AI Writes the Code, You Build the Cage

As AI coding tools become the default, the bottleneck moves from typing speed to architectural judgment. A developer who cannot define module boundaries and failure domains will ship faster but break production more often, because AI optimizes for the happy path and has no model of implicit context or blast radius.

A production outage traced to a missing ErrorBoundary exposes a pattern: AI-generated code works on the happy path but silently omits failure isolation, and developers reviewing it often lack the mental model to spot the gap. The incident forces a return to first principles of software complexity. Accidental complexity—language quirks, type-runtime mismatches, implicit coercion—still demands human muscle memory during review; AI cannot perceive the danger. Essential complexity—the tangled business rules, historical compromises, and implicit context of real systems—leaks when modules lack clear boundaries, and AI only amplifies the mess.

The core discipline shifts from writing code to defining change boundaries. Modules should be grouped by why they change, not by superficial similarity. Interfaces exist primarily to isolate and contain complexity, not for reuse; reuse is often a premature bet. A five-layer boundary framework—business, change, data, interface, and failure—keeps complexity from leaking across modules. The hard rule that follows: never merge code you cannot read and explain, because incomprehensible code is a signal that complexity has escaped its cage.

The lone comment suggests a pragmatic, iterative approach: continue using AI coding tools until the failure-boundary problem is understood well enough to solve, then document the solution. No competing views or deeper technical arguments surfaced.