更便宜的 AI token 推高需求，这正是黄仁勋最想看到的局面
Cheaper AI tokens are driving more demand, and that's Jensen Huang's best-case scenario
The Decoder 援引 Ornn、Silicon Data 和 Bloomberg 截至 2026 年 8 月的数据指出，AI token 价格持续下降，但 H100 GPU 租金保持稳定甚至上涨，a16z 称这是 AI 市场的教科书式杰文斯悖论。更便宜的 token 解锁了 AI 智能体、自动化和新应用，使使用量增速快于单价下降速度。文章同时提醒，人类使用与智能体自身消耗各占多少尚不清楚，算力需求可能被人为放大；整套逻辑依赖 AI 使用量增长足以抵消 token 降价，一旦需求走平，从芯片、内存到能源和云厂商的链条都会受冲击。
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Jevons' paradox is Jensen's best friend. Data from Ornn, Silicon Data, and Bloomberg (as of August 2026) shows what a16z calls a textbook Jevons paradox in the AI market. Token prices keep dropping, but H100 GPU rental prices hold steady or climb. Cheaper tokens unlock AI agents, automation, and new applications, so volume grows faster than per-unit costs fall. How much demand comes from humans versus the systems themselves isn't clear, since agentic AI burns through tokens at a staggering rate . Compute demand could be artificially inflated, and even modest human usage growth could trigger outsized hardware needs.
The whole system rests on one assumption: AI usage has to grow fast enough to offset falling token prices. As long as it does, hardware stays scarce and expensive. If demand flattens, the chain from chip makers and memory suppliers to energy providers and cloud companies takes a hit. Markets could spiral from there, and how sensitive they already are became clear when US stocks dropped on reports that OpenAI's annualized revenue might be lower than previously reported .