State of AI Visibility · Vol. 1
The Authority Gap
How AI separates trust from recommendation across the buyer journey, and why the overlap between them is far smaller than the market assumes.
April 2026Read →
ARDI™ Research Center · The Intelligence Index
Original research on how AI processes, trusts, and recommends brands.
Published research from the GOSH AI ARDI™ Division, the methodology behind the ARDI™ Intelligence Platform. Methodology-grade observation of how the world's leading AI models behave across the buyer journey, by category, by geography, and over time.
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Latest release · State of AI Visibility, Vol. 1
The Authority Gap
AI cites thousands of brands across the buyer journey. It recommends almost none of them. Fewer than 1% earn both, in every category we measured.
<1%of brands reach Full Authority
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All research.
Every ARDI™ paper, published and in development. Filter by track or search the corpus.
Showing all 6 papers
State of AI Visibility · Vol. 2
The Search vs Learned Divergence
Quantifying the gap between brands AI surfaces from real-time search and brands embedded in foundational training data.
May 2026Read →
Market Structure Report · Edition 1
Health & Wellness, 2026
Five health and wellness categories, five structurally distinct AI recommendation marketplaces, and why AI defers to institutional authority over commercial brands.
June 2026Read →
Coming Q3 2026
Category Report
CPG Brand Authority in the AI Era
How consumer packaged goods brands are being cited as authority sources versus recommended directly to consumers across AI models.
In developmentNotify me →
Coming Q4 2026
Methodology Brief
Inside ARDI™: How We Measure What AI Trusts
A technical overview of the ARDI™ observation platform, including the dual-path methodology, prompt taxonomy, and entity resolution framework.
In developmentNotify me →
Coming Q4 2026
State of AI Visibility · Vol. 3
Model Variance: How AI Choices Differ Across Platforms
ChatGPT, Claude, Gemini, Perplexity, Grok, Microsoft Copilot, and Google AI Overviews do not recommend the same brands. The variance between them reveals how each model forms its judgments.
In developmentNotify me →
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