Copilot Is Training Wheels You Should Eventually Remove

The Claim

AI coding assistants are incredibly useful, but over-reliance stunts growth. Use them to go faster, but make sure you can work without them.

Why I Think This

I’ve seen junior developers become dependent on Copilot in ways that worry me.

When the AI is there, they’re productive. When it’s not-network issues, the service is down, the context is too novel for good suggestions-they freeze. They’ve outsourced the “figure it out” muscle to a tool, and that muscle has atrophied.

The best developers I know use AI as acceleration, not as crutch:

• They understand what the AI suggests before accepting it
• They can write the code themselves if they need to
• They use AI for boring parts so they can focus on interesting parts
• They verify instead of trusting

The danger is when AI becomes a replacement for learning. If you tab-accept your way through everything, you don’t build the mental models that let you solve novel problems.

Training wheels are great for learning to ride. But at some point, you take them off. AI should be similar: a boost while you’re building skills, not a permanent substitute for those skills.

The Counterargument

Maybe the skill of “coding without AI” becomes irrelevant, like “coding without IDE autocomplete.” Why learn to do things the hard way if the easy way is always available?

And gatekeeping “real” programming is tiresome. If someone ships value using AI tools, who cares how they got there?

Where I Might Be Wrong

I’m assuming AI tools stay approximately as they are. If they get significantly better-good enough to handle novel situations reliably-maybe the “understanding” layer becomes less important.

And different roles require different skill depths. A developer building custom systems needs deep understanding. A developer integrating existing solutions might not. Context matters.

The Takeaway

Use AI tools. They’re genuinely useful. But regularly practice without them:

• Turn it off sometimes. Write code from scratch. Remember what you actually know.
• Understand before accepting. Read the suggestion. Know why it works.
• Learn the underlying concepts. Don’t just pattern-match to AI suggestions.
• Debug AI mistakes. When it’s wrong (it often is), figure out why.

The goal is AI-augmented skill, not AI-dependent incapacity.

The junior developers who grow fastest use AI to go faster, then deliberately slow down to understand what they shipped. The slow step matters.