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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.

Meta Unveils 816 Billion-Parameter AI Model For Mac, But It Requires 1TB Of RAM

Meta has introduced what it describes as the most capable open-weight AI model available in the United States, making a strategic bet on Apple’s Mac ecosystem. The release reflects the company’s ambition to strengthen its position in open AI while bringing advanced models into professional development and creative workflows.

A Powerful Model With High Hardware Requirements

Called Muse Spark Open, the model has 816 billion parameters and a context window of 1 million tokens. Meta said benchmark results published on Thursday show it outperforming other widely used open-weight systems across key evaluations.

Natively multimodal, Muse Spark Open is designed for complex coding and agentic tasks, extending beyond conventional chatbot use. However, local deployment requires substantial computing power. According to Engadget, the model needs at least 1TB of RAM, with the best performance expected on a Mac Studio equipped with Apple’s M5 Ultra chip. These requirements may limit local use to developers and organisations with access to high-end hardware.

Built For Real-World Development

Meta has paired Muse Spark Open with Muse Code, its AI coding terminal, and tested it across agentic workflows. In one project, the model helped develop a Mastodon client, handling bug fixes, new features and the build process.

The company also deployed the model in DaVinci Resolve, the professional video-editing platform, where it reviewed and cropped footage. These examples demonstrate Meta’s aim to position open-weight AI as a tool for practical production work, from software development to creative tasks, rather than just research demonstrations.

The Strategic Case For Open-Weight AI

Open-weight models give developers and organisations greater flexibility to customise AI, integrate it into internal tools and build specialised workflows. Depending on deployment, they can also reduce costs and provide more control over infrastructure than relying entirely on proprietary services.

However, access to model weights does not eliminate the cost of running advanced AI. Muse Spark Open’s reported memory requirements could make API access a more practical option for many developers than local deployment.

Expanding Access Beyond Mac Hardware

Meta is making Muse Spark Open available through multiple API platforms, allowing developers to use the model without running it on their own machines. This broadens access beyond users with high-end Mac hardware while retaining the option of local deployment for those able to support it.

The release combines Meta’s push to strengthen its position in the open-weight AI ecosystem with a focus on professional users. Wider adoption will depend on the model’s performance, practical usefulness and the resources required to run it.

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