What you say may be true for consumer or even "web scale" technology, but not for high performance computing (of the science type).
In HPC, people can always use more computing power to improve their simulations or numerical solvers, and we are nowhere close to hitting a limit in usefulness (think e.g. faster/more detailed simulation of large scale structures, say airflow around aircrafts, n-body systems, simulation of bigger quantum systems in physics ...).
While massive parallelization is of course used wherever possible, it's sometimes more difficult to come up with algorithms that scale, and sometimes you simply have to much dependency between your data to parallelize beyound a certain point. This is the place where this stuff could be used first, before it's ready to "trickle down" to consumer electronics.
In HPC, people can always use more computing power to improve their simulations or numerical solvers, and we are nowhere close to hitting a limit in usefulness (think e.g. faster/more detailed simulation of large scale structures, say airflow around aircrafts, n-body systems, simulation of bigger quantum systems in physics ...). While massive parallelization is of course used wherever possible, it's sometimes more difficult to come up with algorithms that scale, and sometimes you simply have to much dependency between your data to parallelize beyound a certain point. This is the place where this stuff could be used first, before it's ready to "trickle down" to consumer electronics.