Foundational AI models will commoditize into a utility layer where companies buy "intelligence on the fly." The real, sustainable profit will be captured by application companies that leverage various models to solve specific business problems, as most enterprises lack the expertise to use raw models effectively.

Creating frontier AI models is incredibly expensive, yet their value depreciates rapidly as they are quickly copied or replicated by lower-cost open-source alternatives. This forces model providers to evolve into more defensible application companies to survive.

Early-stage AI startups should resist spending heavily on fine-tuning foundational models. With base models improving so rapidly, the defensible value lies in building the application layer, workflow integrations, and enterprise-grade software that makes the AI useful, allowing the startup to ride the wave of general model improvement.

While immense value is being *created* for end-users via applications like ChatGPT, that value is primarily *accruing* to companies with deep moats in the infrastructure layer—namely hardware providers like NVIDIA and hyperscalers. The long-term defensibility of model-makers remains an open question.

The AI value stack has evolved from chips (NVIDIA) to models (OpenAI). The next critical phase is the application layer. It's unclear if value will be captured by new application companies or if the underlying model providers will absorb all the profits, a key question for investors and founders.

Much like 'big data' evolved from a competitive advantage into a widely available commodity, AI models will likely follow the same path. So many sources will offer powerful models that they will cease to be a unique differentiator or a durable moat for businesses.

The assumption that building the most advanced AI model creates a defensible, high-margin business is collapsing. With competitors offering comparable performance at lower prices, the sustainable advantage shifts from owning the best intelligence to how that intelligence is productized and integrated.

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.

Despite high valuations, foundation models lack sustainable differentiation. Users will switch providers based on cost-per-token and performance, making it a highly competitive, low-margin commodity business, akin to a utility, that is currently mispriced by the market.