What's Happening

We have noticed a recurring pattern: AI coding tools (Cursor, Copilot, and various Agents) have multiplied code output several times over, but AI-written projects on average enter a "nobody dares touch this" state after three months—fix one thing, break three others. The reason isn't that AI is too dumb; it's that it doesn't maintain project structure.

Engineers in the community are revisiting a method proposed 20 years ago: Domain-Driven Design (DDD, whose core idea is to organize code by business boundaries). The specific approach is to allocate a "room" for each business type—user management, Agent, conversation, and knowledge base each stand independent, collaborating through well-defined interface calls rather than directly modifying each other's data.

This is being resurrected because AI has a very typical tendency: if it can directly access another module's data here, it will modify it on the fly. A single instance is unnoticeable, but after dozens, the entire project becomes what the engineering world calls a "big ball of mud"—lots of files, no one can tell who owns what.

Industry View

Supporters argue that DDD's "bounded context" (which can be understood as "the scope within which each type of business code is allowed to operate") is precisely the right remedy: when AI receives a new task, it searches and modifies only within the corresponding small scope, turning a global problem into a local one.

Unifying business vocabulary is also critical—if a project calls something Agent, don't call it Bot today and Assistant tomorrow, or AI itself gets confused.

Opposition isn't lacking either. One camp holds that DDD itself has many concepts and fine-grained rules, making it a burden for small and mid-sized teams—learning it may be more exhausting than writing code.

A more radical view: this is the responsibility of AI coding tool vendors—products like Cursor and Copilot should have built-in project structure awareness, rather than dumping a 20-year-old design methodology on users to learn.

There's also a sober voice: many AI projects never make it to the "maintenance phase" before being abandoned; DDD is solving a problem that doesn't yet exist.

Impact on Regular People

For Enterprise IT: Companies letting teams use AI to write code at scale should reserve 30-50% of project time early on for architectural governance; otherwise, the faster you develop, the faster technical debt accumulates, and the higher future refactoring costs climb.

For Individual Careers: The core competency of programmers is shifting from "writing code" to "designing boundaries," which actually makes senior engineers more valuable; newcomers need to take a "system design" course, or they'll only be able to write local code.

For the Consumer Market: Short-term impact is limited, but as more AI-written products ship, they will frequently bug out and require refactoring; users will gradually perceive that "this product keeps crashing."