While the cost-per-token is decreasing as models become more efficient, this efficiency gain drives a massive increase in new use cases and overall consumption. This economic principle, Jevons Paradox, explains why total enterprise spending on model inference is skyrocketing, even as the unit cost falls.

While the unit cost of AI inference has plummeted 50x, overall spending on AI is surging. This is a textbook example of Jevons paradox, where radical efficiency gains lead to increased consumption and higher total expenditure as new applications become economically viable.

Counter-intuitively, as AI models become more efficient, the total consumption of compute resources will rise. This economic principle, Jevons Paradox, states that increased efficiency lowers costs, which in turn unlocks more applications and drives greater overall demand.

OpenAI's drastic price reductions aren't a sign of collapsing demand for AI. Instead, they trigger the Jevons Paradox: as efficiency increases and costs fall, overall consumption of AI tokens and underlying GPU resources skyrockets, validating the massive data center buildout.

Contrary to fears that efficient models will curb computing needs, lower costs will attract more users and enable complex applications, leading to higher overall consumption. This is a classic example of Jevon's paradox, where increased efficiency drives greater demand for a resource.

Decreasing the cost per AI query (inference) paradoxically drives up total expenditure. As AI becomes more efficient and accessible, its usage skyrockets for new applications, increasing aggregate spending on infrastructure like chips and data centers. This mirrors the Jevons paradox, where more efficient steam engines increased, rather than decreased, Britain's coal consumption.

While the per-unit cost of using AI has plummeted, total enterprise spending has soared. This is a classic example of the Jevons paradox: efficiency gains and lower prices are unlocking entirely new use cases that were previously uneconomical, leading to a net increase in overall consumption and total expenditure.

Counterintuitively, Anthropic lowered the price of its premium Opus model because it was underutilized. This move triggered the Jevons paradox: the lower price made Opus more accessible, and consumption increased by a far greater multiple than the price decrease, unlocking significant value for customers.

When OpenAI's Luna model price dropped 10x on OpenRouter, usage grew 13x, demonstrating the Jevons paradox. Cheaper AI doesn't just mean lower costs; it unlocks exponentially more usage, ultimately growing the overall market spend.