When English-language content boosts your AI visibility in a non-English market — and when it makes no difference
Most LLMs were trained mostly in English. For companies whose primary market isn't English-speaking, publishing in English can extend AI visibility — but only in specific situations. Here's when it's worth it.
There's a language asymmetry in the world of generative AIs that every company operating in a non-English market should know about: most of the training data for the major LLMs (Large Language Models) is in English. Estimates vary, but the consensus among researchers is that English makes up between 50% and 70% of the training data for ChatGPT, Claude, Gemini, and similar models — with any single non-English language making up a much smaller fraction.
That creates a real asymmetry: what AIs "know" about a topic is far richer in English than in most other languages. For a company whose home market speaks another language, that can mean your brand is more visible in the AIs when related queries are asked in English than when they're asked in your local language — even if you operate exclusively in your home market.
The strategic question is: when is it worth investing in English-language content to extend AI visibility, and when does it make no difference?
The four scenarios where English boosts AI visibility
Scenario 1 — A company with international clients or operations. If a share of your clients is international, or if you have operations abroad, English content is mandatory for obvious reasons — but also because AI models have much richer knowledge of companies with a presence in English-language sources. A technology company with a Product Hunt page, an AngelList profile, an article published in an English-language tech outlet, or English-language reviews on G2 is treated by AIs with a much higher level of familiarity.
Scenario 2 — A technical sector whose primary literature is in English. In sectors where international knowledge production is English-dominant — technology, health sciences, quantitative finance, engineering — publishing technical content in English puts the company in direct competition with global sources. A software agency specializing in blockchain that publishes technical analyses in English on Medium or Dev.to shows up in technical queries asked in English by developers worldwide — and, by reflection, increases the level of "familiarity" the model has with the company.
Scenario 3 — A product or service with global-expansion potential. For SaaS and digital products that could serve clients beyond the home market, publishing English content builds a knowledge base in the AIs for when expansion happens — instead of having to rebuild presence from scratch. The investment made before entering the international market is far more efficient than the one made after.
Scenario 4 — Competing on highly contested local-language queries. For some heavily contested local-language terms (where the top results belong to large companies with far higher domain authority), publishing quality content in English on the same topic and then having that content cited in English-language sources can create an indirect authority-boost effect — which eventually transfers to the local-language queries.
The three scenarios where English makes no difference
Scenario 1 — A local business with exclusively local-language queries. A dental clinic, a neighborhood brokerage, or a restaurant competes on local queries in the local language. Publishing content in English won't show up when someone asks, in the local language, about local services — and the effort is better spent strengthening local-language presence.
Scenario 2 — A sector where the audience doesn't use AIs in English. For many B2C sectors serving a mass-market local audience, the audience searches exclusively in its own language. It makes no sense to invest in English for an audience that will never reach that content — directly or through AI citation.
Scenario 3 — A company without the capacity to produce quality English content. English content with grammatical errors or low-quality machine translation can hurt more than help — both in international users' perception and in the AIs' quality assessment. Better to have excellent local-language content than mediocre English.
The hybrid strategy: what to translate vs. what to create in English
For companies that decide to invest in bilingual content, there's a difference between translating and creating:
Worth translating: research with proprietary data (which already has citation value locally and extends reach in English), customer case studies (which demonstrate capability and create international references), and institutional pages with the company's factual information.
Worth creating originally in English: technical content for English-speaking communities (Stack Overflow, GitHub, technical forums), contributions to international sector publications, and profiles on global platforms (G2, Clutch, Product Hunt).
Not worth the effort: translating generic blog posts with no proprietary data or clear differentiator. The cost of quality translation and the distribution effort rarely pay off for content with no data-based competitive advantage.
How Crowly can help you measure the impact of English
Crowly monitors your visibility in the AIs for queries in your target language. If you start publishing content in English, you can track whether your AI score for local-language queries improves over time — which would be evidence of the cross-language authority effect. That data doesn't exist any other way: you can't measure whether English content is helping your local-language visibility without a tool that monitors AI citations.
Monitor whether your bilingual strategy is affecting your AI visibility. Free diagnostic. Analyze my brand →
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