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The paradox that refuses to go away
Jevons paradox is one of those ideas that feels counterintuitive until you see it play out repeatedly across history. First articulated in the 1860s by British economist William Stanley Jevons, the paradox describes a simple but powerful pattern. When technological progress makes a resource more efficient to use, total consumption of that resource often rises rather than falls. Efficiency lowers cost. Lower cost expands demand. Expanded demand overwhelms the savings gained from efficiency.
Jevons observed this effect while studying coal use in Britain during the Industrial Revolution. More efficient steam engines were expected to reduce coal consumption. Instead, they made coal-powered machinery cheaper to run, encouraging wider adoption across factories, transport and industry. Coal use soared. The paradox was not a failure of logic but a reflection of human behaviour. People do not respond to efficiency by holding consumption constant. They respond by doing more.
This same principle now sits at the heart of AI adoption. As artificial intelligence becomes cheaper, faster and easier to use, its total consumption of compute power, electricity, data and human attention increases sharply. What many critics describe as irrational exuberance around AI is better understood as a predictable economic response to falling costs. Tech companies are not ignoring Jevons paradox. They are actively building their strategies around it.
Why efficiency does not mean restraint
A common assumption in discussions about technology and sustainability is that efficiency leads to moderation. Energy efficient cars should reduce fuel use. LED lighting should reduce electricity demand. Cloud computing should lower overall computing costs. Yet history shows the opposite outcome again and again.
Fuel efficient cars reduce the cost per mile, encouraging people to drive more often and travel further. Energy saving light bulbs reduce the cost of illumination, leading to more lights in more rooms, left on for longer. Data storage becomes cheaper, so people store vastly more data than before. Efficiency removes friction, and friction is often the only thing that keeps usage in check.
Jevons paradox does not claim that efficiency is bad. It explains why efficiency alone does not guarantee lower total consumption. In the context of AI adoption, this distinction matters. Cheaper AI does not reduce demand for AI. It accelerates it.
The internet bubble that was not about software
To understand why AI follows a different trajectory from the dot-com crash, it helps to revisit what actually failed in the early internet era. The popular story focuses on overhyped websites and unprofitable startups. The deeper problem was infrastructure misalignment.
During the late 1990s, telecom companies spent hundreds of billions laying fibre optic cable across continents. This capacity far exceeded what consumers could access. The last mile problem remained unsolved. Homes were still connected by copper telephone lines that were slow, unreliable and monopolised by dial-up connections. Most of the fibre went unused, becoming what the industry called dark fibre.
The demand was theoretical rather than real. The infrastructure existed in the wrong place. When the bubble burst, it was not because people did not want the internet. It was because the system could not deliver it in a usable way.
AI adoption faces a different constraint. There is no equivalent of dark compute. High performance chips are scarce, heavily utilised and operating continuously. Every query, every image, every video frame requires real-time processing across large clusters of specialised hardware. Demand is tangible, measurable and immediate.
Why AI compute behaves differently
Artificial intelligence systems do not consume resources in the same way traditional software does. Predicting a single word in a large language model involves coordinated computation across dozens or hundreds of chips. Generating images, video or real-time reasoning requires orders of magnitude more processing.
This is where Jevons paradox becomes central to understanding AI adoption. When optimisation breakthroughs reduce the cost of running AI models, users do not save money by asking the same number of questions more cheaply. They ask more questions, generate richer outputs and integrate AI into workflows that were previously uneconomical.
The release of more efficient AI models does not reduce demand for data centres. It increases it. Lower cost per unit of intelligence unlocks entirely new use cases. Tasks that were once too slow, too expensive or too limited suddenly become viable at scale.
This is why tech companies respond to efficiency gains by accelerating infrastructure investment rather than slowing it down. They are not planning for static usage. They are planning for explosive growth driven by falling marginal costs.
AI adoption follows the same historical pattern
The pattern described by Jevons paradox can be traced through every major technological transition. Whale oil gave way to kerosene, which made lighting dramatically cheaper. Instead of reducing lighting use, it multiplied it. Electricity reduced the cost further, leading to more lights, more appliances and more energy consumption overall.
The same pattern applies to computing. Early computers were rare and expensive, used sparingly. Personal computers made computation affordable and widespread. Smartphones made it constant. Cloud computing made it effectively limitless. At no point did cheaper computing lead to less computing. It led to more of it.
AI is not a break from this trajectory. It is a continuation of it. What appears to be an AI bubble driven by hype is better understood as the early phase of mass adoption driven by cost reduction. The key question is not whether AI use will grow, but how quickly infrastructure, energy systems and governance can adapt.
Why tech companies are betting on Jevons paradox
Large technology firms understand that adoption depends on habit formation. Research consistently shows that once people integrate a tool into their daily routines, usage becomes sticky. The history of search engines, social platforms and mobile apps supports this pattern.
AI tools exhibit similar dynamics. Early friction causes drop-off, but improvements in speed, accuracy and accessibility bring users back. Once users cross a certain threshold of perceived value, they do not revert to previous methods. They expand usage instead.
This is where pricing strategy intersects with Jevons paradox. When premium tiers offer more capability at a fixed price, users do not spend less overall. They consume more output. More queries, more integrations, more automation. The value proposition shifts from cost saving to capability expansion.
Tech companies are therefore not worried that efficiency improvements will cap demand. They expect efficiency to unleash it. Cheaper intelligence encourages experimentation, creativity and dependency. That dependency drives long-term adoption.
Electricity as the new bottleneck
Every application of Jevons paradox eventually runs into a physical constraint. For AI, that constraint is electricity. Data centres require continuous, high-density power. Unlike consumer devices, they cannot scale usage up and down casually. They must remain online at all times.
This has led to renewed interest in nuclear power, large-scale renewables and even on-site generation. The scale of projected demand is unprecedented in modern history. Electricity consumption has rarely grown this fast, if ever.
Yet even this constraint does not negate Jevons paradox. It reinforces it. The race to secure power is itself driven by the expectation of growing demand. Companies would not invest billions in energy infrastructure if they believed usage would plateau.
The lesson from Jevons is not that growth is guaranteed without limits, but that efficiency alone does not enforce restraint. Constraints shift rather than disappear.
AI adoption is not a greenfield bubble
One of the defining features of speculative bubbles is that they promise value in a space that does not yet exist. Early internet companies promised mass adoption before the infrastructure could support it. AI operates within an already mature digital ecosystem.
The networks, devices, protocols and user habits already exist. AI does not require people to learn how to use computers or connect to the internet. It layers intelligence onto systems people already rely on daily. This dramatically lowers adoption friction.
Seen through this lens, AI adoption resembles the expansion of an existing forest rather than the planting of a new one. Some companies will fail. Some investments will prove misguided. But the underlying demand for cheaper, faster and more capable intelligence is real.
What everyone gets wrong about the AI bubble
The mistake many observers make is assuming that lower costs imply lower total usage. This assumption ignores centuries of economic evidence. From coal to electricity to computing, cheaper resources invite broader application.
AI follows the same rule. As models become more efficient, they will not reduce the need for data centres. They will justify building more. As inference becomes cheaper, it will not limit queries. It will encourage constant use.
This does not mean risks should be ignored. Energy demand, environmental impact and labour disruption are real challenges. But they are consequences of adoption, not signs of collapse.
The future of AI adoption through the lens of Jevons paradox
Jevons paradox offers a clearer framework for understanding where AI is headed. The key insight is behavioural rather than technical. People and organisations respond to lower costs by expanding use, not by exercising restraint.
AI adoption will therefore accelerate as efficiency improves. The real question is how societies manage the externalities that come with that growth. Infrastructure, regulation and energy systems will need to evolve in parallel.
History suggests that they will, unevenly and imperfectly, but ultimately in service of continued expansion. Intelligence has always been one of humanity’s most valued resources. Making it cheaper does not reduce demand. It unleashes it.
In that sense, AI is not repeating the mistakes of the past. It is repeating one of its most consistent patterns.
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