Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines
arXiv:2606.20065v1 Announce Type: cross Abstract: People increasingly get answers straight from AI search engines like ChatGPT, Claude, Perplexity, and Gemini rather than scrolling search results. Brands that once focused on search engine optimization (SEO) must now optimize for how these engines represent, cite, and recommend them -- a shift variously called Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), and AI Search Visibility. We treat AEO and AI Visibility as part of GEO, and study how to measure brand visibility across AI engines: what they value when they cite a
The rapid adoption and increasing reliance on large language models (LLMs) and AI search engines necessitate new optimization strategies for brand visibility.
This research defines a critical new vector for brand engagement and information dissemination, fundamentally altering how organizations must approach digital presence and influence.
Traditional SEO is being superseded by Generative Engine Optimization (GEO), requiring re-evaluation of content strategy, citation influence, and real-time AI representation.
- · Digital marketers specializing in GEO/AEO
- · Brands with high-quality, frequently cited content
- · AI search engine developers
- · Content creators optimizing for structured data and unique insights
- · Traditional SEO agencies slow to adapt
- · Brands relying solely on keyword stuffing for visibility
- · Content farms producing low-quality, undifferentiated content
- · Advertisers solely focused on paid search results
Companies will reallocate significant marketing budgets towards GEO and AI search visibility strategies.
The competitive landscape for information will shift, prioritizing authoritative, verifiable data over traditional web page rankings.
AI engines may become gatekeepers of brand reputation, influencing consumer perception and market share based on their aggregation and synthesis of information.
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Read at arXiv cs.CL