As the AI wave drives rapid growth across the global semiconductor industry, the upstream electronic materials supply chain has become a key bottleneck for AI-related shipments. To keep pace with AI investment, Qnity was spun off from US chemical giant DuPont and listed independently in November 2025.
The annual Shangri-La Dialogue, considered the most important defense and security conference in the Asia-Pacific region, was held this year in Singapore at the end of May. For this year's conference, however, China kept a low profile by sending a deputy president from its National Defense University, a move seen as its attempt to minimize the significance of the conference.
The global memory market is enjoying one of its most profitable cycles in years. AI data center demand has driven DRAM and NAND prices sharply higher, and the three companies that dominate global supply — Micron, Samsung, and SK Hynix — are posting results that would have seemed unlikely two years ago.
Every major consumer electronics company has raised prices this year. The reason, in almost every case, is the same: memory costs have surged, driven by AI data center demand that has overwhelmed global DRAM and NAND supply. Apple raised prices on its MacBook and iPad lines, too. However, to group Apple's move with everyone else's is to miss what is actually happening.
As Taiwan becomes the core of the global AI hardware supply chain, Qnity — the century-old company spun off from US chemicals giant DuPont and separately listed — is likewise expanding its production capacity investment in Taiwan. Asia-Pacific president Dennis Chen said in an interview with DIGITIMES that future investment will center closely on three main battlegrounds: advanced processes, advanced packaging, and thermal management.
Protecting patents around the world is a core value for any R&D-driven company. It is also a commitment to partnering with customers. In 2025, glass giant Corning filed nearly 400 patent applications and close to 1,000 international applications. Its active patent portfolio now totals around 11,400 patents worldwide.
For more than a decade, Apple built one of the industry's most profitable business models by using its purchasing power to drive down memory and component costs before turning hardware upgrades into high-margin revenue. The AI-driven boom in HBM and DRAM is now challenging that strategy.
As the electronics industry enters the second half of 2026, it is approaching what has traditionally been the peak season for demand. However, macroeconomic and geopolitical factors have disrupted normal business cycles across many applications, making seasonal patterns far less predictable. According to industry sources, this season is particularly uncertain. Rising component prices and supply shortages have made downstream procurement behavior and end-market consumption patterns more difficult to predict than in the past. Demand signals that the industry once relied upon have become distorted.
Apple's latest round of price increases for Macs, MacBooks, and iPads has unsettled investors and weighed on Asian technology markets, but the reaction may be disproportionate to the likely impact on demand. While higher prices will inevitably slow some purchases, Apple's premium positioning, loyal customer base, and selective pricing strategy suggest the broader implications for shipments and the supply chain are likely to remain manageable.
The rapid rise of artificial intelligence is reshaping the dynamics of competition in the global technology landscape. From hyperscale data center expansion and government-backed sovereign AI initiatives to surging enterprise demand for high-performance computing, AI-driven investment in infrastructure and applications has become the industry's primary growth engine. In this race, companies that secure key positions across the AI supply chain are expected to hold a competitive advantage for years to come.
Micron Technology is turning the AI memory boom into a new Wall Street story: not just record DRAM, NAND and HBM demand, but stronger free cash flow, long-term customer commitments and a clearer path to shareholder returns.
The race to commercialize physical AI and autonomous robots is running into a fundamental challenge: existing robot safety frameworks were designed for deterministic systems operating in controlled environments, not for autonomous machines making decisions in dynamic, unstructured ones.
The G7 debate over AI has moved beyond regulation and safety pledges into a harder fight over frontier model access: who can use the most powerful systems, under what conditions, and whether governments can switch that access off.
