This paper reexamines the traditional distinction between aleatoric and epistemic uncertainty in AI, arguing that this dichotomy is problematic and hinders practical application, especially with large language models. It presents conflicting definitions and empirical evidence suggesting these two types of uncertainty are intertwined rather than separate. The article advocates for a shift towards a more practical view of uncertainty based on identifying sources and defining uncertainty by the tasks it is intended to address, rather than forcing it into the aleatoric/epistemic categories.