As mentioned, that was off the top of my head. Some of it is "inside baseball" at Google, but there are a bunch of published papers about cluster scheduling.

As mentioned, that was off the top of my head. Some of it is "inside baseball" at Google, but there are a bunch of published papers about cluster scheduling. This one is a good overview, and has numbers, evaluation, lessons learned, etc.: https://research.google.com/pubs/pub43438.html https://research.google.com/pubs/pub43438.html My overall point is that even if learned indexes replace traditional data structures (which is a big if), plenty of important systems problems that remain. Some that I could think of: - Fixing front end performance as mentioned in a sibling comment. - System administration of a billion Android phones. The state of these seems pretty sorry. - Auditing app stores for security bugs. - Eliminating security bugs by construction. Hell, even just eliminating XSS. - Software that runs transparently in multiple data centers. (Spanner is a big step in that direction, but not a panacea.) I could name like 10 more... none of these have anything to do with learned indexes. The point is that the OP is generalizing from a really specific technique to some kind of hand-wavy "machine learning writes all the code" secnario.