Hashrate futures launched in October, BlackRock (BLK.US) bets trillion in new assets

According to WooFunai, global derivatives giant CMEGroup (CMEGroup) and GPU market intelligence agency SiliconData officially announced plans to launch a computing power futures contract on October 5, 2026. The plan is currently awaiting regulatory review.

This move confirms BlackRock (BLK.US) CEO Larry Fink's judgment that computing power will become a new asset class. Against the backdrop of rapid advances in AI infrastructure investment, the risk of computational power price fluctuations not yet covered by traditional financial instruments is being incorporated into the financial hedging system through this standardized contract trial, marking a critical step in computing power from simple technical resources to tradable financial assets.

The explosive growth in AI capital expenditure has spawned an urgent need for financialized hedging tools. In 2026, capital expenditure in the AI sector will reach 765 billion US dollars, surpassing the oil and gas industry's 681 billion US dollars for the first time, becoming the largest single capital investment direction in the global economy. Morgan Stanley (MS.US) predicts that by 2031, the spread of AI in the global economy will create $40 trillion in opportunities, and computing power is the core resource supporting this vision.

However, the lack of a price-locking mechanism leaves industry participants with huge exposure: GPU rental prices soar when demand surges, or plummet when supply is relaxed and new chips are released, making it difficult for AI companies to accurately budget for the biggest cost items. Every time Nvidia (NVDA.US) releases a faster chip, the rental value of the previous generation chip shrinks, directly eroding the collateral value behind hardware loans.

Furthermore, the data center construction cycle lasts two to three years, yet developers lack effective means to lock in computing power costs or benefits. Every investment decision is tantamount to a multi-billion dollar gamble. Historically, attempts to establish futures markets around onions, uranium, DRAM memory chips, and bandwidth have all been blocked by failure to resolve basic market structure issues, and the computing power market has faced the same challenges.

According to data compiled by WooFunAI, drastic fluctuations in the current price of computing power have become the main financial risk variable hindering the actual application of AI infrastructure.

If the computing power futures market is to be scaled, it is necessary to overcome the concentration dilemma and the problem of fungibility. Although buyer demand is scattered across thousands of companies due to the popularity of inference workloads, and the seller base seems broad — new cloud vendors surpassed $25 billion in revenue in 2025, covering more than 60 providers — the underlying supply is still highly concentrated on Nvidia, which supplies most AI chips. This structural concentration increases the risk of market manipulation and liquidity depletion.

The more critical variable is interchangeability: Currently, computing power is quoted per GPU hour, but there is a significant difference in the actual performance of the same GPU model. After running the same workload on 3,500 GPUs from 11 cloud providers, SiliconData and academic collaborators found that even within the same chip model, the performance gap was as high as 34.5%, and the biggest gap in the entire study reached 38%.

This physical level of non-standardization makes simple futures contracts difficult to deliver directly. The first sustainable contracts may require defining multiple levels, locations, and delivery periods to accommodate differences in fuel and performance standards, as in the energy market.

The success of computing power futures will determine whether they can evolve into an asset class with a nominal transaction volume of trillions of dollars and further accelerate the development of the AI economy. If CME.US can design a contract structure that takes into account standardization and flexibility, computing power will shift from a technical resource that is difficult to price to a financial asset with deep liquidity, providing stable price expectations and risk hedging tools for the entire AI industry chain. After cryptocurrencies, this is another important attempt to infiltrate Web3 infrastructure into the traditional financial derivatives market. Its success or failure will profoundly influence the direction of global technology capital flows in the next ten years.