The databases we use today in production have severe limitations and are not even close to what is theoretically possible. Many traditional parts of a database (indexing, caching, scheduling, et al) are AI-complete algorithm problems. Entire sub-classes of database (e.g. graph or spatial) famously have persistently poor scalability and performance because of open questions in the foundational computer science.
Just the fact that increasing the generality, scalability, and performance of databases asymptotically converges on designing AGI suggests that it is, in fact, "deep tech". And this property has to mesh with other practical constraints on database behavior. Many problems in databases are hard with little forward progress in decades.
It is true that most database research is not deep tech but there is ample room for it to be if one is sufficiently ambitious.
Just the fact that increasing the generality, scalability, and performance of databases asymptotically converges on designing AGI suggests that it is, in fact, "deep tech". And this property has to mesh with other practical constraints on database behavior. Many problems in databases are hard with little forward progress in decades.
It is true that most database research is not deep tech but there is ample room for it to be if one is sufficiently ambitious.