The CE approval opens Europe for Cornerstone Robotics as the company expands its global surgical robotics business
Updated
May 29, 2026 4:20 AM
A tray of surgical tools. PHOTO: UNSPLASH
As surgical robotics companies expand beyond domestic markets, regulatory approvals are becoming a critical part of global growth. Companies are no longer competing only on hardware and clinical performance. They are also competing on their ability to enter tightly regulated healthcare systems and build long-term hospital partnerships.
Hong Kong-based Cornerstone Robotics is now moving further into that phase of expansion after its Sentire Endoscopic Surgical System received CE Mark certification under the European Union’s Medical Device Regulation framework.
The approval allows the company to commercialize the system across European markets for minimally invasive procedures in general surgery, gynecology, thoracic surgery and urology. For surgical robotics companies, regulatory approvals often represent more than product validation. They also determine market access, hospital adoption opportunities and long-term commercial scale.
Cornerstone Robotics has already been building clinical operations in the UK ahead of the approval. Since 2025, the company has worked with Portsmouth Hospitals University NHS Trust on clinical investigations involving the Sentire Surgical System. According to the company, the system has been used across procedures involving urology, gynecology and gastrointestinal surgery. The company says the clinical investigation helped generate real-world data to support physician training, research and future adoption efforts.
Alongside the regulatory approval, Cornerstone Robotics is also expanding its local operations in Europe. The company established a UK subsidiary in 2025 and has been developing training, clinical support and after-sales service capabilities for hospitals using the system.
That operational buildout reflects a larger challenge inside surgical robotics. Hospitals adopting robotic systems often require ongoing clinical training, technical support and workflow integration alongside the hardware itself.
Cornerstone Robotics says its strategy centers around vertically integrated development across engineering, software, imaging and robotics systems. The company argues that this structure gives it greater control over product development, supply chain management and long-term operational stability.
Professor Samuel Au, Founder and CEO of Cornerstone Robotics, said: "Receiving CE Certification marks a major milestone in Cornerstone Robotics' evolution from a technology innovator to a global clinical solutions provider. From our first clinical investigation in Portsmouth, UK, to achieving European regulatory approval, each step of the journey reflects our commitment to proprietary innovation, product excellence, and clinical value. Looking ahead, we will continue expanding into key global markets and partnering with leading medical institutions to bring high-quality surgical robotic solutions to more physicians and patients worldwide."
The CE approval also comes several months after the company completed an oversubscribed financing round of approximately US$200 million in November 2025.
The funding and regulatory expansion together signal how surgical robotics companies are increasingly entering a more commercially focused stage of growth. Beyond research and development, companies are now investing more heavily in regulatory approvals, hospital partnerships, physician training and international operational infrastructure as competition expands across global healthcare markets.
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The hidden cost of scaling AI: infrastructure, energy, and the push for liquid cooling.
Updated
January 8, 2026 6:31 PM
The inside of a data centre, with rows of server racks. PHOTO: FREEPIK
As artificial intelligence models grow larger and more demanding, the quiet pressure point isn’t the algorithms themselves—it’s the AI infrastructure that has to run them. Training and deploying modern AI models now requires enormous amounts of computing power, which creates a different kind of challenge: heat, energy use and space inside data centers. This is the context in which Supermicro and NVIDIA’s collaboration on AI infrastructure begins to matter.
Supermicro designs and builds large-scale computing systems for data centers. It has now expanded its support for NVIDIA’s Blackwell generation of AI chips with new liquid-cooled server platforms built around the NVIDIA HGX B300. The announcement isn’t just about faster hardware. It reflects a broader effort to rethink how AI data center infrastructure is built as facilities strain under rising power and cooling demands.
At a basic level, the systems are designed to pack more AI chips into less space while using less energy to keep them running. Instead of relying mainly on air cooling—fans, chillers and large amounts of electricity, these liquid-cooled AI servers circulate liquid directly across critical components. That approach removes heat more efficiently, allowing servers to run denser AI workloads without overheating or wasting energy.
Why does that matter outside a data center? Because AI doesn’t scale in isolation. As models become more complex, the cost of running them rises quickly, not just in hardware budgets, but in electricity use, water consumption and physical footprint. Traditional air-cooling methods are increasingly becoming a bottleneck, limiting how far AI systems can grow before energy and infrastructure costs spiral.
This is where the Supermicro–NVIDIA partnership fits in. NVIDIA supplies the computing engines—the Blackwell-based GPUs designed to handle massive AI workloads. Supermicro focuses on how those chips are deployed in the real world: how many GPUs can fit in a rack, how they are cooled, how quickly systems can be assembled and how reliably they can operate at scale in modern data centers. Together, the goal is to make high-density AI computing more practical, not just more powerful.
The new liquid-cooled designs are aimed at hyperscale data centers and so-called AI factories—facilities built specifically to train and run large AI models continuously. By increasing GPU density per rack and removing most of the heat through liquid cooling, these systems aim to ease a growing tension in the AI boom: the need for more computers without an equally dramatic rise in energy waste.
Just as important is speed. Large organizations don’t want to spend months stitching together custom AI infrastructure. Supermicro’s approach packages compute, networking and cooling into pre-validated data center building blocks that can be deployed faster. In a world where AI capabilities are advancing rapidly, time to deployment can matter as much as raw performance.
Stepping back, this development says less about one product launch and more about a shift in priorities across the AI industry. The next phase of AI growth isn’t only about smarter models—it’s about whether the physical infrastructure powering AI can scale responsibly. Efficiency, power use and sustainability are becoming as critical as speed.