Moravec's paradox suggests that tasks easy for humans, like perception and mobility, are hard for AI, while tasks that challenge humans, like mathematics and logic, are easier for machines. Despite its popularity among AI researchers, the paradox lacks empirical testing. The claims surrounding it often reflect the interests of the AI community rather than objective evidence. The article argues that researchers focus too narrowly on tasks deemed interesting, ignoring many simpler tasks that AI and humans can both handle easily, as well as complex problems that are difficult for both. The evolutionary reasoning behind Moravec's paradox, proposed by Hans Moravec, claims that human sensory and motor skills are a result of extensive evolution, while abstract reasoning is a relatively recent development. This narrative appeals to researchers but lacks strong scientific backing. Tasks such as playing soccer, which are easy for humans but currently difficult for AI, generate interest because they represent challenges that AI could potentially overcome. Conversely, tasks that are easy for AI, like web searching, are essential for augmenting human productivity, although they’re often overlooked in discussions of Moravec’s paradox. The article emphasizes that simplistic predictions based on Moravec's paradox can lead to misguided alarmism about imminent AI breakthroughs or misplaced comfort in areas like robotics. Instead of relying on this flawed framework, the focus should shift to understanding the pace of AI advancement and the diffusion of new capabilities. The current landscape of AI development is uncertain, and researchers need to adopt a more nuanced approach to predicting which capabilities will emerge next.