What I believe GPU programming needs is tools that people can easily add to their current environments or that they can easily extend their current algorithms with. NumbaPro, OpenACC, and Thrust allow that.
In addition, the reason it's hard to express structured data and nonuniform computation is because those aren't things GPU architecture excels at. It excels at doing uniform operations over large chunks of memory. I'm sure that Harlan is pushing the boundary of what you can do on a GPU, but it's not going to exceed the limitations imposed by the hardware.
If you write simple GPU kernels (like those that Theano/Thrust/Copperhead/NumbaPro let you easily express) then you're mostly stuck "doing uniform operations over large chunks of memory". However, the latest GPUs are packed with features for going beyond this simple model. There's a rich set of global atomic operations, a fast register shuffle, better caching and most importantly: dynamic parallelism via nested kernel invocations. We're not programming for the G80 any more, Keplers can run a much larger swath of programs. Sure, you won't reach the theoretical peak FLOPS by traversing irregular structures via recursive kernels but you might still beat the pants off a CPU.
Structured data and nonuniform computations are very hard to express right now on the GPU and typically require a long and terrible process of "creative" CUDA programming. Harlan seems to be pushing the boundaries of what sorts of GPU programs we can effectively/efficiently implement. But, because the syntax is unusual, you're saying it's irrelevant compared to NumbaPro...which is what, a thin wrapper over elementwise array operations?