That's basically what the twitter thread I linked explains. It sounds like there is an automatic propagation mechanism for updating downstream callers if the type hasn't changed, otherwise it sounds like a manual update process.
But then when it comes to ideas like this we just tend to say "we're trading one set of problems or another", as if we can't evaluate the problems in a similar manner. And I'm not picking on you here, I tend to do the same!
Yes, we're trading one set of problems for another, but what if the old set of problems was "O(n²)" and the new set of problems is "O(nlog(n))"? Or maybe it's the other way around. Why isn't it obviour how to apply those earlier skills here?
(0) It is easy to order two real numbers. 1 is greater than 0. But can we order points on the cartesian plane (1,2) or (2,1) or (0,100000000000)? Already at 2nd dimension not all points are easily ordered. There is solution "just give priority to 1st coordinate", but can you really completely disregard memory usage and focus solely on cpu?
(1) Theoretically, to get "best", one needs to evaluate not only O(n) of avg cpu usage, and avg memory usage, but also worst and best cases, while using knowledge of input data distribution (e.g. maybe input is almost sorted). Memory access patterns, cache locality, battery life, suitability for your hardware also must come into play. (many dimensions)
(2) Practially one has to do profiling on real hardware with real configurations / inputs / workloads. Different workloads may favor different algorithms/structures. (again results with many dimensions)
(3) Due to constrained time people will not even go over all algorithms. Real people will immediatly rule out really bad ones, then pick 1 or 2 algorithms that theoretically are good enough match, and see if their profiling results fit in cpu/memory budgets. (even if budget is not on paper, but just part of intuition)