Nvidia drops nearly 17% as China’s cheaper AI model DeepSeek sparks global tech sell-off

• U.S. technology firms like Nvidia plunged after Chinese startup DeepSeek sparked concerns over competitiveness in AI and America’s lead in the sector.
• DeepSeek launched a free, open-source large language model in late December, claiming it was developed in just two months at a cost of under $6 million.
• These developments have bolstered questions about the large amounts of money big tech companies have been investing in artificial intelligence models and data centers.

Nvidia and other U.S. technology firms plunged on Monday, part of a global sell-off as Chinese startup DeepSeek sparked concerns over competitiveness in artificial intelligence and America’s leadership in the sector.

Nvidia, the chip designer who has been a major beneficiary of the AI hype, slid 16.9%. With that, the megacap tech stock was on track to notch its worst day since March 2020.

Shares touched lows in the session not seen since October.

Nvidia’s losses helped drive other AI trades and the broader U.S. market down. Micron and Arm Holdings dropped more than 11% and 10%, respectively. Chipmakers Broadcom and Advanced Micro Devices lost more than 17% and 6%, respectively.

Constellation Energy and Vistra, two of the best-known derivative plays tied to the power buildout for AI, plummeted more than 20% and 28%, respectively.

International markets also felt the impacts. Netherlands-based chip companies ASML and ASM International both pulled back sharply in European trading. In Asia, Japanese chip-related stocks including Advantest and Tokyo Electron were broadly lower.

DeepSeek launched a free, open-source large language model in late December, claiming it was developed in just two months at a cost of under $6 million — a much smaller expense than the one called for by Western counterparts. Last week, the company released a reasoning model that also reportedly outperformed OpenAI’s latest in many third-party tests.

“DeepSeek is an excellent AI advancement and a perfect example of Test Time Scaling,” an Nvidia spokesperson said. “DeepSeek’s work illustrates how new models can be created using that technique, leveraging widely-available models and compute that is fully export control compliant. Inference requires significant numbers of NVIDIA GPUs and high-performance networking. We now have three scaling laws: pre-training and post-training, which continue, and new test-time scaling.”

In a social media post, Marc Andreesen called DeepSeek’s product “one of the most amazing and impressive breakthroughs I’ve ever seen” and a “profound gift to the world.” The Andreessen Horowitz co-founder recently gained notoriety for his support of President Donald Trump.

These developments have stoked concerns about the amount of money big tech companies have been investing in AI models and data centers, and raised alarm that the U.S. is not leading the sector as much as previously believed.

“DeepSeek clearly doesn’t have access to as much compute as U.S. hyperscalers and somehow managed to develop a model that appears highly competitive,” said Srini Pajjuri, semiconductor analyst at Raymond James, in a Monday note.

Pajjuri said DeepSeek could “drive even more urgency among U.S. hyperscalers,” a group of large computing infrastructure players like Amazon and Microsoft. Specifically, the analyst said these companies can leverage their advantage from access to graphics processing units to set themselves apart from cheaper options.

GPUs are a key part of the infrastructure required to train huge AI models. Nvidia is the market leader in GPUs.

The cost of computing has become a key topic of conversation following the DeepSeek news, according to Citi analysts.

While the dominance of U.S. companies on the most advanced AI models may be threatened, they said, a key barrier for competitors is access to the best chips. Because of this, leading AI companies likely won’t move away from the more-advanced GPUs, the analysts said.

Last week’s announcement of the $500 billion Stargate AI project is a “nod to the need for advanced chips,” they added.

To be sure, Bernstein analysts expressed doubt over whether the DeepSeek tool was actually built for less than $6 million. They questioned if that figure left out other costs from prior research and experiments to get the technology to where it is today.

Despite stressing that DeepSeek’s models “look fantastic,” the team said they shouldn’t be thought of as “miracles.” And panic about the “death-knell of the AI infrastructure complex as we know it,” the Bernstein analysts said, was “overblown.”

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