Raytracing is not an ideal match workload wise for a GPU, too much divergence, but there are plenty of workloads where GPUs will have an edge for along time to come.
AMD cards offer comparable compute performance and are sometimes even better. What AMD lacks, though, are ready-to-use software packages like CUDA, cuDNN, cuBLAS, and the like.
NVIDIA basically bought their way into the research community and solidified their strong foothold by keeping CUDA proprietary.
It's fascinating how other companies (Microsoft) get bashed on a regular basis for using the exact same tactics (see Direct3D vs OpenGL on Windows), while people are perfectly fine with NVIDIA doing it...
I assure you, the affected scientists who have to fight tooth and nail for funding are less than impressed that they are dependent on the mercy of a single vendor.
This is really important, and I don't think people quite realise the scale. I was doing a Physics MSc in 2012 and it seemed that almost everybody in computational physics was doing some variant of reimplementing Y with CUDA.
That's not to say it was the wrong tool for many of the jobs – though for some it apparently was, judging by the levels of stress – but I did find it a bit weird how this was an approach to research that pretty much completely ignored vendor lock-in.