HyveGeo’s approach to restoring degraded land stands out at the FoodTech Challenge
Updated
January 21, 2026 11:09 AM
Clusters of sandstone buttes in Monument Valley, Colorado Plateau. PHOTO: UNSPLASH
HyveGeo, a climate-focused startup, has been named one of the global winners of the FoodTech Challenge, an international competition designed to surface practical technologies that strengthen food systems in arid and climate-stressed regions.
The FoodTech Challenge (FTC) is based in the UAE and brings together governments, foundations and agri-food institutions to identify early-stage solutions that address food production, land degradation and resource efficiency. Each year, hundreds of startups apply from around the world. In 2026, more than 1,200 teams from 113 countries submitted entries. Only four were selected.
HyveGeo stood out for its approach to one of agriculture’s hardest problems: how to make desert soil usable again. Founded in 2023 by a group of scientists and researchers, the Abu Dhabi-based company focuses on regenerating degraded land using a process built around biochar, a carbon-rich material made from agricultural waste, enhanced with microalgae. The aim is to accelerate soil recovery in environments where water is limited and land has been heavily stressed.
What caught the judges’ attention was not just the technology itself, but the way it links several challenges at once. The system turns waste into a usable soil input, reduces the time it takes for land to become productive and locks carbon into the ground instead of releasing it into the atmosphere. In short, it addresses land degradation, food production and climate pressure through a single framework.
As a winner of the FoodTech Challenge, HyveGeo will share a US$2 million prize with the other selected startups. Beyond funding, the company will also receive support from the UAE’s innovation ecosystem, including research backing, pilot projects, market access and incubation services to help move from testing into wider deployment.
The team’s plans focus on scaling within the UAE first. HyveGeo aims to work across Abu Dhabi’s network of farms and gradually expand into other arid and climate-stressed regions. Its longer-term target is to restore thousands of hectares of degraded land and contribute to carbon removal through soil-based methods.
Placed in a broader context, HyveGeo’s win reflects a shift in how food and climate technologies are being evaluated. Instead of chasing dramatic breakthroughs, competitions like the FTC are increasingly backing systems that connect waste, land, water and carbon into something usable on the ground. Not futuristic agriculture, but practical repair work for environments that can no longer rely on old farming assumptions. If that direction continues, the next wave of food innovation may be less about spectacle and more about quiet, scalable fixes for places where growing food has become hardest.
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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.