The focus is no longer just AI-generated worlds, but how those worlds become structured digital products
Updated
February 20, 2026 6:50 PM
The inside of a pair of HTC VR goggles. PHOTO: UNSPLASH
As AI tools improve, creating 3D content is becoming faster and easier. However, building that content into interactive experiences still requires time, structure and technical work. That difference between generation and execution is where HTC VIVERSE and World Labs are focusing their new collaboration.
HTC VIVERSE is a 3D content platform developed by HTC. It provides creators with tools to build, refine and publish interactive virtual environments. Meanwhile, World Labs is an AI startup founded by researcher Fei-Fei Li and a team of machine learning specialists. The company recently introduced Marble, a tool that generates full 3D environments from simple text, image or video prompts.
While Marble can quickly create a digital world, that world on its own is not yet a finished experience. It still needs structure, navigation and interaction. This is where VIVERSE fits in. By combining Marble’s world generation with VIVERSE’s building tools, creators can move from an AI-generated scene to a usable, interactive product.
In practice, the workflow works in two steps. First, Marble produces the base 3D environment. Then, creators bring that environment into VIVERSE, where they add game mechanics, scenes and interactive elements. In this model, AI handles the early visual creation, while the human creator defines how users explore and interact with the world.
To demonstrate this process, the companies developed three example projects. Whiskerhill turns a Marble-generated world into a simple quest-based experience. Whiskerport connects multiple AI-generated scenes into a multi-level environment that users navigate through portals. Clockwork Conspiracy, built by VIVERSE, uses Marble’s generation system to create a more structured, multi-scene game. These projects are not just demos. They serve as proof that AI-generated worlds can evolve beyond static visuals and become interactive environments.
This matters because generative AI is often judged by how quickly it produces content. However, speed alone does not create usable products. Digital experiences still require sequencing, design decisions and user interaction. As a result, the real challenge is not generation, but integration — connecting AI output to tools that make it functional.
Seen in this context, the collaboration is less about a single product and more about workflow. VIVERSE provides a system that allows AI-generated environments to be edited and structured. World Labs provides the engine that creates those environments in the first place. Together, they are testing whether AI can fit directly into a full production pipeline rather than remain a standalone tool.
Ultimately, the collaboration reflects a broader change in creative technology. AI is no longer only producing isolated assets. It is beginning to plug into the larger process of building complete experiences. The key question is no longer how quickly a world can be generated, but how easily that world can be turned into something people can actually use and explore.
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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.