Petabytes of training data is only one application of PyTorch, which is going to use tens of thousands of containers, but...

Inference, development cycles, any of the application domains of PyTorch that don't involve training frontier models... all of those are complicated by excessive container layers.

But mostly dev really sucks with writing out an extra 10GB for a small code change.