A City That Rewrites Its Rules
Picture a city that rewrites its own rules as it grows — zoning that adapts in real time to infrastructure capacity, social sentiment, environmental impact, and community needs — a fluid built environment that responds to human life and is not a hindrance; a planning process that favors the difficult whole through inclusion, rather than the easy unity through exclusion. That city isn’t a utopian dream; with AI, it is now technically possible, if we’re bold enough to reimagine how we plan, invest in, and govern urban growth.
Solving Urban Fragmentation
This isn’t a hypothetical shift, but one that AI can help us build toward.
Post-modernist architect Robert Venturi in his 1966 book Complexity and Contradiction in Architecture coined the phrase “less is a bore” and he and his wife and fellow architect Denise Scott Brown lived and taught by that mantra — that architecture should embrace contradiction and complexity, not strip it away for the sake of simplicity. Cities aren’t minimalist objects; they are lived, layered environments full of friction, memory, noise, and maybe most importantly, possibility. It has become clear that both capital and design suffer from the same core problem: too much fragmentation and too little synthesis.
Friction and fragmentation plague the built environment; zoning decisions are locked in decades-old code; investment memos take weeks of disconnected analysis; and planning cycles are outpaced by economic, environmental, and social change. Artificial intelligence — if wielded wisely — can untangle these knots and rewire urban decision making.
Investment memos are the analytical reports investors use to assess a property, summarizing risks, comparables, zoning, demographics, and the market outlook. Traditionally, they take weeks, but AI can reduce that to hours; it can pull structured and unstructured data — zoning codes, economic signals, satellite imagery, sentiment analysis, infrastructure indicators — and produce a coherent investment narrative with rigorous citations and confidence scoring. This is yet another example of the capability of AI to cut through the noise, save time, synthesize the parties in a deal and change the scale at which analysis can happen.
AI Zoning by Adequacy
The deeper potential of AI technology is in how it can inform what we build, where, and why — introducing the concept of zoning by adequacy.
Traditional zoning is static. It prescribes use types, densities and heights based on maps drawn decades ago, often enshrining outdated, exclusionary worldviews. What if zoning became a real-time decision informed by adequacy: Is the infrastructure here sufficient for this use? Is the transit, school capacity, flood protection, and neighborhood sentiment aligned with the proposal? If yes, then let it proceed — with confidence. If not, the system identifies what needs to change: expanded utilities, better access, or reduced scale.
Zoning then becomes a framework for solving problems, rather than a gatekeeping mechanism. AI is essential to making this work. No human team can continuously monitor every variable across an entire city, nor simulate the trade-offs of every building proposal, but AI is more than capable; it can map utilities loads, transit strain, pedestrian safety, insurance risk, social cohesion, among other things, and then synthesize a clear readout of whether a development can be adequately supported and what trade-offs exist.
This model is also more equitable. Today, communities across the country with wealth and resources often use static zoning to block growth or diversity, invoking vague concerns that are hard to disprove. Zoning by adequacy introduces shared standards and shared language; if the numbers show your neighborhood can support more housing, you can’t simply opt out. Conversely, underserved communities gain tools to prove their readiness for investment — with data, not just lobbying.
Generative Design & Continuous Planning
We’re already seeing hints that this future is possible. In architecture, generative AI tools now produce dozens of site-optimized building configurations in minutes, accounting for solar angles, structural cost, code compliance, and user behavior. Architects aren’t replaced; they’re elevated. Their roles become curator, editor, or ethicist, and they guide the machine, setting goals and boundaries.
At the city level, platforms such as City Brain in Hangzhou, China and Singapore’s national digital twin initiative are proving that AI can manage traffic, emergency response, and infrastructure at scale, with responsiveness that would be impossible through manual means. These systems enable real-time urban operations, but more importantly, they allow for continuous planning. Instead of updating a general plan every 20 years, planners can use live data and simulation to adapt weekly, or even daily.
Data-Driven Capital Flows & Investment
AI isn’t about surrendering human judgment to algorithms; it’s about giving communities, planners, and policymakers better tools to exercise judgment. When public forums are informed by real-time data — on emissions, capacity, cost, and benefit — debates become clearer, false narratives peter out, and good, productive ideas come to the fore.
Likewise, AI redistributes capital. In our experience, money flows where information flows. A development in New York or Tokyo attracts funding because the data is rich, the risks legible. But what about secondary or tertiary cities, like Gwangju, South Korea or Columbus, Ohio. AI makes these cities more visible to global capital. It reveals the innovation potential of Gwangju’s AI-based mobility hub and renewable energy zones, or the urban revitalization around Columbus’s Intel-driven tech corridor — not with handwaving or promises, but with data-backed clarity.
In infrastructure, consider Busan, where South Korea is investing heavily to modernize one of the world’s busiest ports with smart logistics and freight orchestration. Investors can now model throughput scenarios, carbon offsets, and resilience against climate shocks — all before pouring concrete. Or in eastern Germany, where billions are being funneled into retrofitting Soviet-era housing blocks for green performance. AI helps model not just energy savings, but renovation phases, tenant impact, and subsidy targeting. These types of projects transition from risky to bankable.
Ethical AI & Urban Values
AI is not a magic fix; it reflects its inputs; it amplifies our values. If we train it on biased data, it will encode injustice. If we build it solely for efficiency, it will cut corners. That’s why human leadership matters. Technology is accelerating, but values still steer the machine.
A new coalition is needed: planners, technologists, designers, policymakers, and investors, working together on the next operating system for the built world. Not to eliminate complexity, but to make it legible. Not to flatten differences, but to coordinate across them. Not to build perfect cities, but adequate ones that are adaptive, inclusive and intelligent.
The time is now; we can stop waiting for perfect consensus or permission. Let’s treat planning not as a ritual, but as a feedback loop. Let’s replace the brittle order of exclusion with the resilient complexity of inclusion. Let’s build cities that grow wisely, not just quickly.
If we do this right, our future cities won’t just be smart, they’ll be humane.
There’s nothing artificial about that.
