Context is the product, not the model

The model is not your product. The experience around the model is your product. Anyone can call Claude or GPT — the API is the same for everyone. Your differentiation is the data you have access to, the skills you’ve built, the UX you’ve designed, the reliability you’ve engineered, and how well you know the industry.

The real work isn’t prompting — it’s turning messy data into clean, structured context the model can use. Everything becomes markdown (narrative), CSV (structured data), or JSON metadata (searchable). This connects directly to why Files are the universal interface between humans and agents — when context is your moat, you need formats that both agents and humans can work with.

This is also why Decision traces are the missing data layer — a trillion-dollar gap represents a trillion-dollar opportunity — the “why” behind decisions is the highest-value context, and no one captures it systematically yet. The companies that own Markdown skill files may replace expensive fine-tuning also own the context layer. But context engineering only matters once the underlying model clears the capability bar — Model-market fit comes before product-market fit — without it, no amount of product excellence drives adoption shows that legal AI succeeded at 87% accuracy while finance AI failed at 56%, regardless of context quality. The enterprise data agent space validates this thesis dramatically — Data agent failures stem from missing business context, not SQL generation gaps shows that even well-connected agents fail without proper business definitions and source-of-truth resolution.