Also there are still considerations like domain, team expertise, org ecosystem etc. to consider. I love to use Rust for most things, but now I'm working with an org that primarily has expertise in Java, and I'm not going to rock the boat for barely any reason. Python is also still useful for most ML stuff, and Django is quite a pleasure to work with (although it wouldn't be my first choice).
The great thing about LLM-assisted coding is that an experienced software engineer can acquire decent familiarity with a language quite quickly. And then has a useful sparring partner for understanding and using the quirks and features of a new language.
Same here, working with a team that knows Java, so I'm letting Claude write Java.
If I compare the results to another team that uses Python with Claude I see slightly better results on the Java side. Not because Claude knows that better, but because the tools are more rigid by default which creates more of a self correcting loop for Claude. The Python side has Pydantic, but it's a bit of an afterthought, while in Java you can't skip the type checking.
In the end you can do the same things on both sides, it's 95% a team/engineering culture difference. So pick the language that the team knows best.