AVX512 is so kludgy that it usually leads to a detriment in performance due to the extreme power requirements triggering thermal throttling.

Edit. Furthermore, i think that none of these (pre-2020) low budget CPUs support AVX2, until Tiger lake released in 2020.

You can treat both SVE and RVV as a regular fixed-width SIMD ISA.

"runtime variable width vectors" doesn't capture well how SVE and RVV work. An RVV and SVE implementation has 32 SIMD registers of a single fixed power-of-two size >=128. They also have good predication support (like AVX-512), which allows them to masked of elements after certain point.

If you want to emulate avx2 with SVE or RVV, you might require that the hardware has a native vector length >=256, and then you always mask off the bits beyond 256, so the same code works on any native vector length >=256.

Also, it doesn't just speed up vector math. Compilers these days with knowledge of these extensions can auto-vectorize your code, so it has the potential to speed up every for-loop you write.

In Apple's case, they have both the GPU and the NPU to fall back on, and a more closed/controlled ecosystem that breaks backwards compatibility every few years anyway. But Qualcomm is not so lucky; Windows is far more open and far more backwards compatible. I think the bet is that there are enough users who don't need/care about that, but I would question why they would even want Windows in the first place, when macOS, ChromeOS, or even GNU/Linux are available.

SIMD is not limited to mathy linear algebra things anymore. Did you know that lookup tables can be accelerated with AVX2? A lot of branchy code can be vectorized nowadays using scatter/gather/shuffle/blend/etc. instructions. The benefits vary, but can be significant. I think a view of SIMD as just being a faster/wider ALU is out of date.