Look! The point is about predicting and intelligence. Doesn't matter what a predictor has around itself. It's just predicting. That's what it does.
And what does a (super)intelligence? It predicts. Very good, probably.
A dichotomy is needless.
Some examples:
I predict, you can't give me a counterexample. Where an obviously intelligent solution can't be regarded as a prediction.
This went under the name of SP theory, long ago. That the prediction, compression and intelligence are the same thing, actually.
Almost tautological, but inescapable.
predicting the best possible move in a given chess position
In order to do this you need training data on what the optimal move is. This may not exist, or limits you to only doing as good as the player you are predicting.
Additionally, predicting is inherently less optimal than search, unless your predictions are 100% perfect. You are choosing moves because you predict they are optimal, rather than because it's the best move you've found. If for example, you try to play by predicting what a chessmaster would do, your play will necessarily be worse than if you just play normally.
Yann LeCun, now of Facebook, was interviewed by The Register. It is interesting that his view of AI is apparently that of a prediction tool:
"In some ways you could say intelligence is all about prediction," he explained. "What you can identify in intelligence is it can predict what is going to happen in the world with more accuracy and more time horizon than others."
rather than of a world optimizer. This is not very surprising, given his background in handwriting and image recognition. This "AI as intelligence augmentation" view appears to be prevalent among the AI researchers in general.