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.
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As industrial drone adoption grows, startups are finding bigger opportunities in infrastructure, inspections and field operations.
Updated
May 25, 2026 3:21 PM
An oil pump on a field. PHOTO: UNSPLASH
As drone adoption grows across industrial sectors, more startups are moving beyond hardware sales and into service-based business models. Instead of simply selling drones, companies are increasingly trying to build recurring revenue through inspection, mapping and infrastructure-monitoring services. That shift is shaping ZenaTech’s latest expansion strategy.
ZenaTech is a Vancouver-based startup that develops AI drone and Drone as a Service (DaaS) technologies. The company has signed an offer to acquire an Alberta-based land surveying and geomatics business operating across Western Canada. If completed, the deal would mark ZenaTech’s first land surveying acquisition in Canada and its first major push into the oil and gas sector.
The move gives the startup something more valuable than just another acquisition target. It provides direct access to an industry where drones are already becoming part of everyday operations.
The Alberta surveying company works with oil and gas producers across Alberta, Eastern British Columbia and Saskatchewan. Its services include land surveying, geomatics, mapping and environmental support for infrastructure and energy development projects.
According to ZenaTech, drones are already used in roughly 80 percent of the target company’s existing projects. That matters because it reduces the operational gap between traditional surveying work and AI-powered automation.
Rather than introducing drones into a completely manual workflow, ZenaTech is entering a business where drone-based data collection is already established. The startup says it plans to build on that foundation by integrating more AI-powered capabilities across surveying, mapping, inspections and infrastructure monitoring.
Shaun Passley, Ph.D., CEO of ZenaTech, said: "This proposed acquisition represents an important strategic expansion of our Drone as a Service business into Canada’s oil and gas sector, one of the most significant energy markets in North America. This company brings an established commercial customer base, strong regional expertise, and extensive experience supporting surveying and geomatics projects including for some large producers. We believe there is a significant opportunity to further enhance these services through AI-powered drone technology for surveying, mapping, inspections, and infrastructure monitoring applications, enabling us to establish a core expertise that we can bring to this fast-growing global industry."
The timing is also significant. ZenaTech pointed to estimates showing the global oil and gas drone inspection services market is currently valued at around US$ 2.3 billion and projected to grow at a compound annual growth rate of roughly 28.5 percent.
Much of that growth is being driven by energy companies looking for faster ways to inspect infrastructure, monitor remote sites and reduce manual field operations.
ZenaTech’s broader strategy centers around building a global DaaS network through acquisitions. Instead of creating local operations from scratch, the startup is acquiring existing service businesses with established customers and then layering drone automation and AI systems into those operations.
The company says its DaaS platform offers businesses and government clients subscription-based or on-demand drone services across areas such as inspections, surveying, maintenance, inventory management and precision agriculture.
The larger opportunity for startups in this space may not be drone manufacturing alone. Increasingly, the focus is shifting toward startups that can build scalable drone service networks and integrate them into industries that already rely on large-scale field operations. Oil and gas appear to be one of the next major targets for that expansion.