AI in English vs. Your Native Language - It Actually Matters

Here is a question I have been thinking about: do you communicate with AI in English or in your native language? For me, the answer has always been English. I grew up in the era when everything in IT was English, and I still use every device, every tool, every interface in English. I have been so accustomed to speaking, presenting, and working in English that I do not even make a distinction anymore. It is simply the language I think in when I am working. But I am aware that this puts me in a specific camp, and that camp has an advantage right now that most people do not talk about.

Large language models are primarily trained on English-language data. The internet is disproportionately English, the academic papers are in English, the technical documentation is in English. This means that when you prompt an LLM in English, you are working with the part of the model that has the deepest training and the most nuanced understanding. Prompt the same model in Finnish, Swedish, or Danish, and the quality of the output often drops. Not dramatically, but noticeably. The reasoning is less sharp, the vocabulary is narrower, the cultural context is thinner. This is not a permanent state of affairs, but it is the reality today.

The tension here is real. Many people quite naturally want to work in their own language because that is where they feel comfortable and productive. If you are drafting a business proposal for a Finnish client, writing it in Finnish with the help of an AI assistant in Finnish feels intuitive. Switching to English, getting the AI output, and then translating back adds friction. But that friction might actually produce a better result right now, and that is a trade-off most people are not even aware they are making.

This creates an unintentional advantage for people who already operate in English daily. Not because they are smarter or more skilled, but because the tools happen to work better in their working language. It is a structural bias baked into the training data, and it affects everything from simple text generation to complex reasoning tasks. If you are making important decisions based on AI-assisted analysis, the language you use matters more than you might think.

I expect this gap to close over time. Model providers are investing heavily in multilingual capabilities, and each generation of models handles non-English languages better than the last. But “it will get better” is cold comfort for people who need good results today. My practical advice: if you are not a native English speaker but you are comfortable enough in English, try running your important AI interactions in English and compare the results. You might be surprised by the difference. And if you are building AI-powered products for non-English markets, keep this limitation front and center in your quality assurance process.