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Every major technological revolution promises greater efficiency. Conventional wisdom says that should reduce demand and lower costs. Yet AI economics appears to be doing the opposite. A 160-year-old economic principle known as Jevons Paradox may explain why. As AI becomes cheaper, faster and more accessible, businesses aren\u2019t using less of it; they\u2019re finding entirely new ways to consume more.<\/p>\n
Jevons Paradox, identified by William Stanley Jevons in 1865 while studying coal, holds that improving resource efficiency does not cut consumption; it lowers barriers, unlocks new use cases, and ultimately drives demand higher.<\/p>\n
Microsoft CEO Satya Nadella<\/a> has repeatedly pointed to Jevons Paradox to explain why advances in AI are driving soaring compute demand rather than reducing it. As models become faster, cheaper and more capable, businesses don\u2019t simply use AI more efficiently; they find entirely new ways to deploy it.<\/p>\n Three companies at the forefront of AI hardware, infrastructure and enterprise software explain how this dynamic is reshaping the competitive landscape for startups and small businesses.<\/p>\n Efficiency That Unlocks New Demand In Hardware<\/p>\n Faster, cheaper hardware is removing bottlenecks and transforming AI economics. Tensordyne<\/a> builds inference racks using 90% less power than alternatives, an advance that embodies Jevons\u2019 insight: lower costs mean organizations run more AI, not less.<\/p>\n \u201cJevons Paradox is gaining momentum because the type of work AI is actually doing has become more complex and intensive,\u201d says cofounder Gilles Backhus. \u201cWhen ChatGPT launched a few years ago, it was often one input in, one output out. Now, a single developer’s agent might be performing hundreds of tasks at a time, each requiring detailed reasoning that uses more compute.\u201d<\/p>\n Customers Value Speed And Model Quality<\/p>\n The shift to agentic systems has already boosted compute use per user and task, but hardware has struggled to keep pace, leaving many facing rising costs even as they rush to adopt AI.<\/p>\n \u201cTo outrun Jevons Paradox, we need to make it much cheaper to run AI workloads, without compromising on what customers value: speed and model quality,\u201d says Backhus. \u201cSoftware will get more efficient, but the biggest needle-mover will be hardware: chips and racks.\u201d<\/p>\n The Tensordyne Napier inference system uses less energy, less space, and less money per token, keeping costs down while still running 13 times higher throughput than Nvidia. Systems, and the AI models served by them, are typically either fast and expensive, or slow and cheap. The company plans to eliminate that compromise, making it less capital-intensive to run high-performance AI workloads.<\/p>\n \u201cStartups have a limited supply of capital to burn, so each token is much more valuable,\u201d says Backhus. \u201cIf chips and data centers are more than 10x cost-efficient, models will be cheaper to run, and the margins of financially constrained early-stage companies would transform overnight. It could be rocket fuel for growth.\u201d<\/p>\n Lower costs could democratize AI R&D, unlocking use cases currently too expensive to test. For founders, the core lesson lies in the underlying economics. \u201cIf a state-of-the-art customer support model currently costs $5 a session, but becomes $0.50 because the hardware is cheaper, then we will see ten or even a hundred times the number of companies adopting it,\u201d says Backhus.<\/p>\n Productivity That Creates New Possibilities In Professional Services<\/p>\n