Cheaper AI tokens are driving more demand, and that's Jensen Huang's best-case scenario
Cheaper AI tokens are lifting usage while H100 rental prices hold, matching Jensen Huang's demand thesis.
Data cited by a16z from Ornn, Silicon Data, and Bloomberg through August 2026 shows token prices falling while H100 GPU rental prices hold or rise, which the firm calls a Jevons paradox. Cheaper tokens are said to spur agents, automation, and new applications so usage volume outpaces per-token price declines. The article argues hardware stays scarce only if usage growth offsets cheaper tokens, and notes US stocks fell on reports that OpenAI's annualized revenue may be lower than expected.
- Token prices fell while H100 rental prices stayed firm or rose through August 2026.
- a16z frames the pattern as Jevons paradox: cheaper tokens drive higher volume.
- Agentic workloads may inflate compute demand beyond direct human usage.
- Weaker OpenAI revenue reports were enough to knock US stocks lower.
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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.
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