Direct answer
Largely, yes — but not out of preference, out of the composition of the data. The large AI models are trained on enormous volumes of text from the web, and that web is massively English-language, with an overrepresentation of American content, brands, and companies. The result: at equal fame, an American brand heavily present in English content has a better chance of being "etched" into the model's memory than an equivalent brand from a smaller market or another language. This isn't a bias of the model in favor of the United States; it's a reflection of what it read the most. Web search rebalances part of the gap, but the memory carries this imprint. For a non-English brand, it's a starting handicap worth knowing about — not a fate, and above all something that can be measured rather than assumed.
The problem
Many leaders of non-English brands make the same unsettling observation: they ask an AI about their sector, and it's American players that come up first — sometimes brands barely present on their own national market. The impression is one of unfairness, or a bug.
It's neither. It's a mechanical consequence of how the models are built. Failing to understand this mechanism leads to two symmetrical mistakes: believing you're "bad" when you're actually subject to a structural effect, or believing you're well placed everywhere because you are on your local market. In both cases, you're reasoning without measuring.
The idea to grasp
An AI model has no opinion about countries. It has a memory, and that memory is the product of what it read during training. Three facts follow one from another:
- The training web is dominated by English. A very large majority of the texts available online — and therefore of the training data — are in English, with a high proportion of American content. Other languages, including French, take up a far smaller share.
- What is abundant is learned better. A model retains an entity all the better when it appears often and in varied contexts. A brand omnipresent in English content leaves a stronger imprint than a brand cited mainly in a less represented language.
- Memory comes before the question. When the AI answers without web search, it draws on this memory. The brands most firmly anchored there come up first, whatever the language of the question asked. (What an AI knows from memory, and what it goes to fetch)
Put end to end: a benchmark American brand can be cited spontaneously, even on a question asked in French, because it's solidly present in the model's memory. An equivalent French brand, less represented in the data, may not be.
In our tests, the contrast was stark: on the same need and the same AI, a very well-known American brand in its sector came up systematically from memory, with no web search at all, whereas a French brand in another sector never appeared from memory and emerged only a little when the AI went to search the web. Same method, opposite behaviors — not because one brand is "better," but because one is massively present in the training data and the other is not.
Two important caveats, for honesty's sake:
- It's not a fate. Web search (the live web) partly rebalances things: a brand absent from memory can perfectly well appear when the AI goes to fetch up-to-date pages. And fame in a given language counts: on very locally rooted questions, local players may dominate.
- These aren't exact figures. The precise composition of the training data isn't public, and our observations cover a small number of cases. We describe a mechanism and an observed tendency, not a certified proportion or a universal law.
What you hear everywhere
"The AI is American, so it pushes American brands on purpose." No — there's no intent. The effect comes from the composition of the data, not from programmed favoritism. The distinction matters: a data bias can be measured and partly worked around; a conspiracy can't.
"If I translate my site into English, I'll be in the AI's memory." Not for the memory of an already-trained model: it's frozen. English content can help via web search (the live web), and possibly weigh on future training runs — but nothing immediate or guaranteed, and that's the realm of action (GEO), not measurement.
"I'm the leader in France, so the AI is bound to cite me." Not necessarily. Leading your market doesn't mean dominating the global training data. That's exactly the gap only a measurement reveals.
Our stance: only the facts. That English speakers have a memory advantage is a plausible structural tendency, and one we observe; the exact magnitude, for your brand, can't be guessed — it's measured, AI by AI, memory and web kept separate.
Our approach: measure the gap, don't assume it
From here on, the register changes: we describe the instrument.
The language bias is not a reason to give up — it's a reason to measure precisely where you stand. For a non-English brand, that means:
- Separate memory and web search: it's in the memory that the language bias weighs most; the web can tell a different story.
- Measure AI by AI: the models don't all have the same data composition or the same search behavior.
- Compare local presence and AI presence: the gap between "leader on my market" and "cited by the AI" is precisely the useful information.
- Repeat and date: the place of languages evolves as the models are retrained.