What 250M AI Responses Reveal About AI Search

After examining over 250 million AI responses across eight major answer engines, patterns emerge that reshape how brands should approach visibility in AI-driven search.

This analysis, presented by Josh Blyskal of Profound, reveals five uncomfortable truths about AI search—and what they mean for your strategy.

Finding 1: AI Search Operates Independently from Google

The first finding challenges a common assumption: that ranking well in Google automatically translates to AI visibility.

The data shows ChatGPT’s source selection overlaps with Google results by only 39%. More than 60% of what ChatGPT cites doesn’t come from top Google results.

But here’s the more important insight about how that selection differs:

Unlike humans who concentrate attention on top SERP positions (position 1 gets dramatically more clicks than position 10), language models distribute focus more evenly across positions 1-10.

“Visibility now comes from being the best answer, not the highest SERP rank.”

AI systems evaluate content for relevance and quality rather than relying on position as a proxy. A page ranking #8 that directly answers the question may be cited over a #1 result that’s less relevant.

Implication

Don’t assume Google success equals AI success. Monitor AI visibility separately. Content that doesn’t rank highly may still earn citations if it provides excellent answers.

Finding 2: Google Indexing Remains Foundational

Despite operating independently for source selection, AI systems still depend on traditional search for content discovery.

The analysis found companies absent from Google’s index don’t appear in ChatGPT’s results. Not underrepresented—completely absent.

“SEO is no longer the finish line. It is the qualifying round.”

Traditional search visibility remains prerequisite infrastructure. AI systems discover content through search engines, then apply their own evaluation criteria to select citations.

The genuine competition occurs at the citation layer. But you can’t compete at that layer without first qualifying through traditional indexation.

Implication

Don’t abandon SEO for GEO. Maintain strong traditional SEO as the foundation that enables AI visibility. Then layer AI optimization on top.

Finding 3: Transition to Structured Product Feeds

OpenAI is shifting from HTML scraping toward structured data feeds—a change with significant implications for how products appear in AI responses.

Products with comprehensive, machine-readable information receive preferential treatment:

Stock status and availability
Ratings and reviews
Specifications and attributes
Pricing information
Feature comparisons

This mirrors Google Shopping’s evolution, where clean data architecture determines success. AI systems increasingly prefer structured data they can parse reliably over unstructured content they must interpret.

Implication

For ecommerce and product-focused businesses, structured data investment becomes increasingly important. Comprehensive product feeds—not just basic schema markup—affect AI citation.

Finding 4: AI Generates Multiple Intent-Based Queries

When a user submits a query, AI doesn’t just search that exact query. It generates approximately 2.4 underlying queries on average—a process called query fanout.

A user asking “What’s the best CRM for small business?” might trigger AI searches for:

“CRM software comparison small business”
“best CRM features small teams”
“affordable CRM options”
“CRM ease of use ratings”

“You no longer rank for keywords. You rank for the intents the model derives from the user’s question.”

The AI interprets user intent and searches for information addressing multiple aspects of that intent. Your content needs to satisfy not just the explicit query but the implicit related queries AI generates.

Implication

Optimize for intent clusters, not individual keywords. Create content that addresses the full scope of what AI might search when users ask questions in your space.

Map your content to AI query clusters rather than individual search terms.

Finding 5: Content Architecture Determines Citation

The analysis examined characteristics of highly-cited products versus those receiving few or no citations. The differences were substantial:

High-citation products contained:

848% more FAQs than low-citation products
103% more videos
36% higher average ratings
Longer, more descriptive titles

Structured, explicit, and contextually rich content receives preferential citation from AI models.

This isn’t about word count or keyword density. It’s about information architecture that makes content:

Easy to parse and understand
Rich with extractable facts
Structured for specific question-answer patterns
Comprehensive in coverage

Implication

Content architecture matters as much as content substance. FAQ structures, video content, comprehensive coverage, and clear organization all improve citation likelihood.

Audit your content not just for quality but for structural characteristics that enable AI extraction.

The Uncomfortable Conclusion

The analysis concludes with a prediction: “Most brands will experience quiet visibility decline.”

AI search is growing. Traditional search is flat or declining. Brands that don’t adapt will gradually become invisible as the discovery landscape shifts.

But here’s the opportunity embedded in that warning: “Those adapting early will compound advantages dramatically.”

The compounding nature of AI visibility—where early movers build authority that becomes harder to displace—creates urgency. Brands establishing AI visibility now gain sustainable competitive advantages.

Practical Applications

Based on these findings, prioritize:

1. Independent AI Monitoring

Don’t assume Google rankings indicate AI visibility. Monitor AI platforms directly:

ChatGPT visibility for target queries
Citation sources AI references
Competitive visibility comparison

The 39% overlap means most of what happens in AI search is invisible to traditional SEO tracking.

2. Structural Content Investment

Given content architecture’s impact, invest in:

Comprehensive FAQ development
Video content creation
Detailed product information
Clear, explicit answers to common questions

Structure enables citation.

3. Intent Cluster Mapping

Map your content to intent clusters, not just keywords:

What questions do users ask?
What related questions does AI likely search?
Does your content address the full cluster?

Coverage across query fanout improves visibility.

4. Structured Data Investment

For product-focused businesses:

Comprehensive product feeds
Complete attribute coverage
Real-time availability and pricing
Rich review and rating data

Clean data architecture enables AI parsing.

5. Foundation Maintenance

While building AI visibility:

Maintain traditional SEO fundamentals
Ensure continued indexation
Keep technical infrastructure sound

The qualifying round remains essential.

Direct Measurement Over Guesswork

The final insight from this research: direct measurement replaces guesswork.

With 250 million responses analyzed, patterns emerge that speculation can’t reveal. The brands succeeding in AI search measure AI visibility directly, not as an afterthought to traditional SEO metrics.

What you can measure:

Actual brand mentions in AI responses
Citation frequency and sources
Competitive share of voice
Sentiment and context of mentions
Trends over time

What you should stop doing:

Assuming Google rankings predict AI visibility
Relying on traffic metrics that miss zero-click discovery
Optimizing for AI based on SEO intuition alone

The data is available. The patterns are learnable. The brands pulling ahead are measuring directly and acting on what they find.

RivalHound provides direct AI visibility measurement across all major platforms—exactly what this research shows is essential for AI search success. Start your free trial to measure your AI presence with confidence.