Yes, now, after letting the whole ecosystem die by not supporting OpenCL 2.x for ~10 years, which in turn caused the whole ML field to turn into a Nvidia/CUDA monoculture.
Going by wikipedia it took AMD almost two years to release an SDK with full OpenCL 2.0 support and Intel wasn't that much faster. Most of the Open Source implementations also seem to have died around the OpenCL 1.2 mark, with some having incomplete 2.0 support. So while you can probably blame NVIDIA for kicking a dead horse it looks as if something else got to it first.
I believe OpenCL 2.0 had a mandatory feature that nvidia couldn't support (maybe something about sharing pointers between GPU and CPU?). OpenCL 3.0 solved this by making many features optional.
I tried to create a ML framework[0] that would work on both CUDA and OpenCL (and natively on the CPU) around 2015/2016, which included creating FFI wrappers for both CUDA and OpenCL. This is where my experience on the subject (and my contempt for NVIDIA) comes from.
Me memory isn't perfect, but IIRC the situation was roughly the following: We were quite short on resources (both devtime and money), which meant that we had to choose our scope wisely. Optimally we would have implemented both CUDA and OpenCL 2.0, but we had to settle for OpenCL 1.2 (which offered reduced performance, but was "good enough" for inference). IIRC OpenCL 2.0 was very very similar in what capabilities it assumed and offered to the CUDA version at the time, and cards like the GTX Titan X had "compute capabilities" that supported features like shared virtual memory between CPU and GPU in CUDA at the time. In fact the advances around memory management (and async copying) that were present in CUDA and not in OpenCL 1.x were the main source for the performance differences between the two.
From everything that I can tell at that point in time, if NVIDIA would have wanted to support OpenCL 2.0 they could have done so based on technical requirements. What the reason for not doing so is, is just pure speculation (lack of internal resources due to focusing on devtools?), but to me it always looked like they were using the edge they got via their proprietary libraries like cuDNN to get a foot into the field of ML and then purposefully neglected OpenCL to prevent any competitors from catching up. Classic Embrace, Extend, Extinguish.
Maybe you're right, however OpenCL 2.2 came out 4 years ago and (almost) nobody has adopted it yet, so the problem could be with the spec. OpenCL 3.0 was adopted by nvidia (although OCL 3.0 is the same as 1.2 with some optional features added)
But on that very same wikipedia page you can see it took less than a year for both intel and AMD to release openCL 2.0 drivers, yet nvidia didn't even start evaluating that for years.
Then again, it sounds like OpenCL 2.0 required some flexibility the nvidia drivers or hardware wasn't able to provide.
It's pretty hard to speculate to which degree nvidia was intentionally sandbagging here, and to which degree it really was stuck.
However, it's a member of khronos, and it's hard to believe they as such a major manufacturer could not have either said beforehand that the spec was a problem or simply complied with it; as both AMD and intel did.
Also, given CUDA's success it's all rather convenient for nVidia - at the very least it looks like they didn't mind leveraging their market position for continued market dominance, even if it's unclear whether that was an intentionally anti-competitive aim from the get go, or simply a fortunate happenstance they didn't try to avoid.
Then again; with antitrust enforcement mostly remaining in vaporware mode it's a little hard to blame them.
