Spec-driven development emerges as answer to AI coding complexity
This approach turns AI from a code generator into a more reliable development partner—one that builds what you actually need.
Engineering teams struggle with AI-generated code that looks polished but solves the wrong problem. Ankit Jain's piece in The New Stack captures why this happens and what we can do about it. Sean Grove from OpenAI makes the case that specifications—not prompts or code—are becoming the fundamental unit of programming. The real bottleneck isn't writing code; it's knowing what to build and gathering requirements properly.
The article explains how AI doesn't remove software complexity, it just moves it to debugging and deployment. When you prompt AI to "implement user permissions," you might get clean RBAC code that completely misses your need for temporary access windows or existing identity provider integration. Spec-driven development requires engineers to document specific input/output formats, business rules, integration constraints, and security requirements before any code gets written.
For product and legal teams, this matters because it forces clarity about what we're actually building before we build it. The specs become version-controlled documentation that agents can validate against. Jain also introduces "Runbooks" as a way to make AI coding collaborative across teams, which addresses the knowledge transfer problems we see when engineers leave.
This approach turns AI from a code generator into a more reliable development partner—one that builds what you actually need.
Ankit Jain
