Have you ever noticed that your AI assistant seems more responsive, articulate, or even smarter when you're speaking to it in English? Maybe you've asked a complex question in your native language and received a vague or oddly phrased reply—only to get a more complete, coherent answer when you ask the same thing in English. If that sounds familiar, you're not imagining it. There's a technical and cultural reason behind this bias, and understanding it is key to grasping where AI stands today—and where it's headed.
The dominance of English in AI isn't just a coincidence. It reflects the way most large language models are trained, using vast collections of internet text. And as it turns out, the internet is far from linguistically balanced. Let's explore how this has shaped the capabilities of today’s AI, especially in how it understands, processes, and responds to different human languages.
The Internet’s Language Landscape: An English-First World
Think of the internet as a massive global library—and that’s exactly what many language models use as their training data. But here’s the twist: most of the books in that library are written in English. According to a 2023 analysis of the Common Crawl dataset (one of the largest open repositories of web text), nearly 46% of its content is in English. That’s over eight times more than Russian, which holds second place with less than 6%.
In practical terms, this means that the data used to train language models like GPT-4 is heavily skewed toward English. And since these models learn from what they "read," they naturally become much more proficient in English than in other languages. The result? AI that sounds almost native in English, but often struggles with nuance, grammar, or context in other tongues.
The Problem with “Low-Resource” Languages
While it’s true that many languages are spoken by millions around the world, that doesn't mean they’re equally represented in digital spaces. Languages like Punjabi, Marathi, or Amharic have large numbers of speakers, yet extremely limited representation in major web corpora. These are often labeled as “low-resource” languages—not because of a lack of speakers, but due to the scarcity of digital data available for training.
Consider Punjabi. With more than 113 million speakers globally, it has a larger speaker base than German. Yet in Common Crawl, its presence is almost negligible. This disconnect highlights a fundamental inequity in how language data is collected and used. If a model never sees enough examples of a language, how can it be expected to understand or generate meaningful content in it?
The implications go beyond inconvenience. When AI tools underperform in a user’s native language, it widens the digital divide. Those who speak English (or another well-represented language) get better tools, faster service, and more reliable results. Everyone else is left with second-tier experiences.
AI Performance in Practice: A Closer Look at GPT-4
How does this language imbalance show up in real-world performance? One illustrative case is GPT-4’s performance on the MMLU (Massive Multitask Language Understanding) benchmark. This test measures a model’s knowledge across 57 different subjects, from science to law. Unsurprisingly, GPT-4 performs best in English. But its results in other languages are often disappointing, especially those with less training data available.
In one study, GPT-4 was three times more likely to solve math problems correctly when presented in English than in Armenian or Farsi. For languages like Burmese or Amharic, GPT-4 failed to answer even basic math problems. Not because math changes between languages—but because the model simply hasn’t seen enough examples of how to talk about math in those languages.
Even more troubling, some of the worst-performing languages in MMLU testing—such as Telugu, Marathi, and Punjabi—also had the lowest representation in the Common Crawl dataset. The correlation is hard to ignore. In short, the fewer examples a model sees in training, the weaker its real-world performance in that language.
Is Translation the Answer?
One seemingly obvious solution is to use machine translation as a bridge. Ask your question in your native language, translate it to English, let the AI process it, then translate the answer back. It sounds logical. But in practice, this strategy has serious flaws.
First, good translation requires good training—especially in both the source and target languages. For “low-resource” languages, translation models often suffer from the same data scarcity that affects general language models. The result is a game of telephone where meaning can get diluted, distorted, or even lost entirely.
Second, some languages carry cultural and social nuances that don’t translate easily. Vietnamese, for example, has a rich system of pronouns that reflect age, gender, and social hierarchy. A simple “you” in English might have several different equivalents in Vietnamese, depending on context. When those distinctions vanish in translation, so does a significant part of the meaning.
So while translation can be a temporary workaround, it’s not a true solution. If we want AI to genuinely understand and respect the full diversity of human languages, it must be trained to understand those languages directly—not just through the lens of English.
Unexpected Challenges: Tokenization and Cost
Even the mechanics of how language models operate can create disparities between languages. Language models process text by breaking it into units called “tokens.” For English, tokenization tends to be efficient—words are short and relatively consistent. But in many non-Latin scripts or morphologically complex languages, the same sentence might require far more tokens to express.
In the MASSIVE dataset, for instance, the median number of tokens for an English sentence was 7. For Hindi, that number jumped to 32. For Burmese, it climbed to 72—more than ten times higher. This means a model might need ten times more computational effort to process the same idea in Burmese than in English. Since many AI services charge by token usage, this translates to higher costs and slower performance for users of non-English languages.
It’s an invisible tax on linguistic diversity—one that most users aren't even aware they're paying.
Biases, Misinformation, and Cultural Blind Spots
The imbalance goes beyond performance. It affects how AI systems interpret the world and what kind of information they generate. In a 2023 investigation, NewsGuard found that ChatGPT was more likely to produce false or misleading information in Mandarin than in English. This raises deeper questions about how training data influences not just language skills, but also cultural knowledge, assumptions, and biases.
If most of the data comes from English-speaking countries, then the worldview encoded in AI systems may also reflect that background. For users in other regions, this can lead to subtle cultural misunderstandings, skewed representations, or even the erasure of local contexts.
A Glimmer of Hope: Building Truly Multilingual AI
Despite these challenges, there’s growing momentum to build AI systems that serve the full spectrum of human language. Specialized models are emerging that prioritize non-English languages. Chinese-language models like ChatGLM and YAYI are pushing boundaries. In Vietnam, the PhoGPT project is gaining traction. France has its CroissantLLM. Arabic speakers are exploring Jais. These efforts are still in their early stages, but they mark an important shift in focus.
In parallel, open datasets and community-led initiatives are working to improve digital representation for under-resourced languages. Projects like Masakhane in Africa and IndicNLP in South Asia aim to create high-quality language resources where none previously existed. These grassroots movements play a critical role in closing the digital gap.
More inclusive AI doesn’t just mean more languages. It means better tools, fairer access, and a deeper understanding of how language shapes our identity. And it means recognizing that communication is about more than words—it’s about the cultures and communities those words represent.
The Road Ahead: Why It Matters
Language is central to how we think, relate, and connect. When AI can truly understand not just English, but the full range of human expression, it becomes more than a tool—it becomes a bridge. But until then, the disparities in performance, cost, and accuracy are real, and they affect how billions of people experience and trust these technologies.
So the next time your AI assistant stumbles in your native tongue, remember: it's not just a glitch. It's a reflection of deeper design choices—choices we can influence. As users, researchers, and developers, we all have a role to play in making AI more multilingual, more inclusive, and more representative of the world it serves.
What’s Your Experience?
Have you ever noticed your AI assistant performing differently based on the language you use? Do you think the current English-first design of AI tools limits their usefulness or fairness? And what changes would you like to see in how AI engages with your language and culture?
The conversation around language equity in AI is just beginning—and your voice matters.
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