Earlier quoted context omitted.
You should be considered that you are intentionally missing the point by using an overly narrow and domain-irrelevant interpretation of "supercomputer". Do you think that the users of any of those supercomputers would turn down an offer to run their projects on Google or Facebook's fleet instead of their "one" supercomputer (given some time to adapt their software)
This brings me back to something I discussed here in the past - are there scientific problems that are simply not compatible with running in the cloud, and require a supercomputer? I can't, off the top of my head, think of anything where the performance would degrade enough with the increase in communication latency between components, that the increase in available (on-demand!) resources wouldn't keep up.
Edit: for clarity, finite difference and finite element simulations both involve discretizing a 3D volume into individual cubes (I believe other geometries exist, but are less common) and running an update loop which performs calculations for each of millions or more cells. Of course, models with spatially small divisions, and/or those designed to handle high frequency wave propagation, require potentially enormous amounts of memory (e.g. climate simulation, or large seismic dataset processing). Depending on what math you're solving, there are heuristics which you may be able to use to update only a subset of active cells, or divide up the model to split among multiple machines with minimal loss of accuracy while retaining high precision.