> Why are supercomputers still popular?
Scaling.
A very easy out for me to explain is imagine if you could only simulate two molecules in a Molecular Dynamics simulation and both had to have 100 atoms or less. There's plenty of opportunity, sure. But what if you wanted to simulate an entire strand of RNA interacting with a protein? There are techniques and stable approximations to avoid the O(n^2) complexity of a molecular dynamics simulation but the next-generation hardware allows you to run the same simulations with the same limitations at a much bigger scale, or the previous-sized problem without as many assumptions. edit: There are other considerations of course; I am vastly oversimplifying with this example.
Also, the more simulations you can run the more uncertainties you can account for in Monte Carlo computations, so models can evolve to contain more details and less assumptions.
adding response to:
> That way instead of doing a "big bang" upgrade such as this, you just upgrade individual nodes as the technology allows and are almost always "current."
The problem is that programming targeting a supercomputer usually involves taking advantage of specific features of the hardware to get the bleeding edge performance. Mixing and matching hardware quickly makes deployment of the code difficult in addition to writing the code to reliably squeeze every ounce out of every chip.