That's a rather closed view of history. Vector processors were the norm in supercomputers until the early 2000s, when they were killed off by general purpose CPUs like SPARC and POWER. Intel itself has made two big bets on alternative architectures--Itanium (essentially VLIW) and Xeon Phi (CMP gone insane), the former of which failed miserably, and the latter of which appears to be quietly dying.

One of the major problems with developing alternative highly-parallel architectures is that most algorithms, particularly as they are written in source code today, simply aren't parallelizable. There's only so much ILP that can be extracted, and data parallelism isn't achievable without very heavy annotation of source code. Getting high data parallelism (as GPGPU does) is sometimes possible with algorithmic rewrites, but you're talking about rewriting several portions of your code to make that happen. That's the reason NVidia is successful--they've got an offload model, so you only need to rewrite the kernels that need parallelism, and they've got the proprietary lock-in on CUDA since no one wants to rewrite that code several times. But even then, there are classes of code that can't be run on GPGPUs very well because you just don't have the necessary data parallelism.