Can your AI adapt to multiple cultures?

"In the race to deploy large language models and generative AI across global markets, many companies assume that "English model → translate it" is sufficient. But if you're an American executive preparing for expansion into Asia, Europe, the Middle East, or Africa, that assumption could be your biggest blind spot. In those regions, language isn't just a packaging detail: it's culture, norms, values, and business logic all wrapped into one. If your AI doesn't code-switch, it won't just underperform; it may misinterpret, misalign,"

"For example, a study found that non-English and morphologically complex languages often incur 3-5X more tokens (and hence cost and compute) per unit of text compared to English. Another research paper places around 1.5 billion people speaking low-resource languages at higher cost and worse performance when using mainstream English-centric models."

"Take Mistral Saba, launched by French company Mistral AI as a 24B-parameter model tailored for Arabic and South Asian languages (Tamil, Malayalam, etc.) Mistral touts that Saba "provides more accurate and relevant responses than models five times its size" when used in those regions. But it also underperforms in English benchmarks. That's the point: context matters more than volume. A model may be smaller but far smarter for its locale."

Companies often assume English-trained models can be translated for other markets, but language embodies culture, norms, values, and business logic. English-centric training creates higher token counts, greater compute costs, and poorer performance for morphologically complex and low-resource languages. Studies show non-English languages can incur 3-5x more tokens and that about 1.5 billion people speak low-resource languages facing worse results with mainstream models. Regional models tailored to local languages can outperform larger general models in relevance and accuracy. Market entry requires models that code-switch and reflect cultural-linguistic infrastructure to avoid misinterpretation and misalignment.

Read at Fast Company