The battle for AI chips intensifies, with liquid cooling technology gaining widespread adoption in AI data centers.

Benefiting from increased capital expenditures by large CSPs in North America and the rise of sovereign clouds in various countries, the demand for AI data center construction is expected to be strong in 2026, with global AI server shipments projected to increase by more than 20% annually. NVIDIA, the AI market leader, will face even more intense competition. First, AMD will follow NVIDIA's GB/VR rack solution and launch the MI400 full-rack product, targeting CSP customers. Second, North American CSPs will continue to strengthen their self-developed ASICs. Finally, influenced by geopolitics, ByteDance, Baidu, Alibaba, and Tencent are developing their own ASICs, while Huawei, Cambricon, and others are strengthening their independent AI chip R&D, pushing the competition in the AI market to a fever pitch.

As AI chip computing power increases, the thermal design power (TDP) of a single chip will rise from 700W for NVIDIA H100 and H200 to over 1,000W for B200 and B300. Server racks will need liquid cooling systems to meet the high-density heat flux requirements, pushing the liquid cooling penetration rate of AI chips to 47% by 2026. Microsoft has also proposed a new generation of microfluidic cooling technology at the chip packaging level. Overall, in the short to medium term, the market will still be dominated by water-cooled plates, and the CDU architecture will shift from L2A (Liquid-to-Air) to L2L (Liquid-to-Liquid) design. In the long term, it will evolve towards more refined chip-level heat dissipation.

Breaking bandwidth limitations and achieving high-speed transmission, HBM and optical communication construct a new intelligent computing system.

The data volume and memory bandwidth requirements for AI computing, from training to inference, are growing explosively, bringing transmission speed and energy consumption bottlenecks to the forefront. To address the limitations of AI computing on memory bandwidth and data transmission rate, HBM and optical communication technologies are gradually becoming the core breakthroughs for next-generation AI architectures.

Currently, HBM effectively shortens the distance between the processor and memory through 3D stacking and TSV technology. The upcoming mass-produced HBM4 will incorporate higher channel density and wider I/O bandwidth to support the ultra-large-scale computing of AI GPUs and accelerators. However, as model parameters exceed megabyte levels and GPU cluster sizes expand exponentially, memory bandwidth bottlenecks become prominent again. Currently, memory manufacturers are improving the local bandwidth of AI chips through HBM stacking structure optimization, packaging and interface innovation, and co-design with logic chips.

Besides addressing memory transfer bottlenecks, data transfer across chips and modules remains a new bottleneck limiting system performance. To overcome this limitation, optoelectronic integration and Co-Packaged Optics (CPO) technology are gradually becoming key R&D focuses for mainstream GPU manufacturers and cloud providers. Currently, 800G/1.6T pluggable optical modules are in mass production, and starting in 2026, higher bandwidth SiPh/CPO platforms are expected to be integrated into AI switches. These new optical communication technologies enable high-bandwidth, low-power data interconnection and optimize overall system bandwidth density and energy efficiency.

Looking at the overall trend, the memory industry is moving towards making "bandwidth efficiency" its core competitiveness. New optical communication technologies that handle cross-chip and cross-module communication are also the best solution to overcome the limitations of electrical interfaces in long-distance and high-density data transmission. Therefore, high-speed transmission technology will become a key direction for the evolution of AI infrastructure.

NAND Flash suppliers enhance AI solutions to accelerate inference processes and reduce storage costs.

AI training and inference tasks require high-speed access to massive datasets with unpredictable I/O patterns, creating a performance gap with existing technologies. To address this, NAND Flash vendors are accelerating the development of specialized solutions, including two key products: Storage Class Memory (SCM) SSDs/KV Cache SSDs/HBF technology, positioned between DRAM and traditional NAND, offering ultra-low latency and high bandwidth, ideal for accelerating real-time AI inference workloads. The other is Nearline QLC SSDs. QLC technology is being applied at an unprecedented rate to warm/cold data storage layers for AI, such as model checkpointing and dataset archiving. QLC offers 33% more storage capacity per die than TLC, significantly reducing the unit cost of storing massive AI datasets. It is projected that by 2026, QLC SSDs will achieve a 30% market penetration rate in the Enterprise SSD market.

Energy storage systems are poised to become the core energy source for AI data centers, with demand expected to experience explosive growth.

AI data centers are evolving towards ultra-large-scale clustering, characterized by significant load fluctuations and stringent requirements for power stability. This is driving energy storage systems to shift from "emergency backup power" to becoming the "energy core of AI data centers." It is projected that within the next five years, in addition to existing short-term UPS backup power and power quality improvements, the proportion of medium- to long-term energy storage systems (2-4 hours) will rapidly increase to simultaneously meet backup power, arbitrage, and grid service needs. Deployment methods will also gradually shift from centralized data center-level BESS (battery energy storage systems) to distributed BESS at the rack or cluster level, such as battery backup units, to provide faster instantaneous response.

North America is expected to become the world's largest AI data center energy storage market, dominated by hyperscale cloud vendors. China's "East-to-West Data" strategy will drive the migration of data centers to the green-energy-rich west, and AI data centers combined with energy storage will become standard equipment for large-scale bases in the west. Global new capacity for AI data center energy storage is expected to surge from 15.7 GWh in 2024 to 216.8 GWh in 2030, representing a compound annual growth rate of 46.1%.

AI data centers are moving towards 800V HVDC architecture, driving demand for third-generation semiconductors.

Data centers are undergoing a radical transformation of their power infrastructure, with server rack power rapidly increasing from kilowatts (kW) to megawatts (MW). Power supply is shifting towards 800V HVDC (High Voltage Direct Current) architectures to maximize efficiency and reliability, significantly reduce copper cabling, and support more compact system designs. Third-generation semiconductors, SiC/GaN, are key to this transformation, and several semiconductor suppliers have announced their participation in NVIDIA's 800V HVDC initiative. SiC is primarily used in the front-end and mid-end of data center power supply architectures, handling the highest voltage and power conversion operations. While SiC power semiconductors currently lag behind traditional Si in terms of maximum voltage ratings, their superior thermal performance and switching characteristics are crucial for next-generation solid-state transformer (SST) technology. GaN, with its high frequency and high efficiency, plays a vital role in the mid- and end-end of the power supply chain, pursuing ultimate power density and dynamic response. The penetration rate of third-generation semiconductors SiC/GaN in data center power supply is projected to rise to 17% by 2026 and is expected to exceed 30% by 2030.

The race to the front lines in semiconductors: Mass production of 2nm GAAFETs and heterogeneous integration of 2.5D/3D packaging lead to next-generation breakthroughs.

With 2nm entering mass production, the commercial competition in advanced process technology has led to a trend of pursuing higher transistor density internally and larger package size externally. At the same time, it emphasizes heterogeneous integration capabilities, which meet the needs of high-performance computing and artificial intelligence applications by stacking multi-chips with different functions and combining different technology nodes.

In the pursuit of higher transistor density, semiconductor wafer manufacturing has officially shifted from FinFET to GAAFET. By completely encapsulating silicon channels with Gate-Oxide, more efficient current control is achieved while pursuing high computing power. Externally, 2.5D and 3D packaging technologies provide high-density packaging solutions for multi-chip stacking, enabling faster interconnection between chips and lower power consumption, bringing breakthroughs to next-generation data centers and high-performance computing.

As various companies enter mass production of 2nm GAAFETs, TSMC, Intel, and Samsung have launched 2.5D/3D packaging technologies such as CoWoS/SoIC, EMIB/FOVEROS, and I-Cube/X-Cube, respectively, providing integrated front-end and back-end foundry services. Achieving a balance and commercial advantage among capacity utilization, reliability, cost, and yield will be the core challenge for major wafer foundries and packaging companies.

Humanoid robot shipments are projected to grow by over 700% in 2026, focusing on AI adaptability and scenario-based applications.

2026 will be a pivotal year for the commercialization of humanoid robots, with global shipments projected to increase more than sevenfold annually, exceeding 50,000 units. Market momentum will focus on two main themes: AI Adaptivity technology and application-oriented scenarios. AI Adaptivity technology, combined with the evolution of high-efficiency AI chips, sensor fusion, and Large Language Models (LLM), enables robots to learn and make dynamic decisions in real time in unstructured environments, demonstrating the ability to "plan before acting."

Against this backdrop, new humanoid robots in 2026 will no longer focus solely on specifications or flexibility as their selling point. Instead, they will be designed from the outset to offer value for specific scenarios, supporting complete tasks across various environments, from initial manufacturing and material handling to warehousing and sorting, and even inspection assistance. 2026 will mark a new industrial phase for humanoid robots, driven by AI and centered on applications.

A New Era for OLED: High-End Laptop Displays and Foldable Phones Moving Towards Mainstream

OLED displays are ushering in a generational turning point. Chinese and South Korean panel manufacturers continue to expand their high-generation (8.6-generation) AMOLED production lines. With the continuous improvement of cost structure and yield, OLED display technology is accelerating its coverage of full-size products from small to large, while simultaneously driving up the average selling price (ASP) of related supply chain components such as driver ICs, TCONs, touch modules and thermal design, as well as the bargaining power of suppliers.

OLED, with its self-emissive, high-contrast, thin and light design and variable refresh rate, breaks through the physical limitations of LCD in terms of thickness and energy consumption, meeting Apple's dual requirements for image accuracy and energy efficiency. Apple expects to officially introduce OLED panels into the MacBook Pro in 2026, which will drive the shift of high-end laptop display specifications from mini-LED to OLED. It is estimated that the penetration rate of OLED laptops will reach 5% in 2025, and after 2026, driven by Apple, it is expected to increase to 9-12% in 2027-2028.

Furthermore, with Apple poised to officially enter the foldable phone market between the second half of 2026 and 2027, it will redefine the value of foldable phones through its integrated hardware and software, brand trust, and supply chain synergy, shifting market focus from "technical innovation" to "productivity and enhanced user experience." This is projected to drive global foldable phone shipments to exceed 30 million units by 2027. Currently, foldable phones still face the final hurdle to mainstream adoption—hinge reliability, flexible panel packaging, yield rates, and cost control. Apple's cautious approach to product verification and quality reflects its emphasis on timing and user experience, highlighting that foldable phones still require time and resources to overcome before truly reaching maturity.

Meta drives global leap forward in near-eye displays; LEDoS technology accumulates growth momentum.

As AI applications deepen, Meta has launched the Meta Ray-Ban Display AR glasses, featuring a display function. Targeting "information provision" applications, this glasses aim to bring AI closer to daily life and reshape user behavior. Through first-person data collection and feedback, they enhance the two-way interactive experience between AI and users. The display technology utilizes LCoS, which has proven robust in terms of full-color capabilities and maturity. This provides time for the still-developing LEDoS technology to mature, while also building market awareness through a superior user experience.

With market expectations and Meta's iterative product planning progressing, the trend is pointing towards LEDoS technology with higher brightness and contrast to expand application scenarios. Coupled with the continued investment from manufacturers such as Apple, Google, RayNeo, INMO, Rokid, and Vuzix, costs are expected to rapidly decrease to the expected sweet spot, which is beneficial to the development of LEDoS. It is estimated that more mature full-color LEDoS solutions will emerge in 2027-2028, and Meta is also expected to launch a new generation of AR glasses equipped with LEDoS.

Self-driving technology is rapidly becoming more widespread: passenger cars are increasingly equipped with driver assistance systems as standard, and Robotaxi is expanding its global footprint.

It is estimated that by 2026, the penetration rate of Level 2 (and above) assisted driving systems will exceed 40%, with intelligent technology succeeding electric vehicles as the driving force for the automotive industry's growth. Level 2 assisted driving technology is nearing maturity, and widespread adoption will significantly reduce key steering costs. The cockpit-driver integrated single-chip and controller, which will help reduce the overall system cost, will enter mass production in 2026, initially targeting the mid-range car market in China. Traditional automakers are also actively promoting the intelligent transformation of gasoline vehicles, which is another driving force behind the widespread adoption of assisted driving as a standard feature in vehicles.

On the other hand, Robotaxis, aiming for Level 4 autonomous driving, are experiencing a global expansion wave. Besides the easing of regulations in various regions, the more proactive adoption of Robotaxis by fleet platform providers and service providers, and developers' exploration of more generalizable AI models such as end-to-end (E2E) and VLA (Vision Language Action), all contribute to the expansion of the Robotaxis market. It is projected that by 2026, Robotaxis will accelerate their coverage in markets such as Europe, the Middle East, Japan, and Australia, no longer limited to China and the United States.