Last summer I built an AI chatbot almost entirely in Claude Code. It worked. I never pushed it to GitHub. I felt that putting my name on a public repo felt like making a claim I couldn't back up.
There is a particular kind of quiet that follows building something you don't feel entitled to. No matter how rewarding the project feels, somewhere behind your ribs a voice says: you didn't actually do that.
If you've felt it, you already know the argument I'm about to make against. The stigma, stated fairly
The criticism deserves better than a strawman, so here it is at full strength.
Skill comes from struggle. When you sit with a bug for three hours, you're not just fixing the bug - you're building a mental index of how this kind of thing breaks. The frustration is the encoding mechanism. Hand the struggle to a model and you get the fix without the index. Do that a thousand times and you've shipped a thousand features while learning almost nothing, and you won't find out until the day the model is wrong and you have no idea it's wrong.
There's a second, harsher version: that AI-dependent developers are pricing themselves as engineers while functioning as typists, and the industry hasn't caught up yet.
I think both of these are pointing at something real. I just think they've misidentified the cause.
The real failure mode
Here's the honest part, and I want to say it before the defense, because a defense that skips it isn't worth much.
AI absolutely can make you worse. I've watched it happen, and I've done it. The mechanism is specific: you accept output you haven't read. That's it. That's the whole failure. Not "using AI" - accepting without reading.
It's seductive because it works. The code runs. Nothing punishes you. You get a small hit of progress and you move on, and the debt is invisible because the thing you failed to learn doesn't announce itself. You only meet it later, usually at 11pm, when something breaks in a layer you never looked at.
A developer in that loop isn't a weak coder using a strong tool. They're a person who has outsourced the part of the job that was the job.
So yes. The critics are describing a real animal. But look at what they're describing: passivity. Not assistance.
Why training wheels are the right metaphor - and why most people use it wrong
People reach for the training-wheels comparison constantly, and they almost always use the lazy version: you start with support, then you take it off, happy ending.
The interesting thing about training wheels is that they're a genuinely mediocre teaching tool when used badly. They don't teach balance. They teach pedaling and steering while actively preventing the thing that matters. A kid whose wheels stay bolted at the same height for three years does not slowly become a cyclist. They become very good at riding a four-wheeled vehicle. Take the wheels off and they fall over exactly as hard as on day one.
The reason training wheels ever work is that a parent raises them. Half an inch at a time. The bike gets tippier, the kid gets uncomfortable, and the discomfort is the point - it's the balance loop finally getting a signal to learn from.
That's the whole argument, and it's not "AI is fine." It's this:
AI is a support that only helps if you keep raising it. Bolt it at one height and it will quietly hold you at the skill level you had when you started.
What raising the wheels actually looks like
Concrete, because the abstract version is useless. These are the rules I've converged on. They cost time - that's not a bug, it's the entire price of the thing.