The danger here is that in optimizing for fast and cheap you may be prematurely optimizing yourself away from being able to do "good" without a complete re-write. Fast and cheap has a seductive nature of getting good feedback on broad concepts, but it also tends to lead to very shallow implementation of these concepts. Like a lot of evolutionary algorithms, it is easy to get caught in a local maxima and not realize that you have only reached the top of a foothill while a competitor that can see where the mountain really is will have made less progress but will end up miles ahead if you discover that you climbed the wrong slope...