The common argument I've heard: because then you would have to decide how many experts models are required, train and evaluate them separately, and overall make

The common argument I've heard: because then you would have to decide how many experts models are required, train and evaluate them separately, and overall make your architecture dependent on this choice. If your expert is wrong and miscalculates how many models are required then your entire architecture is also likely to be wrong (humans, am I right?). Researchers at Google's scale prefer a single model where you throw all your data in a single bin and get perfect performance out, no tweaking and no pesky humans required.