AI Computing Power Demand Undergoes Structural Transformation: New Global Semiconductor Supply Chain Landscape in H2 2026
As artificial intelligence technology continues to evolve and become more widespread, global AI computing power demand is undergoing a profound structural transformation. The market landscape in H2 2026 shows that the shift from training to inference, industry verticalization and specialization trends, and energy efficiency becoming key factors are reshaping the ecosystem of the global semiconductor supply chain. This article will provide an in-depth analysis of the latest developments in the current AI computing power market, explore the business logic behind strategic adjustments by tech giants, and address the opportunities and challenges facing the Asian semiconductor supply chain.
Structural Transformation in AI Computing Power Demand: Strategic Shift from Training to Inference
Since 2026, global AI computing power demand has been undergoing a strategic transformation from large-scale training to efficient inference. This shift is driven by two main factors: on one hand, the peak demand during the basic model training phase has been largely met; on the other hand, the demand for low-latency, high-efficiency inference capabilities in real-world applications is growing exponentially.
According to market research data, global AI inference computing power demand is expected to exceed training computing power in 2026, becoming the dominant market force. This transformation means semiconductor suppliers need to redesign their product strategies, shifting from purely pursuing computing power density to balancing computing power with energy efficiency. For example, NVIDIA's newly launched Blackwell Ultra architecture, while maintaining powerful training capabilities, particularly enhances inference performance and energy efficiency ratio, directly responding to market demand changes.
Furthermore, industry verticalization and specialization trends are becoming increasingly apparent. The differentiated computing power demands across different application scenarios have led to the rise of specialized AI chips. Vertical fields such as medical imaging analysis, autonomous driving, and financial risk control have unique computing power requirements, driving the rapid development of semi-customized chips like ASIC (Application-Specific Integrated Circuit) and FPGA (Field-Programmable Gate Array).
Strategic Adjustments by Tech Giants: Shifting Capital Expenditure Focus
In response to the structural changes in AI computing power demand, global tech giants are adjusting their strategic priorities and capital expenditure directions. Cloud service providers like Microsoft, Google, and Amazon are rebalancing their AI infrastructure investments, shifting from simply expanding training cluster sizes to optimizing inference capabilities.
Microsoft revealed in its Q3 FY2026 financial report that its Azure cloud service's AI revenue grew by 45%, while also emphasizing strengthening the deployment of inference services. The company's CFO stated in the earnings conference: "Over the next 12 months, we will allocate 30% of our AI-related capital expenditures to improving inference efficiency, which will help us better meet customer demands for real-time AI applications."
Google has adopted a different strategy, strengthening its vertical integration capabilities in the AI field through its self-developed chip route with TPU (Tensor Processing Unit). In Q2 2026, Google announced the launch of the next-generation TPU v5e, optimized for large-scale inference tasks, with mass production expected in H2 2026. This move shows that Google is reducing its reliance on third-party suppliers through self-developed chips while conducting specialized optimizations for inference scenarios.
In the chip manufacturing sector, NVIDIA continues to lead the AI computing power market, but its strategic focus is also beginning to shift toward inference. The company announced the early mass production of the Blackwell Ultra architecture, particularly enhancing its performance in inference applications. Meanwhile, AMD and Intel have also launched product lines optimized for inference scenarios, attempting to secure a position in this rapidly growing market.
Structural Opportunities in the Asian Semiconductor Supply Chain
The structural transformation in AI computing power demand has brought new development opportunities for the Asian semiconductor supply chain. Advanced process manufacturers like TSMC and Samsung are adjusting their capacity layouts to meet the continuous demand for advanced processes in AI chips.
TSMC clearly stated in its 2026 technology roadmap that it will strengthen the production capacity of AI chips using 3nm and 2nm processes. The company's chairman, Liu Deyin, pointed out at the shareholders' meeting: "AI applications are one of our most important growth drivers for the next 3-5 years. We have already reserved 30% of our advanced process capacity for AI chip customers, and this proportion will be further increased in H2 2026."
The packaging and testing sector is also embracing innovation opportunities. As AI chips continue to scale up, traditional packaging technologies can no longer meet the demands of high bandwidth and low power consumption. Demand for TSMC's CoWoS (Chip-on-Wafer-on-Substrate) and InFO (InFO) technologies has surged. According to industry insiders, TSMC's CoWoS capacity is already scheduled through the end of 2027, in a fully sold-out state.
High Bandwidth Memory (HBM), as a key component of AI computing power, is also experiencing a demand explosion. Storage giants like Samsung, SK Hynix, and Micron are expanding their HBM capacity. Market analysis institutions predict that the global HBM market size will exceed $20 billion in 2026, with an annual growth rate exceeding 60%. Among these, next-generation products like HBM3e and HBM4 will become market mainstream, bringing huge business opportunities for Asian storage chip manufacturers.
Investment Strategies and Risk Warnings
In the face of structural changes in AI computing power demand, investors need to adjust their strategies, seize relevant opportunities while being vigilant about potential risks.
Sector Rotation Opportunities: Historical data shows that structural transformations in AI computing power demand often trigger sector rotation trends. Investors can focus on the following directions: first, specialized AI chip companies in the chip design sector; second, technological innovators in the packaging and testing sector; third, high-end storage chip suppliers like HBM; fourth, data center operators providing AI inference cloud services.
Risk Points to Watch: First, the uncertainty in AI technology development may lead to inaccurate forecasting of computing power demand; second, geopolitical factors may affect the stability of the global semiconductor supply chain; third, overinvestment may lead to excess capacity, especially in popular areas like HBM; finally, technological substitution risks cannot be ignored, as new computing architectures may challenge the dominant position of traditional GPUs.
Medium to Long-Term Investment Value Analysis: Despite short-term fluctuations, the medium to long-term growth prospects of the AI computing power market remain bright. According to market research institutions, the global AI computing power market will maintain a compound annual growth rate of over 30% from 2026 to 2030. Among these, the growth rate of the inference computing power market will exceed that of training computing power, bringing continuous business opportunities for related companies.
Conclusion: Seizing Investment Opportunities During the AI Computing Power Transformation Period
The global AI computing power market in H2 2026 is in a critical period of structural transformation. The strategic shift from training to inference, industry verticalization and specialization trends, and energy efficiency becoming key factors are reshaping the ecosystem of the global semiconductor supply chain.
For investors, it is necessary to deeply understand the essence of this transformation, seize the sector rotation opportunities, while being vigilant about potential risks. The Asian semiconductor supply chain, especially advanced process manufacturers like TSMC and Samsung, as well as leading enterprises in packaging, testing, and HBM fields, will benefit from this transformation process.
Ultimately, the development of the AI computing power market depends not only on technological progress but is also influenced by multiple factors such as application scenario expansion, policy support, and industrial collaboration. Investors need to maintain a long-term perspective and dynamically adjust their strategies to achieve stable returns in this market full of opportunities and challenges.
