Over the past few months, we have worked with several clients on AI visibility strategies. Almost all of them started from the same foundation: English-language guides, frameworks, and case studies about GEO, structured data optimization, and getting featured in AI Overviews. The implementations were often solid. The results in Romanian, however, were consistently disappointing.
The problem was not execution. It was the premise. Strategies built on English-market logic do not automatically transfer to non-English markets. And this is not a subtle nuance. It is a fundamental principle that most marketers overlook.
A recent article on Search Engine Journal addresses this problem head-on, introducing a concept worth remembering: "Language Vector Bias." The idea is simple, but the implications are significant for any brand operating outside the English-speaking world.
Language Vector Bias: Why Translation Falls Short
AI models do not process translated content the same way they process content written natively in a language. Duane Forrester, the article's author, explains the mechanism: regional AI models were built from local cultural identity, language structure, and national data corpora. Translated content arrives in these models as what he calls "a foreign object with no parametric presence."
In practical terms: you can have a grammatically perfect translation, but the local AI model will not assign it the same weight as natively written text. This is not a linguistic quality problem. It is a structural bias baked into how these systems were built from the ground up.
The numbers back this up. Meta's Llama 3.1 model series, marketed as "state-of-the-art in multilingual performance," contains only 8% non-English training data out of 15 trillion total tokens. Research from the Massive Multilingual Text Embedding Benchmark (MMTEB), presented at ICLR 2025 and covering 250+ languages, confirms that benchmark distributions are heavily skewed toward high-resource languages, with English dominating at every stage from data filtering to final dataset construction.
For languages like Romanian, the volume of training data these models have seen is tiny compared to English. That directly affects how well they can evaluate the authority and relevance of Romanian-language content.
Europe Is Responding, But Unevenly
The good news is that Europe is not sitting idle. France has committed 109 billion euros in AI infrastructure investment, and Mistral's Le Chat became the number one free app upon launch. Germany has Aleph Alpha, trained in five languages with EU regulatory compliance, backed by Bosch and SAP. Italy is developing Velvet AI through Almawave and Sapienza University, built specifically for the Italian language and cultural context.
At the European level, the OpenEuroLLM initiative (launched in 2025) is building open-source models covering all 24 official EU languages. Switzerland is developing Apertus through EPFL and ETH Zurich, supporting over 1,000 languages with 40% non-English training data.
The less encouraging news for markets like Romania: there is no national AI model or dedicated initiative yet. Romanian brands operate in an AI ecosystem dominated by models trained primarily on English content. That does not make AI visibility impossible, but it means the approach must be strategically different from what works in English-speaking markets.
Culture Shapes AI More Than You Think
A 2024 Cornell University study tested five GPT models with questions from a global cultural values survey. The findings were striking: responses consistently aligned with the values of English-speaking and Protestant European countries, regardless of the language the query was asked in.
AI models do not just "think" predominantly in English. They filter information through the cultural lens of their training data. The authority signals they recognize, including cited institutions, reference formats, and professional registers, are those from the English-language ecosystem. Content written from a Romanian market perspective, referencing local institutions and using a register natural for Romanian audiences, may be evaluated as less authoritative by models trained on American standards.
This is not a translation defect. It is structural bias. And for an agency working with brands in the Romanian market, it is a strategic factor that cannot be ignored.
What a Localized AI Visibility Strategy Looks Like
Based on our experience at difrnt., here is what actually works:
Original content in the market language, not translated. The difference is significant. An article written natively in Romanian, with local market references and the terminology Romanian marketers and business owners actually use, has a better chance of being accurately picked up and cited by AI systems. Translations, however polished, carry the structural footprint of the source language.
Market-level AI visibility audits. When we assess a brand's AI visibility, we do it separately for each language and relevant platform. Queries must be constructed by native speakers, not translated from English. One client recently showed us that their brand appeared correctly in ChatGPT responses in English but with incomplete or incorrect information when queried in Romanian. Two languages, two completely different realities.
Build authority in your market's language. Authority signals that work in English (mentions in American publications, backlinks from .com sites, citations from English-language studies) carry different weight for Romanian content. Invest in presence on local publications, in case studies relevant to the Romanian market, and in relationships with Romanian institutions and organizations. Authority is built locally, not imported. We detailed this mechanism in our article on brand visibility in AI engines.
Monitor the platforms that matter. Do not assume Google and ChatGPT are the only relevant platforms. The AI landscape evolves rapidly, and GEO requires knowing exactly where your audience searches and how those platforms work. For the Romanian market, this includes tracking how European models from initiatives like OpenEuroLLM will integrate Romanian-language content as they mature.
Think long-term. The European AI ecosystem is under construction. Investing in original, well-structured content in Romanian is not just about today's visibility. It is a foundation for when European models become mature enough to shift market dynamics. Brands that already have a solid corpus of native content will have an advantage over those scrambling to translate everything retroactively.
Copy the Thinking, Not the Playbook
The biggest risk we see with new clients is not the absence of an AI visibility strategy. It is that their strategy is a translated copy of one designed for the American market. The principles in English-language guides are valuable, but direct application in a non-English market produces mediocre results.
For Romanian brands, the challenge is twofold: building AI visibility in a language with limited training data, on platforms not optimized for your market. But that is also the opportunity. Competition in the Romanian AI visibility space is still low. Brands that invest now in native, structured, AI-optimized content will build a competitive advantage that is difficult to replicate later.
AI visibility is not a translation project. It is a ground-up construction project, with different materials, for a different market. And the brands that understand this first will be the ones that win.
