As a programmer, I'm paid to solve novel problems, things that can't be solved by just glueing existing things together. But that's all LLM-generated code can do. And it's a qualitative problem, not a quantitative one. By its very nature, an LLM cannot come up with something novel. Which is why all of the examples in this article are highly formulaic and derivative ideas based on existing stuff, interesting as they may be to the people asking for them.
I can get quite well-running scripts from GPT for well-defined, menial tasks that I know would be easy to do, but just can't be bothered to fight with the mechanics and syntax in order to get it right. But for solving new and specific problems, that doesn't work.
Plus, technologies and languages evolve. If LLMs displaced programming, it would mean that progress has stopped. No LLM is going to come up with a nifty new technique or feature. It can only regurgitate what many ceative people first had to come up with.
You can have LLMs do your boilerplate, your helper scripts or your clearly defined, self-sufficient algorithms for you. But a real-world application is already impossible because an LLM will always be incapable of covering the big picture, of assembling parts into a whole that doesn't only work and make sense, but is also performant, secure, maintainable and extensible. LLMs won't kill the craft of programming, it will just lead those who fall for that to producing loads of crap that doesn't fulfill any of the above requirements, and then end up having to be trashed and redone from scratch by "real programmers".
The Go analogy doesn't work for the same reason. Go has well-defined rules everybody has to follow, and clear goals. Attacking this algorithmically is the extreme opposite of the idea of general artificial intelligence. That doesn't mean it's easy, but it's not even remotely a related problem.
There's a reason why the people swooning over GPT-based coding are always script kiddies, not professional developers.