While prompts are easy to copy, the complex engineering work to ensure reliability—validation, versioning, cost controls, and error handling—creates a true competitive moat. This "AI systems engineering" layer is where a product's long-term value and defensibility are built.
For an LLM's output to be useful in a software system, it cannot be treated as ambiguous text. It must be forced through a "hard boundary"—a strict schema or contract—that constrains, validates, and types the data, making it observable and safe for downstream services to trust and consume.
The industry's critical need is for engineers who can build the entire support system for an LLM: contracts, validation, observability, cost controls, and failure handling. This "AI systems" skill set is more valuable than simply being able to craft a clever prompt for a single input.