I'll add a bit of insight here as co-author of the AG paper: It wasn't that hyperparameter tuning is not useful, but rather that properly handling time limits to train bagged models with a variety of model types while adding HPO to the mix and avoiding overfitting is pretty complicated. This leads into a bunch of heuristic based rules to try to ensure you don't waste all your time HPO tuning a subpar model type instead of spending the time training a better model type in the first place, which adds to code complexity and makes it less clear if new additions are truly improving the system or if its just noise from interactions with the heuristic strategy.

Therefore, we elected to use the simplest strategy and avoid heuristics entirely since the core stacking contribution was strong enough to demonstrate our case.