# GEO engine: 23 percent of news citations came from Grok, and our registry reads none of them

> A study published on 28 July 2026 by the AI visibility vendor Goodie, covering 31 million AI citations recorded between October 2025 and July 2026, puts the largest share of news citations on Grok at 23 percent, ahead of AI Overviews at 19.5 and Gemini at 13.5. Lantad's own answer engine registry, read on 7 September 2026, asks Grok with no retrieval and reads no citations back from it.

- Canonical page: https://lantad.co/blog/geo-engine-grok-23-percent-of-news-citations
- This file: https://lantad.co/blog/geo-engine-grok-23-percent-of-news-citations.md
- Last substantive update: 2026-09-07

## Key facts

- **Published:** 2026-09-07
- **Category:** Findings
- **Author:** Lantad
- **Length:** 3168 words
- **Takeaway 1:** A GEO engine is not one kind of object: the eleven surfaces named in Goodie's study of 31 million AI citations, published 28 July 2026, mix chat products, a search results feature and an assistant inside a shopping app, and each exposes a different amount of what it read.
- **Takeaway 2:** Goodie's study reports Grok as the single biggest citer of news at 23 percent of earned citations, then AI Overviews at 19.5, Gemini at 13.5, ChatGPT at 11.9, AI Mode at 10.6, DeepSeek at 7.9 and Claude at 5.1, which are seven shares summing to 91.5 of the 100 points available.
- **Takeaway 3:** Three of those seven named surfaces belong to Google, and their published shares add to 43.6 percent, which is more than any single row in the table and is a fact about vendors rather than about any one engine.
- **Takeaway 4:** Read out of core/src/engines.ts on 7 September 2026, Lantad's registry holds 8 engine rows, names 7 of the 11 surfaces in that study, and records live retrieval and returned sources on only 5 of them, so the two shares it cannot read at all, Grok at 23 percent and AI Mode at 10.6, are 33.6 points of the news citations the study measured.
- **Takeaway 5:** Every share above is a share of news citations, and the same study states that news makes up between 1.3 and 1.9 percent of all citations in any given month, so none of these figures describes the whole citation surface.

## Summary

Ask which GEO engine matters and the honest first answer is that the phrase names at least three different things. Part of what gets called an engine is a model you can send a prompt to over an API. Part of it is a consumer product with its own retrieval system behind it. Part of it is a block inside a search results page that no API exposes at all. Those three fail differently, cite differently, and expose different amounts of their own work, so a single averaged visibility percentage across them hides the question a buyer is actually asking. Our [generative engine optimization](https://lantad.co/glossary/geo) entry defines the practice. It does not say how many engines are in the field, or where the citations are actually coming from.

One published count is worth reading against that question, and it did not come from us. Goodie, an AI visibility vendor and a competitor of ours, published a study on 28 July 2026 covering 31 million AI citations recorded between October 2025 and July 2026, monitored across eleven named surfaces, with a per-engine breakdown of the news slice. Lantad has not run that study, cannot audit its collection, and has published nothing at that scale, so what follows reports their figures with attribution and then does the part they had no reason to do, which is set those figures against what one measurement tool can actually ask and read back. Their page is at higoodie.com/blog/publishers-and-ai-search-study/, written here as plain text because this site does not link competitors.

## What is a GEO engine, and how many of them are there?

There is no register of generative engines and no definition that decides membership, so any count is a count of what somebody chose to monitor. Goodie's study states that it monitors brand visibility across ChatGPT, Gemini, Claude, Perplexity, AI Overviews, AI Mode, Copilot, Grok, DeepSeek, Meta AI and Amazon Rufus. That is eleven, and the interesting part is not the number but how unlike each other the members are.

Four of them are chat products with a retrieval tool attached. Two of them, AI Overviews and AI Mode, are features of a search results page rather than products you open. One of them, Amazon Rufus, is an assistant inside a shopping app, where the corpus it answers from is a retail catalogue rather than the open web. Grouping those under one word is convenient, and it is also why two vendors can report different numbers for the same brand without either being wrong: they counted different sets of machines.

The distinction that matters for measurement is not the product type but what each surface hands back. An engine that returns the URLs it consulted can be measured for citations. An engine that answers from what a model retained during training returns nothing to count, and a tool asking it is measuring memory rather than the live web. Both produce a fluent paragraph naming brands, and nothing in the shape of the answer separates them, which is the argument our [AI visibility](https://lantad.co/glossary/ai-visibility) entry makes and the reason a report should say which kind it asked.

Two of the eleven are separately worth pulling apart, because they are often treated as one thing. Google's AI Overviews and Google's Gemini app are different surfaces with different retrieval paths and different citation behaviour, which is why we argued that [a Gemini citation tool is not an AI Overview tool](https://lantad.co/blog/a-gemini-citation-tool-is-not-an-ai-overview-tool) rather than a cheaper version of one. The same care applies to the older label: [answer engine optimization](https://lantad.co/glossary/aeo) grew up around question answering surfaces, and a good deal of what it describes still holds, but the set of surfaces it was written for is not the set above. Before any of this matters, a page has to be fetchable and readable at all, which is [the other layer entirely](https://lantad.co/blog/two-layers-decide-if-ai-can-read-your-site) and the one most sites fail first.

## Where the news citations landed, according to one 31 million citation panel

The study's own summary of the collection is that between October 2025 and July 2026 Goodie recorded 31 million AI citations, and that news makes up between 1.3 and 1.9 percent of all citations in any given month. Both halves of that sentence are load bearing. The engine shares below are shares of the news slice, and the news slice is a small minority of everything these surfaces cite, so nothing here describes the whole citation surface for an ordinary business.

Within that slice, the study reports Grok as the single biggest citer of news at 23 percent of all earned citations, then AI Overviews at 19.5 percent, Gemini at 13.5, ChatGPT at 11.9, AI Mode at 10.6, DeepSeek at 7.9 and Claude at 5.1. Those are seven of the eleven surfaces it monitors, and the seven published shares sum to 91.5, which leaves 8.5 points spread across the four surfaces the study does not give a news share for in what we could read: Perplexity, Copilot, Meta AI and Amazon Rufus.

The shape is the finding. No engine holds a quarter of the news citations, the top two are eleven points apart, and the fourth largest is the one most people would have named first. A visibility number quoted from one engine is therefore a number about somewhere between 5 and 23 percent of the citations in this slice, which is the same problem in a different form as the one where [more AI citations did not mean more of your page in the answer](https://lantad.co/blog/citation-count-is-not-answer-influence).

The publisher side of the same study is the sharper number. It reports that Forbes holds 33 percent of all news citations, that the top five publishers take 66 percent and the top ten take 84, and that of 495,000 citations to 37 major news domains, 34 recorded any citations at all. Concentration like that is consistent with what has been measured elsewhere about who gets cited: our reading of publisher deals found [10.2 ChatGPT citations per page against 6.9](https://lantad.co/blog/ai-publisher-licensing-deals-10-2-citations-against-6-9) for licensed publishers, and separately that [citations reached 6.8 percent of ChatGPT prompts and the visit landed on the homepage](https://lantad.co/blog/chatgpt-citations-are-rare-and-land-on-the-homepage). For a business that is not a newsroom, the more useful published pattern is that [most cited domains are other companies](https://lantad.co/blog/ai-citations-mostly-point-at-other-companies) rather than the brand being asked about.

## Three of the seven named engines are Google, and they add to 43.6 percent

Add the three Google surfaces in that table and the arithmetic is 19.5 for AI Overviews plus 10.6 for AI Mode plus 13.5 for Gemini, which is 43.6 percent of the news citations in the study's sample. That sum is ours, computed from their published figures, and it is worth stating because it is invisible in a per-engine ranking where Google appears three times under three names.

It would be wrong to read that as one system with three faces. Two of the three are documented as sharing an eligibility gate: [Google's AI features documentation](https://developers.google.com/search/docs/appearance/ai-features), carrying Last updated 2025-12-10 UTC, says that AI Overviews and AI Mode surface relevant links to help people find the information they are looking for quickly and reliably, and that to be eligible to be shown as a supporting link in AI Overviews or AI Mode, a page must be indexed and eligible to be shown in Google Search with a snippet. That is a shared gate for two of the surfaces. The Gemini app is not named in that requirement.

So a site owner working on Google has one control that plausibly moves two of the three surfaces, and we have measured how rarely that control is even touched: across 81 home pages that answered on 6 September 2026, [none had blocked their own snippet](https://lantad.co/blog/how-to-get-content-cited-in-google-ai-overviews-zero-nosnippet). The harder half is that the fetching side of these features is not a crawler anyone can point at with confidence, which is why [we do not model an AI Overviews crawler](https://lantad.co/blog/ai-overviews-and-the-crawler-we-do-not-model) and say so on the report rather than inventing one.

The same vendor arithmetic runs the other way at Microsoft. Copilot appears once in the study's surface list with no published share, and its grounding behaviour is unusually legible compared to Google's: Bing exposes the query it ran, which is why we could write that [Bing names the grounding query and Google names none](https://lantad.co/blog/microsoft-copilot-seo-bing-names-the-grounding-query). Legibility and citation volume are different properties, and a vendor can be strong on one and absent on the other.

## What this scanner asks, and the 33.6 points of news citations it cannot read

Set that study against our own registry and the gap is specific rather than rhetorical. Read out of core/src/engines.ts on 7 September 2026, Lantad ships 8 answer engine rows: an open-weight chain running on Cloudflare Workers AI, then Gemini, DeepSeek, Grok, Perplexity, ChatGPT, Claude, and Google AI Overviews captured from the results page. The registry names each of those by vendor rather than by the exact model called, which is an admin detail. Seven of those name a surface the study also monitors. The eighth, the open-weight chain, is not a consumer surface at all.

Of the seven, five are configured to retrieve live and return the sources they grounded in. Two are not. The DeepSeek and Grok rows both record retrieval as none and citations as false, and in the Grok case the registry comment states the reason plainly: xAI's Live Search is deliberately not wired because it bills per source used, which no flat per-call booking can honestly cover. That is a cost decision this product made, not a finding about xAI, and the distinction between a setting and a measurement is one we try hard not to blur. The full retrieval split is in [3 of the 8 engines we ask cannot search the web](https://lantad.co/blog/ai-visibility-tracking-three-of-eight-engines-cannot-search).

Put the two together and the inconvenient number falls out. The surface the study ranks first for news citations, Grok at 23 percent, is one this scanner asks without retrieval and reads no citations from. AI Mode at 10.6 percent is not asked at all. That is 33.6 points of the study's news citations that our citation numbers cannot see, against 50.0 points from the four surfaces where we both ask and read sources and where the study publishes a share: AI Overviews, Gemini, ChatGPT and Claude. Perplexity is a fifth such surface with no published share to add.

None of that is unique to us, and the category's shape is the reason: reading pages and asking models are two different machines sold under one name, which is what we found when [ten AI visibility products were read at their own pages and three of them fetched anything](https://lantad.co/blog/ten-ai-visibility-products-three-fetch-your-pages). Coverage claims are worth checking against the vendor's own documentation on both halves. On the crawler half, the tokens are at least published: [OpenAI's crawler documentation](https://developers.openai.com/api/docs/bots) states that OAI-SearchBot is for search, that GPTBot is used to make their generative AI foundation models more useful and safe, that ChatGPT-User covers certain user actions in ChatGPT and Custom GPTs, and that OAI-AdsBot validates the safety of pages submitted as ads. Four tokens, four purposes, one vendor. Our own [methodology page](https://lantad.co/methodology) is where the equivalent disclosure for this product lives.

## What to check before you trust any per engine visibility number

The practical value of a distribution like this is that it turns a vague worry into four questions you can put to any tool, ours included, and get a specific answer or a silence.

Which surfaces were asked, named individually. A tool that says it covers the major AI engines has told you nothing, because the set is unstable and the shares are not close to even. If the biggest single share in the news slice belongs to a surface a tool does not cover, that tool's total is not wrong so much as it is about a different population.

Whether each surface retrieved or remembered. A model answering with no search tool attached returns no sources, so any citation figure for it is either absent or derived from something else. This is where a report either states the retrieval mode per engine or quietly averages two different measurements together.

How many runs and how many prompts the number rests on. Answers move between identical runs, and we measured that [AI visibility took seven runs per prompt to settle](https://lantad.co/blog/ai-visibility-took-seven-runs-per-prompt-to-settle) before the variance stopped mattering, and separately worked out [how many prompts a measurement needs](https://lantad.co/blog/how-many-prompts-an-ai-visibility-measurement-needs) before it means anything. A single run against a single prompt is an anecdote with a percentage sign attached.

Whether the crawler half was checked at all. Being cited requires being readable, and blocking is per token rather than per vendor, which is why a robots.txt edit is a weaker instrument than it looks: a canary study found [a block did not stop 12 of 18 AI chatbots](https://lantad.co/blog/robots-txt-block-did-not-stop-twelve-chatbots) from returning the content. Our [AI crawler reference](https://lantad.co/tools/ai-crawlers) lists the tokens worth naming, and [what AI says about a domain](https://lantad.co/what-ai-says) is the answer side of the same question. Neither is a substitute for the other, and a tool that sells one while implying both is the failure this category keeps repeating.

## Questions and answers

**What is a GEO engine?**

It is any surface that generates an answer and may cite sources inside it, rather than returning a list of links. The label covers three unlike things: chat products with a retrieval tool, features inside a search results page such as Google's AI Overviews and AI Mode, and assistants embedded in other apps such as Amazon Rufus. They differ in whether they return the URLs they consulted, which decides whether citations can be counted at all.

**How many GEO engines are worth tracking?**

There is no register, so any count is a choice. Goodie's study, published 28 July 2026, monitors eleven: ChatGPT, Gemini, Claude, Perplexity, AI Overviews, AI Mode, Copilot, Grok, DeepSeek, Meta AI and Amazon Rufus. Lantad's registry, read on 7 September 2026, holds 8 rows covering 7 of those 11, and returns citations from 5.

**Which engine cites news the most?**

Grok, on this evidence. In Goodie's sample of 31 million citations recorded from October 2025 to July 2026, Grok took 23 percent of earned news citations, ahead of AI Overviews at 19.5 percent and Gemini at 13.5. Those are shares of news citations only, and the same study reports news as between 1.3 and 1.9 percent of all citations in any given month.

**Does Lantad measure every engine in that list?**

No. Four of the eleven surfaces, AI Mode, Copilot, Meta AI and Amazon Rufus, have no row in the registry as read on 7 September 2026, and two more, Grok and DeepSeek, are asked with no retrieval and return no sources. Adding a surface is a cost decision as much as an engineering one, and the Grok row records that xAI's Live Search bills per source used.

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Lantad measures whether AI crawlers can actually read a page: it fetches as a non-rendering
crawler, renders as a browser, and reports the gap. Free scan, one URL, no signup.

Method and weights: https://lantad.co/methodology | All pages as markdown: https://lantad.co/md | Crawler policy: https://lantad.co/bot