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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AI growth is increasingly becoming a manufacturing, packaging and deployment challenge — not just a computing one.
Updated
May 26, 2026 5:28 PM
Taipei 101 and Taipei Nan Shan Plaza, viewed from Elephant Mountain. PHOTO: UNSPLASH
As AI companies continue scaling larger models and data centers, the pressure is no longer falling only on chip design. Manufacturing capacity, advanced packaging and infrastructure deployment are becoming equally important parts of the AI race. AMD’s latest investment announcement reflects how quickly that shift is accelerating.
The US chipmaker announced plans to invest more than US$10 billion across Taiwan’s semiconductor and manufacturing ecosystem to support next-generation AI infrastructure. The investment focuses on expanding partnerships and increasing advanced packaging capacity needed for future AI systems.
The announcement highlights a growing reality across the AI industry. Building powerful AI chips is no longer enough on its own. Companies now also need the manufacturing networks, packaging technologies and supply chain coordination required to deploy AI infrastructure at global scale.
AMD’s investments center heavily around advanced chip packaging, an area becoming increasingly critical as AI systems demand higher performance and greater power efficiency. Traditional chip architectures are struggling to keep pace with the size and complexity of modern AI workloads. Advanced packaging helps connect processors, memory and computing systems more efficiently while managing power and cooling limitations inside large-scale AI environments.
The company said it is working with Taiwan-based partners including ASE, SPIL and PTI to develop next-generation packaging technologies for its upcoming 6th Gen AMD EPYC processors, codenamed “Venice.” AMD also said it had qualified what it described as the industry’s first 2.5D panel-based EFB interconnect technology alongside PTI.
At the center of the broader strategy is AMD Helios, the company’s rack-scale AI infrastructure platform scheduled for deployment beginning in the second half of 2026. The platform combines AMD Instinct MI450X GPUs, 6th Gen EPYC CPUs, networking systems and AMD’s ROCm software stack into integrated AI infrastructure systems designed for hyperscale deployment.
Rather than selling individual processors alone, companies are increasingly building complete AI infrastructure platforms that combine hardware, software, cooling systems and power management into unified deployments. That transition is reshaping how AI infrastructure is designed, manufactured and delivered.
Taiwan is also becoming more deeply embedded in that process. AMD’s investment spans not only semiconductor packaging companies but also manufacturing and system integration partners including Sanmina, Wiwynn, Wistron and Inventec. The partnerships reflect Taiwan’s growing role as one of the operational centers of the global AI infrastructure economy.
Dr. Lisa Su, Chair and CEO of AMD, said: “As AI adoption accelerates, our global customers are rapidly scaling AI infrastructure to meet growing compute demand. By combining AMD leadership in high-performance computing with the Taiwan ecosystem and our strategic global partners, we are enabling integrated, rack-scale AI infrastructure that helps customers accelerate deployment of next-generation AI systems”.
Power efficiency is becoming another major challenge shaping AI infrastructure decisions. As AI workloads consume more electricity and generate more heat, infrastructure providers are increasingly being forced to rethink cooling systems, interconnect technologies and deployment economics.
AMD’s announcement signals how the AI competition is evolving beyond model development and raw computing power. The next stage may depend just as heavily on who can manufacture, package and deploy AI infrastructure fast enough to support global demand.