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.

Doug from Semi Analysis argues that the primary deflationary threat isn't just cheaper tokens, but the emergence of low-end models that can commoditize entire AI-powered solutions, creating a race to the bottom that erodes pricing power for everyone.

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.

AI data centers produce "tokens," a commodity whose price falls 70-80% annually. Investors provide capital based on fixed-return expectations (like real estate cap rates), but the underlying revenue-generating asset is rapidly deflating, creating a fundamental economic mismatch.

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 primary short-term risk for the AI sector isn't capital expenditure but the high cost of token generation. For AI applications to become ubiquitous, the unit economics must improve. If running a single query remains prohibitively expensive for businesses, widespread, sustainable adoption will be impossible, threatening the entire investment thesis.

The massive capital expenditure to train a frontier AI model becomes nearly worthless in months as competitors release superior models. This makes trained models a uniquely fast-depreciating asset, creating immense pressure on labs to monetize quickly through API access or investor hype before their technological advantage evaporates completely.

The current affordability of AI tokens is not sustainable; it's propped up by venture capital funding AI companies operating at a loss. Businesses should treat this as a temporary window for aggressive learning and experimentation before prices inevitably rise to reflect true operational costs.

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.