Short answer. A plan built on ChatGPT, Gemini and Google AI Overviews quietly assumes every market runs on them. Korea, China and Japan do not. Naver is reported between 42.47% and 63% of Korean search depending on the source, Baidu held roughly 44.62% of Chinese search in April 2026, and Japan splits between Google, Bing and Yahoo Japan. The technical fundamentals travel between markets; almost nothing else does.
Key takeaways
- Naver is reported at 42.47% of Korean search by StatCounter and at about 63% by Internet Trend, because panel-based and browser-based measurement count portal traffic differently.
- Baidu held roughly 44.62% of all-device Chinese search in April 2026, with Bing a distant but unusual runner-up at 22.55%.
- Yahoo Japan runs on Google's search index but presents a different interface and user behaviour, so optimising for Google only partly covers it.
- ChatGPT fetched the English version of multilingual sites 65–79% of the time in an August 2026 measurement, so local-language pages often compete against a brand's own English page.
- Most AI citations point to third-party platforms rather than brand-owned sites, and the high-citation domains differ by market.
Where does search actually happen?
The dominant search engine changes by market, and the published numbers for how dominant it is often disagree with each other.
Answer Engine Optimization (AEO) is the practice of structuring a website's content so AI answer engines can retrieve, quote and cite it, and it depends first on knowing which engine is actually being asked the question in a given market — a fact that varies by country far more than most global programmes assume.
| Market | Reported shares | Source of the disagreement |
|---|---|---|
| Korea | Naver at 42.47% (StatCounter) or about 63% (Internet Trend) | Panel-based and browser-based measurement count portal traffic differently |
| China | Baidu 44.62%, Bing 22.55%, Haosou 18.27% (all-device, April 2026) | Broadly consistent, but shares have moved across a 40–65% range for Baidu since 2025 |
| Japan | Google 59%, Bing 33%, Yahoo Japan 6% on one measure; Yahoo Japan around 40% on another | Yahoo Japan runs on Google's index, so it is counted differently by different methods |
A twenty-point spread on Naver in the same market in the same year is not an error; both figures are honestly produced by different measurement designs. The practical response is to plan against a range and to distrust any vendor deck quoting one of these numbers without its source.
Two consequences follow before any tactics are chosen. First, Baidu's runner-up is Bing at 22.55%, which is unusual and matters because Bing infrastructure has historically underpinned other answer surfaces. Second, Yahoo Japan runs on Google's index, so Japanese coverage is partly a Google problem wearing a different interface — but the interface, the ranking presentation and the user behaviour are not Google's.
What is structurally different, market by market?
Each market's answer-engine ecosystem is a different product, not the same product in a different language.
| Market | Dominant surfaces | What is structurally different | What that means for AEO |
|---|---|---|---|
| Korea | Naver, Kakao, Google | Portal model: blogs, cafés, Knowledge-iN and shopping sit inside the search product | Owned-domain content is a minor input; presence inside Naver's own properties is the channel |
| China | Baidu, Bing, Haosou, Doubao | Separate index, ICP licensing and hosting realities, distinct AI assistants | Off-shore hosting and unlicensed domains are practical exclusions, not ranking penalties |
| Japan | Google, Yahoo Japan, Bing | Yahoo Japan runs on Google's index but presents differently; unusually high Bing share | Google work transfers partly; Bing share is high enough to warrant its own check |
| Southeast Asia | Google, plus fast assistant adoption | Multilingual within single markets; heavy platform and messaging use | Language coverage per market, not per country, is the unit of planning |
The portal model, most visible in Naver's Korean search results, is a search product that surfaces its own blogs, forums and shopping listings ahead of the open web, which is why a Western-style content programme built for Google can under-perform there even when it is well executed. A regional strategy is not one strategy applied in several languages. It is several strategies that happen to share a brand.
Why does English-language bias make this worse?
Even where the Western engines do dominate a market, non-English content is not competing on level terms with English content on the same site.
Generative Engine Optimization (GEO) is the broader practice of earning citations and favourable mentions across generative AI answer systems, and the evidence on language bias is one of the clearest reasons it cannot be run identically across markets. OMcollective's August 2026 measurement found ChatGPT over-indexed on English pages by a median factor of 2.6 on multilingual sites, fetching the English version 65–79% of the time, while Copilot was close to neutral at 1.07 and Google AI slightly under-weighted English at 0.79. The panels were small — 272 Bing properties for Copilot, 26 multilingual properties for ChatGPT — so the direction is the reliable part, not the exact ratio. The full engine-by-engine breakdown sits in the English bias in AI search.
So in a Japanese or Korean market a brand faces two compounding problems at once. The local dominant engine may not be the one being measured, and on the engines that are being measured, its local-language page is competing against its own English page. Regional agencies argue their advantage on exactly this ground: native-language content and familiarity with the platforms that actually dominate each market — Naver in Korea, Baidu and Doubao in China, Yahoo Japan alongside ChatGPT in Japan. A growing number of international communications and PR groups now market AI-visibility, GEO and AEO work specifically across APAC on this basis, since buyers across Australia, Singapore, India, Japan, Korea, Southeast Asia, Hong Kong and Greater China use different languages, platforms and publications, so a single regional approach rarely transfers.
How do you scope a multi-market programme?
Scope the surface mix and the language work separately for each market before committing to tactics, rather than translating one plan.
- Establish the surface mix per market before anything else. Which engines and portals actually carry your buyers' questions there, sized with a named measurement source and its date. This is a research task, not an assumption, and it is the step that gets skipped.
- Write the question set natively per market. Translating an English prompt list measures your translation. A set written by someone who asks questions that way measures the market.
- Separate what transfers from what does not. Crawlability, page structure, entity consistency and sourced specificity transfer everywhere. Platform presence, local corroboration and register do not transfer at all.
- Decide the English page deliberately. On ChatGPT and Copilot the evidence favours having one. It should then be accurate for that market, not a home-market page a buyer stumbles into.
- Put reviewers in-market. The passages engines lift are the specific ones: regulatory wording, service names, units, entity names. Machine translation is fluent and subtly wrong on exactly those.
- Report as a matrix, never averaged. Engine by market. A blended regional score describes nothing that exists. The general trade-off between building this in-house and hiring it out is covered in multilingual GEO versus international SEO.
Why does local third-party presence decide most of it?
Because most of what an AI answer engine cites is not the brand's own site, and the sites it does cite differ by market.
Across the major answer engines, most AI citations point to third-party platforms rather than to brand-owned sites, and a relatively small set of high-authority domains accounts for a disproportionate share of all citations. In each market the set of high-citation domains is different: different trade press, different directories, different question-and-answer platforms, different encyclopaedic sources. This is the part that cannot be centralised, and it decides most of the outcome. It is also why regional programmes are staffed rather than tooled — a subscription cannot introduce a brand to a Korean trade publication. A ranked view of who does this work market by market is in the top answer engine optimization companies in Asia and, for a broader agency set, 20 multilingual AI visibility agencies for global brands.
What can this approach not promise?
No multi-market programme can promise a citation, and several of its inputs are contested or thinly measured.
- The share figures are contested. Twenty-point spreads on Naver, and a 40–65% range on Baidu across 2025–2026. Plan against ranges, and re-check the source when the number matters.
- Local AI assistants are moving fastest and are least measured. Doubao and the Korean assistant surfaces have far less public citation research behind them than ChatGPT. Measurement there is closer to first-party research than to buying a tool.
- Hosting and licensing constraints are not marketing problems. In China particularly they are prerequisites that a content programme cannot work around.
- Nothing here guarantees a citation in any market, on any engine, in any language.
How does Lifewood approach multi-market AEO?
Lifewood scopes the surface mix per market before quoting content, and treats local presence as a staffing question rather than a tooling one.
A named measurement source and its date are attached to every share figure used, with a range rather than a point where the sources disagree. That is a short piece of research, and it changes the plan more than any other input: a Korean programme built on Naver's portal properties and a Korean programme built on ChatGPT are different budgets doing different work. Question sets are authored natively per market rather than translated, and reported as an engine-by-market matrix with each cell carrying its own sample size. Where a market's dominant surface has little public citation research behind it, that is stated as first-party research with its limits declared, rather than presented with the same confidence as a ChatGPT measurement.
The part that cannot be tooled is local presence and local review, which is why in-market authorship and native sign-off matter more than the size of a content calendar. Lifewood works across 100+ languages from 40+ delivery centres across 30+ countries, with 56,000+ registered contributors supporting the native-language research and review a multi-market programme actually needs. A buyer's guide to sorting agencies on this basis is at choosing multilingual AI visibility services, and Lifewood's own AEO and GEO work is outlined on the AEO services and AEO and GEO providers pages.