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.

The comparison of the AI hardware buildout to the dot-com "dark fiber" bubble is flawed because there are no "dark GPUs"—all compute is being used. As hardware efficiency improves and token costs fall (Jevons paradox), it will unlock countless new AI applications, ensuring that demand continues to absorb all available supply.

A paradox exists where the cost for a fixed level of AI capability (e.g., GPT-4 level) has dropped 100-1000x. However, overall enterprise spend is increasing because applications now use frontier models with massive contexts and multi-step agentic workflows, creating huge multipliers on token usage that drive up total costs.

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.

The cost of AI, priced in "tokens by the drink," is falling dramatically. All inputs are on a downward cost curve, leading to a hyper-deflationary effect on the price of intelligence. This, in turn, fuels massive demand elasticity as more use cases become economically viable.

While the cost to achieve a fixed capability level (e.g., GPT-4 at launch) has dropped over 100x, overall enterprise spending is increasing. This paradox is explained by powerful multipliers: demand for frontier models, longer reasoning chains, and multi-step agentic workflows that consume exponentially more tokens.