Overview
- AI coding assistants speed routine work such as boilerplate, common patterns, unit tests, and even quick reads of legacy code or simple data conversion.
- The generated code is a starting point without project context, so engineers must review it, correct it, and fit it into their specific systems.
- Code that looks clean can still skip required error handling or use libraries in risky ways, which plants hidden bugs, security flaws, and future technical debt.
- As typing matters less, higher-value work shifts to system architecture, clear scoping from vague needs, complex debugging across services, and mentorship.
- The article urges teams to treat AI like a fast junior, give small tasks, and verify each line against house style, allowed dependencies, and behavior under load.