Semiconductor Cycle Differentiation Under AI: Reshaping the "Dual Circulation" from Advanced Process to Traditional Fields

Semiconductor Cycle Divergence Driven by AI: Reshaping the "Dual Circulation" from Leading-Edge to Traditional Fields

Introduction

Recent semiconductor industry data reveals a profound structural shift: the massive demand from AI has not brought about a unified cyclical recovery but has instead led to a "dual circulation" divergence in the industry. On one hand, sectors driving AI accelerators, HBM, and advanced packaging are in a phase of capacity-constrained boom with strong capital expenditure; on the other hand, the growth in traditional sectors (such as mature nodes, automotive, and consumer electronics) is relatively stable or in a period of adjustment. This severe asymmetry in prosperity requires us to no longer view the entire semiconductor industry as a single cycle but to identify and manage the independent cycles of different sub-sectors.

This article will deeply analyze the impact of this divergence on the semiconductor ecosystem from multiple dimensions, including the industry chain, technology roadmap, market competition, and regional influence, pointing out which links are major beneficiaries of the AI dividend and which face challenges in capacity utilization and valuation adjustments.

Background

Corporate and Technological Background The core driver of the current semiconductor industry has shifted from traditional cyclical demand to the structural demand for AI infrastructure. AI chip design companies, represented by NVIDIA and AMD, and foundry manufacturers like TSMC and Samsung Foundry, are undergoing unprecedented capital investment and rapid technological roadmap iteration. Technically, advancements in HBM (High Bandwidth Memory), advanced packaging technology, and minor progress in process nodes are becoming key indicators of the cycle position.

Market Background According to the latest WSTS forecast, global semiconductor sales are expected to grow by nearly 90%, but the structure of this growth is highly concentrated. The growth rate of memory and logic (Leading Edge Logic) far exceeds that of areas like analog and sensors, clearly depicting a strong pull from AI on specific technology stacks rather than uniform expansion across the entire industry.

In-depth Analysis

1. Technology Roadmap: Drivers of the AI Infrastructure Cycle

The demand for semiconductors from AI is structural, giving rise to two parallel but interconnected cycles:

• AI-Driven Leading-Edge Process Cycle (AI Infrastructure Cycle): This cycle is mainly concentrated in areas such as logic chips, HBM, advanced packaging, and high-speed interconnects.* AI-Driven Advanced Process Cycle (AI Infrastructure Cycle): This cycle is mainly concentrated in areas such as logic chips, HBM, advanced packaging, and high-speed interconnects. The design demands of NVIDIA GPUs, Google TPUs, and various AI ASICs directly drive concentrated investment in leading-edge processes of 7nm and below. This is not only reflected in chip design but also in the immense demand for advanced packaging to achieve heterogeneous integration and high-bandwidth data transfer. The technical barriers have shifted from simply shrinking the transistor size to controlling process yield, the difficulty of HBM integration, and the complexity of advanced packaging.
• Traditional/Cyclical Cycle: Sectors such as automotive, industrial, and consumer electronics are still influenced by macroeconomic conditions and inventory cycles. Capital expenditure in these sectors is driven more by the overall economic environment and corporate cash flow rather than purely by AI computing power needs.

2. Industry Chain Impact

The differentiation brought by AI is profoundly reshaping the distribution of prosperity across the entire semiconductor industry chain:

• Upstream (Equipment and Materials): The concentration of capital expenditure is evident in orders for equipment from ASML, Applied Materials, Lam Research, etc., and in the procurement of specialized materials (such as those for advanced packaging). Due to the limited capacity of leading-edge processes, upstream equipment manufacturers receive extremely high premiums on orders, forming a strong support for the "AI cycle."
• Midstream (Foundry and IP): Leading-edge foundries like TSMC and Samsung Foundry are in a period of extreme prosperity, and the utilization rate and gross margins of their advanced nodes are highly correlated with AI demand. At the same time, the IP and architecture design from Fabless companies (like AMD, Qualcomm) is the core in capturing the opportunities of AI computing power explosion. IDM manufacturers face pressure to balance traditional business with new AI businesses.
• Downstream (System Integration and Applications): Demand shows a highly structural preference. AI chips directly drive the data center and HPC markets, while applications in traditional sectors rely more on the capital decisions of the enterprises themselves. This makes downstream supply chain management extremely complex, as the inventory and order rhythms of different application scenarios are completely decoupled.

3. Changes in Competitive Landscape

The competitive landscape is shifting from "scale competition" to "barrier competition."Competition Landscape Changes

The competitive landscape is shifting from "scale competition" to "barrier competition."

• Foundry Competition: The focus is on who can more effectively manage the yield of advanced processes and the complexity of advanced packaging. TSMC's leading position in 2nm and below nodes, and its lock-in effect within the AI customer base, gives it a very strong position in the current cycle. Intel Foundry is trying to find new growth points in the high-end market by adjusting its technology roadmap and rapidly expanding capacity.
• Design Competition: Fabless companies must build inimitable software and architectural barriers in AI algorithm optimization and customization for specific application scenarios (Custom Silicon) to withstand fluctuations in foundry capacity.

Regional Impact

United States As the center for AI computing power demand, the US layout in AI chip design, HBM, and advanced packaging is central to global competition. Domestic capital expenditure is driving the explosion of advanced processes, but geopolitical export controls pose uncertainty to key equipment and technology roadmaps. The US is committed to building a technology-led supply chain system.

China China maintains a strong scale advantage in mature processes and some mid-end nodes and is actively promoting the research and development of domestic AI chips. However, the reliance on imports in ultra-advanced processes and key equipment remains high, which constitutes a bottleneck and external risk to its technological development.

Europe and Japan Europe and Japan need to find a balance between the transformation of traditional manufacturing and emerging AI applications. Japan remains strong in materials and precision equipment, while Europe is seeking to establish a presence in specific high-value areas (such as industrial automation) with policy support.

Investment Perspective

Capital Market Focus The focus of the capital market has shifted from "judging the cycle stage" to "identifying the sub-cycle." Investors need to deeply analyze the specific "sub-cycle" of each company—for example, TSMC's utilization rate in 3nm and 2nm; the delivery cycle for HBM suppliers; and the degree of reliance of fabless companies on AI models.

Long-Term Outlook Over the next 3-5 years, the expansion of AI infrastructure will continue to provide strong support for advanced processes and advanced packaging, which will be a source of certainty for semiconductor investment. However, the sustainability of this growth will depend on the depth of AI application scenarios and the companies' confidence in capital expenditure. The recovery in traditional sectors will be a slower, more resilient supplement.

Summary

The impact of AI on the semiconductor industry is not a simple "acceleration" or "decline," but rather "differentiation" and "reshaping." It will divide the industry chain into a high-growth "AI infrastructure chain" and a stable "traditional cycle chain." The key to corporate success is no longer chasing a single industry cycle, but precisely positioning oneself within the sub-cycle one is in, and building long-term value based on technological barriers and the sustainability of capital expenditure. AI is the catalyst, but the cycle itself remains the foundation.