YouTube Citations Split Wildly by Platform; llms.txt Delivers Nothing
New data confirms that AI citation behavior varies dramatically across platforms, Perplexity cites YouTube at 38.7% while Gemini sits at 0.2%, and a multi-study consensus now puts the citation lift from llms.txt at zero. Treating AI search as one channel, or llms.txt as a shortcut, are both strategic errors.
This Week's Signals
Google Engineers Detail Why Stateless MCP Was Unavoidable at Cloud Scale
Why it matters for your score
Google hit a hard wall deploying MCP across cloud-native infrastructure because the original protocol required persistent state, handshakes, and session pinning. The 2026-07-28 spec drops session state for a stateless core, and all four Tier 1 SDKs already speak the new version. For any business building or buying agent-connected services, this spec change is now the baseline; older MCP integrations may not be compatible with current clients or servers.
What to do this week
- 1
Audit your video content strategy by platform rather than treating AI search as one channel, the data shows Perplexity at 38.7% and Gemini at 0.2% for YouTube citations, so prioritize long-form video content and check where your target audience's AI tools actually surface video results.
- 2
Run your site through the Faro AI Readiness Scan (/tools/ai-readiness-scan) to confirm your visibility signals are based on content structure and schema rather than relying on llms.txt, which two independent studies now show produces no measurable citation lift.
- 3
If you have an MCP-connected service or are evaluating one, verify whether your integration targets the 2026-07-28 spec, the legacy HTTP+SSE transport is officially deprecated with a one-year offramp, and servers on the new revision may not work with older clients.