A gem > Dawson’s first law of computing: O(n^2) is the sweet spot of badly scaling algorithms: fast enough to make it into production, but slow enough to make things fall down once it gets there
I think the other reason O(n^2) is a "sweet spot" is that it often arises from one O(n) algorithm calling another O(n) algorithm in each iteration, resulting in O(n^2) overall. Very often it's ultimately because the inner O(n) algorithm should have been implemented as O(1), but nobody bothered because it was never intended to be called in a loop.
sometimes it was correct to choose O(n) instead of O(1). if you know you're only going to have very few elements, linear search on a vector can be faster than a hashtable lookup or rb-tree traversal and use less memory for the data structure. later on some clown decides to expose your private function for their own convenience and starts calling it with thousands of elements.