Is English Actually Easier For AI To Understand Because Of Its Syntax?

English has become something of a default language for artificial intelligence, and in many ways, it feels as if that’s something that’s kind of slipped under the radar. Many of the world’s leading AI models are developed with English as a primary language, and their performance in English often sets the benchmark against which their wider capabilities are measured.

The obvious explanation is that English is widely used online, it’s the most commonly spoken language in the world (as per most studies on the topic, even if most English speakers aren’t native English speakers) and as a result, features heavily in the text available to train these models. But there’s another, more intriguing issue at hand here which diverges a little further from the mere fact that English is so commonly spoken. That is, is there more to English than its “popularity”, cruelly speaking, that has made it the most conducive to being used in AI? That is, does the structure of English itself give AI an advantage?

Tackling this question makes the whole question less about the usual discussion of language dominance and the complicated social reasons behind this and more about how large language models (LLMs) actually learn to process language.

Does English Have A Structural Advantage?

Syntax refers to the rules governing how words are arranged to form both phrases and sentences. In English, the typical sentence follows a subject-verb-object structure. For instance, consider the sentence, “The founder launched a startup.” The subject (“the founder”) comes first, followed by the action (“built”) and then the object (“a startup).

This pattern is relatively consistent throughout the language, although English has plenty of interesting (and often infuriating) exceptions. Other languages, however, organise sentences differently. Japanese, for instance, tends to place the verb at the end of a sentence, while Russian uses grammatical case endings that help indicate the relationships between words and allow for more flexible word order. Neither is good or bad, right or wrong or better or worse; they’re just different rules of syntax.

Humans learn to navigate these differences, often without consciously thinking about grammar at all. But AI models, on the other hand, learn statistical patterns from the data they are trained on, because they’re all about analysing patterns.

So, this begs the question: could English’s relatively consistent word order make certain grammatical relationships easier for a model to recognise and predict?

Well, English isn’t necessarily simpler overall. In fact, in many ways, it’s quite the opposite. English has irregular spellings, ambiguous words and complicated grammatical constructions of its own. Meanwhile, features that make one language more complex in one respect may actually help a model identify meaning in another.

And so, perhaps the question should shift slightly. It shouldn’t be whether English is objectively the easiest language for AI to work with, but rather whether particular features of its syntax might make it easier for certain AI models to learn.

A Little Insight from Research Say

There’s evidence that language models can, in fact, develop preferences for grammatical structures associated with English, even when processing other languages. Indeed, A 2023 study published in the “Findings of the Association for Computational Linguistics” examined multilingual BERT, a language model trained across multiple languages. Researchers found that the model favoured English-like grammatical patterns, including explicit pronouns and subject-verb-object word order, over alternatives more natural in Spanish and Greek.

The researchers described this as a form of grammatical structure bias, where patterns associated with higher-resource languages influence how models handle lower-resource ones.

More recently, research published by “Apple Machine Learning Research” in 2025 found that multilingual large language models could produce unnatural grammatical patterns in languages including French and Chinese, reflecting English-influenced tendencies in their outputs.

These findings seem to suggest that English can influence how AI handles the grammar of other languages, but they don’t establish the fact that English syntax is inherently easier for AI to understand. In fact, what this does seem to suggest is that models may actually favour English-like structures simply because they’ve encountered them more frequently during training, rather than because those structures are intrinsically easier to learn. And that’s a pretty important conclusion.

Is It About Syntax, Or Just More Practice and Exposure?

English has a significant practical advantage in that there’s an enormous amount of English-language material available online, from books and news articles to technical documentation and software resources. Generally speaking, far more than any other language. As a result, AI developers also have strong incentives to prioritise English when building and evaluating models.

This makes it difficult to separate the effects of syntax from those of training data. If a model performs better in English than in Japanese, for example, researchers need to account for differences in training material, tokenisation and model design before they can just go ahead and attribute that gap to grammar, no matter how tempting it may be.

So to establish whether syntax does actually make a meaningful difference, researchers would need to compare languages under carefully controlled conditions, testing how models handle different grammatical structures while accounting for other variables. And this is really important, because the consequences extend beyond mere linguistic curiosity. That is, if models consistently favour familiar grammatical patterns, they may produce less natural language or misunderstand certain constructions when working across languages. That could affect how useful AI tools are to people and businesses operating in multilingual markets. Thus, in the most basic sense, English speakers would be at an advantage while non-English speakers would be at a slight disadvantage, so to speak.

Could The Future Of AI Be Less English-Centric?

English’s dominance in AI is unlikely to have a single explanation. As we’ve discussed, training data, commercial priorities, model architecture and linguistic structure may all play a role, although the relative importance of each remains an open question, and will most likely remain that way until more extensive studies can be conducted.

But still, investigating syntax could help researchers understand why models behave differently across languages and how to build systems that handle linguistic diversity more effectively. The other thing it brings up is that if the way in which a language is structured influences how well a model learns it, that may mean that developers might need to pay more attention to differences in grammar when they’re training and evaluating multilingual systems.

So for now, there’s no convincing basis for declaring English the perfect language for AI, and that’s absolutely not the objective. But it does seem as though there’s at least some evidence that models can carry English-like grammatical preferences into other languages, which has the potential to have ramifications on a broader level.

So, are we building systems that understand language in all the different possible varieties, or are the AI systems we’re building just getting very good at recognising the patterns we’ve spent the most time teaching them?