Well the problem with that is "this is a curve that roughly fits the data" is a bad way to go about constructing a model. It's a useful and neat observation, but that doesn't make it a good model. It might model random accesses to memory reasonably well (such as traversing a linked list, the example used there), but it doesn't model a scan over an array well. That doesn't make for a useful model. In contrast, the external memory model assumes a fast internal memory of size M (e.g., cache), which can be accessed in constant time, and a slow external memory of infinite size (e.g., RAM) which can only be accessed in blocks of size B (e.g., a cache line). Then you count how many blocks the algorithm needs to read or write. Now, scanning an array of size N takes O(N/B) I/Os, whereas scanning a linked list of the same size takes O(N) I/Os. The complexity of sorting is O(N/B log(N/B)/log(M/B)) I/Os. This models the same behaviour in a much cleaner way, applies equally to all levels of the memory hierarchy (you can also view RAM as the internal memory and a hard disk/SSD as the external memory), and is widely used in what you called "serious academic work" :) See also https://en.wikipedia.org/wiki/External_memory_algorithm

Furthermore, the introduction in the article you linked misunderstands Big-O notation so incredibly fundamentally that I don't think the author has done their background reading on machine models and Big-O notation.