Anthropomorphizing artificial intelligence (AI) -- that is, attributing human characteristics to it -- is prevalent in public discourse. Using human-like voices or appearances is a prominent example. Similarly, the language used to describe AI applications is anthropomorphic. AI is "fed" with data, it "learns", "acts", and "decides". However, little is known about the effects of using such anthropomorphisms when describing AI. Previous research has shown that anthropomorphic appearance and design increase trust among people interacting with AI applications. To test whether this translates to anthropomorphism used in descriptions of AI, we conducted two pre-registered experiments comparing two conditions in which an AI application is either linguistically anthropomorphized or not. In the first experiment, participants read a fictitious newspaper article about the usage of an AI application in radiology. In the second experiment, participants completed a classification task using AI. Using a response-elicitation approach, participants had to judge their trust and distrust towards the AI system, and reliance was compared in the second experiment. We found no significant differences in trust, distrust, or reliance between anthropomorphic and non-anthropomorphic conditions. Subgroup analyses accounting for domain expertise and general attitudes towards AI yielded similar null results. These results suggest that anthropomorphic descriptions alone are insufficient to shift trust-related concepts (trust, distrust, and reliance). Further, the results indicate boundaries for metaphorical framing effects in general and, specifically, for trust.