Artificial intelligence is set to reshape the global technology landscape in 2026, according to new projections from market intelligence firm TrendForce. The company has outlined 10 emerging trends that will redefine data center architectures, semiconductor development, and cloud infrastructure worldwide, driven by escalating AI workloads and geopolitical shifts that are accelerating competition in advanced computing.
TrendForce expects AI server shipments to grow more than 20% year-over-year in 2026 as hyperscalers in North America increase capital spending and as sovereign cloud initiatives expand across Europe, the Middle East, and Asia. This surge marks a new phase of infrastructure investment in which AI models, GPUs, and distributed computing architectures dictate the pace of global data center transformation.
The competitive landscape is evolving quickly. NVIDIA remains the dominant supplier of AI accelerators, but the report finds that rivals are preparing more aggressive challenges. AMD is expected to introduce its MI400 full-rack solution, designed to compete directly with NVIDIA’s GB and VR system architectures.
North American cloud providers are simultaneously pushing deeper into custom ASIC development to optimize performance for their own AI clusters, while China’s technology firms accelerate in-house chip programs in response to geopolitical constraints. Companies such as Huawei, Baidu, Tencent, Cambricon, and ByteDance are expected to increase investment in domestic AI silicon, intensifying the global race for processor leadership.
With more powerful chips comes substantially higher heat output. TrendForce highlights that thermal design power for leading AI processors has already climbed from 700 watts in NVIDIA’s H100 generation to more than 1,000 watts in upcoming models such as the B200 and B300. By 2026, liquid cooling is projected to be incorporated into nearly half of all AI server deployments. Cloud providers are evaluating multiple cooling strategies, though cold-plate liquid cooling remains the dominant medium-term solution. Some, including Microsoft, are experimenting with chip-level microfluidic cooling to push thermal efficiency further.
Performance bottlenecks are no longer confined to compute. As models grow into the trillion-parameter scale and inference traffic expands, memory and I/O throughput have emerged as major constraints. High-bandwidth memory continues to evolve, with HBM4 introducing wider interfaces and greater channel density, but even these improvements are not enough to fully offset the bandwidth demands of next-generation accelerators.
To overcome interconnect limitations, GPU makers and cloud operators are turning to optical communication technologies. Pluggable 800G and 1.6T optical modules are being deployed in volume, and the industry is preparing for wider adoption of co-packaged optics and silicon photonics starting in 2026. These technologies are expected to play a critical role in enabling low-power, high-density data transfer across racks and between AI compute nodes.
The storage sector is also undergoing major change. AI workloads produce unpredictable and intensive I/O patterns, creating performance gaps that conventional storage cannot address efficiently. NAND flash manufacturers are responding with two main product categories: storage-class memory solutions that sit between DRAM and SSDs to support real-time inference, and nearline QLC SSDs designed to lower the cost of large-scale data retention. TrendForce forecasts QLC SSDs to account for 30% of enterprise SSD shipments by 2026.
Power requirements are shifting from a background factor to a core architectural design constraint. AI data centers increasingly require stable, flexible, and long-duration power systems. As workloads become more variable and compute density increases, energy storage systems are moving from backup usage to primary power infrastructure.
TrendForce expects medium- and long-duration storage solutions in data centers to grow rapidly, supporting grid services and hybrid power models that blend renewables with traditional supply. Installed capacity for AI data center energy storage is projected to expand from 15.7 GWh in 2024 to 216.8 GWh by 2030.
The underlying electrical architecture of data centers is also in transition. The industry is moving toward 800-volt high-voltage DC systems to support megawatt-scale racks while reducing electrical losses and copper cabling. This shift is accelerating adoption of third-generation power semiconductors such as silicon carbide (SiC) and gallium nitride (GaN). TrendForce predicts combined adoption of SiC and GaN in data center power systems will reach 17% by 2026 and exceed 30% by 2030 as operators pursue higher efficiency and compact system designs.
Executive Insights FAQ
Why is liquid cooling becoming unavoidable in next-generation AI data centers?
AI chips now exceed 1,000 watts of thermal output, far beyond the limits of traditional air cooling. Liquid cooling offers the density and efficiency needed to maintain performance at scale.
What is driving renewed interest in custom ASICs among cloud providers?
Hyperscalers are optimizing silicon for their own workloads to improve efficiency, reduce reliance on third-party GPU suppliers, and accelerate proprietary AI platform development.
Why are optical interconnects critical for future AI clusters?
As GPU counts increase, electrical signaling limits long-distance, high-bandwidth data transfer. Optical links reduce power consumption and enable higher throughput across large distributed systems.
How will QLC SSD adoption affect storage economics?
QLC’s higher density lowers cost per bit, enabling more affordable large-scale AI dataset storage while maintaining adequate performance for warm and cold data tiers.
What makes SiC and GaN pivotal to next-generation data center power design?
Both materials offer superior thermal performance, higher switching efficiency, and better voltage tolerance than silicon, making them essential for 800V HVDC systems.
