This article aims to define and demystify the concept of smart cities and connect it to the services and vision of Building. We know that this topic can carry some weight and that's why it's important to ensure alignment.
People experience a wide array of feelings when they hear “smart cities”—a term that has not only become more popular in recent years but is now, arguably, overused. It is most often assigned to any instance of urban technology and, unfortunately, used in the context of surveillance. This is because, in order to function, technology must interact with humans; therefore, it must study humans—observe, learn, and imitate them.
Startups, corporations, institutions, and governments have all tried to claim the label, promising to usher in some sort of innovation that will change the way people work, live, and play. A new product or service is typically accompanied by a utopian vision, and its creators will tout progress.
The confusion of nomenclature arises when two vastly different urban environments can both describe themselves as smart cities. For example, an urban center with brutalist architecture, CCTV, and a strong police presence may deem itself “smart” in the sense that it is orderly and safe, while another—with neo-Gothic structures, greenery, and a rehabilitative legal system—believes smart cities are democratic and sustainable.
At Building, we don’t believe there is a uniform definition of a smart city, and we certainly don’t believe a city needs to be high-tech to earn the title. This approach works because the intelligence of a city depends on how well it conforms to the needs of its end users—the people who live there. And those needs will differ depending on history and culture.
First, it’s important to be clear about what a smart city is not. The intelligence of an urban environment is not determined by the number of sensors, apps, or Internet-of-Things (IoT) networks it contains. This is especially true if these devices are used against the residing population. They should also not worsen the human condition. For example, a city may install surveillance cameras under the guise of improving safety, yet these same systems can be weaponized to monitor citizens, enforce conformity, or suppress dissent.
Second, we do not believe that technology alone equates to progress. Complexity should not be confused with sophistication. A doorway that uses electronics to open automatically for foot traffic is actually an impediment if the sensors are faulty—there’s no alternative during a power outage, or the doors may open substantially slower than what a human could do.
Third, efficiency should not come at the cost of humanity. Efficiency is merely a means to an end. We need to design our cities so that successful implementation is distributed equitably; a prime example would be developing private roadways that may impede existing residents, creating noise and air pollution. It’s true that the roadway may help certain individuals arrive faster to the city center, but it also disturbs the lives of hundreds of other residents.
Rather, we identify the intelligence of urban environments through six pillars: Governance, Economy, Environment, Living, Mobility, and People. All smart-city indices use some variant of this framework. The characteristics of these pillars are:
All cities will rank somewhere along this spectrum, and the aggregate score determines their overall rank. That said, this is not a perfect calculation, and the opinions of residents (perceived service versus institutional metrics) should be considered as well.
While this is not entirely a recent development — because cities aren’t built overnight — one change in design practice that we are starting to see more often (or at least advocated for) is the creation of urban environments strategically architected around the needs of residents. This requires a degree of cultural anthropology, but when done correctly, it creates a city layout that becomes an extension of the personalities of the local people.
At the same time, there is seamless — not insistent — integration of technology. This distinction matters because, while a small village in antiquity may have complemented the needs of its residents, it would struggle with scale. Therefore, the success of the aforementioned six pillars depends not only on end-user satisfaction but also on efficiency, data-drivenness, sustainability, and scalability.
This leads to an active discussion about automation versus empathy — but I won’t do much more than mention it, as this article is intended to be informative rather than philosophical.
At Building, this balance between empathy and automation defines our approach to urban intelligence — using data not to replace human judgment, but to amplify it.
Shifting gears to digital infrastructure, we have to start with the oil of the 21st century: data. This is a powerful resource that fuels nearly every decision and operation — in smart cities, in business, and arguably, in life itself.
Data naturally exists in an unstructured format, and it persists whether we study it or not — much like physics. Regardless of whether we’re calculating mass and velocity, when you drop an item, it will fall. The same is true for the built environment: buildings will have dimensions, corners will have angles, and climate will test structural integrity.
Thinking about data is quintessential to smart cities — and we should clarify that data and technology are not the same thing. One could study foot traffic and discover that people prefer simple, analog entrances over high-tech ones. Regardless of what the data implies, it must be captured and understood.
This is what Building aims to do across the real estate lifecycle. Starting with design, then moving through construction, financing, operations, and even demolition, we seek to capture, structure, and activate data about the built environment — so people can make better decisions.
What we’ve recognized, however, is that this is easier said than done. Construction and real estate are complex ecosystems where data is often unavailable, and when it is, it’s at risk of being incomplete, outdated, unverified, or even manipulated. This causes a ripple effect of issues: mismanaged project information between architects and engineers can trigger rework, delay financing, confuse operators, and even lead to lawsuits or safety risks in the future.
While there are hundreds of use cases for data, our focus — given our work in blockchain and artificial intelligence — is on capital markets. Thousands of studies and decades of experience in this industry reveal the same conclusion: the disconnect between real estate’s current practices and its potential lies in data integrity.
With this in mind, let’s dive into Building’s vision for data, tokenization, and the future of smart cities.
At Building, we see data not as a byproduct of urban life but as its nervous system — the connective tissue that allows cities to sense, learn, and respond. The problem is that most of this data exists in isolation. Architectural drawings, environmental assessments, financial ledgers, inspection records — they all live in separate silos, disconnected from one another and from the people who need them most. Our vision is to bridge those divides, transforming fragmented information into structured, auditable, and finance-ready intelligence.
We approach the built world as a living ecosystem rather than a collection of static assets. Each property generates a continuous stream of information throughout its lifecycle: design, construction, operation, maintenance, renovation, and eventually, decommissioning. By capturing and structuring this information from the start, we enable cities and stakeholders to measure integrity, efficiency, and value in real time. This continuous thread of information — what we call the Golden Thread — ensures that every decision made about a property is based on verified truth rather than assumption.
But structured data alone isn’t enough. It must also be activated. Building’s platform turns data into action through a secure, collaborative environment where developers, asset managers, auditors, and investors can each view what they need, when they need it. Our integration layer connects lifecycle documentation with valuation models, compliance frameworks, and tokenization workflows. This allows real estate assets to become digital financial instruments — verifiable, tradable, and transparent — without compromising privacy or regulatory integrity.
This foundation supports a broader civic goal. Cities built on accurate, interoperable data can operate more efficiently, attract sustainable capital, and better serve their citizens. Whether it’s accelerating project approvals, streamlining financing, or embedding ESG verification into every layer of the built environment, the result is the same: more trust in how cities function, and more agency for the people who live in them. We believe this trust — not technology — is the real measure of a smart city’s intelligence.
Ultimately, Building’s vision is to redefine how the world measures progress. A truly intelligent city is not one filled with gadgets or sensors, but one where data and humanity move in sync — where every building, policy, and investment contributes to a system that learns and improves over time. By making the invisible visible, and the complex manageable, we aim to help build cities that are efficient, sustainable, safe, clean, and, above all, human.
If data is the nervous system of a city, then capital is its bloodstream. The health of an urban economy depends on how efficiently value circulates — not just between buyers and sellers, but between developers, financiers, and the communities who give a city its meaning. Today, that circulation is fragmented. Information moves slowly, transactions are opaque, and the value created within the built environment is often trapped inside institutional walls.
A circular urban economy seeks to change that. Instead of treating buildings as endpoints — projects to be completed and forgotten — it views them as nodes in a continuous flow of value. When data about a property’s performance, sustainability, and compliance is verifiable, it can be repurposed: used to secure better financing, justify retrofits, or even collateralize new developments. The result is a feedback loop where every project contributes data, and every new project benefits from it.
This is where transparency becomes a force multiplier. Through tokenization, property data and financial rights can be represented digitally, giving participants a shared, tamper-proof record of truth. Investors gain visibility, developers gain liquidity, and regulators gain confidence that compliance is not performative but provable. The same infrastructure that powers a real estate transaction can, over time, support public infrastructure, housing, and energy systems — forming the foundation for a truly circular economy.
Such a system doesn’t eliminate profit; it redefines it. Value creation is no longer a zero-sum game between private interest and public good. When trust and data integrity align, growth compounds. Cities attract cleaner capital. Innovation scales responsibly. Stakeholders, regardless of size or geography, operate on a shared layer of verifiable information.
This is the economy we are building toward — one where sustainability is not an ideal but an outcome, and where the systems of finance and the systems of life finally begin to speak the same language.
The promise of smart cities has never been about technology for its own sake. It has always been about creating systems that learn — cities that become smarter not because they are automated, but because they are aware. Awareness is what turns information into wisdom, and infrastructure into civilization.
At Building, we don’t imagine the future as a distant skyline waiting to be constructed. We see it as a process already underway — one decision, one dataset, one collaboration at a time. Our mission is to give that process structure and transparency, so progress can compound without losing sight of the people it’s meant to serve.
The cities of tomorrow won’t be defined by how much they know, but by how well they listen: to their data, to their citizens, and to the world around them. The future is not a destination we arrive at; it’s a direction we refine — and we’re building the tools to help it take shape.