Cheaper AI tokens are driving more demand, and that's Jensen Huang's best-case scenario
Quick Answer
Jensen Huang's scenario thrives on Jevons' paradox as cheaper AI tokens drive demand, despite stable or rising H100 GPU rental prices.
Quick Take
This growth hinges on AI usage outpacing token price declines, with potential risks if demand flattens, impacting the entire supply chain from chip makers to cloud providers.
Key Points
- Token prices are decreasing while H100 GPU rental prices remain stable or increase.
- Cheaper tokens enable more AI agents and automation, boosting overall demand.
- Demand from AI systems could artificially inflate compute needs, complicating usage metrics.
- A slowdown in AI usage growth could negatively impact hardware supply chains.
- Recent stock drops indicate market sensitivity to changes in AI revenue forecasts.
📖 Reader Mode
~1 min read
Oct 11, 2026
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.
— Originally published at the-decoder.com
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