TL; DR
ICE, which owns the New York Stock Exchange, has partnered with index provider Ornn to launch cash-settled futures contracts linked to GPU computing costs. The move comes just days after rival CME Group announced its own computing futures, signaling Wall Street’s race to turn AI computing power into a standardized, tradable commodity.
Intercontinental Exchange, the parent company of the New York Stock Exchange, preparing to launch futures contracts is tied to the price of computing power, which suggests Wall Street sees AI infrastructure as the next big commodity market.
ICE announced on Monday that index products will develop new contracts with financial infrastructure firm Ornn, which tracks GPU computing costs in real-time. Futures will be USD denominated, cash settled and referenced to Ornn indices covering various major GPU types. The plans remain subject to regulatory approval.
What ICE and Ornn are building
The partnership brings together one of the world’s largest exchange operators with a startup quietly building the plumbing for computational price discovery. Ornn, officially Ornn AI Inc, publishes the Ornn Compute Price Index, which tracks live spot prices for compute GPUs across hardware types, including Nvidia’s H100, H200 and B200 chips. Now available on Bloomberg Terminal, the index is based on real transaction data from live GPU markets and has attracted more than 400 data center operators, investors and AI companies to its platform.
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Trabue Bland, ICE’s senior vice president of futures markets, described the move as a response to a market that has overwhelmed informal pricing mechanisms. As AI moves from research labs to a central driver of the global economy, the computing market is “in dire need of a globally accepted pricing mechanism and risk management tool,” he said.
Contracts will be settled in cash, rather than through physical delivery familiar from energy and financial futures. For AI companies planning large model trainings or cloud providers locking in capacity, tools will offer a way to protect against this type. Variable computing costs accompanying Big Tech’s $650 billion capital increase in 2026.
Two horse race with CME
ICE is not alone in finding this opportunity. CME Group, the world’s largest derivatives exchange, announced on May 12 that its compute futures contracts are partnering with Silicon Data to create products based on daily GPU benchmark rental prices. CME’s contracts will reference the Silicon Data H100 Rental Index, which tracks the rental price of high-end GPUs used for AI training workloads.
The fact that two of the world’s most established futures exchanges went live within days of each other shows that institutional confidence in commodity-like computing has reached its breaking point. This reflects the early days of energy futures in the 1980s, when competing exchanges raced to create benchmark contracts for crude oil and natural gas. The exchange that captures the most liquidity early on will likely set the reference price for the industry, just as ICE Brent and CME WTI do for oil.
Competitive dynamics also extend beyond the big two. Architect Financial Technologies partnered with Ornn in January to launch exchange-traded perpetual futures on GPU and RAM prices through its AX platform, and prediction market Kalshi offered contracts that let users bet on Nvidia GPU compute prices. But ICE and CME bring something newer entrants lack: deep institutional liquidity, regulatory credibility and clearing infrastructure large-scale GPU-a service providers and customers will demand.
Why calculation is needed in the futures market
Ornn co-founder and CEO Kush Bavaria made the scale of the problem clear. Computing, he said, “has become a trillion-dollar market, but still lacks the pricing and risk transfer infrastructure that every major commodity relies on.”
This gap has real consequences. GPU rental prices were highly volatile, with Ornn’s own index showing that Nvidia Blackwell spot rental prices rose 48% between mid-February and mid-April 2026, from $2.75 to $4.08 per GPU-hour. For AI companies whose training can cost tens of millions of dollars, this kind of price swing can go over budget with little warning. Financing of cloud providers, data center operators and lenders Billions of dollars in building AI infrastructure they face similar exposure.
A functioning futures market would allow these participants to lock in forward prices, transfer risk to willing counterparties, and plan capital expenditures with greater certainty. It would also create transparent price signals that the broader market currently lacks, giving investors, analysts and policymakers a clearer picture of where computing spending is going.
Broader implications for the AI economy
The emergence of computing futures reflects a deeper structural change. As AI moves from experimental technology to mainstream economic infrastructure, the inputs that power it are funded in much the same way as energy, metals and agricultural products were in previous decades. The increasing demand for advanced semiconductors has already reshaped chip supply chains and managed record capital investment in the technology sector.
Futures contracts add a new layer to this ecosystem. They create standardized criteria that can support credit decisions, insurance products and investment strategies linked to AI infrastructure. For example, a bank financing a new data center might use computing futures to value the facility’s projected revenue with forward GPU pricing, just as energy lenders use oil futures to value drilling projects.
Of course, there are complications. Unlike oil sitting in a tank, computing is what traders call a streaming commodity, consumed in real time and not storable. Ornn solved this by designing its futures with Asian-style settlement, meaning contracts are settled based on the arithmetic average of daily index values over the duration of the contract, rather than a single expiration day price. This structure aligns the financial instrument with the actual purchase and consumption of the account.
Whether ICE or CME ultimately captures the lion’s share of this market will depend on liquidity, the breadth of GPU types covered, and which index providers gain the most institutional credibility. But the direction of travel is clear. Computing power, the resource that supports everything power-hungry AI data centers to the development of autonomous vehicles, custom procurement is transformed from a headache to a standardized, tradable financial asset. For an industry accustomed to negotiating GPU access through opaque, bilateral deals, this is a significant shift.
