Without local languages, ‘AI is essentially useless’: Hong Kong’s Votee AI is taking on English and Mandarin’s AI dominance with a Cantonese model

The AI boom is being driven by two languages: English and Mandarin Chinese.

The leading AI developers—OpenAI, Anthropic, DeepSeek, Moonshot AI, Z.ai , and others—are almost all based in either the U.S. or mainland China, and so their models perform best in their native tongues.

That leaves countless other languages and dialects , even those spoken by tens of millions of people, by the wayside.

“The whole AI revolution is in English and Mandarin,” says Pak-Sun Ting, CEO of Votee AI, a Hong Kong-based startup that’s striving to build AI models for Cantonese and other neglected tongues.

“There’s only a very small fraction that represents other languages.” Votee is one of a growing number of companies that are trying to tackle AI’s neglect of other languages.

It takes open-weight models from developers like Meta and Alibaba , retrains them on Cantonese data, and sells the result to banks, universities, and government departments.

“It’s more than just culture.

Cantonese is used in education, healthcare, and police communications,” Ting says.

“If those don’t get covered, then AI is essentially useless.” How Cantonese is different Cantonese is often referred to as a “dialect” of Chinese, but that moniker undersells just how different it is from Mandarin, the most commonly spoken version of the Chinese language.

Cantonese uses different grammar than Mandarin, as well as different vocabulary, particularly in the city of Hong Kong, where speakers often switch between English and Cantonese words, sometimes in the same sentence.

Over 80 million people speak Cantonese, roughly equal to the number who speak Korean, and more than those who speak Italian or Thai.

Yet its large speaker base has not led to a comparably deep pool of standardised written data, particularly for colloquial Cantonese.

Leading models aren’t completely useless at handling Cantonese.

HKCanto-Eval —a set of benchmarks developed by researchers at Kyushu University, the Education University of Hong Kong and the local AI community hon9kon9ize, and sponsored by Votee—reports that while mainstream models can handle everyday Cantonese at a reasonable level, they routinely fail when it comes to cultural and local knowledge.