Cultural Encoding in Large Language Models: The Existence Gap in AI-Mediated Brand Discovery

TL;DR

The study documents Cultural Encoding in LLMs, showing training-data geography creates an Existence Gap where brands may be invisible in AI-generated recommendations despite quality. Using 1,909 pure-English query–LLM pairs across six LLMs (two geographies: International and Chinese) and 30 brands, the authors demonstrate a 30.6 percentage point higher mention rate in Chinese LLMs ($CE$ differences) and validate the Existence Gap with a Zhizibianjie case study ($0\%$ vs $65.6\%$). They introduce the Data Moat framework to quantify AI-visible content as a VRIN resource and define Algorithmic Omnipresence as comprehensive brand visibility across LLM ecosystems, offering an 18-month managerial roadmap to build semantic coverage, depth, and localization. Methodologically, the work combines quasi-experimental design with rigorous language validation, chi-square and t-tests, and logistic regression, achieving statistically significant support for H1–H3 (mention rates, sentiment, and query-type moderation). The paper contributes to theory by extending Resource-Based and Institutional perspectives to AI-mediated markets and provides practical guidance for brands to overcome the Existence Gap through cross-linguistic content strategy and community engagement, ultimately redefining what it means to achieve market visibility in an era of Generative Engine Optimization.

Abstract

As artificial intelligence systems increasingly mediate consumer information discovery, brands face algorithmic invisibility. This study investigates Cultural Encoding in Large Language Models (LLMs) -- systematic differences in brand recommendations arising from training data composition. Analyzing 1,909 pure-English queries across 6 LLMs (GPT-4o, Claude, Gemini, Qwen3, DeepSeek, Doubao) and 30 brands, we find Chinese LLMs exhibit 30.6 percentage points higher brand mention rates than International LLMs (88.9% vs. 58.3%, p<.001). This disparity persists in identical English queries, indicating training data geography -- not language -- drives the effect. We introduce the Existence Gap: brands absent from LLM training corpora lack "existence" in AI responses regardless of quality. Through a case study of Zhizibianjie (OmniEdge), a collaboration platform with 65.6% mention rate in Chinese LLMs but 0% in International models (p<.001), we demonstrate how Linguistic Boundary Barriers create invisible market entry obstacles. Theoretically, we contribute the Data Moat Framework, conceptualizing AI-visible content as a VRIN strategic resource. We operationalize Algorithmic Omnipresence -- comprehensive brand visibility across LLM knowledge bases -- as the strategic objective for Generative Engine Optimization (GEO). Managerially, we provide an 18-month roadmap for brands to build Data Moats through semantic coverage, technical depth, and cultural localization. Our findings reveal that in AI-mediated markets, the limits of a brand's "Data Boundaries" define the limits of its "Market Frontiers."