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.

The rise of efficient, cheaper models pressures the profit margins of frontier AI labs. However, this could trigger a Jevon's Paradox effect, where lower costs cause demand to explode. This would dramatically expand the overall market, allowing both frontier and efficient models to thrive in a much larger pie.

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.

Despite enterprises hitting AI budget limits, the market is not collapsing. Competition is forcing AI providers to lower token prices, triggering the Jevons paradox: as a resource's cost falls, its consumption increases, sustaining demand for underlying infrastructure like NVIDIA chips.

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.

The AI market has two opposing trends: a dramatic collapse in token prices for equivalent models (down 150x in 21 months) and unprecedented revenue growth. This indicates that the explosion in utilization and value creation is massively outpacing cost reductions, signaling a healthy, expanding market.