's paper Software Libraries and the Limits of Reuse: Entropy, Kolmogorov Complexity, and Zipf's Law
keeps triggering insights. One is that high-level programming abstractions are essentially a compression mechanism: instead of writing a few dozen lines of C each time we want to invoke a function via a pointer embedded in a struct, for example, we derive one class from another. Similarly, if we want to construct one list by iterating over the elements of another, transforming each independently, we use a list comprehension instead of a loop, a conditional, and an assignment.
But here's the thing. Suppose you represent an image with three bytes per pixel (red, green, and blue). The worst thing a one-bit error can do is mess up one color channel at one point in the image (e.g., turn low green to high green). If you compress the image, though, then a one-bit error will almost certainly have a much greater effect: it can change the number of pixels of a particular color (if you're using run-length encoding), or affect every other pixel "after" a certain point (if you're using a more sophisticated adaptive encoding scheme).
I think the same is true of programs. If you mess up a function in a C program, you've messed up one function. Mess up a method in a class near the root of your inheritance hierarchy, and you've affected dozens of things; mess up a metaclass, or a generic that you're using in a bunch of different places, and the effects spread even more widely. I'm therefore wondering (after a particularly nasty debugging session) whether there's some fundamental tradeoff at work: eventually, the cost of each error in the high-level program outweighs the time saved by using the abstractions involved. Equivalently, the redundancy in lower-level programs might actually be a good thing, for the same reasons that redundancy is good in other evolved and engineered artefacts: it limits the damage that can result from something going wrong at any particular point.