An analysis of 1.3 million citations found that translated sites gain 327% more visibility in AI Overviews than single-language sites. A brand ranking first in English for its category can be entirely absent when the same question is asked in German.
Most brands treat international AI visibility as a translation problem. It is closer to a starting-over problem.
Authority compounds almost everywhere else in GEO. Across language boundaries, it does not, and that single exception creates the largest unclaimed opportunity currently available in AI search.
Key Takeaways
- Language authority does not transfer: each language behaves as a partially independent knowledge space inside the model.
- Translation alone is insufficient: original content per language outperforms translated content for citation probability.
- Opportunity runs inverse to language size: the thinner the training data, the emptier the citation set.
- Position consolidates quickly: once a domain becomes the cited source in a language, displacing it is difficult.
- Geography compounds the effect: user location influences citation behaviour independently of query language.
Why Doesn't English Authority Transfer to Other Languages?
English authority does not transfer because large language models hold language-specific associations between topics and sources. A model answering a German query draws primarily on what it associates with that topic in German. Your English-language authority is largely invisible to that retrieval path.
The practical result is uncomfortable for established brands. A company with dominant English coverage and no German presence loses to a German competitor with weaker overall authority but stronger local signals.
The training data hierarchy
Model training corpora are heavily English-weighted, with over half the data in major systems originating from English sources. Spanish, despite roughly 580 million native speakers, occupies a much smaller share. German, French, and Japanese face similar disparities.
That creates three practical tiers. High-resource languages where models hold deep knowledge and firm brand associations. Medium-resource languages with substantial but thinner coverage. And low-resource languages where the model has comparatively little to work with.
Why Thin Coverage Is an Opportunity, Not a Problem
The instinct is to read sparse training data as a reason to deprioritise a market. The opposite conclusion follows. Sparse data means few established sources, which means the citation set for most queries in that language is close to empty.
In many sectors, competition to be cited for non-English queries is minimal. A business publishing a comprehensive, original, well-structured resource in Spanish, German, or Polish has a strong probability of becoming the reference source models reach for in that language.
Opportunity runs inverse to language size. The markets with the least model coverage are the ones where a single well-built asset can own the category outright.
Positions consolidate and then hold
Language models establish durable associations between topics and sources. Once a domain consolidates as the cited source for a topic in a given language, maintaining that position is easier than displacing an incumbent.
This is the same dynamic that makes English-language GEO so difficult for challengers, running in your favour for once. The window is open specifically because so few brands have moved. Our AI Recommendation Gap report covers how quickly those positions harden once claimed.
Translation vs Original Content
| Machine translation | Localised translation | Original per language | |
|---|---|---|---|
| Cost | Low | Moderate | High |
| Citation probability | Baseline lift | Better | Best |
| Handles local vocabulary | Poorly | Yes | Yes |
| Reflects local buying context | No | Partially | Yes |
| Best used for | Coverage floor | Secondary markets | Priority markets |
Translation establishes a floor, and the lift is real. But models weigh how well content answers a question as it is actually asked in that language, and question phrasing differs by market in ways translation does not capture.
Geography Compounds Language
Query language is not the only variable. Testing across locations found that user geography influences citation behaviour independently, meaning the same query in the same language can return different sources depending on where it is asked.
This matters for measurement more than for content. Testing your prompts from a single location produces a misleading picture of where you stand, which is a common blind spot when tracking AI search visibility across international markets.
How Should You Prioritise Languages?
Prioritise by the intersection of revenue exposure and citation emptiness, not by market size. A large market with an established local incumbent is a worse target than a mid-sized market where the citation set is unclaimed.
Run the audit first
Take your twenty core buyer questions and run them in each target language, from each target geography. Record which sources appear. In many markets you will find the same two or three domains repeatedly, or nothing coherent at all. The second case is the opportunity.
Pick depth over breadth
Comprehensive coverage in two languages outperforms shallow coverage in eight. The asset that becomes the cited reference is the one that answers the question completely, and partial coverage rarely achieves that in any language.
Build local third-party signals
The same rules apply as in English. Local editorial coverage, local review platforms, and local community presence carry weight, and the source sets differ by market. Since platforms already draw on largely different sources, layering language on top multiplies the fragmentation.
Fix the technical layer
Each language version needs its own indexable URL, correct hreflang, and translated schema. Content that exists only behind a client-side language switcher is invisible to the crawlers feeding these systems.
Who Should Ignore This
Not every brand should act on it. If your revenue is single-market, if your product cannot be sold across borders for regulatory reasons, or if your English-language position is still weak, multilingual work is premature.
The signal that it is not premature is simple: you already earn revenue in a market where you have never checked what a model says about you in the local language. Most companies with international revenue have never run that check.
The Reframe
English-language GEO is a contest against every well-funded competitor in your category. The same work in a medium or low-resource language is frequently a contest against nobody.
That asymmetry will not last. It exists because most brands are still treating international AI visibility as a translation line item rather than a distinct competitive surface, and the positions being claimed now will be defended for years.
FAQs
1. Does my English content help me get cited in other languages?
Only marginally. Models hold language-specific associations between topics and sources, so a query in German draws primarily on German-language associations. Brands with strong English authority and no local-language presence are frequently absent from non-English answers entirely.
2. Is translating my site enough for multilingual AI visibility?
Translation produces a measurable lift and establishes a coverage floor, with one analysis of 1.3 million citations finding translated sites gain 327% more AI Overview visibility than single-language sites. Original content written for each market performs better, because question phrasing and buying context differ in ways translation does not capture.
3. Which languages should I prioritise?
Prioritise by the intersection of your revenue exposure and how empty the citation set is, rather than by market size. A mid-sized market with no established cited source is a better target than a large market with an entrenched local incumbent.
4. Why are smaller languages easier to win?
Because model training data is heavily English-weighted, smaller languages have fewer established sources competing for citation. In many sectors, competition for non-English queries is minimal, so a single comprehensive resource can become the reference source models cite.
5. Does location affect AI citations separately from language?
Yes. Testing found user geography influences citation behaviour independently of query language, so the same question in the same language can surface different sources depending on where it is asked. Prompt testing from a single location gives a misleading picture.
Figures reference published analyses including Weglot's study of 1.3 million citations and research into training corpus composition. Citation behaviour varies by sector, language, and platform, and shifts as models are updated.