In this article, I describe a novel position on the semantics of artificial intelligence. I present a problem for the current artificial neural networks used in machine learning, specifically with relation to natural language tasks. I then propose that from a metasemantic level, meaning in machines can best be interpreted as radically contextualist. Finally, I consider what this might mean for human‐level semantic competence from a comparative perspective.

Linguistic meaning underdetermines what is said. This has consequences for philosophical accounts of meaning, communication, and propositional attitude reports. I argue that the consequence we should endorse is that utterances typically express many propositions, that these are what speakers mean, and that the correct semantics for attitude reports will handle this fact while being relational and propositional.