Artificial intelligence systems like ChatGPT and Claude are fundamentally changing how consumers discover products, with some brands achieving dramatically higher AI visibility than seemingly equivalent competitors despite similar market positions, traditional search rankings, and marketing investments. This paper provides the first systematic empirical analysis of competitive Generative Engine Optimization across six diverse consumer product categories, examining how optimization sophistication relates to AI citation outcomes under varying competitive conditions. We systematically measure optimization levels for six brands across plant-based protein and running shoes category using structured coding of website characteristics across four dimensions including structured data implementation, citation quality, content comprehensiveness, and technical optimization. We query four major AI platforms—ChatGPT, Claude, Perplexity, and Google Gemini—with thirty to fifty category-relevant queries per category and code citation patterns. We employ logistic regression with category fixed effects and clustered standard errors to examine relationships between optimization investment and citation outcomes while controlling for market share, brand age, and baseline competitive position. We find that optimization sophistication strongly predicts AI citation frequency, with patterns suggesting that challenger brands capture asymmetric competitive advantages relative to market leaders for equivalent optimization investments. These findings reveal that AI-mediated discovery creates novel competitive dynamics where optimization responsiveness matters more than traditional brand equity, with important implications for marketing strategy as commerce becomes increasingly AI-intermediated.

Generative Engine Optimization, competitive dynamics, AI citations, brand positioning, market structure, digital strategy, consumer behavior, search optimization

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