Despite recent breakthroughs in deep learning and large language models (LLMs), language technologies continue to exhibit critical failure modes in multilingual and multicultural contexts. This talk provides examples of these systemic weaknesses across a wide range of language technologies from real-world case studies and benchmark evaluations. We highlight how language models often underperform or behave unpredictably when dealing with non-dominant languages, dialects, or culturally-specific content. These failures, ranging from dangerous mistranslations to culturally insensitive chatbot responses, reveal persistent gaps in data, modeling and evaluation. We call for deeper attention to inclusion, representativeness, and equity in language AI development.