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Nvidia Faces Historic Market Loss As DeepSeek Dents Confidence In AI’s Future

Nvidia experienced the largest single-day market cap drop in history on Monday, as its stock tumbled by 17%, shedding nearly $600 billion in value. This staggering loss is directly linked to a new development in the AI space—DeepSeek, a Chinese AI firm that unveiled its version of ChatGPT, raising concerns over the cost-efficiency and competitive positioning of U.S. AI companies.

Key Details

Nvidia’s shares experienced a severe decline, marking its worst daily percentage drop since March 2020, during the initial shock of the COVID-19 pandemic. On Monday, Nvidia lost a record-breaking $589 billion in market capitalization, more than doubling the previous one-day loss of $279 billion in September 2024. To put it into perspective, this is significantly more than Meta’s $251 billion market cap loss in February 2022.

As a result, Nvidia’s market valuation dropped from $3.5 trillion to $2.9 trillion, slipping behind Apple and Microsoft as the world’s most valuable company. Nvidia’s dramatic fall led a broader retreat in U.S. stocks, with the S&P 500 losing 1.5% and the Nasdaq dropping 3.1%. Other major players in the AI industry, such as chipmakers Arm and Broadcom, alongside Oracle, saw their stocks plummet by at least 10%.

The DeepSeek Effect

The cause of Nvidia’s catastrophic loss lies in DeepSeek’s release of its large-language model, which has cast doubt on the continued dominance of U.S. companies in generative AI. Initially, this might not seem like a negative development for Nvidia, as DeepSeek’s model was also powered by Nvidia’s powerful graphics processing units (GPUs), just like many other AI technologies. However, DeepSeek revealed that it spent just $5.6 million on Nvidia’s technology to develop its model. While experts believe this figure is likely a significant underestimation, it still calls into question the very foundation of Nvidia’s meteoric stock rise.

In recent years, Nvidia’s profits have skyrocketed, with projections indicating net profits could soar from $4.8 billion in 2022 to $66.7 billion in 2024, largely due to the soaring demand for its high-priced GPUs, which can cost up to $25,000 each. U.S. tech giants such as Meta, Tesla, and OpenAI have been among Nvidia’s biggest customers. However, if companies like these can replicate DeepSeek’s cost-efficient approach by using cheaper GPUs, Nvidia could face significant challenges in maintaining its market dominance.

As Ed Yardeni of Yardeni Research pointed out, this shift could be an unwelcome development for Nvidia.

Surprising Statistic

Nvidia’s near-$600 billion market cap loss on Monday exceeds the market values of all but 13 American companies, surpassing industry giants like UnitedHealth, Exxon Mobil, and Costco.

CEO’s Losses

Nvidia CEO Jensen Huang saw his wealth take a massive hit, losing $21 billion in a single day. His net worth dropped from $124.4 billion to $103.1 billion, according to Forbes estimates. Huang remains the largest individual shareholder in Nvidia, owning a 3% stake in the company.

Nvidia’s colossal market cap loss highlights the growing uncertainties in the AI sector, as DeepSeek’s cost-effective alternative to American AI models threatens to disrupt the industry’s balance. With AI becoming an increasingly competitive and global field, Nvidia’s future may hinge on how it adapts to these emerging challenges.

Mirendil Signs $100 Million Google Cloud Deal To Advance Self-Improving AI

AI startup Mirendil has signed a multi-year agreement worth more than $100 million with Google Cloud to secure computing infrastructure for its self-improving AI research.

The partnership reflects growing competition among AI companies to lock in access to high-performance computing, while cloud providers race to attract promising startups developing next-generation AI models.

Backing The Next Stage Of AI Research

Mirendil plans to use Google’s Tensor Processing Units (TPUs), Nvidia GPUs and managed training infrastructure to develop AI systems capable of improving their own performance over time.

Known as recursive self-improvement, the concept focuses on building AI that can refine its knowledge and capabilities with minimal human intervention. The technology is attracting growing interest across the industry, with several startups and leading AI labs exploring similar approaches.

According to co-founder and Chief Executive Behnam Neyshabur, the long-term goal is to develop AI that can automate scientific research and accelerate discoveries in fields such as medicine, biology and materials science.

Compute Capacity Becomes A Strategic Asset

Training increasingly advanced AI models requires enormous computing resources, making long-term infrastructure agreements a critical competitive advantage.

Mirendil said Google’s combination of TPUs and GPUs allows workloads to be matched with the most suitable hardware, improving efficiency while reducing costs for customers.

For Google Cloud, the agreement strengthens its position in the race to provide infrastructure for frontier AI developers, while giving the company exposure to one of the industry’s emerging approaches to next-generation artificial intelligence.

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