AI poses both an opportunity and an existential risk for minority languages. While tools like Abair (Irish), Aina (Catalan), and Project Vaani (Indian languages) show promise, 97% of the world's languages are now categorized as 'in danger.' Key problems include biased training data (93% of GPT-3's data was English), tokenizer inefficiency that multiplies compute costs for non-English words, energy infrastructure gaps in developing nations, and performance disparities (Claude Sonnet scores 94% in English reasoning vs. 76% in Yoruba). Solutions proposed include collecting high-quality native-speaker data, government and nonprofit support, small language models (SLMs), improved tokenizers, and accountability for cross-language performance metrics. A creative example from the Iñupiat people — a video game preserving their language — illustrates unconventional approaches to language preservation in the AI age.
