Thanks. One benefit of AutoML is that you are certain to get a high performing model. Which is not guaranteed in manual hyperparameter tuning. So I think in many cases it will be worth it.
I wonder instead of creating a model from scratch, if it is possible to take a known architecture that has performed well in a similar task, and tune from there. In that way a huge computational power might be saved. Kind of like Transfer Learning in NN.