The US$50.8 million deal strengthens TECO’s push into modular infrastructure and faster data center deployment across Southeast Asia.
Updated
May 26, 2026 5:39 PM
Kuala Lumpur, Malaysia. PHOTO: UNSPLASH
TECO Electric & Machinery is expanding further into Southeast Asia’s AI data center infrastructure market through a new acquisition in Malaysia.
The Taiwan-based company has signed an agreement to acquire approximately 78 percent of Malaysian engineering firm Dynaciate Engineering in a deal valued at around MYR 200 million (US$50.8 million). According to TECO, the acquisition is aimed at strengthening its modular data center manufacturing capabilities and supporting its expansion across Southeast Asia’s data center infrastructure sector.
Under the agreement, Dynaciate will become TECO’s global manufacturing hub for modular data center and power equipment products. The company will also serve as an engineering hub supporting TECO’s regional expansion efforts, particularly in AI data center infrastructure projects.
TECO Chairman Morris Li said the integration between both companies has improved execution efficiency and increased the company’s in-house modular prefabrication capabilities. According to the company, the collaboration has reduced data center delivery timelines to as little as six months.
Dynaciate is headquartered in Johor Bahru, Malaysia. Its facilities span approximately 36,000 square meters and include eight production buildings focused on stainless steel and carbon steel fabrication. The company said the site is also eligible for export tax incentives that support future global supply chain deployment.
According to TECO, Dynaciate has experience in engineering, steel fabrication and large-scale industrial projects for multinational corporations. The company added that Dynaciate has expanded into the data center engineering market since 2025 through projects involving international cloud service provider clients.
TECO estimates that after the acquisition, around 65 percent of future data center-related revenue will come from modular data centers and prefabricated products, while the remaining 35 percent will come from AI data center engineering projects. The company also forecasts that data center-related revenue within its Power & Energy Business Group will rise from below 10 percent to 30 percent this year.
Dynaciate CEO Ng Kim Thiea said the company is entering a new phase of growth through the partnership with TECO. He added that Dynaciate has extensive experience supporting engineering and industrial projects across the region.
The acquisition marks a further expansion of TECO’s presence in the AI data center infrastructure sector as companies continue increasing investments in modular infrastructure and regional engineering capacity.
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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.