I spent the last two weeks doing something I'd been putting off: reading every large-sample AI search study published in the past six months. Not the "AI is coming, here's what to do" marketing fluff — I mean studies with clear sample sizes, statistical methods, and reproducible results.
I started with ~100 pieces. Only 26 made the cut, spanning December 2025 to August 2026. The rest were either tool tutorials or thinly-veiled product pitches.
This post distills the most valuable conclusions from those 26 reports, each tagged with its sample size. Many contradict the "GEO best practices" you've been sold — which is exactly why they're worth sharing.
Disclaimer: personal synthesis + cross-validation. Not infallible. Happy to debate.
What This Post Covers Six hard findings — if you only read one section, read this one Debunked tactics — these "GEO service packages" need to stop Page-level writing framework — no tools needed, just change how you write Technical accessibility — if AI can't crawl you, nothing else matters Fan-out queries — AI search's real unit of topic planning Off-site signals — AI learns about you from everyone else Measurement — track average visibility, not single-shot answers 90-day execution checklist Six Hard Findings
These six come from six independent large-sample studies. They cross-validate each other. If you only read one screen, read this one.
A study of 75,000 brands measured Spearman correlation between various factors and AI visibility. YouTube mentions scored 0.740 in AI Mode, 0.737 in ChatGPT, and 0.712 in AI Overviews — outranking everything else.
And mention count matters more than view count — a mention in a low-view video counts just the same.
Same 75K-brand study: the correlation between total indexed pages and AI visibility is just ~0.194. Domain Rating only reaches 0.266–0.326. The old playbook of "publish more + build DR" doesn't convert in AI search.
A study of 1.4 million ChatGPT prompts found that URLs retrieved via the search channel had an 88.46% citation rate. News dropped to 12.01%, Reddit to just 1.93%. Traditional SEO isn't the opposite of AEO — it's the prerequisite.
Across 1.2M ChatGPT citations, 44.2% referenced content in the first 30% of the page, 31.1% the middle, 24.7% the end. Bury your answer in paragraph 12 and your citation probability drops to roughly 1/2.5.
Across 863K SERPs and 4M AIO citations, only 37.9% of cited URLs appeared in the top 10 organic results. Positions 11–100 accounted for 31.2%, and beyond 100 accounted for 31.0%. Six months ago, the top-10 share was ~76%.
One platform shared its own data: AI search visitors made up just 0.5% of traffic but drove 12.1% of signups — a 23x conversion rate over organic search. An independent SaaS reported 20.15% vs 7.06%. Stop judging this channel by traffic share. Debunked Tactics
This section is worth more than the positive advice: every item below is actively being sold as a "GEO service package" — and every one has been disproven by large-sample data. Stop these first.
The correlation looks compelling: 53% of cited pages had schema; cited pages were ~3x more likely to have JSON-LD than uncited pages. But matched difference-in-differences killed the causal claim: tracking 1,885 pages that added JSON-LD against 4,000 controls yielded AI Overviews −4.6% (statistically significant), AI Mode +2.4%, ChatGPT +2.2% (indistinguishable from zero). A separate packet-capture study confirmed none of the five major AI systems actually parse schema during live retrieval.
→ Keep schema for rich results and Knowledge Graph. Don't budget it as an AEO lever.
A scan of 137,210 domains: 28% had published a valid llms.txt, but 97% received zero requests during the study period. The remaining 3% captured all ~22,000 requests — 96% from bots. Real AI retrieval bots accounted for just 1.1%; the largest traffic source was SEO audit tools (21.7%).
→ Only worth it for developer documentation sites targeting coding agents. For marketing sites, let your CMS auto-generate it.