GEO Is Changing How Brands Earn Visibility

The rise of AI-powered search engines like ChatGPT, Perplexity, Gemini, and Claude is fundamentally reshaping how brands maintain visibility online. Unlike Google SERPs, AI search engines generate synthesized, recommendation-style answers backed by citations and user trust signals. This shift requires search marketers to adopt a new discipline: Generative Engine Optimization (GEO).

AI search engines behave less like crawlers and more like analysts. LLMs don’t simply retrieve content. They evaluate, summarize, and recommend content using signals that differ meaningfully from traditional SEO. For example, earned media (third-party reviews, editorial coverage, analyst reports, and expert commentary) is weighted far more heavily in AI-generated answers than brand-owned content.

That shift should change how marketing teams invest in visibility—because in AI search, trust is earned externally, not claimed internally. Below, we outline how AI search differs from Google and why GEO as a channel must become the object of any modern marketing team’s desire.

The End of “Ranking” as the Primary Visibility Model

For decades, SEO has revolved around a familiar paradigm: optimize content to rank highly in Google’s list of blue links. Visibility was earned through a mix of technical hygiene, content relevance, backlinks, and authority signals.

AI search disrupts that model.

Generative engines don’t show ten links to users and ask them to choose. Instead, they synthesize answers (often in the form of shortlists) with explanations, tradeoffs, and recommendations. In many cases, the user never clicks through to a website at all.

This shift has three immediate consequences:

1. Visibility is a zero-sum game: Brands are either visible or invisible; they cannot be both.
   
2. Justification matters more than keywords: AI engines look for reasons to recommend, not just relevance to rank.
   
3. Authority comes from the outside: What others say about your brand matters more than what you say about yourselves.

These new rules demand a different approach—starting with understanding how AI search engines actually evaluate and select which brands to recommend.

How AI Search Engines Actually Behave

AI engines aren’t trying to return the “best page.” They’re trying to produce the “best answer.” That means they behave less like search engines and more like research analysts: synthesizing information, selecting what feels credible, and constructing a recommendation that can be defended.

AI Search Strongly Favors Earned Media

AI engines overwhelmingly prioritize authoritative third-party sources: industry publications, institutional reviews, analyst commentary, and expert-authored content.

Brand-owned content still appears, but typically in a supporting role, or only in high-intent transactional queries where official specifications, pricing, or policies are required.

Google, by contrast, maintains a more balanced mix of earned and owned media. This is the first major break from traditional SEO. You can have an exceptional content engine and still fail to appear in AI-generated results.

Social and UGC Coverage Varies by Platform

While Google continues to surface Reddit threads, forums, and user-generated content for many queries, certain AI experiences tend to prefer institutional/edited sources over forums—though some engines and query types cite Reddit and other UGC outlets more heavily. This is less about “social vs. not social,” and more about perceived reliability. AI systems appear to favor institutional trust in lieu of raw popularity or community consensus.

Overlap with Google Is Surprisingly Low

In many consumer and software categories, the overlap between Google’s top results and the sources cited by AI engines is limited. In other words, a brand that ranks well on Google may not appear at all in AI-generated answers. Strong SEO performance does not guarantee AI visibility.

Query Behavior Has Shifted from Retrieval to Delegation

AI search hasn’t replaced Google yet. However, it is changing how users ask questions. Users interfacing with LLMs are more likely to:

• Ask for shortlists rather than exhaustive lists
• Request comparisons and tradeoffs
• Delegate decisions (“Which option should I choose?”)
• Extend across the entire customer journey, from discovery to post-purchase support

In other words, users increasingly treat AI as an agent rather than an index. This shift reinforces the importance of justification signals: sources that explain why a brand is trusted, not just what it offers.

Earned Media Is a Major Signal in AI Visibility

Brands with strong third-party citation footprints, e.g., analyst mentions, editorial reviews, expert commentary, tend to be:

• Mentioned more frequently
• Introduced earlier in AI responses
• Framed as “trusted,” “leading,” or “widely adopted”

Conversely, brands with extensive owned content libraries but limited third-party validation are often excluded from AI responses. This can be true even when brands have high domain authority, strong backlink profiles, or broad Google visibility.

The implication is clear: In AI search, authority is earned, not claimed. You cannot publish your way into trust.

Not All AI Engines Are the Same

While there appears to be a persistent eared media bias across LLM search, each AI engine creates its own information ecosystem.

ChatGPT and Claude

• Most conservative
• Extremely earned-media heavy
• Minimal inclusion of social or niche sources
• Strong bias toward established market leaders

Perplexity

• More diverse citation set
• Will include YouTube, retailers, and select social content
• Slightly more open to niche and challenger brands

Gemini

• Most brand-leaning among AI engines
• Closer to Google’s ecosystem in some categories
• More willing to reference brand-owned sources

The overlap in cited domains between engines is often surprisingly small. Optimizing for “AI search” generically is insufficient; engine-specific strategies are required.

The Big Brand Bias and How to Break It

AI systems default to market leaders for unbranded queries. This “big brand bias” is especially pronounced in ChatGPT and Claude. Niche and emerging brands are underrepresented unless they have:

• Strong third-party validation
• Coverage in respected specialist publications
• Clear expert positioning

Perplexity shows more openness to niche brands, particularly when supported by video content, reviews, or deep technical coverage. For challengers, the path to AI visibility runs through expert ecosystems, not broad awareness campaigns.

The Generative Engine Optimization Framework

If AI engines default to market leaders, reward third-party validation, and vary by platform, the takeaway is simple: you can’t treat LLM search like a slightly different version of Google. You need a playbook built specifically for how generative engines decide what to recommend. and how your brand earns a seat in that shortlist. Below are a few tips on how to get your playbook started.

Engineer for Machine Scannability

Treat your website as an API for AI agents.

• Implement structured data/schema where appropriate
• Make pricing, specs, reviews, and policies explicit
• Reduce ambiguity wherever possible

Dominate Earned Media

Supplement investment in owned content volume with:

• Analyst relations
• Editorial reviews
• Expert commentary
• Independent research and benchmarks

Optimize for Justification, Not Keywords

AI engines look for reasons to recommend.

• Comparison tables
• Pros and cons
• Clear value propositions
• Explicit tradeoffs

Use Engine-Specific Tactics

Each engine trusts different sources.

• Map which publications, platforms, and formats each engine favors
• Target those ecosystems deliberately

How Niche Brands Break Through

Over-invest in depth and specialization.

• Specialty publications
• Technical explainers
• YouTube and review ecosystems where applicable

SEO Alone Is Not Enough

Traditional SEO is not obsolete—but it is no longer sufficient. In an AI-dominated search future, visibility is earned through:

• Third-party authority
• Justification-ready content
• Engine-specific strategies
• Continuous monitoring and competitive intelligence

GEO is not a one-time project. It’s an ongoing investment—one that will increasingly separate brands that are recommended from those that are ignored. In AI search, the question is no longer “How do we rank?” It is “Why should the engine recommend us at all?”