IANAEE (I am not an electrical engineer).

Something nice about GPUs is that graphics rasterization is a very parallel problem. If you can handle thermals, power consumption, memory bandwidth, etc. that go with it, you can pretty much just add more of the parts that do the processing (in this case, CUDA cores - 3,584 of them) and get a more powerful GPU. A 1080 TI has 12 billion transistors. The 1080 had 7.2 billion. The 980 had 5.2 billion.

CPUs handle more branching problems, so it's harder to get improvements by just adding cores.

They're also smaller than the pre-1080 - 16nm down from 28nm, which lowers power consumption and therefore heat. It's a huge help and it's part of what allows them to just add more transistors in the first place - but in this area, GPUs will run into the same problems CPUs do. It's hard to shrink transistors.

Why not just write "I am not an electrical engineer"? Defeats the purpose if you still have to explain it.

It makes sense from the perspective of the fact that not all users may understand what the acronym means at first, and doing this is a great courtesy (also, it removes ambiguity in instances where you may be using an acronym similar or same as that for another domain - perhaps the domain of the forum or medium you are communicating on).

A considerable chunk of it is process improvements. Take a look at the chart at http://wccftech.com/nvidia-pascal-gpu-analysis/ that's about halfway down. nVidia's current generation GPUs are at 16nm. For comparison, Intel is at 14nm for their processors (though their 14nm process is better than most other chipmakers 14nm process) and hopes to start delivering 10nm this year, a year or so later than planned. So nVidia has a couple more process bumps to go, whenever they become affordable, and then they hit the wall like everyone else and have to focus mainly on architectural improvements for further performance gain.

CPUs are stuck at clocking 4Ghz and beyond, so graphics cards still have some headroom to grow into.