As language technologies have become more sophisticated and prevalent, there have been increasing concerns about bias in natural language processing (NLP). Such work often focuses on the effects of bias instead of sources. In contrast, this paper discusses how normative language assumptions and ideologies influence a range of automated language tools. These underlying assumptions can inform (a) grammar and tone suggestions provided by commercial products, (b) language varieties (e.g., dialects and other norms) taught by language learning technologies, (c) language patterns used by chatbots and similar applications to interact with users. These tools demonstrate considerable technological advancement but are rarely interrogated with regard to the language ideologies they intentionally or implicitly reinforce. We consider prior research on language ideologies and how they may impact (at scale) the large language models (LLMs) that underlie many automated language technologies. Specifically, this paper draws on established theoretical frameworks for understanding how humans typically perceive or judge language varieties and patterns that may differ from their own or their perceived standard. We then discuss how language ideologies can perpetuate social hierarchies and stereotypes, even within seemingly impartial automation. In doing so, we contribute to the emerging literature on how the risks of language ideologies and assumptions can be better understood and mitigated in the design, testing, and implementation of automated language technologies. [This paper was published in: "Universal Access in Human-Computer Interaction (HCII 2024) Proceedings, LNCS 14696," edited by M. Antona and C. Stephanidis, 2024, pp. 271-290.]