The quiet infrastructure shift powering the next generation of data centers
Updated
January 30, 2026 11:42 AM
Peripheral Component Interconnect Express (PCIe) port on a motherboard, coloured yellow. PHOTO: UNSPLASH
Modern data centers operate on a simple yet fundamental principle: computers require the ability to share data extremely quickly. As AI and cloud systems grow, servers are no longer confined to a single rack. They are spread across many racks, sometimes across entire rooms. When that happens, moving data quickly and cleanly becomes harder.
Montage Technology, a Shanghai-based semiconductor company, builds the chips and connection systems that help servers exchange data without delays. This week, the company announced a new Active Electrical Cable (AEC) solution based on PCIe 6.x and CXL 3.x — two important standards used to connect CPUs, GPUs, network cards and storage inside modern data centers.
In simple terms, Montage’s new AEC product helps different parts of a data center “talk” to each other faster and more reliably, even when those parts are physically far apart.
As data centers grow to support AI and cloud workloads, their architecture is changing. Instead of everything sitting inside one rack, systems now stretch across multiple racks and even multiple rows. This creates a new problem: the longer the distance between machines, the harder it is to keep data signals clean and fast.
This is where Active Electrical Cables come in. Unlike regular copper cables, AECs include small electronic components inside the cable itself. These components strengthen and clean up the data signal as it travels, so information can move farther without getting distorted or delayed.
Montage’s solution uses its own retimer chip based on PCIe 6.x and CXL 3.x. A “retimer” refreshes the data signal so it arrives accurately at the other end. This allows servers, GPUs, storage devices and network cards to stay tightly connected even across longer distances inside large data centers.
The company also uses high-density cable designs and built-in monitoring tools so operators can track performance and fix issues faster. That makes large data centers easier to deploy and maintain.
According to Montage, the solution has already passed interoperability tests with CPUs, xPUs, PCIe switches and network cards. It has also been jointly developed with cable manufacturers in China and validated at the system level.
What makes this development important is not just speed. It is about scale. AI models, cloud services and real-time applications demand massive amounts of data to move continuously between machines. If that movement slows down, everything else slows with it.
By improving how machines connect across racks, Montage’s AEC solution supports the kind of infrastructure that next-generation AI and cloud systems depend on.
Looking ahead, the company plans to expand its high-speed interconnect products further, including work on PCIe 7.0 and Ethernet retimer technologies.
Quietly, in the background of every AI system and cloud service, there is a network of cables and chips doing the hard work of moving data. Montage’s latest launch focuses on making that hidden layer faster, cleaner and ready for the scale that modern computing now demands.
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How ECOPEACE uses autonomous robots and data to monitor and maintain urban water bodies.
Updated
January 23, 2026 10:41 AM
A school of fish swimming among debris and waste. PHOTO: UNSPLASH
South Korea–based water technology company ECOPEACE is working on a practical challenge many cities face today: keeping urban water bodies clean as pollution and algae growth become more frequent. Rather than relying on periodic cleanup drives, the company focuses on systems that can monitor and manage water conditions on an ongoing basis.
At the core of ECOPEACE’s work are autonomous water-cleanup robots known as ECOBOT. These machines operate directly on lakes, reservoirs and rivers, removing algae and surface waste while also collecting information about water quality. The idea is to combine cleaning with constant observation so changes in water conditions do not go unnoticed.
Alongside the robots, ECOPEACE uses a filtration and treatment system designed to process polluted water continuously. This system filters out contaminants using fine metal filters and treats the water using electrical processes. It also cleans itself automatically, which allows it to run for long periods without frequent manual maintenance.
The role of AI in this setup is largely about decision-making rather than direct control. Sensors placed across the water body collect data such as pollution levels and water quality indicators. The software then analyses this data to spot early signs of issues like algae growth. Based on these patterns, the system adjusts how the robots and filtration units operate, such as changing treatment intensity or water flow. In simple terms, the technology helps the system respond sooner instead of waiting for visible problems to appear.
ECOPEACE has already deployed these systems across several reservoirs, rivers and urban waterways in South Korea. Those projects have helped refine how the robots, sensors and software work together in real environments rather than controlled test sites.
Building on that experience, the company has begun expanding beyond Korea. It is currently running pilot and proof-of-concept projects in Singapore and the United Arab Emirates. These deployments are testing how the technology performs in dense urban settings where waterways are closely linked to public health, infrastructure and daily city life.
Both regions have invested heavily in smart city initiatives and water management, making them suitable test beds for automated monitoring and cleanup systems. The pilots focus on algae control, surface cleaning and real-time tracking of water quality rather than large-scale rollout.
As cities continue to grow and climate-related pressures on water systems increase, managing waterways is becoming less about occasional intervention and more about continuous oversight. ECOPEACE’s approach reflects that shift by using automation and data to address problems early and reduce the need for reactive cleanup later.