Can a city use AI to cut red tape, fill potholes faster, and shave minutes off commutes—without sliding into surveillance? We sit down with San José’s mayor, Matt Mahan, to unpack how a highly regulated public institution can adopt AI pragmatically and responsibly. In this episode, we dig into the playbook: pilots that become policy, guardrails that build trust, and workforce upskilling that actually moves the needle.

We cover how bus routes now hit fewer red lights, why real-time translation boosts civic inclusion, what “privacy by design” looks like for license-plate readers, and how a 10-week AI curriculum is turning city staff into hands-on builders. We also press on the risks—bias, privacy, and transparency—and explore where city AI is headed next: transit, permitting, and procurement.

Highlights

• From pilots to scale: Bus route optimization with Light AI cut red-light hits by 50%+ and reduced travel time by 20%+, now rolling out citywide.
• Inclusion by default: Real-time multilingual access (e.g., Wordly) and improved translations informed by San José’s deep Vietnamese-language data.
• Eyes on the street, not faces: No facial recognition, strict retention, no third-party data sharing, and tightly controlled access to ALPR data.
• Upskilling at scale: A 10-week AI curriculum (plus a data track) with San José State; staff build custom GPTs (including a budget-analysis GPT) to speed analysis.
• Culture that ships: A “coalition of the willing,” clear problem statements, and a Mayor’s Office of Technology & Innovation to operationalize change.
• Road ahead: Smarter mass transit, faster permitting, and streamlined procurement—practical abundance without new tax dollars.

If you’re new here, we’d love your support—subscribe on Apple, Spotify, or YouTube, and consider leaving a quick rating or sharing this episode with a colleague who’s wrestling with real-world AI adoption.

Does AI make us more creative—or quietly replace us?

In this episode of AI-Curious, we sit down with Dr. Maya Ackerman—author of Creative Machines: AI, Art, and Us—to probe where human creativity ends and machine creativity begins, and how incentives in Big Tech and venture capital shape the tools we all use.

We explore why today’s dominant systems skew “convergent” (safe, samey, oracle-like) instead of “divergent” (surprising, generative), what that means for artists, and how to design AI that actually elevates human imagination rather than displacing it.

Why listen

We wrestle with uncomfortable truths: bias mirrored back at us, investor pressure to “replace” vs. “augment,” and the risk of a cultural sea of slop. We also map a constructive path forward—collaborative systems, richer human–AI interfaces, and a 10-year horizon where AI expands human creative range.

Guest

Dr. Maya Ackerman — AI researcher, entrepreneur, and author of Creative Machines: AI, Art, and Us.

Takeaways

• AI reflects us. Bias in → bias out; representation fixes are not enough without cultural understanding.
• Incentives matter. Many well-funded tools are architected to replace creators; augmentation tools are underfunded.
• Creativity ≠ autocomplete. Today’s LLMs are optimized for correctness and convergence, not genuine divergence.
• Better interfaces beat bigger models. Beyond “text-to-X,” human-centred, interactive tools can coach, not usurp.
• A hopeful arc. With the right design, collaborative AI can measurably raise human creative ability—and stick.

Dr. Ackerman's new book: Creative Machines

https://www.amazon.com/Creative-Machines-Future-Human-Creativity/dp/1394316267

What does hiring look like when AI is embedded into the world’s largest professional network—and how should leaders, recruiters, and job-seekers adapt?

We sit down with Deepak Agarwal, LinkedIn’s Chief AI Officer, for a practical playbook on AI at work: production-grade AI agents for hiring, how semantic job search changes discovery, why “relevance” is the antidote to spammy outreach, and how to build a culture of responsible AI that scales. We unpack where humans stay firmly in the loop—and how AI can reduce friction, close information asymmetries, and free more time for real human connection.

Highlights
•LinkedIn’s AI agents (incl. Hiring Assistant) are in market with paying customers; routine sourcing drops from ~40 hours to a few, while humans focus on candidate fit and relationship-building.
•Semantic job search moves beyond keywords to plain-English intent and better matching across people, jobs, and knowledge.
•Responsible AI is baked in: bias detection/mitigation, rigorous pre-launch testing, and governance—treated as a must-have, not an afterthought.
•“Relevance is the key currency”: better matching reduces spray-and-pray outreach and AI-to-AI noise.
•Guidance for leaders: embrace discomfort, start from the problem (not the tool), choose the right autonomy level, and rethink testing for non-deterministic systems.
•Guidance for job-seekers: be authentic, upskill, and optimize for the next five years—not the next five months.
•Future of work: AI shrinks the 80% “prep” to expand the 20% creative/strategic work; humans remain in control.

If you’re curious about our AI & Leadership event, The Drawing Room at The Explorers Club in NYC, learn more at TheDrawingRoom.ai. If you found this useful, follow the show, rate/review, and share with a hiring leader or job-seeker who needs a clear view of what’s coming.

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