Beijing-based Zhipu AI, one of China’s four AI “tigers”, raised an unknown sum in a new investment round headed by a state-backed investor, valuing the startup at 20 billion yuan.
Lead investor Beijing Zhongguancun Science City Innovation Development, a state-backed investment vehicle, said in a WeChat post , that Zhipu AI’s latest financing round, estimated to be worth “billions of yuan” according to local media outlet 36Kr, will “support technological innovation and ecosystem buildout” for its large language models (LLMs).
According to research firm IDC, the tech unicorn’s GLM series of LLMs, which powers generative AI applications like ChatGPT, is among the top AI models utilized by mainland businesses.
Beijing Zhipu Huazhang Technology, formerly known as Zhipu AI, did not immediately reply to a request for comment.
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The company’s latest funding round demonstrates how, in spite of Washington’s efforts to stop Beijing from pursuing technological advancements, investors have continued to have a healthy appetite for AI-related businesses on the mainland.
Alibaba Group Holding, Tencent Holdings, Meituan, GL Ventures, and Legend Capital are some of the other significant investors in Zhipu AI. The South China Morning Post is owned by Alibaba.
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The start-up Zhipu AI and Chinese smartphone manufacturer Honor signed a strategic cooperation agreement earlier this week to establish a joint AI large model laboratory in Beijing. This financing announcement coincides with that agreement. Through Honor’s line of gadgets, the partnership hopes to provide users more clever and practical AI services.
Zhipu AI released Ying, a text-to-video AI model akin to Sora, in July. Ying can produce six-second video clips in about 30 seconds by responding to both text and visual stimuli.
In the same month, Baichuan, another Chinese AI tiger, raised over 5 billion yuan in a fresh round of funding, valuing the company at over 20 billion yuan.
About Zhipu AI
Zhipu AI trains machines to think like humans. The world-leading AI team has created cutting-edge large language and multimodal models (e.g., ChatGLM, GLM-130B, CodeGeeX, CogVLM, and CogView) and high-precision billion-scale knowledge graphs, enabling us to create a powerful data- and knowledge-driven cognitive engine for AGI.
