Everything you have described, apart from on-call, I think LLMs can/will be able to do. Explaining code, reviewing code, writing code, writing test, writing tech docs. I think we are approaching a point where all these will be done by LLMs.
You could argue about architecture/thinking about the correct/proper implementations, but I'd argue that for the past 7 decades of software engineering, we are not getting close to a perfect architecture singularity where code is maintainable and there is no more tech debt left. Therefor, arguments such as "but LLMs produce spaghetti code" can be easily thrown away by saying that humans do as well, except humans waste time by thinking about ways to avoid spaghetti code, but eventually end up writing it anyways.
people using GPT to write tech docs at real software companies get fired, full stop lol. good companies understand the value of concise & precise communication and slinging GPT-generated design docs at people is massively disrespectful to people's time, the same way that GPT-generated HN comments get downvoted to oblivion. if you're at a company where GPT-generated communication is the norm you're working for/with morons
as for everything else, no. GPT can explain a few thousand lines of code, sure, but it can't explain how every component in a 25-year-old legacy system with millions of lines and dozens/scores of services works together. "more context" doesn't help here
You could argue about architecture/thinking about the correct/proper implementations, but I'd argue that for the past 7 decades of software engineering, we are not getting close to a perfect architecture singularity where code is maintainable and there is no more tech debt left. Therefor, arguments such as "but LLMs produce spaghetti code" can be easily thrown away by saying that humans do as well, except humans waste time by thinking about ways to avoid spaghetti code, but eventually end up writing it anyways.