Large Language Models (LLMs) invite a re-evaluation of the concept of intelligence by pointing to the unclear boundary between human and machine intelligence. The representational view of intelligence posits that intelligence requires: a) the existence of semantic representations, and b) the causal role of representations in driving behavior. This paper argues that the representational view is not well-founded. First, I challenge the idea of semantic representations as a requirement for intelligence as a consequence of our non-scientific and anthropocentric conception of intelligence. On a second step, I argue that even if such representations exist within AI systems, there are not sufficient reasons to claim they causally drive intelligent behavior. Finally, I propose some observable criteria for a less suspect concept of intelligence, drawing on scientific approaches from the cognitive sciences. These criteria suggest that LLMs, despite presumably lacking semantic representations, may still exhibit some degree of intelligence worthy of recognition. I conclude that requiring mental representations, and particularly their causal role, not only unjustifiably anthropomorphizes the concept of intelligence but also imposes unnecessarily high standards for intelligence attribution, including humans themselves. Therefore, a more operational and deflationary view of intelligence is preferable for understanding both human and machine intelligence.