AI总结
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核心结论：随着AI代理（agents）普及，企业正从盲目追求“tokenmaxxing”转向精细化成本控制，以防止AI支出失控。
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结构逻辑：
- 现象：AI代理运行消耗巨大算力，导致企业运营成本激增。
- 背景：过去鼓励消耗token（“tokenmaxxing”）作为技术贡献指标，现已不可持续。
- 数据：Ramp数据显示企业AI支出一年内增长13倍，Uber等企业甚至提前耗尽预算。
- 对策：停止鼓励行为、差异化使用模型（选择低成本模型）、设置支出上限、引入效能导向的定价模式。
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关键信息：
- 概念：Tokenmaxxing（追求高token使用量）、AI代理（AI agents）、推理模型。
- 关键数据：Top 1% AI支出企业月均人均费用约$7,450；部分企业月度token账单达$5亿。
- 行业与公司：科技行业领跑；Meta、Amazon、Uber、OpenAI、Anthropic、Moonshot AI（Kimi）、Thoughtworks。
- 热点趋势：企业从扩张转向预算管理；模型厂商面临利润诉求与市场份额的平衡挑战。
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问题导向：
- 核心问题：AI成本持续攀升，企业如何平衡创新与财务可持续性？
- 核心回答：通过模型分层使用（按任务价值分配）、实施硬性预算限额、优化供应链（选择更廉价模型）以及倒逼服务商改革定价模式。
正文
For the heaviest users, tokenmaxxing is losing its appeal
“It’s going to be an absolute nightmare,” says an executive at a big American tech company. He is talking about an emerging problem for businesses that use artificial intelligence. AI agents—bots that can read, interpret and act—use masses of processing power and have started to run up huge bills. As they proliferate, the problem will grow. Big companies, the executive points out, typically use hundreds of software programs. If each of those offers agents (as they probably will), AI costs could spiral out of control.
Budget management is a new worry for AI adopters. Not long ago employees were encouraged to binge on the technology. Burning through vast numbers of tokens—the chunks of text that models process, which are often used as a unit of pricing—became a badge of honour; techies dubbed it “tokenmaxxing”. Companies showed off staff’s AI use on internal leaderboards. Meta’s display awarded top users titles like “Token Legend”.
Such incentives partly explain the boom in AI spending. So does a change in the way enterprises use the technology. Token-heavy applications, such as reasoning models and agents, are growing more popular. In some cases agents build their own agents, sending costs higher still. Ramp, a corporate-credit-card provider, analyses its clients’ transaction data to shed light on their AI use. It reckons their spending has risen 13-fold in the past year. In April Uber said that it had spent its annual AI budget in four months. Other firms have similar problems. One reportedly spent $500m on AI tokens in a month. Sam Altman, the boss of Open AI, has called mounting customer costs “a huge issue”.
For now, the problem is concentrated. The top spenders tend to be tech firms, because they are early adopters and because AI is particularly good at writing software. Ramp reckons that the 1% of clients that spend the most on AI per employee are racking up average monthly bills of about $7,450 per person; the median client pays $11. And although the big spenders’ AI bills are low compared with the cost of hiring a developer in San Francisco, they are high compared with employing one in Delhi.
Companies are responding in various ways. Although AI laggards are still racing to adopt the technology, for the heaviest users tokenmaxxing is out. Some, including Meta and Amazon, have scrapped their leaderboards. Many are thinking more carefully about model choice. Plenty of tasks do not require pricey, cutting-edge models. Aran Khanna of Archera.ai, which helps companies reduce their cloud costs, points out that in some cases Sonnet, a lagging-edge model from Anthropic, can cost a twentieth of what Opus, a leading-edge one, does. And Kimi, an open-source model from Moonshot AI, a Chinese startup, can cost a twentieth of that.
Another approach is spending caps. Uber has limited its employees to $1,500-worth of tokens each month per coding tool. How firms choose to allocate tokens depends on where they can generate the most value from AI, says Rachel Laycock of Thoughtworks, a consultancy. That often means companies apportion the most tokens to their core business function, such as engineers in a tech firm, she adds.
Some software vendors are experimenting with new pricing plans to ease clients’ concern over costs. Intercom, one such supplier, offers a service whereby the customer pays only for queries that are resolved by its IT -support agent. The big cloud providers have launched cost-management services, too, including budgeting tools and ways to triage queries to the most appropriate model.
For the model-makers, the balancing act is trickier. They want their biggest customers to use as many tokens as possible without balking at the cost. Today the cost of providing AI services is subsidised by the labs. Indeed, Open AI’ s plan for winning customers from Anthropic reportedly involves drastic price cuts. But eventually the model-makers will need to turn a profit, which will mean raising prices. Pressure to do so will only intensify later this year if, as expected, Anthropic and Open AI go public. Their customers, then, can look forward to more jaw-dropping AI bills. ■
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