When AI chips fight, your portfolio makes money
The artificial intelligence (AI) revolution is here. And international companies are aligning their strategies. Almost all big tech names have started designing their own chips. There are also a few smaller and profitable companies listed that could make for interesting stock picks.
According to reports from McKinsey and PwC, the AI revolution could have an annual impact of $13-15 trillion on global GDP by 2030. Well, some of these numbers aren’t the latest estimates, but they give us a good idea of where we’re headed. Including recent developments in AI could significantly increase these estimates. For those looking to invest internationally in the US tech industry and global chipmakers, here is an interesting investment idea.
We believe that the state of the art in current AI is so advanced that companies have attempted to hide or downplay the capabilities to avoid scaremongering by the media, hysterical reactions from the general population and calls for strict and very restrictive regulations the policy on the use of data and AI techniques.
Either way, the AI revolution is here, as ChatGPT strongly demonstrates. In a previous article, we discussed the threats and opportunities that ChatGPT is a harbinger of from an investment perspective.
If AI is related to the human mind, which thinks and generates insights, then AI chips are similar to the human brain, which stores and processes sensory input. The processing of the new sensory inputs and stored inputs as well as thoughts by the human brain leads to new cognitions by the human mind. AI chips also store sensory inputs in the memory chips and process those inputs to generate insights.
In this article, we focus on some of the key companies in the AI chip design and manufacture ecosystem and potential investment opportunities. Of course, the mention of company names should not be taken as a recommendation to buy their stock. While a company can play an important role in the ecosystem, an investment decision needs to be far more sophisticated than buying the best company. It should consider the strength of its resources including monetary, human and intangible assets, its competitive advantages and market price versus intrinsic value. We have discussed the science investment framework in several other articles.
Why artificial intelligence will shake up technology
AI requires large amounts of memory and fast storage and retrieval, and this brings with it the need for specialized memory chips designed for AI applications. It typically requires around five times more bandwidth and can be three times more expensive. Micron, SK Hynix and Samsung are some of the companies focusing on these memory chips.
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The more interesting battle, however, takes place with the AI-focused processor chips. While traditional CPUs can be used for AI computing, they are much slower and inefficient. Chips for AI include graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs).
The typical AI process requires training in the first step, in which large amounts of data are fed into the AI engine and a model is created from it. Now this model can be provided with new data and it will draw conclusions from it and share them with the user. The system requirements for training are significantly more intense and this is where the GPU chips have an advantage. FPGA chips are better suited for inference. ASICs can be used for both.
Mapping of the companies with their strategies
One of the top names in AI chips is NVIDIA. However, there is competition from Intel, AMD, Qualcomm and many others. Interestingly, almost all of the big tech names have started designing their own chips. For example, Google’s TPUs (Tensor Processing Units) are ASICs designed to solve matrix and vector operations for deep learning neural network models.
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Amazon has been developing chips for nearly a decade and made the pivotal acquisition of Annapurna Labs in 2015. Currently, its two chips – Trainium and AWS Inferentia – are obviously designed for training and inference, respectively. The Graviton series is another powerful AWS chip.
Then there’s the AIU (Artificial Intelligence Unit), another ASIC, from one of the pioneering companies in AI, IBM. This is the latest development of their AI chip Telum. IBM’s AI Hardware Center was founded in 2019 with a goal to train and run AI models 1,000x faster by 2029.
Microsoft’s Project Brainwave also focuses on specialized FPGA-based AI chips. Microsoft recently acquired a chip design company called Fungible.
Apple has been working on its own chips called Apple Silicon for quite some time. These chips are used in all of their own hardware, from the iPhone to the iPad, Mac, etc. Apple has a specialized AI chip called ANE (Apple Neural Engine). ANE is an NPU (Neural Processing Unit) designed to speed up matrix operations and convolutions required for neural network operations.
While Meta Platforms (Facebook) has also dabbled in designing its own chips, for the near future it has partnered with Qualcomm and Broadcom to develop custom chips for its AI, VR and Metaverse needs.
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Although we haven’t detailed NVIDIA’s GPUs, they are currently the best-selling chips for AI. Intel, AMD and Qualcomm are the other major mainstream players. These are the traditional, well-known semiconductor chip suppliers, and so we wanted to rather highlight the other efforts being made alongside these in the AI battle.
What should investors do?
There are also numerous specialized AI companies, but many of them may be unlisted, undersized, or not yet turning a profit. But they offer a glimpse of what is to come.
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Investors looking to invest in the U.S. tech sector — and even some of the other global chipmakers — must use this as a starting point to delve deep into this highly interesting AI chip battle. This is just the beginning. Going forward, this room will discuss many other exciting aspects of the growth vector of the AI revolution.
Disclaimer: Please note that the mention of company names does not constitute a recommendation to buy, sell or hold. Investments are subject to market risks. Past performance is no guarantee of future performance. Global investing comes with additional risks. One should invest based on the advice of their financial advisor based on their investment objectives, financial situation and risk profile. OmniScience Capital, its management and employees, and its customers may buy, sell or hold any of the named companies.