TL;DR
ICE, the owner of the New York Stock Exchange, is partnering with index provider Ornn to launch cash-settled futures contracts tied to GPU computing costs. The move comes days after rival CME Group announced its own computing futures, signaling that Wall Street is racing to turn AI computing power into a standardized, tradable commodity.
Intercontinental Exchange, the parent company of the New York Stock Exchange, prepares to launch futures contracts tied to the cost of computing power, marking the latest sign that Wall Street sees AI infrastructure as the next big commodity market.
ICE announced Monday that it will partner with Ornn, a financial infrastructure company whose indexed products track GPU computing costs in real time, to develop the new contracts. The futures will be denominated in US dollars, settled in cash and will be referenced to Ornn indices covering a variety of major GPU types. The plans remain subject to regulatory approval.
What ICE and Ornn are building
The partnership unites one of the world’s largest exchange operators with a startup that has quietly built the pipelines for computational price discovery. Ornn, formally Ornn AI Inc, publishes the Ornn Computing Price Index, which tracks live-traded spot prices for GPU computing on all types of hardware, including Nvidia’s H100, H200, and B200 chips. The index, now available on the Bloomberg Terminal, is based on real trading data from live GPU markets and has attracted more than 400 data center operators, investors and artificial intelligence companies to its platform.
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Trabue Bland, senior vice president of futures markets at ICE, framed the move as a response to a market that has outgrown its informal pricing mechanisms. The computing market, he said, “desperately needs a globally accepted pricing mechanism and risk management tool” as AI moves from research labs to become a central driver of the global economy.
The contracts will be settled in cash rather than physical delivery, a familiar structure in energy and financial futures. For AI companies planning large model training runs or cloud providers locking down their capacity, instruments would offer a way to protect against the kind of volatile computing costs that have accompanied the $650 billion increase in capital spending by big tech companies in 2026.
A two horse race with CME
ICE is not the only one to detect this opportunity. CME Group, the world’s largest derivatives exchange, announced its own computer futures contracts on May 12, partnering with Silicon Data to create products based on daily GPU benchmark rental rates. CME’s contracts will reference the Silicon Data H100 rental index, which tracks the cost of renting high-end GPUs used for AI training workloads.
The fact that two of the world’s most established futures exchanges have gone computing within days of each other indicates that institutional conviction in computing as a commodity has reached a tipping point. It reflects the early days of energy futures in the 1980s, when competing exchanges raced to establish benchmark contracts for crude oil and natural gas. The exchange that captures the most liquidity early on will likely set the benchmark price for the industry, just as ICE Brent and CME WTI did for oil.
The competitive dynamic also extends 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 has offered contracts that allow users to bet on Nvidia’s GPU computing prices. But ICE and CME bring something new entrants lack: deep institutional liquidity, regulatory credibility, and the necessary clearing infrastructure. Large-scale GPU-as-a-service providers and your clients will demand.
Why computing needs a futures market
Kush Bavaria, co-founder and CEO of Ornn, bluntly expressed the magnitude of the problem. Computing, he said, “has become a trillion-dollar market, but it still lacks the pricing and risk transfer infrastructure that all other major commodities depend on.”
That gap has real consequences. GPU rental prices have been wildly volatile, with Ornn’s own index showing that Nvidia Blackwell’s spot rental price increased 48% between mid-February and mid-April 2026, from $2.75 to $4.08 per GPU hour. For AI companies whose training sessions can cost tens of millions of dollars, that kind of price fluctuation can derail their budgets without warning. Cloud providers, data center operators and financing lenders billions of dollars in AI infrastructure development face similar exposure.
A functioning futures market would allow these participants to set forward prices, transfer risk to willing counterparties, and plan capital expenditures with greater certainty. It would also generate transparent price signals that the broader market currently lacks, giving investors, analysts and policymakers a clearer view of where computing costs are headed.
Broader implications for the AI economy
The rise of computing futures reflects a deeper structural change. As AI moves from an experimental technology to a core economic infrastructure, the inputs that drive it are becoming financialized in much the same way that energy, metals, and agricultural products were in previous decades. He Growing demand for advanced semiconductors. has already reshaped chip supply chains and driven record capital investment across the technology sector.
Futures contracts add a new layer to this ecosystem. They create standardized benchmarks that can support credit decisions, insurance products and investment strategies linked to AI infrastructure. A bank financing a new data center, for example, could use computer futures to evaluate the facility’s projected revenue against future GPU prices, in the same way that energy lenders use oil futures to evaluate drilling projects.
Of course, there are complications. Unlike oil stored in a tank, computing is what marketers call a fluid product, which is consumed in real time and cannot be stored. Ornn has addressed this problem by designing its futures with Asian-style settlement, meaning that contracts are settled based on the arithmetic average of the daily index values over the contract term rather than based on a single price on the expiration day. This structure aligns the financial instrument with the way computing is actually purchased and consumed.
Whether ICE or CME ultimately captures the majority of this market will depend on liquidity, the breadth of GPU types covered, and which index providers garner the most institutional trust. But the direction of travel is clear. Computing power, the resource that underpins everything from energy-intensive AI data centers to the development of autonomous vehicles, is moving from a bespoke procurement headache to a standardized and marketable financial asset. For an industry accustomed to negotiating GPU access through opaque bilateral deals, that’s a significant change.
