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San Jose Mayor Matt Mahan and NVIDIA's Jumbi Edulbehram reveal how the nation's third-largest city by population is pioneering AI adoption in civic operations. (05:21) The conversation explores San Jose's comprehensive approach to becoming an "AI city," from real-time multilingual translation in public meetings to optimizing bus routes through intelligent traffic signal coordination. (20:51) Mayor Mahan discusses the city's groundbreaking AI upskilling program for over 1,000 employees and the nation's first civic AI grant program, while Jumbi shares insights on transforming urban operations through spatial intelligence and data analytics.
• **Main Theme:** How cities can leverage AI to enhance public services, improve citizen engagement, and drive civic innovation while building sustainable ecosystems for urban technology advancement.Matt Mahan serves as the sixty-sixth mayor of San Jose, California's third-largest city with one million residents, having taken office in 2023. (00:56) A former teacher turned Silicon Valley entrepreneur, he built two startups before transitioning to city leadership. His administration has positioned San Jose as a national leader in civic AI adoption, launching the nation's first civic AI grant program and establishing the GovAI Coalition that now serves over 800 public agencies nationwide.
Jumbi Edulbehram oversees global business development for smart cities and spaces at NVIDIA, where he drives adoption of AI solutions for spatial intelligence and urban applications. (01:42) With a PhD and extensive background spanning chip design and leadership roles, he brings deep technical expertise to transforming city operations through AI. As a San Jose resident, he directly observes the impact of the city's AI initiatives and contributes to NVIDIA's partnerships with municipal governments worldwide.
Mayor Mahan emphasizes that San Jose approaches AI as "labor enhancement" rather than labor replacement, focusing on empowering city workers rather than threatening their jobs. (15:55) The city created a voluntary 10-week AI upskilling program where employees learn to build custom GPTs and find applications in their daily work. This approach has already saved over 10,000 hours of staff time across the first cohorts, demonstrating how proper change management and employee empowerment can drive successful AI adoption in large organizations.
Both leaders stressed the importance of establishing clear guidelines and frameworks before widespread AI deployment. San Jose launched the GovAI Coalition, now serving over 800 public agencies with policy manuals, vendor registries, and incident response plans. (12:08) Rather than waiting for massive regulation, they created basic guidelines that gave other cities confidence to experiment. This "permission structure" approach enables innovation while maintaining accountability and risk management.
San Jose's bus route optimization partnership with Light AI demonstrates how AI can create measurable improvements in core city services. (20:51) By using sensors on buses and AI-controlled traffic signals, they reduced the likelihood of buses hitting red lights by over 50%, speeding up routes by 20% without buying more buses or hiring additional drivers. This approach shows how cities should focus on specific, measurable outcomes rather than implementing AI for technology's sake.
San Jose's approach extends beyond internal operations to creating a broader AI ecosystem including partnerships with San Jose State University, NVIDIA, and local startups. (26:03) Their first-ever civic AI grant program attracted 170 applications and selected four winners working on diverse challenges from maternal health to food waste reduction. This ecosystem approach, including monthly innovators meetups and office leasing incentives, demonstrates how cities can become innovation hubs rather than just service providers.
Both speakers emphasized the importance of beginning with pilot programs and specific use cases rather than attempting wholesale transformation. (40:03) San Jose's ADU permitting pilot uses an AI copilot to guide applicants through the process, addressing the fact that 90% of initial applications were previously returned for incomplete information. This targeted approach allows for learning, iteration, and building confidence before expanding to more complex applications.