August 30, 2023
No, artificial intelligence (AI) cannot predict when will the AI bubble will burst. The more financial gurus keep saying that NVIDIA's stock is in a bubble, the more the stock will continue rising. Once everybody is absolutely convinced that NVIDIA will continue rising in value forever, and that it is "impossible"for NVIDIA to go down in value, that's when the bubble will burst. We predict this will happen this year in 2023, or in the first half of 2024. Find a way of bookmarking this article so that you can hold us accountable. Just to put our money where our mouth is, we are shorting 20 stocks (approximately $10k) in NVIDIA stock since August 25, 2023. Let's see what happens.Below is some general information about NVIDIA, GPUs, and AI.
Golden State Wonders: NVIDIA and Founder Jensen Huang
NVIDIA Corporation is an American multinational technology company based in Santa Clara, California. It was founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem. NVIDIA is best known for its Graphics Processing Unit (GPU) products, which are high-performance hardware used in computers for rendering graphics. Over the years, NVIDIA's focus and product range have expanded considerably. Today, NVIDIA leads the artificial intelligence (AI) GPU sector, and has benefited incredibly from what seems to be a super stock valuation bubble.
NVIDIA Corporation is an American multinational technology company based in Santa Clara, California. It was founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem. NVIDIA is best known for its Graphics Processing Unit (GPU) products, which are high-performance hardware used in computers for rendering graphics. Over the years, NVIDIA's focus and product range have expanded considerably. Today, NVIDIA leads the artificial intelligence (AI) GPU sector, and has benefited incredibly from what seems to be a super stock valuation bubble.
Jensen Huang (born February 17, 1963) is a Taiwanese-American computer engineer and businessman best known as the co-founder, President, and CEO of NVIDIA, a leading American technology company known for its Graphics Processing Unit (GPU) products.
Huang was born in Taipei, Taiwan. In the early 1970s, his family moved to the United States. He later attended Oregon State University, where he earned his undergraduate degree in electrical engineering. He went on to achieve a master's degree in electrical engineering from Stanford University.
Huang was born in Taipei, Taiwan. In the early 1970s, his family moved to the United States. He later attended Oregon State University, where he earned his undergraduate degree in electrical engineering. He went on to achieve a master's degree in electrical engineering from Stanford University.
Forty years ago, in 1993, Jensen Huang, along with Chris Malachowsky and Curtis Priem, co-founded NVIDIA. Under Huang's leadership and legendary vision, NVIDIA initially focused on the then fairly new and rapidly expanding gaming market with their GeForce product line. Huang is known for his hands-on leadership style and deep technical knowledge. He's played a key role in guiding NVIDIA's strategic direction and its expansion into new markets to this day, when NVIDIA is leading the AI revolution.
Throughout his career, Huang has received numerous accolades. For instance, in 2017, he was named Fortune's Businessperson of the Year. He has also been recognized on Forbes' list of America's Most Innovative Leaders, among other honors.
Under Huang's leadership, NVIDIA has expanded its focus from primarily gaming GPUs to a broader range of applications, including AI, deep learning, automotive tech (self-driving cars), and more. Huang has often emphasized the role of GPUs in the future of computing, particularly in areas requiring significant computational power and parallel processing.
Jensen Huang's journey with NVIDIA exemplifies the rapid evolution of the technology sector over the past few decades, from the rise of PC gaming to the current forefront of artificial intelligence and machine learning research.
Throughout his career, Huang has received numerous accolades. For instance, in 2017, he was named Fortune's Businessperson of the Year. He has also been recognized on Forbes' list of America's Most Innovative Leaders, among other honors.
Under Huang's leadership, NVIDIA has expanded its focus from primarily gaming GPUs to a broader range of applications, including AI, deep learning, automotive tech (self-driving cars), and more. Huang has often emphasized the role of GPUs in the future of computing, particularly in areas requiring significant computational power and parallel processing.
Jensen Huang's journey with NVIDIA exemplifies the rapid evolution of the technology sector over the past few decades, from the rise of PC gaming to the current forefront of artificial intelligence and machine learning research.
GPU is the new gold
NVIDIA's primary product line for much of its history has been graphics processing units (GPUs). GPUs were originally designed for computer gaming but have become essential in many other industries, such as professional graphics workstations, design, and research.
A GPU is a specialized electronic circuit designed to accelerate the processing of images and videos to be displayed on a computer's monitor. It is a type of processor that offloads graphical tasks from the main Central Processing Unit (CPU), ensuring that the CPU can work on other tasks without getting bogged down by graphics processing.
Specialized Diversification
Over the years, the capabilities of GPUs have expanded immensely, with substantial increases in processing power, memory, and features. Modern GPUs are not only about raw power but also support advanced graphics techniques like ray tracing (simulating the way light interacts with objects to create realistic images).
NVIDIA and AMD are the two primary vendors of discrete GPUs for consumer PCs. Intel also produces integrated GPUs that are part of their CPU packages. While discrete GPUs are separate components (often on dedicated graphics cards) that are installed on the motherboard, integrated GPUs are built directly into the CPU or the motherboard itself. Integrated GPUs tend to be less powerful than discrete GPUs but are more energy-efficient and cost-effective.
Parallel processing
NVIDIA's primary product line for much of its history has been graphics processing units (GPUs). GPUs were originally designed for computer gaming but have become essential in many other industries, such as professional graphics workstations, design, and research.
A GPU is a specialized electronic circuit designed to accelerate the processing of images and videos to be displayed on a computer's monitor. It is a type of processor that offloads graphical tasks from the main Central Processing Unit (CPU), ensuring that the CPU can work on other tasks without getting bogged down by graphics processing.
GPUs are essential for tasks like rendering 3D graphics in video games, handling complex visual effects, and processing high-definition videos. They ensure smooth, high-quality visuals by rapidly manipulating and altering memory to accelerate the creation of images intended for output to a display. GPUs are foundational to many of the visual experiences users have come to expect in modern computing, from watching high-definition movies to playing graphically intensive video games. With the rise of AI and machine learning, they have also become critical components in many research and enterprise settings.
Specialized Diversification
Historically, GPUs were primarily designed for graphics rendering. However, due to their parallel processing capabilities, they have become useful for other types of computations not necessarily related to graphics. This has led to the concept of General-Purpose Graphics Processing Unit (GPGPU), where the GPU is used for general computational tasks, including physics simulations, financial modeling, and particularly, deep learning and other artificial intelligence (AI) tasks.
Over the years, the capabilities of GPUs have expanded immensely, with substantial increases in processing power, memory, and features. Modern GPUs are not only about raw power but also support advanced graphics techniques like ray tracing (simulating the way light interacts with objects to create realistic images).
NVIDIA and AMD are the two primary vendors of discrete GPUs for consumer PCs. Intel also produces integrated GPUs that are part of their CPU packages. While discrete GPUs are separate components (often on dedicated graphics cards) that are installed on the motherboard, integrated GPUs are built directly into the CPU or the motherboard itself. Integrated GPUs tend to be less powerful than discrete GPUs but are more energy-efficient and cost-effective.
Parallel processing
One of the defining characteristics of a GPU is its ability to handle multiple operations simultaneously, making it highly efficient at tasks like rendering graphics, where many pixels need to be processed at the same time. This parallel architecture allows GPUs to process thousands of threads concurrently, making them particularly effective for tasks well-suited to parallelization.
AI
AI
NVIDIA's GPUs, due to their parallel processing capabilities, are very well-suited for AI's machine learning or "deep" learning tasks. As a result, NVIDIA has positioned its products as essential tools for AI research and development. For example, NVIDIA has ventured into the autonomous vehicle industry, offering platforms like NVIDIA DRIVE, which combines deep learning, sensor fusion, and surround vision to create solutions for self-driving cars.
The AI bubble
NVDIA has been the #1 beneficiary of the AI financial bubble. As all bubbles, the NVIDIA bubble will burst. We are still in the expansion stage. Expansion will continue accelerating until an "unpredictable" event bursts the NVIDIA bubble. AI, of course, will continue rising just like the internet continued rising after the dot.com bubble.
NVDIA has been the #1 beneficiary of the AI financial bubble. As all bubbles, the NVIDIA bubble will burst. We are still in the expansion stage. Expansion will continue accelerating until an "unpredictable" event bursts the NVIDIA bubble. AI, of course, will continue rising just like the internet continued rising after the dot.com bubble.
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