As much as we like to say lots of software jobs are just plumbing, the current state of consumer software indicates we have a long way to go in terms of quality.
Whatever training data is fed to an AI will not be better than the data used by human engineers to write code at the macro level. Ergo, the code will be worse in quality.
> Whatever training data is fed to an AI will not be better than the data used by human engineers to write code at the macro level. Ergo, the code will be worse in quality.
No, that's wrong, generally speaking. There's successful work on self-play for text generation. E.g. you can have AI to generate 1000 answers, then to evaluate quality of all of them, then to make it learn the best, and so on. As with self-play in player-vs-player games I'd expect this technique to be able to achieve superhuman results.
What are objective metrics for generated source code? "It compiles" is just the baseline. You could look at coupling and cyclometric complexity to start. But optimizing those doesn't necessarily produce great code (though I realize that was never the goal).
That's a detail irrelevant to your argument and my counterargument. The point is that there's data beyond human generated available for training, therefore you can't conclude it will forever be restricted to human-level.
Whatever training data is fed to an AI will not be better than the data used by human engineers to write code at the macro level. Ergo, the code will be worse in quality.