Artificial Intelligence Offers Real Opportunities In Super Computing For Investors
The evolution of AI is a disruptive technology megatrend that will issue in the 4th Industrial Revolution by improving business efficiency and democratizing information.
Artificial Intelligence Offers Real Opportunities In Super Computing For Investors
Summary
- The evolution of AI is a disruptive technology megatrend that will issue in the 4th Industrial Revolution by improving business efficiency and democratizing information.
- Several high end supercomputing solutions are being developed by companies like Google, Nvidia, AMD, and Super Micro, ushering in the new world of AI adoption.
- In this review I evaluate and suggest opportunities for investors with a long-term horizon to benefit from this "iPhone moment" for AI.
Unless you have been living under a rock for the past few months, you may have heard about the hype surrounding Artificial Intelligence, especially with the recent introduction of ChatGPT. The generative AI application was developed by OpenAI, a private company backed by Microsoft (MSFT). In fact, in January Microsoft announced a multi-billion dollar investment in ChatGPT. The product was introduced as a free application for people to “try out” as part of a research phase for the company to obtain user feedback.
“We formed our partnership with OpenAI around a shared ambition to responsibly advance cutting-edge AI research and democratize AI as a new technology platform,” said Microsoft Chairman and CEO Satya Nadella in a statement.
One result of the experiment that has been observed since the introduction of the app in November 2022 was the sudden and explosive reaction to widespread use of this new AI tool by everyday folks. Until recently, many of the applications of AI have been hidden behind closed doors in secret laboratories doing research for the government or private industry and on university campuses with doctoral students working on tools and programs to make software “smarter”.
Some of the earliest work in AI began back in the 1950s with work by mathematicians including my late grandfather, Merrill Flood, PhD, whom you can read about in my biography that I published last year. He collaborated with the likes of Claude Shannon (founder of Information Theory), Johnny Von Neumann, Norbert Weiner (the father of Cybernetics), Alan Turing, and others while at Rand Corp and during his research years with MIT and the University of Michigan. Much of the early work was around “machine learning” and robotics, and the interplay between human beings and machines. In an article on Slate, the author wonders what Wiener might think about the current state of AI:
Like Alan Turing, whose Turing test suggested that computing machines could give responses to questions that were indistinguishable from human responses, Wiener was fascinated by the notion of capturing human behavior by mathematical description.
A new Dawn for Artificial Intelligence
Fast forward to the 21st century and the evolution of AI has become an integral part of the 4th Industrial Revolution which I recently wrote about. It has now become clear that the business case for AI has arrived. In this article from UBS, the importance of AI as a unique set of tools that will move businesses forward into the future are clearly elucidated. Some of the advantages are summarized in this paragraph:
The main business advantages of AI over human intelligence are its high scalability, resulting in significant cost savings. Other benefits include AI's consistency and rule-based programs, which eventually reduce errors (both omission and commission), AI's longevity coupled with continuous improvements and its ability to document processes - some of the few reasons why AI is drawing wide interest.
The generative AI applications like ChatGPT and DALL-E-2 are interesting and have brought widespread attention to the improvements that have occurred in AI; however, those are not the only types of AI applications that will be needed to create the business advantages that UBS is suggesting. Self-learning systems that integrate data mining, pattern recognition, natural language processing, and predictive analytics are examples of the types of applications that will likely be beneficial to businesses and are still in early stages of development.
Google AI
Another experiment in bringing AI to the masses is Bard from Google (GOOG). Google has more aspirational goals for Bard that are similar to what ChatGPT offers. That is, more of a tool for widespread use by ordinary folks rather than business tools to help streamline and automate business processes. This is what Google says about Bard:
We have long seen the potential of AI to make information and computing more accessible and useful to people. As part of this journey, we have made pioneering advancements on large language models (LLMs) and have seen great progress across Google and in this field more broadly. For several years, we have applied LLMs in the background to improve many of our products, such as autocompleting sentences and helping us in Google Search. Now, we are using LLMs to power Bard, an experiment that allows people to collaborate directly with generative AI.
Supercomputing and AI
The forays into AI by Google do not stop with Bard though. In fact, Google recently announced the development of a new supercomputer that competes with Nvidia (NVDA) for AI applications, which currently dominates the AI supercomputing market. According to this news story about the new supercomputer offering, Google has been tinkering with Tensor Processing Units or TPUs for AI since 2016. The new Google supercomputer is based on TPU v4, an optically reconfigurable supercomputer for machine learning with hardware support for embeddings.
In response to innovations in machine learning [ML] models, production workloads changed radically and rapidly. TPU v4 is the fifth Google domain specific architecture (DSA) and its third supercomputer for such ML models. Optical circuit switches (OCSes) dynamically reconfigure its interconnect topology to improve scale, availability, utilization, modularity, deployment, security, power, and performance.
Nvidia – AI Chip Market Leader
Nvidia is the dominant force in AI supercomputing with its workhorse A100 chip. The A100 chip is a GPU (graphics processing unit) that was initially developed for 3D rendering for gaming applications but can be configured to run inside data centers to support the massive computations required for machine learning. The newest H100 chip built on the Hopper architecture offers 30x the performance of the A100 for AI and HPC (high performance computing) applications.
At their recent GTC 2023 Developer conference, Nvidia CEO Jensen Huang remarked that this is the “iPhone moment for AI” as the company unveiled several new partnerships and products including a Quantum computing platform for AI. They also disclosed new partnerships with Medtronic and AT&T, as well as new agreements with Microsoft and Oracle related to data center acceleration.
Morgan Stanley recently upgraded NVDA to Overweight based on the AI megatrend.
The rise of AI applications and the hype surrounding it has been good for NVDA. By some estimates, NVDA has captured 90% or more of the AI supercomputing market. On the Q422 earnings call, CFO Colette Kress remarked that,
“Generative large language models with over 100 billion parameters are the most advanced neural networks in today's world. NVIDIA's expertise spans across the AI supercomputers, algorithms, data processing and training methods that can bring these capabilities to enterprise. We look forward to helping customers with generative AI opportunities.”
NVDA stock has surged over 80% in the first quarter of this year, so the stock may not be a good buy at the current price but it is clearly benefitting from the AI revolution that is occurring.
AMD – Chasing the AI Trend
Another horse in the AI race for supercomputing hardware is Advanced Micro Devices (AMD). While AMD has been making advances in data centers and HPC applications including AI, they have a bit of catching up to do relative to NVDA. I wrote about the new MI300 chip from AMD when I last covered them in January. With the acquisitions of Xilinx and Pensando, AMD has been able to make advances in high end data center and HPC applications by merging CPU, GPU, and FPGA with 3D stacking technologies, resulting in energy efficient, high-performance chipsets like the MI300. According to a review from Tom’s hardware, the MI300 is a monster device:
Make no mistake, the Instinct MI300 is a game-changing design - the data center APU blends a total of 13 chiplets, many of them 3D-stacked, to create a chip with twenty-four Zen 4 CPU cores fused with a CDNA 3 graphics engine and 8 stacks of HBM3. Overall the chip weighs in with 146 billion transistors, making it the largest chip AMD has pressed into production.
That chip is expected to be available in mid-2023 so it remains to be seen how much market share that AMD will be able to capture once it hits the market. The outlook for growth in data center and embedded systems are the primary focus for AMD going forward and will likely result in continued market share improvement, based on comments made by CEO Lisa Su during a discussion on Q422 earnings when she stated that AMD is "in a good position to gain share" in the embedded data center market, even with what she said were expected to be "elevated levels of inventory" in the first half of the year that should improve after summer and toward the end of 2023.
AMD has also seen its share price rise over 40% since the start of 2023, yet still represents a better long-term value in my opinion than NVDA with a forward P/E of 30 versus NVDA FWD P/E of about 60.
Super Micro Computer – Putting the AI Hardware Together
Perhaps the best value and possibly one of the biggest longshots in the horse race for supercomputing market share is Super Micro Computer (SMCI). With nearly 60% YOY revenue growth, SMCI is an industry leader in integrated systems that rely on chipsets from AMD, NVDA, and other vendors to assemble fully integrated systems for HPC, AI, ML, and data center applications. Recognizing the evolving trend and building upon the recent AI hype, SMCI recently announced a whole new AI platform built on the NVDA AI Enterprise solution.
"This exciting new GPU system will also have a completely built-in liquid cooling system, allowing leading-edge CPUs and GPUs to run at maximum performance without additional infrastructure costs," the company said.
In January, SMCI was rated the #1 stock pick for 2023 by Steven Cress of SA Quant fame. Shortly after that, the company was the target of a short report that clobbered the share price. On January 10, SA analyst Jeremy Blum wrote a rebuttal to the Spruce Point capital short report that outlined the reasons why SMCI was still a screaming buy at a price below $80 and the stock has risen by more than 25% since then.
As I am writing this article on April 6, 2023, the share price of SMCI dropped by nearly 10% today at market open to below $100, offering savvy investors another opportunity to get in while the price is still reasonable. Not everyone agrees that SMCI is a strong buy with one Wall Street analyst from Wedbush downgrading the stock to Neutral. At the current share price, SMCI trades at a low FWD P/E of about 10, with strong revenue growth and excellent Quant factor grades across the board.
Summary and Conclusion
In this article, I focused on the hardware side of supercomputing applications for AI versus the software side. There are also many companies focused more on software applications that employ AI to help businesses to compete at the enterprise level and in a future article I intend to highlight some of those companies.
As the UBS article from 2016 predicted, AI is “coming of age” now in the 2020s. Back then the expectation was for about 20% CAGR in revenues for the AI industry from 2016 to 2020.
The rise of AI software applications will rapidly advance with new and innovative solutions in multiple industries as computing power gets stronger, faster, cheaper, and more integrated. We are already seeing this in 2023 and the growth in AI applications is just beginning to unfold.
Software companies will take up the mantle and charge ahead, pushing the boundaries of automation, search and social media. Dubbed a machine's brain, AI will likely power automation in sectors like autonomous vehicles and unmanned drones. And AI software will create significant business opportunities and societal value.
For example, virtual assistants or chatbots will offer expert assistance; smart robots or robot advisors in the fields on finance, insurance, legal, media and journalism will provide instantaneous research or findings; and within the healthcare field, AI software will assist with medical diagnosis and assistance. Other benefits include significantly improving efficiencies in R&D projects by reducing time to market, optimizing transport and supply chain networks, and improving governance by better decision-making processes.
The growing field of AI for enterprise business applications is still young and evolving and is not just some passing fad that will go away after the luster wears off. As more businesses recognize the inherent value that AI applications can offer, the growth will continue and mature leading to significant long-term opportunities for investors who can identify the leaders in this emerging industry.
This article was written by
Analyst’s Disclosure: I/we have a beneficial long position in the shares of SMCI, AMD either through stock ownership, options, or other derivatives. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.
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