DeepSeek is reportedly planning to purchase 160,000 Huawei chips for its large 1 GW data center currently under construction in the Chinese province of Inner Mongolia. This would make it among the largest clusters of Huawei chips deployed to date, as the Chinese authorities attempt to wean the country off Nvidia chips, but still struggle with insufficient domestic manufacturing capacity.
The same AI boom that is filling handsets with new features is draining the memory supply that makes cheap phones possible—and in India, the world's second-largest smartphone market, buyers are paying for both. Xiaomi India's leadership told HT Tech that prices will keep climbing through the second half of 2026 as the global chip shortage persists, even as the company doubles down on AI features it says should be felt rather than seen.
HyperVault, a Tata Consultancy Services (TCS) subsidiary, has secured 264 acres in Hyderabad to build a large AI data center campus capable of reaching 1GW of capacity. The project could strengthen India's role in global artificial intelligence infrastructure, while influencing jobs, energy demand, and digital supply chains worldwide.
JM-Applied, a Taiwanese semiconductor gas equipment manufacturer and supply chain member for Micron and TSMC, said on September 4 that revenue in the first half of 2026 exceeded NT$600 million (approx. US$18.98 million), up more than 50% year-over-year. Growth was driven by the continued demand for artificial intelligence (AI) and high-performance computing (HPC), which is driving global semiconductor expansion and boosting demand for gas supply systems and equipment at wafer fabs.
Anthropic is moving toward a potential blockbuster stock-market debut this fall, but the company's unusual governance structure could become an important issue for investors alongside its growth prospects.
As AI data centers scale rapidly, Eaton is expanding beyond traditional power management into modular power deployment, next-generation DC conversion and liquid cooling, positioning itself to address infrastructure requirements from the electrical grid to AI chips.
AI-driven automation is reshaping the cybersecurity requirements of semiconductor manufacturing, with zero-trust architecture, trusted data, and interoperable standards becoming increasingly important as fabs move toward more autonomous operations.
The most consequential safety technologies are often judged by what happens after something goes wrong: how fast an alarm sounds, how quickly rescuers arrive, or how effectively protective equipment limits injury.
Taiwanese automotive microcontroller (MCU) maker SiliconAuto showcased its products at SEMICON Taiwan 2026, with CEO Gene Liu saying the company is now spotlighting AI chiplet architecture for automotive and industrial edge AI as carmakers seek more flexible chip designs.
Pegatron announced on September 3 that it had signed a memorandum of understanding with Deloitte Taiwan, combining Pegatron's self-developed enterprise AI digital twin platform with Deloitte's consulting expertise in AI strategy, governance, and implementation to offer corporate clients a one-stop AI solution from planning to deployment.
AI is driving a new wave of technological demand and changing how the semiconductor industry innovates, according to Benjamin Hein, member of the executive board and CEO of the Electronics Business Sector at Merck. As AI becomes an integral part of semiconductor innovation, future competitive advantages in the industry will depend less on any single process or material, but rather on the ability to combine materials, processes, packaging, metrology, and software into complete processes. In this sense, AI-driven growth in the semiconductor industry is like "Moore's Law on steroids," and collaboration will become the industry's next Moore's Law.
As AI computing power surges, the energy consumption of AI servers has become a major bottleneck that must be solved. The issue is spreading from chips into power delivery systems as Nvidia's AI racks ramp up, rapidly pushing up the power draw of a single AI rack.
Taiwan Mobile announced that it is participating for the first time with Systex in the "2026 Build for NextGen – International Sustainable Intelligent Building & Intelligent Materials Expo," where the two companies are jointly showcasing three smart and sustainable operations-management solutions: an AI digital twin smart management platform, ESG and energy integration services, and the Smart Building 360 smart building operations management platform.
Facing rapid demand expansion across the semiconductor and AI industries, Tongtai Group (TT Group) has aggressively promoted an import-substitution and localization strategy in recent years. The group is extending its traditional machine tool expertise into hard and brittle material processing, advanced packaging, and smart manufacturing production-line solutions required for semiconductor fabrication.
The G20 Innovation Ministerial was held in North Carolina, US, from September 1 to 2, with artificial intelligence (AI) taking center stage. The US urged governments to pursue a light-touch regulatory approach, while officials and technology executives also addressed data centers and copyright rules governing AI training data. The following are the key takeaways from the meeting.
AI and high-performance computing (HPC) demand is changing the semiconductor industry's technology and investment priorities. As chip power consumption, computing density, and data transfer requirements rise rapidly, competition is no longer limited to front-end process scaling; 3D stacking, advanced packaging, high-density interconnects, thermal management, and memory technologies are also becoming more important.
Quantum computing has a trust problem. When a quantum machine produces an answer no ordinary computer can feasibly produce, there is no obvious way to tell whether the answer is right.
South Korea's safety-tech sector may be increasingly filled with AI cameras, robots, drones, and predictive systems, but BEXCO general manager Tom Choi argues that the force pushing many of those technologies toward actual deployment begins somewhere less glamorous: government policy.
System integration remains a key hurdle as the AI chip industry pushes for higher power efficiency and faster transmission speeds, drawing intense attention to when short-reach optical communications between racks and chips will enter practical use. Industry players say the maturity of system integration is still the main consideration for commercialization.
As AI model training, inference, and AI agent workloads continue to grow, AI accelerators are increasingly being optimized for different tasks. Amin Vahdat, Google's senior vice president and chief technologist for AI and infrastructure, said at SEMICON Taiwan 2026 that if a specific workload reaches sufficient scale and investment in custom chips is economically viable, Google could develop additional dedicated chips for different computing needs.
ADATA Technology said it will integrate the products, technologies and solutions of its TRUSTA and ADATA Industrial brands to target enterprise servers, edge computing, system integration, smart homes, mobile devices and wearables. This comes as AI infrastructure, edge computing and smart systems accelerate. The memory module maker aims to expand AI and intelligent application opportunities by focusing on business use cases.
AI is forcing companies to rethink not only how software is built but how quickly ageing systems must be replaced, with Cisco warning that increasingly capable frontier models are turning technical debt into a board-level cybersecurity concern.
Trustworthy supply chain concerns are extending beyond semiconductors to drones and other autonomous systems, now emerging as national strategic industries. At a Semicon Taiwan 2026 side forum, defense and industry experts from Taiwan and the US called for stronger production capacity, standardized interfaces, testing infrastructure and predictable government procurement to turn Taiwan's supply chain strengths into a globally trusted base for unmanned systems.
A decade ago, when Nvidia CEO Jensen Huang was developing the world's first NVLink-enabled deep learning system, the DGX-1, the tech industry was deeply skeptical about the future of artificial intelligence (AI).
