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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Bindwell is testing a simple idea: use AI to design smarter, more targeted pesticides built for today’s farming challenges.
Updated
January 8, 2026 6:33 PM
Researcher tending seedlings in a laboratory environment. PHOTO: FREEPIK
Bindwell, a San Francisco–based ag-tech startup using AI to design new pesticide molecules, has raised US$6 million in seed funding, co-led by General Catalyst and A Capital, with participation from SV Angel and Y Combinator founder Paul Graham. The round will help the company expand its lab in San Carlos, hire more technical talent and advance its first pesticide candidates toward validation.
Even as pesticide use has doubled over the last 30 years, farmers still lose up to 40% of global crops to pests and disease. The core issue is resistance: pests are adapting faster than the industry can update its tools. As a result, farmers often rely on larger amounts of the same outdated chemicals, even as they deliver diminishing returns.
Meanwhile, innovation in the agrochemical sector has slowed, leaving the industry struggling to keep up with rapidly evolving pests. This is the gap Bindwell is targeting. Instead of updating old chemicals, the company uses AI to find completely new compounds designed for today’s pests and farming conditions.
This vision is made even more striking by the people leading it. Bindwell was founded by 18-year-old Tyler Rose and 19-year-old Navvye Anand, who met at the Wolfram Summer Research Program in 2023. Both had deep ties to agriculture — Rose in China and Anand in India — witnessing up close how pest outbreaks and chemical dependence burdened farmers.
Filling the gap in today’s pesticide pipeline, Bindwell created an AI system that can design and evaluate new molecules long before they hit the lab. It starts with Foldwell, the company’s protein-structure model, which helps map the shapes of pest proteins so scientists know where a molecule should bind. Then comes PLAPT, which can scan through every known synthesized compound in just a few hours to see which ones might actually work. For biopesticides, they use APPT, a model tuned to spot protein-to-protein interactions and shown to outperform existing tools on industry benchmarks.
Bindwell isn’t selling AI tools. Instead, the company develops the molecules itself and licenses them to major agrochemical players. Owning the full discovery process lets the team bake in safety, selectivity and environmental considerations from day one. It also allows Bindwell to plug directly into the pipelines that produce commercial pesticides — just with a fundamentally different engine powering the science.
At present, the team is now testing its first AI-generated candidates in its San Carlos lab and is in early talks with established pesticide manufacturers about potential licensing deals. For Rose and Anand, the long-term vision is simple: create pest control that works without repeating the mistakes of the last half-century. As they put it, the goal is not to escalate chemical use but to design molecules that are more precise, less harmful and resilient against resistance from the start.