There's no reason you can't do both: prototype a simple model to get a baseline performance, then use AutoML to fine-tune it, plus you now have a value to sanity check against.

Those hours of hyperparameter search aren't blocking. You can do other things while it's searching, or do the search when not actively using the resources (e.g. overnight).

It depends on what algorithms you use in AutoML. If you decide to use simple algorithms: logistic regression, decision tree, random forest then you will have a simple model very quickly. Using Neural Networks in AutoML requires much more computational resources.

if you know optimal hyperparameters from the beginning, then yes, you can build such model manually. But in most cases this is not the case.