By building a feature that competes directly with startups using its own API, Anthropic demonstrates the "platform risk" inherent in the AI ecosystem. Like Amazon with its Basics line, foundation model companies can observe usage, identify valuable applications, and integrate them, creating a kill-zone for dependent companies.

Enterprises are currently overspending on tokens by sending all queries to the most powerful LLMs. A new software category will emerge to intelligently route requests to smaller, cheaper models when possible, creating a critical efficiency and cost-saving layer between companies and foundational model providers.

A key value proposition for vertical AI applications is being model-agnostic. They act as a strategic layer for enterprises, allowing them to route tasks to the best available LLM at any given time. This de-risks enterprise AI strategy from being locked into a single model provider whose performance may be surpassed.

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.

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.

Dan Sundheim argues that the biggest threat to LLMs is not their addressable market, which is nearly infinite, but the temptation to pursue too many verticals at once. Spreading a fixed-cost asset (the model) is economically rational, but history shows that companies rarely succeed when they simultaneously attack consumer, enterprise, and science without a focused A-team.

Leading AI companies like Anthropic are positioning themselves as the infrastructure layer for intelligence, akin to how AWS provides infrastructure for computing. Their strategy is to partner with and enable existing SaaS companies, not to destroy them by competing directly at the application level.

While AI labs could build competing enterprise apps, the required effort (sales teams, customizations) is massive. For a multi-billion dollar company, the resulting revenue is a rounding error, making it an illogical distraction from their core model-building business.

The AI value chain flows from hardware (NVIDIA) to apps, with LLM providers currently capturing most of the margin. The long-term viability of app-layer businesses depends on a competitive model layer. This competition drives down API costs, preventing model providers from having excessive pricing power and allowing apps to build sustainable businesses.