Despite fears that cheaper, open-source models would commoditize the market, the opposite is happening. While token usage for cheaper models is rising, the actual share of economic value (wallet share) is increasingly flowing to expensive frontier labs like Anthropic and OpenAI.

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.

The narrative of a zero-sum 'AI race' is misleading. Demand for agentic AI capabilities is expanding so rapidly that the market can support multiple winners. Even second or third-tier labs will likely be 'sold out of tokens,' indicating the industry is a rapidly growing pie rather than a winner-take-all fight for market share.

Contrary to the "bubble pop" narrative, a market shift away from high-margin frontier models toward cheaper alternatives could boost overall AI usage. This would redirect revenue from labs like OpenAI to infrastructure players who provide the most efficient, low-cost compute.

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.

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 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.

The market isn't a battle between proprietary frontier models and open-source alternatives. Instead, both are seeing parabolic growth. While open-source becomes more capable for simple tasks, the demand for cutting-edge capabilities unlocked by frontier models is also expanding rapidly, creating a positive-sum environment.

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.