2026 AI Brand Visibility Index: How 8 Major Brands Score in AI Search

We scanned 8 major brands across ChatGPT, Claude, Kimi, and DeepSeek to measure how visible they are in AI-generated recommendations. Here are the results.

Methodology: Each brand is scored across 12 query scenarios — recommendation queries, comparison queries, trust queries, beginner queries, and direct brand queries. Discovery Score (60% weight) measures unprompted AI recommendations. Brand Score (40% weight) measures sentiment accuracy and narrative consistency. Combined score is 0–100.

The Rankings

Key Findings

1. Brand awareness ≠ AI discoverability

OpenAI — the company that built ChatGPT — scores 71/100 on AI visibility. Their Discovery Score is 62, meaning in one-third of recommendation scenarios, AI doesn't bring them up even when the topic is directly relevant.

Nike, one of the world's most recognized brands, scores 58. In direct comparison queries ("Nike vs Adidas"), AI coverage is solid. But in problem-solution queries ("what should I wear for marathon training?"), Nike's presence drops sharply.

High brand recognition built through traditional media does not automatically translate into AI recommendation visibility.

2. Comparison content is the highest-leverage GEO asset

Brands with strong "vs competitor" content coverage score systematically higher on Discovery. Notion scores 66 on Discovery partly because there's a large body of "Notion vs Obsidian," "Notion vs Roam Research," and "Notion vs ClickUp" comparison content that AI models have absorbed.

Brands without this content stack struggle in comparison query types — which are among the highest purchase-intent queries.

3. Chinese brands perform surprisingly well in bilingual AI coverage

小红书 (RedNote) scores 89/100 — the highest in this index. Its Discovery Score of 84 reflects extensive coverage in both Chinese and English AI training data, plus strong community-generated content that AI models treat as social proof.

完美日记 scores 71 with a Brand Score of 84, reflecting accurate and consistent narrative across AI models despite being primarily a Chinese market brand.

4. The Discovery Score gap is where GEO work pays off

Most brands have adequate Brand Scores (AI describes them reasonably accurately when asked directly). The gap is almost always in Discovery Score — being found when users don't know to ask for you.

Every brand in this index has a higher Brand Score than Discovery Score. The opportunity is consistent: improve discoverability, not brand description accuracy.

What Moves the Discovery Score

Based on these scans, the highest-leverage actions for improving Discovery:

Content structure matters more than volume. AI models are more likely to cite structured content (comparison articles, FAQ pages, "best X for Y" formats) than blog posts or brand announcements.

Third-party citation is weighted heavily. A mention in an independent review, Reddit thread, or industry newsletter carries more weight than 10 branded blog posts. AI models are trained to recognize citation patterns.

Consistency across AI models requires consistency in source material. If ChatGPT recommends you but Claude doesn't, the gap usually traces back to uneven content coverage across the platforms each model weights differently.

Keyword + context pairing. Appearing in a "best [category]" query requires content that explicitly connects your brand to that category in a recommendation context — not just content that describes your features.

The Measurement Problem

None of these brands can see this data in their standard analytics stack. Google Analytics shows traffic. Search Console shows keyword rankings. Neither shows AI recommendation frequency.

That's the GEO measurement gap: the work is visible, the results are not — unless you scan for them directly.

This index will be updated quarterly. Brands can scan their own visibility at anchor.agentese.ai.

Data collected April 2026. Scores are based on AI responses at the time of scanning and will change as AI models update. Score variance ±10 is normal across scanning sessions.