The AI Coding Trap
Chris Loy’s article The AI coding trap provides a structured framework for AI-assisted development. His core insight is that we should treat AI coding agents as exceptionally fast but limited junior engineers, applying traditional software engineering practices to avoid technical debt.
The fundamental shift
Traditional development involves extensive problem-solving before implementation. With AI, this reverses - we generate code immediately but then face the harder task of understanding what was produced. Loy calls this “the AI coding trap.”
AI as junior engineers
This perspective helps explain why productivity gains are often modest rather than revolutionary:
| Aspect | Junior Engineer | AI Coding Agent |
|---|---|---|
| Velocity | Improves with experience | Consistently high |
| Learning | Grows through experience | Limited to context engineering |
| Domain Knowledge | Develops over time | None without explicit context |
Applying structured practices
What I find valuable about Loy’s approach is how it systematises AI collaboration using proven software development practices:
- Specification first: Use AI to explore requirements before implementation
- Documentation focus: Generate and review documentation upfront
- Modular design: Scaffold architectures to control context scope
- Test-driven development: Create tests before writing code
- Coding standards: Apply house styles through careful prompting
This structured approach transforms AI from a code generator into a collaborative tool. The practices that work with human teams—clear specifications, documentation, and standards—prove equally essential when working with AI.
“By understanding that delivering software is so much more than just writing code, we can avoid the AI coding trap and instead hugely amplify our ability to deliver working, scalable software.”
The framework acknowledges that while AI handles implementation at unprecedented speed, human oversight remains essential for design, architecture, and maintaining system coherence.
