"The reason LLMs are successful in writing code is because we’ve made a feedback loop that feeds the errors back to the LLM and loops until most errors are solved or hidden. Remember that LLMs can and do cheat too."
I've heard, and somewhat seen, that AI makes more code than necessary when fed large code bases. Then the context window isn't big enough to encompass the whole thing anymore, so it starts to create the issues you'll make it solve later. This feedback loop manifests in longer debug times, prompt adjustments, and greater lack of understanding overall.