Ai languages: One Ruler to Measure Them All

Artificial intelligence speaks many tongues, yet not all languages are treated equally. Most large language models are trained on vast English datasets, leaving other languages to fill in the gaps with whatever online text exists. For years, this asymmetry shaped an assumption that English would always yield the best results in interacting with AI systems. Recent research has begun to challenge that notion. 

 A study from the University of Maryland titled “One Ruler to Measure Them All” explored how large language models handle long and complex documents in twenty-six languages. The surprising outcome placed Polish at the top of the performance rankings, ahead of English, French, and Spanish. The research team tested how models preserve and reason over information in very long texts—up to 128,000 tokens—and found that models responded with higher consistency and accuracy in Polish than in several high-resource languages. This benchmark, known as ONERULER, was co-developed with Microsoft and provides one of the first systematic multilingual evaluations of long-context reasoning. While the study focused on one specific dimension of performance, it opens a wider discussion about linguistic structure and how it affects AI comprehension. Polish and other Slavic languages rely heavily on inflection, word endings, and flexible syntax to convey meaning. This grammatical richness may help models encode relationships more precisely, especially when handling context-dense instructions. In contrast, languages like English often depend on word order, which can be harder for a model to track over very long sequences. 

 Other studies have shown complementary patterns. Meta’s “No Language Left Behind” project demonstrated that languages with well-defined morphology, such as Finnish or Turkish, can outperform expectations once sufficient training data is available. Research by Google on multilingual models like Gemini and by OpenAI on GPT-4 indicates that cross-lingual representations can transfer surprisingly well, suggesting that the structure of a language sometimes compensates for lower data volume. These findings remind us that linguistic diversity is not an obstacle for artificial intelligence but a source of strength. Each language offers a different logic, rhythm, and way of connecting ideas. As models evolve toward greater inclusivity, understanding how grammar and morphology influence reasoning will help shape more balanced and universal AI systems. The story of Polish outperforming English in one benchmark is not a competition but an invitation to look deeper at how language itself molds intelligence—both human and artificial.