Source Overlap Between Search Engines and AI Recommendations

RankCaster AI | April 2026

Queries studied: 4 · Platforms: 5 · AI-system runs: 10 per query per system · Measurement date: 15 April 2026

Executive Summary

The marketing industry starts from a convenient assumption: that a brand which ranks well in Google and Bing will automatically appear in the responses of ChatGPT, Gemini, and DeepSeek. This study tests that assumption empirically.

GEO (Generative Engine Optimization) is a new discipline focused on a brand’s visibility within the generative answers produced by AI assistants. Unlike SEO (Search Engine Optimization), where the metric is the position on the SERP, in GEO the metric is the frequency with which a source is cited in an LLM’s response. This study sets out to show where SEO and GEO diverge.

We took four B2B queries connected to the marketing of AI visibility and ran them through five platforms: Google Search, Bing Search, ChatGPT, Gemini, and DeepSeek. For the AI systems, each query was executed ten times in order to capture not a one-off but a stable pattern of model behaviour. In parallel, we reverse-engineered the network traffic and the internal response structures of each AI system in order to understand from which API fields the “sources” are actually extracted, and how they are ranked inside the system.

The main finding: the overlap between search-engine results and citations in AI is minimal. We ran four queries (Q1–Q4, introduced in the Methodology): two tool-selection queries Q1 and Q2, a closely related phrasing Q3, and a conceptual query Q4.

For the two tool-selection queries Q1 and Q2, the overlap between the Google/Bing top-10 and the stable AI citations, computed at the specific-URL level (a particular article rather than merely the domain), falls in the 0–10% band per pair (search-engine × AI-system). The 12 measured pairs decompose as 2 queries (Q1, Q2) × 2 search engines × 3 AI systems; across them we recorded just four URL matches (4 of 120 SEO top-10 positions, or 3.3%). The distribution is not smooth but bimodal — it splits cleanly into two groups with nothing in between: eight of the 12 pairs show zero overlap, and the remaining four show exactly one URL in ten (10%). All four matches fell on the Bing side — none on Google. ChatGPT in particular shows zero URL-level matches with either Bing or Google on Q1 and Q2. The four matches are distributed one per pair across exactly four pairs — Bing × Gemini Q1, Bing × DeepSeek Q1, Bing × Gemini Q2, and Bing × DeepSeek Q2 — with nothing on the Google or ChatGPT side.

A note on statistical precision. All numeric figures in this study come from N=10 runs per query, which implies a confidence interval for any single APR value on the order of ±15–20 percentage points; we rely on the qualitative shape of the picture rather than on exact point estimates.

For Q3, Google and Bing return essentially the same top-10 as for Q2 (search engines do not distinguish these closely related phrasings), so Q3 is not included separately in the quantitative overlap calculation: its SEO signal is already accounted for within Q2.

Q4 is a conceptual query (“what is GEO?”): on the SEO side it consists of explanatory articles, while the AI systems produce their own sets of sources. At the URL-level these are again different records, but at the domain level an overlap becomes visible — most notably between Bing and Gemini (Semrush, Moz, HubSpot, Contentful), with a smaller Google↔Gemini overlap (Writesonic, a16z, plus Contentful also shared with the Bing set). In total, six unique publishers are touched across both search engines (Semrush, Moz, HubSpot, Contentful, Writesonic, a16z) — all via different URLs under the same domains. In Q4 the overlap is therefore observed at the domain level but not at the URL-level.

(A side note on Q4: the Google SERP for this query returns only 9 stable organic results in the top-10 rather than the usual 10; the per-query Q4 section documents this Google/Bing asymmetry.)

Each AI system operates by its own selection rules, which coincide neither with SEO ranking nor with the rules used by the other AI systems.

This means that, in the category studied, the strategy “optimise for Google and AI will follow” does not work. A separate approach to AI-response visibility is required — with an understanding of which specific API fields produce the “source” in each AI system (and with the caveat that, in our analysis, the decoded meanings of certain Gemini API fields are hypotheses based on traffic analysis, not Google documentation). Extension to other product categories should be done with care (see the section “Selection-bias disclosure”).

Introduction: the main research question

With the emergence of AI assistants as a fully fledged channel for consuming information, marketers face a practical question: do the authoritative sources that an LLM selects for its response overlap with those that rank in search engines?

If the overlap is substantial, old SEO strategies continue to work by default. If it is minimal, then AI visibility demands a separate discipline with its own rules.

You cannot answer this from first principles or by reading LLM vendors’ official blogs. What is required:

Controlled testing. Run identical queries and record the results side by side.

Repeatability. A single run in ChatGPT tells you nothing — LLMs are stochastic, and patterns only emerge across repeated runs.

Technical understanding. A “source” in an LLM’s response is not one entity but the result of specific fields in a specific system’s API; without tracing those fields the picture is incomplete.

Our study covers all three layers.

Methodology

Query sample

We selected four English-language queries that reflect the real-world tasks of a B2B marketer working on AI visibility:

• Q1. Which AI marketing platform offers the best Answer Presence Rate (APR) tracking?
• Q2. Which tools are best for AI search visibility tracking in 2026?
• Q3. Which tools are best for proactive AI visibility marketing?
• Q4. What is Generative Engine Optimization (GEO)?

The queries were selected to cover different types of user tasks: comparative tool selection, conceptual definition, and an open-ended search query.

Platforms

Five platforms were tested:

• Google Search and Bing Search — the organic top-10 was recorded.
• ChatGPT, Gemini, DeepSeek — three AI systems covering different architectures: a Western commercial one, the Google ecosystem, and a Chinese one with an open model.

Testing was conducted from “maximally neutral” sessions in order to exclude the influence of personalised history and profile-based signals. By “neutral session” we mean the following: for ChatGPT, the anonymous path was used (the /backend-anon path prefix on chatgpt.com — an internal ChatGPT endpoint without authorisation; see the technical section for details) without a signed-in account; for Gemini, a baseline Google session without a tie to the researcher’s personal Google account (mandatory authorisation at the level of Google infrastructure was nonetheless preserved, since Gemini is unavailable without it — this is a technical constraint of the platform, not a methodological choice); for DeepSeek, the web client without a signed-in user profile. For Google and Bing, the English-language SERP was captured from a clean browser profile. All measurements were taken on the same day — 15 April 2026. For ChatGPT, Gemini, and DeepSeek, the public web interfaces were used with the web-search option enabled (it is this option that generates the block of cited sources; without it the LLM works on its own weights and does not return citations through any of the per-system channels examined in this study — url_citation objects in ChatGPT, the obfuscated Wiz source fields in Gemini, or search_results[] in DeepSeek).

The SEO SERP was recorded for all four queries, but the quantitative calculation of the SEO↔AI overlap percentage includes only Q1 and Q2, for the following reasons. In our measurements, Google and Bing return essentially the same top-10 for Q3 (“proactive AI visibility marketing”) as for Q2 (“AI search visibility tracking”): the search engines do not distinguish these closely related phrasings. This is confirmed by two independent signals in the raw SERP — the Google annotation “Missing: proactive | Show results with: proactive” on a subset of results, and a Bing video block titled “Videos of Which Tools Are Best for Proactive AI Visibility Marketing”. Since the SEO signal for Q3 is already captured inside Q2, we do not add it to the overlap calculation separately — this would be double-counting the same URLs. For Q4 (“What is GEO?”) the SEO SERP exists and is stable, but it is conceptual in character — these are explanatory articles about what GEO is (Writesonic, Contentful, Jasper, Semrush, Moz, TechTarget, Ahrefs, HubSpot, Frase), rather than a list of tools in the category. For Q4 the three AI systems answer with different kinds of sources: an academic paper from ChatGPT, a GEO-explainer cluster from Gemini (articles and guides on what GEO is), news and research from DeepSeek. The URL-level overlap for Q4 is zero, but at the domain level Bing and Gemini overlap noticeably (Semrush, Moz, HubSpot, Contentful); a smaller Google↔Gemini domain overlap is also present (Contentful, Writesonic). This is the most pronounced case in the study of an SEO↔AI domain-level overlap, and we describe it separately, qualitatively, in Synthesis §1 below. The headline quantitative estimate “0–10% per pair, 4 URL matches across 12 pairs” is driven by Q1 and Q2. Operationally, by a “stable top-10” we mean a list in which at least eight URLs overlap as a set (irrespective of position) across repeated measurements over the course of a single day — this criterion is met by all four queries, with the caveats noted above for Q3 (the SERP coincides with Q2) and Q4 (conceptual intent).

How the SEO ↔ AI overlap was computed (Q1 and Q2)

Before the per-pair calculation, note the direction of the metric: the denominator in each pair is the SEO top-10 (10 URLs), not the AI-system citation count. Overlap means “the share of Google/Bing top-10 URLs that also appear among the stable AI citations” — not the reverse. The overlap was computed at the specific-URL level (an individual article/page), not at the domain level. This is fundamental: two different articles on the same domain (madgicx.com/blog/x and madgicx.com/blog/y) are counted as different sources, and only records with a matching URL enter the overlap (or, if the full URL was not preserved in the logs, a clearly identifiable match on the same article by title). The choice is deliberate: from the perspective of a user of an AI response, “the same domain, but a different article” is a different recommendation, not the same one. For each AI system, up to 10 cited sources that cleared a baseline threshold of APR ≥ 20 (the APR metric is defined below in the section “Metric: APR”) were taken and matched against the top-10 of the corresponding search engine. For pairs where the AI table contains fewer than 10 rows (ChatGPT Q1 — 9 rows, ChatGPT Q2 — 7 rows), the denominator of the percentage remains equal to ten, because the count is anchored on the SEO side: we count the share of URLs from the Bing/Google top-10 that appear among the stable AI citations. In our sample the distribution came out as follows: of 12 pairs (search-engine × AI-system), four pairs produce exactly 1 match out of 10, and eight pairs produce 0 out of 10. The range we quote in the text and in the executive summary is 0–10% per pair (a detailed breakdown by search engine is given in Synthesis §1 below). All four recorded URL matches fall on pairs with Bing — none on the Google side; this is an asymmetry that we flag here so the reader holds it in mind during the data sections, and that we examine in detail in Synthesis §1 below.

Selection-bias disclosure

All four queries lie within a single product category — “AI-visibility and GEO-monitoring platforms”. This is the core category of RankCaster AI, which naturally creates a risk of bias: the topic of the sample was chosen by the producer of a product operating in the same niche. The specific phrasings of Q1–Q4 were also authored by the researchers — they are not drawn from an external registry of popular B2B queries, which introduces an additional source of bias (the researcher picks queries typical for their own funnel rather than representative of the market as a whole). The conclusion “SEO does not coincide with AI” is examined within a specific category and does not automatically extend to all B2B topics. Broader validation (finance, healthcare, e-commerce, engineering categories), as well as the use of an externally assembled query list, are tasks for future research. All quantitative figures (including the 0–10% share per search-engine × AI pair in Q1–Q2 and any references to APR values of specific sources) should be read within this frame — within the AI-visibility-platform category and on the query set curated by the authors.

Conflict-of-interest disclosure

The study was conducted by RankCaster AI, the producer of a platform in the very category it studies. The author’s own domain (rankcaster.ai) was excluded from the rankings across all four queries in all AI systems, so that the report shows only external data. In the raw logs, the domain appears predominantly in ChatGPT Q1 (where rankcaster.ai held one of the leading positions — which is precisely why the ChatGPT table for Q1 in the final report contains nine rows rather than ten) and with substantially lower frequency in the other combinations. The competitor source type in the tables means “a product in the same category as RankCaster AI” — this is a positional, not an evaluative, definition.

Repeatability

A key property of LLMs is stochasticity. The same query, across different runs, can yield different sources. In order to capture stable rather than random patterns, each query was executed ten times in each AI system. This makes it possible to assess not merely “did a source appear” but how often it appears.

Metric: APR (Answer Presence Rate)

To measure the stability of citations we use the APR metric: the percentage of runs out of ten in which a given source was cited in the LLM’s response. The scale runs from 0 to 100. APR = 30 means that the source appeared in three runs out of ten. APR is RankCaster AI’s internal metric; in this study it is used simply as a frequency count of how often a given source was cited. In the DeepSeek tables the same quantity is recorded in the form frequency n/10 (for example, 7/10 is equivalent to APR = 70) — this is the same metric written in an alternative form; the difference in form is a legacy of how the data was collected and is preserved for transparency.

Sample-size caveat: at N = 10 runs, the confidence interval for each APR point is wide (on the order of ±15–20 percentage points). We deliberately do not claim a precise APR value for each source — rather, we take an interest in the qualitative picture: which sources enter the system’s responses at all, and how they rank relative to one another.

Threshold for entry into the table. Sources enter the AI-system tables in the “Per-Query Results” section with APR ≥ 20 (that is, cited in at least two runs out of ten) — this is the baseline threshold for citation stability. The table is capped at the top 10 sources; if the number of stable sources in a query is fewer than ten, the table contains exactly those that crossed the threshold, without topping up with less stable entries. This is why the number of rows in AI tables may differ from ten: for example, the ChatGPT table for Q2 contains seven rows — the number of sources that crossed APR ≥ 20 on this query. This is a property of the query itself, not of missing data.

Source typology

In the tables, each source is assigned a type: blog, forum, news, aggregator, wiki, academic, documentation, press-release, tool, competitor. A “competitor” is a domain promoting its own product in the same category as RankCaster AI (AI-visibility and GEO-monitoring platforms). A “tool” is a utilitarian service that is adjacent in function but is not a direct category competitor (for example, monitoring dashboards, mention trackers, or general-purpose SEO tools). The distinction is positional, not evaluative; borderline cases can be interpreted either way.

Result format

Each query run in an AI system was logged in the rankcaster-monitoring-test-v1 format, with the fields: test date, number of runs, brand, competitors, list of sources with type.

Per-Query Results

Q1. Which AI marketing platform offers the best Answer Presence Rate (APR) tracking?

Google Search — top-10

Pixis — “We Ranked 20 AI Marketing Platforms: Here Are the Winners”

ChatRank — “Best MultiPlatform AI Ranking Trackers for 2026”

Reddit r/ProductMarketing — “Top 5 tools to monitor your brand’s presence in AI search”

Cometly — “9 Best AI Marketing Analytics Platforms 2026 Review”

Sanja Singh (LinkedIn) — “25 Best AI Search Rank Tracking and Visibility Tools”

ConvertMate — “7 Best AI Marketing Tools for Small Businesses”

The Rank Masters — “11 Best AI Search Visibility Tools 2026”

Atomic AGI — “17 Best AI Search Monitoring Tools to Use in 2026”

GrowthOS — “10 Best AI Brand Visibility Monitoring Tools for 2026”

Onrec — “10 Best AI Visibility Tools in 2026 for Tracking Brand”

Bing Search — top-10

Cometly — “9 Best AI Marketing Analytics Platforms 2026 Review”

Averi AI — “We Tested 11 AI Marketing Platforms: Here Are the Winners (2025)”

Mostafa Elbermawy — “9 Best AEO Tools for AI Search”

Topify AI — “6 AI Brand Monitoring Services Tested”

Influencer Marketing Hub — “Top 12 AI Marketing Platforms for 2025”

Latenode — “15 Best AI Marketing Automation Platforms in 2025”

Madgicx — “11 Best AI Analytics Platforms for Performance Marketing”

Trymeridian — “10 Best Answer Engine Optimization Platforms in 2026”

Seranking — “8 Best AI Visibility Tools You Need to Know In 2026”

SurgeGrowth — “Top AI Marketing Automation Tools”

ChatGPT — sources by APR (10 runs)

APR 90 — presenc.ai — “Track & Optimize Your AI Visibility” (blog)

APR 60 — airrscore.com — “AI Search Visibility Measurement” (blog)

APR 50 — rampiq.agency — “The Best AI Visibility Trackers In 2026” (blog)

APR 40 — searchinfluence.com — “AI SEO Tracking Tools 2026” (blog)

APR 30 — topify.ai — “The Best AI Answer Tracking Tools in 2026” (competitor)

APR 30 — gainforge.ai — “March 2025 Complete AI Marketing Tools PDF” (blog)

APR 20 — home.norg.ai — Live AI Mention Tracker Dashboard (tool)

APR 20 — trakkr.ai — “AI Brand Visibility Tracking” (blog)

APR 20 — attensira.com — “Prompt Tracking” (blog)

Gemini — sources by APR (10 runs)

APR 60 — zenmedia.com — “What is Answer Share: AI Era PR Metric” (blog)

APR 50 — tryprofound.com — Profound, enterprise visibility (competitor)

APR 40 — faii.ai — “Tools for Tracking Brand AI Presence” (competitor)

APR 40 — sleepinggiantmedia.co.uk — “Marketing when AI replaces clicks” (blog)

APR 30 — landerlab.io — “AI Marketing Tools” (blog)

APR 30 — topify.ai — “AI Answer Tracking” (competitor)

APR 30 — salesforce.com — “Best AI Marketing Tools” (blog)

APR 30 — averi.ai — “We Tested 11 AI Marketing Platforms” (blog)

APR 20 — hubspot.com — “HubSpot AEO Grader” (competitor)

APR 20 — zapier.com — “Best AI Visibility Tool” (blog)

DeepSeek — sources by frequency (10 runs)

The “Frequency” column is given in the form N/10 and is numerically equivalent to APR (N/10 ↔ APR = N·10; for example, 10/10 ↔ APR 100, 4/10 ↔ APR 40).

APR 100 — amplitude-docs.vercel.app — “AI Visibility General FAQ” (documentation)

APR 90 — madgicx.com/blog/ai-analytics-platforms (blog)

APR 70 — madgicx.com/blog/best-martech-platform-for-ads (blog)

APR 70 — investors.amplitude.com — “Amplitude Launches AI Visibility” (press-release)

APR 60 — aitoolnet.com/trakkr (aggregator)

APR 60 — madgicx.com/blog/ai-tools-comparison (blog)

APR 50 — madgicx.com/blog/marketing-automation-platform (blog)

APR 40 — singular.net — “Singular Marketing Analytics” (blog)

APR 40 — hubspot.com/blog/best-ai-visibility-tools (competitor)

APR 30 — btmagazine.com.au — “Amplitude Insights” (news)

Observation. On the APR-tracking query, the three AI systems in our sample form three largely non-overlapping sets of results. The Google and Bing top-10 are dominated by listicle reviews from aggregators and marketing blogs: Pixis and Cometly are characteristic of the Google top, Topify AI and Madgicx of the Bing top. ChatGPT concentrates on specialised GEO tools (the table contains nine rows because the author’s own domain has been removed from the ranking — see “Conflict-of-interest disclosure”). Gemini mixes analytical media (Zen Media) with a broad slate of competitor products in the same category (Profound, faii.ai, topify.ai, and others). DeepSeek shows a noticeable presence of Amplitude-related sources (documentation, investor release, news coverage), as well as four different URLs on the madgicx.com domain — these are four independent blog articles under /blog/..., and by methodology they are counted as four independent sources. For Q1, at the URL-level between the Bing top-10 and the AI citations, two URL matches are recorded: the Averi AI article “We Tested 11 AI Marketing Platforms” (Bing #2 ↔ Gemini Q1) and the Madgicx article “11 Best AI Analytics Platforms for Performance Marketing” — in the DeepSeek logs this is the URL madgicx.com/blog/ai-analytics-platforms (Bing #7 ↔ DeepSeek Q1). topify.ai deserves a separate comment: the domain is present in Bing #4 (the article “6 AI Brand Monitoring Services Tested”), in ChatGPT Q1 (“The Best AI Answer Tracking Tools in 2026”), and in Gemini Q1 (“AI Answer Tracking”) — but these are different articles on the same domain, and when matched at the URL level such cases do not enter the overlap (see the methodology section for detail). Net: two confirmed URL matches in Q1, both on the Bing side (Averi AI ↔ Gemini; Madgicx ↔ DeepSeek).

Q2. Which tools are best for AI search visibility tracking in 2026?

Google Search — top-10

SE Visible — “8 best AI visibility tracking tools explained and compared”

Reddit r/DigitalMarketing — “Best AI Visibility Tools (2026)”

Reddit r/SEO_tools_reviews — “Best AI search visibility tracking tools for 2026”

Quora — “What are the best free AI tools for digital marketing in 2026?”

Medium / Tim Soulo (Ahrefs CMO) — “Best AI SEO Tools for 2026”

tryvizup.com — “15 Best AI Visibility Tools Compared for 2026”

Campaign Monitor — “Best AI Marketing Tools for 2026”

LLMClicks.ai — “Best AI Visibility Tracker Tools: 11 Platforms Tested”

Airtop — “7 Best SEO Visibility & AI Search Tools for 2026”

Mersel AI — “7 Best AI Visibility Tools for Mid-Market Teams”

Bing Search — top-10

surferstack.com — “Best AI Visibility Platforms for Marketing Teams in 2026”

semrush.com — “The 8 Best AI Visibility Tools to Win in AI Search (2026)”

zapier.com — “The 8 best AI visibility tools in 2026”

emailanalytics.com — “We Ranked The Top 24 AI Visibility Tools (2026)”

webfx.com — “30+ AI Visibility Tools for Brand Monitoring in 2026”

ranktracker.com — “The Best AI Visibility Tools in 2026”

LinkedIn — “Top AI Visibility Tools for 2026: Practical Comparison”

tryprofound.com — “7 Best AI Visibility Tools for Marketing Agencies”

indexly.ai — “Best AI Search Visibility Tools for Agencies 2026”

cometly.com — “9 Best AI Visibility Tools for Marketing Optimization”

ChatGPT — sources by APR (10 runs)

APR 90 — iconizer.io — AI-tool aggregator (aggregator)

APR 90 — Wikipedia (en) — Evertune AI article (wiki)

APR 80 — searchable.ai — Searchable AI product (competitor)

APR 60 — nogood.io — NoGood marketing blog (blog)

APR 50 — snezzi.com — AI visibility analysis (blog)

APR 50 — therankmasters.com — “11 Best AI Search Visibility Tools” (blog)

APR 50 — metehan.ai — AI visibility tools guide (blog)

In Q2 for ChatGPT, seven sources cleared the baseline citation-stability threshold (APR ≥ 20, see the methodology in the “Metric: APR” section) — the rest did not cross APR ≥ 20 over ten runs and therefore did not enter the table.

Gemini — sources by APR (10 runs)

APR 70 — zapier.com — “The 8 best AI visibility tools in 2026” (blog)

APR 60 — topify.ai — “AI Answer Tracking” (competitor)

APR 50 — topify.ai — “Best AI Answer Tracking Tools” (competitor)

APR 50 — topify.ai — AI Visibility Tools page (competitor)

APR 40 — seranking.com — “8 Best AI Visibility Tools” (blog)

APR 40 — topify.ai — “AI Answer Tracking System” (competitor)

APR 40 — tryprofound.com — Profound (competitor)

APR 20 — reddit.com r/DigitalMarketing (forum)

APR 20 — bluefishai.com — AI visibility (blog)

APR 20 — generatemore.ai — AI visibility guide (blog)

DeepSeek — sources by frequency (10 runs)

APR 90 — TMCnet — AI Visibility Platform news (news)

APR 80 — emailanalytics.com — “Top 24 AI Visibility Tools” (blog)

APR 70 — MarketScreener — AI Visibility press coverage (news)

APR 70 — mi-3.com.au — Havas Red / Sefiani AI Visibility (news)

APR 70 — fellow.ai — “25 Best AI Marketing Tools” (blog)

APR 60 — creatify.ai — “13 Field-Tested AI Marketing Tools” (blog)

APR 60 — frase.io — “Top 10 AI Visibility Tools” (blog)

APR 50 — GlobeNewswire — Webflow AEO / V2 Communications (press-release)

APR 40 — markets.businessinsider.com — Wellows AI Search (news)

APR 40 — Informa TechTarget — GEO Content Solutions (news)

Observation. Google gives priority to forum discussions (Reddit, Quora) and analytical blogs. Bing — to tool reviews featuring paid services (Semrush, Surferstack, Cometly). ChatGPT relies on an aggregator listing (iconizer.io) and a single product-focused Wikipedia article (Evertune AI) — combined with topical blogs. Gemini systematically surfaces products of the same category (AI visibility / GEO monitoring): four different URLs on the topify.ai domain appear in the top (these are four independent pages, not duplicates of a single address). DeepSeek on Q2 relies almost entirely on news and press-release sources: TMCnet, MarketScreener, GlobeNewswire — that is, on the layer formed by press-release distribution channels (news-wires) and B2B news outlets. The URL-level SEO↔AI matches in Q2 are: the zapier.com article “The 8 best AI visibility tools in 2026” (Bing #3 ↔ Gemini Q2) and the emailanalytics.com article “Top 24 AI Visibility Tools” (Bing #4 ↔ DeepSeek Q2). (Note on disambiguation: two distinct zapier.com articles appear across the tables in this study — “The 8 best AI visibility tools in 2026” (the listicle that matches Bing Q2, also present in Gemini Q2 and in the DeepSeek Q3 table as “The 8 best AI visibility tools”) and a separate, earlier page titled “Best AI Visibility Tool” that surfaces in Q1 Gemini and Q3 Gemini. They are different URLs and are counted as different sources.) A separate example: tryprofound.com appears in Bing #8 (the article “7 Best AI Visibility Tools for Marketing Agencies”) and in Gemini Q2 (the product page “Profound”), but these are different URLs — when matching at the URL-level they are not counted as a match. Forum threads (Reddit r/DigitalMarketing in Google Q2 #2 and in Gemini Q2) also represent different discussion branches, not the same URL, and do not enter the overlap. Net: two confirmed URL matches — few, but real.

Q3. Which tools are best for proactive AI visibility marketing?

Google Search — top-10

SE Visible — “8 best AI visibility tracking tools explained and compared”

Reddit r/DigitalMarketing — “Best AI Visibility Tools (2026)”

Reddit r/SEO_tools_reviews — “Best AI search visibility tracking tools for 2026”

Quora — “What are the best free AI tools for digital marketing in 2026?”

Medium / Tim Soulo (Ahrefs CMO) — “Best AI SEO Tools for 2026”

tryvizup.com — “15 Best AI Visibility Tools Compared for 2026”

Campaign Monitor — “Best AI Marketing Tools for 2026”

LLMClicks.ai — “Best AI Visibility Tracker Tools: 11 Platforms Tested”

Airtop — “7 Best SEO Visibility & AI Search Tools for 2026”

Mersel AI — “7 Best AI Visibility Tools for Mid-Market Teams”

Methodological note. The Google and Bing SEO SERPs for Q3 (“proactive AI visibility marketing”) coincided in the study with those for Q2 (“AI search visibility tracking”) at the top-10 level: in our sample the search engines do not distinguish these closely related phrasings as separate topics. This is confirmed in the raw SERP by two independent signals — the Google annotation “Missing: proactive | Show results with: proactive” on a subset of results, and a Bing video block titled “Videos of Which Tools Are Best for Proactive AI Visibility Marketing”. The coincidence of the top-10 is itself the observed fact about SEO behaviour on the Q2/Q3 pair, not missing data.

Bing Search — top-10

surferstack.com — “Best AI Visibility Platforms for Marketing Teams in 2026”

semrush.com — “The 8 Best AI Visibility Tools to Win in AI Search (2026)”

zapier.com — “The 8 best AI visibility tools in 2026”

emailanalytics.com — “We Ranked The Top 24 AI Visibility Tools (2026)”

webfx.com — “30+ AI Visibility Tools for Brand Monitoring in 2026”

ranktracker.com — “The Best AI Visibility Tools in 2026”

LinkedIn — “Top AI Visibility Tools for 2026: Practical Comparison”

tryprofound.com — “7 Best AI Visibility Tools for Marketing Agencies”

indexly.ai — “Best AI Search Visibility Tools for Agencies 2026”

cometly.com — “9 Best AI Visibility Tools for Marketing Optimization”

ChatGPT — sources by APR (10 runs)

APR 90 — iconizer.io — AI-tool aggregator (aggregator)

APR 90 — Wikipedia (en) — Evertune AI (wiki)

APR 80 — Wikipedia (en) — Searchable AI (wiki)

APR 60 — techradar.com — AI marketing tools (blog)

APR 60 — nogood.io — AI visibility marketing guide (blog)

APR 50 — snezzi.com — proactive AI visibility (blog)

APR 50 — therankmasters.com — “11 Best AI Search Visibility Tools” (blog)

APR 50 — metehan.ai — AI visibility tools (blog)

APR 50 — Wikipedia (en) — Ranketta (wiki)

APR 50 — compare.growthmarshal.io — GrowthMarshal (aggregator)

Gemini — sources by APR (10 runs)

APR 80 — semrush.com — “Best AI Content Marketing Tools 2026” (blog)

APR 70 — marketermilk.com — AI Marketing Tools guide (blog)

APR 60 — evertune.ai — “10 Best AI Visibility Tools” (competitor)

APR 60 — campaignmonitor.com — Best AI Marketing Tools (blog)

APR 50 — zapier.com — Best AI Visibility Tool (blog)

APR 50 — cometly.com — Best Top AI Visibility Tools (blog)

APR 50 — seodogs.com — “Top AI Marketing Tools for 2026” (blog)

APR 50 — gwi.com — AI Marketing Tools (blog)

APR 50 — improvado.io — Best Predictive Analytics Tools (blog)

APR 50 — darkroomagency.com — “12 AI SEO Tools that Deliver” (blog)

DeepSeek — sources by frequency (10 runs)

APR 100 — inlinks.net — InLinks (AI entity-based SEO) (tool)

APR 100 — ventureradar.com — VentureRadar AI tools aggregator (aggregator)

APR 90 — zapier.com — “The 8 best AI visibility tools” (blog)

APR 90 — 42dm.com — 42DM agency: AI visibility marketing (blog)

APR 80 — getairefs.com — GetAIRefs: AI referral tracking (tool)

APR 70 — cometly.com — Cometly AI visibility marketing (blog)

APR 60 — emailanalytics.com — Email Analytics AI visibility (blog)

APR 50 — withgauge.com — Gauge: AI presence tracking (tool)

APR 30 — storyzee.com — Storyzee content platform (blog)

APR 30 — toolhunt.net — Toolhunt AI tools aggregator (aggregator)

Observation. On the SEO side, Q3 reproduces Q2: Google and Bing in our sample return essentially the same top-10 for both phrasings. This is itself a notable signal — the search engines interpret “AI search visibility tracking” and “proactive AI visibility marketing” as a single topic. The AI systems, meanwhile, behave differently — each produces its own set of sources, and those sets coincide with the SEO top only at isolated points. ChatGPT reuses sources from Q2 (iconizer, the Wikipedia articles on Evertune and Searchable AI), which points to the semantic proximity of Q2 and Q3 for the model. Gemini interprets the query as “best AI tools for marketing” and surfaces Semrush with high frequency. DeepSeek offers the most original set of the entire study: InLinks and VentureRadar are stable across all 10 runs; GetAIRefs and Gauge are present only in DeepSeek and are visible neither in Google/Bing nor in the other AI systems. Because the SEO top-10 for Q3 coincides with Q2, a few Q2 URL matches also show up mechanically in Q3 — notably zapier.com --- The 8 best AI visibility tools in 2026 (Bing #3 on both queries), which appears in the Gemini Q2 table and in the DeepSeek Q3 table. These are the same SEO URLs as in Q2; we do not count them as additional matches, because doing so would double-count the same Bing top-10 against two queries that the search engines treat as one. The quantitative overlap figure (4 URL matches across 12 pairs) is therefore computed on Q1 and Q2 only; nothing is hidden by this exclusion — the matches are the Q2 matches, visible in the Q2 tables. Net: the Q3 observation is about intent collapse between Q2 and Q3 on the SEO side, not about a new overlap signal.

Q4. What is Generative Engine Optimization (GEO)?

Google Search — organic top (9 stable results)

For Q4, Google returned 9 stable organic results plus an expanded “People also ask” block and a video section — a typical picture for a conceptual query with explanatory intent. The tenth organic position did not stabilise across repeated measurements and is omitted from the table.

Writesonic — “GEO vs SEO: What’s The Difference And Why It Matters?”

Contentful — “What is Generative Engine Optimization (GEO) and how is it different from SEO?”

Reddit r/digital_marketing — thread on Generative Engine Optimization (80+ comments)

Jasper.ai — “What is Generative Engine Optimization? GEO vs AEO vs SEO Guide”

Strapi — “GEO vs SEO in 2025: Optimizing for AI & Search Ranking”

Global Skill Development Council (GSDC) — “What Is Generative Engine Optimization (GEO) & How Does It Work?”

Coursera — “What Is Generative Engine Optimization?”

Andreessen Horowitz (a16z) — “How Generative Engine Optimization (GEO) Rewrites the Search Stack”

YouTube — James Dooley, “SEO vs GEO: Understanding the Key Differences” (video)

Bing Search — top-10

Seer Interactive — “What Is Generative Engine Optimization (GEO) & How Does It Impact SEO”

Semrush — “Generative Engine Optimization: A Practical Guide”

Moz — “What Is Generative Engine Optimization (GEO) [Tips & Workflows]”

TechTarget — “GEO vs. SEO: What’s the difference?”

Ahrefs — “SEO vs. GEO: 5 Key Differences Despite the Overlap”

HubSpot Blog — “Generative engine optimization: What we know so far”

SEO.com — “GEO vs. SEO: Understanding the Future of Search”

Foundation Marketing — “SEO vs. GEO: How to Optimize for Search Engines (with AI)”

Contentful — “What is Generative Engine Optimization (GEO) and how is it different from SEO?”

Frase — “What is Generative Engine Optimization (GEO)? 2026 Guide”

ChatGPT — sources by APR (10 runs)

APR 100 — arxiv.org — “GEO: Generative Engine Optimization” (2311.09735) (academic)

APR 90 — en.wikipedia.org — Generative Engine Optimization (wiki)

APR 90 — generative.inc — Generative Inc. (GEO practices) (blog)

APR 70 — wikipedia.org (pt) — Otimização de motor generativo (wiki)

APR 70 — itpro.com — GEO explained for IT professionals (blog)

APR 50 — wordstream.com — “What is GEO?” (blog)

APR 40 — techradar.com — Generative Engine Optimization guide (blog)

APR 30 — conductor.com — “What is GEO?” (competitor)

APR 30 — geohq.ai — GEO practitioners hub (blog)

APR 20 — thehoth.com / builtin.com — GEO overview ‡ (blog)

‡ thehoth.com and builtin.com each surfaced at low frequency across the 10 runs and are grouped here for compactness; APR 20 is the combined count across the two domains. Under the study’s “different-URL = different source” rule each domain would enter as a separate row, but the raw log did not preserve a per-domain split for this tail entry. The same convention applies to the grouped-outlet rows in the DeepSeek table below.

Gemini — sources by APR (10 runs)

APR 90 — evertune.ai — Complete GEO guide (competitor)

APR 80 — conductor.com — “What is GEO?” (competitor)

APR 80 — infinitymkt.com — GEO for brands (blog)

APR 70 — searchengineland.com — GEO definition & tips (blog)

APR 70 — hubspot.com — “What is Generative Engine Optimization?” (competitor)

APR 70 — contentful.com — GEO content strategy (blog)

APR 50 — a16z.com — GEO research & VC perspective (blog)

APR 40 — semrush.com — Complete Guide to GEO (blog)

APR 30 — moz.com — GEO ultimate guide (blog)

APR 20 — writesonic.com — “What is GEO?” (blog)

DeepSeek — sources by frequency (10 runs)

APR 90 — arxiv.org — 2311.09735 GEO (Princeton/Columbia) (academic)

APR 80 — Harvard GEO research paper † (academic, unidentified)

APR 80 — Princeton GEO research paper † (academic, unidentified)

APR 70 — Informa TechTarget — GEO Content Solutions for B2B (news)

APR 60 — eMarketer — GEO marketing analytics (analytics)

APR 50 — Ragan Communications — GEO for PR (news)

APR 50 — arxiv.org — “Beyond SEO: GEO and LLM optimization” (academic)

APR 40 — writesonic.com — “What is GEO?” (blog)

APR 30 — Martech Zone / ARY News — GEO news (news)

APR 30 — 数位时代 (BusinessNext, Taiwan) and 阿里云 (Alibaba Cloud, China) — GEO coverage in Chinese-language tech media (two separate outlets) (news)

† For the rows “Harvard GEO research paper” and “Princeton GEO research paper”, DeepSeek in its responses referred to academic works from the respective institutions without a full URL — in the raw logs a stable link to a specific PDF was not preserved, so the descriptive name of the source has been retained in the table. The most likely candidate for “Princeton GEO research paper” is the already-mentioned arXiv 2311.09735, whose co-authors are affiliated with Princeton/Columbia; the Harvard publication could not be separately identified from the available logs. This means that arxiv.org — 2311.09735 (9/10) and “Princeton GEO research paper” (8/10) possibly cite the same work referenced by the model in two different ways — in which case their frequencies do not add up but instead reflect two modes of referring to a single publication. When interpreting the ranking, these two rows should be read with the caveat of an unidentified source and potential overlap with arXiv 2311.09735.

Observation. The query about GEO shows a gap between SEO and AI recommendations for conceptual queries. On the SEO side, Google and Bing return explanatory articles (Writesonic, Contentful, Jasper, Semrush, Moz, TechTarget, Ahrefs, HubSpot, Frase) — the classic “explain GEO to me” set for a marketer. The AI systems distribute themselves differently: ChatGPT treats GEO as a scientific concept and surfaces the original academic paper arXiv 2311.09735 with APR 100 — one of the few points of maximum stability in the study, joining three peak-stability DeepSeek sources observed elsewhere in the dataset: amplitude-docs.vercel.app (10/10 in DeepSeek Q1), inlinks.net, and ventureradar.com (both 10/10 in DeepSeek Q3). Gemini treats GEO as a tool-oriented discipline, and in doing so overlaps noticeably with Bing at the domain level (Semrush, Moz, HubSpot, Contentful); a smaller Google↔Gemini domain overlap is also present on Q4 (Contentful, Writesonic). Q4 is the query where SEO↔AI domain-level overlap is most clearly expressed in the study. DeepSeek combines academic works (Princeton, Harvard, arXiv) with international news outlets, including Chinese-language ones — and it barely overlaps with the SEO SERP. Across a single SEO SERP for Q4, the three AI systems form three different sets of sources. Net: zero URL-level matches in Q4, but domain-level overlap (Semrush, Moz, HubSpot, Contentful) between Bing and Gemini, with a smaller Google↔Gemini overlap (Contentful, Writesonic).

Reverse engineering: how AI systems actually assemble “sources”

Who this section is for. The next three subsections are technical. They will be useful to engineers, architects, product marketers of the product, and technical SEO specialists. A marketing reader who is not interested in the API details can skip straight to the section “Synthesis: What the Data Says” — all practical conclusions are stated there in non-technical terms.

Methodological caveat. The analysis below is based on an examination of the public network traffic of the web clients of the three AI systems and the structure of their JSON responses (including Gemini’s JSON-encoded, Protobuf-shaped payloads). Neither ChatGPT, nor Gemini, nor DeepSeek publishes its internal response schemas. The decodings of obfuscated fields (in particular, for Gemini: Mf, SR, rs, ls, y6, K1b, GK, tM) are hypotheses, built from the behaviour of the client side and from comparison with the UI: they should be read as informed conjecture, not as official definitions.

Nature of the observations. The names of headers, paths, fields, and parameters are reproduced in the form in which they were recorded in our DevTools sessions. The web clients of ChatGPT, Gemini, and DeepSeek are actively updated, and the set of headers sent with each request, the names of obfuscated fields, internal endpoints, and defensive mechanisms may differ across builds, regions, and accounts. The aim of this section is to describe our own reproducible trace, not to treat it as the canonical protocol of the three platforms.

Typesetting and copying note. The name of the Gemini CSRF token referred to below is written as SNlM0e — the third character is the lowercase Latin letter L (l), not the digit 1, and the fifth character is the digit zero (0), not the uppercase letter O. In certain monospaced fonts both pairs are visually difficult to tell apart; when preparing printed versions of the document the transcription should be checked for correctness.

The lists of sources that enter an LLM’s response do not share a single abstraction. Behind each of the three AI systems studied sits a distinct implementation: its own protocol, its own response format, its own fields from which the front-end extracts “citations”. Without an understanding of this layer, discussion of GEO misses the mechanics. The three subsections that follow examine each system in turn.

ChatGPT

Transport. JSON-over-HTTPS, with RPC-style POSTs to action endpoints (e.g. /conversation, not a resource-oriented REST API in the Fielding sense) and Server-Sent Events for the streaming leg — in our sessions partly over HTTP/1.1, partly over HTTP/2; SSE works over both. The base domain is chatgpt.com, with three path prefixes observed for the backend API: /backend-api, /backend-alt, and /backend-anon. The last of these allows work without a user account, though not without any tokens: a device-scoped identifier and Cloudflare/Sentinel artefacts are still required, so “anonymous” here means “logged-out”, not “unauthenticated”. Limited functionality is available on this path. The endpoint and token names below were recorded in our DevTools sessions in April 2026 and may differ between ChatGPT builds.

Request flow. In our sessions, a preliminary request was observed before the main exchange (what we will informally call the “prepare stage”), in the response to which the client received a token we refer to as conduit_token. The message was then sent to the /conversation endpoint with two custom request headers, which we refer to with the working labels x-conduit-token and chatreq_token; these match most closely, in role, the publicly discussed openai-sentinel-* / chat-requirements-style headers in the Sentinel family (e.g. openai-sentinel-chat-requirements-token), whose wire names have changed across ChatGPT builds. The validation flow — a preflight that issues a session-bound proof-of-work or attestation before the primary /conversation call — matches the Sentinel family of mechanisms. Additionally, in a subset of sessions a Cloudflare Turnstile challenge was issued through that same chat-requirements preflight when the server demanded a higher-assurance proof-of-human; we flag this as a secondary challenge path inside the same flow, not as an independent edge-level anti-bot layer running in parallel. The response arrives as an SSE stream: first service messages about status, then chunks of the generated text, then a closing message. Important caveat. The names conduit_token, x-conduit-token, chatreq_token, as well as the “prepare stage” itself, are working names from our DevTools trace of April 2026, not components of a documented protocol, and we do not attempt a precise mapping onto names used in third-party reverses (for example, Sentinel, chat-requirements, or, historically, Arkose Labs’ FunCaptcha — a single product, mentioned under either name in the literature). The reader should treat the chain presented here as an observation of our particular session, not as the canonical scheme.

Citations. Sources live inside the annotations[] structure, and more specifically inside url_citation objects with the fields url, title, start_ix, end_ix. The last two fields are offsets into the generated text, bounding the segment of the response that the given source is attached to. OpenAI’s public Responses API documents start_index/end_index on citation annotations as UTF-16 code units; the web-client fields start_ix/end_ix observed here are distinct from the public API fields and are not documented by OpenAI, but by analogy with the public API and because JavaScript strings are themselves indexed in UTF-16 code units (so a simple text.slice(start_ix, end_ix) in the browser produces the cited segment), these are almost certainly UTF-16 code units too. Practical consequence: characters outside the Basic Multilingual Plane (most emoji, some CJK extensions) count as two units (a surrogate pair); any reimplementation that treats the offsets as bytes or Unicode code points will misalign citations for responses containing such characters. That is, a source in ChatGPT is anchored to a specific segment of the response, not to the response as a whole. This has practical significance: for a brand to land in url_citation, it is not enough for the model to “know” about it — the model must use content associated with the brand when it generates that particular segment of the response.

Authentication. The Authorization header carries a Bearer token with a header.payload.signature shape (JWT-like in form). The primary web-session credential is a cookie (__Secure-next-auth.session-token); the Bearer value observed in outgoing chat requests is therefore best understood as a short-lived access token issued against that cookie rather than a long-lived credential. We did not decode the payload to verify standard JWT claims. For authenticated sessions this is accompanied by additional tokens (x-conduit-token; where a Turnstile challenge was presented, a resolved Turnstile token as well). The anonymous path is separate — via the /backend-anon prefix.

Gemini

Transport. Streaming delivery of the response inside Google’s Wiz framework. HTTP/2 was observed in our sessions, but the defining feature is the Wiz envelope, not the specific HTTP version. Wiz is Google’s internal JavaScript framework for first-party web apps (Docs, Maps, Photos, Search Labs, and so on all ship on it); the batchexecute endpoint is the standard Google-wide RPC-shaped batching endpoint exposed by Wiz-built frontends, not a Gemini-specific invention. On the server side, rpcid values (e.g. hNvQHb, observed on the send-message call in our April 2026 capture; note that rpcid values across batchexecute builds tend to drift, and public write-ups have associated this particular id with several conversation-path RPCs) are dispatched to BardFrontendService-family handlers; on the client side, the call is a POST to batchexecute (not gRPC) with a streaming response. The base URL is https://gemini.google.com/_/BardChatUi/data/.

Body format. The outer request body is application/x-www-form-urlencoded. Inside it, two form fields matter: f.req (the payload) and at=<SNlM0e> (the CSRF token described under “Authentication and protection”). The f.req value itself is commonly a two-element JSON envelope of the form [null,"<stringified-envelope>"], where <stringified-envelope> is the JSON-encoded, positionally-indexed request payload written in the Wiz/JSPB convention (JSPB, also known as PBLite, is Google’s compact array-based encoding for Protobuf messages: on the wire a message becomes a JSON array in which position — rather than a key — maps to the corresponding Protobuf field number; in practice there are some sparse-field subtleties, but the canonical form is positional text rather than Protobuf wire bytes, with no JSON keys on the wire — the obfuscation is the index-only layout, not the JSON encoding itself). The stringified envelope has the canonical shape [[["<rpcid>","<args-json-string>",null,"<request-id>"]]], where position 2 is itself a nested JSON array encoded as a string. Despite the Protobuf-shaped layout, the wire format on the user-facing batchexecute endpoint is JSON, not Protobuf wire bytes. This is fundamentally different from ChatGPT’s JSON-over-SSE approach and requires empirical field mapping — public .proto definitions for this endpoint are not available, and neither are its positional-index assignments.

Citations. Sources in Gemini are described by a set of fields with obfuscated names. Two layers should be kept separate here: the presence of these fields in the Wiz stream is observable (the names Mf, SR, rs, ls, y6, K1b, GK, tM genuinely appear in the web-client’s traffic); the semantics of each name is a hypothesis (see the methodological caveat above). Below are our working decodings:

• sourceUrl — the field that carries the source URL itself. Unlike the others in this list, the URL string is directly observable (it is not a hypothesis in the semantic sense). However, the label sourceUrl is one we use as a convenient name: in the wire payload this field arrives by position within an indexed, obfuscated (JSON-encoded) array whose field layout mirrors Google’s internal Protobuf schemas, not under a literal string key, so the specific name is a working label rather than a documented Google identifier.
• Mf — presumably the source title;
• SR — presumably a summary;
• rs — presumably reliability_score, an internal rating of domain trustworthiness;
• ls — presumably last_seen_date, the date the source was last observed;
• y6 — presumably the citation snippet;
• K1b — presumably a URL-validity flag;
• GK — a character range within the response (by analogy with the ChatGPT start_ix/end_ix fields and because Wiz runs in JavaScript, very likely UTF-16 code units rather than bytes or Unicode code points — we flag this as a working hypothesis, not independently verified for multi-byte or emoji payloads; functionally analogous to start_ix/end_ix in ChatGPT, i.e. an anchor to text, not a signal of source quality);
• tM — merge type (values such as MERGED and others appear in the traffic).

Two-letter names such as rs and ls have other plausible expansions (ranking_signal, retrieval_score, relevance_score for rs; label_state, latency_slot and similar for ls); the decodings given are the most natural ones from the point of view of the observed client behaviour, but without Google’s internal documentation we cannot strictly choose between them. Because the “domain-quality score” reading follows directly from the reliability_score expansion, the two are two steps of one inference, not two independent pieces of evidence. The qualitative observation we carry forward to the Synthesis — “Gemini carries a family of internal signals correlating with source authority” — does not depend on the accuracy of any particular decoding: it rests on the fact that this family of fields exists, not on exactly how each abbreviation is resolved. The stronger claim “Google already applies a domain-quality score at the level of source ranking inside Gemini” is a plausible interpretation, not a proven fact, and should be read in that register.

Authentication and protection. SAPISIDHASH is not a separate header but a label used inside the single Authorization header, whose value in our Gemini sessions was a space-separated concatenation of up to three such labelled hashes: SAPISIDHASH <ts>_<hex> SAPISID1PHASH <ts>_<hex> SAPISID3PHASH <ts>_<hex>. The hex portion is SHA1(<timestamp> " " <cookie-value> " " <origin>), where <timestamp> is seconds since the Unix epoch (not milliseconds) and is the same value prefixed to the hash in the wire form <ts>_<hex>, and <origin> for Gemini is the Fetch-spec origin https://gemini.google.com (scheme + host, no trailing slash). Each of the three labels reads a different Google auth cookie: SAPISIDHASH reads the legacy SAPISID cookie; SAPISID1PHASH reads the first-party-context __Secure-1PAPISID cookie; SAPISID3PHASH reads the third-party-context __Secure-3PAPISID cookie. The SHA1 construction is identical in all three cases; only the cookie whose value is hashed differs, and which of the three labels is required varies by property and context. These auth labels travel alongside the broader set of Google session cookies. CSRF protection uses Google’s double-submit-token pattern: the bootstrap value SNlM0e is scraped from the initial HTML of gemini.google.com (from the window-level Wiz bootstrap data) and then echoed back on every batchexecute POST as the at= form field; the server requires the submitted at= value to match the token it issued at HTML-render time. This pattern is used in addition to the SAPISIDHASH/Authorization mechanism described above because SAPISIDHASH rides on cookies (and cookies alone are cross-site-submissible, so they would be insufficient as CSRF protection on their own). Anonymous access is not supported: Gemini requires valid Google authorisation.

DeepSeek

Transport. HTTP/JSON + SSE over HTTPS (HTTP/1.1 and HTTP/2 observed in different sessions), following the OpenAI-compatible wire contract — a response format close to the public API api.deepseek.com/v1/chat/completions, with web-only extensions (in particular, the search_results[] array — this is a web-client extension, not part of the OpenAI-compatible public contract). The wire format closely resembles OpenAI’s public contract; a practitioner-facing description of the API schema lies outside the scope of this technical section. The observations in this section were made via the web client chat.deepseek.com without a signed-in profile, which uses the same JSON contract; the search_results[] field described below was observed precisely in the traffic of the web client.

Body format. JSON. Request and response bodies are compatible with the OpenAI format, with the exception of the search-related extensions.

Citations. Sources in DeepSeek arrive in the search_results[] array with the fields index, url, title, snippet, publish_date. The structure is significantly simpler than in Gemini: there is no internal reliability score, no information about when a source was last observed. In effect, these are results retrieved by a search back-end at the time the response is generated, with a minimal metadata layer on top.

Authentication and protection. Two distinct layers need to be kept apart here. User authentication on the web client chat.deepseek.com is handled by standard application session cookies. In front of that, at the network edge, AWS WAF appears to enforce bot-defence rules via a separate challenge-solution token (the aws-waf-token cookie and response headers such as x-amzn-waf-action, observed in our sessions, not documented configuration); this WAF token is a bot-defence artefact, not an auth credential. Additional tracking parameters from third-party analytics stacks are observed in the network traffic; their specific attribution requires separate verification and was not performed within this study. The programmatic API via the OpenAI-compatible contract we also did not test. The chat is reachable without a signed-in user profile, but a valid WAF challenge-solution token is still required — a client that presents neither an application session nor a resolved WAF token does not work.

Comparison of the three systems

Application layer

• ChatGPT: RPC-style POSTs to action endpoints over JSON-over-HTTPS + SSE for streaming (not a resource-oriented REST API).
• Gemini: RPC-shaped HTTP endpoint (batchexecute within the Wiz framework; not gRPC).
• DeepSeek: JSON-over-HTTPS + SSE for streaming (OpenAI-compatible contract + web-only extensions, see search_results[]).

Transport

• ChatGPT: HTTPS (HTTP/1.1–HTTP/2 observed in our sessions).
• Gemini: HTTPS (HTTP/2 observed in our sessions).
• DeepSeek: HTTPS (HTTP/1.1–HTTP/2 observed in our sessions).

Body format

• ChatGPT: JSON.
• Gemini: application/x-www-form-urlencoded outer body; the f.req form field carries a JSON-encoded, positionally-indexed payload (Protobuf-shaped layout, JSON-text wire).
• DeepSeek: JSON.

Streaming

• ChatGPT: SSE (Server-Sent Events).
• Gemini: streaming Wiz envelope.
• DeepSeek: SSE (Server-Sent Events).

Authentication

• ChatGPT: Bearer token (JWT-shaped) + session tokens.
• Gemini: SAPISIDHASH + Google cookies.
• DeepSeek: session cookies + what appears to be AWS WAF (web client chat.deepseek.com).

Additional entry-point protection

• ChatGPT: Sentinel-style preflight / chat-requirements issuing conduit_token / chatreq_token; a Cloudflare Turnstile challenge was observed in a subset of sessions, issued through that same preflight as a secondary challenge path rather than as an independent edge layer.
• Gemini: SNlM0e → at= (Google’s double-submit-token CSRF pattern: SNlM0e is scraped from the initial HTML, echoed back as the at= form field).
• DeepSeek: no dedicated anti-bot/CSRF mechanism observed in the traffic; protection appears to be implemented via AWS WAF rules (see “Authentication”).

Anonymous access

• ChatGPT: yes (path prefix /backend-anon).
• Gemini: no (Google auth required).
• DeepSeek: without a user profile — yes; fully anonymous without a WAF session cookie — no.

Source field

• ChatGPT: array annotations[].url_citation (core fields: url, title; text-anchor and additional-metadata fields are broken out into the two separate items below).
• Gemini: the field whose decoded meaning is the source URL (referred to here as sourceUrl; the name is a label we use, since the actual wire payload is indexed and obfuscated), together with a family of other obfuscated fields in the Wiz stream — see the Gemini paragraph above.
• DeepSeek: array search_results[] (core fields: index, url, title; additional descriptive fields are broken out into the item below).

Anchor to response text

• ChatGPT: start_ix, end_ix (character range; almost certainly UTF-16 code units).
• Gemini: GK (character range; presumed UTF-16 code units by analogy with the ChatGPT fields and because Wiz runs in JS).
• DeepSeek: not observed.

Source metadata

• ChatGPT: no additional fields.
• Gemini: presumably enriched: rs, ls, tM, plus y6, K1b — the decoding of these names is not unambiguous, see the methodological caveat in the paragraph above.
• DeepSeek: snippet, publish_date.

Practical takeaway: a source in an AI response is a different entity in different systems. In ChatGPT it is a text fragment anchored to a segment of the response. In Gemini — an element of an enriched source catalogue with a reliability score. In DeepSeek — a search result attached to the query. Optimisation for one of the three models does not automatically transfer to the others, because what is being optimised are fundamentally different mechanisms.

Synthesis: What the Data Says

1. The overlap between SEO and AI responses is minimal

For Q1 and Q2, the overlap with the stable AI citations, computed at the level of the specific URL (a particular article rather than merely the domain), falls in the 0–10% band per pair of search-engine × AI-system. In total, across the 12 measured pairs (2 queries × 2 search engines × 3 AI systems), four URL matches are recorded — two in each of the two queries, on four distinct pairs (Bing×Gemini Q1, Bing×DeepSeek Q1, Bing×Gemini Q2, Bing×DeepSeek Q2). In aggregate, this amounts to 4 out of 120 SEO top-10 positions, or 3.3% (120 = 12 pairs × 10 SEO top-10 positions per pair). The distribution across pairs is bimodal rather than smooth: eight of the 12 pairs produced zero URL matches, and the remaining four produced exactly one match each (1 of 10, or 10%). In Q1: the Averi AI article “We Tested 11 AI Marketing Platforms” (Bing Q1 ↔ Gemini Q1) and the Madgicx article “11 Best AI Analytics Platforms for Performance Marketing”, that is the URL madgicx.com/blog/ai-analytics-platforms (Bing Q1 ↔ DeepSeek Q1). In Q2: the zapier.com article “The 8 best AI visibility tools in 2026” (Bing Q2 ↔ Gemini Q2) and the emailanalytics.com article “Top 24 AI Visibility Tools” (Bing Q2 ↔ DeepSeek Q2). The majority of sources that enter an LLM’s response did not appear on the first page of either Google or Bing in the form of the same specific article. Cases in which the same domain is present in both responses but through different URLs (topify.ai in Q1, tryprofound.com in Q2, Reddit threads in Q2 — specifically, r/DigitalMarketing threads that surfaced in both Google Q2 and Gemini Q2 but as different URLs) were not counted as overlap — these are different materials, even where the brand coincides. Q3 is not included in the calculation as a separate point: in our measurements, Google and Bing return essentially the same top-10 for Q3 as for Q2 (see the methodology section and the Q3 observation), so its SEO signal is already accounted for within Q2 — adding Q3 would be double-counting the same URLs. Q4 is also not added as a separate point to the quantitative calculation: its SEO SERP is conceptual (explanatory articles about GEO), while the AI systems on Q4 respond heterogeneously — the URL-level overlap is zero. But it is precisely in Q4 that we record the most pronounced case in the study of an overlap at the domain level. Bing↔Gemini coincide on four domains — Semrush, Moz, HubSpot, Contentful — and Google↔Gemini adds Writesonic and a16z, and shares Contentful with the Bing set. The full Q4 domain-level overlap across both search engines therefore spans six unique publishers (Semrush, Moz, HubSpot, Contentful, Writesonic, a16z), in each case via different URLs under the same domain. This is a qualitative observation, and we keep it separate from the headline quantitative estimate, since the metric is computed by URL, not by domain.

Two asymmetries by platform are worth noting. First, none of the recorded URL matches fall on Google — all four points (the Averi AI, Madgicx, zapier.com, and emailanalytics.com articles) fall on the Bing side. Second, none of the four matches involve ChatGPT: all four come from Gemini-Bing and DeepSeek-Bing pairs, while ChatGPT on Q1 and Q2 produces zero URL-level overlap with either Google or Bing. Together these strengthen the main thesis: in our sample, a correlation “Google top → AI citation” is not detectable even at the URL-level, and ChatGPT in particular relies on sources that do not rank on the first page of the traditional search engines we tested. Plausible mechanisms for these asymmetries — different crawl pipelines feeding the two search engines, different RAG back-ends used by each AI system (ChatGPT’s web tool and Gemini’s search grounding need not share a provider with Google or Bing), or simply small-N variation at N=10 — are not testable with this sample and lie outside the scope of the study; we flag them as observed patterns, not as explained causes.

For Q1 and Q2 this is a gap, not a correlation. The assumption that “good SEO automatically yields good AI visibility” is not supported by our data — within a single product category.

2. Each AI system has its own repertoire

• ChatGPT — on conceptual queries (Q3, Q4) tends towards academic sources (arXiv) and Wikipedia; on product queries (Q2) towards specialised tools and aggregators. In Q1 neither academic nor Wikipedia sources entered its top-10 — there, blogs and a single tool predominate, so the generalisation “ChatGPT = academia + Wikipedia” holds only for the conceptual layer of queries.
• Gemini across Q1–Q4 surfaces marketing platforms and products in the category: Profound (Q1, Q2), Evertune (Q3, Q4), Conductor (Q4), HubSpot (Q4), Semrush (Q3, Q4), Topify (topify.ai; Q1, Q2). Google’s own products and domains (YouTube, Blogger, support.google.com, developers.google.com, and so on) were not recorded in Gemini’s top-10 in our measurements — what is systematically represented are external SaaS platforms.
• DeepSeek uses news aggregators, press releases, B2B news outlets, and academic works. On Q4 specifically, two separate Chinese-language outlets also appear — 数位时代 (BusinessNext, Taiwan) and 阿里云 (Alibaba Cloud, China) — and they are the only Chinese-language sources recorded in the study; the pattern should not be generalised beyond Q4.

In our sample, a strategy that raises a brand’s visibility in one system does not automatically transfer to the other two.

3. Wikipedia is one of the few points of potential overlap

In Q2–Q4, Wikipedia stably appears in AI responses, but the character of the articles depends on the query. (In Q1, Wikipedia does not enter the stable AI-citation top for any of the three systems.) In ChatGPT for Q4 (the conceptual definition of GEO), it is the conceptual article en.wikipedia.org — Generative Engine Optimization that is cited, with APR 90, together with its Portuguese version wikipedia.org (pt) — Otimização de motor generativo with APR 70 — that is, the conceptual article on GEO is reproduced across several language editions. In Q2 and Q3, ChatGPT cites product Wikipedia articles — about specific tools in the category, not general conceptual entries. In Q2 there is one such article: Wikipedia (en) — Evertune AI (APR 90). In Q3 there are three: Evertune AI (APR 90), Searchable AI (APR 80), and Ranketta (APR 50). In Q2, the product domain searchable.ai (APR 80) also appears as a separate row — this is not a Wikipedia article but the site of the product of the same name. This is important for practical GEO strategy: the presence of a Wikipedia article on the brand/product itself may be a mechanism for entry into AI citations no less significant than general conceptual articles. In the recorded Google/Bing top-10 for Q1 and Q2, Wikipedia does not appear explicitly, but at the behavioural level it is one of the few platforms that both ecosystems (SEO and AI) traditionally treat as an authoritative source. This is a targeted opportunity, not a regularity of mass overlap.

4. Academic works dominate conceptual queries in two out of three AI systems

The arXiv 2311.09735 paper was the single most stable citation recorded for ChatGPT in the study (APR 100 on Q4) and appeared with high frequency in DeepSeek (9/10; see the note to the DeepSeek Q4 table on possible duplication with the “Princeton GEO research paper” row (8/10)). Its ChatGPT APR 100 figure puts it in the same peak-stability band as the 10/10-in-DeepSeek sources recorded elsewhere in the study — amplitude-docs.vercel.app (DeepSeek Q1), inlinks.net, and ventureradar.com (both DeepSeek Q3) — though arXiv itself did not hit 10/10 in DeepSeek. For conceptual queries, reliance on academia is a noticeable pattern. For Google and Bing, academic sources do not appear with such frequency.

5. Within each AI system, citation is the result of a specific API structure

This is not some abstract “source authority” but the result of specific fields at work: url_citation.start_ix/end_ix in ChatGPT, rs/ls/GK in Gemini (where the readings of rs, ls, and GK are informed hypotheses from traffic and client-behaviour analysis, not official Google definitions), and search_results[] in DeepSeek. Working on AI visibility without accounting for these differences is optimising different mechanisms as if they were one. As noted in the technical section, the working decodings of Gemini’s obfuscated fields (rs as reliability_score, ls as last_seen_date, GK as a character range) remain hypotheses; the qualitative observation — that Gemini carries a family of internal signals correlating with source authority — does not depend on the accuracy of any specific field decoding.

6. Visibility niches are not symmetric

The same platform may have a dominant presence in one engine and be absent from another. Amplitude has a noticeable presence in DeepSeek for Q1 (documentation, investor release, and news coverage) and does not appear in significant positions in either ChatGPT or Gemini. Topify appears consistently in Gemini for Q2 (four different URLs on a single domain in the top-10), whereas in ChatGPT and DeepSeek for Q2 this domain does not appear in the top-10 (in Q1, Topify is present in ChatGPT and Gemini, but through a single URL with APR 30 — without the characteristic Q2-Gemini “cluster” of several URLs). This means that, within our sample, the category of “AI visibility” is not a single magnitude but a set of non-coincident niches.

Practical implications for brands

If one accepts the picture we observed in our sample as a working hypothesis about the gap between SEO and AI visibility, several direct conclusions follow from it.

Separate the visibility strategy into two disciplines. It makes sense to plan SEO and GEO as two independent lines of work with partially overlapping resources, rather than as a single line with an “AI add-on”. The intermediate artefacts — keywords, landing pages, content formats — may coincide only in part.

A distinct citation profile for each AI system. In our sample, ChatGPT more often drew on academic publications and Wikipedia articles. Gemini — on large marketing and SaaS platforms with their own guides. DeepSeek — on press releases, B2B news, and product documentation. A single “material for all AI” in such a landscape risks working unevenly — only where the system’s profile coincides with the material’s style.

Monitoring should be conducted in units of APR, not positions. The classical “position on the SERP” metric does not transfer directly to LLMs: across ten runs, the sources are different. A frequency-based measurement — how many of N runs the model cited a given source — gives a more reproducible picture and allows sources to be compared against one another.

Account for structural API fields. The three AI systems cite different “kinds” of things, and what counts as a citation-ready piece of content differs accordingly. In marketer-facing terms:

• For ChatGPT, citations are anchored to specific segments of the generated response (the start_ix/end_ix fields inside url_citation). In practice this rewards content that is decomposable into quotable factual claims — clear definitions, short benchmark statements, numbered lists — so that when the model generates a particular sentence it has something specific to point to. Pages built as long narratives without extractable claims tend to be skipped.
• For Gemini, citations live inside an enriched source object with metadata fields the system appears to use as quality signals (rs plausibly reliability_score, ls plausibly last_seen_date; the readings remain hypotheses — see the caveat at the start of the technical section). In practice this rewards being hosted on a domain Google already treats as reputable in its broader systems and keeping the content recent; the family of internal signals Gemini exposes correlates with what is in practice treated as an authoritative source.
• For DeepSeek, citations are structured as search results with publish_date and snippet. In practice this rewards freshness and a well-formed opening paragraph/meta-description that reads cleanly as a stand-alone snippet.

Wikipedia and academic works are a distinct route into AI citations for conceptual queries. If a brand’s category or term is semantically pulled towards conceptual queries, a separate strategy around Wikipedia and academic publications may yield a disproportionate effect in AI responses, difficult to achieve through SEO.

Regional and linguistic asymmetry. In our sample — visibly in Q4 — DeepSeek surfaced two separate Chinese-language outlets (数位时代, BusinessNext / Taiwan, and 阿里云, Alibaba Cloud / China) that did not appear in ChatGPT or Gemini; these are the only Chinese-language sources recorded in the study. We note the pattern without generalising across queries: a single query is a thin basis. For brands with an Asian market, this may nonetheless be a significant channel that remains invisible through Western SEO tooling.

Conclusion

We asked a simple question: do the sources on which AI systems rely overlap with the results produced by search engines? Empirically — on four queries, three AI systems, ten runs each, within a single product category — the answer is: they overlap minimally. At the URL-level — 0–10% per search-engine × AI pair; four matches in total across the 12 pairs of Q1+Q2 (3.3% of the combined 120 SEO top-10 positions). Eight of the 12 pairs produced zero matches, the remaining four produced exactly one match each; all four matches fall on the Bing side and none involve ChatGPT (details and the Bing/Google asymmetry — in Synthesis §1 above). Q3 is not included separately in the quantitative calculation, because the Google and Bing SEO SERPs for Q3 coincide with Q2 — the search engines do not distinguish these close phrasings, and adding Q3 would be double-counting. For Q4 — the conceptual query about GEO — the URL-level overlap is zero, but at the domain level Bing and Gemini coincide on four publishers (Semrush, Moz, HubSpot, Contentful), with a smaller Google↔Gemini domain overlap also present (Contentful, Writesonic). Q4 is the case in the study where SEO↔AI domain-level overlap is most pronounced, and it relates precisely to the conceptual layer of the query, not to the product-oriented choice of a tool. SEO and AI visibility are different disciplines that operate by different logics, rely on different data structures, and form different maps of authority — at least within the category of AI-visibility platforms. Given the confidence interval of roughly ±15–20 percentage points per individual APR value at N=10 runs (see the Methodology), the signal here is qualitative — the bimodal 0/10% shape of the per-pair distribution — rather than a precise point estimate; we treat the “3.3% across 120 positions” figure as a headline summary, not as an exact parameter.

For the industry, this means that visibility in AI responses is best treated as a separate engineering and analytical discipline — with its own metrics and its own structural definition of what counts as a “source” inside each AI system.

— The RankCaster AI research team