WIRobotics is betting that years of real-world movement data could shape the next generation of humanoid robots
Updated
May 19, 2026 5:10 PM
3D render of a person in various colours. PHOTO: UNSPLASH
Investor interest in humanoid robotics is continuing to grow as startups race to build systems capable of working alongside humans in real-world environments. That momentum was reflected after WIRobotics announced a KRW 95 billion (USD 68 million) Series B funding round to accelerate development of its humanoid robotics platform, ALLEX.
The Seoul-based startup said the funding comes roughly two years after its KRW 13 billion Series A round in 2024. JB Investment led the financing alongside investors including InterVest, Hana Ventures, Smilegate Investment, SBVA, NH Investment & Securities, Company K Partners, GU Investment and FuturePlay.
WIRobotics has spent the past several years building wearable robotics systems designed to assist human movement. The startup is now using that foundation to expand deeper into humanoid robotics and Physical AI, a category focused on AI systems that can interact with the physical world through movement, perception and manipulation.
Its humanoid platform, ALLEX, is being developed to support human-level object manipulation and interaction capabilities. The startup was recently selected for NVIDIA’s Physical AI Fellowship, a global robotics and AI development initiative aimed at supporting next-generation robotics research.
Rather than building humanoid systems entirely from scratch, WIRobotics is drawing on movement data collected through its wearable walking-assist robot, WIM. Over the past three years, the startup says it has built large real-world datasets around gait patterns, mobility and human movement control.
That wearable robotics business has also started showing commercial traction. WIM has sold more than 3,000 cumulative units and expanded into overseas markets including Europe, China, Türkiye and Japan. Revenue grew from KRW 560 million in 2023 to KRW 1.3 billion in 2024, then to KRW 2.79 billion in 2025. According to the startup, first-quarter 2026 revenue has already surpassed its full-year 2024 total.
The startup believes that real-world movement data collected through wearable robotics could become a competitive advantage as humanoid systems move closer to commercial deployment. WIRobotics is also expanding its global footprint alongside its robotics development efforts. The startup said it is establishing a North American entity in California while growing partnerships with overseas distributors and healthcare networks.
Its humanoid ambitions are moving into a more operational phase as well. Beginning later this year, WIRobotics plans to supply a research-focused version of its Mobile ALLEX platform to global research institutions and international partners for testing and collaborative development. The startup is also in discussions with a global automotive manufacturer around manufacturing-focused platform validation projects.
Yeonbaek Lee said: "This investment represents global recognition that the real-world movement data and control technologies accumulated through wearable robotics can evolve into next-generation humanoid robotics. We aim to accelerate the arrival of humanoid robots capable of interacting naturally with people".
Yongjae Kim added: "All investors from our previous Series A round participated again in this Series B financing, demonstrating strong confidence in WIRobotics' technological capabilities and growth potential amid intensifying global humanoid competition. Our mission is to realize humanoids capable of fundamentally human-like interaction and force control, driving a paradigm shift in high-performance manipulation technologies".
As competition intensifies across humanoid robotics, startups are increasingly trying to differentiate themselves through real-world deployment data rather than simulation alone. WIRobotics is positioning its wearable robotics business as the foundation for that transition, betting that years of human movement data could help shape the next generation of humanoid systems.
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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.