At the heart of AI applications to scholarly communication are processes of summarisation and the ranking  of “too much” information. Considering how unequal dynamics of attention, such as the Matthew effect, have shaped the existing scholarly literature, Jefferson Pooley argues academic AI tools will smuggle these biases back into new academic hierarchies in ways that are increasingly difficult to audit.