# Rohan Paul：推理成本下降将引发智能领域的杰文斯悖论

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-07-11 16:39
- AIHOT 分数：40
- AIHOT 链接：https://aihot.virxact.com/items/cmrg4hv9800tliha7edz6pszv
- 原文链接：https://x.com/rohanpaul_ai/status/2075862510100476294

## AI 摘要

Rohan Paul 引用斯坦福报告指出，GPT-3.5 级别推理成本在不到两年内已下降 280 倍。他认为智能领域将出现杰文斯悖论：更便宜的推理会创造远超当前的需求。当 Opus 4.8 级别的开源模型价格再降 4 倍时，多智能体系统将无处不在。他还引用预测称，6 个月内将出现质量接近 fable 5 但价格便宜 3-4 倍的模型，12 个月内 Opus 4.8 级别模型可在本地设备运行，发生概率超 50%。

## 正文

100% agree.

That famous Stanford report already found GPT-3.5-level inference costs has already fallen 280X in under 2 years.

And so we will see Jevons paradox for intelligence： cheaper inference will create far more demand.

Multi-agent systems will be everywhere when we have Opus 4.8 class open-source model at 4X cheaper prices.

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> Aravind Srinivas：Imagine a fable 5 quality model that's 3-4x less expensive in less than 6 months. And an Opus 4.8 grade model that can run on a local device in less than 12 mon...