While educators and educational researchers in many fields are struggling to develop policies and pedagogical approaches that include or at least address the use of generative artificial intelligence tools, it is particularly challenging in computer science education since such tools are fundamentally changing the state of the profession. In this position paper, we take a look at pedagogical approaches from other subjects with a longer history and a more extensive body of educational research, hoping that doing so can help us gather some insights on how this challenge can be met. We draw on recent neurological research to find subjects that share cognitive commonalities with computer science and extend the comparison that others have drawn between language and programming education. We consider how the reading wars'' andmath wars'' have shaped literacy and mathematics education, which we see as conflicts between less effective top-down pedagogy and more effective bottom-up pedagogy, and reflect on what would be comparable approaches in teaching computing. We find that approaches that make heavy use of large language models without teaching fundamentals can be compared to the top-down pedagogy of teaching reading and mathematics and are likely to be ineffective. Therefore, like many others, we caution against their use among novices. However, we also acknowledge that the social science surrounding computer science education is complex and that effectiveness only tells a part of the story, with other factors such as engagement, motivation and social dynamics also being important.