> I’m not talking about CPU architecture here at all. I’m talking about Information Theory, which they absolutely should be taught, and Physics, which often eno

> I’m not talking about CPU architecture here at all. I’m talking about Information Theory, which they absolutely should be taught, and Physics, which often enough is a requirement for graduation. At least at schools where CS is lumped into the college of engineering. I would expect any reasonable reputable CS institution to have do this as part of their curriculum. It just is not relevant for the theory of Big-O. If you want to use some of that knowledge in practical applications, then it is relevant, but I expect graduates to be able to make that connection by themself. It is also part of advanced algorithmic theory anyway. > Nobody has ever sorted strings in O(nlogn) time. Nobody has ever inserted String->Object mappings into a hash map in O(1) time. Stop lying to intellectuals. They either learn to distrust you or you break them if they don’t. Big-O is not about "sorting in O(n log n) time", it's about the growth of the algorithm to do so as the input grows. It can obviously only hold as long as the assumptions hold, in this case that comparison is constant time. What that constant is, is of no importance for what you use Big-O for. I'd rather teach intellectuals what Big-O is (upper limit on growth when input size increases), rather than trying to get them to use it for something it isn't (measure runtime as input increases). It's in the very definition of the concept, and at least where I got my degree, it was drilled into our head "it's an upper bound, constants do not matter as the input grows sufficiently".