Your OS, the linear algebra libraries themselves, much of the user-facing software that you use (latency sensitive rather than throughput sensitive), image/video encoding/decoding, most of the language runtimes that you use, high volume webservers, high volume data processing (where your data is not already some nice flat list of numbers you're operating on with tensor operations), for some examples.
Really, for almost any X, somebody somewhere has to do X with strict performance requirements (or at very large scale, so better perf == savings)
Most of these python libraries are only fast for relatively large and relatively standard operations in the first place. If you have a lot of small/weird computations, they come with a ton of overhead. I've personally had to write my own fast linear algebra libraries since our hot loop was a sort of modified tropical algebra once.
They asked for examples of non-numpy/tf/had use cases and I gave some including my own experience? No disagreement, HPC Python in practice is heavily biased towards numpy and friends
Very limited view which ignores way too many areas.