DeepSeek focuses on AGI breakthroughs over quick profits a month after shocking the world

Chinese artificial intelligence (AI) start-up DeepSeek has been prioritising research over quick financial gains in the month following the release of its reasoning model that sent shock waves across the world, according to sources and media reports.

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Since its R1 reasoning model made headlines in Silicon Valley and Wall Street, the Hangzhou-based start-up founded by Liang Wenfeng and spun out of a hedge fund business, has maintained a low profile, with minimal communication with the public outside the developer community.

The 40-year-old founder has not made any public comments or accepted media interviews in the past month. Although he was briefly shown by China’s state broadcaster attending a high-profile symposium chaired by President Xi Jinping last week, he did not contribute any quotes to the state media’s readout of the meeting.

Liang’s only public presence of late has been as a co-author on a paper titled “Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention”, along with 14 others. At least 12 of the 15 authors on that paper also contributed to the paper on R1, showing that Liang was directly involved in the research alongside DeepSeek’s young scientists.

DeepSeek founder Liang Wenfeng meets Xi Jinping. Photo: CCTV
DeepSeek founder Liang Wenfeng meets Xi Jinping. Photo: CCTV

A source close to the company, who declined to be named, said DeepSeek is in no rush to conduct further fundraising or engage in new commercial activities. Instead, Liang is focused on advancing artificial general intelligence (AGI) by improving model efficiency and capabilities with minimal resources. AGI refers to a type of AI that reaches or surpasses human cognitive capabilities.

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“Whether it is a wise choice, and how long it can sustain the research, only time can tell,” the person said about DeepSeek’s key priorities. “A key problem is that the scaling law still exists, and it is hard to maintain a leading edge just by algorithm improvement.”

  

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