Video playback only has dedicated hardware for the formats that exist when your CPU was made. A cpu with AVX-512 will do a lot better on AV1 decode/encode than one without until someone actually makes a cpu with hardware for it. Funnily enough, the best ML acceleration hardware on CPUs is AVX-512. For small neural nets, a well tuned CPU implementation will do way better than GPUs.
I'm not saying that there's no value in it, but rather that while you're right that it can produce a fair improvement over a traditional CPU implementation things like video are not the best examples because they're _so_ common that they get dedicated hardware which is usually considerably faster / more power-efficient so there's a narrow window for a couple of years where that SIMD implementation is most valuable.
ML is similar – many people are running these apps now but, for example, many millions of them are running Apple ML models on Apple hardware with acceleration features so again, while SIMD is unquestionably useful, I'm not surprised that the average working programmer doesn't feel a huge need to dive in rather than using something like a library which will pick from multiple backends.
