The AI Coding Trap Date: 28th September 2025 Author: Chris Loy Reading time: 9 minutes --- The Nature of Software Development Coding is often just the final step in a larger problem-solving process. Most developer time is spent thinking — understanding requirements, domain, abstractions, side effects, incremental testing, and debugging. Writing code is akin to filling in answers on a complex crossword puzzle. How AI Changes the Coding Process AI coding agents (like Claude Code) can write code quickly but lack full understanding of complex systems. Large Language Models (LLMs) can't hold an entire app's context, causing human developers to spend time decoding AI-generated code. This leads to a disparity between fast code generation claims (10X faster) and actual delivery improvements (~10%). The Tech Lead’s Dilemma Experienced engineers who become tech leads face a choice: Fair delegation: Sharing work fairly, promoting team growth but potentially slowing delivery. Mollycoddling: Taking on hardest work themselves for speed, which risks team brittleness and burnout. Mollycoddling yields short-term delivery gains but long-term team fragility as knowledge becomes siloed. Balancing Delivery and Team Growth The solution is a middle path: Implement practices that minimize rework. Promote sustainable collaboration. Encourage learning and ownership in team members. Examples of such practices: Code reviews Incremental delivery Modular design Test-driven development Pair programming Quality documentation Continuous integration AI as Lightning-Fast Junior Engineers Modern AI coding agents: Deliver code far faster than junior engineers. Do not truly learn, only improve via better prompts or new models. Developers can approach using LLMs in two ways: AI-driven engineering: Using best practices for sustainable, understandable code. Vibe coding: Rapid, unstructured coding that sacrifices maintainability and eventually fails. Vibe coding can work for small prototypes but breaks down with complexity. Avoiding the AI Coding Trap Engineers must act as tech leads for AI agents, managing and guiding their output. Integrate AI throughout the software lifecycle, not just coding: Specification: Refining requirements and edge cases. Documentation: Creating clear, reusable docs upfront. Modular Design: Structuring code for clarity and context control. Test-Driven Development: Writing tests before code. Coding Standards: Enforcing style and best practices via prompt engineering. Monitoring & Introspection: Using AI to analyze logs and system behavior. By acknowledging that software delivery is more than code writing, developers can harness AI as a powerful tool to scale delivery without falling into the trap of unsustainable, messy code. --- Summary: The article highlights the challenge of integrating AI coding agents effectively into software development. While AI accelerates code writing, successful delivery requires human-led processes to ensure maintainability, team growth, and sustainable engineering practices. Treating AI as super-fast junior engineers and applying proven team practices throughout the software lifecycle can avoid the pitfalls of "the AI coding trap."