Nvidia is about to become a trillion dollar company thanks to AI chips

The AI ​​craze helped Nvidia's stock surge 25% on May 25th, pushing the company's market capitalization to approximately $950 billion. Prior to that, Nvidia's market capitalization was only $755 billion on May 24th. According to CNBC, if expectations are met, the chip giant will become the fifth US company to reach a $1 trillion valuation.

Nvidia is changing the way it builds computers to further increase profitability. Huang Jensen stated that components used to build data centers could create a market worth $1,000.

The most important component in computers and servers is the central processing unit (CPU). That market is dominated by Nvidia's rivals, Intel and AMD. But with the advent of AI applications that demand powerful computing capabilities, Nvidia now dominates the GPU market.

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Huang said that data centers previously relied heavily on CPUs for file retrieval, but in the future, they will be retrieving general data. Instead of retrieving data, you will be retrieving a portion of the data, but generating most of the data using AI. So, instead of using millions of CPUs, you will need far fewer, but they will be connected to millions of GPUs, the Nvidia CEO added.

That's one reason why Nvidia's data center business grew 14% in Q1 2023. Meanwhile, Intel's data center and AI segment saw a 39% drop in revenue to $3.7 billion, while AMD's growth remained unchanged.

Furthermore, Nvidia's GPUs tend to be significantly more expensive than central processing units. Intel's latest generation of Xeon CPUs can cost up to $17,000 at list price. An Nvidia H100 chip can even be resold for $40,000 on platforms like eBay.

Nvidia faces increasing competition as the AI ​​market heats up. Its two major rivals, AMD and Intel, both have their own GPU lines. In addition, tech giants like Google and Amazon are also designing AI chips. However, Nvidia's high-end GPUs remain the top choice for AI training. Analysts believe Nvidia continues to lead in AI chips thanks to its proprietary software, which facilitates AI applications.

Mr. Huang shared that the company's software would not be easily copied because you have to design all the software, all the libraries, all the algorithms, integrate them, and optimize the frameworks and optimize it for the architecture—not just a single chip, but the architecture of the entire data center.

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