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Data centers could require less land, power and equipment to produce a given amount of computing in the years ahead. That would not necessarily mean fewer data centers or less total electricity use: more efficient, cheaper computing could also encourage much greater demand. The distinction matters when communities and investors weigh the costs and long-term value of new infrastructure.
What does it mean for the data center boom to shrink?
It means the infrastructure required for a given quantity of computing may get smaller or more efficient—not that total computing demand is certain to fall. In an October 6, 2026, opinion essay, Todd G. Buchholz asks what a data center will look like in 10 or 20 years, and how much land, electricity and equipment will be needed to produce a unit of computing.
Buchholz points to more efficient chips, stacked chips, better cooling and leaner software as possible ways to reduce infrastructure intensity. These are reasons to question whether today’s facilities will retain their current scale and configuration over the long term, not proof of a particular future footprint.
His historical contrast is ENIAC: Buchholz writes that the 1946 computer weighed 30 tons, could fill a house and used 140 kilowatts for arithmetic. The example illustrates how computing hardware can change radically; it does not by itself predict the size or energy use of future AI infrastructure.
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Why efficiency may not reduce total electricity use
Efficiency lowers the resources needed for each unit of computing. But if it also makes computing cheaper, people and businesses may use far more of it. This is the Jevons effect: efficiency can spur enough additional use to increase total resource consumption.
Applied to AI, that leaves two outcomes possible at once: infrastructure intensity per unit of computing could fall while total computing—and the energy needed to serve it—rises. Buchholz puts it this way: “Two apparently contradictory things can therefore happen at once: The world can consume vastly more computing while requiring less infrastructure for each computing unit.” His essay does not quantify whether efficiency or increased demand will dominate.
What the Ohio project example does—and does not—show
Buchholz describes a developing Ohio campus backed by SoftBank, with commitments from OpenAI and Nvidia, planned power capacity of 10 gigawatts and long-term contracts. That is an account of plans and commitments, not evidence that the full capacity will be built, connected to the grid or used.
The example raises a practical question for large projects: who bears the cost if utilities expand generation or transmission for a major customer and the expected demand does not materialize? Buchholz warns that households could be left paying for infrastructure built for that customer. He frames this as a bad contract, not an inevitable consequence of computing or data-center growth.
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What the reported electricity-price finding means
Buchholz summarizes a June study by the Electric Power Research Institute as finding that data-center growth from 2015 to 2024 modestly reduced U.S. residential electricity prices. The reported explanation is that large, steady customers spread fixed grid costs across more kilowatt-hours.
The underlying study is not available here for independent assessment, and the essay’s summary does not establish a general causal rule. Whether households benefit depends on how utilities plan and allocate costs, including whether large customers pay for the upgrades they require.
What communities and investors should examine
The essay’s central investment point is that a data center’s useful life may not match the life of every asset inside it. Buchholz argues that grid access, transmission access, contracted power and fiber may outlast aging chips or a building shell. That is an investment thesis, not a guaranteed valuation outcome.
Before treating a proposed facility as a durable asset—or approving its costs—communities, utilities and investors can test the assumptions that determine who benefits and who carries the risk:
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- Power and grid upgrades: What capacity is actually contracted, who pays for generation and transmission work, and who bears costs if demand falls short?
- Water and operating costs: What demands will the facility place on local water resources and the grid over time?
- Infrastructure intensity: How much computing output is expected per unit of land and electricity, and how might that change as chips, cooling and software improve?
- Hardware obsolescence: How exposed is the investment to accelerators and other equipment that may lose value faster than the site’s power or fiber connections?
- Alternative use: If the building or computing equipment becomes less useful, could grid, transmission or fiber access retain value for another use?
These are decision questions, not a ranking of facilities. Their answers depend on project contracts, local infrastructure and how technology and demand develop.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the headline does not prove
“The data center boom will shrink” is Buchholz’s opinion thesis, not an established forecast. The case for less infrastructure per unit of computing does not establish that total data-center demand, land use or electricity consumption will decline. Those totals will also depend on how quickly AI and other computing uses grow, and whether efficiency gains offset the resulting demand.
For long-term decisions, the useful takeaway is not to assume either permanent expansion or imminent contraction. It is to examine who pays for infrastructure, whether contracts protect ratepayers, and which assets could remain useful if computing hardware or facility designs change.
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