Can AI actually help eradicate poverty for real people, right now—not in some vague future?

We talk with two leaders from Opportunity International who are trying to do exactly that, using AI to support smallholder farmers and low-cost private schools across Africa and beyond.

In this episode of AI-Curious, we sit down with Ama Akuamoah and Paul Essene from Opportunity International’s Digital Innovation Group. We explore how they’re deploying AI chatbots over WhatsApp to help farmers diagnose crop diseases, optimize planting decisions, and access localized agricultural advice, and how they’re building classroom tools that give overstretched teachers better lesson plans and more time for their students.

We hear the origin story of their farmer chatbot—from a mud-brick home in Malawi to pilots now running in five countries—and the 80-year-old farmer who saved her okra crop by using an AI tool through a trusted “farmer support agent.” We also dig into how they use retrieval-augmented generation (RAG) grounded in local government content, why “human in the loop” is non-negotiable, and what it really takes to make AI work in communities with limited electricity, spotty connectivity, and low digital literacy.

Along the way, we talk about ethics and trust: data consent, privacy for highly vulnerable populations, and the risk of leaving people behind in this new wave of AI. And we zoom out to the bigger picture—why conversational AI in local languages could be a genuine game-changer for economic development if infrastructure, funding, and partnerships keep pace.

What we cover

• [01:00] Opportunity International’s mission and why they focus on farmers, teachers, and micro-entrepreneurs
• [08:00] The Malawi farm-floor moment that sparked their AI journey
• [09:00] How a WhatsApp-based chatbot helps thousands of farmers, and how “farmer support agents” multiply its impact
• [13:40] Using RAG and local government content to keep answers accurate and context-aware
• [15:30] Bringing AI into crowded, low-resource classrooms and supporting teachers with lesson plans and copilots
• [20:15] The hard parts: infrastructure gaps, low-cost devices, digital literacy, and why this work is heavy lifting
• [24:30] Human-centered design in action: co-creating with communities, iterating in the field, and learning from pilots
• [37:50] Guardrails, consent, and building trust around AI in vulnerable communities
• [41:00] What’s needed for real scale: infrastructure, funding, language support, and the right partners
• [43:00] Their hopeful vision for AI as a lever for economic development—if no one gets left behind

If you’re interested in AI for social impact, global development, or what it really takes to deploy AI outside Silicon Valley, this conversation is a grounded, hopeful look at what’s already working—and what still needs to change.

Is AI making us smarter or dumber—and how do we make sure we’re on the right side of that divide?

In this episode of AI-Curious, we talk with Professor Vasant Dhar, author of the new book Thinking With Machines: The Brave New World of AI. Vasant isn’t just a historian of AI; he’s part of the story. In the 1990s, he helped bring machine learning to Wall Street, founded one of the world’s first ML-based hedge funds, and became the first professor to teach AI at NYU Stern, where he’s now the Robert A. Miller Professor of Business. He also hosts the podcast Brave New World.

We explore how AI evolved from early efforts around “thinking, planning, and reasoning” to the long era of pure prediction and machine learning, and then to today’s general-purpose models that blur the line between expertise and common sense. Vasant explains why the autocomplete problem turned out to be a gateway to something like “general intelligence,” and why that matters for how we define knowledge, understanding, and reasoning.

We then dive into finance and the search for “edge.” Vasant shares war stories from his days at Morgan Stanley, where machine learning systems quietly reshaped trading strategies and risk-taking. We unpack his work on “the DaBot,” an AI built on the writings and valuation framework of Aswath Damodaran, and what happens when every analyst and firm can tap this kind of supercharged valuation machine. Does AI erase the edge—or simply raise the bar for everyone?

Finally, we zoom out to careers, education, and everyday life. Vasant argues that AI is likely to bifurcate humanity into those who become “superhuman” by thinking with machines, and those who outsource their thinking and fall behind. We discuss how classrooms will change, why many teachers (and professors) may be more automatable than they realize, and how each of us can periodically test whether AI is making us smarter or dumber.

If you’re curious about how to work with AI rather than be replaced or outpaced by it, this conversation offers a grounded, big-picture way to think about your edge in the age of intelligent machines.

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.