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, or mis-serve your new market. 

The multilingual and cultural gap in LLMs 

Most of the major models are still trained predominantly on English-language corpora, and that creates a double disadvantage when deployed in other languages. 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. 

The result: a model that works well for American users may stumble in India, the Gulf, or Southeast Asia, not because the business problem is harder, but because the system lacks the cultural-linguistic infrastructure to handle it. 

A regional example worth noting 

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. 

For a U.S. company entering the MENA region (Middle East & North Africa) or the South-Asia market, that means your “global” AI strategy isn’t global unless it respects local languages, idioms, regulation, and context. 

Token costs, language bias, and global ROI 

From a business perspective, the technical detail of tokenization matters. A recent article points out that inference costs for Chinese may be 2X English, while for languages like Shan or Burmese, token inflation can be 15X. 

That means if your model uses English-based encoding and you deploy in non-English markets, your usage cost skyrockets, or your quality drops because you cut back tokens. And because your training corpus was heavily English-centric, your “underlying model” may lack semantic depth in other languages. 

Add culture and normative differences into the mix: tone, references, business practices, cultural assumptions, etc., and you arrive at a very different competitive set: not “were we accurate” but “were we relevant.” 

Why it matters for executives expanding abroad 

If you’re leading a U.S. corporation or scaling startup into international markets, here are three implications: 

1. Model selection isn’t one-size-fits-all: you may need a regional model or a specialized fine-tuning layer, not just the largest English model you can license. 
2. Cost structure will vary by language and region: token inflation and encoding inefficiencies mean your unit cost in non-English markets will likely be higher, unless you plan for it. 
3. Brand risk and user experience are cultural: A chatbot that misunderstands basic local context (e.g., religious calendar, locale idioms, regulatory norms) will erode trust faster than a slower response. 

How to build a culturally aware multilingual AI strategy 

For executives ready to sell, serve, and operate in global markets, here are practical steps: 

• Map languages and markets as first-class features. Before you pick your largest model, list your markets, languages, local norms, and business priorities. If Arabic, Hindi, Malay, or Thai matter, treat them not as “translations” but as first-class use-cases. 
• Consider regional models or joint-deployment. A model like Mistral Saba may handle Arabic content more cheaply, more accurately, and more natively than a generic English model fine-tuned. 
• Plan for token-cost inflation. Use pricing comparison tools. A model may have a U.S. cost of $X per 1 M tokens, but if your deployment is Turkish or Thai, the effective cost may be 2X or more. 
• Fine-tune not just for language, but for culture and business logic. Local datasets shouldn’t just include language, they should capture regional context: regulations, business customs, idioms, risk frameworks. 
• Design for active switching and evaluation. Don’t assume your global model will behave locally. Deploy pilot tests, evaluate on local benchmarks, test user-acceptance, and include local governance in your rollout. 

The bigger ethical and strategic lens

When AI models privilege English and Anglophone norms, we risk reinforcing cultural hegemony. The technical inefficiencies (token cost, performance gap) are symptoms of a deeper bias: which voices, languages, economies are considered “core” versus “edge.” 

As executives, it’s tempting to think “we’ll translate later.” But translation alone fails to address token inflation, semantic mismatch, cultural irrelevance. The real challenge is making AI locally grounded and globally scaled. 

If you’re betting on generative AI to power your expansion into new markets, don’t treat language as a footnote. Language is infrastructure. Cultural fluency is a competitive advantage. Token costs and performance disparities are not just technical: they are strategic. 

In the AI world, English was the path of least resistance. But your next growth frontier? It might require language, culture, and cost structures that act more like differentiators than obstacles. 

Choose your model, languages, rollout strategy not on the size of the parameter count, but on how well it understands your market. If you don’t, you won’t just fall behind in performance: you’ll fall behind in credibility and relevance.