I don't know if it's an appropriate / relevant question to ask, but how does it impact on a majority of programmers / ML practitioners (if they are not into the

I don't know if it's an appropriate / relevant question to ask, but how does it impact on a majority of programmers / ML practitioners (if they are not into the mathematical details)? Don't get me wrong, solving linear systems with more efficient methods is very appreciated but in my experience the coder only needs to choose which solver to use in a wrapped up library unless you are implementing a very low-level algorithm yourself. For a real example, if the solver isn't good at yielding a solution in a practical time frame (say if I'm fitting models on large genomic dataset of tens of thousands of genes without narrowing down the set of interests), then I might be wrong to use it at all. Isn't that right? Or am I wrong as the article might suggest you could do that in the future?