I don't know specifically about OpenCL, but the big thing about CUDA is that it's supported across operating systems and across the whole spectrum of NVidia GPUs. AMD has ROCm, but they have to be dragged kicking and screaming to support any of the consumer grade GPUs. ROCm is still pretty much Linux only, with limited support on Windows and only the high-end consumer offerings (7900XT and 7900XTX) are officially supported for ROCm. Because they want to push compute users (and AI users) into workstation grade GPUs. In comparison, CUDA is usable and supported even on mid range NVidia GPUs such as RTX 3060. And most of the software around things like AI is done by enthusiasts with gaming PCs, so they write their software for the interface that supports them.
I have an AMD card that I try to use for AI stuff and it's an uphill battle. The most popular workloads such as Stable Diffusion or Llama more or less work. But as soon as you go into other, less mainstream workloads it starts to fall apart. Projects install CUDA versions of torch by default. To make them work you have to uninstall them manually and install the ROCM versions of torch. Then it turns out some Python dependency also uses CUDA, so you also have to find a ROCM fork for that dependency. Then there's some other dependency which only has a CUDA and CPU version and you're stuck.
I have an AMD card that I try to use for AI stuff and it's an uphill battle. The most popular workloads such as Stable Diffusion or Llama more or less work. But as soon as you go into other, less mainstream workloads it starts to fall apart. Projects install CUDA versions of torch by default. To make them work you have to uninstall them manually and install the ROCM versions of torch. Then it turns out some Python dependency also uses CUDA, so you also have to find a ROCM fork for that dependency. Then there's some other dependency which only has a CUDA and CPU version and you're stuck.