Traditional SaaS switching costs were based on painful data migrations, which LLMs may now automate. The new moat for AI companies is creating deep, customized integrations into a customer's unique operational workflows. This is achieved through long, hands-on pilot periods that make the AI solution indispensable and hard to replace.

AI capabilities offer strong differentiation against human alternatives. However, this is not a sustainable moat against competitors who can use the same AI models. Lasting defensibility still comes from traditional moats like workflow integration and network effects.

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.

The AI industry is not a winner-take-all market. Instead, it's a dynamic "leapfrogging" race where competitors like OpenAI, Google, and Anthropic constantly surpass each other with new models. This prevents a single monopoly and encourages specialization, with different models excelling in areas like coding or current events.

An enterprise CIO confirms that once a company invests time training a generative AI solution, the cost to switch vendors becomes prohibitive. This means early-stage AI startups can build a powerful moat simply by being the first vendor to get implemented and trained.

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.

AI models are becoming commodities; the real, defensible value lies in proprietary data and user context. The correct strategy is for companies to use LLMs to enhance their existing business and data, rather than selling their valuable context to model providers for pennies on the dollar.

Unlike traditional SaaS where high switching costs prevent price wars, the AI market faces a unique threat. The portability of prompts and reliance on interchangeable models could enable rapid commoditization. A price war could be "terrifying" and "brutal" for the entire ecosystem, posing a significant downside risk.

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.