How AI’s energy constraints are driving the infrastructure investments that will enable ubiquitous computing
In 1865, economist William Stanley Jevons observed a paradox in Britain’s coal industry: as steam engines became more efficient and coal-powered technology advanced, total coal consumption increased rather than decreased. More efficient engines didn’t reduce coal demand—they made coal-powered applications economically viable in new contexts, from railways to factories to ships. The same pattern has repeated across every major energy transition since. As automobile engines became more fuel-efficient, total gasoline consumption soared because cheaper per-mile costs enabled more driving, more cars, and more applications for transportation.
While Jevons’ paradox doesn’t apply universally—many efficiency gains do reduce total consumption—computing has consistently followed this pattern. Personal computers didn’t reduce total computing energy use; they made computation cheap enough for everyone. The internet didn’t decrease data transmission; it made information sharing ubiquitous.
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