Data centers could require less land, power and equipment to deliver a given amount of computing in the future, even as the world uses far more computing overall. That distinction is central to Todd G. Buchholz’s argument in an October 6, 2026, Washington Post opinion essay: efficiency may shrink the infrastructure needed per unit of computing, but it does not guarantee that total demand—or total energy use—will fall.
What does it mean for the data center boom to shrink?
“Shrink” need not mean fewer calculations, fewer AI services or even fewer data centers. It can mean that a facility needs less land, electricity and equipment to produce a given quantity of computing. If chips, cooling and software become more efficient, each unit of output could require a smaller physical footprint.
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Buchholz frames the practical question this way: “How much of that development’s land, electricity and equipment will be required to produce a unit of intelligence?” His essay contrasts that question with the common assumption that today’s facilities will retain their scale and configuration over the next 10 or 20 years.
The distinction matters because efficiency per unit and total consumption are different measures. Buchholz puts it succinctly: “Two apparently contradictory things can therefore happen at once: The world can consume vastly more computing while requiring less infrastructure for each computing unit.”
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What could make facilities more efficient?
The essay points to several possible sources of lower infrastructure intensity:
- More efficient chips: improved computing hardware could deliver more work for a given amount of electricity.
- Stacked chips: changes to chip design and packaging could affect how much equipment and space are needed for computing capacity.
- Better cooling: improved cooling could reduce the infrastructure burden of keeping computing equipment within operating limits.
- Leaner software: software that uses computing resources more efficiently could reduce the hardware needed for a given task.
These are mechanisms for making a unit of computing less resource-intensive, not proof that future facilities will use less power in total. The essay does not provide a measured forecast of future data-center footprints or quantify how much each change might save.
Why efficiency may not lower total energy use
When efficiency makes a service cheaper, people and businesses may use more of it. If the added use is large enough, it can offset or exceed the savings per unit. This is often called the Jevons effect: greater efficiency can lead to greater total consumption when lower costs encourage enough additional demand.
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Applied to AI, more efficient computing could make new services affordable or practical, prompting more people and organizations to use them. The result could be lower infrastructure needs for each unit of computing alongside higher aggregate demand. Buchholz’s essay raises this possibility but does not quantify the net effect on electricity use. It therefore supports neither a confident prediction that total data-center energy use will fall nor a claim that efficiency savings will necessarily be overwhelmed.
What the essay says about the grid and communities
Large customers can affect who pays for grid infrastructure
Buchholz’s essay describes an Electric Power Research Institute study from June as estimating that data-center growth from 2015 to 2024 modestly reduced U.S. residential electricity prices. The explanation reported in the essay is that large, steady customers spread fixed grid costs across more kilowatt-hours. The essay does not give a percentage, and the underlying study is not available here to establish the result independently; treat it as an attributed estimate, not settled causal evidence.
A poor contract can leave households with the bill
The essay also warns of the opposite outcome: a utility could build infrastructure for a large new customer, then leave households paying for costs that the customer does not cover. Buchholz characterizes this as a bad contract, not an inevitable consequence of data-center growth. For communities weighing a project, the practical issue is how the contract allocates the cost of new generation, transmission and other grid upgrades—not simply whether a large customer is present.
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Plans are not the same as built capacity
As an example of the scale of current ambitions, the essay describes an Ohio campus backed by SoftBank, with commitments from OpenAI and Nvidia, plans for 10 gigawatts of power and long-term contracts. That is the article’s account of a developing project. It should not be read as confirmation that the planned capacity will all be built or used.
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What should communities and investors examine?
Buchholz’s argument is a reason to test long-term assumptions, not a verified forecast that data centers will become smaller. The following questions help make that distinction concrete:
- Who pays for power and grid upgrades? Identify which costs the data-center customer is contractually responsible for and which, if any, could be passed to other ratepayers.
- What are the water and grid-cost exposures? Consider the project’s resource demands and who bears their financial or community impacts.
- How much computing output is expected per unit of land and electricity? Compare the expected output with the resources committed to support it, while treating future efficiency as uncertain.
- How exposed is the investment to aging chips? Hardware can lose value as technology changes; a long-lived facility should not be valued as though its current equipment will remain useful indefinitely.
- What remains useful if the building or computing equipment changes? Buchholz’s investment thesis is that grid access, transmission access, contracted power and fiber may outlast the value of aging chips or a building shell. That is a thesis about what could retain value, not a guarantee of future returns.
What the historical comparison does—and does not—show
To illustrate how much computing hardware can change, Buchholz’s article says that ENIAC, in 1946, weighed 30 tons, could fill a house and used 140 kilowatts for arithmetic. These historical details are reported by the essay; they are an illustration of technological change, not a direct forecast of what modern data centers will become.
The analogy helps explain why today’s physical configuration should not automatically be treated as permanent. It cannot, on its own, establish how quickly facilities will change, how much smaller they will get or whether aggregate electricity demand will decline.
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