For more than a year the town of The Dalles, Oregon, fought in court — its legal costs paid by Google — to stop a newspaper finding out how much water the local data centre used. When the city gave up, the records showed a single building consuming more than a quarter of the town's water, and rising.1
Much of the worry about artificial intelligence in 2026 is physical: the power its data centres draw, the water they evaporate, the planet supposedly drained to run a chatbot. On those terms the panic does not survive arithmetic. A single query uses about a quarter of a watt-hour, the energy of a second in a microwave oven. The world's data centres consume roughly 1.5% of its electricity, less than a fifth of what air-conditioning uses; and a third of the data-centre total is spent mining bitcoin, not running AI.2
But The Dalles is not a global average, and the costs that bite are not the ones in the global total. They are concentrated: on one small grid, in one town's water, in the six gigawatts that Northern Virginia's "data-centre alley" now draws by itself. They fall at the margin, where the newest data centre's electricity is dirtier than the average and can keep a coal plant open. And overall demand is rising because the technology is becoming more efficient, not despite it: as each computation gets cheaper, more computing gets done, so total demand climbs even as the cost of a calculation falls.3 AI's data centres can be a small share of the world's electricity and still see their demand double by 2030.
What actually binds
The binding physical limit is not the one people argue about. It is not the query, which is trivial. It is not thermodynamics: a chip runs about a million times hotter than the floor physics sets for erasing a bit of information, leaving scope for decades of efficiency gains. Nor is it land. What binds is duller: the stacks of high-bandwidth memory beside each processor, and the local grid. Fetching a number from that memory takes roughly 100 times as much energy as the calculation done with it, so the machines spend most of their power shifting data rather than computing. That memory is also the most carbon-intensive part of a chip to make, and manufacturers have sold its advanced packaging years in advance.4
A rational way to lose money
Why, then, spend some $600bn a year, heading towards $700bn, on warehouses of chips that will be worthless in three or four years?5 The easy answer is "a bubble". But the spending has a rational core. A firm with a durable lead in machine intelligence could take a share of the work it replaces — part of a global wage bill of around $60trn a year,6 far more than is now being spent to chase it. Even at long odds, that can justify huge and risky bets. And most of the money is spent by landlords: the few firms that own the buildings and the chips earn rent whichever model-maker wins.
Rational for each firm is not the same as sensibly priced overall. Britain's railway mania of the 1840s was rational competition over a transformative technology, and its investors earned less than government bonds paid for a decade.7 What separates a land-grab from a mania is not the motive — both are rational — but the timing: whether demand arrives before the chips are written off. Railways, and the dark fibre laid in the late 1990s, could sit idle for 30 years and still be worth using cheaply later. A data centre's chips are written off in three or four years, and firms increasingly buy them with debt at interest rates that are no longer near zero.
What the firms are buying
What the firms are buying is not the model. Models are becoming commodities: a Chinese laboratory, DeepSeek, built one to rival the best for a small fraction of the cost, and open versions trail the leaders by months.8 What does not commoditise is use, and the data it generates. Intelligence increasingly looks like something a system gains by acting in the world and getting feedback, not by absorbing a fixed store of text. The scraped internet is a record of other people's experience; the program that beat the world's best players at Go learned by playing, not by reading old games. If that is right, the advantage lies in access to a rich, live environment with real consequences — which may be found less in chatbot logs than in the physical economy, and in work itself.
There is a catch. Machines learn best where the environment gives cheap, clear feedback: a game with a winner, a proof a checker can verify, code that either runs or does not. Most of the economy offers no such signal. Whether the method works beyond these tidy cases is what the whole build-out is betting on.
Where the bill lands
However it ends, the losses are unlikely to come as a crash. The losers' chips will simply stop earning; the winners will pass on what costs they can. Not yet to ordinary bills: Virginia's legislative auditor found that data centres in the state pay their full share today. But the same report expects that to change. And in the wholesale market it already has: the monitor of America's largest grid blames data-centre demand for billions of dollars in higher capacity costs, which every household on the network helps pay.9 Political choices, not markets, will decide who ends up paying, and when. None of which makes ordinary people bystanders: pension funds hold the shares, the phones run the tools, and towns compete to host the warehouses for the tax revenue.
The surplus problem
The bigger risk may run the other way. The worry is scarcity — that the machines will use too much power and water. The older economic danger is the opposite. A machine that can do a growing share of human work is a machine for producing more than a society can easily use, and the harder problem in an economy has seldom been making enough of something. It has been absorbing what gets made.
Keynes expected his grandchildren to work 15-hour weeks by now. Productivity rose as he predicted; the leisure did not, because societies found uses for the hours — much of it work whose chief product is that someone is kept busy.10 That is one way to absorb a surplus. Societies have found uglier ones. Unable to sell enough to a China that wanted little it made, 19th-century Britain manufactured the demand: it got the country addicted to opium, reversed the flow of silver and went to war when China moved to stop the trade.11 A surplus gets absorbed one way or another — as make-work, as manufactured demand, at the extreme as war — and the way chosen first is usually the one that costs the powerful least.
Which one a society chooses is not set by the technology. It depends on what people want, and people want more than economic models assume: to be needed, to have a place, to keep busy, sometimes to have someone below them. A machine that can do the work sharpens that choice without making it. The same machine can be used to let more people think, or to let fewer people decide; it settles neither. How much water it consumes is the easy question. What a society will do with a machine that can do its work is the hard one.