First, let's show that I can write a job that can produce more data that it inputs. I have a map of user to score, and want to compute pair wise summed scores for every user pair. This is clearly O(n^2) outputs. Computing pair wise scores is a common algorithm for recommender systems (I realize in practice you generally do not compute the entire space because it will be too slow. However your output will be closer to n^2 than n, ie, it will be much larger than your input.)
Now, this is a complicated example. A simpler example is "I want to compute aggregations on all the fields my log file." If you have N fields, hadoop is going to sort the data N times. Ie, you will be producing lots of intermediate data, almost certainly more than the input size, just by using map reduce (the code doing this merge sort is not your code, it's hadoop.)
But again, the point you keep missing and conveniently ignore when you quote my posts is that the point of map reduce on Aws is not about data locality (obviously) but about downstream flexibility. I can run 100 jobs, in parallel, on 10k machines, and output much more data than I input, without running a cluster of my own and I get to pay by the hour. I am isolated from other devs and spin the machines down when finished. If you buy the argument that this is a useful feature (as any EMR customer would attest too) than this too is a separate more pragmatic reason why the author has no idea what he is taking about when he says all EMR customers are a cargo cult.
