Variable selection for predictive modeling really needed in 2016?

This question has been asked on CV some yrs ago, it seems worth a repost in light of 1) order of magnitude better computing technology (e.g. Let's assume the goal is not hypothesis testing, not effect estimation, but prediction on un-seen test set. So, no weight is given to any interpretable benefit. Second, let's say you cannot rule out the relevance of any predictor on subject matter consideration, ie. Third, you're confront with (hundreds of) millions of predictors.