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.

OpenAI and Anthropic form a powerful duopoly at the "frontier" of AI, commanding premium prices like Apple. A second, commoditized tier of open-source and lagging models exists, where value is captured through compute and services, not the model itself. This creates a clear market separation between premium and "good enough" AI.

OpenAI's decision to slash prices on its smaller models isn't a discount sale due to struggling sales. It is a strategic maneuver to compete in the increasingly crowded market for more efficient models. This allows them to secure the lower end of the market while demand for their high-priced, frontier models remains incredibly strong.

To avoid having their core inference services commoditized, frontier labs like OpenAI and Anthropic will inevitably move up the stack. They will build applications that compete directly with their largest customers, such as those in legal tech or design, posing an existential risk for any startup building on their platform.

Current AI pricing models, which pass on expensive LLM costs to users, are temporary. As LLM costs inevitably collapse and become commoditized, the winning companies will be those who have already evolved their monetization to be based on the value their product delivers.

Anthropic is outpacing OpenAI by targeting enterprise clients. This market has fewer free substitutes and is less price-sensitive than the consumer market, leading to more reliable, high-margin recurring revenue and faster growth.

Gurley notes that major AI model providers like OpenAI and Anthropic are shifting from solely selling API access to building their own applications. This move up the stack signals a fear that being a pure model provider is not a defensible moat and could lead to commoditization.

To capture market share, AI labs are offering access to their latest models at prices far below their actual cost. This creates a short-term "price war" that benefits users with heavily subsidized access but highlights the industry's shaky unit economics.

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.