How Does AI Decide What to Cite?I Tested 125 Keywords Across 4 AI Engines

By Irina Saprykina, working in SEO and digital marketing since 2006.

How does AI decide which sources to cite? My data suggests there are two separate problems: getting into the candidate pool, and being selected once you're there. Google rank is the strongest observable path into that pool, but it doesn't explain most citations on its own. Among pages Google already ranks, page-level links, freshness, query-format match and AI Overview presence help separate cited from uncited pages - and each AI engine weighs those signals differently.

To test that, I pulled the real citations from ChatGPT, Claude, Gemini, Perplexity and Google's AI Overview for 125 keywords across seven topic areas, measured the pages each AI search engine cited against the ones it ignored, then re-ran part of it to see what moved. (My earlier 41-factors guide collected what the research says about AI citations; this is what my own runs show.)

The distinction that runs through everything:

Getting into the candidate pool (mostly a matter of ranking) and getting picked from it (a different, smaller set of signals) are two different jobs - and it's easy to treat them as one.

The Short Version: What Moved Citations

If you want the bottom line before the evidence, here is what mattered in my data. Each factor is unpacked below, and the full statistical model - with odds ratios and confidence intervals - is at the end.

And it is not one target: each engine weights these signals differently, so "optimize for AI" really means optimizing for several machines at once (more in Part 3).

Further down I also dig into the ecommerce picture (reviews vs storefronts), the per-engine differences, and how much citations move from one run to the next.

How I Tested This

For each keyword I captured Google's top 40, Bing's top 20, the AI Overview sources, and the live citations from all four assistants (ChatGPT, Claude, Gemini, Perplexity) - then enriched every URL with its page-level backlinks, Domain Rating, word count, fact density, publish date, schema and content type.

Three runs:

• 30 keywords across six verticals - SaaS, health, affiliate, recipes, data/statistics and SEO (~1,500 pages),
• a 64-keyword SaaS deep-dive across eight query formats - "what is X", "X vs Y", "best X", pricing, how-to, review and more, unpacked in Part 2 (~3,000 pages),
• and a 36-keyword ecommerce run (~1,600 pages).

That is 130 keyword-run observations representing 125 unique keywords - the SaaS deep-dive re-used the five SaaS keywords from the cross-vertical run. Plus re-runs at day 5 and day 32 to measure churn, two fan-out experiments plus a 160-call observability control, and a repeatability study (12 prompts × 8 identical runs × 3 engines). US, desktop.

Platform giants (Reddit, YouTube, Wikipedia) are excluded from the factor analysis - their scale and platform dynamics make them structural outliers, and less actionable for most sites.

Three scope notes up front:

• First, the main quantitative ChatGPT dataset was first collected on gpt-4o with web retrieval through the API - one specific ChatGPT setup, not ChatGPT as a whole. Because that model has since moved on, I re-ran the core analysis on the current systems (GPT-5.6 Sol, Claude Sonnet 5, Gemini 3.1 Pro Preview and Perplexity Sonar Pro), and the main findings held - the full before-and-after is in the update near the end. A separate follow-up also showed that ChatGPT's reasoning mode retrieves differently, so these tests describe at least two retrieval regimes, not a claim that there are only two.
• Second, most citations could be matched to the exact page I had measured: about 70%. Another ~23% pointed only to the domain, and ~7% to a different page on the same site. So the page-level analysis is based mostly on exact matches. Gemini is the exception: only about 31% of SaaS citations and 27% of ecommerce citations resolved to an exact measurable page. I therefore treat Gemini's page-level results more cautiously and measure its repeatability at the domain level.
• Third, even when I sent the same prompts through two different DataForSEO interfaces, the citation counts were very different. The LLM Scraper returned about 5 citations per response, while DataForSEO's LLM Responses endpoint returned about 12-13. So citation counts from different interfaces should not be compared directly as if they were measuring the same process.

The article runs in three lenses:

1. Discovery - what gets into the pool at all (rank, the off-SERP tail, fan-out).
2. Selection - what separates cited from uncited among ranked pages (links, freshness, format, AI Overview).
3. Measurement regimes - four engines, two ChatGPT surfaces, sampling noise vs real drift.

The closing model then puts every factor in one place.

I'll grade each finding:

• ★★★ - replicated across verticals or rank-controlled
• ★★ - solid in one large run
• ★ - suggestive

The SaaS findings then replicated on ecommerce - a structurally opposite vertical - which is why I trust the ★★★ ones. Where an outside study agrees (or seems to disagree), I say so.

Part 1 - Getting Seen

Google's Top 10 is the Strongest Citation Gate I Measured

★★★

Rank is the gate. I split Google's top 40 into two bands - the top 10, and positions 11-40 below it (roughly pages two to four). A top-10 page was cited by at least one of the four assistants 78% of the time in SaaS and 69% in ecommerce; an 11-40 page, 27% in both. Across six verticals the top-10 range was 50-87% versus 16-36% below it. (Throughout, "cited" means picked by one of the four AI assistants - ChatGPT, Claude, Gemini or Perplexity; Google's AI Overview is a separate source, analysed later as a supporting signal.) Ahrefs' 863K-SERP study found the same shape - 37.9% of AI Overview citations come from top-10 pages.

But ranking is never enough - in two directions. Depending on the vertical, 13-50% of top-10 pages got no citations at all. And more surprising: while an individual top-10 page has the best odds, most citations don't come from the top 10 at all. Count where every cited page actually ranks and only 19% sit in Google's top 10; 25% are at 11-40, and 56% don't rank in the top 40 at all (64% in ecommerce). Odds favour the top 10; sheer volume doesn't. The same odds-versus-volume gap comes up again - with page formats, and with each engine's habits.

Links Separate Pages Below the Top 10

★★★

Links to the exact page (its referring domains) were the most consistent authority signal I measured - but they only tell cited and uncited pages apart below the top 10. In the 11-40 band, the cited page had more links than the uncited one in 89% of keywords (93% in ecommerce) - measured per keyword, so a few heavily-linked topics can't skew it. Inside the top 10, that fell to a coin flip - 50% in both verticals. In other words, links work mostly through ranking - once you're in the top 10, raw link count stops separating cited from uncited on its own. (Even with rank held equal, the full model still finds a smaller independent link effect - roughly 1.4× more citation odds each time a page's backlink count roughly triples.)

How much more linked are cited pages? It depends on the topic (cited vs uncited median referring domains): health 119.5 vs 2, affiliate 117.5 vs 40.5, SEO 15 vs 1, SaaS 5 vs 1 - huge where big institutions and small sites share a SERP, small where everyone is equally linked. "Build links" is still right - just treat it as a ranking lever, not a direct citation trick. (Two caveats: links partly stand in for brand familiarity, which I didn't measure; and inside the top 10 they still predict how many engines cite you, not just whether - more later.)

Domain Rating Adds Much Less Once Page Links Are Controlled

★★★

Most of the Domain Rating gap between cited and uncited pages comes from links, not the domain score itself. Group pages by how many backlinks they have, and within each group cited and uncited pages sit at almost the same DR (52/54, 73/71, 88/84). A small DR effect of its own does remain once everything else is accounted for, but it is minor.

A residual effect survives stricter controls (1.13× odds per +10 DR in the original model; 1.20× in the September refresh). So DR is not irrelevant, but it is not a hard barrier either: high DR alone did not rescue pages with no page-level links. Among ranked pages with zero page links, a DR≥80 was cited 20% and a DR<50 18%. In affiliate, cited DR 86 = uncited DR 86.

This lines up with Ahrefs' 75K-brand study, which found domain-level link metrics "very weak" for AI visibility. They measured the domain; I measured the page. Same conclusion from both ends: the page is where authority counts.

Ranking in Bing too Helps - But Bing Alone Rarely Earns a Citation

★★

Ranking in Bing as well as Google helps. Among pages at the same Google rank, those also in Bing's top 10 were cited far more often (11-40 band: 39-67% vs 24-27%). Ranking in two engines, not one, is an extra sign of relevance.

But Bing on its own rarely wins a citation: pages that ranked in Bing but not Google were cited just 3-8% of the time. The old "ChatGPT runs on Bing" shortcut didn't hold either - only 8-12% of ChatGPT's citations matched Bing, versus 23-24% for Google. Where the rest of ChatGPT's citations come from is a separate question, which I come back to below.

Part 2 - Getting Quoted

On Informational Queries, Homepages are Never Quoted

★★★

Zero homepages were cited as a content source - 0 of 109 in SaaS, 0 of 49 in ecommerce. Landing pages 24-29%. What gets quoted is editorial: comparison pages 52%, blog articles 44%, definitions 40%.

You may have seen studies saying homepages get the MOST AI traffic - Similarweb's May 2026 clickstream analysis found that after ChatGPT began surfacing prominent brand links, the share of referrals landing on homepages jumped from roughly 26-32% to around 60%.

Both are true, and they're not in conflict. Those clickstream datasets count referral clicks across all real prompts and include the navigational and branded behavior ("Notion", "HubSpot pricing") that my informational keyword sample deliberately excludes. And assistants often make brand-level, domain-only mentions - Gemini especially - which can render as homepage links. Their own explanation matches mine: the model names a company, the user lands on the homepage to evaluate it.

AI links your homepage as a brand; it quotes your editorial pages as sources. Publish the explainer or comparison for the query you want cited.

Match the Format to the Query

★★★

Across every measurable query format I tested, format follows intent: "vs" returns comparison pages, "what is" returns definitions, "best" returns roundups, "pricing" returns the vendor's pricing page. And the expected page type performed better within the top-10 rank band. In SaaS, the right type added 12-15 points; it replicated in ecommerce (definition 80% vs 67%, roundup 50% vs 20%, how-to 67% vs 14%, review 82% vs 50%). The "dominant page-type in the top 10" is a direct what-to-produce signal.

Some Formats Have a Low Ceiling - "Best X" Worst of All

★★★

Winning the top 10 pays off differently by format. Validation and definition queries cite their top-10 pages 85-90% of the time; "best X" roundups only ~47-48%, in both verticals. Half your hard-won top-10 roundups get cited, and most "ranked-but-ignored" pages are this format. Ecommerce buy-intent queries sit just as low (47%).

You may have seen studies calling listicles the most-cited format. That's mostly a matter of volume: commercial queries return so many listicles that they add up to a big slice of all citations. But any single top-10 listicle still gets cited only about half the time. A big share of the citations, modest odds per page - share isn't the same as odds.

Concrete Facts: Common on Data-Query Pages, But Not a Citation Lever

★★

For data queries ("average salary", "conversion benchmarks"), cited pages were far fact-denser (15.6 vs 8.7 facts per 1,000 words). For step-by-step queries, no difference at all (4.5 vs 4.2). Within SaaS, only price/spec formats showed a lift. That descriptive pattern was still visible in the refresh, but it disappeared once the other measured factors were controlled. So I no longer treat fact density as an independent citation lever - it looks more like a characteristic of the kinds of pages data queries reward. Don't stuff numbers into a how-to; do put them on a benchmarks page. (Self-contained, unhedged claims help too, per the GEO literature - I didn't measure that one directly.)

Freshness Matters Modestly Everywhere

★★★

Freshness is a modest but real factor, and statistically it behaves the same wherever a page sits: holding rank, page-level links and everything else equal, a recently updated page had roughly 1.9× the citation odds of a stale one, both inside the top 10 and below it.

What changes with rank is how much that edge is worth in practice. Below the top 10, citation rates are low, so the same odds boost shows up as a big jump in the actual rate: at positions 11-40, fresh pages were cited 32% of the time vs 18% for pages over a year old, a 14-point gap. Inside the top 10, where most pages get cited anyway, it only widens an already-high rate a little (80% fresh vs 72% stale). Same statistical effect, bigger real-world difference where citations are otherwise scarce.

Two practical notes:

• expose a real updated date (ecommerce penalised undated pages even inside the top 10),
• freshness mattered most descriptively among weakly linked pages - in the 11-40 band, fresh pages with few links were cited 27% vs 16% for stale ones. So if you have few links, keeping the page freshly updated is one of the levers most in your control.

Length, Schema and Title-Match: The Proxies and the Myths

★★

Length: longer pages were cited a bit more across the whole sample, but the effect disappeared inside the top 10 - so length looks like a proxy for thoroughness, not a lever you pull. Aim for answer density rather than word count (retrieval reads long pages only partially anyway).

Schema presence is flat (Ahrefs' 1,885-page controlled test agrees - no lift). But specific types still correlate after rank control: FAQPage 86% vs 73%, Article 85% vs 69% inside the top 10. I can't fully separate "the markup helps" from "Q&A-shaped content helps and the markup tags along," so treat it as correlation. Boilerplate types (WebSite, Organization) showed no positive association. Add valid Article/FAQ markup as cheap hygiene, not as a lever.

Title-to-query overlap was NOT a citation factor in my data - flat once I controlled for rank. It plausibly helps you rank (and citation follows rank), but adds nothing on top.

A Note on Nofollow

★

Many of us nofollow external links by default. Does it help your own page get cited? The data says no: cited pages used plain follow links 89% of the time, and nofollow was slightly more common among uncited pages. This is a weak, suggestive signal, not something to build a firm rule on - but leaving your outbound links as plain follow links clearly doesn't hurt your own chances of being cited. If anything, I'd be especially careful not to nofollow the links to genuinely useful data sources you're citing - those are exactly the ones worth passing real credit to.

AI Overview Presence is a Strong Co-Signal

★★★

Among Google-ranked pages, being a source in Google's own AI Overview was one of the strongest co-signals I measured: AIO-source pages were cited ~74% of the time in both verticals, versus ~30-33% for ranked pages outside the AI Overview. Part of that is rank selection - AIO sources rank well - but the effect survives rank control, and it follows the now-familiar pattern: the gap is widest in the 11-40 band (59% vs 25% in SaaS, 56% vs 26% in ecommerce, roughly a 2-3× higher citation rate) and compresses inside the top 10. That makes AI Overview presence a third signal that separates cited from uncited pages most clearly at positions 11-40, alongside page links and freshness.

The AI Overview itself shows up on factual queries and fades on judgment ones (what-is 7/8, is-worth 3/8, when/why 1/8) - and in the query formats where the AIO appeared less often, the assistants tended to cite more sources of their own.

Where the Off-SERP Citations Come from: The Fan-Out Trail

One thread runs through the factors above: page-level links, freshness and AI Overview presence all mattered most in the 11-40 band, not inside the top 10. A likely reason is that these factors help a page rank - just not always for the query you're watching. AI assistants often expand your query into related sub-queries ("fan-outs") and search those too, so a page that ranks nowhere for the main keyword can rank well for a sub-query you never see, and get cited from there. That would also explain the big off-SERP tail from Part 1: many cited pages don't rank for the main query at all.

I tested this directly. Where ordinary sub-queries were visible, cited off-SERP pages ranked for them far more often than matched non-cited controls (same-domain top-20: 30% vs 6%; exact-URL top-10: 28% vs 5%) - so hidden-query ranking is one real pathway to a citation.

When ChatGPT reasons more before answering - the "thinking" effort you can turn up in the app - it searches differently, and its off-SERP citations fall into two patterns:

• Targeted fetching (about 56%). A same-call site:-targeted search aimed straight at the cited domain - the model deciding to fetch a specific source and citing from it.
• Hidden-query ranking (about 16%). A top result of an ordinary open sub-query - the pathway above, where a page ranks for a query you never see.

The targeted pattern dominates. In a controlled test that varied only this thinking effort, about 80% of the domains cited at high effort were ones the model had site:-searched in that same run, versus about 21% at minimal effort. So much of the off-SERP tail is the model deliberately fetching specific sources it chose to check, with hidden-query ranking as a smaller, second pathway.

(These are small, descriptive samples; absence from the visible sub-queries is not proof the model used none.)

Part 3 - ChatGPT, Claude, Gemini and Perplexity Cite Different Pages

The Four AI Engines Barely Overlap on What They Cite

Take the same keyword and compare what any two of the four AI search engines - ChatGPT, Claude, Gemini and Perplexity - cite: median overlap never exceeded 16% in SaaS, 11% in ecommerce. Four assistants, four different source sets. Outside audits land in the same place - Averi reports ~11% ChatGPT - Perplexity domain overlap, citing a 680M-citation analysis, and similarly low pairwise agreement in other large audits. So "optimize for AI" isn't one target - here it's four, and in general it's as many as there are engines you care about.

The September current-model refresh showed almost the same picture: even the most similar pair, Claude and Perplexity, averaged only 16.6% cited-domain overlap across keywords, while the mean across all six engine pairs was 10.3%.

Perplexity Tracks Google Closely; ChatGPT Strays Furthest Off-SERP

Share of each engine's citations that sit in Google's top 10: Perplexity 34% / Claude 31% / Gemini 22% / ChatGPT 17%. Off-SERP share (cited but not ranking in Google's top 40): ChatGPT ~68% / Gemini ~55% / Claude ~46% / Perplexity ~34%. Perplexity is closest to your rankings; ChatGPT strays furthest off-SERP.

One subtlety worth stating carefully: among ranked pages, ChatGPT is actually the most backlink-selective of the four - but most of its citations aren't ranked pages at all.

What Each Engine Rewards

I modeled each engine separately, so read these as patterns rather than head-to-head tests. They don't reward the same things:

• ChatGPT rewards authority. It leans on a domain's overall strength more than the others, and it is the harshest on sites that block AI crawlers - block one and a ChatGPT citation becomes very unlikely.
• Perplexity follows Google ranking. It sticks closest to the search results (a top-10 page is about 8× more likely to be cited than one below it), and its picks are the most consistent from one run to the next.
• Gemini rewards backlinks. The more links point to a page, the more likely Gemini is to cite it.
• Claude has no single standout. No one factor dominates for Claude the way rank does for Perplexity or authority for ChatGPT; it is the most balanced of the four.

I ran an earlier version of this test on the previous generation of models. Most of these patterns held when I re-checked on the current ones - ChatGPT's authority lean, Perplexity's rank-following and Gemini's link sensitivity all carried over. The one that changed is Claude: on the older models it stood out as the freshness engine, and that lead has since faded. The full before-and-after is in the update near the end.

Scope Update: "ChatGPT" Is Not One Retrieval Condition

The original ChatGPT benchmark used gpt-4o with web retrieval through an API. I have since repeated the main factor analysis on GPT-5.6 Sol, and the same broad factor map survived. I also checked this against the ChatGPT app itself - GPT-5.6 Sol in its "Instant" mode, extra reasoning off and web search on - on 14 test prompts. The app and the API behaved alike: both searched the web on 13 of the 14, and their top source matched on 12 of 14. So the original findings are not simply an artifact of the old gpt-4o API.

But reasoning effort changes how the same model searches - and, it turns out, what it cites. The "thinking" dial (the reasoning effort you can turn up in the app) runs from none, where the model answers with minimal thinking, to high, where it thinks hard first. I ran the same 28 prompts four times at each end, in one short window, changing nothing else.

Turning thinking up changed three things:

• It searched more, and more narrowly. Searches per answer went from 2.6 to 5.8, and most shifted to targeted site: lookups (27% to 62% of searches).
• It cited a bit more. Sources per answer rose from 2.3 to 2.9 (p=0.004).
• It cited a different set of sources. Two answers at the same setting shared about 53% of their cited domains; a minimal-thinking answer and a high-thinking one shared only about 40% (p<0.001) - beyond run-to-run noise.

Where the extra sources came from: about 80% of the domains cited at high thinking were ones the model had site:-searched in that same run (versus about 21% at minimal thinking). The shift ran toward vendor, product and tool sites, with some primary sources, and away from third-party media and aggregators - though not toward .gov/.edu specifically, which stayed flat (16.3% vs 16.6%).

(It took four runs at each setting to see this - a single high-thinking run can't be told apart from noise.) I keep this separate from the factor model because it answers a different question: not which page attributes correlate with a citation, but how the system goes looking for evidence, and reaches a different set of it, in the first place.

What a Page Cited by All Four Engines Looks Like

Sort pages by how many engines cite them. Pages cited by 0/1/2/3/4 engines have median page-links of 2 → 2 → 5 → 14 → 38, DR 74 → 91, top-10 share 5% → 78%, AIO presence 9% → 50%.

These aren't universal thresholds - they're medians across the specific keywords I checked, so treat the exact numbers as illustrative. But the direction is clear and, frankly, unsurprising: the more engines agree on a page, the more of these signals it tends to have stacked up at once. The portrait of a universal citation magnet: well-linked, top-ranked, present in the AI Overview.

Part 4 - The Ecommerce Twist

Most of the study so far pools informational and SaaS queries. Shopping queries behave differently enough to deserve their own look, so I ran a separate 36-keyword ecommerce set (~1,600 pages) - things like "best robot vacuum for pets", "running shoes for flat feet" or "air fryer vs oven" - this time letting retailers (Amazon, Walmart and the like) count as competitors alongside editorial pages. Three patterns stood out.

AI Prefers Reviews to Storefronts

★★

The first question was simple: across these product and shopping queries, among pages that rank, which type gets cited more - editorial pages (reviews, guides, comparisons) or retailer/storefront pages? Editorial won comfortably. Storefronts were cited at roughly half the editorial rate - 25% vs 42% among ranked pages. Amazon was cited three times across 36 keywords; Dick's, REI and Home Depot edged it out. SE Ranking's AI-Mode shopping research saw the same thing (Amazon largely missing, eBay leading). AI reaches for reviews and guides much more often than product listings.

Storefronts Earn Citations Mainly on Buy and Branded Intent

★★

Next I split those citations by the intent behind the query, to see when a storefront does get picked. The retailer share of citations was 25% on transactional queries and 14% on branded/review queries - but under 6% on informational ones (what / best / vs). A product page is citable where the query is "buy", rarely cited where it's "best".

Even a Brand's Own Page Loses Out on Review Queries

★★

The retailers above are resellers; the manufacturer's own product page (Dyson.com, Purple.com) is a different actor. It doesn't fare much better on judgment queries: on "X review" queries the brand's own page was cited only 2/4. A review pulls in third-party opinion by design, so even the brand's own site rarely wins one - the manufacturer gets cited for facts about its product, not for verdicts on it.

Part 5 - How Stable are AI Citations?

For this part I stopped asking what gets cited and asked whether a citation is even a stable thing to measure: I ran the same prompts many times over, then re-checked the same keywords across a day and again across a month, to separate real change from noise. Citation measurement itself turned out to be the finding. In repeated identical runs (12 prompts × 8 runs × 3 engines), a single ChatGPT answer exposed only ~29% of the URLs observed across the eight runs - and ten of twelve prompts never returned the same citation set twice - while Perplexity exposed ~89% and its sets repeat.

A 24-hour re-test separated time from that noise: ChatGPT's overlap across a full day (14%) exactly matched its overlap within minutes; what looks like overnight churn is mostly per-response sampling. Perplexity overlapped 90% at both distances - whatever the backend mechanism, its measurements are stable, so its multi-day movements are interpretable as real change.

The month-scale layer reads accordingly. Re-scanning 24 keywords at day 5 and day 32 gave a front-loaded retention curve - 100% → 63% → 57% - with 28% of the "lost" domains cited again by day 32: flicker, not exit. That curve mixes sampling noise with genuine drift and should not be read as pure decay; the stable core is anchored mostly on rank (30% top-10 share vs ~7% in the churn groups).

Practical read: measure ChatGPT and Gemini as distributions over repeated runs, not snapshots; Perplexity is much more defensible to track with single snapshots.

The Final Model - What Holds up with Every Factor Controlled at Once

As a closing pass I put every measured factor into one model - query, rank, page links, Domain Rating, freshness, AI Overview presence, robots blocking, schema, length, fact density, all controlled simultaneously across ~2,400 ranked pages, with keyword-clustered confidence intervals. The factors that still stand out when they all compete at once:

A few notes on reading the table:

• Each odds ratio is on its own unit, so the column is not a ranking of importance - you can't say top-10 "beats" AI Overview just because 5.42 > 3.11.
• The page-links row is on a natural-log scale - the 1.34 is "per log-unit", meaning each roughly 2.7× increase in a page's backlink count multiplies its citation odds by about 1.34, so the first links matter far more than the thousandth.
• llms.txt never entered this model - a separate descriptive check found no difference in citation rates with or without it (55% vs 54% cited).

As a final check, I re-ran the same all-factors-at-once model from the table above, this time using only citations that resolve to a specific, exact page - dropping the domain-only matches, which are noisier - to make sure the results weren't an artifact of fuzzy URL matching. The main conclusions held (top-10 4.70, page-links 1.34, AI Overview 2.36, robots 0.55), which is the strictest test I could put this data through.

I Re-ran the Study on Current Models - and the Same Map Still Fits

There was an obvious problem with publishing a study built partly on models that had already moved on. So in September 2026 I repeated the core analysis using the current successor conditions: GPT-5.6 Sol, Claude Sonnet 5, Gemini 3.1 Pro Preview and Perplexity Sonar Pro.

For the cleanest comparison, I first restricted the refresh to the same 100 SaaS and ecommerce queries used in the original deep analysis and re-ran the same query-fixed-effects model with the same feature definitions. The historical pipeline also reproduced its published numbers exactly from the frozen backup before I made the comparison.

The result was surprisingly stable:

The important part is not that every coefficient stayed numerically identical. It didn't. The important part is that the structure survived. The top-10 gate is almost unchanged in raw terms: 78% vs 27% originally, 79% vs 27% now. Page-level links still retain an association after rank and query are controlled. Domain authority remains a much smaller residual signal. Freshness and AI Overview inclusion still matter. Blocking AI crawlers is still associated with roughly half the citation odds. Schema and fact density remain effectively null in the pooled controlled model.

The stricter exact-page analysis tells the same story. The current top-10 point estimate is larger - 4.70× originally versus 10.00× now - while page links moved from 1.34× to 1.37×, AI Overview inclusion from 2.36× to 2.77×, and robots blocking from 0.55× to 0.61×. One small historical result did disappear: the negative schema association in the old exact-page model did not replicate.

That does not mean GPT-5.6 "caused" the higher rank coefficient. These are two temporal snapshots, not a model-change experiment. SERPs changed, pages changed, Google's index changed, provider retrieval changed, and the ChatGPT API surface changed as well. The defensible conclusion is narrower: the same factor map still describes the current data surprisingly well.

The engine-level fingerprints were less perfectly stable. Perplexity stayed strongly rank-coupled (its top-10 coefficient moved from 6.52× to 8.25×) and ChatGPT stayed the authority outlier (Domain Rating 2.54× to 2.68×). Gemini's own page-link coefficient barely moved (1.14× to 1.18×), though it is no longer the least link-sensitive engine. But Claude lost its distinctive "freshness engine" position: its freshness effect fell from 3.21× to 1.77×, and on the current models ChatGPT's freshness estimate is higher. Some provider-specific patterns are strikingly persistent between the original benchmark and the current-model refresh; others are not.

One thing definitely did not become stable: the individual pages. Across four repeated GPT-5.6 Sol runs, any two runs shared only about 19% of the exact pages they cited; a single run covered a median 35% of everything the four runs cited between them; and 69% of the pages I saw appeared in only one of the four runs. So the aggregate relationships above are reproducible even while the specific pages ChatGPT cites keep shifting from one run to the next.

A smaller format check also survived directionally: within the top 10, intent-matched page types were cited on 87% of candidates versus 77% for other formats on the matched 100-query set - a 10-point gap versus roughly 12-15 points originally. The map is coarse, so I treat that as directional rather than another precise effect estimate.

So How Do You Get Cited by AI?

If I restrict the recommendations to what this dataset actually measured:

1. Win the classic ranking for your priority queries. Links are strongly tied to ranking, but they also retain a smaller independent association with citation after rank is controlled.
2. Match page type to intent: dedicated editorial pages (explainer / comparison / roundup / guide) for informational queries; product and retailer pages have their clearest citation opportunity on buy-intent.
3. Use concrete facts where the query calls for them, but treat this as content fit and quotability rather than a proven independent citation lever.
4. Be not-stale; expose a real updated date.
5. Know your engine mix - Perplexity tracks Google rank most closely, and ChatGPT's ranked selections skew toward domain authority.

Other GEO literature also points to answer-first structure, self-contained claims and original rather than commodity content; plausible, but I did not test those directly here.

None of it is a trick, and none is a guarantee - only better odds. It's SEO pointed at a new surface, not a separate ritual.

Want to see where a page stands? Run any URL through the free AI Readiness Checker - it scores a page against most of these factors and shows whether it's already appearing in AI answers.

Method & Reliability (Appendix)

• Grades: ★★★ replicated across verticals or rank-controlled · ★★ one large run · ★ suggestive. Verticals: SaaS, ecommerce, health, affiliate, recipes, data/statistics, SEO.
• Giants (Reddit/YouTube/Wikipedia) excluded from stats. Single-snapshot noise is real; aggregates only. I measured a subset of factors, not all of them.
• Setup, for reproducibility: US · desktop · English. Original ChatGPT benchmark: gpt-4o with web retrieval via API. Current refresh: GPT-5.6 Sol, Claude Sonnet 5, Gemini 3.1 Pro Preview and Perplexity Sonar Pro, using the web-enabled configurations documented in the method files. Matched reasoning experiment: GPT-5.6 Sol, 28 prompts × 4 runs at reasoning=none and 4 runs at reasoning=high, with only reasoning effort varied. Original runs began July 17; refresh and follow-up experiments continued through September 2026.

I'm Irina Saprykina, and I've worked in SEO and digital marketing since 2006, mostly in-house, promoting international SaaS companies. I'm a mathematician by training, which is probably why I'd rather test and automate a process than take a claim on faith. datairi is where I publish those experiments and the tools that come out of them.

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