The next war for AI data centers: Who will benefit from electricity, networks, and infrastructure?

In the past few years, the logic of the AI industry's development has been very clear: whoever has more powerful computing power is more likely to gain a market advantage. Therefore, GPUs have become the most sought-after core assets in the AI era, with the market launching a sustained investment boom around AI chips.

However, as large language models continue to scale up, the AI industry is entering a new phase.

Training a large AI model requires tens of thousands of GPUs to work together, and these GPUs do not operate independently. They need high-speed network connections, massive power support, stable data center environments, as well as advanced storage and cooling systems to maintain long-term operation.

This means that the bottleneck for AI development is shifting from "whether there is enough computing power" to "whether such a large scale of computing power can be supported."

In the future, competition in AI data centers may no longer be just about chips, but about competition between entire infrastructure systems.

AI Data Centers Are Entering the Infrastructure Competition Phase

There are clear differences between AI data centers and traditional data centers. Traditional cloud computing data centers mainly serve web pages, databases, enterprise software, and other workloads, with relatively stable computing demands. AI data centers, on the other hand, need to support large-scale parallel computing, imposing higher requirements on energy, networking, and hardware.

Especially in the context of the rapid development of generative AI, enterprise demand for GPU clusters continues to increase. A single AI data center may deploy tens of thousands or more AI accelerators, and when these devices run simultaneously, they generate enormous energy consumption and data exchange needs.

In the past, the market focused on:

• Who can produce more GPUs? Now the market is beginning to focus on:
• Who can build more AI data centers?
• Who can provide enough electricity?
• Who can make tens of thousands of GPUs work together efficiently?

This is also why the AI industry chain is expanding from chip companies to a broader range of infrastructure fields.

Why Electricity Has Become a New Bottleneck for AI Expansion

One of the biggest changes in AI data centers is the significant increase in energy demand. Although traditional data centers also consume a lot of electricity, AI computing tasks typically require higher-density computing resources. Massive numbers of GPUs running for long periods lead to higher power demands.

As global tech companies continue to increase investment in AI infrastructure, power supply is becoming a new limiting factor. A large AI data center not only requires server equipment but also a stable and reliable power system, including:

• Grid connection capacity
• Power generation resources
• Power management systems
• Data center energy optimization technologies

This means that the winners in the AI era may include not only chip manufacturers but also energy infrastructure companies. In the past, the tech industry and the energy industry were relatively independent, but AI is changing this relationship. In the future, building an AI data center is not just about purchasing GPUs; it also requires solving the problem of "where to get enough electricity." This is an important reason why the market has recently begun to focus on data center power supply, grid upgrades, and new energy infrastructure.

Network Interconnection Is Becoming Key to AI Cluster Efficiency

Beyond electricity, networking is another major bottleneck for AI data centers. Large AI model training typically requires many GPUs to work together on tasks. If the data transfer speed between GPUs is insufficient, even with a large number of computing resources, full performance cannot be achieved.

Therefore, AI data centers need higher-speed, lower-latency network architectures. Among these, the importance of high-speed switching chips, optical interconnect technology, and data center networking equipment is increasing. For example, in traditional server environments, networks primarily serve data exchange functions; but in AI clusters, the network has become an important component affecting computing efficiency. Computing power determines capability; HBM determines data supply speed; the network determines how computing resources collaborate.

This is also why the market has recently begun to pay attention to AI networking chip companies. Compared to merely manufacturing computing chips, network infrastructure companies may become another type of beneficiary in the AI expansion process.

Data Center Infrastructure Ushers in a New Growth Cycle

The development of AI data centers is also driving the upgrade of the entire infrastructure industry.

Server infrastructure. AI servers differ from traditional servers, requiring support for higher-performance GPUs, more complex cooling systems, and stronger power management capabilities.

Cooling technology. As chip performance improves, traditional air cooling technology is increasingly under pressure, and advanced cooling solutions such as liquid cooling are gaining attention.

Data center construction. AI data centers require larger spaces, more stable energy supplies, and more complete network environments.

Therefore, the AI industry chain is forming a new infrastructure ecosystem:

Upstream: AI chips, HBM, advanced packaging.

Midstream: Servers, networking equipment, data center construction.

Downstream: Cloud computing services, AI applications, and enterprise intelligence.

In the future, AI value may not be concentrated solely on models and chips, but will gradually spread across the entire infrastructure system.

Beyond NVIDIA, What Other Beneficiaries Are There in the AI Industry Chain?

In the past, when discussing AI investment, NVIDIA was almost the most central keyword. But as AI infrastructure enters an expansion phase, the market is looking for more beneficiaries.

The first category is network infrastructure companies. The larger the AI cluster, the higher the demand for high-speed interconnection, and the importance of networking chips, switching equipment, and optical communication technology will increase.

The second category is memory companies. HBM has become an important component of AI chips, and memory manufacturers like SK Hynix, Samsung Electronics, and Micron are benefiting from the growth in AI data center demand.

The third category is data center infrastructure companies. This includes server equipment, power management, cooling systems, and data center operators.

The fourth category is energy-related companies. The long-term expansion of AI data centers requires a more stable energy supply, which may drive increased investment in power infrastructure.

Therefore, the future AI investment logic may expand from a single chip opportunity to opportunities across the entire industry chain.

What Risks Does AI Infrastructure Investment Face?

Although the long-term trend for AI data centers is clear, investors still need to pay attention to multiple risks.

Capital expenditure risk. Global tech companies are currently investing heavily in building AI infrastructure. If the commercialization speed of AI falls short of expectations in the future, it could affect corporate investment pace.

Supply-demand risk. The semiconductor, server, and data center industries all have cyclical characteristics. When many companies expand production simultaneously, there may be periodic oversupply.

Technology change risk. AI technology is developing very fast, and future computing architectures may change. If new technical routes emerge, demand for some infrastructure could be affected.

In addition, the energy issue is also a long-term challenge. AI data centers require a large and stable energy supply, but grid construction usually takes a long time, which may limit the expansion speed of AI infrastructure in some regions.

Therefore, although AI infrastructure offers long-term opportunities, it is not a simple one-way growth market.

AI Data Center Competition Is Going Global

AI data center construction has become an important part of global technology competition.

• The United States has leading AI chip companies and cloud computing firms, making it a current major center for AI infrastructure.
• South Korea, with its HBM memory technology, holds an important position in the AI hardware supply chain.
• Hong Kong (China) and other Asian markets are also promoting the development of AI applications, cloud computing, and technology industries.

In the future, the AI industry chain will not be concentrated in a single market but will form a global collaborative system.

This also means that investors need to observe changes in the AI industry from a global perspective.

Gate Stock Trading: Focus on Global AI Infrastructure Chain Opportunities

As the AI industry chain continues to expand, the targets of investor attention have also gradually extended from single AI chip companies to multiple fields including memory, networking, power, and data centers.

Gate Stock Trading supports 7×24 hour trading of U.S., Hong Kong, and South Korean stocks, allowing users to more flexibly track changes in the global AI industry chain. From U.S. AI chip companies to South Korean HBM memory companies to global technology infrastructure-related assets, investors can focus on development opportunities in different markets and different links based on market changes.

Investment opportunities in the AI era are shifting from a single track to a complete ecosystem, and cross-market observation of industry chain changes is becoming more important.

Summary: The Next Competition in AI Is Infrastructure Competition

In the first phase of AI development, the market fought over computing power. GPUs became the most core assets, and chip companies became the focus of the capital market. But as AI enters the scaling phase, the factors that truly limit industry development are changing.

Electricity, networking, storage, servers, and data center construction capabilities are becoming new infrastructure competitions in the AI era. In the future, the AI market may no longer just be about finding the most powerful chip companies, but about identifying the key bottlenecks in the entire AI system. Whoever can solve the energy, connectivity, and infrastructure problems during AI expansion may become an important beneficiary in the next phase.

FAQs

Q1: Why do AI data centers need more electricity?

Because AI training and inference require a large number of GPUs to run for long periods, and the computing density is much higher than traditional data centers, resulting in significantly increased energy consumption.

Q2: What are the biggest bottlenecks for AI data centers?

Currently, they mainly include power supply, network interconnection, memory bandwidth, and data center construction capabilities.

Q3: Besides GPUs, which other parts of the AI industry chain are worth attention?

Including HBM memory, networking chips, optical interconnects, servers, cooling, and energy infrastructure.

Q4: Will AI data center investment keep growing forever?

Long-term demand is still strong, but short-term it may be affected by capital expenditure, economic conditions, and technological changes.

Q5: Why is networking important for AI?

Because large AI models require a large number of GPUs to compute collaboratively, and high-speed networks can improve the efficiency of the entire computing cluster.