AI-Generated Code Works Fine — But Maintaining It Is a Growing Engineering Problem

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AI tools have sharply reduced the time and effort needed to write code, but the resulting implementations often touch dozens of files, duplicate existing dependencies, or include tests that lack meaningful coverage. Engineers reviewing AI-assisted pull requests frequently struggle to understand why certain abstractions exist or how to safely modify them later. This gap between code that passes tests and code that a team can confidently maintain is emerging as a core challenge in AI-assisted development. Experts argue that 'reviewability' should be treated as a hard engineering constraint alongside correctness and performance. The recommended approach is to use AI to accelerate implementation while relying on human engineering judgment to ensure only the simplest, clearest solutions enter the codebase.

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