Tag: civic tech

  • Is Technology Still Net Positive? A Product Leader’s Reckoning and Playbook for Humane Growth

    Is Technology Still Net Positive? A Product Leader’s Reckoning and Playbook for Humane Growth

    I’ve spent my career building products on top of the internet, championing social media, and now scaling AI. Lately, I keep returning to an uncomfortable but necessary question: are we still building a net positive future—or have we drifted into something else entirely?

    A recent long-form conversation in my podcast queue challenged me to do a deeper self-audit. If you want to hear the debate that sparked this reflection, you can listen on: Spotify | Apple Podcasts. What follows is my synthesis as a product management leader: the hard truths, the hopeful paths forward, and the practical actions I’m taking with my teams.

    The moment that hit me hardest was a family member’s blunt assessment that the internet has become “net negative.” That phrase landed like a wake-up call—a reminder that those of us inside tech often operate in an echo chamber. We see our roadmaps, our metrics, our progress; the rest of the world experiences the second-order effects. As a leader, I have to seek out those outside-in perspectives with the same rigor I apply to any product discovery practice.

    Another truth I can’t ignore: somewhere along the way, parts of our industry slid from “make people’s lives better” to “extract maximum value at any human cost.” You can see it in incentives that prioritize growth at all costs, in waves of layoffs that treat people as an expense line, and in platform behaviors that resemble a modern tycoon era. This isn’t just a moral critique—it’s a product strategy risk. Extractive models erode trust, weaken retention, and invite regulatory and reputational headwinds that no amount of optimization can out-execute.

    The loneliness crisis is real, and technology has too often replaced human connection instead of augmenting it. Spend a week in San Francisco and you’ll notice what I call “isolation by design”—QR-code menus, autonomous Waymos, frictionless everything, but fewer genuine human moments. It’s efficient, yes, but alienating. No algorithm can substitute for physical touch, care, and community. As builders, we should design products that create on-ramps to real-world connection, not cul-de-sacs of infinite scroll.

    We still have agency. “Don’t be evil” shouldn’t be a nostalgic slogan; it should be a minimum bar. Responsible product management means being a citizen of the ecosystems we influence: naming trade-offs clearly, instrumenting for externalities, and building AI risk management into our operating cadence. It also means stepping outside the industry narrative to ask neighbors, parents, teachers, and small business owners how our products actually land in their lives.

    One idea that gives me hope is “mom and pop tech”: AI-enabled, hyper-local tools crafted for specific neighborhoods and communities. Think “inch wide, mile deep”—software that solves a real problem for a defined community rather than chasing a horizontal total addressable market. Consider ride share. The extractive platform playbook maximized liquidity but squeezed drivers and frayed local fabric. A community-owned alternative could optimize for safety, fair wages, and neighborhood vitality over blitz-scaled margins. That’s civic tech with a viable product strategy.

    I’m also watching how social norms evolve. At a recent Elternabend at a German primary school, parents collectively agreed to delay smartphones until age 11 or 12—a striking shift from just five years ago when many 7–8 year olds had devices. Culture moves, sometimes faster than we expect. Product-led growth that ignores cultural momentum (or ethical guardrails) is fragile growth.

    So what do we do on Monday morning? First, rebuild our discovery muscles outside the echo chamber: continuous discovery with the people most affected by our products, not just our power users. Second, measure what matters: add well-being, community impact, and qualitative trust signals to the same dashboards that track activation and retention. Third, resist technology FOMO—choose fewer bets and go deeper, especially where AI can be applied responsibly to unlock real-world value. Fourth, cultivate communities of practice that normalize responsible experimentation, privacy-by-design, and transparent communication. Finally, narrate the change: as product people, we are educators as much as we are builders; our stories shape what teams believe is possible.

    If you’re looking for frameworks to anchor this work, revisit classics like Bowling Alone: The Collapse and Revival of American Community for context on social capital, and pair that with modern conversations on local resilience and community spaces. The future isn’t written yet. With clear principles, careful incentives, and the courage to narrow our scope in service of depth, we can still build technology that strengthens the bonds that make life worth living.

    I’d love to hear how you’re approaching this in your organization—especially examples of “mom and pop tech,” AI Strategy in service of community, or product strategies that trade a little scale for a lot of human good. Join the conversation in the comments.


    Inspired by this post on Product Talk.


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  • Turning Community Noise into Action: My Product Lessons from Zencity’s AI That Listens

    Turning Community Noise into Action: My Product Lessons from Zencity’s AI That Listens

    I’m constantly looking for ways to turn messy, multi-source signals into decisions leaders can trust. Recently, I dug into how Zencity powers government decision-making with community voices—and it’s a masterclass in building AI products that are both responsible and useful.

    Noa Reikhav, Head of Product, Zencity; Andrew Therriault, VP of Data Science, Zencity; and Shota Papiashvili, SVP of R&D, Zencity share a comprehensive view of how they designed an AI that listens and acts without sacrificing rigor.

    How do you use AI to help city leaders truly hear their residents?

    I was struck by the clarity of their platform vision—“They share how Zencity brings together survey data, 311 calls, social media, and local news into a unified platform that helps cities understand what people care about—and act on it.” That single line captures the essence of a unified analytics platform done right.

    You’ll hear how the team built their AI assistant and workflow engine by being thoughtful about their data layers, how they combined deterministic systems with LLM-driven synthesis, and how they keep accuracy and trust at the core of every AI decision.

    It’s a fascinating look at how modern AI infrastructure can turn noisy, messy civic data into clear, actionable insight.

    Here are the takeaways that resonated with me most, and they align closely with how I approach AI Strategy and product management leadership. Data architecture defines what AI can do. Guardrails and transparency matter more than flashy outputs. Agentic systems become powerful when grounded in real, multi-tenant data. AI in the public sector can make democracy more responsive—if built responsibly.

    The team’s layered data model is the backbone that enables trustworthy synthesis: raw data → elements → highlights → insights → briefs. As a product leader, I love how each layer introduces meaning and structure while preserving traceability. It’s the difference between a demo-friendly prototype and a durable platform.

    Why context is everything when building AI for civic use. That’s not a platitude—it’s a requirement. Community conversations are hyper-local, emotionally charged, and policy-laden. Without context and rigorous data governance, you risk misclassification, bias, and broken trust.

    How the team designed their AI assistant using MCP servers to safely negotiate data access. This is a smart pattern for privacy-by-design: let the assistant request access, let the system adjudicate, and make the boundary explicit and auditable. In multi-tenant environments, that clarity is the difference between scaling confidently and shipping risk.

    Balancing agentic flexibility with deterministic trust. I’ve found this to be the most practical framing for real-world agentic AI: give the system room to explore, but bind its outputs to deterministic rails where it matters—taxonomy, citations, permissions, and evaluation criteria.

    Evaluating accuracy when latency matters: how they think about evals, citations, and model-as-judge systems. I appreciate the pragmatism here. In production, you don’t have the luxury of slow truth-finding. You need tight feedback loops, interpretable citations, and layered evals to keep both precision and speed.

    Using workflows like annual budgeting or crisis communication to deliver AI-generated briefs to the right people at the right time. This is where product-market fit shows up: not in features, but in end-to-end workflows aligned to real decision cycles and stakeholders.

    Why government workflows are the ultimate “jobs to be done” framework. When the job is a public process—with deadlines, accountability, and high scrutiny—you don’t just need insights; you need timely, contextualized briefs that match the cadence of the work.

    From my lens, the magic isn’t any single model. It’s the orchestration: deterministic systems with LLM-driven synthesis, strong guardrails, transparent citations, and an orchestration layer that routes the right brief to the right role at the right moment. That’s how you turn community noise into legitimate signal—and signal into action.

    If you’re building AI for regulated, high-stakes environments, take note: invest in your data layers, make context a first-class citizen, embrace privacy-by-design with clear access negotiation, and treat evaluation as a living system. Do that, and you’ll earn the trust that makes your AI assistant—and your organization—indispensable.


    Inspired by this post on Product Talk.


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