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.

The focus in AI has evolved from rapid software capability gains to the physical constraints of its adoption. The demand for compute power is expected to significantly outstrip supply, making infrastructure—not algorithms—the defining bottleneck for future growth.

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.

The current compute crunch isn't just a supply issue. It's because new AI models are so much more capable that they unlock a total addressable market (TAM) of valuable tasks that grows exponentially, far outpacing the linear or geometric growth of compute supply.

The massive growth in AI token consumption isn't a sign of waste but of ambition. While the cost per "unit of intelligence" is decreasing, companies are immediately applying that efficiency to solve exponentially harder problems. Our appetite for more capable AI is growing faster than the cost is falling, leading to sustained, exponential spending.

Efficiency gains in new chips like NVIDIA's H200 don't lower overall energy use. Instead, developers leverage the added performance to build larger, more complex models. This "ambition creep" negates chip-level savings by increasing training times and data movement, ultimately driving total system power consumption higher.