Have you seen the results when they let a trained model manage Borg? The power reductions were immediate, non-trivial, and performance stayed the same. There's your scheduling result for you.

Look at it this way. As the paper points out, a Hashtable is just a heuristic that works fairly well in the worst case and reasonably well in the average case. No one would argue that you couldn't hand-roll an algorithm that is better for a specific, narrowly defined data set. This paper is demonstrating that you don't have to do it by hand, you can train a model automatically without the expected caveats like: size (doesn't have to be huge), latency (the model trains very quickly) or specialized hardware (they used bog-standard CPUs, not GPUs or TPUs).

This is obviously just my opinion, but I think it's pretty big. It's not big because of the paper itself, although that's amazing in isolation (they improved on hashtables!). As I said above, it _points the way_. They have a throw-away sentence about co-optimizing data layout along with lookup and not showing those result. My guess is that beats a hashtable in every way. More importantly, if a model can beat bloom filters, b-trees, and hashtables, you'd better hold onto your socks because most algorithms are just waiting to fall. To me this paper is Deep Blue vs Kasparov. We all know what comes next.