Alright, with proof of work you indeed waste energy, but it's still just tangentially related - if you do it on CPU, you are probably wasting even more. However

Alright, with proof of work you indeed waste energy, but it's still just tangentially related - if you do it on CPU, you are probably wasting even more. However, I see e.g. my Core-M laptop as vastly underused - if I run Deep Learning there, it's on CPU, which is inefficient, but given it has only Intel HD graphics with OpenCL for which I don't have a working TensorFlow/Keras, I might have it 100x slower than I could. But I still use CPU for simpler Deep Learning models when I need it "on the road", though wish I could use iGPU. OTOH when I have a problem that requires AVX-512, it's much more efficient to compute it on latest Xeon, but if I only have a stack of single-precision GPUs, I might use those even knowing my results will be less precise and they would take longer to compute?