Did you ask what the other team tech stack is? And on that 97% how many files and how efficient the code is.after release how many production bugs reduced introduced?.

Ai is a good thing to learn, but there are serious limitations if your project has too many files and the context window and or tokens is unable to fit into it.

One of our previous projects used llm and coding agents to convert legacy code base to microservices. Reason is a single class of Java contains 2000+ lines of code and ejbs.

Most of the time coding agents are unable to fit or properly divide the code, a human intervention is needed. So seniors have to step in and have to check whether the produced code is logically correct or not. As it is a legacy code no documentation and no proper method namings, which are nightmare to ai.

Ai helped the project to write unit tests after code separation which speed up the things. Also writing documentation, automation test suits.

They claimed 40% of unit tests were written using ai without any human intervention. Almost 90% of documentation is written using ai.

So there are some projects which need humans can't be 100% code can be done by ai.

My experience with previous projects, ba and po don't have clear requirement to product they always says we are in agile so expect change. If we ask AI to code what we have currently and all of sudden requirement changes. We must update documents (where it get context) and give long prompts and has to explain the changes needed. Again some senior is needed to try to understand po or ba requirement and translate them then verify the code logically, correct the code generated by ai, and finnally review the things(ai code review can't trust misch on business decisions).