Will Artificial Intelligence Make Cities Obsolete?

Will Artificial Intelligence Make Cities Obsolete?

For decades, each new communications technology has sparked predictions that cities would become obsolete. The telephone, the fax machine, email, videoconferencing, cloud computing—each was expected to “free” workers from the need to cluster in metropolitan areas. Yet every era of digital progress has produced the opposite effect: cities have grown more important, more productive, and more central to national economic life.

Artificial intelligence (AI)—more powerful, more rapidly adopted, and more transformative than its predecessors—is now inspiring similar claims. If AI enables remote work, automates mental labor, manages logistics, and creates personalized digital worlds, why should anyone still choose to live or work in dense metropolitan areas? Why wouldn’t AI unleash a new wave of sprawl?

The answer is simple. AI amplifies the forces that make cities valuable in the first place. Far from displacing urban life, AI is poised to enhance it, accelerating the revival of older cities, increasing the competitiveness of walkable districts, and making dense, connected, infrastructure-rich places more economically efficient and socially vibrant. The emerging geography of the AI economy is just beginning to emerge. It’s ultimate pattern will depend on proximity, infrastructure, human collaboration, energy, and culture—all of which favor cities over dispersed hinterlands. The story of AI and the future of cities will not be a story of dispersal. It will be a story of intensified clustering for a variety of reasons.

1. AI Increases the Values of Proximity and Human Coordination.

Cities have always been engines of innovation. Cities facilitate unplanned encounters, dense labor markets, rapid job matching, and tight feedback loops across firms, universities, supply chains, and capital. Economists call these “agglomeration economies”—and AI heightens the importance of agglomeration.

AI-driven industries rely on highly specialized research talent, advanced computing infrastructure, venture capital and corporate innovation networks, and rapid collaboration across engineering, design, and business teams. All these factors flourish where dense concentration accelerates creativity.

Even as digital tools have expanded, the industries that depend most on technology—software, biotech, advanced manufacturing, robotics, finance—have become more concentrated in major metropolitan areas, not less. AI brings an additional layer. Innovation cycles are so fast and iterative that physical proximity becomes a competitive advantage. When breakthroughs occur weekly rather than yearly, the places that allow the fastest exchange of ideas will lead.

AI’s most valuable knowledge is the strategic wisdom to best leverage its capabilities. This includes choices about how to build models, which models to build, how to align them ethically, how to adapt them for industry use, and how to best integrate AI into physical systems. This knowledge is the most difficult to automate. The processes that create and extend this type of knowledge requires constant human coordination. Cities supply the environments that foster human coordination in ways remote work cannot match.

Much of the conversation around AI focuses on automation. But the more tasks AI performs, the more valuable uniquely human skills become. These skills include judgment, creativity, negotiation, trust-building, mentorship, and improvisation. These abilities thrive in in-person environments.

The “paradox of telecommunication” observed over a century ago remains true: the easier it becomes to communicate over great distances, the more productive face-to-face interactions become for high-value work. As routine tasks become automated, the remaining work becomes more collaborative, strategic, and interpersonal.

Cities foster these interactions that often form the basis for important collaborations. They include chance encounters on sidewalks, dense meeting schedules, formal and informal industry meetups and conferences, informal learning networks, mentorship cultures with overlapping members, and hybrid workplaces that can anchor organizational identity. AI strengthens—not weakens—the need for physical places where people can think collectively.

2. AI Requires Massive Infrastructure Best Located in Cities.

AI is not a frictionless cloud floating above geography. It is a physical system dependent on enormous electrical capacity, high-voltage transmission infrastructure, robust water supplies for cooling, dense fiber networks, specialized industrial land, and skilled technicians and engineers.

These needs make infrastructure-rich cities and older industrial corridors far more competitive than rural or highly sprawled exurban regions. The growth of energy-intensive AI data centers is already clustering near places with existing substations, brownfields zoned for industrial use, proximity to universities, and robust transportation access.

Urban innovation districts—from Pittsburgh’s Hazelwood Green to Cleveland’s Health-Tech Corridor to Chicago’s Fulton Market and Boston’s Seaport—are examples of where AI-related investment gravitates, precisely because infrastructure is abundant and expandable. Sprawl, by contrast, is expensive to serve. Extending transmission lines, broadband, and industrial utilities to dispersed areas costs more and generates less economic return. AI’s appetite for power and connectivity rewards the density and redundancy of cities.

3. AI Helps Solve the Traditional Problems of Urban Living.

Cities historically lose people to suburbs when the downsides of density exceed the benefits. AI, however, offers the possibility of mitigating many of these downsides by creating strategic advances in managing complex urban problem-solving.

· Congestion: AI-optimized traffic control reduces delays, manages signal timing dynamically, and enables more efficient freight movement. Early pilot projects show 15–30% reductions in travel times. Combined with automated delivery and improved transit scheduling, this makes density more feasible.

· Crime: Predictive analytics (implemented with safeguards) can help deploy resources more effectively, identify high-risk spaces for environmental design, and reduce harm without over-policing. AI-enhanced lighting, building security, and public space monitoring can make neighborhoods safer.

· Housing and permitting: AI can streamline zoning reviews, generate multiple design options for infill housing, and reduce regulatory friction. This makes redevelopment cheaper and faster, reducing pressure for greenfield sprawl.

· Climate resilience: AI enables cities to model flooding, optimize stormwater systems, manage heat islands, and operate energy-efficient buildings. As climate risks rise, well-managed cities may become safer than suburban fringe zones exposed to flooding, fire, or excessive heat.

AI doesn’t eliminate the challenges of urban life, but it gives cities powerful new tools to manage them—making the trade-off between city and suburb tilt more toward urban living.

4. AI Makes Transit-Oriented, Walkable Development More Competitive.

Public transit stands to benefit enormously from AI. Substantial improvements in real-time routing, automated fleet optimization, demand-responsive service, predictive maintenance, and seamless, customized trip planning will reduce costs and raise reliability for all types of public transit. Systemwide improvements will create strong incentives to live in transit-rich areas rather than auto-dependent suburbs and exurbs.

Meanwhile, autonomous freight and autonomous delivery systems will both reduce the footprint needed for parking. This will enable cities to reclaim more land for housing and public space. AI-enhanced planning tools can help design walkable, mixed-use districts that balance transportation, housing, and green space more effectively. Cities that leverage AI for mobility will make living without a car cheaper and easier—something sprawl cannot match.

5. AI Will Help Revitalize Older Industrial Districts.

AI significantly reduces the cost and complexity of redeveloping older urban areas. Tools such as digital twins, generative engineering, automated site assessments, AI-enhanced environmental remediation, and energy modeling will make it easier to transform old factories, warehouses, and waterfronts into new hubs of innovation.

This gives older industrial cities like Cleveland, Pittsburgh, Buffalo, Detroit, St. Louis, Milwaukee, and even Chicago a potential competitive advantage. These cities already possess the historic industrial footprints that AI industries require. Newer cities and sprawled suburban and exurban regions surrounding older industrial cities lack this existing foundation.

6. AI Strengthens the Cultural Advantages of Cities.

Cities are not just labor markets—they are complex, human-centered, cultural ecosystems. AI-era workers are increasingly choosing places based on essential quality of life factors such as arts and culture, social diversity, high-quality universities and public libraries, vibrant food and nightlife districts, walkability, and accessible public spaces. Cities deliver these in abundance. As AI automates routine work, the factors that define quality of place become even more important in attracting talent. The cities that combine cultural vitality with AI-friendly infrastructure will become magnets for the next generation of workers.

7. AI Encourages “Centralized–Distributed” Work Models That Still Favor Cities.

The workplace of the future will be neither fully remote nor fully office based. Instead, AI-enabled firms are adopting a permanently hybrid model with several components. Firms will have one or two large, dense, collaborative hubs in urban districts. The hubs will be supported by smaller satellite offices in suburban or secondary cities and by networks of remote workers who will perform routine tasks.

This model strengthens cities because firms need centralized hubs for strategic decision-making, R&D clusters, training centers and experimentation labs, and strategic spaces that build organizational identity. These “hub functions” will perform best in lively, transit-served urban districts, including revitalized traditional urban downtowns. AI may decentralize some roles, but it centralizes leadership and innovation.

Conclusion: AI Will Be a Catalyst for Urban Renaissance.

AI will not liberate society from geography. On the contrary, it will bind economic activity more tightly to places where talent, infrastructure, and culture overlap. Cities are the only environments that offer the density, diversity, and connectivity needed to harness AI’s potential.

The 21^st century AI era will be a century of cities—older cities revitalizing, newer cities densifying, innovation corridors forming, and regions knitting themselves together through stronger infrastructure. The urban fabric will evolve, but its central role in human progress will endure.

The challenge now is not whether cities will matter in the age of AI—they will. The real challenge is ensuring they are prepared, inclusive, resilient, and ready to seize the advantages AI offers.

If cities rise to the occasion, AI could help deliver the most significant urban renewal since the Industrial Revolution—and do so in ways that promote equity, creativity, and shared prosperity.

Bob Gleeson