I assume that this primarily benefits games and not any deep learning right? The most attractive aspect of Mac M1 is the huge memory boost. Might not be great for training due to the inability to distribute across multiple cards, but it makes for a great inference engine for stable diffusion, llama, and other large models.
SYCL is Khronos Group's vendor-neutral, high-level programming framework. Application support is limited, but hopefully with Intel's backing, the situation would gradually improve. Meanwhile, Vulkan Compute sidesteps the entire headache with compute shaders. But I'm not familiar with it in terms of application support.
SYCL can be implemented on top of OpenCL and OpenCL's SPIR-V extension. It soon turned out that this route is unfeasible due to prevalent vendor lock-in that's not going to change anytime soon, so it has largely been abandoned by everyone else but Intel and Mesa. Right now SYCL is usually implemented by backends to GPU vendor's respective APIs, like ROCm, HIP or CUDA. Doing the same for Metal would be very challenging.
Mesa already has experimental support of OpenCL w/ SPIR-V on Intel and AMDGPU, so theoretically it can be extended to Apple Silicon. Difficulty of implementing OpenCL's SPIR-V extension should be comparable with Vulkan compute shader (which also uses SPIR-V). However, currently OpenCL on Apple Silicon is entirely unsupported. The last time I checked, it's on the roadmap.