English Dominance in ChatGPT’s Fan-Out Queries: Insights from Peec AI

Examining the Language Trends in AI Search Analytics

Understanding the Impact of Language on ChatGPT’s Search Queries: Insights from Peec AI’s Recent Report

As the landscape of AI-driven search evolves, understanding how AI models like ChatGPT process language and gather information is crucial. A recent report from the AI search analytics firm Peec AI has highlighted some interesting findings regarding ChatGPT’s handling of non-English prompts. Specifically, the report reveals that even when users input queries in languages other than English, a significant portion of the follow-up queries—termed "fan-outs"—are conducted in English.

What Are Fan-Out Queries?

To grasp the implications of Peec AI’s findings, it’s important to first understand the concept of fan-out queries. According to OpenAI’s ChatGPT Search documentation, when a user asks a question, ChatGPT typically reformulates the original query into targeted sub-queries and sends them to various search partners. This process may involve multiple rounds of querying to refine the answers provided.

Peec AI refers to these sub-queries as "fan-outs." Their report focused on which languages were utilized in these fan-out queries, drawing on a massive dataset that included over 10 million prompts and 20 million fan-out queries.

Peec AI’s Key Findings

Peec AI distilled its data to analyze only cases where the language of the prompt matched the user’s IP location, ensuring a fair examination of language usage. The findings revealed that 43% of fan-out steps for non-English prompts were carried out in English. What’s more, 78% of non-English prompts included at least one English fan-out query, with notable discrepancies across languages:

• Turkish prompts were most inclined towards English fan-outs at 94%.
• Spanish prompts had the lowest percentage at 66%, yet still demonstrated a significant reliance on English sub-queries.

This consistent pattern indicates that regardless of the original prompt’s language, ChatGPT frequently incorporates English when formulating follow-up queries.

Real-World Examples from Peec AI’s Report

Peec AI provided specific instances demonstrating how this language pattern impacts outcomes. For example:

• In a case where a Polish user inquired about the best auction portals, ChatGPT focused more on global platforms like eBay, rather than highlighting Allegro, Poland’s dominant e-commerce entity.

• Another case involved a request for German software companies, which resulted in a list devoid of any German options.

• Similarly, a Spanish-language prompt on cosmetic brands yielded results that neglected local options in favor of global names. In one instance, ChatGPT generated English fan-out queries, modifying a Spanish request by adding the word “globales,” implicitly steering the response toward international brands.

These examples underscore a potential bias where local entities get overshadowed by more universally recognized names, reflecting the dominance of English-language sources.

Implications for SEO and Content Strategy

The implications of Peec AI’s findings are significant for content creators and SEO teams operating in non-English markets. The reliance on English fan-out queries may create an unlevel playing field, where local competitors struggle to gain visibility if search models prioritize global entities over regional ones.

This raises an important question: Will the current trends necessitate the incorporation of English-language content as a strategy for AI search optimization? Or will AI search platforms evolve to ensure that local contexts are properly considered in the search results?

Methodology and Authorship

Peec AI employed a data collection method involving the automation of customer-defined prompts, interacting directly with AI platforms through their web interfaces rather than APIs. The findings are derived from Peec AI’s platform, with limited details on the types of prompts analyzed.

Authored by Tomek Rudzki—a respected voice in technical SEO—the report contributes to ongoing discussions about how AI systems operate and the potential biases that can arise from their functionality.

Conclusion: A Look Ahead

Peec AI’s report shines a light on the mechanics of language processing in AI search. While the patterns observed may not be representative of all instances of ChatGPT’s behavior, they certainly warrant attention. As AI technology continues to integrate into search functionalities, understanding these dynamics will be essential for market players seeking to optimize their content for diverse audiences.

The future trajectory of AI search optimization and its responsiveness to local markets remains an open question, one that stakeholders will need to monitor closely as technology evolves.

By dissecting the nuances of language usage in search queries, we begin to unlock a broader understanding of how AI influences information retrieval—a shift that holds vast implications for content creation and online visibility.