Having worked in the high performance computing field and in cloud hosted commercial applications, I can agree with the article but for entirely different reasons. The reason why some scientific computing shouldn't be done on AWS has to do with networking and latency between compute nodes. Supercomputers often use specialized networking hardware to get single digit microsecond latencies for data transfer between compute nodes and much higher network bandwidth than what you would normally find between EC2 nodes. This allows simulations to efficiently operate on really large data sets that span hundreds or thousands of nodes. The network topology between these nodes is often denser than a tree (think a 2D or 3D grid topology) and offers shorter paths between nodes.

All of this allows you to run code that you can't run in AWS unless it fits on one computer only. It's also way more expensive than clusters of commodity hardware.

For problems that are trivially parallelizable without much communication between nodes - I don't think that most universities can actually operate those cheaper than renting them from cloud computing services. A lot of these calculations don't take the staff to operate data centers, the cost of the building itself or the opportunity cost of using lots of space for this purpose vs something else into account. Economics of scale also kick in here. It's way cheaper per computer for AWS to admin a data center because they do this for orders of magnitude bigger data centers than your typical university.