Can innovation truly deliver affordable housing to those who need it most?
Updated
November 27, 2025 3:26 PM
Close up of a 3D printer nozzle pouring concrete. PHOTO: ICON
The affordable housing crisis has become one of the most pressing challenges of our time. Across the globe, millions of people are struggling to secure a roof over their heads. In cities like San Francisco, housing prices are so high that even middle-income families find themselves shut out of the market.
The root of this crisis lies in a persistent imbalance: the supply of housing has failed to keep pace with growing demand. Factors such as high construction costs, bureaucratic hurdles, and limited available land in urban areas have made it increasingly difficult to build enough homes quickly and affordably. The result is a market where housing remains inaccessible to millions, even as the need becomes more urgent.
Technology is now stepping in to address these challenges in ways that were unimaginable just a decade ago. From streamlining construction processes to introducing new financing models and data-driven tools, tech innovations are rethinking how homes are built, financed, and accessed. But while these advancements offer hope, they also raise important questions: can they truly address the root causes of the housing crisis, or are they simply patching up a fractured system?
The housing crisis begins with supply shortage: we simply aren’t building enough homes. Traditional construction methods are expensive, slow, and reliant on labor that is increasingly hard to find. This is where technology is making its most significant impact. Startups likeICON and Veev are leading the charge, using cutting-edge solutions to make housing more efficient and affordable.
ICON, for instance, uses 3D printing to build homes faster and at a lower cost. By printing the structure of a house directly on-site, ICON reduces waste, labor requirements, and construction time. Entire neighborhoods of 3D-printed homes are already being built, showcasing how this technology can scale.
Veev, on the other hand, focuses on prefabricated construction. By manufacturing high-quality components like walls and steel frames in a controlled factory environment, Veev eliminates inefficiencies associated with on-site building. These components are then assembled on location, drastically reducing construction time and costs. This approach mirrors the principles of mass production seen in industries like automotive manufacturing, where efficiency and scalability are key.
While building more homes is essential, access to housing often depend son financing. For many people, especially those with low or irregular incomes, the traditional mortgage system presents insurmountable barriers. Fintech innovations are stepping in to make housing financing more inclusive and flexible.
Access to affordable housing often hinges on financing, and innovative financial technology (fintech) solutions are beginning to change the landscape. Some platforms are offering new ways for individuals to transition from renting to owning, while others are introducing shared equity models that reduce the traditional barriers of large down payments and strict credit requirements. For example, companies like Point use shared-equity financing, where homeowners receive funds in exchange for a percentage of their home’s future value instead of taking on traditional debt. Meanwhile, startups are building tools that automate and simplify and revolutionizing the mortgage process, making it easier for underserved populations to access loans tailored to their needs.
Blockchain technology is also changing the game. By digitizing land titles and creating secure records of financial transactions, blockchain reduces the complexity and difficulty of accessing credit, especially for those with limited traditional credit. This is particularly impactful in regions where informal economies dominate and traditional proof of income is scarce. These tools create a pathway to homeownership for individuals who would otherwise be excluded from the system.
Beyond building and financing, technology is transforming how we understand and address housing needs. Artificial intelligence (AI) is revolutionizing risk assessment in the mortgage industry by analyzing a broader range of financial behaviors, such as rent and utility payments, to provide a more inclusive picture of creditworthiness.
At the same time, AI and big data are helping policymakers and developers make smarter decisions about where and how to build. By analyzing population trends, commuting patterns, and infrastructure needs, these tools ensure that new housing developments are built in the right places, reducing wasteful construction and improving urban planning.
For example, startups are using 3D scanning and machine learning to map informal settlements and identify buildings at risk of collapse. These insights not only improve safety but also guide investment toward areas where housing is most desperately needed.
The housing crisis is one of the most complex challenges of our time, and technology alone cannot solve it. But it can provide powerful tools to address specific pain points, from streamlining construction to expanding access to financing. Startups like ICON, Veev, and Landis are proving that innovation can lower costs, improve efficiency, and make housing more inclusive.
However, the ultimate solution lies in a combination of technology, policy reform, and community engagement. Governments must work alongside tech innovators to create urban environments that prioritize affordability, sustainability, and accessibility.
The future of housing isn’t just about building more homes; it’s about building smarter, greener, and fairer cities where everyone has a place to call home. By integrating cutting-edge technologies with forward-thinking policies, we can move closer to a world where affordable housing is not an aspiration but a reality.
The question is no longer whether technology can solve the housing crisis—it’s how we will use it wisely to create lasting change.
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The hidden cost of scaling AI: infrastructure, energy, and the push for liquid cooling.
Updated
December 16, 2025 3:43 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.