LLMs are becoming commoditized. Like gas from different stations, models can be swapped based on price or marginal performance. This means competitive advantage doesn't come from the model itself, but how you use it with proprietary data.

AI companies built to fill feature gaps on top of foundation models are at high risk. As core models rapidly improve, they often absorb these adjacent features, disintermediating the "wrapper" companies. Their early-adopter customers are also the quickest to switch to better tools.

The top-performing Large Language Model has changed multiple times in just a few years, from OpenAI's ChatGPT to Google's Gemini to Anthropic's Claude. This rapid evolution indicates that establishing a durable competitive advantage, or moat, in the foundational model space is extremely difficult.

As customers increasingly adopt model orchestration—routing tasks to the most efficient model for the job—value shifts away from individual frontier models. This trend commoditizes the raw intelligence layer, posing a significant threat to companies focused solely on building the largest models.

Contrary to the assumption of strong vendor lock-in, companies spending heavily on AI APIs are highly motivated and able to switch providers. If an open-source model can perform 90% of the work at 10% of the cost, this presents a significant structural problem for incumbent API providers.

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.

Large enterprises are avoiding commitment to a single AI provider like OpenAI or Anthropic. Instead, they're building control planes and abstraction layers that allow them to hot-swap the underlying models, mitigating technology risk and preventing dependence on one provider's terms of service.

The core business of creating AI foundation models is weak due to a lack of compounding advantage and defensibility. With leads being fleeting and tools like Open Router allowing customers to switch models instantly, companies are forced into a brutal price competition, making regulatory capture their only viable long-term strategy.

Unlike the cloud market with high switching costs, LLM workloads can be moved between providers with a single line of code. This creates insane market dynamics where millions in spend can shift overnight based on model performance or cost, posing a huge risk to the LLM providers themselves.