Software architecture has always been more about constraints than capabilities. A good architecture does not just describe what a system can do — it defines what it will not do, where the boundaries are, and how components relate to one another in ways that can be understood, tested, and changed over time. The value of architectural discipline is not immediately visible. It manifests as the absence of problems that would otherwise accumulate quietly and expensively.

For most of software’s history, vague requirements were expensive but survivable. A developer who received an underspecified ticket could ask a question, make a reasonable inference, or build a small prototype and get feedback. The cost of ambiguity was absorbed in back-and-forth, rework, and the gradual clarification that happened naturally when humans were iterating together. AI-assisted development has changed the economics of ambiguity in a way that most teams have not fully reckoned with.