Course Syllabus

AI & THE FUTURE OF CITIES

Columbia University Graduate School of Architecture, Planning & Preservation

Spring 2026 | Tuesdays, 5-7 PM

Instructors:

  1. Course Summary

How can city builders leverage the benefits and minimize the negative externalities of artificial intelligence (AI) and other emerging technologies to build more attractive, equitable, and dynamic places? And how can tech companies best engage city leaders, developers, and the public as they seek to shape the urban built environment?

Catering to both aspiring policymakers and budding entrepreneurs, this course provides a high-intensity introduction to the challenges and opportunities of technological innovation in cities. Over the course of the semester, students will engage with academic perspectives on the social, economic, historical, and political dimensions of “smart cities” and “urban tech,” as well as lectures from industry leaders and practitioners on how technology is shaping the way we live, work, consume, and play in cities.

The class is organized into three parts. First, the course will establish key themes and concepts, including the role of innovation in local economic development, the diverse nature of partnerships between cities and tech companies, and the range of actors involved in fostering innovation ecosystems. Second, the course will zoom out to explore how urban systems – from city streets to utility systems – are being reshaped by AI and the infrastructure needed to support it. The third section will focus on how AI is integrated into all aspects of urban life including government services, the public realm, and the urban economy. Throughout, we will interrogate the implications of AI and other technologies for diversity, equity, and democracy in the city, as well as how the widespread adoption of AI is accelerating or disrupting existing urban systems and institutions.

  1. Student Learning Objectives

By the end of the semester, students will be able to evaluate and articulate:

  • The major opportunities and challenges related to the development of urban tech for the betterment of city building and the urban environment.
  • Analytical methods for evaluating the impact of algorithmic systems on the built environment and identifying strategies to improve the urban environment in thoughtful partnership with technology firms.
  • The public policy dynamics between the urban built environment and evolving technologies, including the rapid adoption of AI.
  • How to develop a public benefits case of a disruptive technology that influences how we live, work, and play in cities.
  • Best practices for designing public-private partnerships with technology companies.

 

  1. Course Structure

The first 60 minutes of each class period will generally be a seminar-style lecture, in which the instructors (and occasionally a guest lecturer) will present material while providing opportunity for input, discussion, and questions. The remaining 60 minutes will include a classmate’s presentation, demonstration of an AI product that is influencing urban life, and a student-led debate related to the topic of the class. Not every class will include all three components; formats may rotate depending on the topic and guest speakers.

 

  1. Assignments & Grading

Class Participation (20%)

Attendance is required at all classes. One excused absence will be permitted, but please notify the teaching team as far in advance as possible. The course will be most fun and interesting if everyone is engaged in a lively discussion.

Recognizing that AI itself is evolving rapidly, this course will combine foundational texts with additional sources identified by students.

Prior to each class, students must read an assigned text (approx. 20 pages), posted to CourseWorks/Canvas at least one week in advance. Students are also expected to spend at least one hour researching and preparing a one-page, bullet-pointed summary for each class that cites at least five sources. These may include industry publications, academic research, policy reports, and other publications that contextualize the class topic.

While AI can assist in research, the weekly one-page summaries must reflect each student’s own critical synthesis of the material.

Student Presentation (15%)

On the first day of class, students will sign up to prepare a five-minute presentation on an AI product or service that is connected to a specific urban system (e.g. transportation, utilities, commerce, government services). In their presentation, the student needs to describe the product, articulate the aspect of urban life it is interacting with, describe the impact the technology will have on urban life, and discuss whether any new policies or modifications to existing policies need to be made to amplify positive impacts or mitigate negative impacts.

Presentations will be evaluated based on clarity of explanation, critical assessment of impacts, and thoughtfulness of policy implications.

Memos (45%)

Three short memos, each worth 15% of the grade, will relate to class discussions and be assigned throughout the semester. Late memos will be penalized 5% per day.

  • Memo 1: What physical and digital infrastructure investments does the growth of AI demand? Focusing on one major type of investment (e.g., energy, broadband, transportation, compute capacity), analyze the role of local governments, businesses, and residents in delivering and using the infrastructure. What are the objectives, risks, and limitations as seen from the perspective of each stakeholder?

    Due on Friday, February 27

 

  • Memo 2: How is AI being used to deliver a specific city service today? Students will choose an AI-powered government service and personally use the product or a publicly available demo, pilot, or simulation (e.g., using a public sandbox for a GenAI permitting assistant or a dashboard simulation for a predictive policing pilot). Does this use of AI improve service delivery? What are the risks and benefits? How financially sustainable might this approach be at scale? How would residents’ and city workers’ experiences change as a result of widespread adoption?

    Due on Friday, March 27

 

  • Memo 3: How might AI’s second-order impacts change how individuals interact with cities? Assume AI is going to improve people’s efficiency such that they have eight additional hours a week that they can allocate in new ways. What would be the impact on cities if they spent a portion of those eight hours doing things besides work or housework? How would urban life change as a result, and what are the spatial, physical, and policy ramifications of this new behavior?

    Due on Friday, April 24

 

Group Project (20%)

Students will conduct a project throughout the second half of the semester in groups of 3 to 4. All groups will present the outcomes of their projects during the last class for their peers and a panel of outside reviewers. Each group will select an AI product or service influencing urban life and conduct a deep-dive analysis into one of three domains: physical planning, workforce development, or data and privacy. While the final deliverable must demonstrate a comprehensive scoping of the product's impact across all three areas, the core of the project should focus on providing actionable policy or design recommendations within the chosen domain.

Grades will depend on individual contributions and overall group performance, supported by individual and peer assessments.

Presentations will take place on Tuesday, April 28.

Individual Meetings

Students will be required to meet one-on-one with the course instructors during the first 2-3 weeks of the semester to discuss professional goals, academic interests, and any outstanding questions related to the course. Further details on meeting timing will be shared once enrollment has been finalized.


Generative AI Policy

As this course explores the frontier of urban technology, the responsible use of Generative AI (e.g., ChatGPT, Gemini, Claude) is permitted for brainstorming, drafting, and technical assistance. However, per Columbia University’s Generative AI Policy, students are held personally accountable for the final output. Usage must adhere to these three rules:

  1. Mandatory attribution: Any use of AI must be disclosed in a short statement at the end of every assignment (e.g., AI Disclosure: I used [Model] to [Summary of use]. I have verified all facts/citations.).
  2. Fact-checking: AI models often “hallucinate.” Students are responsible for verifying every claim, data point, and citation. Inaccurate AI-generated content will be treated as a failure of research rigor.
  3. Data privacy: Do not input confidential, personal, or non-public data into AI tools.

 

  1. Class Descriptions

Module A: AI + Urban Tech Public Policy

Class 1 | AI and the Future of Cities

Topics: What are the goals, structure, and expectations of the course? What is the background of the students and the teaching team? What is the 21st century model of economic development, and how does this relate to AI? How can municipalities most effectively regulate and partner with private entities to maximize the social benefits of AI and other technologies? How has the shift from 2010s “Smart City” data collection to 2026 “Agentic AI” models reshaped the future of urban innovation and the role of the city as a technology platform?

 

Class 2: | Cities and Tech: Rules of Engagement

Topics: In what ways have municipal governments and technology companies historically collaborated to deliver products and services to city residents? How have these modes of engagement shifted over time? Under what circumstances might cities and tech companies come into conflict – or, conversely, establish partnerships? What is a “joint venture” and how can the concept be used to structure effective deals between cities and tech companies?

 

Class 3 | Evaluating the Costs and Benefits of Urban Tech

Topics: What methods do tech companies, real estate developers, policymakers, advocates, and others use to evaluate the impacts of AI and other forms of urban tech? What types of impacts do they (and can they) measure? What is economic impact, fiscal impact, and cost-benefit analysis? What is a benefits case? How have these methods been used to promote or critique urban tech projects, products, or programs? 

 

Class 4 | Tech-Focused Economic Regional Development

Topics: How can public policy catalyze regional innovation ecosystems? This session explores the strategies behind "tech hubs," drawing on the history of Cornell Tech and recent initiatives like the CHIPS Act and Regional Tech Hubs. We will analyze New York’s Empire AI consortium as a model for state-led AI compute projects, and how Micron’s investment serves as a case study for the infrastructure, energy, and labor requirements of regional AI leadership.

 

Module B: Systems + Infrastructure

Class 5 | From Physical to Digital Infrastructure

Topics: What is the urban infrastructure of the future? What will this new digital infrastructure enable households, businesses, institutions, tourists, and city makers to do? How will cities need to adapt to plan, finance, deliver, and maintain this new infrastructure? How does the transition toward AI-driven “Active Management” allow physical assets like the curb or the power grid to respond to real-time demand? What role will machine learning play in the design and use of future public space? What are the implications on accessibility, equity, and privacy?

 

Class 6 | Urban Development in an Age of Automation

Topics: How and why is the volume of data being generated in cities increasingly at an exponential rate? What are the potential benefits and unanticipated consequences of cities using agentic systems and autonomous decision-making? How do these approaches complement or conflict with more democratic, participatory, or “crowd-sourced” models of urban governance? How can public, private, and community-based actors work together to promote data privacy, accountability, and transparency?

 

Class 7 | AI, Energy, and Climate

Topics: As AI adoption grows, so does its physical footprint. This session investigates the 24/7 power demands of data centers and the broader environmental implications of the AI boom. Will AI exacerbate climate change through increased consumption, or will its physical burden be offset by innovations including grid resilience and Virtual Power Plants (VPPs)? We will analyze the tradeoffs and explore interventions designed to mitigate the climate impacts of our increasingly digital world.

 

Class 8 | Intro Class to Final Project and Group Work

Topics: This workshop launches the group project. Working in teams of 3-4, students will select an AI product or service currently influencing urban life. The assignment requires groups to conduct a comprehensive evaluation of the technology's implications across three critical domains: physical planning, workforce development, and data privacy. We will review the prompt, form teams, and begin scoping the analysis.

 

Module C: AI and the Urban Experience

Class 9 | AI and City Services

Topics: How is AI transforming the delivery of civic services? How are cities leveraging Generative AI to enhance citizen engagement and streamline administrative processes? What role is AI likely to play in decision-making for urban planning, infrastructure management, and city administration? We will examine the shift toward “Autonomous Administration,” as cities like New York, San Jose, and Seattle use AI for procurement, automated permitting, and 311 resolution, while exploring the practical and ethical guardrails needed to mitigate the risks posed by AI “hallucinations,” embedded biases, and data privacy considerations.

 

Class 10 | AI and Public Safety

Topics: How is AI reshaping public safety and criminal justice? What are the risks and benefits of services today, and how are they likely to evolve? We will critically examine the capabilities and ethical controversies of predictive policing, facial recognition, and automated surveillance. We will discuss frameworks that balance technological efficacy with civil liberties and community trust, including exploring Privacy by Design and transparent consent mechanisms.

 

Class 11 | AI and City Building

Topics: How will AI transform the physical construction and economic landscape of cities? This session explores the intersection of AI with real estate development, workforce dynamics, and the construction industry. We will discuss the rise of Generative Design in urban planning and how AI-optimized supply chains and job-site robotics are disrupting traditional city-building timelines. We will examine which jobs may remain or disappear, the need for workforce development, and how AI might streamline or disrupt traditional city-building processes.

 

Class 12 | AI and City Life

Topics: Beyond infrastructure and services, how will AI change the daily experience of urban life? We will explore impacts on retail, entertainment, and travel. Will AI-driven productivity gains allow us to "work less and do more," effectively creating a new leisure class, or will it exacerbate inequalities? We examine the potential shifts in how we live, shop, and play in cities.

 

Class 13 | AI and the Future of Cities

Topics: Looking forward, what emerging technologies will be needed to amplify the benefits of AI for cities? We will discuss the ecosystem of innovations—from IoT to quantum computing—that could synergize with AI. The session also addresses the role of incubators, accelerators, and government policy in fostering a new generation of urban tech products and services that maximize social benefit.

Class 14 | Final Presentations

Topics: In this culminating session, student groups present the outcomes of their projects to a panel of outside reviewers. Teams will evaluate the real-world implications of their chosen AI product or service, detailing its impact on physical space, labor markets, and privacy rights. Grades will be determined by group performance and individual contributions, incorporating peer assessments.