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Nvidia (NVDA), Broadcom (AVGO), and Constellation Energy (CEG) are positioned closest to AI infrastructure trends, with Nvidia dominating AI GPUs while utilities like Constellation command premium valuations as data-center electricity demand is projected to double by 2030. Goldman Sachs estimates AI-related data centers could consume 8% of total U.S. electricity demand by decade’s end versus roughly 3% today.
BlackRock’s Larry Fink argues AI infrastructure shortages in compute, chips, memory, and electricity could spawn a trillion-dollar asset class of “futures on compute” contracts guaranteeing future access to AI processing capacity, similar to how oil and electricity evolved into massive futures markets.
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Artificial intelligence has already reshaped the stock market. Semiconductor stocks have rallied, utilities are suddenly growth plays again, and hyperscalers are spending hundreds of billions of dollars building data centers across the U.S.
At the same time, President Donald Trump has pushed for more domestic manufacturing, energy production, and AI infrastructure investment as part of a broader effort to keep the U.S. ahead in the global technology race. But what if AI’s next phase doesn’t just create new companies -- what if it creates an entirely new asset class?
That’s the argument Larry Fink recently made during a public discussion about AI infrastructure and capital markets. The BlackRock (NYSE:BLK) chief executive warned that AI is already creating shortages across four critical markets -- compute power, chips, memory, and electricity -- as companies race to build ever-larger AI systems.
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Those shortages are also driving a wave of U.S. infrastructure spending tied to semiconductor manufacturing, power generation, and domestic data-center construction. Whenever shortages emerge in essential economic resources, Wall Street usually finds a way to financialize them. Oil, natural gas, and electricity all evolved into massive futures markets.
Fink believes AI infrastructure could follow the same path, potentially creating a trillion-dollar asset class centered on “futures on compute” -- contracts tied to future access to AI computing capacity.
Let’s start with what “compute” actually means.
Every AI model -- whether it’s ChatGPT, Gemini, Claude, or enterprise AI software -- runs on computing power supplied by high-end chips and massive data centers. Those systems require:
GPUs from Nvidia (NASDAQ:NVDA) and Advanced Micro Devices (NASDAQ:AMD)
Server infrastructure from Dell Technologies (NYSE:DELL) and Super Micro Computer (NASDAQ:SMCI)
Cloud capacity from Amazon (NASDAQ:AMZN), Microsoft (NASDAQ:MSFT), and Alphabet (NASDAQ:GOOG)(NASDAQ:GOOGL)
That highlights how AI doesn’t work without enormous physical infrastructure behind it.
Analysts at Goldman Sachs estimate global AI-related infrastructure spending could approach $1 trillion over the next several years. Microsoft, Amazon, Alphabet, and Meta Platforms (NASDAQ:META) are expected to spend $710 billion or more in combined capital expenditures this year alone, much of it tied to AI infrastructure.
As demand for compute rises, pricing power rises with it. That’s where Fink’s idea comes in. Instead of simply renting cloud capacity, companies may someday buy contracts guaranteeing future access to AI compute resources. It could materialize in::
It would resemble oil futures contracts, where airlines lock in fuel prices months ahead of time. Only instead of barrels of crude, companies would hedge the future cost of AI processing power.
Beyond stocks and bonds: AI is forging a trillion-dollar asset class that rivals the energy markets of the past.
Financial markets thrive on scarcity and predictability. AI compute increasingly has both.
During Nvidia's most recent earnings cycle, CEO Jensen Huang noted demand for its Blackwell AI chips exceeded supply for multiple quarters. Microsoft executives have similarly acknowledged AI infrastructure shortages constrained some cloud growth.
Once scarcity appears, Wall Street usually builds financial products around it. Electricity futures already exist. So do carbon-credit markets, uranium funds, and bandwidth pricing contracts. Compute could become the next step because AI has transformed processing power into an economic input rather than just a technology expense. That could radically alter investing.
Here’s what the numbers tell us about the companies already positioned closest to this trend:
What that is showing is the market is no longer valuing AI solely as software. Infrastructure owners are commanding premium valuations because investors increasingly view compute capacity as strategic.
Granted, most investors still think of AI as a semiconductor story. In reality, it may become an energy story disguised as a technology revolution.
The U.S. Energy Information Administration projects electricity demand from data centers could more than double by 2030. Goldman Sachs estimates AI-related data centers may consume as much as 8% of total U.S. electricity demand by the end of the decade versus roughly 3% today. That helps explain why utility stocks suddenly entered AI conversations.
Companies such as Constellation Energy, Vistra (NYSE:VST), and NextEra Energy (NYSE:NEE) have all benefited from investor interest in supplying future AI power demand. That's because compute requires:
In short, AI’s next phase may reward infrastructure owners just as much as software developers.
Larry Fink’s “futures on compute” idea may sound abstract today, but the market already behaves as though compute has become a scarce commodity. Nvidia's supply constraints, hyperscaler spending races, and the sudden investor obsession with data-center electricity all point in the same direction.
When all is said and done, this isn’t merely about AI chatbots. It’s about whether computing power itself becomes a tradable financial asset. If that happens, sharp investors may need to think beyond software and focus on the companies controlling the infrastructure behind AI -- chips, power, cooling, networking, and data centers. Because in the next phase of the AI boom, owning the “digital oil fields” could prove just as valuable as building the applications running on top of them.
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