AI to Reshape the Global Technology Landscape in 2026, Says TrendForce

TAIPEI, Nov. 26, 2025 /PRNewswire/ -- TrendForce has identified 10 key technology trends that will define the tech industry's evolution in 2026. The highlights of these findings are outlined below:

AI Chip Competition Intensifies as Liquid Cooling Gains Widespread Adoption in Data Centers

In 2026, the high demand for AI data center construction—fueled by increased capital spending by major North American CSPs and the rise of sovereign cloud projects worldwide—is anticipated to boost AI server shipments by over 20% year-over-year.

NVIDIA, the leading name in AI today, will face stronger competition ahead. AMD plans to challenge NVIDIA by introducing its MI400 full-rack solution, which mirrors NVIDIA's GB/VR systems and is aimed at CSP clients. Meanwhile, major North American CSPs are increasing their in-house ASIC development. In China, geopolitical tensions have sped up the drive for technological self-sufficiency, with companies like ByteDance, Baidu, Alibaba, Tencent, Huawei, and Cambricon boosting efforts to create their own AI chips. This is set to intensify the global competition.

Thermal design power (TDP) per chip is increasing rapidly as AI processors become more powerful, jumping from 700W for NVIDIA's H100 and H200 to over 1,000W for the upcoming B200 and B300. This increase in heat output is leading to a widespread adoption of liquid-cooling systems in server racks, with usage expected to reach 47% by 2026.

Microsoft has introduced advanced chip-level microfluidic cooling technology to enhance thermal efficiency. In the near to midterm, cold-plate liquid cooling will remain the primary solution, with CDUs transitioning from liquid-to-air to liquid-to-liquid setups. Over the long term, the market is likely to move toward more detailed chip-level thermal management.

Breaking Bandwidth Barriers: HBM and Optical Communications Redefine AI Cluster Architectures

The rapid increase in data volume and memory bandwidth needs, driven by expanding AI workloads from training to inference, is challenging system design by exposing bottlenecks in transmission speed and power efficiency. To address these limitations, HBM and optical interconnect technologies are emerging as critical enablers of next-generation AI architectures.

Current generations of HBM leverage 3D stacking and through-silicon via to significantly reduce the distance between processors and memory, achieving higher bandwidth and efficiency. The upcoming HBM4 generation will introduce greater channel density and wider I/O bandwidth to further support the massive computational demands of AI GPUs and accelerators.

However, as model parameters surpass the trillion-scale level and GPU clusters expand exponentially, memory bandwidth once again emerges as a major performance bottleneck. Memory manufacturers are addressing this issue by optimizing HBM stack architectures, innovating in packaging and interface design, and co-designing with logic chips to enhance on-chip bandwidth for AI processors.

While these advances mitigate memory-related bottlenecks, data transmission across chips and modules has become the next critical limitation to system performance. To overcome these limits, co-packaged optics (CPO) and silicon photonics (SiPh) are emerging are strategic focus areas for GPU makers and CSPs.

Currently, 800G and 1.6T pluggable optical transceivers have already entered mass production, and starting in 2026, even higher-bandwidth SiPh/CPO platforms are expected to be deployed in AI switches. These next-gen optical communication technologies will enable high-bandwidth, low-power data interconnects, optimizing overall system bandwidth density and energy efficiency to meet the escalating performance demands of AI infrastructure.

Overall, the memory industry is rapidly evolving toward bandwidth efficiency as its core competitive advantage. Advances in optical communications—designed to handle data transmission across chips and modules—are emerging as the most effective solution to overcome the limitations of traditional electrical interfaces in long-distance, high-density data transfers. As a result, high-speed transmission technologies are set to become a key pillar of AI infrastructure evolution.

NAND Flash Suppliers Advance AI Storage Solutions to Accelerate Inference and Reduce Costs

AI training and inference tasks demand quick access to massive datasets with unpredictable I/O behavior, leading to a widening performance gap with current storage options. NAND Flash manufacturers are tackling this issue by speeding up the development of tailored solutions, concentrating on two main product types.

The first category includes storage-class memory SSDs, KV cache SSDs, and HBF, which are placed between DRAM and traditional NAND Flash. These options offer extremely low latency and high bandwidth, making them perfect for speeding up real-time AI inference tasks.

The second category includes nearline QLC SSDs, which are rapidly being adopted for warm and cold AI data storage layers like model checkpoints and dataset archiving. QLC significantly lowers the cost per bit for storing large AI datasets, offering 33% higher per-die storage density than TLC. TrendForce projects that by 2026, QLC SSDs are expected to make up 30% of the enterprise SSD market, highlighting their increasing importance in enhancing storage capacity and cost efficiency in AI infrastructure.

Energy Storage Systems Emerge as the Power Core of AI Data Centers and Are Set for Explosive Growth

As AI data centers develop into large-scale clustered systems, their variable workloads require much more stable power. This shift is turning energy storage systems from mere backup sources into the core energy infrastructure of AI data centers.

Over the next five years, AI data centers are expected to significantly transform energy storage systems. In addition to traditional short-duration UPS backup and power quality stabilization, the share of medium- to long-duration storage systems (2 to 4 hours) will increase sharply to support backup power, energy arbitrage, and grid services simultaneously.

Deployment models will also evolve from centralized, data center-level battery energy storage systems to distributed architectures at the rack or cluster level that incorporate modular battery backup units capable of instantaneous response. This shift will improve system resilience and energy efficiency while satisfying the increasingly demanding power stability needs of AI-driven infrastructure.

North America is expected to become the largest global market for AI data center energy storage, led by hyperscale cloud providers. In China, the "Eastern Data, Western Computing" initiative is driving data centers toward renewable energy-rich western regions, where AI data centers paired with energy storage systems will become standard infrastructure for large-scale campuses. Globally, the installed capacity of AI data center energy storage is projected to surge from 15.7 GWh in 2024 to 216.8 GWh by 2030, representing a CAGR of 46.1%.

AI Data Centers Transition to 800V HVDC Architecture, Driving Demand for Third-Generation Semiconductors

Data centers are experiencing a major upgrade in power infrastructure as server rack ratings increase from kilowatts to megawatts. The industry is quickly adopting 800V HVDC architectures to boost efficiency, enhance reliability, cut down on copper cabling, and support more compact system designs. Advanced third-generation semiconductors like SiC and GaN play a crucial role in this shift, with numerous semiconductor providers now participating in NVIDIA's 800V HVDC project.

SiC is vital in the front-end and mid-stage power conversion within data center architectures, managing the highest voltages and power loads. While SiC devices currently have lower maximum voltage ratings compared to traditional silicon, their enhanced thermal efficiency and switching performance are essential for the development of next-generation solid-state transformers (SSTs).

Meanwhile, GaN, known for its high-frequency and high-efficiency properties, is becoming increasingly popular in mid- and end-stage power conversion. It supports ultra-high-power density and quick dynamic responses. TrendForce predicts that the adoption of SiC and GaN in data center power systems will reach 17% by 2026 and exceed 30% by 2030.

Next-Generation Semiconductor Race: 2nm GAAFET Production and 2.5D/3D Heterogeneous Integration Lead the Next Breakthrough

The semiconductor industry is currently undergoing two simultaneous trends: the shift to 2nm process technology in mass production to achieve higher transistor density, and its growth into larger package sizes, driven by advances in heterogeneous integration. This approach merges multiple chips with different functionalities and technology nodes to meet the performance and efficiency demands of AI and HPC applications.

Wafer fabrication is transitioning from FinFET to GAAFET architecture, where the gate oxide fully encircles the silicon channel. This design provides improved current control while maintaining high performance. In packaging, 2.5D and 3D technologies enable dense multi-chip stacking, resulting in faster interconnects and lower power consumption. These innovations are essential for future data centers and HPC systems.

TSMC, Intel, and Samsung are each adopting unique 2.5D/3D packaging solutions—TSMC with CoWoS and SoIC, Intel with EMIB and Foveros, and Samsung with I-Cube and X-Cube—to provide integrated front-end and back-end foundry services as they ramp up 2nm GAAFET production. Their main challenge will be to manage capacity, reliability, cost, and yield effectively to secure sustainable competitive advantages in the next phase of semiconductor development.

Humanoid Robot Shipments to Surge over 700% in 2026, Driven by AI Adaptivity and Scenario-Based Applications

The year 2026 will mark a pivotal turning point for the commercialization of humanoid robots, with global shipments expected to surge more than sevenfold to surpass 50,000 units. Market momentum is set to revolve around two core pillars: AI adaptivity and application-oriented design.

The advancement of AI adaptivity, driven by powerful AI chips, sensor fusion, and LLM integration, allows humanoid robots to learn on the spot and make flexible decisions in unpredictable settings, reaching new heights of situational awareness and reasoning prior to acting.

In line with this trend, the next generation of humanoid robots in 2026 will shift focus from just showcasing specifications or dexterity. Instead, they will be tailored to specific operational scenarios like manufacturing logistics, warehouse sorting, and inspection support, with each capable of performing complete, task-oriented functions. This signals a formal shift of humanoid robotics into a new phase—an AI-driven, application-focused industrial evolution.

OLED Enters New Phase: Premiumization of Notebook Displays and the Rise of Foldable Smartphones

OLED technology is undergoing a significant transformation in various device segments. As Chinese and Korean panel makers expand their Gen 8.6 AMOLED production, improvements in cost structure and yield rates are speeding up OLED adoption in both small and large displays. This shift is also increasing ASPs and strengthening the bargaining position of upstream components like driver ICs, TCONs, touch modules, and thermal solutions.

OLED boasts self-emissive pixels, offering better contrast, a slimmer design, and adaptable refresh rates. It overcomes LCDs' physical constraints in thickness and energy use, aligning with Apple's emphasis on image quality and power efficiency. Expected to arrive in the MacBook Pro line in 2026, OLED panels will likely lead to a shift from mini-LED to OLED in premium notebooks. TrendForce predicts OLED laptop market share will hit 5% by 2025, increasing to 9–12% between 2027 and 2028, driven by Apple's adoption.

Meanwhile, Apple's planned launch of its foldable smartphones around late 2026 to 2027 may transform the market by leveraging its hardware-software synergy, strong brand, and robust supply chain. The industry's attention is likely to move from aesthetic appeal to boosting productivity and improving user experience, with worldwide shipments of foldable devices projected to surpass 30 million units by 2027.

Nonetheless, mainstream adoption continues to confront obstacles such as hinge durability, flexible panel encapsulation, yield rates, and cost management. Apple's careful product validation approach emphasizes its focus on quality and timing, suggesting that the foldable market's development will ultimately rely on technological advances and robust manufacturing capabilities.

Meta Accelerates the Global Advancement of Near-Eye Displays as LEDoS Builds Momentum

Meta has introduced its Meta Ray-Ban Display AR glasses as AI integration becomes more advanced. These glasses are aimed at information delivery applications that integrate AI into daily life, transforming how humans and AI interact. By gathering and analyzing data from a first-person view, they improve two-way communication between users and AI.

Current displays employ LCoS, providing reliable full-color performance and maturity. This approach supports the still-developing LEDoS technology and helps build market awareness with an accessible and polished user experience.

Looking ahead, both market expectations and Meta's product roadmap are aligning towards LEDoS displays, which provide higher brightness and contrast and enable a wider range of applications. Companies like Apple, Google, RayNeo, INMO, Rokid, and Vuzix are actively investing in this technology, and production costs are expected to drop quickly, making it more accessible. TrendForce predicts that by 2027–2028, the industry will have more advanced full-color LEDoS solutions, with Meta likely launching its next-generation AR glasses featuring LEDoS displays.

Autonomous Driving Accelerates: Passenger Cars Standardize Assisted Driving While Robotaxi Expands Globally

It is projected that the adoption rate of L2 and higher assisted-driving systems will surpass 40% by 2026, making vehicle intelligence the next key growth driver in the automotive sector after electrification. Emphasis will shift to cost reduction as L2 technology becomes widespread, with integrated cockpit-driving SoCs and controllers entering mass production in 2026. This primarily targets China's mid-range vehicle market. Traditional automakers are also enhancing the vehicle intelligence of internal combustion vehicles to further drive the widespread adoption of ADAS as standard equipment.

Meanwhile, the Robotaxi sector is entering a phase of global expansion as it aims for L4 autonomy. Looser regulations, growing enthusiasm among fleet operators and mobility service providers, and advances in AI models, such as E2E and VLA architectures, are all accelerating market growth. By 2026, Robotaxi services are expected to grow rapidly across Europe, the Middle East, Japan, and Australia, moving beyond their current strongholds in China and the US—signaling a new chapter in autonomous mobility.

For more information on reports and market data from TrendForce's Department of Semiconductor Research, please click here, or email the Sales Department at [email protected] 

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About TrendForce

TrendForce is a global provider of the latest development, insight, and analysis of the technology industry. Having served businesses for over a decade, the company has built up a strong membership base of 500,000 subscribers. TrendForce has established a reputation as an organization that offers insightful and accurate analysis of the technology industry through five major research divisions: Semiconductor Research, Display Research, Optoelectronics Research, Green Energy Research, ICT Applications Research. Founded in Taipei, Taiwan in 2000, TrendForce has extended its presence in China since 2004 with offices in Shenzhen and Beijing.

SOURCE TrendForce