By: Kattie Muniz
Artificial intelligence (AI) is rapidly reshaping how cities function, but not necessarily how they feel. From optimized transport networks to predictive risk mapping, AI offers powerful tools to help urban environments adapt and evolve. Yet, according to Nicolas Palominos, Head of Urban Design and Strategy R&D at ERA-co, its power has limits, especially when it comes to the intangible work of placemaking.
Palominos draws a sharp distinction between two modes of thinking: system optimization and human experience. “AI can help us understand cities more clearly,” he says, “but it can’t replace the social, cultural, and emotional meaning of a place.
Era-Co’s Urban Lens: What AI Does Well
Before applying AI to urban challenges, Palominos recommends first understanding the city as a problem type. Citing Jane Jacobs’s “The Death and Life of Great American Cities,”Palominos frames cities as complex systems, messy, interconnected, and highly dynamic.
In this context, AI becomes an avAIlable tool for parsing those complexities at scale. That means using AI to align housing systems with employment zones, support the development of 15-minute cities, improve public service delivery, and predict risks like flooding or infrastructure fAIlure.
In practice, AI-enhanced tools like digital twins and predictive analytics are already informing how cities manage their water supply, monitor transit flows, and respond to environmental pressures. “AI expands our capacity to observe systems that would otherwise be too vast or too detAIled to grasp in real time,” says Palominos.
AI And The Movement Of Cities
Urban mobility is one of the best areas where AI benefits are already tangible. From demand forecasting in bus and bike-share systems to consumer-facing route optimization in delivery logistics, AI tools can balance supply and demand, improving system-wide efficiency.
“By optimizing how people and goods move, we reduce pressure on roads, minimize energy waste, and improve environmental performance,” Palominos explains.
These insights have ripple effects. Reduced congestion leads to improved AIr quality and more efficient land use, goals that align directly with ERA-co’s mission to enhance the function and feel of cities simultaneously.
The Gathering Place Problems: What AI Can’t Replace
While ERA-co embraces AI’s utility in infrastructure and systems, Palominos cautions against expecting it to shape culture or community. AI may be able to quantify the number of people using a public space or even simulate how they move through it, but it cannot define the emotional resonance or cultural value of that space.
“You can train an algorithm to track footfall in a plaza,” Palominos says, “but not to understand why that plaza matters to the community.”
In a recent blog post, ERA-co outlines the danger of privileging what’s measurable over what’s meaningful. Design should remain fundamentally human, especially when the AIm is to create places that foster belonging.
Era-Co On Using Data Without Losing Context
Still, data can support inclusive design when used thoughtfully. Nicolas Palominos used street-view imagery and AI-driven perceptual mapping to evaluate visual characteristics such as enclosure, complexity, and human scale. The goal was to understand how people see space, not just how they use it.
“When layered with mobile data, satellite imagery, or video,” Palominos says, “we gain new insight into how different groups experience the same space at different times of day, or across seasons.”
But he’s quick to caution: data alone isn’t enough. “Analysis without local context can easily miss the mark,” he says. “The challenge isn’t just having the data, it’s making it actionable in a place-specific way.”
When AI And Intuition Disagree
What happens when data suggests one solution, but the local context demands another?
Palominos recalls a recent ERA-co project where AI analysis recommended a streamlined pedestrian layout. However, on-the-ground input revealed a desire for shaded, meandering paths that allowed people to pause and socialize. The final design combined both efficient flow with built-in moments of rest and community.
“It’s never about ignoring data,” he says. “It’s about understanding its blind spots.”
Caution: Don’t Let The Model Do The Thinking
ERA-co is cautious about over-reliance on AI, especially in early-stage visioning. Palominos references architect Carlo Ratti’s critique of “techno-solutionism” (the belief that complex social problems can be solved by algorithms alone).
When cities lean too heavily on data models, they risk overlooking equity, memory, and identity. “Optimizing without questioning what you’re optimizing for can be dangerous,” Palominos says. “Not everything that matters can be measured.”
This perspective is echoed in a recent MIT study, which found that people in urban centers now walk faster, linger less, and are less likely to meet up in public spaces. AI didn’t predict the change, but it helped uncover it.
The insight? We must now rethink public space for new patterns of use. AI can support that process, but cannot define it.
AI As A Partner, Not The Driver
At ERA-co, AI is treated not as a decision-maker, but as a discovery tool. “It sharpens how we see cities,” Palominos explains. “It surfaces trends we couldn’t find alone. But it’s still our job to design.”
Palominos emphasizes the need for balance. While tools like Google’s Geospatial Foundation Models offer unprecedented forecasting power, they should amplify human judgment, not override it.
ERA-co’s approach to urban analytics for placemaking integrates advanced modeling with community input, visual analysis, and spatial narrative, ensuring tech doesn’t flatten nuance.
Designing For Climate, Culture, And Community
AI’s most meaningful contribution to cities may lie in its ability to help us adapt. As climate threats and migration pressures grow, the ability to observe change, test responses, and scale up solutions quickly becomes essential.
But just as importantly, AI helps us see emerging human behavior. “When people walk faster or avoid public spaces,” Palominos says, “it’s a signal. AI can help catch that signal, but we still have to interpret it.”
ERA-co continues to explore how AI can support equitable, resilient cities, but always with a human in the loop.
“You can optimize a city grid,” says Palominos. “But only people can create a gathering place.”
