1 AI Can Hinder Fundamental Skill Development
When AI generates code, juniors may not fully understand the intent, making it harder to identify and fix hallucinations or subtle errors. This creates a dangerous "knowledge gap" where developers deliver code without understanding how it works.
Key learning processes that AI can interrupt:
- Gaining proficiency through repetition
- Learning by correcting your own mistakes
- Spending "quality time with the debugger"
- Understanding code intent and architecture
One contributor describes this as "Dunning-Kruger on steroids" - where younger developers "just trust it now, because they literally don't know better."
2 AI is Augmentation, Not a Shortcut for Inexperience
The consensus is clear: AI works best when you already understand what you're trying to accomplish. It's a powerful tool for acceleration, not education.
AI is excellent for:
- Learning more about concepts you're already familiar with
- Automating repetitive or "boring" parts of coding
- Getting a "first draft" to refine manually
- Small to mid-scope changes you understand
Agentic tools that automate extensive code generation are particularly dangerous for juniors, as they encourage turning your "brain off" and rewriting features "until there aren't any errors left" without ensuring correctness or understanding.
Think of AI like "training wheels for programming" - they can be counterproductive for learning the dynamic balance skills needed to truly master development.
3 Critical and Deliberate Approach is Essential
To mitigate risks, both juniors and seniors must approach AI with deliberate intention and critical evaluation.
Best practices for AI usage:
- Ask AI to explain its approach and reasoning
- Request resources for concepts you need to understand better
- Review and understand all generated code before using it
- Use AI for "first drafts" then manually refine and optimize
- Focus on small to mid-scope changes you can verify
The ultimate goal: "Focus on understanding how the code works instead of learning how to code manually" - learning from each project to improve for the next.
The Chef Analogy
Using AI for programming is like a chef using a powerful new kitchen appliance. While it can speed up preparation of familiar dishes, a new cook still needs to master fundamental cooking techniques and understand the ingredients to truly create a good meal, rather than just relying on the machine to "blink" a dish into existence.
Quick Takeaways
- AI should enhance your existing skills, not replace learning fundamentals
- Spend time debugging and understanding code manually to build real expertise
- Use AI as a learning partner, not an automated solution generator
- Always review and understand AI-generated code before implementing it
- Focus on small, manageable AI-assisted changes you can verify
- Avoid "agentic" tools that encourage mindless code generation