AI Computing Power Demand Faces Structural Transformation: New Global Semiconductor Supply Chain Landscape in H2 2026
The global technology market in H2 2026 is undergoing a profound AI-driven transformation. As generative AI applications move from experimental stages to commercial deployment, AI computing power demand is experiencing a structural shift, not only reshaping the strategic layouts of tech giants but also bringing unprecedented challenges and opportunities to the global semiconductor supply chain. This article will provide an in-depth analysis of the latest dynamics in the current AI computing power market, interpret the strategic shifts of tech giants, and explore how Asian semiconductor supply chains are responding to this round of industrial transformation.
Structural Changes in AI Computing Power Demand: Transition from Training to Inference
According to the latest data from multiple market research institutions, in H2 2026, global AI computing power demand is undergoing a structural transformation from "large-scale training" to "efficient inference." This transformation is mainly reflected in three aspects: first, the diversification of application scenarios, with AI expanding from simple text generation to image, audio, video and other multimodal processing, showing diversified characteristics for computing power demand; second, the improvement of computing efficiency requirements, as applications are deployed, enterprises focus more on energy efficiency ratios rather than pure computing scale; finally, the surge in real-time inference demand, with scenarios like autonomous driving and smart factories requiring higher standards for low-latency inference.
This transformation has had a profound impact on the semiconductor industry. Traditional GPU-based computing architectures are facing challenges, with technical routes such as specialized AI chips, heterogeneous computing architectures, and memory-compute integration receiving increasing attention. Advanced manufacturers like TSMC and Samsung are adjusting their capacity layouts, investing more resources in special process areas suitable for AI applications, such as 3nm, 2nm advanced nodes, and special wafer production lines optimized for AI.
Strategic Shifts of Tech Giants: Reallocation of Capital Expenditure
In this round of AI computing power demand transformation, the strategic layouts of US tech giants have shown significant changes. According to Q2 2026 financial report data, the capital expenditure structure of the tech seven giants (NVIDIA, Microsoft, Google, Apple, Amazon, Meta, Tesla) is undergoing significant changes: NVIDIA continues to increase investment in AI chip R&D and capacity expansion, while Microsoft and Google are allocating more resources to AI application development and cloud service expansion, with Apple focusing on the development of edge-side AI chips.
Specifically, NVIDIA's capital expenditure in Q2 2026 increased by more than 50% year-over-year, mainly used for the mass production of Blackwell Ultra chips and HBM memory capacity expansion. Microsoft Azure's AI-related capital expenditure proportion increased from 35% in the same period last year to 48%, showing its acceleration in building cloud AI infrastructure. Google has shifted its capital expenditure focus from traditional data centers to the development and deployment of TPU v5 chips.
Notably, the strategic shifts of tech giants are also reflected in stock performance. From July to August 2026, NVIDIA's stock price rose by more than 15%, while some traditional cloud service providers showed relatively weak performance, indicating that the market is reassessing the AI strategic value of different tech companies.
Asian Semiconductor Supply Chain: From Follower to Leader
Against the backdrop of structural changes in AI computing power demand, the Asian semiconductor supply chain is facing historic opportunities. Leveraging its 3nm advanced process technology, TSMC has become the main foundry partner for AI chips from tech giants like NVIDIA and Apple. According to market news, TSMC's 3nm production line was fully loaded in H2 2026, with AI-related chips accounting for over 60%.
South Korea's Samsung Electronics has made breakthroughs in the HBM (High Bandwidth Memory) field, with its HBM3E products certified by NVIDIA for use with Blackwell Ultra chips. According to reports, Samsung's HBM capacity in 2026 increased by more than 80% year-over-year, but still struggles to meet market demand, leading to a 30% increase in HBM prices.
Japanese semiconductor equipment manufacturers have also benefited from this AI boom, with orders for advanced process equipment from companies like Tokyo Electron and Screen Holdings significantly increasing. Statistics show that Japanese semiconductor equipment manufacturers' order value in Q2 2026 increased by 45% year-over-year, reaching a historic high.
Global Capital Flows: AI Computing Power Chain Becomes a Favorite
In terms of global capital flows, AI computing power-related sectors continue to attract investment. According to Bloomberg data, from July to August 2026, global investment in related fields such as AI chips, cloud services, and semiconductor equipment exceeded $500 billion, reaching a historic high for the same period. Among them, the US market accounted for about 65%, the Asian market for about 30%, and the European market for about 5%.
In the US stock market, AI computing power-related stocks have shown strong performance. AI-related component stocks in the Nasdaq index achieved an average increase of 18% in Q2 2026, far exceeding the 8% increase of the overall Nasdaq index. Among them, core stocks of the AI computing power chain such as NVIDIA, Microsoft, and Broadcom all increased by more than 20%.
Related stocks in the Asia-Pacific region also performed impressively. Asian semiconductor leader stocks such as TSMC, Samsung Electronics, and ASML all reached historic highs in H2 2026. According to statistics, TSMC's stock price increased by about 25% from July to August 2026, outperforming the market index.
Challenges and Risks: Supply Chain Bottlenecks and High Valuations
Despite strong AI computing power demand, supply chain bottlenecks remain prominent. On one hand, advanced process capacity is limited, with TSMC's 3nm production line already booked through 2027, and the advanced process capacity of Samsung and Intel also in a tight state. On the other hand, insufficient supply of key components like HBM has led to delivery delays for some AI chips.
In addition, valuations of AI computing power-related stocks are at historic highs. Taking NVIDIA as an example, its 2026 dynamic P/E ratio reached 65 times, far exceeding the technology industry average of 25 times. High valuations may mean that the market's expectations for AI computing power growth are overly optimistic, posing adjustment risks.
Geopolitical factors also pose challenges to the global AI computing power supply chain. The intensification of US chip export restrictions to China may lead to a fragmentation trend in the global AI computing power supply chain. It is reported that some Asian semiconductor equipment manufacturers are considering establishing new production bases in Europe and Southeast Asia to diversify geopolitical risks.
Future Outlook: 2027 AI Computing Power Market Forecast
Looking ahead to 2027, the AI computing power market will show several major trends: first, the diversification of computing architectures, with specialized chips such as NPU, FPGA, and ASIC increasing their share; second, the accelerated commercialization of memory-compute integration technology, which will effectively solve memory bottleneck problems; finally, the rise of edge AI, with demand for low-power, high-efficiency edge-side AI chips growing significantly.
According to forecasts from multiple market research institutions, the global AI computing power market size in 2027 will reach $1.2 trillion, with an annual growth rate exceeding 40%. Among them, cloud AI computing power will account for about 60%, and edge AI computing power for about 40%. In terms of industrial structure, US tech giants will continue to dominate the AI application layer, while Asian semiconductor supply chains will maintain a leading position in the basic hardware layer.
For investors, the AI computing power sector still has long-term investment value, but risk control needs attention. It is recommended to focus on AI chip design companies with technical barriers, semiconductor foundries with advanced processes, and key component suppliers such as HBM. At the same time, be wary of short-term overvaluation risks and appropriately diversify investment portfolios.
Conclusion: Seizing AI Computing Power Transformation Opportunities
In H2 2026, global AI computing power demand is undergoing a structural transformation, shifting from large-scale training to efficient inference, and from centralized cloud to distributed edge. This transformation brings historic opportunities for tech giants and Asian semiconductor supply chains, but also comes with challenges such as supply chain bottlenecks and high valuations.
For investors, it is necessary to deeply understand the structural changes in AI computing power demand, grasp the strategic shifts of tech giants, and understand the development trends of Asian semiconductor supply chains. In terms of specific investment strategies, attention should be paid to AI chip design companies with technical advantages, semiconductor foundries with advanced processes, and key component suppliers such as HBM. At the same time, geopolitical risks and valuation bubbles need to be wary of, and investment portfolios should be adjusted in a timely manner.
Overall, as the core driving force of future technological development, the AI computing power sector still has long-term investment value. Investors should remain rational, seize structural opportunities, respond to market fluctuations, and obtain stable returns in the industrial transformation of the AI era.
