Huawei is betting that the future of AI infrastructure will depend as much on energy systems as on computing power
Updated
May 19, 2026 5:43 PM
Blue light painting with a lightbulb. PHOTO: UNSPLASH
As AI companies build larger models and deploy more AI agents, the industry is running into a new constraint: electricity. The challenge is no longer just about computing power. It is increasingly about how to supply, manage and sustain the energy needed to run AI infrastructure at scale.
That was the central argument behind Huawei’s latest AI data center strategy unveiled at its Global AIDC Industry Summit in Dongguan.
The company introduced what it calls a grid-interactive AIDC strategy, focused on redesigning AI data centers around power supply, cooling systems and energy management. AIDC refers to AI data centers built specifically for large-scale AI computing workloads.
The announcement reflects a broader shift happening across the industry. As AI systems grow larger, data centers are consuming more electricity and generating more heat than traditional computing infrastructure was designed to handle. Companies are now being forced to rethink not just chips and servers, but the physical systems supporting them.
Huawei argues that future AI infrastructure will need closer coordination between computing systems and energy grids. The company says traditional data center designs are struggling to keep up with fluctuating AI workloads, rising power density and the growing use of renewable energy sources.
Hou Jinlong, Director of the Board of Huawei and President of Huawei Digital Power, said: "The booming AI industry, widely adopted large models, and numerous AI agents are creating huge energy demands, set to boost the global AIDC capacity. Electricity is essential for computing; energy is the foundation for AI long-term development. Computing and electricity will deeply synergize and empower each other, progressively building an integrated framework that brings together new power systems and AI infrastructure."
A large part of Huawei’s strategy focuses on power architecture. AI workloads can create sudden spikes in electricity demand, especially in high-density computing environments. To manage that, Huawei says it plans to develop new power systems that combine grid-friendly UPS infrastructure with energy storage technologies.
Cooling is becoming another major pressure point. AI servers generate significantly more heat than traditional enterprise systems and Huawei says liquid cooling is now becoming essential for large-scale AI deployments. The company introduced a liquid cooling system designed to improve long-term thermal management inside high-density AI environments.
Huawei is also pushing modular construction methods to reduce deployment times for AI data centers. Instead of building infrastructure entirely onsite, parts of the system can be prefabricated and tested in factories before installation.
Bob He, Vice President of Huawei Digital Power, said: "The global AI industry is booming, and the token demand surges. As such, the AIDC industry is entering the Token era."
As part of that shift, Huawei introduced a proposed measurement system called the TokEnergy Index. The company says the metric is designed to measure the relationship between energy consumption and AI computing output, rather than relying only on traditional data center efficiency metrics such as PUE.
The broader message behind the strategy is that AI infrastructure is becoming an energy engineering problem as much as a computing problem. As global demand for AI continues to rise, companies across the sector are beginning to realise that the future of AI may depend not only on better models, but also on whether power grids and data centers can keep up with them.
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How Korea is trying to take control of its AI future.
Updated
January 13, 2026 10:56 AM
SK Telecom Headquarters in Seoul, South Korea. PHOTO: ADOBE STOCK
SK Telecom, South Korea’s largest mobile operator, has unveiled A.X K1, a hyperscale artificial intelligence model with 519 billion parameters. The model sits at the center of a government-backed effort to build advanced AI systems and domestic AI infrastructure within Korea. This comes at a time when companies in the United States and China largely dominate the development of the most powerful large language models.
Rather than framing A.X K1 as just another large language model, SK Telecom is positioning it as part of a broader push to build sovereign AI capacity from the ground up. The model is being developed as part of the Korean government’s Sovereign AI Foundation Model project, which aims to ensure that core AI systems are built, trained and operated within the country. In simple terms, the initiative focuses on reducing reliance on foreign AI platforms and cloud-based AI infrastructure, while giving Korea more control over how artificial intelligence is developed and deployed at scale.
One of the gaps this approach is trying to address is how AI knowledge flows across a national ecosystem. Today, the most powerful AI foundation models are often closed, expensive and concentrated within a small number of global technology companies. A.X K1 is designed to function as a “teacher model,” meaning it can transfer its capabilities to smaller, more specialized AI systems. This allows developers, enterprises and public institutions to build tailored AI tools without starting from scratch or depending entirely on overseas AI providers.
That distinction matters because most real-world applications of artificial intelligence do not require massive models operating independently. They require focused, reliable AI systems designed for specific use cases such as customer service, enterprise search, manufacturing automation or mobility. By anchoring those systems to a large, domestically developed foundation model, SK Telecom and its partners are aiming to create a more resilient and self-sustaining AI ecosystem.
The effort also reflects a shift in how AI is being positioned for everyday use. SK Telecom plans to connect A.X K1 to services that already reach millions of users, including its AI assistant platform A., which operates across phone calls, messaging, web services and mobile applications. The broader goal is to make advanced AI feel less like a distant research asset and more like an embedded digital infrastructure that supports daily interactions.
This approach extends beyond consumer-facing services. Members of the SKT consortium are testing how the hyperscale AI model can support industrial and enterprise applications, including manufacturing systems, game development, robotics and autonomous technologies. The underlying logic is that national competitiveness in artificial intelligence now depends not only on model performance, but on whether those models can be deployed, adapted and validated in real-world environments.
There is also a hardware dimension to the project. Operating an AI model at the 500-billion-parameter scale places heavy demands on computing infrastructure, particularly memory performance and communication between processors. A.X K1 is being used to test and validate Korea’s semiconductor and AI chip capabilities under real workloads, linking large-scale AI software development directly to domestic semiconductor innovation.
The initiative brings together technology companies, universities and research institutions, including Krafton, KAIST and Seoul National University. Each contributes specialized expertise ranging from data validation and multimodal AI research to system scalability. More than 20 institutions have already expressed interest in testing and deploying the model, reinforcing the idea that A.X K1 is being treated as shared national AI infrastructure rather than a closed commercial product.
Looking ahead, SK Telecom plans to release A.X K1 as open-source AI software, alongside APIs and portions of the training data. If fully implemented, the move could lower barriers for developers, startups and researchers across Korea’s AI ecosystem, enabling them to build on top of a large-scale foundation model without incurring the cost and complexity of developing one independently.