This isn't true, often even in trivial cases. Auto-vectorization is actually quite fragile in 2026. The reason is that it's subject to (a) scalar float semantics (i.e. the resulting code must not produce different results from the scalar version), and (b) a number of opaque compiler heuristics that sometimes work out, sometimes don't.

For example, consider you want to compute the average of a list of floats. The compiler cannot autovectorize this, because float addition is not commutative. However, it's much faster to do component-wise addition in groups, then a horizontal sum at the end, and then divide. Whether it matters depends on your use case, and the compiler unfortunately can't read your mind, so it has to be conservative.

The autovectorizer afaik rarely emits optimizations for the different vector units to support + efficiently caches the CPUid check to happen once on program start. It’s a good baseline but the continuum (today) is scalar -> auto vectorized -> portable SIMD -> hand rolled explicit. That portable SIMD lets you bridge into hand rolled explicit ergonomically is a power auto-vectorization doesn’t have. Either the compiler does it or doesn’t but you have no way to even have a check that says “fail to build the program if this function isn’t vectorized”. This is important if you’re relying on that property and someone accidentally adds a data dependency and breaks the optimization without you realizing. Portable and explicit SIMD don’t have this problem by definition.

but like, this isn't the case