Allegro.pl is Poland’s dominant e-commerce platform. It’s not a niche player or an upstart. It is the market leader, full stop. So when a Polish user, searching from a Polish IP address, asks ChatGPT in Polish for the best auction portals, you’d expect Allegro to appear prominently in the results.
It doesn’t. In many cases, it’s buried or missing entirely. eBay shows up instead.
This isn’t a one-off quirk. Tomek Rudzki shared a recent analysis from Peec AI, based on over 10 million ChatGPT prompts and 20 million background queries, reveals a structural pattern: when users search in non-English languages, ChatGPT conducts a significant chunk of its research in English anyway. The result is that local market leaders get systematically displaced by global brands that happen to dominate English-language content.
The fan-out problem
To understand why, you need to know how ChatGPT actually retrieves information. It doesn’t just answer from memory. It breaks your question into smaller sub-queries (called “fan-outs”) and searches the web to assemble its response.
Here’s where the bias enters. Peec’s data shows that 43% of these fan-out queries are conducted in English, even when the original prompt was in another language. ChatGPT typically starts searching in the user’s language, then switches to English for subsequent queries. The logic is understandable: English-language content is more abundant, has stronger authority signals (more backlinks, more citations), and is easier for the model to evaluate for quality.
But the downstream effect is significant. A German user asking about German software companies in German gets a response built partly on English-language sources that naturally favor international brands. In Peec’s testing, not a single German software company appeared in the results. A Spanish user asking about cosmetics brands in Spanish gets recommendations shaped by English-language listicles that ignore the Spanish market entirely.
The 78% number
Peec filtered their dataset to eliminate noise: only queries where the user’s location matched the query language. Polish queries from Poland. German queries from Germany. No mixed signals.
The finding: in 78% of non-English ChatGPT sessions, at least one sub-query is performed on the English web. Turkish queries switch to English 94% of the time. Even Spanish, the lowest in the dataset, hits 66%. No non-English language falls below 60%.
This isn’t a bug in the traditional sense. It’s a design consequence. English-language content is the largest, most interlinked corpus on the internet. When an AI model optimizes for answer quality using signals like backlinks and citations, it will naturally gravitate toward English sources. The problem is that “quality” as measured by these signals and “relevance to this specific user in this specific market” are not the same thing.
What this actually means for B2B
The Peec research focuses on consumer examples (auction sites, cosmetics, clothing brands), but the implications for B2B SaaS are worth thinking through carefully.
Consider a mid-market SaaS company that dominates the DACH region. Strong German-language content, solid brand recognition, the obvious choice for buyers in that market. If a German buyer asks ChatGPT for software recommendations in their category, that company may not appear, because ChatGPT is pulling half its research from English-language sources where the company has no presence. The response will skew toward companies with strong English-language footprints: G2 profiles, English blog posts, mentions in English-language publications.
This creates an interesting strategic question. For companies in non-English markets, AI search visibility may increasingly depend not just on being strong in your home language, but on having a deliberate English-language content presence. Not a full translation of everything. A targeted English footprint in the specific content types that AI models tend to cite.
The strategic calculus
The temptation is to treat this as a localization problem. Translate your key pages into English, check the box, move on.
That misses the point. The question isn’t whether you have English content. It’s whether you have English content in the places ChatGPT actually looks. Peec’s data suggests AI models pull from specific source types: comparison sites, industry publications, product directories, certain kinds of long-form content. Understanding which source types get cited in your category matters more than simply having an English version of your homepage.
This is also a competitive intelligence problem. If your English-speaking competitors are getting cited in AI responses for queries in your home market, that’s a visibility gap that traditional SEO metrics won’t surface. You could be winning every traditional search ranking in your language and still be invisible in the AI layer.
A bias that compounds
The deeper issue is that this bias is self-reinforcing. As more users shift research behavior toward AI tools, the companies that show up in AI responses get more attention, more backlinks, more citations, which makes them more likely to show up in future AI responses. Companies that are invisible in that loop fall further behind, even in markets where they’re the established leader.
For now, the practical move is straightforward: audit your visibility in AI search responses for your category, understand which English-language sources are being cited, and make deliberate decisions about where to build an English-language presence. But the bigger story here is about who gets to be the default recommendation for 80% of internet users who don’t speak English as their primary language. Right now, the answer is skewing heavily toward whoever shows up in English.