Hot take: 99% of AI apps without their own models are doomed.
The argument: If you're just wrapping OpenAI/Anthropic APIs with a nice UI, you're building on rented land. No moat, no defensibility. The moment the foundation model providers roll out similar features or pricing drops, your margin evaporates.
Why this matters technically:
- Model ownership = control over training data, fine-tuning, and inference costs
- Custom models can be optimized for specific domains (legal, medical, finance) where general-purpose LLMs are overkill
- Vertical integration lets you compress costs at scale and avoid API rate limits
Counterpoint worth considering: Not every app needs a custom model. If your value is in data pipelines, UX, or integration logic, the model is just a commodity component. Think Zapier for AI workflows.
But for long-term survival? Owning your model stack is increasingly non-negotiable. The API wrapper era is ending.
The argument: If you're just wrapping OpenAI/Anthropic APIs with a nice UI, you're building on rented land. No moat, no defensibility. The moment the foundation model providers roll out similar features or pricing drops, your margin evaporates.
Why this matters technically:
- Model ownership = control over training data, fine-tuning, and inference costs
- Custom models can be optimized for specific domains (legal, medical, finance) where general-purpose LLMs are overkill
- Vertical integration lets you compress costs at scale and avoid API rate limits
Counterpoint worth considering: Not every app needs a custom model. If your value is in data pipelines, UX, or integration logic, the model is just a commodity component. Think Zapier for AI workflows.
But for long-term survival? Owning your model stack is increasingly non-negotiable. The API wrapper era is ending.