What happens to the learning process when the difficult part of coding becomes optional?
Today, you can paste the error into an AI assistant and get a possible explanation almost immediately.
This distinction becomes even more important when you're learning something new.
If I already understand Python and ask AI to help me optimize a piece of Python code, that's one thing.
If I'm learning Python and ask AI to write every function I don't understand, that's very different.
In the second case, I may finish the project without actually learning how I built it.
That would be like saying developers should stop using debuggers because debugging used to be harder.
If I'm spending thirty minutes trying to remember a syntax detail that I could verify in ten seconds, I don't think that struggle is valuable.
But if I'm trying to understand why an architecture behaves the way it does, immediately asking AI for the answer may deprive me of an important learning opportunity.
If I encounter a problem I believe I can solve, I give myself some time to reason about it before asking AI.
I might: read the error inspect the code form a hypothesis test the hypothesis search the documentation try a solution
The AI's answer becomes much more useful because I have a mental model of the problem.
Instead of always asking AI to produce something, we can ask it to challenge us.
As AI becomes better at answering questions, the skill of asking good questions becomes more valuable.
If I ask AI everything immediately, I may become extremely efficient at retrieving answers.
But someone who never learns basic arithmetic because a calculator exists has a different problem.
The difference is whether we're using AI to avoid thinking or to improve our thinking.
Problem ↓ Think ↓ AI assists ↓ Code ↓ Test ↓ AI challenges ↓ Human evaluates ↓ Learn