Can innovation truly deliver affordable housing to those who need it most?
Updated
January 8, 2026 6:35 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.
Keep Reading
How Korea is trying to take control of its AI future.
Updated
January 13, 2026 10:56 AM
SK Telecom Headquarters in Seoul, South Korea. PHOTO: ADOBE STOCK
SK Telecom, South Korea’s largest mobile operator, has unveiled A.X K1, a hyperscale artificial intelligence model with 519 billion parameters. The model sits at the center of a government-backed effort to build advanced AI systems and domestic AI infrastructure within Korea. This comes at a time when companies in the United States and China largely dominate the development of the most powerful large language models.
Rather than framing A.X K1 as just another large language model, SK Telecom is positioning it as part of a broader push to build sovereign AI capacity from the ground up. The model is being developed as part of the Korean government’s Sovereign AI Foundation Model project, which aims to ensure that core AI systems are built, trained and operated within the country. In simple terms, the initiative focuses on reducing reliance on foreign AI platforms and cloud-based AI infrastructure, while giving Korea more control over how artificial intelligence is developed and deployed at scale.
One of the gaps this approach is trying to address is how AI knowledge flows across a national ecosystem. Today, the most powerful AI foundation models are often closed, expensive and concentrated within a small number of global technology companies. A.X K1 is designed to function as a “teacher model,” meaning it can transfer its capabilities to smaller, more specialized AI systems. This allows developers, enterprises and public institutions to build tailored AI tools without starting from scratch or depending entirely on overseas AI providers.
That distinction matters because most real-world applications of artificial intelligence do not require massive models operating independently. They require focused, reliable AI systems designed for specific use cases such as customer service, enterprise search, manufacturing automation or mobility. By anchoring those systems to a large, domestically developed foundation model, SK Telecom and its partners are aiming to create a more resilient and self-sustaining AI ecosystem.
The effort also reflects a shift in how AI is being positioned for everyday use. SK Telecom plans to connect A.X K1 to services that already reach millions of users, including its AI assistant platform A., which operates across phone calls, messaging, web services and mobile applications. The broader goal is to make advanced AI feel less like a distant research asset and more like an embedded digital infrastructure that supports daily interactions.
This approach extends beyond consumer-facing services. Members of the SKT consortium are testing how the hyperscale AI model can support industrial and enterprise applications, including manufacturing systems, game development, robotics and autonomous technologies. The underlying logic is that national competitiveness in artificial intelligence now depends not only on model performance, but on whether those models can be deployed, adapted and validated in real-world environments.
There is also a hardware dimension to the project. Operating an AI model at the 500-billion-parameter scale places heavy demands on computing infrastructure, particularly memory performance and communication between processors. A.X K1 is being used to test and validate Korea’s semiconductor and AI chip capabilities under real workloads, linking large-scale AI software development directly to domestic semiconductor innovation.
The initiative brings together technology companies, universities and research institutions, including Krafton, KAIST and Seoul National University. Each contributes specialized expertise ranging from data validation and multimodal AI research to system scalability. More than 20 institutions have already expressed interest in testing and deploying the model, reinforcing the idea that A.X K1 is being treated as shared national AI infrastructure rather than a closed commercial product.
Looking ahead, SK Telecom plans to release A.X K1 as open-source AI software, alongside APIs and portions of the training data. If fully implemented, the move could lower barriers for developers, startups and researchers across Korea’s AI ecosystem, enabling them to build on top of a large-scale foundation model without incurring the cost and complexity of developing one independently.