Wedoany.com Report on Mar 16th, As the NVIDIA GPU Technology Conference (GTC) is about to commence, the global AI industry's focus is shifting from a singular competition in model capabilities to a deeper transformation of AI infrastructure—an industrial era known as the "AI Factory" is approaching.

The AI Factory is defined as a vertically integrated system whose core mission is to transform electricity, semiconductors, memory, and data into intelligent models. Unlike traditional software companies, the essence of an AI Factory is closer to heavy industry: it requires massive physical infrastructure, complex global supply chains, and gigawatt-scale energy support. Currently, tech giants like NVIDIA, Amazon, Microsoft, Google, and Meta have invested hundreds of billions of dollars in this domain, and the industry widely anticipates that capital expenditure in the next phase could approach $1 trillion.

However, the expansion of AI Factories is facing multiple structural constraints, which are redefining the competitive rules of the AI industry.

First is the "appreciation paradox" of GPUs. AI chips, represented by NVIDIA's H100, do not depreciate over time; instead, their value continues to appreciate due to the ongoing enhancement of model capabilities. This makes GPUs the most critical value-preserving assets in the digital economy and intensifies the competition for computing power.

Second is the memory bottleneck. High Bandwidth Memory (HBM), a key enabler for unleashing GPU computing power, is seeing its costs rise rapidly. Industry forecasts suggest that by 2026, up to 30% of capital expenditure by hyperscale enterprises may be allocated to memory procurement. This trend is reshaping the resource allocation landscape of the semiconductor industry, strengthening the bargaining power of HBM suppliers like SK Hynix and Samsung.

Third is the supply rigidity of upstream equipment. Dutch company ASML is the world's sole manufacturer capable of producing Extreme Ultraviolet (EUV) lithography equipment, with an annual capacity limited to producing only 70 to 100 EUV lithography tools. This physical ceiling directly constrains the expansion rate of advanced process chips, becoming the "narrowest bottleneck" in the entire AI computing power supply chain.

Beyond hardware resource constraints, energy is emerging as another core variable for AI Factories. The power consumption of a hyperscale AI cluster has already reached gigawatt levels, equivalent to the output of a small nuclear power plant. To address challenges related to grid capacity and electricity price volatility, tech companies are accelerating the deployment of behind-the-meter power systems, including natural gas generation, energy storage facilities, and even small modular nuclear reactors.

Simultaneously, AI Factories are expanding beyond centralized data centers. The rise of the hyper-converged edge is extending AI computing capabilities to frontline scenarios such as factory floors, hospital imaging centers, and port control rooms. The construction of this distributed intelligent network requires AI Factories to balance the synergy between central training and edge inference in their architectural design.

On the dimension of global competition, AI sovereignty is becoming a new focal point of national strategy. The United States, China, Europe, and multiple Middle Eastern countries are investing in sovereign-level AI computing capabilities, viewing AI Factories as strategic infrastructure as critical as ports, power grids, and communication base stations. This "national computing power sovereignty" mindset is accelerating the fragmentation and regionalization of global AI infrastructure.

Overall, AI Factories are driving the AI industry from a capital-light software logic to a capital-heavy industrial logic. Over the next four to five years, the outcome of the AI race will be determined not only by algorithmic breakthroughs but also by supply chain assurance capabilities—including access to lithography machines, allocation of memory wafers, and supporting power facilities. As a foundational pillar of the digital economy, the construction race for AI Factories has already begun.