The so-called NASDAQ Seven Sisters include Apple, Microsoft, Meta, Amazon, Alphabet, NVIDIA, and Tesla. These are the world's top technology companies. In the series on the NASDAQ Seven Sisters, the author will introduce the development history and core competitiveness of these companies, as well as some of the problems and challenges they face.

The first article in the series will introduce NVIDIA, the new top star in the current technology circle.

Two years ago, NVIDIA mainly earned money by selling graphics cards. Although it was also a household name, it was limited to the most loyal PC gamers.

Today, this chip design company has become one of the most valuable companies in the world, a major beneficiary of the AI boom, and an embodiment of the new technology economy that is expected to dominate the next decade.

01. Status in the Industry: From Graphics Card Player to New King in the AI Era

On June 18th of this year, NVIDIA surpassed Apple and Microsoft to become the company with the highest market value in the world, with a market value of $3.34 trillion. Although the market value has since declined, the moment of reaching the top has become a highlight for the company.

Looking back at NVIDIA's market value growth trajectory, its development speed is indeed remarkable. It took the company 24 years from its IPO to reach a market value of $1 trillion; and it only took 10 months for its market value to increase from $1 trillion to $2 trillion; even more astonishingly, the leap from $2 trillion to $3 trillion took only 3 months. Behind this series of rapid growth figures are the strong market demand for NVIDIA's high-end chips and the company's forward-looking strategic layout in the AI era.

As we all know, NVIDIA started as a game graphics card company. So how did it evolve from a graphics card player to a king in the AI era?

First, let's go back to 1999, which was an extremely special year for NVIDIA. In this year, NVIDIA not only successfully went public, but also released the world's first GPU (Graphics Processing Unit, i.e., "Graphics Processor") in August - the landmark GeForce 256.

The emergence of GeForce 256 directly changed the competitive landscape in the industry. Because before that, tasks that could only be completed with high-end CPUs and graphics cards could now be done with cheaper CPUs and GeForce 256, and with better smoothness.

NVIDIA accomplished something revolutionary by doing the same thing with less money and more efficiency. Thanks to this outstanding performance, NVIDIA successfully secured an order for the graphics processor of Microsoft's first XBOX game console in March 2000.

But who knew that NVIDIA got too big for its britches at that time, and the high price quote caused a rift between the two companies. Microsoft turned around and gave the order to rival ATI, causing NVIDIA's share price to plummet to $2.64.

Through this incident, Huang Renxun realized that they must create their own differentiated competitive advantage. In 2000, NVIDIA introduced Huang's Law, which states that while Moore's Law in the industry dictates that new products are introduced every 18 months with doubled performance, NVIDIA would invest three times the manpower to do the same thing and introduce new products every six months. Compete with speed.

02. GPU Became the Natural Shovel for AI

If NVIDIA solidified its position in graphics cards by outperforming its peers with Huang's Law, its rise to dominance in AI chips was more a matter of luck and the favor of the times.

In fact, many classic deep neural network architectures have been proposed as early as the second half of the 20th century, but due to the lack of computing hardware to train them, many studies could only be theoretical, and development stagnated for a long time.

GPU, when it was first introduced, never imagined that it would be so well-suited to the computational needs of AI. From the very beginning, GPU was not designed for training neural networks, but for graphics. More specifically, it was created to free the CPU from the drudgery of graphics display.

The turning point came in 2012. That year, Andrew Ng, a leading authority on global artificial intelligence and machine learning and a Chinese scientist, led Google Brain in successfully identifying a cat from among 10 million images, shocking the industry.

But behind this result was an investment of $1 million, involving 1,000 computers and 16,000 CPUs, which led him to consider whether there was a faster and cheaper way.

He thought of NVIDIA. Four years earlier, he had pioneered the use of NVIDIA's graphics processing chips (GPUs) instead of Intel's CPUs to build a deep learning model. This time, he wanted to take another gamble. The result was surprising: he accomplished the same thing with just 16 computers and 64 GPUs.

03. Unique Skill: Computing Power Chips and CUDA Dominate the Industry

Meanwhile, looking around, there are currently no companies in the entire chip industry that can truly challenge NVIDIA. Players in other industries have their own troubles.

For example, while Intel's CPUs still have an advantage in traditional computing, they lag far behind NVIDIA in AI computing. Samsung Electronics, on the other hand, relies primarily on its memory chip business, but as the memory chip market becomes saturated and competition intensifies, its revenue has been severely affected.

Even AMD struggles to compete with NVIDIA's computing power and software advantages. And NVIDIA's competitive advantage is not limited to its hardware performance, but also lies in its deep technical ecosystem. The company pioneered the CUDA (Compute Unified Device Architecture) architecture, a programming language and platform designed specifically for GPUs, which enables efficient programming and optimization for workloads that support AI applications. The widespread adoption and application of CUDA has subtly deepened the industry's reliance on NVIDIA's hardware ecosystem.

04. Weaknesses and Vulnerabilities: NVIDIA Cannot Afford to Make Mistakes

But that doesn't mean NVIDIA's future is worry-free. First of all, all highly profitable industries attract many players, and NVIDIA must continue to be correct in its future innovation directions and technology paths to maintain its unique advantages.

From an external perspective, the key to NVIDIA's continued success lies in its ability to work with technology giants such as Microsoft and Google to tap into the unlimited commercial potential of AI and help these giants turn their huge investments in high-performance chips into tangible business value.

And as NVIDIA's market position becomes increasingly prominent, antitrust regulators are also focusing their attention on whether NVIDIA is using its market dominance to create barriers that prevent customers from switching to other suppliers.

It is also worth noting that on August 29th, NVIDIA released disappointing sales performance forecasts, which immediately led to its largest one-day drop in share price in four weeks, sparking a strong market reaction.

Therefore, for NVIDIA at this time, the cost of making mistakes has become increasingly high. Under the scrutiny of the market, industry, and regulators, whether NVIDIA can continue its growth myth is the biggest concern for the future of this new top star in the technology circle.