The search visibility gap: What 5,000 queries reveal about AI versus Google

A Toronto research team maps the new rules of online discovery

A study that changes how we think about search

Researchers at the University of Toronto ran thousands of queries through ChatGPT, Claude, Perplexity, Gemini, and Google. Same questions, wildly different answers. Not the content of the answers, but where they pulled their information from. In September 2025, they released their research paper named “Generative Engine Optimization: How to Dominate AI Search”. In this post, let me walk you through what the Toronto team actually did.

The experiment design that reveals everything

In August 2025, the research team ran over 1,000 base queries across ten verticals including consumer electronics, automotive, software, and local services. Each query went through all major AI engines plus Google for comparison.

They classified every single source into three categories. Brand sources from company-owned websites. Earned sources from third-party media and review sites. Social sources from Reddit, YouTube, and forums. Simple framework, profound implications.

But here’s where it gets interesting. They didn’t just run straight queries. They tested paraphrase sensitivity with seven variations of each query to see if slight wording changes affected results. They examined three query types: informational…