The query itself represents information. If you can anticipate 100% of the ways in which you intend to query the information (no surprises), I'd argue there might be an ideal way to store it.
Requiring perfect knowledge of how information will be used is brittle. It has the major benefit of making the algorithm design problem tractable, which is why we do it.
An alternative approach is to exclude large subsets of queries from the universe of answerable queries without enumerating the queries that the system can answer. The goal is to qualitatively reduce the computational intractability of the universal case by pruning it without over-specifying the queries it can answer such as in the traditional indexing case. This is approximately what "learned indexing" attempts to do.
Given the domain name, I was expecting something about the physics of information storage, and some interesting law of nature. Instead, the article is a bad introduction to data structures.
"No single best way", meaning "it depends."
But don't let something like literacy get in the way of a opportunity to engage in meaningless outrage.
I'd love to be clued in on more interesting architectures that either attempt to optimize both or provide a more continuous tuning knob between them
https://en.wikipedia.org/wiki/Optimal_binary_search_tree#Dyn...
A consequence of there being no generally superior storage mechanism is that technologists as a community should have an agreed default standard for storage - which happens to be relational.
I'd say this is spiritually what the no-free-lunch theorems are about... Because whatever "AI model" / query system you build -- it is implicitly biased towards queries coming from one slice of futures.