Market Updates on AI Data Center and Silicon Photonics

4:40 pm - 5:05 pm

The rapid rise of AI, especially Large Language Models (LLMs), is transforming data centers and creating massive demand for high-performance interconnects. A widening gap between compute and interconnect bandwidth is making traditional electrical and optical solutions increasingly unsustainable in terms of power and cost.

To overcome these bottlenecks, optical interconnects—particularly Silicon Photonics (SiPh)—are advancing quickly. SiPh leverages mature CMOS processes to deliver integrated, high-bandwidth, low-power optical components. Innovations like Co-Packaged Optics (CPO), Optical I/O (OIO), and Linear Drive Optics (LPO/LRO) are reducing power and latency, while companies like Google and NVIDIA are deploying all-optical network scheduling to boost performance.

The optical component market is strong, with data centers shifting from 400G to 800G by 2026 and beginning 1.6T deployments in 2025. AI is also accelerating photonic device design and enabling smarter network operations—key enablers for scalable, efficient AI infrastructure.

Key Technologies Covered

The primary driver of market shifts is the rapid rise of AI and its escalating computational demands.
CPO addresses the power and bandwidth bottlenecks of copper by co-integrating optics with ASICs, making it essential for scalable, high-performance AI data centers
Advanced packaging, such as CoWoS (Chip-on-Wafer-on-Substrate), is essential for integrating multiple chips, including AI accelerators and CPO components, within a single package.
Optical Interconnect for AI Server Networks
CPO, OBO, NPO, and CPO

Featured Speakers

Mr. Brady Wang

Associate Director, Counterpoint Research

Hi, I’m Brady Wang, a seasoned professional with over 20 years of experience in the high-tech industry, spanning semiconductor manufacturing, market intelligence, and strategic advisory roles. Currently, I serve as an analyst at Counterpoint Research, where I specialize in semiconductors with a focus on advanced applications such as automotive, server platforms, and cutting-edge process nodes.

My core research centers on AI servers and their key components, including GPUs, custom accelerators, high-bandwidth memory (HBM), CPUs, and advanced packaging technologies. I also track the evolution of AI server architectures, interconnect technologies, and data center deployment trends. By combining deep technical knowledge with market insight, I help clients navigate the fast-changing AI infrastructure landscape and make strategic, data-driven decisions.