Domain-Driven Design matters more when AI writes your code
AI can shorten implementation without shortening discovery. This essay gets the center of Domain-Driven Design right: the valuable part is not a diagram, a document, or a fashionable folder structure. It is the repeated work of learning the business, naming concepts precisely, separating contexts, and turning that shared understanding into software. An agent can draft an impressive model, but the team has gained little if nobody can explain which assumptions it made or recognize when the real domain contradicts it.
The useful tension is that a domain model still needs artifacts. Code records the implemented model, tests hold selected invariants, and decision notes preserve reasons, but none of them is the whole understanding. That makes DDD more valuable and easier to counterfeit at the same time. The practical standard is not how much domain language an agent can generate. It is whether engineers and domain experts can use the same vocabulary to challenge a design, keep changes small enough to review, and notice when an apparently correct implementation solves the wrong problem.