I think this is essentially the same situation as Proton+DXVK for Linux gaming. I think that that is a net positive for Linux, but I'm less sure about this. Getting good performance out of GPU compute requires much more tuning to the concrete architecture, which I'm afraid devs just won't do for AMD GPUs through this layer, always leaving them behind their Nvidia counterparts.
However, AMD desperately needs to do something. Story time:
On the weekend I wanted to play around with Stable Diffusion. Why pay for cloud compute, when I have a powerful GPU at home, I thought. Said GPU is a 7900 XTX, i.e. the most powerful consumer card from AMD at this time. Only very few AMD GPUs are supported by ROCm at this time, but mine is, thankfully.
So, how hard could it possibly to get Stable Diffusion running on my GPU? Hard. I don't think my problems were actually caused by AMD: I had ROCm installed and my card recognized by rocminfo in a matter of minutes. But the whole ML world is so focused on Nvidia that it took me ages to get a working installation of pytorch and friends. The InvokeAI installer, for example, asks if you want to use CUDA or ROCm, but then always installs the CUDA variant whatever you answer. Ultimately, I did get a model to load, but the software crashed my graphical session before generating a single image.
The whole experience left me frustrated and wanting to buy an Nvidia GPU again...