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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Where smarter storage meets smarter logistics.
Updated
January 8, 2026 6:32 PM
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Kioxia's flagship building at Yokohama Technology Campus. PHOTO: KIOXIA
E-commerce keeps growing and with it, the number of products moving through warehouses every day. Items vary more than ever — different shapes, seasonal packaging, limited editions and constantly updated designs. At the same time, many logistics centers are dealing with labour shortages and rising pressure to automate.
But today’s image-recognition AI isn’t built for this level of change. Most systems rely on deep-learning models that need to be adjusted or retrained whenever new products appear. Every update — whether it’s a new item or a packaging change — adds extra time, energy use and operational cost. And for warehouses handling huge product catalogs, these retraining cycles can slow everything down.
KIOXIA, a company known for its memory and storage technologies, is working on a different approach. In a new collaboration with Tsubakimoto Chain and EAGLYS, the team has developed an AI-based image recognition system that is designed to adapt more easily as product lines grow and shift. The idea is to help logistics sites automatically identify items moving through their workflows without constantly reworking the core AI model.
At the center of the system is KIOXIA’s AiSAQ software paired with its Memory-Centric AI technology. Instead of retraining the model each time new products appear, the system stores new product data — images, labels and feature information — directly in high-capacity storage. This allows warehouses to add new items quickly without altering the original AI model.
Because storing more data can lead to longer search times, the system also indexes the stored product information and transfers the index into SSD storage. This makes it easier for the AI to retrieve relevant features fast, using a Retrieval-Augmented Generation–style method adapted for image recognition.
The collaboration will be showcased at the 2025 International Robot Exhibition in Tokyo. Visitors will see the system classify items in real time as they move along a conveyor, drawing on stored product features to identify them instantly. The demonstration aims to illustrate how logistics sites can handle continuously changing inventories with greater accuracy and reduced friction.
Overall, as logistics networks become increasingly busy and product lines evolve faster than ever, this memory-driven approach provides a practical way to keep automation adaptable and less fragile.