

He said that, based on talks with Uber's senior engineering leaders, he realized higher token usage did not translate into a proportional increase in useful consumer features.

"That link is not there yet, right?" he said. "I think maybe implicitly there is more that is getting shipped, but it's very hard to draw a line between one of those stats and, 'Okay, now we're actually producing 25% more useful consumer features.'"

He said that the trade-off costs from AI are harder to justify because he can't draw a direct link. Earlier this month, CEO Dara Khosrowshahi said in an earnings call that Uber was slowing hiring to counter its investments in AI.

Macdonald added that AI can seem free if you're "just a user sitting there coming up with interesting use cases" without paying for it. But ultimately, the company foots the bill.

While Big Tech is going hard on tokenmaxxing —using AI as much as possible — and evaluating employees by their AI usage, some companies are starting to go the other way.

Duolingo, for instance, walked back its decision to include AI usage in performance reviews after employees asked whether they had to use AI for the sake of using it.

"It felt like, rather than being held accountable for the actual outcome, we were trying to just push something that in some cases did not fit," Duolingo CEO Luis von Ahn said in a podcast interview in April.

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