With the rapid development of artificial intelligence technology, global AI computing demand is undergoing an unprecedented structural transformation. In the second half of 2026, this trend is not only reshaping the semiconductor industry landscape but also profoundly affecting the layout and adjustment of global supply chains. This article will provide an in-depth analysis of the current trends in AI computing demand and how this transformation impacts the global semiconductor supply chain, offering forward-looking market insights for investors.
Structural Transformation in AI Computing Demand
Since 2026, AI computing demand has shifted from purely pursuing computational scale to focusing on energy efficiency and scenario-specific optimization. This transformation is mainly driven by several key factors:
- High training costs for large language models are prompting companies to focus more on computational efficiency
- AI applications are penetrating from general scenarios to vertical domains, increasing demand for specialized computing power
- Rising energy costs and carbon neutrality pressures are driving the development of green AI computing
- Growing demand for edge computing is promoting changes in distributed AI computing architectures
According to data from industry analysis firms, the global AI chip market is expected to reach $520 billion in 2026, a year-on-year increase of 38%, but the growth rate has significantly slowed compared to 65% in 2025. This data indicates that the AI computing market is transitioning from a high-growth phase to a more mature phase that emphasizes quality and efficiency.
Shift from General to Specialized Computing Demand
Over the past two years, AI computing demand has primarily focused on general-purpose GPUs and other high-performance computing chips to meet the needs of large language model training. However, with model optimization and specialized chip technology development, the market is gradually shifting toward scenario-specific specialized computing power.
Take NVIDIA as an example. Although its GPUs still dominate in AI training, the company has increased investment in specialized inference chips. In the second quarter of 2026, NVIDIA's Hopper architecture inference chips increased their market share in data centers by 25% compared to the same period last year, showing that AI computing demand is extending from training to inference.
Energy Efficiency Becomes a Key Consideration
As AI models continue to expand in scale, computational power consumption is growing exponentially, making energy costs a critical factor in AI computing deployment. In the first half of 2026, energy costs for major global data centers increased by an average of 30%, forcing companies to reassess their AI deployment strategies.
In this context, more energy-efficient AI chip solutions are gaining market favor. Traditional chip manufacturers like AMD and Intel are actively launching low-power chips optimized for AI, while emerging AI chip startups such as Cerebras and Graphcore are also making energy efficiency a core consideration in product design.
Adjustment and Restructuring of the Global Semiconductor Supply Chain
The structural transformation in AI computing demand is reshaping the global semiconductor supply chain landscape. From chip design and manufacturing to packaging and testing, every link is facing new opportunities and challenges.
Intensified Competition for Advanced Process Capacity
The increasing reliance of AI computing chips on advanced process technologies has made capacity at advanced foundries like TSMC and Samsung a scarce resource. In the second half of 2026, TSMC's 3nm and 2nm process capacities are nearly sold out, with delivery cycles extended to over 18 months.
This capacity shortage has forced chip design companies to plan their capacity allocations in advance and consider multi-supplier strategies to mitigate risks. Meanwhile, some AI chip manufacturers are beginning to invest in building their own fabs to ensure supply chain security.
Enhanced Importance of Advanced Packaging Technologies
As Moore's Law approaches physical limits, advanced packaging technologies have become crucial for enhancing AI chip performance. In 2026, the adoption of Chiplet architectures and 2.5D/3D packaging technologies in AI chips has significantly increased.
Packaging and testing giants like ASE and JCET are actively expanding advanced packaging capacity to meet AI chip demand. According to industry data, in the first half of 2026, the penetration rate of advanced packaging in AI chips reached 45%, an increase of 15 percentage points compared to the same period in 2025.
Changes in Storage Chip Demand Structure
Changes in AI computing demand have also affected the storage chip market structure. High-performance computing continues to drive demand for High Bandwidth Memory (HBM), while the traditional DRAM market faces challenges of slowing growth.
In the first half of 2026, the HBM market reached $18 billion, a year-on-year increase of 65%, accounting for 28% of the entire storage chip market. Storage giants like Samsung, SK Hynix, and Micron are actively expanding HBM capacity to meet AI computing demands.
Opportunities and Challenges for the Asian Semiconductor Supply Chain
As an important base for global semiconductor manufacturing, the Asian region faces both opportunities and challenges in the transformation of AI computing demand.
Taiwan's Leading Position in the Semiconductor Industry
Taiwan occupies an irreplaceable position in the global semiconductor supply chain, especially in advanced processes and packaging. TSMC is not only the primary manufacturing partner for AI chip giants like Apple and NVIDIA but is also actively developing specialized AI chip designs.
In the first half of 2026, TSMC's AI-related revenue accounted for 35% of its total revenue, an 8 percentage point increase compared to the same period in 2025. The company has announced plans to build a new advanced packaging factory in the Hsinchu Science Park in Taiwan to meet the growing demand for AI chip packaging.
Mainland China's Pursuit in the Semiconductor Industry
Mainland China is accelerating the development of its semiconductor industry and has made breakthroughs in the AI chip field. Domestic AI chip design companies like HiSilicon, Cambricon, and Horizon Robotics are showing competitiveness in specific areas.
However, limited access to advanced processes has led mainland Chinese AI chip manufacturers to focus more on mature processes and specialized chip design. In the first half of 2026, product shipments from mainland Chinese AI chip design companies using mature processes (28nm and above) increased by 45% year-on-year, demonstrating the effectiveness of their differentiated competitive strategies.
Strategic Adjustments by Japanese and Korean Enterprises
Japanese and Korean semiconductor companies are adjusting their strategic directions based on changes in AI computing demand. The Japanese government is supporting semiconductor industry revitalization through policies, focusing on advanced materials and equipment. Meanwhile, South Korean Samsung and SK Hynix continue to expand investments in memory chips and advanced processes.
In 2026, Samsung Electronics announced a $20 billion investment to build a new fab in Pyeongtaek, South Korea, focused on AI chip foundry services. At the same time, SK Hynix also announced plans to expand HBM capacity, aiming to increase its HBM market share to 40% by 2027.
Investment Strategies and Market Outlook
In the face of structural transformation in AI computing demand, investors need to adjust their investment strategies and seize key opportunities in the semiconductor supply chain.
Focus on Vertical Integration Trends
To address supply chain risks, the semiconductor industry is experiencing a trend of vertical integration. Chip design companies are extending downstream into manufacturing and packaging, while traditional foundries are expanding upstream to provide design services.
Investors can focus on companies making progress in vertical integration, such as NVIDIA strengthening AI computing and data center interconnection capabilities through acquiring Mellanox, and AMD expanding into adaptive computing through acquiring Xilinx.
Seize Opportunities in Specialized Chips
With the diversification of AI application scenarios, the specialized AI chip market is expected to grow rapidly. Investors can focus on companies with technical advantages in specific AI application areas, such as leading enterprises in segments like autonomous driving chips and edge AI chips.
Evaluate Supply Chain Security Risks
Geopolitical factors and supply chain security have become important considerations in the semiconductor industry. Investors need to evaluate companies' progress in supply chain diversification and localization, as well as their ability to respond to geopolitical risks.
Conclusion
In the second half of 2026, AI computing demand is undergoing a structural transformation from scale-oriented to efficiency-oriented, a change that profoundly affects the layout and adjustment of the global semiconductor supply chain. For investors, understanding this transformation trend, seizing investment opportunities, and assessing related risks will be key to achieving excess returns.
With the continuous development of AI technology and expansion of application scenarios, the semiconductor industry will enter a new growth cycle. Those companies that can accurately grasp the changing trends in AI computing demand and occupy key positions in the supply chain are expected to stand out in future market competition.
