NVIDIA once again showcased its Rubin-series chips and rack systems—scheduled for launch by the end of 2026—at GTC 2026 on March 16th. Compared to the previous generation, the Rubin series boasts significantly larger substrate sizes, higher layer counts, and increased rack board complexity.
Furthermore, the shift toward cableless design architectures is driving demand for components such as midplanes and orthogonal backplanes, while the introduction of the inference-focused Rubin LPX rack is further boosting demand for high-end glass fiber cloth.
However, the supply side is facing severe constraints. Nittobo, which controls approximately 90% of the global T-glass market and 60–70% of the NER-glass market, is not expected to bring new capacity online until mid-2027 at the earliest. This implies that supply gaps for critical materials will persist over the next year, with direct implications for lead times and cost trends across the AI server supply chain. This issue warrants close attention from all industry stakeholders.
This report provides an in-depth analysis of:
(1) The technology background and development of glass fiber cloth;
(2) AI-driven demand trends for glass fiber cloth;
(3) Nittobo’s capacity bottlenecks and their impact on pricing.
The objective is to offer a comprehensive view of demand momentum, pricing trends, and competitive dynamics in the high-end fiberglass cloth market.
As data centers adopt HVDC architectures to reduce grid-to-chip power losses, demand for advanced power conversion and next-gen semiconductors surges. Meanwhile, Asian manufacturers continue to dominate the PCB and specialized substrate supply chains.
AI chips represent the core cost component of computing infrastructure. Recently, reports that Google’s TPU has been adopted by several leading companies have drawn attention to the possibility of more cost-efficient alternatives to GPUs.
This report focuses on Google’s TPU, analyzing the long-term demand drivers behind its development within the broader AI arms race. It also examines the product’s competitive advantages and identifies supply chain participants that may benefit from its growing adoption.
As AI compute demand grows exponentially, the physical limits of traditional silicon-based chips are rapidly becoming apparent. The world is accelerating into a disruptive “post-GPU era,” in which quantum computing is no longer confined to laboratory theory but is emerging as a double-edged force capable of reshaping drug discovery and financial risk management—even triggering a “quantum crisis” by undermining national-level encryption systems.
From NVIDIA’s hybrid-computing strategy to the competing technical roadmaps of IonQ and Rigetti, and from the national-level arms race unfolding across the United States, China, Europe, and Japan, the next decade will be defined by a high-stakes contest for computational supremacy. Against this backdrop, Taiwan’s supply chain faces a defining question: how will it position itself in the era of quantum computing?
The global AI hardware market continues to expand, driven by increasing CSP capital expenditures and accelerated procurement of advanced computing resources. In response, companies are partnering with Google to adopt TPUs, while TPU technology evolves with enhanced architectures and supply chain strategies to support next-generation AI models. AI applications are increasingly recognized as a key enabler of high-performance transformation.
The global satellite market is expected to reach $392 billion in 2026. Competition will intensify as Starlink continues expanding satellite broadband and direct-to-cell (D2C) services into emerging markets, prompting MEO/HEO/GEO satellite operators to accelerate multi-orbit deployment strategies to counter Starlink’s growing influence.
Meanwhile, early-stage 6G deployment is underway. As global satellite service markets rapidly scale, Taiwanese manufacturers are shifting production bases to Southeast Asia while increasing shipments of key satellite components.
The rise of AI and HPC workloads has significantly increased GPU power consumption, from a few hundred watts to over 2,300W in the Rubin generation. This evolution has elevated cooling from a mere support component to a critical factor for maximizing computational performance. As traditional liquid-cooling methods near their physical limits, the industry is turning to a next-generation solution: Microchannel Lids (MCL). Quietly, a new “cooling revolution” is underway.
A clear uptrend is taking shape for 2026, with tighter DRAM supply and broad-based price increases now firmly in sight. The primary growth catalyst is CSPs, which are accelerating data center expansion to support AI workloads. This is not only driving higher global server shipments but also a notable increase in memory content per server.
In the NAND Flash market, enterprise demand will serve as the core growth engine in 2026, while the consumer segment is expected to remain muted until a more visible economic recovery boosts purchasing power and revitalizes demand.
Looking ahead to 2026, continued strength in AI server demand—combined with suppliers' profitability-first strategy—will keep both DRAM and NAND Flash prices on an upward trajectory, reinforcing a structural pricing shift across the memory industry.
Given the complexity and diversity of the challenges faced by urban transportation, the parallel development of intelligent transportation systems (ITS) and autonomous driving aims to address congestion and enhance system resilience. AI-powered ITS will toward greater proactivity, strengthening real-time sensing and decision-making capabilities as regulations and technologies for autonomous vehicles evolve. However, data standardization and platform integration remain critical challenges that must be resolved.
Current progress in humanoid robotics is centered on optimizing vision-language-action (VLA) models, integrating multimodal data, and enhancing instruction comprehension as well as the ability to interpret human intent. Training relies heavily on world models, human video data, and VR-based remote training, with increasing emphasis on first-person perspectives to strengthen perception. While the ultimate goal is to achieve general-purpose humanoids, development remains constrained by significant challenges, leading Western and Chinese companies to pursue divergent technological pathways.
