This is not at all comparable to that insane example you just gave, but a lot of people are just tool users in non tech companies. I know because I've seen it in finance. They're absolutely brilliant in their statistical concepts, but their programming skills leave a lot to be desired for because they don't have the background.
For instance, when you move from C++ to Python, you kinda realise after a month or so that normal loops won't cut it for stuff like numpy arrays etc due to the huge overhead. I realised this personally because I knew how quick C++ was and I wondered why the same code in Python was so massively slow, and I read up online to understand it. Similarly in matlab, for loops are very fast but still inefficient. So now when I write code, I use inbuilt libraries as much as possible and in matlab, I make everything into a matrix operation so it is blazingly fast once I scale it to a million data points.
My colleagues have no idea what that shit is. They'll know every step of a complicated algorithm and implement it simply using loops.
For instance, when you move from C++ to Python, you kinda realise after a month or so that normal loops won't cut it for stuff like numpy arrays etc due to the huge overhead. I realised this personally because I knew how quick C++ was and I wondered why the same code in Python was so massively slow, and I read up online to understand it. Similarly in matlab, for loops are very fast but still inefficient. So now when I write code, I use inbuilt libraries as much as possible and in matlab, I make everything into a matrix operation so it is blazingly fast once I scale it to a million data points.
My colleagues have no idea what that shit is. They'll know every step of a complicated algorithm and implement it simply using loops.