The gulf between the world Python was written to address and the world we live in continues to grow. Python's native memory layout is actively hostile to any sort of performance on modern systems, so it's going to continue to have the problem that interacting with a faster runtime will require a ton of copying.

I've never been impressed by the claim that you can just drop in other faster languages into Python; the costs of communication are so staggeringly high that you have to write around it to have any gain, and by the time you've moved effectively your entire task into the underlying faster language, Python's gain is often quite minimal. NumPy works to some extent because it can fire off huge tasks from a single Python call, a lot of less numerical code can't do that, and I think the ML community still generally underestimates and fails to understand how much performance they leave on the floor in the Python portions of their code, at least based on my interaction with them.

Many ML practitioners aren't software engineers. I don't expect that cohort (non-engineers) would manage a deployment well in any language.

Can you give me an example from your recent memory?

I've been coding since the 90s, ASP, PHP, JS, C, Perl, I transitioned from Perl to Python back in 2012-2013. Dabbled in Go when it first came out because I was a Plan9 fanatic and recognized some of the source files, but never went further than tutorials.

Honestly I find very little wrong with the Python ecosystem, except the general insecurity of using package managers. But that applies to most package management, it's a social/infosec issue that Fedora has mitigated fairly well, if you want role models.

The languages that I found most annoying, as a user and developer, were C, Javascript, Typescript and Ruby.