AI to Reshape the Global Technology Landscape in 2026, Says TrendForce
PatternForce has recognized 10 key know-how developments that may outline the tech trade’s evolution in 2026. The highlights of those findings are outlined under:
AI Chip Competition Intensifies as Liquid Cooling Gains Widespread Adoption in Data Centers
In 2026, the excessive demand for AI knowledge heart development—fueled by elevated capital spending by main North American CSPs and the rise of sovereign cloud tasks worldwide—is anticipated to enhance AI server shipments by over 20% year-over-year.
NVIDIA, the main title in AI as we speak, will face stronger competitors forward. AMD plans to problem NVIDIA by introducing its MI400 full-rack resolution, which mirrors NVIDIA’s GB/VR programs and is aimed toward CSP shoppers. Meanwhile, main North American CSPs are growing their in-house ASIC growth. In China, geopolitical tensions have sped up the drive for technological self-sufficiency, with firms like ByteDance, Baidu, Alibaba, Tencent, Huawei, and Cambricon boosting efforts to create their very own AI chips. This is about to intensify the international competitors.
Thermal design energy (TDP) per chip is growing quickly as AI processors turn out to be extra highly effective, leaping from 700W for NVIDIA’s H100 and H200 to over 1,000W for the upcoming B200 and B300. This improve in warmth output is main to a widespread adoption of liquid-cooling programs in server racks, with utilization anticipated to attain 47% by 2026.
Microsoft has launched superior chip-level microfluidic cooling know-how to improve thermal effectivity. In the close to to midterm, cold-plate liquid cooling will stay the major resolution, with CDUs transitioning from liquid-to-air to liquid-to-liquid setups. Over the long run, the market is probably going to transfer towards extra detailed chip-level thermal administration.
Breaking Bandwidth Barriers: HBM and Optical Communications Redefine AI Cluster Architectures
The speedy improve in knowledge quantity and reminiscence bandwidth wants, pushed by increasing AI workloads from coaching to inference, is difficult system design by exposing bottlenecks in transmission velocity and energy effectivity. To handle these limitations, HBM and optical interconnect applied sciences are rising as essential enablers of next-generation AI architectures.
Current generations of HBM leverage 3D stacking and through-silicon through to considerably cut back the distance between processors and reminiscence, reaching larger bandwidth and effectivity. The upcoming HBM4 era will introduce better channel density and wider I/O bandwidth to additional help the huge computational calls for of AI GPUs and accelerators.
However, as mannequin parameters surpass the trillion-scale degree and GPU clusters increase exponentially, reminiscence bandwidth as soon as once more emerges as a significant efficiency bottleneck. Memory producers are addressing this situation by optimizing HBM stack architectures, innovating in packaging and interface design, and co-designing with logic chips to improve on-chip bandwidth for AI processors.
While these advances mitigate memory-related bottlenecks, knowledge transmission throughout chips and modules has turn out to be the subsequent essential limitation to system efficiency. To overcome these limits, co-packaged optics (CPO) and silicon photonics (SiPh) are rising are strategic focus areas for GPU makers and CSPs.
Currently, 800G and 1.6T pluggable optical transceivers have already entered mass manufacturing, and beginning in 2026, even higher-bandwidth SiPh/CPO platforms are anticipated to be deployed in AI switches. These next-gen optical communication applied sciences will allow high-bandwidth, low-power knowledge interconnects, optimizing general system bandwidth density and power effectivity to meet the escalating efficiency calls for of AI infrastructure.
Overall, the reminiscence trade is quickly evolving towards bandwidth effectivity as its core aggressive benefit. Advances in optical communications—designed to deal with knowledge transmission throughout chips and modules—are rising as the simplest resolution to overcome the limitations of conventional electrical interfaces in long-distance, high-density knowledge transfers. As a consequence, high-speed transmission applied sciences are set to turn out to be a key pillar of AI infrastructure evolution.
NAND Flash Suppliers Advance AI Storage Solutions to Accelerate Inference and Reduce Costs
AI coaching and inference duties demand fast entry to huge datasets with unpredictable I/O conduct, main to a widening efficiency hole with present storage choices. NAND Flash producers are tackling this situation by dashing up the growth of tailor-made options, concentrating on two principal product sorts.
The first class consists of storage-class reminiscence SSDs, KV cache SSDs, and HBF, that are positioned between DRAM and conventional NAND Flash. These choices supply extraordinarily low latency and excessive bandwidth, making them excellent for dashing up real-time AI inference duties.
The second class consists of nearline QLC SSDs, that are quickly being adopted for heat and chilly AI knowledge storage layers like mannequin checkpoints and dataset archiving. QLC considerably lowers the value per bit for storing massive AI datasets, providing 33% larger per-die storage density than TLC. PatternForce tasks that by 2026, QLC SSDs are anticipated to make up 30% of the enterprise SSD market, highlighting their growing significance in enhancing storage capability and value effectivity in AI infrastructure.
Energy Storage Systems Emerge as the Power Core of AI Data Centers and Are Set for Explosive Growth
As AI knowledge facilities grow to be large-scale clustered programs, their variable workloads require rather more steady energy. This shift is popping power storage programs from mere backup sources into the core power infrastructure of AI knowledge facilities.
Over the subsequent 5 years, AI knowledge facilities are anticipated to considerably remodel power storage programs. In addition to conventional short-duration UPS backup and energy high quality stabilization, the share of medium- to long-duration storage programs (2 to 4 hours) will improve sharply to help backup energy, power arbitrage, and grid providers concurrently.
Deployment fashions may even evolve from centralized, knowledge center-level battery power storage programs to distributed architectures at the rack or cluster degree that incorporate modular battery backup items able to instantaneous response. This shift will enhance system resilience and power effectivity whereas satisfying the more and more demanding energy stability wants of AI-driven infrastructure.
North America is predicted to turn out to be the largest international marketplace for AI knowledge heart power storage, led by hyperscale cloud suppliers. In China, the “Eastern Data, Western Computing” initiative is driving knowledge facilities towards renewable energy-rich western areas, the place AI knowledge facilities paired with power storage programs will turn out to be commonplace infrastructure for large-scale campuses. Globally, the put in capability of AI knowledge heart power 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 facilities are experiencing a significant improve in energy infrastructure as server rack rankings improve from kilowatts to megawatts. The trade is rapidly adopting 800V HVDC architectures to enhance effectivity, improve reliability, reduce down on copper cabling, and help extra compact system designs. Advanced third-generation semiconductors like SiC and GaN play an important function in this shift, with quite a few semiconductor suppliers now taking part in NVIDIA’s 800V HVDC mission.
SiC is important in the front-end and mid-stage energy conversion inside knowledge heart architectures, managing the highest voltages and energy masses. While SiC gadgets presently have decrease most voltage rankings in contrast to conventional silicon, their enhanced thermal effectivity and switching efficiency are important for the growth of next-generation solid-state transformers (SSTs).
Meanwhile, GaN, recognized for its high-frequency and high-efficiency properties, is changing into more and more standard in mid- and end-stage energy conversion. It helps ultra-high-power density and fast dynamic responses. PatternForce predicts that the adoption of SiC and GaN in knowledge heart energy programs will attain 17% by 2026 and exceed 30% by 2030.
Next-Generation Semiconductor Race: 2nm GAAFET Production and a pair of.5D/3D Heterogeneous Integration Lead the Next Breakthrough
The semiconductor trade is presently present process two simultaneous developments: the shift to 2nm course of know-how in mass manufacturing to obtain larger transistor density, and its progress into bigger bundle sizes, pushed by advances in heterogeneous integration. This strategy merges a number of chips with completely different functionalities and know-how nodes to meet the efficiency and effectivity calls for of AI and HPC purposes.
Wafer fabrication is transitioning from FinFET to GAAFET structure, the place the gate oxide totally encircles the silicon channel. This design gives improved present management whereas sustaining excessive efficiency. In packaging, 2.5D and 3D applied sciences allow dense multi-chip stacking, ensuing in quicker interconnects and decrease energy consumption. These improvements are important for future knowledge facilities and HPC programs.
TSMC, Intel, and Samsung are every adopting distinctive 2.5D/3D packaging options—TSMC with CoWoS and SoIC, Intel with EMIB and Foveros, and Samsung with I-Cube and X-Cube—to present built-in front-end and back-end foundry providers as they ramp up 2nm GAAFET manufacturing. Their principal problem will probably be to handle capability, reliability, value, and yield successfully to safe sustainable aggressive benefits in the subsequent part of semiconductor growth.
Humanoid Robot Shipments to Surge over 700% in 2026, Driven by AI Adaptivity and Scenario-Based Applications
The 12 months 2026 will mark a pivotal turning level for the commercialization of humanoid robots, with international shipments anticipated to surge greater than sevenfold to surpass 50,000 items. Market momentum is about to revolve round two core pillars: AI adaptivity and application-oriented design.
The development of AI adaptivity, pushed by highly effective AI chips, sensor fusion, and LLM integration, permits humanoid robots to be taught on the spot and make versatile choices in unpredictable settings, reaching new heights of situational consciousness and reasoning prior to appearing.
In line with this development, the subsequent era of humanoid robots in 2026 will shift focus from simply showcasing specs or dexterity. Instead, they are going to be tailor-made to particular operational eventualities like manufacturing logistics, warehouse sorting, and inspection help, with every able to performing full, task-oriented features. This indicators a proper shift of humanoid robotics into a brand new part—an AI-driven, application-focused industrial evolution.
OLED Enters New Phase: Premiumization of Notebook Displays and the Rise of Foldable Smartphones
OLED know-how is present process a major transformation in varied machine segments. As Chinese and Korean panel makers increase their Gen 8.6 AMOLED manufacturing, enhancements in value construction and yield charges are dashing up OLED adoption in each small and enormous shows. This shift can be growing ASPs and strengthening the bargaining place of upstream elements like driver ICs, TCONs, contact modules, and thermal options.
OLED boasts self-emissive pixels, providing higher distinction, a slimmer design, and adaptable refresh charges. It overcomes LCDs’ bodily constraints in thickness and power use, aligning with Apple’s emphasis on picture high quality and energy effectivity. Expected to arrive in the MacBook Pro line in 2026, OLED panels will probably lead to a shift from mini-LED to OLED in premium notebooks. PatternForce predicts OLED laptop computer market share will hit 5% by 2025, growing to 9–12% between 2027 and 2028, pushed by Apple’s adoption.
Meanwhile, Apple’s deliberate launch of its foldable smartphones round late 2026 to 2027 might remodel the market by leveraging its hardware-software synergy, robust model, and sturdy provide chain. The trade’s consideration is probably going to transfer from aesthetic enchantment to boosting productiveness and enhancing consumer expertise, with worldwide shipments of foldable gadgets projected to surpass 30 million items by 2027.
Nonetheless, mainstream adoption continues to confront obstacles corresponding to hinge sturdiness, versatile panel encapsulation, yield charges, and value administration. Apple’s cautious product validation strategy emphasizes its give attention to high quality and timing, suggesting that the foldable market’s growth will finally depend on technological advances and sturdy manufacturing capabilities.
Meta Accelerates the Global Advancement of Near-Eye Displays as LEDoS Builds Momentum
Meta has launched its Meta Ray-Ban Display AR glasses as AI integration turns into extra superior. These glasses are aimed toward info supply purposes that combine AI into day by day life, remodeling how people and AI work together. By gathering and analyzing knowledge from a first-person view, they enhance two-way communication between customers and AI.
Current shows make use of LCoS, offering dependable full-color efficiency and maturity. This strategy helps the still-developing LEDoS know-how and helps construct market consciousness with an accessible and polished consumer expertise.
Looking forward, each market expectations and Meta’s product roadmap are aligning in direction of LEDoS shows, which give larger brightness and distinction and allow a wider vary of purposes. Companies like Apple, Google, RayNeo, INMO, Rokid, and Vuzix are actively investing in this know-how, and manufacturing prices are anticipated to drop rapidly, making it extra accessible. PatternForce predicts that by 2027–2028, the trade could have extra superior full-color LEDoS options, with Meta probably launching its next-generation AR glasses that includes LEDoS shows.
Autonomous Driving Accelerates: Passenger Cars Standardize Assisted Driving While Robotaxi Expands Globally
It is projected that the adoption fee of L2 and better assisted-driving programs will surpass 40% by 2026, making automobile intelligence the subsequent key progress driver in the automotive sector after electrification. Emphasis will shift to value discount as L2 know-how turns into widespread, with built-in cockpit-driving SoCs and controllers getting into mass manufacturing in 2026. This primarily targets China’s mid-range automobile market. Traditional automakers are additionally enhancing the automobile intelligence of inside combustion automobiles to additional drive the widespread adoption of ADAS as commonplace tools.
Meanwhile, the Robotaxi sector is getting into a part of world enlargement because it goals for L4 autonomy. Looser laws, rising enthusiasm amongst fleet operators and mobility service suppliers, and advances in AI fashions, corresponding to E2E and VLA architectures, are all accelerating market progress. By 2026, Robotaxi providers are anticipated to develop quickly throughout Europe, the Middle East, Japan, and Australia, shifting past their present strongholds in China and the US—signaling a brand new chapter in autonomous mobility.
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