Together with next-generation ML accelerators in the CPU, the high-performance GPU, and higher-bandwidth unified memory, the Neural Engine makes M4 an outrageously powerful chip for AI. In case it is not abundantly clear by now: Apple's AI strategy is to put inference (and longer term even learning) on edge devices. This is completely coherent with their privacy-first strategy (which would be at odds with sending dat…
I know a shop who's doing this and it's a very promising approach. The ability to offload the costs of cloud GPU time is a tremendous advantage. That's to say nothing of the decreased latency, increased privacy, etc. The glaring downside is that you are dependent upon your users to be willing and able to run native apps (or possibly WASM, I'm not sure) on bleeding edge hardware. However, for some target markets (e.g. video production, photography, designers, etc.) it's a "safe" assumption that they will be using the latest and greatest Macs.
I've also been hearing people talk somewhat seriously about setting up their own training/inference farms using Macs because, at least for now, they're more readily available and cheaper to buy/run than big GPUs. That comes with a host of ops problems but it still may prove worthwhile for some use cases and addresses some of the same privacy concerns as edge computing if you're able to keep data/computation in-house.