Or it could be like code=data and homoiconity and other CS fundamentals that were figured out 40+ years ago, but are still mostly ignored by software industry/culture.

I doubt such designs will find practical uses (iceberg maybe but the pure math designs seem unlikely).

I'm hoping because Quanta's explanation was approachable, but ultimately, wrong when I try applying it the following way:

Theorists will spend an enormous amount of time developing algorithms that are O(kNlog(N)) that are impractical in practice because

- K approximates infinity

- It is well-known K approximates infinity.

- It is not expected for K to decrease.

Working at a place who kept losing customers to a competitor whose software was less than 2x as fast as ours fundamentally changed how I view optimization and how I view constant overhead C. And crystalized once I saw how delivering steady gains milestone after milestone can buy a lot more goodwill than one fast and dirty optimization.

Speed doesn't matter if you're the only game in town (a monopoly). For everything else it matters.

So the DRAM experiments are apples to apples. It's actually the PMEM experiments, I think, that are comparing a new hash table on a new technology to previous hash tables that weren't designed specifically for that technology.