Model Consensus
Different AI systems agreeing on what a brand is and is known for.
Aingworth Concept
Full explanation
Model consensus is the degree to which different AI systems, such as ChatGPT, Claude, Gemini, and Perplexity, arrive at similar understandings of a brand, its expertise, and its relevance.
It is measured by comparing outputs across systems for the same query: the same category description, the same named strengths, the same competitors mentioned alongside a brand. High model consensus means those answers converge. Low model consensus means each system has formed a different, sometimes contradictory, picture from the sources it retrieved.
Model consensus differs from Consensus in scope. Consensus describes agreement among independent sources, human and machine. Model consensus narrows that to agreement among the AI systems themselves, which matters because each system trains on different data, retrieves from different sources, and updates on its own schedule.
Why this matters
Agreement across independent models is a stronger signal than performance in any one of them, and it is far less vulnerable to a single platform changing how it works.
Example
A brand asks ChatGPT, Claude, and Perplexity to describe its specialism. Two return a consistent answer; one, trained on older data, describes a service the brand phased out two years ago. That gap is model consensus breaking down, not a single wrong answer.
Where this sits
Model Consensus belongs to Retrieval, Evidence & Consensus, one of the seven layers of the Aingworth Glossary. Information exists, then it is retrieved, then sources are selected, then they corroborate one another, then consensus emerges. This is where Aingworth's Consensus Over Rankings thesis lives.
Related terms
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