1. How Does an AI Model “Learn” About a Brand? Training-Data Distribution Decides Everything
Mainstream LLM training data is predominantly English
For mainstream models such as ChatGPT, Gemini, and others, the training data is overwhelmingly English. While none of the vendors disclose the exact proportions, the industry consensus is that English vastly outweighs all other languages combined.
Within the remaining non-English portion, Simplified Chinese (web content from China) also clearly makes up a larger share than Traditional Chinese. Japanese, Korean, and various European languages each hold a certain weight, and Traditional Chinese is a minority within the minority across the entire data distribution.
What this means for Taiwanese brands
AI learns about a brand based on “how many credible descriptions of that brand it has seen in its training data.” If your brand’s information lives mainly in Traditional Chinese media and Traditional Chinese websites, you are essentially accumulating visibility in the “marginal segment” of AI’s overall training data.
Take two SaaS companies of the same kind: a mid-sized company in the United States might have several hundred English media articles, English blog posts, and English reviews; a Taiwanese company of comparable scale might have only a few dozen Traditional Chinese articles and almost no English data. In AI’s eyes, the gap in “entity clarity” between these two companies is enormous.
A Taiwanese brand owner asks ChatGPT: "Tell me about [our company name]." The description AI gives back: the founding year is off by three years, the main product line is listed as a competitor's, and the founder is written up as a different, same-named Chinese entrepreneur.
The brand owner is stunned—"My company has been around for 12 years; all of this is searchable online, isn't it?" The answer is: it's searchable on Traditional Chinese web pages, but AI's underlying perception comes mainly from English-language data, and within that English-language data there is extremely little credible information about this company.
2. Why Do Even Big Brands Get Written Up Wrong by AI?
Large scale ≠ clear AI perception
Many large Taiwanese brand owners assume, “We’re already a well-known domestic brand, so AI must understand us clearly.” In reality, domestic recognition and clarity of AI perception are two different things.
Domestic recognition is built on ad spend, media exposure, and consumer word of mouth—but most of this happens through Traditional Chinese channels, which AI’s English-dominated training data barely picks up.
Clarity of AI perception is built on English media coverage, an English Wikipedia article, cross-border review platforms, and English-language industry analysis—areas where Taiwanese brands have invested relatively little.
Three of the most common AI perception biases
| Bias type | How it shows up | Root cause |
|---|---|---|
| Same-name confusion | AI mistakes you for another same-named entity (especially a same-named Chinese brand) | Your brand’s entity record is thin in English-language data |
| Wrong background | Founding year, product line, or founder written incorrectly | Outdated or fragmentary data dominates AI’s perception |
| Misjudged scale | AI describes a big brand as an “emerging / small company” | A lack of credible English sources to corroborate the actual scale |
The hidden losses for international business
These biases have limited impact on brands that serve the domestic market only, but they are a serious problem for any brand with international business, cross-border partnerships, or overseas procurement intent—when your potential international customers use AI to look up your company, what they see may be an incorrect or muddled description, and that first impression is hard to reverse.
This problem is especially severe for Taiwanese B2B brands—B2B buyers make heavy use of AI to shorten their shortlist during cross-border evaluation. If AI's perception of your company is vague or wrong, you may be eliminated before you are ever contacted.
3. Why This Isn’t Just an SEO Problem, but a Matter of “Brand Information Sovereignty”
From SEO to GEO to information sovereignty
In the SEO era of the past, the power to present a brand’s information lay mainly in Google’s hands—where your site ranked, what the snippet displayed. But once a consumer clicked through to your official site, control returned to the brand itself.
The AI era is different: a user asks AI a question, gets an answer, and that’s the end of it—they don’t click through to your official site at all. In other words, how AI describes your brand often is the entirety of what your target audience perceives of you.
Comparing the power to describe and the power to correct
| SEO era | AI era | |
|---|---|---|
| Who generates the description | Your official site / the media | The AI model |
| Path of influence | Edit your site, add a press release—direct | Systematically build credible external sources + structured markup—requires method |
| Time to take effect | Days to weeks | Begins accumulating in weeks, position shifts visible in months |
| Who holds control | Directly editable | Influence requires the right strategy—and the strategy exists |
“Information sovereignty” sounds like a nation-level issue, but for a brand it is very concrete: can you still influence how the outside world (through AI) perceives you?
The key difference is not “English-language brands hold control, Taiwanese brands don’t”—it is that the content dividend enjoyed by English-language brands lets them benefit while doing nothing, whereas Taiwanese brands must deliberately and with the right method build their position within AI’s perception. The method exists, and it works once applied; brands that do nothing are simply handing this slice of sovereignty away for free.
4. The Remediation Path for Taiwanese Brands
It’s not about writing more Traditional Chinese content
If the root of the problem is the English bias of AI training data, then the solution is not to “write even more Traditional Chinese articles”—that only accumulates more content in a segment AI already barely cares about. There are three genuinely effective directions for remediation:
First: accumulating credible English-language sources
Proactively invest in English media coverage (the English Asia editions of outlets such as TechCrunch, Nikkei Asia, Reuters), English-language industry review platforms (G2, Capterra), and English blogs. These are “your existence” as AI can read it within English-language data.
Second: an English Wikipedia article
For brands that meet the notability threshold, an English Wikipedia article is one of the highest-value investments available—it is a high-weight source in LLM training data, almost equivalent to securing a spot in AI’s “official record.”
Third: structured English schema
Structured markup—Organization schema on your official site, Product schema on product pages, Article schema on articles, and so on—needs an English version. This lets AI read a clear entity record directly when it crawls your English-language web pages.
A Taiwanese SaaS company decides to prepare for international expansion. They spend a year doing the following: 1) investing in an English G2 review page and accumulating 80 user reviews; 2) earning independent coverage in three English tech media outlets; 3) establishing an English Wikipedia article.
A year later, when overseas prospects ask ChatGPT for "Taiwan-based B2B SaaS for X," the company consistently appears in the top three of the recommendation list. Overseas inquiries rise noticeably, and the customers reaching out already hold a correct understanding of the company's background by the first contact, shortening the sales cycle.
Continuity is the key
There is no such thing as “doing these three remediation paths once and being done.” AI training data is dynamic, the competitive landscape is dynamic, and your business is dynamic too. This is exactly why more and more Taiwanese brands with international ambitions choose a managed GEO service—not because these things can’t be done in-house, but because the discipline and resources required to keep doing them consistently rarely hold up internally.
5. Where Do You Start Assessing?
If you want to know your brand’s current “cross-language visibility” status within AI, the 12-dimension scoring of the free GEO health check can give you a starting point—particularly the “external credibility” and “structured information” dimensions.
If you need a tailored plan for building international brand visibility, get in touch: [email protected]
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