Jevons paradox
In plain wordsThe observation that making something more efficient can make people use more of it overall because it gets cheaper — as with coal in the 1800s, and with AI computing today.
The tendency for improvements in the efficiency with which a resource is used to increase rather than reduce total consumption of it, because the lower effective cost creates enough new demand; it is the extreme case of what energy economists call the rebound effect. In AI it is invoked to argue that cheaper inferenceinference cost, n.: What it costs to use an AI model after it has been built: the computing power, electricity and hardware needed every time it answers, usually billed per chunk of text. and more efficient models — such as those behind the DeepSeek momentDeepSeek moment, n.: The shock of January 2025, when a little-known Chinese company released a free AI model nearly as good as the best American ones at a fraction of the reported cost, briefly wiping hundreds of billions off chip stocks. of January 2025 — will expand total demand for computecompute, n.: The raw computing power used to train and run AI, usually measured by how many specialized chips are used and for how long. With data and algorithms, it is a main driver of AI progress., chips and energy rather than shrink it, because falling prices per tokentoken, n.: The small chunk of text an AI language model reads and writes — often a word, part of a word or a punctuation mark. AI usage, limits and prices are counted in tokens. make new uses such as agents, long reasoning and video economical. Skeptics note that the paradox holds only when demand is highly price-elastic, that the evidence for it is mixed even for energy, and that it says nothing about which companies capture the value.
Inference got ten times cheaper and our token bill tripled: Jevons paradox in one invoice.
Origin
Named after the English economist William Stanley Jevons, who argued in The Coal Question (1865) that James Watt's more efficient steam engine had increased Britain's coal consumption. The idea re-entered AI discussion in late January 2025, when Microsoft chief executive Satya Nadella cited it as DeepSeek's low-cost models unsettled markets.
Related
- DeepSeek momentDeepSeek moment, n.: The shock of January 2025, when a little-known Chinese company released a free AI model nearly as good as the best American ones at a fraction of the reported cost, briefly wiping hundreds of billions off chip stocks.
- inference costinference cost, n.: What it costs to use an AI model after it has been built: the computing power, electricity and hardware needed every time it answers, usually billed per chunk of text.
- computecompute, n.: The raw computing power used to train and run AI, usually measured by how many specialized chips are used and for how long. With data and algorithms, it is a main driver of AI progress.
- environmental impact of AIenvironmental impact of AI, n.: The effects of AI on the planet: the electricity, water, materials and land used to make chips, build data centers and train and run models, and the pollution that results.
- AI bubbleAI bubble, n.: The worry that investors are pouring far more money into AI companies, chips and data centers than AI will earn back soon, so that prices could crash as in the dot-com bust of 2000.
- algorithmic progressalgorithmic progress, n.: Improvements in AI methods — not faster chips — that let a model reach the same ability with less computing power. Researchers estimate this has cut the computing needed by half every eight to sixteen months.
Sources
- Jevons, W. S. (1865). The Coal Question. London: Macmillan.
- Wikipedia contributors. Jevons paradox. Wikipedia (accessed October 2026). en.wikipedia.org/wiki/Jevons_paradox
Mentioned in
environmental impact of AIenvironmental impact of AI, n.: The effects of AI on the planet: the electricity, water, materials and land used to make chips, build data centers and train and run models, and the pollution that results., general-purpose technologygeneral-purpose technology, n.: A technology, like electricity or the computer, that spreads through the whole economy, keeps improving and sparks many other inventions. Many economists think AI is one., productivity paradoxproductivity paradox, n.: The puzzle that a powerful new technology, like computers in the 1980s or AI today, can be everywhere without showing up as faster growth in how much workers produce., Green AIGreen AI, n.: A movement to make AI research and products use less energy and computing power, and to report those costs openly alongside accuracy.