I have one: scientific software. There's a growing recognition that Python is creaking under the weight of scientific applications that lean on it. It's very difficult to write efficient, maintainable, large-scale software systems with it.

An example that comes to mind is data reduction pipelines for the bytes coming out of a major telescope, like that of LSST. I worked on those pipelines, and a lot of the effort was around figuring out how to consistently build the dang thing given the looseness of Python's systems. An equally large amount of the effort was on trying to eke out the performance of Python via Numpy tricks, Numba, etc, which impinged readability and maintainability.

In some places, we fall back on a C++ core, but nobody is happy about that, for all the usual reasons. Some of us advocated for Rust... but it was never really plausible, because the interactions with Python numerical and scientific libraries are so immature. People are starting to work on it but it's hard to compete with the decades of work put into C++ and Python as the core of scientific stacks.