AI-Curious with Jeff Wilser

• Can AI Help Eradicate Poverty? How AI is Helping African Farmers and Teachers, w/ Opportunity International's Ama Akuamoah & Paul Essene

  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 behindIf 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.

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• How We Got Here and Where We're Going: AI History (and Future) w/ Vasant Dhar, Author of Thinking with Machines

  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.

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• How San Jose is Harnessing AI (and What We Can Learn From It), w/ Mayor Matt Mahan

  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.HighlightsFrom 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.

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• The Complicated Intersection of AI and Creativity, w/ Dr. Maya Ackerman

  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 listenWe 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.GuestDr. Maya Ackerman — AI researcher, entrepreneur, and author of Creative Machines: AI, Art, and Us. TakeawaysAI 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 Machineshttps://www.amazon.com/Creative-Machines-Future-Human-Creativity/dp/1394316267

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• LinkedIn's Chief AI Officer, Deepak Agarwal, on AI Agents, Building Responsible AI, and the Future of Work

  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.LinkedInAI-Curious

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