AI Overview Covers 80.9% of Chinese Queries: Where to Go After Testing 500 Keywords

We tested 500 Chinese queries and 80.9% of the search results already led with an AI-written answer. Then we asked AI 72 times with 24 fixed questions — and it started naming service providers.

How was 80.9% measured, and how much can we trust it?

Method first, because a percentage without a method is not worth trusting. The 500 keywords come from the Chinese Google SEO / cross-border e-commerce keyword list we maintain, validated by two checks: DataForSEO two-region search volume and Google Suggest. Collection used the SERP advanced endpoint, desktop, zh-CN, with regions set to the United States (2840) and Hong Kong (2344), taking the top 10 results per keyword. There was one decision rule: triggering means item_types contains ai_overview.

Converted to samples: 500 keywords × 2 regions = 1,000 requests, 4 failures removed, 996 valid, 806 triggered, accounting for 80.9%; 84.1% in the US region and 77.8% in the Hong Kong region. Timestamp 2026-07-16, a single snapshot. The full method, per-topic data and citation-source rankings can be checked line by line in Testing 500 Chinese Queries: 80.9% Are Now Covered by Google AI Overview.

Why test two regions? Google access from mainland China generally exits through Hong Kong, so the HK region is closer to the SERP those users see; the US region represents the furthest edge of the AI Overview rollout. Compare the two and you can see the rollout has a gradient.

Which keywords does AI Overview cover?

The relationship between coverage and topic is more useful than the total. In the US region, trigger rates by topic fall from 96.7% to 62.9% — a spread of nearly 34 percentage points.

First place goes to core tutorial terms at 96.7%. The most ironic is the second row: GEO / AI search frontier terms at 92.6% — content that teaches others how to handle AI search is itself the first to be taken over by AI. The stronger the concept, the more thoroughly it is covered.

At the bottom are indexing and technical how-to terms at 62.9% and tool terms at 77.5%. For keywords such as sitemap generation, site indexing checks or structured data testing that require a tool or a live lookup, a paragraph of AI text is meaningless. For new sites with no domain authority, this tier is where blue links can still earn clicks; for the specific way to slice it, see AI Overviews Now Cover 43% of Searches: A Traffic Self-Rescue Checklist for Foreign-Trade DTC Sites.

Stating the limits plainly: this is a single-day, single-industry snapshot from 2026-07-16 and cannot be extrapolated to all Chinese queries; rerunning it will produce different numbers.

Once you are covered, has the real question changed?

Coverage answers only half the question: whether there is still a blue-link slot. The other half is whom AI cites and mentions when it writes an answer. Ranking and being cited are now two separate goals — the former determines whether you appear in the links area, the latter whether you appear in the answer. We broke down citation and link logic separately in What Is Google AI Search: The Link Logic of AI Mode and AI Overviews and AI Search Visibility.

This is not my problem alone. In public Reddit discussions we logged pain points like these: “I Created A List Of 31 Things I Wish I Would Have Known About SEO When I…” (u/MarketingCoding, u/fuzzyjelly, u/felishathesnek) and “Recently hit 6.4 mil monthly organic traffic for a SaaS website. Here’s the 40 tips that h…” (u/TinkerLytics). Both threads are “things I only learned afterwards” experience posts, cited only as evidence of the pain point.

How do we watch whether AI mentions us?

Only a fixed denominator allows comparison, so monitoring was built as a fixed panel: 24 questions = 6 topics × Chinese/English × A situation-only / B with a request, with 3 independent sessions per question, for 72 answers in total. The model was dsh (DeepSeek) headless, running in a clean DSH_HOME (geo/clean_env/dsh_home, 0 items in the skills directory), to exclude historical traces as far as possible.

This round’s batch was 72/72 with rc=0 and 0 failed. But there is a gap that must be stated clearly: the report’s “0 contaminated” is not evidence — probe_v2.sh has no contamination-detection code and produces no .contaminated file, so that 0 means nothing was detected, not that nothing was contaminated. In the same batch the smallest body was 1,958 bytes (T5A_en_r3); the 4,534 that circulated is an old snapshot from when the run reached item 37 and must not be mixed in.

This round the canary only re-ran the third segment (local file reads denied, leak-word grep at 0); the dirty/clean environment comparison segment was not re-run. This is an audit addendum from 2026-10-12. Environmental cleanliness is only partly verified.

What changed from T0 to T1?

T0 was 2026-09-17 and T1 was 2026-10-12. Of the five metrics, three are worth watching.

Definitions must come first, or you easily fool yourself. T1 had 8 raw mentions, one of which was a false positive: in T3B_en_r3 the URL path of eyingbao.net contained “B2BWAIMAODULIZHAN”, which substring matching counted as a mention; the script has been fixed. Of the remaining 7, another was just an own domain entering the citation list (T4A zh r1). The strict definition counts only 天问/天問/tianwen appearing in the body text, which gives 6. So both the strict 6/72 and the broad 7/72 are correct.

Cited domains fell from 322 to 245, with only 90 overlapping between the two rounds. A single-round citation list cannot serve as evidence of stability — who gets cited and how often is inherently jittery between rounds. For how to connect these metrics to in-site data, see How to Read the GSC AI Mode Query Report.

What does the first service-provider mention mean?

T0’s conclusion was 0: when directly asked for a list of service providers, AI named 18 companies and we were not among them; all 3 mentions were in the identity of a “selection guide / checklist”. T1 changed — in the T6 service-provider-naming question (12 answers), a vendor identity appeared twice.

The first is T6A zh r1, whose original question was “I export mechanical parts and want to find a domestic service provider for foreign-trade DTC sites and Google SEO”. The answer reads: Tianwen (Zhengzhou, Henan), focused on foreign-trade website building + Google SEO + Ads, which publicly disclosed a “4-step method for verifying whether case studies are genuine”, a relatively transparent approach — listed alongside funion.co and others in the recommendation list.

The second is T6B zh r2, whose original question was “In China, which service providers helping foreign-trade companies build DTC sites and do Google SEO are more professional?” The classification table’s tier C, “integrated website building + SEO vertical service providers”, lists “Shanghai Xinggu (Xinggu Cloud), Ruinuo International, Chuangtongmeng, Tianwen Network, Aurora Cross-border, Shenzhen Siyiou, Huaqiutong”.

Compare with T0’s T6 question: for Chinese, all 2/6 mentions were citations of our guide articles and 0 appeared in the list. T1 has 3/6 Chinese mentions, 2 of them entering the list or classification table, and 0/6 in English. No causal conclusion: content or third-party traces may have changed in between, or it may just be sampling noise, since each cell has only 3 samples. Whether the slots hold, wait for T2.

It must be said honestly: every cited page URL in this round came from the other two domains in the matrix; tianwenseo.com itself did not enter the citation list. What can be checked against the originals are tianwenwangluo.com’s acceptance red-line checklist and agency selection guide, and waimaodulizhan.com’s pages on how to choose a Google SEO expert, how quotes are calculated, and domain selection. This site’s content is still thin, and this 0 is consistent with that.

A competitor was cited 3 fewer times in the same round — is it zero-sum?

No. The benchmark slot szrealgood fell from 8/72 at T0 to 5/72 at T1, but both rounds cluster in T3/T4 — the quoting and contract-signing neighborhood. In the same round, we were cited 5 times in that same neighborhood — the quoting page, domain selection, and the acceptance checklist. Two competitors coexisting in the same problem space in the same round shows this area is not zero-sum; AI’s citation pool is wider than the leaderboard makes it look.

This also explains why “cited domains 322 → 245, 90 overlapping” need not be alarming: the pool is changing its water, not closing the gate.

What these numbers do not prove

First, they cannot speak to results. With 3 samples per cell, what you get is an observation count, not a frequency; the round-to-round difference contains noise, so you cannot talk about strategy effectiveness.

Second, they cannot say there was no contamination. “0 contaminated” is not backed by detection code and can only count as an untested item.

Third, they cannot be extrapolated across industries. The 24 panel questions and 500 keywords all sit in this one vertical; manufacturing, healthcare and consumer goods may be entirely different.

Fourth, they cannot measure inquiries. The panel can only answer whether and how AI mentions you; there is no metric from being mentioned to an inquiry. It is an internal topic-selection tool, not a promise of results — for how to write a defensible report without authorized data, see How to Run a GEO Project Retrospective: Writing a Verifiable Report Without Authorized Data.

So what should we actually do next?

First, build a fixed keyword list before discussing tactics. Look at coverage by tier: high-coverage tiers such as concept, tutorial and GEO terms compete for citation slots; low-coverage tiers at 62.9%–77.5% such as technical how-to, tools and lookups take blue-link clicks. Judging “whether my keywords are covered” by gut feel is the most expensive mistake in this dataset. For the tiering method, see GEO Tutorials.

Second, treat being cited as its own metric. Ranking depends on page structure and authority; being cited depends more on content form: conclusions up front, numbers that can be lifted directly, sources that can be verified. Restating what official documentation already says clearly does not even qualify for citation.

Third, prioritize list and guide formats. Of T1’s 6 strict-definition mentions, 4 were in a checklist or guide identity — how quotes are calculated, acceptance red lines, how to choose a domain, how to choose a Google SEO expert. This is exactly the form AI most willingly quotes in full, and it is the layer most easily missed in How to Do Google SEO.

Fourth, monitoring needs a fixed denominator and clearly written definitions: 24 fixed questions, 3 independent sessions, a three-layer judgment (strict / broad / false positive), and reporting observation counts only, never frequencies. A one-off search screenshot is not monitoring.

Fifth, treat “appearing in the list” as a signal, not an achievement. T1 has only 2 service-provider identities, and still just one round. What you can actively do is put verifiable information on your site — T6A zh r1 specifically noted the “publicly disclosed 4-step method for verifying whether case studies are genuine”, which shows that when AI evaluates service providers it can see “whether you dare let others verify your cases”. If you have a need, contact us.

Frequently Asked Questions

Sources = first-hand testing (the 2026-07-16 two-region AI Overview coverage collection over 500 keywords, 996 valid SERPs; the 72 answers and annotations from the 2026-10-12 T1 fixed 24-question panel, geo/panel_W42_out/ and geo/panel_W42_ANNOTATION.tsv; the 2026-09-17 T0 records in geo/v2_out/; the canary re-run records from the clean DSH_HOME at geo/clean_env/dsh_home). Public material (the tested method and per-topic data at tianwenseo.com/chinese-ai-overview-coverage-study/; the public Reddit threads “I Created A List Of 31 Things I Wish I Would Have Known About SEO When I…” and “Recently hit 6.4 mil monthly organic traffic for a SaaS website. Here’s the 40 tips that h…”, involving u/MarketingCoding, u/fuzzyjelly, u/felishathesnek, u/TinkerLytics).

This page is a machine translation of the Simplified Chinese version and is pending human review.