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.

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.

OpenAI's drastic model price cuts led to a disproportionate, 13x surge in usage. This demonstrates Jevons' Paradox in AI: lower token costs don't just make existing tasks cheaper, they cross a threshold that makes previously uneconomical applications viable, causing a massive net increase in consumption as new use cases go from '0 to 1'.

A radical improvement in compute efficiency won't just lower costs; it will trigger Jevons' paradox, where consumption increases by more than the price drops. Making AI compute 1000x cheaper will unlock currently unimaginable applications, creating a market far larger than linear projections and potentially the largest in human history.