With the rapid advancement of AI technology, new demands are emerging across various industries, presenting both opportunities and challenges. At the core of this transformation is AI computing, which is driving growth in four key areas within the semiconductor industry: ASICs, silicon intellectual property (IP), memory modules, and sensors.
In recent years, AI systems have evolved rapidly, shifting the competition from software development to hardware innovation. The rise of network technologies has led to an explosion of big data, enabling AI to process complex real-world scenarios with speed and precision. Fubon Securities highlights that the semiconductor supply chain is benefiting significantly from AI-driven demand, particularly in the fields of ASICs, IP, memory modules, and sensors.
As AI continues to advance in machine learning, Fubon Securities forecasts that the AI application market will grow at a compound annual growth rate (CAGR) of 38% through 2025, reaching $230 billion. Meanwhile, the value of semiconductor chips used in AI applications is expected to surge from around $900 million to over $70 billion by 2025, growing at a CAGR of 62%. Among these, memory solutions for vehicle use, deep learning, voice recognition, and other applications are expected to see the highest demand.
Edge computing is also gaining momentum, increasing the need for specialized chips like ASICs. Fubon Securities notes that during the learning process, machines rely on "processing chips" to handle data computation and function derivation. This makes chip performance a critical factor for future manufacturers. These chips can be categorized into CPUs, GPUs, FPGAs, and ASICs, each suited for different applications such as cloud computing or edge computing.
Cloud computing is well-suited for large-scale data processing and long-term operations, requiring high power consumption and strong performance—commonly used in data centers and supercomputers. Major players like Google, Nvidia, and Intel are heavily investing in this space. On the other hand, edge computing focuses on terminal devices, where power efficiency and compact size are crucial. FPGAs and ASICs are becoming the preferred choices for edge-based AI applications.
Fubon Securities predicts that AI chip shipments will exceed 40 million units by 2025, with ASICs leading the way, accounting for nearly 60% of total shipments—around 24 million units. Revenue from AI chips is expected to grow from over $10 million in 2016 to $33.4 billion by 2025. ASICs offer advantages such as low latency, low power consumption, and high performance, making them essential for AI applications like smart surveillance, autonomous vehicles, robotics, drones, and virtual reality.
While ASICs are rising, GPUs remain a strong player due to their inherent performance advantages in AI processing. With the need to handle massive and real-time data from big data, GPUs continue to dominate in cloud-based systems and car networking. By 2025, AI chip shipments are expected to exceed $12 billion, with GPUs alone surpassing $5 billion, representing more than 40% of the market.
Beyond ASICs and GPUs, there are significant opportunities in silicon IP, memory, and sensor technologies. As information volumes grow, storage costs are decreasing, and the popularity of deep learning is making storage and sensing devices indispensable. In AI modules, the optimization between processing chips and storage systems directly impacts system performance, especially in real-time decision-making and deep learning tasks. Memory modules must prioritize customization and stability, with industrial applications already showing mature development and clear benefits.
Fubon Securities estimates that the demand for deep learning SSDs for AI applications will grow from 863 million GB in 2016 to 41.2 billion GB by 2025, at a CAGR of 53%, with revenue expected to jump from $260 million to $12.3 billion. In the sensing device sector, the rise of machine learning data is boosting demand for robots, industrial applications, machine vision, and audio systems. Analog data output is also expected to increase significantly, driving higher demand for MCUs and sensors.
Taiwan’s semiconductor industry, with its strong wafer foundry capabilities and a complete supply chain, is well-positioned to benefit from AI growth. Led by TSMC, the industry is expected to lead the development of related sectors such as ASICs, IP, memory modules, and sensors. Investors should pay close attention to companies in these areas as they represent promising investment opportunities in the AI-driven future.
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