To build a durable business on top of foundation models, go beyond a simple API call. Gamma creates a moat by deeply owning an entire workflow (visual communication) and orchestrating over 20 different specialized AI models, each chosen for a specific sub-task in the user journey.

The "AI wrapper" concern is mitigated by a multi-model strategy. A startup can integrate the best models from various providers for different tasks, creating a superior product. A platform like OpenAI is incentivized to only use its own models, creating a durable advantage for the startup.

The notion of building a business as a 'thin wrapper' around a foundational model like GPT is flawed. Truly defensible AI products, like Cursor, build numerous specific, fine-tuned models to deeply understand a user's domain. This creates a data and performance moat that a generic model cannot easily replicate, much like Salesforce was more than just a 'thin wrapper' on a database.

The key for enterprises isn't integrating general AI like ChatGPT but creating "proprietary intelligence." This involves fine-tuning smaller, custom models on their unique internal data and workflows, creating a competitive moat that off-the-shelf solutions cannot replicate.

Strong AI products require a tight feedback loop where the product and model are deeply integrated. Thin wrappers around third-party models create weak, short-lived features that will be subsumed by the platform. A durable AI business treats the model *as* the product itself.

The best application-focused AI companies are born from a need to solve a hard research problem to deliver a superior user experience. This "application-pull" approach, seen in companies like Harvey (RAG) and Runway (models), creates a stronger moat than pursuing research for its own sake.

Most successful SaaS companies weren't built on new core tech, but by packaging existing tech (like databases or CRMs) into solutions for specific industries. AI is no different. The opportunity lies in unbundling a general tool like ChatGPT and rebundling its capabilities into vertical-specific products.

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.

To escape platform risk and high API costs, startups are building their own AI models. The strategy involves taking powerful, state-subsidized open-source models from China and fine-tuning them for specific use cases, creating a competitive alternative to relying on APIs from OpenAI or Anthropic.