Competition among GPU vendors, foundry expansion, and national chip strategies shaping AI hardware access

The global AI hardware landscape is rapidly evolving as industry giants, regional powers, and geopolitical tensions reshape the manufacturing, supply chains, and technological foundations necessary for AI advancement. From 2024 through 2026, this period is poised to be pivotal, marked by aggressive capacity expansions, technological breakthroughs, and a geopolitical chess game that influences access to cutting-edge chips and models alike.

Foundry Expansion and Capacity Scaling: Meeting Explosive AI Demand

To keep pace with surging AI model complexity—particularly large language models and real-time inference systems—leading semiconductor firms are investing heavily in expanding manufacturing capacity:

• TSMC’s Japan Initiative: Announcing a $17 billion investment, TSMC aims to establish state-of-the-art fabs in Japan centered on sub-3nm EUV process nodes. Leveraging ASML’s EUV lithography systems, these fabs are critical for producing next-generation AI accelerators capable of handling massive models. This move aligns with Japan’s broader strategic goal to enhance regional technological sovereignty and lessen reliance on Taiwan amidst rising Indo-Pacific tensions and export restrictions targeting China.

• Micron’s Foundry 2.0 Strategy: Valued at over $200 billion, Micron’s multi-faceted plan emphasizes regional diversification, especially in Singapore, and investments in advanced packaging technologies like CoWoS and 3D stacking. These developments aim to address the persistent memory shortages, particularly in HBM4, which are vital for both AI training and inference workloads. Industry leaders note that memory famine—exacerbated by capacity constraints at suppliers like Samsung and full commitments from companies such as Western Digital—continues to challenge AI deployment timelines and operational costs.

• Additional Capacity Boosts: Industry giants and startups alike are ramping up manufacturing footprints. Samsung commands premium prices for its HBM4 chips amid memory famine, while startups like Taalas and MatX focus on architectures optimized for large-model training and inference, highlighting a competitive push to diversify supply sources.

Technological Innovations: Scaling Nodes and Packaging for AI

Advances in EUV lithography and advanced packaging techniques underpin the industry’s push toward more powerful, energy-efficient AI chips:

• EUV and Sub-3nm Nodes: TSMC’s expansion into 3nm fabrication, utilizing ASML’s EUV systems, underscores the quest for smaller, more efficient transistors. These nodes are essential for producing high-performance AI accelerators and edge devices capable of supporting complex models.

• Advanced Packaging: Techniques like CoWoS and 3D stacking enable higher memory bandwidth and lower latency, critical for large-model training and agentic AI systems. Industry reports indicate that TSMC shipped over 15,000 CoWoS wafers in 2025, primarily for Nvidia’s Hopper and Blackwell GPUs, demonstrating the importance of packaging innovations in scaling AI hardware.

Geopolitical Tensions and Ecosystem Fragmentation

Despite TSMC’s dominance as the “Silicon Shield”, geopolitical tensions and export controls are threatening the integrity of the global supply chain:

• Export Restrictions and Bifurcation: The U.S. has imposed restrictions on high-performance GPU exports to China, aiming to slow Chinese AI progress. While well-intentioned, these measures are causing industry bifurcation, with parallel supply chains and standards emerging across regions. This fragmentation risks interoperability and collaborative innovation, potentially slowing overall AI development.

• Regional Sovereignty Efforts: Countries like Japan and India are investing heavily to develop indigenous AI chip ecosystems. Japan’s alliance with TSMC and India’s goal to achieve self-reliant 2nm process technology exemplify efforts to bolster regional resilience against supply chain disruptions and geopolitical pressures.

• Supply Chain Challenges: Industry voices, including Google DeepMind’s CEO, warn that memory shortages, especially in HBM, are delaying AI deployment and increasing operational costs. Capacity constraints at ASML and other critical equipment suppliers further threaten the pace of transistor node scaling, which is vital for sustaining AI hardware growth.

Industry Dynamics: Competition, Inference, and Autonomous Design

While Nvidia maintains a dominant position in AI acceleration, its leadership faces mounting challenges:

• Rising Competitors: Intel and AMD are aggressively expanding their AI accelerator portfolios. AMD’s $60 billion deal with Meta signals a significant shift in the competitive landscape. Meanwhile, startups like Graphcore and SambaNova are gaining prominence with power-efficient, low-latency inference chips tailored for real-time applications.

• Shift Toward Inference Accelerators: The industry is witnessing a move from GPU-centric training toward dedicated inference accelerators, which are optimized for power efficiency and low latency. Companies like Google (with Maia 200), SambaNova, and Hailo are innovating in this space, aiming to meet the burgeoning demand for real-time AI systems and edge deployment.

• Agentic AI and Autonomous Chip Design: A groundbreaking development involves agentic AI systems capable of designing and optimizing chips independently. Industry leaders such as Agentrys’ CEO Mark Ren emphasize that agentic AI could accelerate hardware innovation cycles, enabling faster, more tailored chip development. Additionally, AI-driven Electronic Design Automation (EDA) tools are lowering barriers to chip customization, democratizing hardware innovation further.

Recent Developments: Model-Level Restrictions and Geopolitical Battles

Adding complexity to the hardware landscape are recent reports of model-layer withholding and disputes over model access:

• DeepSeek’s V4 Model Withholding: Reports indicate that DeepSeek has withheld its latest V4 model from Nvidia, reflecting model-level restrictions that are effectively weaponizing access to AI models within the broader chip war. Such disputes may force hardware providers and users to navigate fragmented model ecosystems and differing standards.

• US–China Model Battles: Tensions over models like Claude highlight the ongoing US–China AI rivalry. The US government’s restrictions aim to limit Chinese access to advanced models, but this also accelerates regional AI ecosystems and parallel development. A recent YouTube discussion on these battles underscores how policy and geopolitics are directly shaping access to AI capabilities.

• Expert Insights: Chris Miller, in recent commentary, emphasizes that AI demand continues to grow exponentially, but chip supply bottlenecks, especially in memory and lithography capacity, threaten to slow the AI supercycle. The convergence of policy restrictions, supply chain constraints, and technological innovation will determine whether the industry can sustain rapid growth or face long-term constraints.

Outlook: Strategic Choices for 2024–2026

The next two years will be decisive in shaping a multipolar, fragmented AI hardware ecosystem:

• Regional Resilience vs. Interoperability: Investments like TSMC’s Japan fabs, India’s indigenous process ambitions, and Japan’s alliances will bolster regional resilience, but parallel standards may emerge, challenging interoperability and collaborative innovation.

• Supply Chain Robustness: Resolving memory shortages and expanding lithography capacity—particularly for sub-3nm nodes—will be critical. Advanced packaging will also remain vital for scaling AI hardware efficiently.

• Technological Breakthroughs: Progress in node scaling, packaging, and agentic AI-driven chip design will determine whether the industry can sustain its AI supercycle or face long-term constraints due to geopolitical fragmentation.

In summary, 2024–2026 are shaping up as a period of intense competition, strategic regional investments, and technological innovation. The industry’s ability to balance resilience with interoperability, while pushing the frontiers of chip technology, will be crucial in maintaining AI’s rapid progression and broad adoption in the coming years. The ongoing battles over model access and supply chain control further underscore that the chip war has now extended into the model layer, adding new complexities to an already complex ecosystem.