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.
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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.
Simply offering the latest model is no longer a competitive advantage. True value is created in the system built around the model—the system prompts, tools, and overall scaffolding. This 'harness' is what optimizes a model's performance for specific tasks and delivers a superior user experience.
For specialized, high-stakes tasks like insurance underwriting, enterprises will favor smaller, on-prem models fine-tuned on proprietary data. These models can be faster, more accurate, and more secure than general-purpose frontier models, creating a lasting market for custom AI solutions.
Instead of simply adding AI features, treat your AI as the product's most important user. Your unique data, content, and existing functionalities are "superpowers" that differentiate your AI from generic models, creating a durable competitive advantage. This leverages proprietary assets.
While today's focus is on text-based LLMs, the true, defensible AI battleground will be in complex modalities like video. Generating video requires multiple interacting models and unique architectures, creating far greater potential for differentiation and a wider competitive moat than text-based interfaces, which will become commoditized.
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.
If a company and its competitor both ask a generic LLM for strategy, they'll get the same answer, erasing any edge. The only way to generate unique, defensible strategies is by building evolving models trained on a company's own private data.
The concept of "sovereignty" is evolving from data location to model ownership. A company's ultimate competitive moat will be its proprietary foundation model, which embeds tacit knowledge and institutional memory, making the firm more efficient than the open market.
Instead of offering a model selector, creating a proprietary, branded model allows a company to chain different specialized models for various sub-tasks (e.g., search, generation). This not only improves overall performance but also provides business independence from the pricing and launch cycles of a single frontier model lab.