As of September 2026, AI-search research has an evidence problem, not primarily a content problem: a handful of genuinely rigorous studies (Ahrefs, the Princeton GEO paper) carry most of the field's reliable findings, while most circulating statistics are vendor-reported from private corpora or, in several documented cases, simply don't reconcile with each other. This snapshot inventories what's solid, what's genuinely open, and what this site has built toward closing that gap so far.
Why a snapshot now
This site's research program, the AI Citation Index and everything built around it, launched with a specific thesis: the field is saturated with advice and starved of evidence. Fifty research pages later, that thesis holds up. It's worth stating plainly, with receipts, rather than as an abstract claim made once at the start and never revisited.
The landscape, in five findings
Google ranking no longer strongly predicts AI citation. Overlap ranges from ~12% (ChatGPT/Gemini/Copilot) to ~38% (AI Overviews, down from ~76% a year earlier) — see most-cited domains and AI Overview statistics.
Engines barely agree with each other. Reported ChatGPT/Perplexity domain overlap sits around 11% — see cross-platform concordance.
The schema debate has real evidence on both sides, and it's unresolved. Observational data shows no clear lift; live-fetch tests show schema often ignored entirely — see the schema RCT design.
Three separate studies collapsed into one "58%" figure that gets misapplied across contexts — the clearest documented case of statistic conflation in the field, per the provenance audit.
Not one randomized, causal test of a GEO tactic existed in public before this year's pre-registrations. Every published finding in the field prior to this site's Tier 3 program is correlational.
- Statistics & platform pages 20
- Pre-registered studies 15
- Reference & framework 9
- Definitions & glossary 6
What these five findings mean together
Read individually, each finding above is a discrete fact about one narrow slice of AI search. Read together, they describe something more structural. The traditional shortcut, rank well on Google, get cited by AI, no longer reliably works. A strategy tuned for one engine doesn't transfer to the next.
Even a widely recommended technical tactic, schema, has genuinely mixed evidence, not a settled answer. Sloppy statistic handling is common enough to produce a documented three-studies-collapsed-into-one case. And almost nothing has been tested with a method that could actually support a causal claim. Put plainly: the practical implication isn't "GEO doesn't work." It's "almost nobody has actually tested what works." That's precisely the gap this site's research program exists to close.
Fifty pages into building an independent AI-search research site, the finding holds: the field has an enormous amount of published conclusion and very little published evidence.
Share on XThe five biggest unanswered questions
- What does Claude actually cite? The least-measured major surface — see Claude citation statistics.
- How many sub-queries does AI Mode really generate per question? Folklore says 8–16; nobody has published the data — see the Fan-Out Corpus design.
- Does anything a site controls causally change its citation rate? Nearly everything published is correlational — see the Tier 3 RCT program (schema, freshness, author bio).
- How long does a citation last? Every study is a snapshot; none track duration — see citation half-life.
- Do AI crawlers render JavaScript? A binary, foundational, and still-untested technical question — see the rendering experiment.
What surprised us most while building this
Two things stood out more than expected going into this project. First, how often a specific, precise-sounding statistic, "47.9% of ChatGPT citations are Wikipedia," turned out, on tracing, to originate from exactly one vendor-reported source, repeated verbatim across dozens of secondary articles. The appearance of broad consensus masked a single underlying data point.
Second, how genuinely thin the causal-research layer is. Not a niche gap in one corner of the field, but essentially the entire field, across every major platform and tactic, prior to the pre-registrations this site has begun publishing this year. Neither is a criticism of any individual source. Most are transparent about being vendor-reported. But the aggregate picture, once actually traced hop by hop, is thinner than the sheer volume of published content about AI search would suggest.
What this site has published so far
Forty-nine research pages as of this snapshot. The Citation Index's pre-registration and its core metrics. Thirteen curated and cross-platform statistics pages. Ten pre-registered original studies. Six Claude-Code-for-SEO practitioner pages. And a reference layer: the AI Bot Registry, the tactic scoreboard, the Null-Results Registry, the glossary, and two definitional pillars.
Methodology lessons from the first fifty pages
A few practical lessons worth stating plainly, since they shaped how later pages on this site were built. Tracing a statistic to its actual origin routinely takes longer than writing the page around it. It's worth doing anyway, since it's the entire basis for this site's credibility claim.
A pre-registered hypothesis is only meaningful if the prediction is genuinely stated before data collection, not written to match a result decided in advance. That's why several studies here register a directional prediction that could turn out wrong, rather than a safe, hedge-everything framing.
And a null-result commitment only means something if an actual null result eventually gets published, not merely stated as an intention. That's exactly why the Null-Results Registry exists as a standing, checkable mechanism, not a one-time promise.
What comes next
Data collection for Index v1 begins Q1 2027. Every pre-registered study above moves from design to result on that same timeline. The next State of AI Search edition, in Q4 2027, will report actual findings against every prediction registered here — including the ones we predicted would fail.
How this snapshot was built
A first-party inventory and synthesis of this site's own published research as of the date shown, cross- referenced against the provenance grades established throughout. No new data collection underlies this specific page — it's a synthesis of work already published and linked above.
Namdev, R. (2026). The state of AI search: September 2026 (v1). Retrieved from https://ritiknamdev.com/blog/state-of-ai-search-september-2026 Published under CC BY 4.0 — reuse freely with attribution.
Every finding and gap below links to its own dedicated page. Start with the AI Citation Index if you're new to this research program.