At SEMICON Taiwan 2026, Google outlined the direction of its AI infrastructure, and DIGITIMES notes that the discussion extended well beyond its next-generation TPU. Rather than focusing on a single chip, Google described how it links large numbers of TPUs into an integrated computing system spanning compute clusters, data centers, and ultimately multiple data centers, while also addressing the power constraints that emerge as the system continues to scale.
Anthropic CEO Dario Amodei's call to slow frontier AI development has stirred debate over safety, but chip industry sources say global demand for AI infrastructure is likely to keep rising. For readers worldwide, the larger issue is not whether AI spending stops, but how quickly it spreads across clouds, devices, and industries.
South Korea is updating its artificial intelligence security guidance as companies deploy AI agents capable of operating with limited human supervision, while Microsoft is proposing model-level rules requiring its systems to remain subject to human interruption and shutdown.
Taiwan's original equipment manufacturer/electronics manufacturing services (OEM/EMS) sector is witnessing a structural split between explosive demand for artificial intelligence (AI) infrastructure and softening demand for legacy consumer device assembly in 2026. Monthly revenue data highlight how suppliers anchored to high-density compute racks, GPU-based servers, and high-speed network switches are delivering massive year-over-year gains, whereas manufacturers more reliant on standard notebooks face component supply bottlenecks.
At this year's China International Optoelectronic Expo (CIOE), Sivers Semiconductor brought two products to its booth that trace the trajectory of the optical networking industry: a 1.6T pluggable optical transceiver currently ramping toward mainstream adoption, and a prototype module built for external light source (ELS) architectures, a technology that could define the next generation of data center interconnects.
As US AI executives warn that artificial intelligence development may need to slow down, Chinese companies are pressing ahead with open source tools and large-scale deployment. The gap matters far beyond China, because the next phase of AI competition may be shaped as much by collaboration and standards as by chips and model size.
