I think you could be right. I do use excessive error-handling code and verbose comments — that's true.

But most of my time is spent on delivery, and the biggest problem with delivery is that if a bug occurs during runtime, the client curses me out. So to me, GPT code feels meticulous.

Open source contributors might be different. Most of them write code after long periods of deliberation. They take their brightest ideas and put them into open source. Those pieces of code are probably the best answers those programmers can give.

But for someone like me, who works primarily on delivery, we mostly plug in proven patterns and focus on getting things done. 'It works' and 'it's beautiful' are different terms, after all. In that sense, I highly value the meticulousness of GPT code — the very thing you called verbose. Because even if it's inefficient, at least it runs, and it catches and wraps around far more of the parts where things break.

Given a month, I could probably write code at GPT's level, at least to some degree. The problem is the difference between one hour and one month. At its core, AI code is still based on training data.