In software development, the rise of generative AI assistants like GitHub Copilot promises efficiency but raises critical questions about skill development. One experienced developer has instituted a firm rule for junior team members: turn off AI code generation entirely. This stance stems from a practical, high-stakes example that exposes deeper risks beyond mere bugs—risks that threaten the very foundation of becoming a proficient engineer.

The Silent Corruption Trap in AI-Generated Code

The author recounts merging two SQLite databases with differing schemas, a task requiring precise handling of ID mappings to avoid data corruption. Using an AI tool (likely Claude or DeepSeek-R1 via aider.chat), they requested a script to merge 'houses' and 'rooms' tables while updating relational IDs. The AI produced functional-appearing code quickly, but with a critical flaw:

new_id = id_mapping.get(old_id, old_id)

This line uses Python's dictionary .get() method to avoid crashes by defaulting to the original old_id if a mapping is missing. However, this 'silent fallback' could corrupt data if the mapping is incomplete—for instance, due to concurrent database writes or future code changes. Unlike a deliberate crash, which alerts developers to investigate, this error propagates invisibly. As the author notes:

"Correct output matters, and I like that. For the project I’m currently working on, 'silently wrong output' is one of the very worst things we can do."

Why Human Errors Differ From AI-Generated Pitfalls

Junior developers make similar mistakes, but the root cause and remediation differ starkly. When a human writes flawed code, mentors can probe their reasoning (e.g., "Why did you choose .get()?") and correct misconceptions through dialogue. This fosters growth, turning errors into teachable moments. In contrast, AI's 'black box' output lacks explainable intent—there’s no 'why' to discuss, only a pattern-missed output. Reviewing such code becomes a superficial exercise, bypassing the cognitive engagement needed for skill acquisition.

The Expertise Void: How AI Short-Circuits Seniority

Three scenarios illustrate the problem:
1. Seniors using AI: Time pressure and automation bias reduce thorough code reviews, increasing risk in critical systems.
2. Juniors writing code manually: Mistakes become opportunities for mentorship, building intuition and accountability.
3. Juniors relying on AI: The developer disengages from the creative process, missing repetitions and feedback vital for deliberate practice.

Referencing Veritasium's research on expertise, the author emphasizes that mastery requires a "valid environment, many repetitions, timely feedback, and deliberate practice." AI-assisted coding erodes this foundation, particularly for juniors. Even mid-level developers risk stagnation; as the author observes, "If you offload significant parts of programming to an LLM, you may be faster at outputting things of a similar level, but you won’t tackle fundamentally harder projects."

For senior developers, avoiding AI isn’t just about risk mitigation—it’s about preserving the joy of craftsmanship. Programming at the right abstraction level offers intellectual satisfaction that prompts alone can’t replicate. As the tech industry grapples with AI's role, this cautionary tale underscores that true proficiency isn’t just about writing code, but understanding it deeply enough to prevent disasters only humans can foresee.

Source: Based on the blog post by Luke Plant, available at https://lukeplant.me.uk/blog/posts/why-im-not-letting-the-juniors-use-genai-for-coding/