Evolutionary artificial life systems have demonstrated many exciting behaviors. However, there is a general consensus that these systems are missing some element of the consistent evolutionary innovation that we see in nature. Many have sought to create more “open-ended” evolutionary systems in which no stagnation occurs, but have been stymied by the difficulty of quantifying progress towards such a nebulous concept. Here, we propose an alternate framework for thinking about these problems. By measuring obstacles to continued innovation, we can move towards a mechanistic understanding of what drives various evolutionary dynamics. We propose that this framework will allow for more rigorous hypothesis testing and clearer applications of these concepts to evolutionary computation.