I hated AI.
Not on principle. Because I had spent years digging. Stack Overflow at two in the morning, GitHub issues, reading a file until I understood why it actually broke rather than why it appeared to. Effort was the filter. It was the thing that separated the people who could from the people who could not, and I had paid for my side of that line.
Then a kid with two months of experience started shipping a working project in five minutes.
I want to be precise about what I felt, because I think a lot of people feel it and do not say it. It was not fear of being replaced. It was something smaller and uglier: the suspicion that the thing I had bought with my nights had just been handed out for free.
The sentence that moved me
Two people changed my mind. One was a teacher of mine. The other was a friend of his. Separately, weeks apart, they said the same thing:
You are used to a manual gearbox. Somebody just handed you an automatic. Your advantage is not that you can drive. It is that you know what is happening under the hood.
I nodded politely and did not understand it for months.
What finally landed was the inversion hidden inside it. I had been reading the situation as a loss, as though a scarce skill had been devalued by mass production. The opposite was true. The skill had not been devalued. It had been made optional, which is a very different thing, and optional skills do not disappear. They become uncommon.
Scarcity, not erasure
Before, everyone had to dig. There was no other route from a stack trace to a fix. Because the effort was compulsory, it was also ordinary. Everybody around you had paid the same price, so the price bought you nothing in particular.
Today nobody is forced to. You can ship a feature without ever asking why the library behaves that way, and most of the time it works, which is exactly what makes it dangerous. The understanding is still there to be had. Almost nobody acquires it, because nothing in the daily loop requires it anymore.
That is the whole shift, and it runs the other way from how it is usually told. The supply of people who understand deeply is shrinking. The demand for them has not moved. Anyone who says this makes expertise worthless has the arrow backwards.
What it actually looks like
This is the part that sounds abstract until you have done it, so here is the concrete shape of it.
A model will hand you code that is syntactically perfect, idiomatic, well named, and wrong in a way that will not show up for six months. It will confidently call a method that behaves differently than its name suggests. It will reproduce a pattern from the project's most-copied file without noticing that the file is the one everybody complains about. It will give you the answer that is most common on the internet, which is not the same as the answer that is correct here.
None of that is caught by reading the output carefully. It is caught by already knowing the terrain. By having read the library, by recognising that two siblings in the same class disagree with each other, by having a sense of what this codebase does when it is healthy.
That sense cannot be prompted into existence. It is the residue of having dug, and it is the only part of the job that is getting scarcer rather than cheaper.
Where that leaves me
I use AI every day now. I stopped resisting it a while ago, and resisting it was never the interesting position anyway.
But I am not ahead because I use it. Plenty of people use it. I am ahead, on the days when I am ahead, because I can tell when it is wrong, and because telling when it is wrong is a thing I can only do about the parts of the stack I actually took the time to learn.
So the advice I would give my past self, the one who was quietly resentful watching a beginner ship in five minutes, is not "learn the tool". Everyone learns the tool. It is: keep digging in the places nobody is forced to dig anymore. That is where the margin moved.
I am Ayoub Hammoudi, a backend and AI platforms engineer in Paris. More of my work is at github.com/Ayoubhm07.
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