Indic NLP research maps cultural costs of language AI homogenization

The paper's core claim isn't that Indic NLP is underfunded (true but known) but that infrastructure optimized for scale actively erodes the knowledge systems it aims to preserve. The tension is technical, not just social: homogenization happens through architecture choices, not negligence.

This directly extends the finding from MSQA (July 1) that language fluency doesn't guarantee cultural competence. Where MSQA showed models fail on culturally grounded reasoning despite multilingual training, this work argues the failure is baked into how we build systems. The YOMI-Bench paper on Japanese kanji (same week) surfaces a related constraint: character-level semantics in non-Latin scripts remain unsolved, suggesting that treating all languages as interchangeable data streams misses civilizational structure. Together these papers expose a pattern: scaling multilingual capacity without redesigning for linguistic specificity produces systems that are fluent but hollow.

If researchers release Indic-language benchmarks that explicitly test for preservation of cultural worldviews (not just accuracy on translated tasks), and if those benchmarks show degradation as model scale increases, that confirms the paper's core claim. If instead performance tracks only with training data volume, the homogenization thesis remains theoretical.