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.

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.

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.

Unlike sticky cloud infrastructure (AWS, GCP), LLMs are easily interchangeable via APIs, leading to customer "promiscuity." This commoditizes the model layer and forces providers like OpenAI to build defensible moats at the application layer (e.g., ChatGPT) where they can own the end user.

Unprofitable frontier AI companies are expanding into application-layer verticals like drug development as a defensive strategy. They aim to build defensible, high-margin SaaS revenue streams to prove their business model to investors before their core inference and training services are fully commoditized by cheaper open-source alternatives.

While many scrutinize the low gross margins of AI companies, the real threat is low switching costs. As foundation models rapidly leapfrog each other, application-layer revenue lacks the durability of traditional infrastructure software, making customer retention a primary investor concern.

Frontier model providers like OpenAI and Anthropic are under immense pressure to monetize beyond tokens, forcing them to compete at the application layer. Startups building on their platforms are providing valuable data that will be used to create competitive products, effectively training their own replacement.

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.

Companies like Meta and Microsoft are cutting back third-party model consumption in favor of internal alternatives. Because AI models function largely as interchangeable matrix-multiplication infrastructure rather than sticky software, switching between providers requires minimal technical effort, threatening foundation lab defensibility.