The performance race in frontier AI models is irrelevant for most business use cases. The vast majority of enterprise AI traffic—an estimated 90%—will run on cheaper, older, or specialized open-source models that are sufficient for day-to-day operational tasks, rather than costly state-of-the-art ones.

The dominant use of AI compute is moving from training massive models to running inference tasks. This shift fundamentally alters the market, enabling broader enterprise adoption via cheaper, open models and changing the demand profile for compute hardware beyond the absolute cutting edge.

Brex data shows a market shift where spending on underlying AI infrastructure (compute, databases) is growing faster than on AI applications. This indicates the ecosystem is moving from a primary focus on new product creation to a phase of scaling, optimization, and tooling for existing applications.

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.

Despite the hype around enterprise AI, the vast majority of current inference workloads are driven by new, AI-native application companies. This indicates that the broader enterprise adoption market is still in its infancy, representing a massive future growth opportunity.

Unlike the dot-com era's speculative infrastructure buildout for non-existent users, today's AI CapEx is driven by proven demand. Profitable giants like Microsoft and Google are scrambling to meet active workloads from billions of users, indicating a compute bottleneck, not a hype cycle.

The future of compute demand is a tale of two opposing forces. Enterprises will use AI to compress redundant data and streamline operations, reducing compute costs. Consumers, however, will demand generative AI for entertainment and personalization (e.g., 'Star Wars with my face'), creating massive new compute needs.

The narrative of insatiable AI compute demand is partially a bubble. It's fueled by inefficient early models ("token maxing") and a culture where tech executives brag about their AI spending as a status symbol, a behavior not seen with traditional cloud costs. This suggests demand could normalize.

The vast majority of spending and market capitalization in AI today is in the infrastructure layer—compute (NVIDIA), foundation models (OpenAI), and data services. The entire application layer's revenue combined is a rounding error in comparison, highlighting a massive, though likely temporary, imbalance in where value is currently being captured.