Jun 5, 2026
Sam Altman seems to have a clearer picture of where artificial intelligence is headed. At the BlackRock U.S. Infrastructure Summit in Washington, D.C., on March 11, the OpenAI CEO told the audience that his company — and every other AI model provider — is building toward a very specific future.
“We see a future where intelligence is a utility like electricity or water and people buy it from us on a meter and use it for whatever they want to use it for,” he said.
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His analogy is that most people don’t generate their own electricity. They connect to a grid and pay for what they use. AI is heading the same way. The problem is that building that grid — the data centers, power lines, cooling systems and chips needed to make AI a utility — is proving harder, pricier and slower than almost anyone expected.
The scale of what’s being spent
The money going into AI infrastructure is massive by any historical measure.
Alphabet, Google’s parent company, just raised $84.75 billion in equity this week — up from an initial $80 billion target announced two days earlier. The raise includes a $10 billion private investment from Berkshire Hathaway and is Alphabet’s first new share issuance since 2005. The company says it will funnel the proceeds into scaling AI infrastructure and global compute, and now expects capital spending for 2026 to land between $180 to $190 billion.
Alphabet is not alone. Tech giants’ combined capital spending is set to exceed $700 billion this year, up from earlier expectations of $600 billion, with Amazon alone committing $200 billion.
At CES in January, AMD CEO Lisa Su told the audience the world will need more than “10 yottaflops” of compute over the next five years. That’s a scale 10,000 times larger than global AI capacity in 2022, all to keep pace with demand.
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Why the build-out is falling behind
Bloomberg and multiple industry trackers show that of the roughly 12 gigawatts of U.S. data center capacity targeted for 2026, only about one-third is under active construction. The rest has been announced but has no physical progress yet, despite typical build times of under 18 months. The Wall Street Journal also reported that America’s data center build-out is falling way behind schedule.
The major hold-up is the electrical infrastructure — specifically transformers, switchgear and grid-tie batteries. Before 2020, lead times for high-voltage transformers took 7 to 14 months to arrive. Today, it takes up to four years for high capacity units, according to Reuters. Plenty of sites can be built with land and capital, but sites that can actually get power within a reasonable timeline are now, as one infrastructure analysis put it, “the scarcest resource in the American economy.”
The consequences are direct. At the BlackRock summit, Altman said that if OpenAI can’t build enough compute to meet demand, it either “can’t sell it or the price gets really high.” That pushes AI access toward the wealthy or forces governments to decide how to distribute limited compute. OpenAI has committed roughly $600 billion to total commute spend through 2030 to try to stay ahead of demand.
What the infrastructure actually requires, and what it costs the planet
A report published June 4, 2026 by the United Nations University Institute for Water, Environment and Health puts real numbers on what scaling AI to utility level actually demands from the physical world.
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In 2025, data centers consumed 448 terawatt-hours of electricity globally—more than Saudi Arabia uses in an entire year. AI accounted for roughly one-fifth of that total. Data centers also used 4.5 trillion liters of water and generated 189 million tons of carbon dioxide emissions.
By 2030, the report projects those figures will roughly double. Annual electricity use is expected to reach 945 terawatt-hours — comparable to Japan’s total national consumption — with AI accounting for 40% of the total.
Water use will rise to 9.3 trillion litres. CO2 emissions will increase to 399 million tons. The physical land occupied by data centers is forecast to expand from 6,900 square kilometers today to more than 14,500 square kilometers — about the size of Connecticut.
“The public debate still often treats AI as software,” the report states, “but AI is also physical infrastructure: data centres, electricity generation, cooling systems, transmission networks, chips, minerals, land and water.”
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What this means for your money
Altman’s utility analogy is useful as a mental model. Electricity and water don’t ask users to think about where they come from. You flip a switch or turn a tap and the resource appears. The tech industry’s $700+ billion bet this year is that AI will work the same way. But it’s hard to know if the infrastructure can be built fast enough and cheap enough to make that happen.
For investors, this creates opportunities and risks. Transformer makers, electrical equipment suppliers, and utilities with data center customers are best positioned. Data center REITs like Equinix and Digital Realty sit on the demand side, while utilities ETFs offer broader grid exposure, but talk to a financial advisor before you invest.
The risk is that the build-out is already taking longer and costing more. Alphabet, for instance, is raising $84.75 billion for data centers it can’t yet power. AI is trying to build, in years, what utilities took decades to construct.
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Godwin Oluponmile is a content specialist, SEO strategist and copywriter with seven years of expertise in finance, Web 3.0, B2B SaaS and technology. His work has been featured in publications such as Entrepreneur, HackerNoon, Blocktelegraph and Benzinga.
