The CGD Podcast: Where AI Meets Development

Guests: Temina Madon (Agency Fund CEO), Han Sheng Chia (CGD Fellow & AI Initiative Director)
Host: Marcus Goldstein (CGD Vice President and Senior Fellow)
Date: October 30, 2025

Episode Overview

This episode explores the integration of artificial intelligence (AI) into global development, focusing on its practical applications, ethical implications, and the unique challenges faced when adapting AI tools to diverse contexts in the Global South. The guests discuss current trends, promising interventions, evaluation hurdles, inclusion, and the future role of local actors in shaping AI for development outcomes.

Key Discussion Points and Insights

1. The Current Landscape of AI in Development

[02:08] Temina Madon:

• Civil Society Leadership: Nonprofits and tech companies are at the forefront, leading much of the AI experimentation; government engagement remains limited but may grow over time.
• Sector Adoption:
  • Agriculture: AI delivering support to farmers via weather information and farm management.
  • Health: Digital chatbots providing health advice (e.g., maternal health, nutrition, TB adherence) due to a pre-existing digital backbone.
  • Livelihoods: AI's impact on job creation is mixed—while it threatens some jobs, it could support creative livelihoods through entrepreneurship.
• Accessibility & Inclusion:
  • "Most of the foundation models and even open source and fine-tuned models are from the global North… in English or other major languages."
  • This creates a lopsided landscape that funders are attempting to address via localization and inclusivity.

2. Rethinking Impact: Technology vs. Outcomes

[04:06] Han Shang Chia:

• CGD’s focus expands from access to improvement in actual development outcomes.
• Quote:

  "Powerful technology and access alone does not necessarily lead to improvements in infant mortality or increases in agriculture yields." [04:18]

3. Where AI Is Making a Difference

[04:55] Temina Madon:

• Health & Education Impact:
  • Increases in user engagement, especially with AI health chatbots ("two, three times the engagement of women").
  • AI tutors in education showing "almost implausible" effects, though long-term impact still to be determined.
  • Quote:

    "I really advocate a deep focus on the user and solving their pain points as opposed to coming in with a central planner's perspective." [06:58]

• Supporting the Front Lines:
  • Bots and AI co-pilots for health professionals could help alleviate absenteeism and support stretched public systems.
• Moral Tradeoffs:
  • Quote:

    "Is it electrification for AI chatbots or is it electrification to provide light in clinics where women are delivering babies?" [08:36]

4. Scaling, Cost Reduction, and Real Constraints

[09:24] Han Shang Chia:

• AI can massively scale interventions—e.g., digital ag extension services to reach wider populations at lower cost.
• But "AI is not a silver bullet"—it can't solve infrastructure deficits or supply chain problems alone.

[11:17] Temina Madon:

• Urges a systems approach: AI needs to connect to physical supply chains, logistics, and the broader ecosystem to be effective.

[12:27] Han Shang Chia:

• Envisions platform-based solutions, e.g., a "digital agronomist" that not only provides advice but also connects farmers with local suppliers, thus bridging digital and physical worlds.

5. Bottlenecks, Systemic Challenges, and Optimism

[13:06] Marcus Goldstein:

• Technology might spotlight bottlenecks (e.g., lack of roads or supplies) but could also drive innovation in tackling these pain points.

6. Agency, Empowerment, and Education

[14:00] Marcus Goldstein:

• AI-informed coaching to foster personal initiative and agency excites many in the sector.

[14:54] Han Shang Chia:

• Raises fundamental questions—does generative AI enhance or diminish user agency?
  • Evidence suggests unguarded AI tools can harm self-directed learning.
  • Quote:

    "Does it enable us to introspect...or does it substitute away from our own self-directed learning?" [14:54]
    "Students used it as a crutch and performed worse than students who were provided a chatbot with guardrails." [15:39]

7. Guardrails, Alignment, and Cultural Context

[16:22] Han Shang Chia:

• Safety should be a foundational expectation, not the final goal—AI must also deliver on development-specific outcomes and pedagogical best practice.

[17:57] Temina Madon:

• Cultural sensitivity is crucial. What is safe or appropriate can differ vastly by context.
  • Quote:

    "There is a component of safety which is around culture, social norms, also language...and it may take a lot of work to develop different kinds of technologies, other foundation models that don't come out of the global north..." [18:38]

• Inclusion means capturing local voices, dialects, and sector-specific language to make AI genuinely useful.

[20:34] Han Shang Chia:

• Who should be responsible for this work—tech companies creating foundational models, or local organizations customizing for context?
  • The question of feedback loops and ownership of cultural knowledge remains open.

8. Funding, Incentive Structures, and Resource Allocation

[21:39] Marcus Goldstein:

• Notes the misalignment of resources: cutting-edge AI is focused where profits are highest, not always where need is greatest.

[22:44] Temina Madon:

• Proposes an "API tax"—a levy on tech companies that could finance the development of socially beneficial AI products.

[24:07] Han Shang Chia:

• Suggests usage credits as incentives for socially valuable use of major AI models; both ideas go beyond traditional corporate social responsibility.

9. Lessons from the AI & Development Accelerator

[24:52] Tamina Madon:

• Accelerator with OpenAI credits supported nonprofits in health, agriculture, and education.
• Key Insight:
  • Understanding barriers and frictions is as important as finding early success stories.
• Challenges Observed:
  • Data scarcity for fine-tuning and evaluation.
  • The difference in experience between digitally native and analog-first nonprofits.

[27:23] Tamina Madon:

• Over-eager evaluation without first achieving product stability or user engagement is a common misstep.
• Nonprofits lack adoption of digital tooling (e.g., Deep Eval, Langfuse); ‘vibe-checks’ replace rigorous evaluation practices.
• There’s a need for user funnel tracking and a better grasp on digital product analytics.

[28:58] Marcus Goldstein & [29:23] Han Shang Chia:

• Irony: digital products provide abundant data, but it's often not leveraged by NGOs due to lack of technical familiarity.

[30:01] Han Shang Chia:

• Impact evaluations must account for the fluidity of AI models—products and outcomes change rapidly, so specifications and user engagement should always be tracked.

Notable Quotes & Memorable Moments

• On Inclusion:

  "Most of the foundation models...are from the global North...a lot of people not involved, not driving adoption, not getting access." — Temina Madon [03:38]

• On Systemic Change:

  "We have to talk about where our priorities lie. And is it electrification for AI chatbots or is it electrification to provide light in clinics where women are delivering babies?" — Temina Madon [08:36]

• On Safety and Context:

  "You can create a perfectly safe AI and yet it does not generate development outcomes that we care about." — Han Shang Chia [16:46]

• On Personal Stories:

  "That sense of dignity of a woman who has been given sort of a second chance at life, to me, that's quite compelling." — Temina Madon [33:29] "We went from in 2017 taking six months to identify, enroll, and pay just 5,000 households to several hundred thousand at weeks. And that's what motivates me…" — Han Shang Chia [35:42]

Timestamps for Important Segments

• [02:08] – Current landscape of AI adoption in the development sector
• [04:55] – Largest effects seen in health and education; user engagement stories
• [08:36] – AI, electrification, and moral tradeoffs
• [09:24] – Scaling and cost considerations in agricultural extension
• [14:54] – AI, agency, and effects on self-directed learning
• [16:22] – Safety vs. development-focused alignment; the need for contextual guardrails
• [22:44] – Proposed API tax and funding mechanisms for inclusive AI
• [24:52] – Overview of the AI accelerator, frictions, and lessons for nonprofits
• [31:39] – Personal stories from guest speakers highlighting AI’s human impact

Tone and Takeaways

The conversation is both practical and philosophical, blending optimism about AI’s ability to empower users and scale programs, with a sober recognition of persistent bottlenecks and risks of exclusion. Guests emphasize the importance of localized, user-driven design, vigilant evaluation, and new funding mechanisms to ensure AI benefits are shared broadly rather than concentrated in the Global North.

Useful For

This summary is ideal for development practitioners, policymakers, technologists, and anyone interested in the intersection of AI and international development. It covers applications, challenges, and strategic questions shaping the field, offering both inspiration and caution.