The massive capital expenditure by hyperscalers on AI will likely create an oversupply of capacity. This will crash prices, creating a golden opportunity for a new generation of companies to build innovative applications on cheap AI, much like Amazon utilized the cheap bandwidth left after the dot-com bust.

Despite clear ROI, Glean's founder argues current AI costs are "absurdly expensive," citing a single internal engineering triage agent that cost one million dollars per month. He believes this is a historical anomaly and predicts that competition and open source will force inference prices to drop by orders of magnitude.

The falling cost of AI infrastructure, driven by competition and supply chain improvements, will make AI so affordable that its adoption and usage will explode. This isn't just incremental growth; it's a paradigm shift in accessibility and scale.

While AI compute demand seems limitless, its price is not infinitely elastic. As inference becomes a core cost of goods sold (COGS) for AI products, excessively high compute prices will break the business models of infrastructure customers, ultimately limiting demand.

A primary risk for major AI infrastructure investments is not just competition, but rapidly falling inference costs. As models become efficient enough to run on cheaper hardware, the economic justification for massive, multi-billion dollar investments in complex, high-end GPU clusters could be undermined, stranding capital.

The cost for a given level of AI capability has decreased by a factor of 100 in just one year. This radical deflation in the price of intelligence requires a complete rethinking of business models and future strategies, as intelligence becomes an abundant, cheap commodity.

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.

Arvind Krishna forecasts a 1000x drop in AI compute costs over five years. This won't just come from better chips (a 10x gain). It will be compounded by new processor architectures (another 10x) and major software optimizations like model compression and quantization (a final 10x).