I somewhat disagree. Maybe there are few compiled applications that can use those resource, but it's easy to write code that uses all of them.

I have a 40 core CPU machine with a GTX 1080 Ti GPU. I run deep learning models with 90% GPU utilization and those 40 cores are barely used.

I would love to have 3 more GPUs to run in parallel to test different neural network architectures. Sometimes I'll run a CPU script at the same that processes machine learning data that uses all 40 cores.

I would use 4000 CPU cores and 10 GPUs in a machine if I could get them and I don't even do machine learning full time. I'm personally happy to see this trend of more core counts.