That’s according to a report Wednesday (June 3) by Bloomberg News, which says the financing would mark one of China’s largest ever startup funding rounds.
Social media giant Tencent is among the investors in the round, sources familiar with the matter told Bloomberg. Also taking part, the same sources said, is the government-backed National Artificial Intelligence Industry Investment Fund.
According to the report, DeepSeek’s senior management has told potential investors that the company will focus on groundbreaking AI research instead of short-term commercialization.
Founder Liang Wenfeng — who is himself investing in the funding round — committed in at least one meeting with investors to continue developing open-source AI models while working toward a larger goal of achieving artificial general intelligence (AGI), Bloomberg added.
The report also points out that AI companies — including those in China — are facing increasing pressure to show results following hundreds of billions of dollars of infrastructure investments. DeepSeek’s “research-first philosophy,” Bloomberg continued, comes as companies like OpenAI to Anthropic are exploring stock market listings and multiple paths to producing revenue.
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The news outlet had reported last month that DeepSeek was hoping to raise $10 billion in a round that would value the company at $45 billion.
DeepSeek sent shockwaves through the AI industry in early 2025 when it introduced an AI model that offered comparable performance to those of American rivals OpenAI and Meta while using substantially fewer Nvidia chips.
The news caused Nvidia to suffer one of the largest drops in market value ever recorded, as investors began to rethink the need to invest in AI hardware.
PYMNTS wrote earlier this year about research by the company illustrating a new way to train large language models that lets performance improve without a proportional increase in training costs. This runs counter to one of the main assumptions that has shaped the way the AI space has evolved over the last several years, the report said.
“If approaches like DeepSeek’s prove reliable beyond research settings, they could alter the economics of AI deployment in sectors that depend on cost discipline, including commerce, payments, and enterprise software,” PYMNTS added.
“Lower training costs make it easier to build and maintain specialized models tailored to specific workflows, rather than relying on generalized systems that are expensive to update.”
