Nvidia Stock Hits Record High Amid AI Demand Surge
AInvestMonday, Oct 14, 2024 6:00 pm ET
1min read
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Nvidia's stock closed at a record high on Monday, marking the first time in approximately four months. Investors remain optimistic about the long-term demand for artificial intelligence technologies. Shares rose 2.4% to $138.07, surpassing the previous record set in June. Since its low in August, the stock has rebounded by 40%, boasting a 179% increase year-to-date.

This upward momentum has contributed over $20 billion to Nvidia's market capitalization in 2024. The company's robust performance is partly attributed to CEO Jensen Huang's reassurances regarding the demand for the Blackwell chip, easing investor concerns about potential delays due to engineering issues.

Over the past month, Nvidia’s stock has seen a 25% surge, approaching historical highs. With major clients like Meta, Microsoft, and Alphabet poised to release financial results soon, Nvidia's stakeholders eagerly await updates on AI investment forecasts. The recent spike in stock value reflects widespread confidence in Nvidia amid the booming AI landscape.

Nvidia has emerged as a significant beneficiary of the AI boom, with companies like Meta, OpenAI, Alphabet, Microsoft, and Oracle heavily investing in technologies and products reliant on its GPUs. In August, Nvidia reported a remarkable 122% year-over-year increase in quarterly revenue, with net profits doubling to $166 billion.

The company provided optimistic expectations for the third quarter and announced that the Blackwell AI chip’s shipments could reach several billion dollars. As demand remains high, Nvidia anticipates continued growth in sales of the Hopper architecture chips over the next two quarters.

Jensen Huang highlighted the "frenzied" demand for the Blackwell chip, revealing that each GPU costs between $30,000 to $40,000 to produce, with production capacity set to rise in the fourth quarter and sustain into fiscal year 2026. Market experts note that Nvidia maintains a dominant position in the AI training and inference chip sector, although potential risks like export restrictions and reduced spending on AI servers loom.

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