Semiconductors are the backbone of technological innovation, powering everything from smartphones and laptops to servers and supercomputers. This sector is expected to become a trillion-dollar industry by 2030. McKinsey predicts that about 70% of growth will be driven by the automotive (particularly electric vehicles or EVs), data storage, and wireless industries.
We are positive about the impact of AI on the semiconductor market and how it will affect government and enterprise supply chain resilience.
Key technologies behind surge in AI
AI is particularly resource-intensive, requiring vast amounts of data and processing power to create new content. Processing semiconductors naturally seem key beneficiaries of AI, but large language models (LLMs) also require adequate memory capacity and bandwidth. Memory, computing and storage semiconductors account for the majority of semiconductor sales. Memory (DRAM) and storage (NAND Flash) chips are primarily used for storing data and instructions, while processing chips (such as the core CPU in a computer or a complementary accelerator chip like a graphic processing unit or GPU) are used for performing calculations and processing data in real time.
With a historical 10% CAGR over the last 10 years, the memory semiconductor market, accounting for 26% of semiconductors’ revenues, is expanding as a result of rising smartphone usage, increased digitalisation, and rising semiconductor usage across numerous sectors, including the automotive and IT industries.
However, the development of AI models, particularly LLMs, require substantially higher levels of memory and storage given the size of the datasets. Indeed, the amount of memory and the speed at which memory is accessed by the processing chips is becoming one of the key bottlenecks in AI model training. The deployment of new memory technologies in semiconductors help optimise this process by speeding up data transfer, improving the performance and increasing energy efficiency.
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For example, an American semiconductor manufacturing company is at the forefront of these memory technologies. Its memory semiconductors are used in everything from computing, networking, and server applications, to mobile, embedded, consumer, automotive, and industrial designs. It has been leading the industry in the move to higher-capacity and higher-speed memory chips and should benefit as AI drives further demand for memory used in servers.
The market for logic processors, accounting for 42% of semiconductors’ revenues, also continues to rise, with technological advancements driving increased productivity, lower power consumption, higher reliability and quality.
This, in turn, continues to improve the overall price/performance trend for processor chips (a trend also known as Moore’s Law), enabling higher consumption of electronic devices overall. Not only is AI a new application enabled by the improvements in logic processors, it is now also a driver of incremental demand for the servers which are processing the AI algorithms, and for the end-devices where the interaction with the AI models occur. Such devices could range from a traditional desktop PC or a smartphone to an embedded device like a robotic arm or a self-driving car.
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For example, a fabless semiconductor chip designer has been one of the main beneficiaries of the growth in computational requirements to power AI algorithms. Its high-end data-centre accelerator chips are optimised for high-speed parallel processing required in AI training, and these chips are now accounting for over half of its revenues.
Semiconductors’ competitive landscape: Few winners taking it all?
Historically, semiconductor companies have benefited from increased consolidation. This trend is driven by factors including: economies of scale and pooling of resources to tackle rising costs of resource-intensive R&D, design and manufacturing, huge demand for ever greater connectivity (e.g. 5G, Internet of Things), ensuring supply chain security, expansion of product portfolios beyond microchips (e.g. software products), growing demand for custom chips and foundries, acquiring highly specialised talent, as well as demand for cloud and data centre management technologies.
For example, a US$38.5 billion acquisition in 2020 aimed to create the industry’s high-performance and adaptive computing leader. The same year, an acquisition by a leader in high-speed data created a US$40 billion company to position it for opportunities in cloud and 5G.
With fewer semiconductor suppliers and designers in the industry, and even fewer leading-edge manufacturers, intrinsic knowledge and highly specialised equipment are key. Some US firms dominate the international AI chip design market, while South Korea and Taiwan’s semiconductor manufacturing leaders remain the titans of global semiconductor fabrication.
For instance, the world’s leading provider of semiconductor manufacturing services in Taiwan has over 50% share of the overall foundry market and more than 90% market share in advanced process technologies used for AI and high-performance computing. It should benefit from AI leaders’ increased demand as it manufactures all chips for key designers and manufacturers of GPU and graphic technologies, and customised AI processors such as the tensorflow processing units (TPUs).
AI increases demand for faster and more efficient computing. It pushes the development of cutting-edge technologies, as well as the related production and design capabilities, while creating higher barriers to entry.
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Semiconductor equipment makers and software companies that enable semiconductor manufacturing should also benefit from the rise of AI.
AI trends also drive demand for semiconductor equipment companies. They provide the chipmaking systems that produce smaller, faster, cheaper, more powerful and energy-efficient microchips. For example, a dominant supplier of lithography equipment to the global semiconductor industry is essential for the fabrication of semiconductor chips. Its unique position in the semiconductor value chain allows it to directly capture the growth of new leading-edge semiconductor applications in high-performance/AI computing. It is the only provider of systems using extreme ultraviolet (EUV) light, which is required to make the most leading-edge microchips used in 5G, AI, and other high-performance computing applications.
The design of advanced integrated circuits would also not be possible without the assistance of computer-aided design software, more specifically electronic design automation (EDA) technologies. For example, the complexity of designing leading-edge microchips to enable AI applications should require an increasing use of software tools which support and automate the design and verification (simulation) of microchips, as well as perform integrity and quality testing. These tools themselves are also AI-enabled, allowing chip designers to reduce the time it takes to design a chip from weeks to days.
Addressing global demand in a multipolar world
Semiconductors are the most important sector in terms of share in global manufacturing. As a critical component of modern computing and due to the complexity of its supply chain, semiconductors have been the subject of intense geopolitical competition. Taiwan and South Korea are the only suppliers of cutting-edge chips, supplying 90% and 10% respectively of the most advanced semiconductors. The US maintains technological supremacy when it comes to the design of the chips, whereas China remains critical to the supply chain (38% of semiconductors’ assembly, packaging, and testing).
Government and industry stakeholders worldwide are making significant efforts to bolster their positions within the global supply chain through subsidies. In Asia, Japan has decided to provide around US$3 billion ($4 billion) a year to fund new subsidies for semiconductor manufacturing, while South Korea announced a US$250 billion funding plan for semiconductors.
In the West, the US Chips Act is driving US$50+ billion in investments in the semiconductor sector (with 80% being manufacturing incentives), while the EU Chips Act represents a US$40+ billion targeted support to increase production capacity.
These subsidies will likely take several years to materialise, assuming cost differences to manufacturing in Taiwan can be solved.
With the strategic re-shoring of semiconductor manufacturing, there is a sustained tailwind for spending on equipment through the remainder of this decade. Indeed, the world’s top three semiconductor manufacturers have already announced plans to invest more than US$300 billion in global capacity through 2030.
Finally, supply and demand mismatches for semiconductors have generated production headaches across industries, with about 75% of all shortage-driven demand involving integrated circuits and discrete semiconductors. Forward-looking companies are using AI to increase supply chain reliance, which gives them near-real-time insights into pricing and demand fluctuations.
Anjali Bastianpillai is senior client portfolio manager, thematic equities at Pictet Asset Management.
