OpenAI embraces the 'platform paradox' by selling API access to startups that compete directly with its own apps like ChatGPT. The strategy is to foster a broad ecosystem, believing that enabling competitors is necessary to avoid losing the platform race entirely.

Unlike social networks where user-generated content creates strong lock-in, AI chatbots have a fragile hold on users. A user switching from ChatGPT to Gemini experienced no loss from features like personalization or memory. Since the "content" is AI-generated, a competitor with a superior model can immediately offer a better product, suggesting a duopoly is more likely than a monopoly.

Counter to fears that foundation models will obsolete all apps, AI startups can build defensible businesses by embedding AI into unique workflows, owning the customer relationship, and creating network effects. This mirrors how top App Store apps succeeded despite Apple's platform dominance.

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 enduring moat in the AI stack lies in what is hardest to replicate. Since building foundation models is significantly more difficult than building applications on top of them, the model layer is inherently more defensible and will naturally capture more value over time.

The founder of Stormy AI focuses on building a company that benefits from, rather than competes with, improving foundation models. He avoids over-optimizing for current model limitations, ensuring his business becomes stronger, not obsolete, with every new release like GPT-5. This strategy is key to building a durable AI company.

Beyond its technical capabilities, OpenAI's app ecosystem within ChatGPT functions as a new distribution platform. For founders, this creates a strategic opportunity to build apps that serve as an interface layer to their product, opening a novel and potentially powerful channel for user acquisition and growth.

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.

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.