There’s nothing artificial about NVIDIA’s relationship with AI

 

 

NVIDIA NASDAQ: NVDA shares have skyrocketed over 220% so far this year, mainly thanks to increased interest in artificial intelligence (AI), causing demand for its chips to soar. Riding the wave of AI stocks excitement, NVIDIA breached $1 trillion in market capitalization at the end of May, making it the first U.S. chipmaker to join the trillion-dollar club and has stated that its sales will likely rise by 170% in the third quarter, reaching a valuation of $16 billion.

Despite fears of market saturation and the potential of strict regulations limiting growth and profitability, interest in AI and its future applications shows no signs of slowing down – and, as such, neither does the profitability of NVIDIA.

AI Potential: The Sky’s the Limit

No longer just a theoretical concept, AI is now being applied across various industries and sectors. Due to its seemingly infinite capabilities, the technology has been well-funded by governments, venture capital firms, and large corporations and is supported by a global community of researchers, developers, and innovators committed to pushing it to its limits. Breakthroughs in natural language processing, computer vision, and reinforcement learning are opening up new possibilities for AI every day.

It also has the potential to impact some of society's most pressing challenges positively; for example, it could improve healthcare accessibility by advancing diagnostic accuracy, drug discovery, personalized medicine, and remote patient monitoring.

NVIDIA originally saw the potential of AI a decade ago when it helped power the deep learning “revolution” and shows no intention of relinquishing its stronghold on the technology. NVIDIA strategically partnered with major players in the tech industry and made calculated acquisitions to bolster its AI capabilities.

Notable examples include the procurement of Mellanox Technologies to enhance data center capabilities and ARM Holdings to expand its presence in mobile and IoT markets.

NVIDIA’s GPU Dominance Reigns

Advances in computing power, particularly in graphics processing units (GPUs) and specialized AI chips, are enabling the training and deployment of more complex and capable AI models. Anticipating this industry need, NVIDIA invested heavily in developing dedicated hardware, like Tensor Cores, which significantly accelerates AI computations and is crucial for handling the matrix operations of AI.

In return, NVIDIA’s data center segment has shown impressive growth through the first two quarters of 2023, responsible for $14.6 billion of the company’s total revenue of $20.7 billion and accounts for just over 70% of Nvidia's top line. It is estimated that OpenAI reportedly used 10,000 NVIDIA GPUs to train ChatGPT.

According to John Peddie Research, NVIDIA currently holds about 88% of the GPU market and, therefore, is uniquely positioned to benefit from the ongoing increased demand for more powerful GPUs. Earlier this year, Citi estimated that ChatGPT usage alone could result in $3-11 billion in sales for NVIDIA in 2023.

“It is not just a GPU computing company, it’s basically an AI supercomputing company,” Gartner analyst Chirag Dekate explains. “NVIDIA has had complete freedom of the AI landscape. They have taken advantage of really sharp, business-savvy investments and focused approaches that basically enable them to dominate their market.”

It doesn’t look like that will change anytime soon as competitors Intel and AMD both suffered drops in stock values - 4% and 7%, respectively, per their last earning’s report - as they struggle to meet demand for AI chips.

Powerful Chips are Essential for AI; AI is Essential for Data Processing

The explosion of digital devices and platforms has resulted in an unprecedented amount of data being generated every second, and AI is needed for tasks like image and speech recognition, natural language processing, and recommendation systems. AI also allows for highly personalized experiences in marketing, e-commerce, content recommendation, and healthcare, which, in turn, enhances user satisfaction and drives engagement.

And it is AI's ability to process and extract valuable insights from this information that is an invaluable resource for businesses and industries looking to make data-driven decisions.

Processing AI algorithms on edge devices (such as smartphones, IoT devices, drones, and autonomous vehicles) rather than relying solely on cloud-based computing creates new opportunities for real-time, low-latency AI applications.

The development of these technologies is poised to revolutionize industries like transportation, logistics, and manufacturing. NVIDIA’s Jetson platform and AGX series are specifically designed for edge computing, making them well-positioned to capitalize on this expanding market.

But Can NVIDIA Sustain This Level of Success?

It appears so. According to 44 analysts who cover the stock, NVIDIA has a 12-month median price target of $623, which indicates a 42% jump from current levels. The company’s strong outlook through next year, coupled with the disappointing earnings and delayed chip production of its competitors, suggest that it will remain the dominant producer of the high-powered chips needed to build and run large AI models for the foreseeable future.

In addition, the dynamic and rapidly evolving nature of AI and its broad applicability across industries means that the enthusiasm and funding for this transformative technology isn’t going away any time soon. As such, the potential of AI to revolutionize various aspects of our lives is only set to increase.

Because NVIDIA recognizes this, it has shaped its long-term vision to ensure that they are well-prepared to meet evolving market demands. It wouldn't be surprising if NVIDIA delivers even better results next fiscal year. In 2024, the company expects to at least triple its shipments of H100 processors, and The Financial Times estimates that number could actually be up to 2 million units. While some investors worry the uptick from AI has already peaked, NVIDIA knows it has only just begun.