AI & Policy

Jevons Paradox & AI: Why AI Will Never Get Cheap

The Jevons Paradox shows that as AI becomes more efficient, the total demand for AI compute skyrockets, leading to higher overall spending, not lower costs.

For decades, the tech industry has operated on a simple, powerful promise: as technology gets more efficient, it gets cheaper. Moore’s Law, which predicted the doubling of transistors on a chip every two years, drove the cost of computing toward zero and powered the digital revolution. Many assume the same will be true for artificial intelligence; as AI models become more efficient, the cost of using them will plummet.

This assumption is dangerously wrong. It ignores a 160-year-old economic principle known as the Jevons Paradox, which states that as technology makes using a resource more efficient, the total consumption of that resource actually increases because demand explodes. This isn’t a niche academic theory; it’s a fundamental law of economics that explains why your AI bill is destined to go up, not down—and why that’s ultimately a sign of incredible progress.

Expert Insight: “The Jevons Paradox, first observed with coal in the 19th century, is happening right now with AI compute. Every time a new, more ‘efficient’ model is released, it doesn’t lead to companies spending less. Instead, it unlocks so many new, previously unimaginable use cases that the overall demand for AI skyrockets. We’re not saving money; we’re just finding more problems to solve.”

This guide breaks down what the Jevons Paradox is, why it’s the single most important economic concept for understanding the future of AI, and how it explains the seemingly endless demand for more powerful and more expensive AI systems.

What is the Jevons Paradox?

In 1865, the English economist William Stanley Jevons made a counter-intuitive observation. He noticed that as technological improvements made steam engines more efficient at using coal, the total consumption of coal in England didn’t decrease—it soared.

The logic is simple:

Efficiency Increases: A new technology makes a resource cheaper to use for a specific task. (e.g., a new steam engine design needs less coal to travel one mile).
Price Drops: Because the task is now cheaper, the price of doing that task falls. (The cost of transporting goods by train goes down).
Demand Explodes: The lower price opens up a vast number of new applications and increases demand for the service. (More businesses can now afford to use trains, and they use them for more things).
Overall Consumption Rises: The explosion in demand is so large that it completely overwhelms the initial efficiency savings, leading to a net increase in the consumption of the original resource (more total coal is burned).

This is also known as the “rebound effect.” If an efficiency gain of 20% leads to a rebound in demand of more than 20%, you get the Jevons Paradox.linkedinyoutube

The Jevons Paradox in the AI Era

The same dynamic that Jevons observed with coal is now playing out with AI compute. Every new generation of AI models is more “efficient”—it can perform a task with fewer computations or at a lower cost per token. But this efficiency is not leading to lower overall AI spending.

The AI Efficiency-Demand Cycle:

A More “Efficient” Model is Released: A company like DeepSeek releases a new model that is 50% cheaper per million tokens than its predecessor.
The “Price” of a Task Drops: A task that used to cost $10 in API calls now costs $5.
Demand Explodes:
- Existing Users Increase Volume: A company that was running 1,000 analyses per day can now afford to run 5,000.
- New Use Cases Emerge: A startup that couldn’t afford AI for video analysis now can. A developer who couldn’t justify using AI to auto-complete every line of code now can.
Overall Compute Consumption Soars: The massive increase in the volume and variety of AI tasks completely dwarfs the initial 50% price drop. The company’s total AI bill doesn’t go down; it goes up by 300% as they find more ways to leverage the now-cheaper intelligence.

This explains the seemingly contradictory headlines we see every day: “New AI Model is 50% Cheaper!” followed by “Data Center Spending on AI to Triple in the Next Two Years!”. It’s not a contradiction; it’s the Jevons Paradox in action.

Why This Is a Good Thing (for Innovation)

The Jevons Paradox is often viewed negatively from an environmental perspective, as it can lead to increased resource depletion. However, in the context of AI, it is a powerful engine for innovation and economic growth.

It Drives Unprecedented Innovation: When the cost of a fundamental input (like intelligence) drops, it unlocks a Cambrian explosion of new applications. Just as cheap electricity didn’t just mean cheaper candles but led to everything from refrigerators to the internet, cheaper AI won’t just mean cheaper chatbots; it will lead to applications we can’t even imagine yet.
It Increases Demand for Human Labor (in new ways): The paradox suggests that AI won’t simply replace jobs. By making certain tasks more efficient, it increases the demand for human workers who can use these powerful new tools to solve bigger and more complex problems. A graphic designer who can use AI to generate 100 concepts in an hour is more valuable, not less, than one who can only create one.
It Fuels the AI Flywheel: The increased demand for AI compute drives massive investment in building more powerful and more efficient models, which in turn makes AI even cheaper, further increasing demand in a virtuous cycle.

Conclusion: Embrace the Inevitable

The dream of an “AI future” where intelligence is free is a misunderstanding of basic economics. The Jevons Paradox guarantees that as long as we can find new and valuable ways to use artificial intelligence, the total amount we spend on it will continue to rise. Instead of fighting this trend, business leaders and investors should embrace it. The rising cost of AI is not a sign of inefficiency; it is the clearest possible signal of its revolutionary and ever-expanding value.

SOURCES

Ansari Alfaiz

Alfaiz Ansari (Alfaiznova), Founder and E-EAT Administrator of BroadChannel. OSCP and CEH certified. Expertise: Applied AI Security, Enterprise Cyber Defense, and Technical SEO. Every article is backed by verified authority and experience.