I think it's really interesting how complexity theory sort of falls apart in the face of hardware-specifics and concrete use cases. The motto in computer architecture is to make the common case fast (it's in like every textbook), whereas the motto in computer science is to make the program scale/to solve generically. When push comes to shove (as this article shows), the computer architects seem to have the final word, at least when it comes to making things go more brrrrrr.

There's so much stress and attention given to complexity theory for software engineers, to the point that people will cram for hours to make it through FAANG interviews. I understand that it's important and it's just something that everyone has to go through... but data structure and algorithm performance is a Wikipedia search away, and then you choose the appropriate STL container and move on with your life. The same can't be said for gaining an understanding of modern processors.

I'm not saying that one is more important than the other or vice-versa, I'm just saying that it seems wrong to me that not knowing algorithms and data structures can break an interview, whereas not knowing hardware is virtually a non-factor.

The big take away for me is this: if you're not benchmarking, you're cargo culting.