Chatbots have in recent years increasingly been used by organizations to interact with their customers. Interestingly, most of these chatbots are gendered as female, displaying stereotypical notions in their avatars, profile pictures and language. Considering the harmful effects associated with gender-based stereotyping at a societal level—and in particular the detrimental effects to women—it is crucial to understand the effects of such stereotyping when transferred and perpetuated by chatbots. The current study draws on the Stereotype Content Model (SCM) and explores how the warmth (high vs. low) of a chatbot’s language and the chatbot’s assigned gender elicit stereotypes that affect the perceived trust, helpfulness, and competence of the chatbot. In doing so, this study shows how established stereotype theory can be used as a framework for human-machine communication research. Moreover, its results can serve as a foundation to explore ways of mitigating the perpetuation of stereotyping and bring forward a broader discussion on ethical considerations for human-machine communication.