Ping me when the software stack for the AMD hardware is as good as CUDA.
Or do you mean the profiler tooling?
I hear everyone say that AMD doesn't have the software, but I'm a little confused --- have you tried HIP? And have you tried the automatic CUDA - HIP translation? What's missing?
CUDA runs on pretty much every NVIDIA GPU, this year they dropped support for GPUs released 9 years ago, and older binaries are very likely to be forward compatible.
Meanwhile my Radeon VII is already unsupported despite still being pretty capable (especially for FP64), and my 5700XT was never supported at all (I may be mixing this up with support for their math libraries), everyone was just led on with promises of upcoming support for 2 years. So "AMD has the software now" is not really convincing.
But if we're talking datacenter GPUs, the software is there. Data centers is where most GPGPU computing happens after all.
It's not ideal when it comes to hobby development, but if you're working in a professional capacity I'm assuming you're working with a modern HPC or AI cluster.
Of course the end goal was to run on a large HPC cluster we had access to, but for efficient development, support on personal machines was necessary. My personal dual 3090 setup has been invaluable for getting through debugging and testing before dealing with the queueing system on the cluster (Side note: it also ended up revealing another important benefit of consumer side support for GPGPU, a 3090 was easily matching the performance of a single node of the CPU-only version of the cluster, thus massively bringing down the cost of entry to an otherwise computationally restrictive topic).
And there wasn't a parts shortage (modulo some cryptocurrency mining, but that impacted both GPU vendors)
And ML models weren't so large as to make 8GB of vram sound meagre.
And there weren't a bunch of venture capitalists throwing money at the work, because the state of the art models were doing uninspiring things. Like trying to tag your holiday photos, but doing it wrong because they couldn't tell a bicycle helmet and a bicycle apart.
Meanwhile, priests like Intel with Itanium, Microsoft with WinRT, FOSS nerds, or AMD with GPUs will continue failing because most people ain't got no time to be preached to about how they achieve something.
From a consumer perspective, I agree. From a datacenter, edge and industrial application perspective though, I think those crowds are content funding an effective monopoly. Hell, even after CUDA gets dethroned for AI, it wouldn't surprise me if the demand continued for supporting older CUDA codebases. AI is just one facet of HPC application.
We'll see where things go in the long-run, but unless someone resurrects OpenCL it feels unlikely that we'll be digging CUDA's grave anytime soon. In the world where GPGPU libraries are splintered and proprietary, the largest stack is king.
The SW story has been bad for a long time, but it is perhaps right now better than you think