To support this era of autonomous decision-making, the “AI Factory” concept has emerged as the definitive blueprint for next-generation AI infrastructure. This approach prioritizes a holistic co-design of computing power, memory, interconnects, power supply, and thermal management. Taiwan-based companies, long pivotal to the industry, are well-positioned to seize new opportunities and reach new heights within this emerging landscape.
The rise of GenAI has revolutionized data center operations, shifting the primary workload to AI and triggering a construction boom for dedicated AI data centers (AIDC) in 2025. Global market intelligence firm TrendForce estimates that the top five US cloud service providers (CSPs) will increase capital expenditure by over 50% for the current year of 2026, thus signaling sustained, robust momentum in AI investments.
These investments highlight a strategic shift in the global AI race: the focus has moved from algorithmic superiority to power supply stability and energy security. Microsoft is restarting Unit 1 at the Three Mile Island nuclear plant, Google has signed the first US corporate agreement for small modular reactors (SMRs) with Kairos Power, and Amazon is partnering with Energy Northwest to deploy up to 12 SMRs. The world now faces a new reality defined by the trinity of “Power = Compute = National Power.”
Demand for AI Servers Drive AI Infrastructure Growth, and Liquid Cooling Goes Mainstream
Looking further ahead, capacity is expected to surge to 152 GW, marking a year-on-year growth rate of nearly 30%. This trajectory underscores the market’s intensifying demand for AI computing infrastructure.
Crucially, this growth is driven almost entirely by AI servers. TrendForce estimates that the total power capacity of AI servers deployed worldwide will reach 55 GW—a staggering 74% YoY increase that accounts for over half (52%) of the total power capacity of all servers deployed worldwide. This marks a major industry watershed: for the first time, power demand for AI servers exceeds that of general-purpose servers. Consequently, global energy allocation and hardware spending will increasingly pivot toward AI infrastructure.
TrendForce Analyst Fion Chiu adds that alongside rising power consumption, the demand for liquid cooling is accelerating due to the drastically increased power density of server racks.
Mirroring global trends, Taiwan’s AI-related electricity demand is climbing sharply. The Ministry of Economic Affairs forecasts an eightfold increase in AI-related power consumption, rising from 0.24 GW in 2023 to 2.24 GW by 2028. Clearly, the rapid expansion of AI development places Taiwan before a critical test regarding the sufficiency of its power supply.
When AI Agents Meet LLMs: Unveiling a New Era of Autonomous Decision-Making
The convergence of long-established AI agents with large language models (LLMs) has ushered in a new era of autonomous decision-making. The evolution from GenAI to Agentic AI marks a pivotal shift: AI has graduated from merely speaking to actively executing tasks. Almost overnight, Agentic AI has become the industry’s hottest trend. Defined as systems capable of autonomously setting goals, formulating plans, executing actions, and adjusting strategies based on feedback, Agentic AI essentially represents the next generation of LLM-based agents.
At the GPU Technology Conference (GTC) 2025, NVIDIA CEO Jensen Huang emphasized that the rise of Agentic AI and advanced reasoning capabilities will push computing power requirements 100 times higher than previously expected. Consequently, global data centers are racing to meet this explosive new demand.
As “Cloud + Edge” Hybrid Architecture Turns Mainstream, Energy Efficiency Also Becomes Top Priority
To minimize latency, enhance privacy, and enable real-time decision-making, nearly two-thirds of computing workloads are expected to shift to the edge in the coming years. This entails running AI models directly on devices where data is generated—such as smartphones, autonomous vehicles, local servers, and edge AI computers equipped with NVIDIA Jetson—rather than in remote data centers.
TrendForce Senior Research Manager PK Tseng estimates that the global Edge AI market reached around USD 36 billion in 2025 and will expand to USD 84 billion by 2029. This represents a CAGR of 23.5% from 2025 to 2029 (see table below).
Global Edge AI Market Size Forecast (2025-2029)
(Source: TrendForce. Note: (E) denotes estimated values, indicating the year is near completion or based on preliminary statistics; (F) denotes forecasted values, based on long-term trend outlooks.)
While Edge AI will decentralize a significant portion of computing, Yang argues this will not result in a zero-sum scenario where “the edge grows and the cloud shrinks.” Instead, the “Cloud + Edge” hybrid architecture will drive an overall increase in overall computing power. While the edge handles more real-time inference and low-latency tasks, the cloud remains indispensable for model training, iteration, cross-domain collaboration, and long-context reasoning.
Consequently, energy efficiency optimization has become a top priority, particularly for data centers. Since reliance on semiconductor manufacturing process advancements alone can no longer fully address power consumption challenges, the focus is shifting toward optimizing upstream power distribution architectures—such as high-voltage direct current (HVDC).
The “AI Factory” Boom is Here! Triggering a Wave of Customized AIDC Construction
Driven by the rise of Agentic AI and Edge AI—and the need to tackle critical challenges regarding power, computing, energy consumption, and thermal management—NVIDIA CEO Jensen Huang has tirelessly promoted the concept of the “AI Factory” at major global venues such as GTC, CES, COMPUTEX, and Davos. Consequently, the term has become the hottest buzzword in the AI sector and serves as the reference architecture for building future AIDCs.
Yang points out that “AI Factory” is essentially a synonym for the next generation of AIDCs. Although NVIDIA has proposed a reference architecture, major CSPs are actively developing customized AIDC designs based on their own business strategies, resulting in a diverse and evolving technological landscape.
The most critical difference between an AI Factory and a traditional AIDC lies in the emphasis on holistic planning and co-design. Its core objective is to maximize system-level efficiency across three major subsystems: computing, power, and switching.
However, these efficiency improvements will not dampen the aggregate demand for computing power and electricity. The overall demand curve is expected to maintain a steep upward trajectory. The ultimate goal of enhancing efficiency is to pack as much computing power as possible into fixed capacity limits, thereby generating greater model value.
EU Plans to Build 5 AI Gigafactories; Such Facilities Are Being Established Globally and in Taiwan
Regardless of how “AI factories” are defined across the industry, the construction of next-generation AIDCs and AI factories has undeniably become a top priority for governments and corporations alike. In the EU, the European High Performance Computing Joint Undertaking (EuroHPC JU) selected seven countries—including Finland, Germany, and Spain—in December 2024 to host the first batch of AI factories. This was followed in March 2025 by the addition of six more sites in countries such as Austria, France, and Poland. Furthermore, in February 2025, the European Commission pledged to mobilize EUR 20 billion through the InvestAI initiative to establish up to five “AI Gigafactories” across the bloc.
Taiwan is not falling behind in this global wave of AI factory construction. Big Innovation Company, a subsidiary of Hon Hai Technology Group (Foxconn), is partnering with NVIDIA to build an AI factory powered by 10,000 NVIDIA Blackwell GPUs. Similarly, US GPU cloud service provider GMI Cloud has announced a USD 500 million investment to collaborate with Taiwan Mobile, transforming the latter’s server room in Taoyuan into an AI factory.
Furthermore, in November of last year, a consortium of companies—including INFINITIX, SignalPro, Supermicro, Macnica, Stark Technology, and He Tong Enterprise—joined forces to build the next-generation “SiGTRON” AI factory near the Southern Taiwan Science Park, aiming to jointly promote a neocloud intelligent computing ecosystem.
Taiwan’s AI Supply Chain Mobilizes to Become “AI Infra Integrators”
Fueled by strong market demand for emerging applications such as Agentic AI and Edge AI, alongside the global construction boom of AIDCs and AI factories, Taiwan’s AI supply chain is undergoing rapid expansion and structural upgrading. The insatiable appetite for AI chips has created supply bottlenecks in advanced manufacturing processes and advanced packaging. In response, TSMC and ASE are actively expanding new facilities to boost overall production capacity.
Taiwanese EMS providers (ODMs) already account for over 80% of global server shipments and more than 90% of global AI server shipments, underscoring their pivotal role in future AI infrastructure. Leading firms such as Foxconn, Quanta, Wistron, Wiwynn, Inventec, MiTAC, and Gigabyte have delivered impressive results amidst this new AI wave. Consequently, their investment focus and delivery capabilities have evolved from simple “production lines and assembly” to “rack-level delivery and data center-level integration.” Furthermore, with the rise of Edge AI, traditional industrial computer manufacturers are pivoting from image and sensor recognition to developing Edge AI systems capable of local inference, tool invocation, and process agency. Key players in this transition include Advantech, Asus, AAEON, IEI, and IBASE.
The rising power density of AI servers is establishing liquid cooling as the mainstream thermal solution for future AIDCs and AI factories. Taiwan-based firms Auras and AVC have achieved significant success with liquid cooling modules, cold plates, and quick disconnects. In the realm of power supply and distribution, major local suppliers—including Delta Electronics, LITE-ON, and AcBel—are not only showcasing higher-power, higher-efficiency AIDC power, thermal, and microgrid solutions but are also actively integrating into the high-voltage ecosystem driven by NVIDIA’s 800V requirement for data center power supply.
AI training and inference clusters demand networks with higher bandwidth and lower latency. To meet this need, the island’s networking companies like Accton and its subsidiary Edgecore are launching high-performance switch portfolios, thus collectively help pushing a wave of upgrades in AI-related switching technology.
Simultaneously, the demand for AI servers, accelerator cards, and switches is propelling high-end PCBs and AGF substrates into a long-cycle growth period. Manufacturers such as ZDT, GCE, and Tripod, along with the “ABF Trio” (Unimicron, Nan Ya PCB, and Kinsus), have emerged as the strongest drivers of this boom.
In conclusion, propelled by the latest wave of demand for Agentic AI and AI factories, Taiwan’s AI supply chain is no longer limited to isolated pockets of growth. Instead, the entire ecosystem—from computing power, packaging, and substrates to board and rack assembly, cooling, power, and networking—is experiencing a comprehensive elevation. The robust demand generated by this trend has spilled over from the cloud and data centers to the edge, accelerating the island’s evolution from a “hardware manufacturing hub” into an “AI infra integrator.”
(Header image source: Shutterstock.)
