I think a distinction between "perfect" and "imperfect" can easily be made, in this case. Computers are great at executing algorithms, and never learning from

I think a distinction between "perfect" and "imperfect" can easily be made, in this case. Computers are great at executing algorithms, and never learning from their experiences. Humans are great at learning from their experiences, but pretty hit-or-miss when it comes to following algorithms. It is probably exactly that learn-ability that trips us humans up: we can learn new and beneficial things, but the flip side is that we can learn the wrong things. Clearly, we need both of these traits (predictability, learn-ability), and should probably delegate the tasks according to each others strengths. Of course, there is nothing fundamentally different between computers and humans (modulo some philosophical assumptions); in principle, we could implement very complex artificial neural networks, to the point that computers being deterministic might be as much of a useless distinction as considering sociological phenomena as deterministic - fine in a deterministic universe, but a pretty useless perspective for all practical purposes given the immense set of variables. We then gain learn-ability, or AI, but we also lose predictability. So we would probably still delegate the task of compiling programs to a very deterministic program over an incredibly advanced AI that, while sometimes having some good and creative insights when it comes to generating output code, sometimes also learns the wrong stuff and gives us just flat-out wrong output.