This is a bit pessimistic. Most STOC papers don't directly give an incredibly practical algorithm. But what they do give is insight, and that insight can often

This is a bit pessimistic. Most STOC papers don't directly give an incredibly practical algorithm. But what they do give is insight, and that insight can often be used to improve the practical state of the art. And new bounds get you thinking; there's power in just knowing we can do better. It's like breaking the 4 minute mile -- a lot follows. From a personal perspective: I quite like some of the techniques they use in the STOC'22 paper, and I have some ideas about how to turn that into a practical improvement for some of my data structures, which _do_ take into account CPU and cache because I'm that kind of geek. (To save you some googling, I'm one of the co-creators of the cuckoo filter, and of the techniques that underlie many practical implementations of cuckoo hashing, particularly those with concurrent access, and some other stuff. A lot of what I do in practical data structures-meets-systems comes from seeing what's happening in theory and finding ways to apply it, even though in doing so I often give up on the optimality bounds, the insights that come from the theory folks are the basis of it.) Also, note that there are a few flavors of theory. A lot of US theory is quite on the math side of things, with a few exceptions (Mitz, quoted in the article, being one of them). But there's a very thriving applied algorithms community in the world, with a lot of energy in the area in both Europe and South America, and the stuff published in those venues -- again, often derived from or inspired by work such as this STOC paper -- is more immediately applicable.