Opinions expressed by Entrepreneur contributors are their own. Many AI startups launch first in English because the models, benchmarks, developer tools and enterprise buyers are easiest to find there. That approach can help a company reach the market quickly, but it can also cause founders to overlook a much larger multilingual opportunity. According to the International Telecommunication Union, 2.2 billion people remained offline in 2025, most of them in low- and middle-income countries.
As more of these users come online, many will expect digital products to work in the languages they use every day — not merely offer translated versions of English-first experiences. They represent a significant growth opportunity for companies prepared to build multilingual products. I have seen this challenge firsthand through my contributions to the Government of India’s BHASHINI and BhashaDaan initiatives and as an expert contributor to C-DAC’s Vikaspedia. BhashaDaan crowdsources speech, text, translation and image-labeling contributions for Indian-language technologies, while Vikaspedia provides knowledge across social-development sectors in India’s scheduled languages.
Contributing to these initiatives reinforced a consistent lesson: You cannot serve a multilingual market by treating language support as a translation feature added at the end. “Tokens” are the fundamental billing unit of generative AI. Tokenization varies by model, and even English words do not always map to one token. However, multilingual studies have found that equivalent content can require materially different numbers of tokens across languages. When a tokenizer fragments a target language more heavily, the application may pay for more input and output tokens to communicate the same meaning.
A simple first estimate is: Estimated multilingual text cost = Comparable English text cost × Token-count multiplier. This estimate does not include differences in model pricing, caching, output length or infrastructure. A language-focused tokenizer or model may materially reduce inference costs, but founders should benchmark it using representative conversations in each target language before making a platform decision. Engineering teams should compare general-purpose and language-focused models using representative regional inputs.
Evaluate token count, response quality, latency, safety, licensing and total cost together. A model that uses fewer tokens is not a better business choice if it produces less reliable answers. High-quality digital and training resources are distributed unevenly across languages, leaving many lower-resource languages with less material for model training, retrieval and evaluation. When startups implement Retrieval-Augmented Generation (RAG) for regional languages, their systems may produce weaker or less grounded results when suitable localized retrieval and evaluation data is sparse, outdated or poorly translated.
Founders should evaluate sovereign and institutional language resources before paying to recreate equivalent data. Before using any resource for retrieval, fine-tuning or commercial deployment, verify its license, provenance, update history, quality, privacy conditions and permitted uses. Government backing should not replace technical and legal due diligence. Properly licensed, relevant resources can improve language coverage and reduce the amount of data a startup must collect independently, but their quality and suitability must still be tested.
When building for the U.S. enterprise market, the default user interface is often a text box and a keyboard. However, mobile-internet research indicates that reading, writing and digital-literacy difficulties are major barriers to mobile-internet adoption. In markets where user research identifies typing, literacy or script entry as meaningful barriers, founders should evaluate voice-enabled and visual interfaces rather than assuming that a text box is sufficient. As I explained in my earlier analysis of conversational AI and “Zero-UI” systems, reaching the next billion users often requires fitting technology into their existing communication habits rather than forcing them to navigate a conventional app If voice is central to the target workflow, design and test the audio pipeline early.
It should be evaluated using representative accents, dialects, noisy environments and code-mixed speech rather than added as an untested wrapper at launch. Multilingual expansion should also begin with one narrowly defined market rather than a simultaneous global launch. Choose a high-value workflow, test it with native speakers, measure task completion and support costs, then use that evidence to decide whether the architecture is ready for the next language. Multilingual markets contain users whose needs are poorly served by English-first products.
Capturing this opportunity requires treating localized AI as a core engineering and product discipline. Audit your token economics to protect your margins, verify the legal and technical quality of regional datasets and design interfaces around how target users actually communicate. An English-only architecture may prevent an otherwise strong AI company from reaching users who prefer to speak, search and transact in other languages. Many AI startups launch first in English because the models, benchmarks, developer tools and enterprise buyers are easiest to find there.
That approach can help a company reach the market quickly, but it can also cause founders to overlook a much larger multilingual opportunity. According to the International Telecommunication Union, 2.2 billion people remained offline in 2025, most of them in low- and middle-income countries. As more of these users come online, many will expect digital products to work in the languages they use every day — not merely offer translated versions of English-first experiences. They represent a significant growth opportunity for companies prepared to build multilingual products.
