One Search Engine, Hundreds of Signals, Millions of Tests. Now There Are multiple Engines and No Rulebook.

By Phil | AI Visibility | 10 min read

SEO had one search engine and 25 years of testing. GEO has multiple AI platforms and no proven playbook. Here is how each LLM works differently and what that means for your brand.

The Luxury of a Single Opponent

For most of the internet's commercial history, digital visibility meant one thing: ranking on Google. Bing existed, Yahoo lingered, but Google commanded over 90% of search traffic. If you understood Google, you understood search.

And understanding Google became a science. Thousands of SEO practitioners ran controlled experiments. They tested title tags, heading structures, link placements, page speed thresholds, schema markup variations, and content length. Over millions of tests spanning two decades, the industry distilled Google's hundreds of ranking signals into a workable framework: content quality, backlink authority, and technical health.

That framework works. It is proven. And it is still essential.

But the landscape has shifted. Today, half of all consumers use AI assistants to research purchases. ChatGPT alone has 800 million weekly users. And here is the problem: there is no single opponent any more. There are at least multiple major AI platforms, each with fundamentally different approaches to deciding which brands get mentioned.

Visualisation of multiple AI platforms showing fragmented visibility landscape

How Google Built the SEO Playbook

Before exploring what is different about AI platforms, it is worth appreciating how the SEO playbook was constructed. It did not appear overnight.

In 2003, practitioners discovered that exact-match anchor text in backlinks could influence rankings. By 2011, Google's Panda update rewarded comprehensive, original content. In 2012, Penguin penalised manipulative link building. In 2015, the mobile-friendly update changed technical requirements. Each algorithm change triggered thousands of new tests and refined the collective understanding.

By 2026, the SEO community has documented hundreds of ranking factors, tested them across billions of pages, and created a mature discipline with reliable best practices. When someone says "focus on content, links, and technical," that advice is backed by an enormous body of evidence.

GEO has none of this. The discipline is perhaps 18 months old. Nobody has run millions of controlled tests on what makes ChatGPT cite one brand over another. The data is emerging, but the playbook is unwritten.

Platform by Platform: Why Each AI Engine Is Different

This is the core challenge of GEO. Unlike SEO, where optimising for Google covers the vast majority of search traffic, AI visibility is fragmented across platforms that work in fundamentally different ways.

ChatGPT: The Market Leader

ChatGPT holds roughly 80% market share among AI assistants. It generates answers based on a combination of its training data and, when using browsing features, real-time web retrieval. The key characteristics that matter for visibility include entity recognition (how clearly your brand is defined in training data), content authority (whether your content reads as authoritative and factual), and structured information (clear comparisons, statistics, and direct answers to specific questions).

Here is a telling statistic: only 17% of sources that ChatGPT cites also rank in Google's organic top 10. That means 83% of what ChatGPT recommends comes from a completely different evaluation process than Google uses. Optimising for Google gives you a small head start with ChatGPT, but it is far from sufficient.

Google AI Overviews: The Hybrid

Google's AI Overviews now appear in 25 to 30 percent of searches, pushing the first traditional organic result below the fold. Unlike ChatGPT, these overviews draw heavily from Google's existing search index. Ninety-two percent of cited sources come from pages already ranking in the top 10.

This makes Google AI Overviews the one platform where strong SEO directly helps. But it also creates a paradox: when AI Overviews appear, organic click-through rates drop by 34 to 61 percent. You might be cited in the AI summary, but fewer people click through to your actual site.

Platform comparison showing different citation patterns across AI engines

Perplexity: The Real-Time Researcher

Perplexity holds about 11% market share and works differently again. It searches the live web for every query, making content freshness far more important than on other platforms. Pages updated within the last 60 days are nearly twice as likely to appear in Perplexity answers.

For brands in fast-moving industries, Perplexity rewards a content strategy built around regular updates and timely analysis. The static "evergreen" content that works well for Google SEO may be invisible on Perplexity if it has not been updated recently.

Claude: The Cautious Responder

Claude, built by Anthropic, takes a more conservative approach to brand recommendations. It tends to qualify its suggestions more heavily and is less likely to make definitive "best product" claims. Content that reads as balanced, well-sourced, and transparent about limitations performs better with Claude than aggressive marketing copy.

Microsoft Copilot

With about 5% market share, Copilot integrates with Bing's search index and GPT-4. It blends traditional web search with AI generation, creating yet another set of citation dynamics that differ from pure ChatGPT or pure search results.

What This Fragmentation Means for Strategy

In SEO, you can run one strategy and cover 90% of search traffic. In GEO, optimising for ChatGPT alone covers roughly 80% of AI assistant traffic, but the other 20% uses platforms with completely different rules. And that 20% includes Google's own AI features, which sit in front of billions of existing Google users.

More importantly, each platform's approach is changing rapidly. ChatGPT's browsing capabilities have evolved significantly over the past year. Google AI Overviews have expanded from 13% to 25% of queries in 12 months. Perplexity has introduced shopping features and advertising. These platforms are moving targets.

In SEO, Google updates its algorithm several times a year in significant ways. In GEO, every major model update can shift which brands get cited and how. There is no stable ground yet.

Timeline showing the rapid evolution of AI platform features and citation changes

The Emerging GEO Framework

Despite the uncertainty, patterns are forming. Across all platforms, several factors consistently improve the likelihood of being cited by AI.

Entity Definition

Every AI platform needs to understand what your brand is. A clear, consistent entity definition that appears across your website, structured data, and third-party sources helps models categorise and recommend your brand correctly.

Structured, Citable Content

AI models favour content that contains specific data points, direct comparisons, clear feature lists, and definitive statements. Content that hedges or uses vague language is harder for AI to cite with confidence.

Multi-Source Authority

Unlike Google, which primarily evaluates your website and its backlinks, AI models pull information from across the entire web. Reviews, forum discussions, news articles, industry reports, and social mentions all contribute to whether an AI assistant recommends your brand.

Freshness and Consistency

Keeping content current matters across all platforms, but especially for Perplexity and Google AI Overviews. Equally important is consistency: if your brand description varies across different sources, AI models may hesitate to cite you confidently.

Running Two Disciplines in Parallel

The practical reality for businesses in 2026 is that you need both SEO and GEO, running as parallel but distinct strategies. Your SEO programme continues to drive traffic from traditional search results. Your GEO programme builds citation authority across AI platforms.

The measurement is different too. SEO success is measured in rankings, traffic, and click-through rates. GEO success is measured in citation frequency, share of voice in AI responses, and mention sentiment across platforms.

There will not be a unified playbook for years. The SEO trinity of content, links, and technical took 25 years and millions of tests to establish. GEO's equivalent framework is still being discovered, one experiment at a time. The brands that start testing, measuring, and learning now will be the ones writing the playbook for everyone else.