Well, if I understand your point you are refering to the fact that in many high level programming languages, the language itself is able to dected the most efficient method to do solve some linaer algebra problems, say solving linear systems.
However QR factorization (or other factorizations) may be usefull when you have to solve different problems but with the same matrix, so that factorizing once and for all gives you an advantage (for example many undetermined linear problems [1]). In this case knowing something about factorizations, or what to use when, may be helpful
In my modest experience the perfect Julia slogan would be:
"fast as C, easy as python, but NEVER the two together"
All the sentences:
"When you’re writing various algorithms, you don’t necessarily want to think about whether you’re on a GPU, or whether you’re on a distributed computer. You don’t necessarily want to think about how you’ve implemented the specific data structure. What you want to do is talk about what you want to compute."
sound nice.
Except in practice, unless someone else bothered doing that for you, you have to do it yourself.
[1] https://en.wikipedia.org/wiki/QR_decomposition#Using_for_sol...