Entrepreneurs
1 Curated Dialogues
Carmen Li
Key Views & Dialogues
EP55 Token Prices Dropping on Model Shifts with Carmen Li, CEO of Silicon Data & Compute Exchange
Token prices are falling as customers shift some consumption from premium models toward cheaper open-source and open-weight alternatives, according to Silicon Data’s tracking of 300 models. The index reflects observed spending choices rather than model quality, while workflow routing remains only a possible explanation because Li cannot see customers’ use cases. Whether the shift signals durable demand for cheaper models or changing workloads remains unclear, and Li does not establish why individual customers switch.
View summary (7)
- Li attributes falling token prices to customers shifting their model mix toward cheaper alternatives. Silicon Data tracks consumption across 300 open-source/open-weight models plus closed models, observing “people shifting from premium models to cheaper models after the token-mixing era.” Doug suggests workflow routing could explain the shift, but Li cannot see customer use cases and does not confirm that mechanism.
- B200 on-demand rates were around $5 per GPU-hour at the global neocloud rate, while its implied forward curve still sloped upward. Last year’s downward-sloping curves had shifted into contango; more recently, A100 and H100 curves flattened. These curves come from reserved contracts, ahead of futures trading, and Li’s warning is explicit: “There are cycles.”
- Older GPUs can retain earning power beyond a simple depreciation schedule. Li sees three-year A100 contracts despite the architecture already being roughly four to six years old, and cites secondary prices of $4,000–$9,000 per GPU. Doug challenges whether that reflects durable economics rather than physical longevity; Li points to cooling constraints, replacement delays, and workflow suitability, while cautioning, “I’m not saying every A100 lasts 9 years.”
- Compute hedging addresses the mismatch between long financing commitments and shorter customer contracts. GPU financing can run five to seven years against reservations lasting three to five, leaving later rental income exposed. CME futures would cover the first 36 months, with longer hedges requiring OTC transactions; the objective is to let operators lock their “cost of production” and make financing easier to underwrite.
- Silicon Data’s commercial opportunity depends on trusted benchmarks becoming embedded in compute finance. Li says CME would launch H100 and B200 futures “in the next few weeks,” with four ETFs already filed, including a 1x inverse product. Her potential $30 trillion mature derivatives market is annual notional activity, not a forecast for data-licensing revenue.
- More chip designers do not automatically eliminate compute supply constraints. Doug stresses that fabrication capacity is expanding; Li’s response is that fabrication, packaging, infrastructure, and approvals must expand together. Otherwise, output remains capped by the slowest-growing part, while specialized inference chips may supplement Nvidia rather than replace its entire role.
- Li leaves the ultimate AI value-capture question open. Doug points to internet platforms that owned customers and distribution, while Nvidia currently captures substantial economic rent. Li calls AI “a gigantic baby”: the infrastructure, models, harnesses, and applications are still developing, and she does not claim to know which future layer will produce the biggest winners.