The Semiconductor Industry Redefined by AI

A robotic arm precisely placing semiconductor chips in a controlled cleanroom manufacturing environment.

Insight | 2026-7-29

10 minute read

AI is not merely expanding the semiconductor market—it is fundamentally redefining computing demand, semiconductor technologies, supply chains, and even the rules of global competition. This paper examines these structural shifts through three dimensions: demand, supply, and technological sovereignty. It argues that competitive advantages in the AI era will depend on treating semiconductors as strategic assets, designing computing strategies around Token-Centric Management, and integrating technology, markets, and public policy into a unified management framework.

1. Why Semiconductors Matter: Rethinking the Industry in the AI Era

As AI moves from experimentation to large-scale deployment, semiconductors have become more strategically important than ever as the foundation of AI computing.

This optimism is underpinned by the explosive growth in AI computing workloads, driven by the rapid expansion of token consumption. Annual token processing volume is estimated to have increased fifteenfold within a year, with some forecasts suggesting that AI-related semiconductor demand could grow by nearly 90% in 2026.

At the same time, the sustainability of this growth remains a subject of debate. Skeptics argue that the current investment cycle resembles previous semiconductor booms and warn of overcapacity, geopolitical risks, and the eventual return of the traditional silicon cycle. Optimists, however, believe that AI's transformative nature, combined with a paradigm shift in semiconductor technologies, is creating the foundation for a new semiconductor Supercycle.

Meanwhile, advances in AI—including multimodal models and AI agents—are reshaping semiconductor demand beyond GPUs and high-bandwidth memory (HBM) to a broader computing architecture encompassing CPUs, NPUs, and specialized accelerators. At the same time, sovereign technology initiatives, export controls, and economic security policies are fundamentally redefining the industry's competitive landscape.

This paper examines these structural shifts through three strategic lenses: demand, supply, and sovereign risk. By analyzing how these forces are reshaping the semiconductor ecosystem, it aims to provide strategic implications for business leaders navigating the AI era.

2. AI-Driven Demand Transformation: How Far Can Computing Demand Scale?

Recent growth in semiconductor demand has been driven by the rapid adoption and advancement of AI, which continues to expand global computing requirements. The rise of multimodal AI, reasoning models, and AI agents is accelerating token consumption and fundamentally reshaping the demand structure of the semiconductor industry.

The most visible indicator of this transformation is the explosive growth in token processing. OpenRouter, which provides unified access to more than 400 large language models (LLMs), increased its annual token volume from approximately 100 trillion in May 2025 to 1.5 quadrillion in May 2026, a fifteenfold increase in just one year (1) .Major AI platforms, including Google, Microsoft Azure, OpenAI, and ByteDance's Doubao, now process trillions to tens of trillions of tokens every day (2) . As reasoning models and AI agents become mainstream, monthly token consumption is projected to increase by another twenty-fourfold by 2030 (3) .

Rising token consumption directly translates into greater computing demand. Supported by this structural shift, the global semiconductor market is projected to grow by approximately 90% in 2026, reaching US$1.51 trillion (Figure 1). Although growth is expected to moderate in 2027, it is still forecast to remain well above historical semiconductor cycles.

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Figure 1. Changes in Global Semiconductor Market Size and Growth Rate

Global semiconductor market size and growth rate forecast from 2018 to 2027. Market revenue increases from $465 billion in 2018 to $1.914 trillion in 2027, with annual growth peaking at 89.9% in 2026.

Source: Created by the author based on WSTS (June 2, 2026) “Global Semiconductor Market Surges Beyond $1.5T 2026”

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Global Semiconductor Market Surges Beyond $1.5T 2026

Demand is also becoming increasingly diversified. In AI servers, the widening gap between processor performance and memory bandwidth has created the "memory wall," making high-bandwidth memory (HBM) a critical bottleneck for AI infrastructure (4) . HBM capacity is already largely sold out through 2026, accelerating the transition toward new computing architectures built around tightly integrated GPUs and HBM. At the same time, AI workloads are expanding beyond cloud data centers to edge devices, driving demand for dedicated AI chips in smartphones, vehicles, and industrial equipment. Looking ahead, Physical AI—including autonomous vehicles and humanoid robots—is expected to become another major source of semiconductor demand.

Many industry observers therefore view today's AI-driven demand as a structural transformation rather than a temporary cycle, marking the beginning of a new semiconductor Supercycle (5) .
Nevertheless, important uncertainties remain. AI monetization is still in its early stages, and hyperscale’s and AI startups have yet to fully justify their massive infrastructure investments through sustainable revenue generation (6) .
In addition, power infrastructure constraints (7) , geopolitical tensions and export controls could all influence the pace and sustainability of future semiconductor demand (8) .

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3. Semiconductor Technology Transformation: How Is Computing Architecture Evolving?

The rapid evolution of AI is reshaping not only semiconductor demand but also the computing architecture that underpins it. As AI progresses from generative models to reasoning systems, AI agents, and Physical AI, computing workloads are becoming increasingly diverse. Different applications now require different processor architectures, shifting the market from a GPU-centric model toward a more heterogeneous ecosystem that includes CPUs, NPUs, and custom AI accelerators (ASICs).

The growing adoption of reasoning models and AI agents is particularly increasing demand for server CPUs, as multi-step inference and reinforcement learning generate significantly more complex computational workflows (9) . While demand for GPUs remains strong, CPUs and NPUs are becoming increasingly important, prompting semiconductor vendors to rebalance their product portfolios. At the same time, hyperscalers and digital platform companies are accelerating the development of proprietary AI accelerators to improve performance, reduce power consumption, and optimize workloads for their own services. Google's TPU, AWS's Inferentia and Trainium, Microsoft's Azure Maia, Tesla's Dojo, and Apple's Neural Engine illustrate how competition is expanding beyond general-purpose GPUs toward application-specific architectures.

At the same time, semiconductor innovation is entering a new phase. As Moore's Law approaches its physical limits, the center of performance improvement is shifting from front-end process scaling to advanced packaging technologies (Figure 2) (10) . Chiplets, 2.5D integration, and 3D packaging enable multiple chips to operate as a tightly integrated computing system, dramatically improving processing performance and memory bandwidth. Among these technologies, TSMC's CoWoS (Chip-on-Wafer-on-Substrate), combined with High-Bandwidth Memory (HBM), has become one of the industry's most important architectural innovations, addressing the growing "memory wall" challenge by enabling ultra-high-density connections between GPUs and memory.

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Figure 2. Share of key AI chip supply chain inputs consumed by major AI chip designers, by value
(2025)

Share of key AI chip supply chain inputs consumed by major AI chip designers in 2025, by value. NVIDIA accounts for 60% of CoWoS packaging demand and 69% of HBM demand, while advanced logic die consumption is largely attributed to other companies at 88%.

Source: Created by the author based on Venkat Somala (Mar 13, 2026) “Advanced packaging and HBM — not logic dies —
were the bottlenecks on AI chip production in 2025”.

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Advanced packaging and HBM — not logic dies — were the bottlenecks on AI chip production in 2025

This technological transition is also being accelerated by supply constraints in leading-edge AI chips. With advanced GPUs remaining in limited supply, maximizing computing performance increasingly depends on packaging, memory integration, and high-speed interconnect technologies rather than processor performance alone.

As a result, competition is expanding beyond computational capability to include thermal management, packaging, and advanced materials. Silicon interposers, glass substrates, and next-generation thermal materials are emerging as critical enablers of AI infrastructure. Competitive advantage is therefore no longer determined solely by transistor scaling, but by the ability to integrate chips more densely and manage heat more efficiently.

Ultimately, competitive advantage in the AI era will depend less on the performance of individual chips than on the ability to design and optimize the entire computing ecosystem by integrating diverse processors with advanced packaging technologies. Alongside these technological shifts, governments are becoming increasingly involved in the semiconductor industry through industrial policy and economic security initiatives. The next chapter examines how technological sovereignty and public policy are reshaping global competition and semiconductor supply chains.

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4. Semiconductors as Strategic National Infrastructure: The Rise of Technology Sovereignty

In the AI era, semiconductors have evolved far beyond their traditional role as enabling components. They have become strategic assets that underpin economic competitiveness, national security, and technological leadership. As advanced AI chips increasingly serve both commercial and defense applications, semiconductor competition is no longer shaped solely by market forces. It is increasingly defined by national strategy, industrial policy, and geopolitical considerations.

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Table 1. Regional Share of the Semiconductor Supply Chain (Percentage of the global total, 2019)

Regional share of the global semiconductor supply chain in 2019. The United States dominates EDA and core IP with a 74% share and logic with 67%, while East Asia leads memory (70%), materials (57%), and wafer fabrication (56%). Assembly, packaging, and testing are concentrated in East Asia (43%) and China (38%).

Source: Created by the author based on SIA/BCG (April 2021) "BCG X SIA Strengthening The Global Semiconductor Value Chain".

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BCG X SIA Strengthening The Global Semiconductor Value Chain

For decades, the global semiconductor industry thrived on a highly specialized supply chain built around comparative advantage (Table 1) (11) . The United States led in electronic design automation (EDA), intellectual property (IP), chip design, and semiconductor manufacturing equipment, while East Asia became the center of advanced fabrication and China specialized in assembly, packaging, and testing. This globally optimized ecosystem delivered remarkable efficiency, but growing geopolitical tensions and the pandemic exposed its structural vulnerabilities, bringing economic efficiency into direct conflict with national resilience.
In response, governments have begun to redefine semiconductors as strategic national infrastructure rather than simply globally traded products. The U.S. CHIPS and Science Act, the European Chips Act, and Japan's Semiconductor and Digital Industry Strategy all seek to strengthen domestic manufacturing capabilities and reinforce supply-chain resilience through substantial public investment. The industry is therefore shifting from a globalization-driven model toward one in which governments and private enterprises jointly shape long-term competitiveness.

At the same time, strategic competition between the United States and China is fundamentally reshaping the global semiconductor landscape. Under the "Small Yard, High Fence" strategy, the United States has progressively expanded export controls on advanced AI chips, semiconductor manufacturing equipment, and High-Bandwidth Memory (HBM), while coordinating closely with key allies to strengthen technology restrictions (12) . The focus of competition has consequently expanded beyond individual products to encompass the entire semiconductor ecosystem.

China, meanwhile, has responded by accelerating its strategy of technological self-reliance. Through large-scale state investment, it is developing domestic capabilities across the entire semiconductor value chain—from chip design and manufacturing to packaging, materials, and computing infrastructure. Companies such as Huawei, HiSilicon, and SMIC are working together to advance indigenous AI semiconductor technologies despite external restrictions. At the same time, China is investing in data centers, power infrastructure, and nationwide computing networks, recognizing that leadership in AI depends on the entire computing ecosystem rather than on advanced chips alone. Although China's most advanced technologies still trail the global frontier in several areas, its rapid progress in developing "good enough" technologies under severe constraints has become an important trend that deserves close attention.

Ultimately, competition in the semiconductor industry is no longer determined solely by technological innovation or market leadership. It is increasingly shaped by governments through industrial policy, export controls, and technological sovereignty. Success in the AI era will therefore depend not only on technological excellence, but also on the ability to navigate an increasingly complex landscape where technology, geopolitics, and national strategy are becoming inseparable.

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5. Strategic Implications: Building Competitive Advantage in the AI Era

This paper has shown that AI is fundamentally reshaping semiconductor demand, computing architecture, and the competitive landscape itself. As these structural shifts accelerate, business leaders should reconsider their strategic priorities from three perspectives.

1) treat semiconductors not as procurement items, but as strategic assets

Semiconductors are no longer components evaluated solely on price or procurement efficiency. They have become strategic resources that determine AI competitiveness, supply-chain resilience, technological sovereignty, and business continuity. Managing semiconductor capabilities should therefore become an integral part of corporate strategy rather than a function of purchasing alone.

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2) design computing strategies around Token-Centric Management (13) , rather than simply deploying AI

In the era of AI agents, enterprises will increasingly operate on massive volumes of token consumption. Competitive advantage will depend not on adopting AI itself, but on how effectively organizations generate, consume, manage, and convert tokens into business value. GPUs, CPUs, ASICs, edge devices, and cloud infrastructure are not strategic objectives in themselves; they are enablers of this transformation. The real challenge is to make token consumption, processing costs, and AI-driven productivity visible, measurable, and continuously optimized through well-defined KPIs. Computing architecture should therefore be designed as the operational foundation for Token-Centric Management, enabling organizations to maximize their return on their computing investments.

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3) integrate technology, public policy, and geopolitics into strategic decision-making

In the AI era, technology, procurement, corporate planning, government affairs, and risk management can no longer operate independently. Organizations must continuously monitor changes in customer demand, technology, and regulatory environments using AI itself, and rapidly feed those insights back into business strategy and computing operations. Sustainable competitive advantage will increasingly belong to organizations capable of learning and adapting in real time.

Taken together, AI is not simply expanding the semiconductor market. It is redefining demand, computing architectures, and even the rules of global competition. Companies that successfully integrate technology, markets, and public policy—while placing Token-Centric Management at the center of their AI strategy—will be best positioned to build lasting competitive advantage in the AI era.

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(9) Intel (May 15, 2026) “E-book: Rising CPU:GPU ratios in AI infrastructure”
(9) Luke James(April 24, 2026)“CPU requirements for AI workloads are multiplying, driving intensifying shortages and price hikes — Intel already shifting production from consumer chips to Xeon as inference workloads drive server CPU ratios back toward parity with GPUs”
(10) Venkat Somala (Mar 13, 2026) “Advanced packaging and HBM — not logic dies — were the bottlenecks on AI chip production in 2025”
(11) SIA/BCG (April 2021) "BCG X SIA Strengthening The Global Semiconductor Value Chain".
(12) Stanislav Vynnytskyi(January 31, 2025)“Choking the Silicon Dragon: The Geopolitical Effects of U.S. Restrictions on China’s Chip Industry”
(12) Igor Khrestin (August 21, 2025)“The U.S. needs a comprehensive policy on technology sales to China”
(13) Token-Centric Management: In this paper, Token-Centric Management refers to a management approach that treats token consumption as a strategic business resource by making it measurable, governable, and continuously optimized through KPIs, thereby maximizing the business value generated from computing investments

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Dr. Jianmin Jin (Ph.D in International Economic Law)
Chief Digital Economist Fujitsu Ltd.
Senior Director
Marketing Promotion Office

2020 Fujitsu Ltd., Chief Digital Economist. 1998 Fujitsu Research Institute, Senior Fellow.
Dr. Jin's research mainly focuses on global economic, digital innovation/digital transformation, and Dr. Jin has published books such as ”Towards the Creation of a Japan’s Silicon Valley”(2020), etc.

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