Can AI Compute Become The Next Big Futures Market?
Start-up Silicon Data has partnered with CME Group to launch what could be the world's first AI compute futures market, pending CFTC regulatory approval. The market aims to help companies hedge against volatile GPU rental costs, similar to how airlines use oil futures. Key challenges include standardizing GPU pricing benchmarks and building sufficient market liquidity.
Summary
The video explores a novel financial product being developed by start-up Silicon Data in partnership with CME Group: a futures market based on the price of AI compute. Drawing a parallel to oil futures used by airlines to hedge fuel costs, the concept applies the same hedging logic to GPU rental pricing, which can swing dramatically and unpredictably.
Carmen Li, CEO of Silicon Data, explains that most AI companies do not own their GPU infrastructure outright but instead rent access through cloud providers and neo-cloud platforms. This creates significant cost volatility. While large enterprises can negotiate multi-year fixed-rate contracts with major hyperscalers like Amazon, Microsoft, and Google, doing so locks them into specific chips, providers, and prices — a potential innovation killer in a fast-moving industry. Smaller players lack this option entirely.
Silicon Data has built GPU price indices that track real-time hourly rental costs across providers, with the Nvidia H100 chip serving as the primary benchmark. The proposed futures market would allow compute users (long positions) to lock in future prices, while compute providers (short positions) can protect against price drops. Speculators would also participate, contributing to price discovery and liquidity.
A significant challenge is standardization. Unlike a barrel of oil, a 'GPU hour' is not uniform — Silicon Data has identified over 50 different configurations of the H100 chip alone, each trading at different prices. The company normalizes over 150,000 daily traded prices to create a comparable index. The CFTC must ultimately approve the product, determining contract size, trading times, settlement mechanisms, and whether settlement will be physical or financial.
Key Insights
- Carmen Li claims that compute will eventually surpass all energy combined as the largest human resource, suggesting the AI compute market could dwarf even oil futures in scale.
- Li argues that large enterprises locking into multi-year fixed-rate GPU contracts with hyperscalers gain cost predictability but lose flexibility — a trade-off she describes as a potential 'innovation killer' in a fast-moving industry.
- Silicon Data has identified more than 50 different configurations of the H100 chip alone, each trading at a different price, which the company must normalize before any index calculation can occur — a complexity that distinguishes GPU hours from traditional commodity benchmarks like a barrel of oil.
- A market expert notes that liquidity is not automatic just because AI is widely used, pointing out that most individuals and smaller companies do not consume compute at a scale that would require a futures contract.
- Li describes a three-sided ecosystem required for the futures market to function: natural hedgers on both the user and provider sides, market makers, and speculators — all of whom are necessary for price discovery and to signal market confidence in liquidity.
Topics
Transcript
[0:00] What if AI had its own futures? Oil prices can swing dramatically, so airlines often use futures contracts to lock in fuel costs months ahead of time. Now, a similar idea may be coming to artificial intelligence. AI companies are spending billions of dollars on computing power to train and run models. But the future cost of that compute can be difficult to predict. Start-up Silicon Data has partnered with the CME Group to launch what could be the world's first futures contract based on the price of AI compute, [0:32] pending regulatory approval. Do you think it will one day become as big and prominent as oil... Oil futures? I think will be larger. I think everyone will…
Full transcript available for MurmurCast members
Sign Up to AccessMore from CNBC
Watch AI Dock A Boat
CNBC's Contessa Brewer demonstrates a 36-foot Cray boat equipped with advanced marine technology, including GPS, radar, and newly launched autonomous docking capabilities. The boat's base price is $550,000, with fully equipped models featuring autopilot and autonomous docking reaching $880,000.
What Will TV Look Like In 3 Years?
Industry leaders predict that TV will continue its shift toward streaming and live content over the next three years, with cable subscribers declining further and AI enabling personalized, multilingual viewing experiences. Sports remain a critical driver of viewership as one of the few irreplaceable live experiences, while new aggregator services and platforms like Tubi and Roku Channel are expected to gain significant market share.
To keep growing, Best Buy wants to go smaller
Best Buy is launching small and medium-format stores this summer as part of a strategic pivot following years of declining sales and stock performance post-COVID. New CEO Jason Bonfig will take over from Corie Barry in October and plans to accelerate growth through this revised store strategy while capitalizing on the AI boom.
How Audi hopes to win back consumers
Audi is attempting to regain market competitiveness by launching three new premium SUVs, including the Q9 and SQ9, featuring advanced technologies like smart headlights and OLED lighting. The brand has struggled since 2020, falling behind BMW and Mercedes-Benz in quality rankings and market share, with recent losses attributed to tariffs and the end of federal EV subsidies.
Inside SK Hynix: We Went To Korea To See The World's Biggest AI Memory Buildout
SK Hynix, South Korea's memory chip giant, is undertaking a historic $720 billion expansion to triple HBM (high-bandwidth memory) capacity by 2034, driven by unprecedented AI demand. The company is building massive new facilities in Korea while establishing its first U.S. packaging fab in Indiana, though it faces traditional boom-bust cycle risks and competition from Samsung, Micron, and Chinese manufacturers.