# Sam Altman 承认 AI 预算已成"巨大问题"：外部客户月耗 token 达 603B，智能体加剧隐藏成本

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-06-05 04:22
- AIHOT 分数：70
- AIHOT 链接：https://aihot.virxact.com/items/cmpzy24rk014gsltr3vv093ae
- 原文链接：https://x.com/rohanpaul_ai/status/2062631044382388615

## AI 摘要

Sam Altman 表示 AI 预算正成“巨大问题”。OpenAI 顶级内部用户月耗约 100B 模型 token，而外部客户高达 603B。AI 智能体使成本恶化：agent 不止回答一次，而是规划、调用工具、读取文件、重试失败步骤、检查自身工作，产生大量隐藏 token 消耗。人类问一次，agent 可能一秒内问数百次。公司不再问 AI 是否令人印象深刻，而是问边际 token 是否产生边际价值。杰文斯悖论解释部分陷阱：每 token 成本下降，人们使用更多 token，总账单仍可能上升。

## 正文

Sam Altman admits AI budgets are turning into a "huge issue，" with customers burning more tokens than even OpenAI's top in-house users.

Altman said OpenAI's top internal user spends about 100B tokens/month， while one outside customer hit 603B tokens/month.

The cost problem gets worse with AI agents because they do not just answer once， they plan， call tools， read files， retry failed steps， check their own work， and create long chains of hidden token spending. Every plan， retry， code review， context window， tool call， and verification step becomes metered cognition.

A human asks once； an agent may ask hundreds of times in a second.

Companies are no longer asking whether AI is impressive， but whether the marginal token is producing marginal value.

Jevons paradox explains part of the trap： when AI gets cheaper per token， people use far more tokens， so the total bill can still rise.