While it is amusing to see an entire thread of unqualified bombastic statements, a GPU and CPU are two very effective tools at two separate types of tasks. While in the future GPU + CPUs may live on the same chip, and use similiar compilers, the task they specialize in will remain separate.

A GPU is excellent at batch computation, where it achieves a high throughput of computation by streaming data and keeping many computational threads in flight. That is it can hide the growing memory latency gap by filling it with lots of repetitive calculations. Did you notice OpenCL and CUDA run the same kernels or shaders on large arrays of data? Did you notice that GPU has 100s of cores. On some GPUs hundreds of thread in blocks run lockstep executing the same instruction on different data. Anything like branches, random memory fetches, accessing memory in an incoherent manner will quickly kill your GPU performance.

GPU flexibility will grow where it will match a CPU in terms of features, but if you mis-understand the type of workloads a GPU has been designed for you will be wasting a lot of good silicon.

A CPU on the other is optimized to minimize latency of memory fetches by all sorts of tricks like much larger caches, branch prediction, and much higher clock speeds than GPUs. This allows are each core to run different sequential code with lots of branches and random memory access patterns. That is clearly more suited for operating systems, buisness logic, network stacks, game AI, browser layout etc.

tl;dr:

GPU = MIMD, batch data processing type of work

CPU = SISD, everything else that requires random branching and random data

(I updated grammar and spelling becauseI know that's the most important part of any argument)