Podcast Summary: High-Impact Growth – "What’s New in AI: Equity-enhancing use cases and Open Chat Studio"

Host: Dimagi (Jonathan Jackson, Amie Vaccaro)
Guest: Brian Direnzi (Head of Research and Data, Dimagi)
Date: June 13, 2024

Main Theme & Purpose

This episode delves into the rapidly evolving landscape of artificial intelligence (AI) and its implications for global health and development, focusing on how AI can be steered toward enhancing equity rather than widening gaps. The hosts, Jonathan Jackson and Amie Vaccaro, alongside guest Brian Direnzi, explore Dimagi’s latest activities in AI, including direct-to-client interventions, AI support for health workers, and the development of Open Chat Studio—a platform designed to democratize chatbot creation and testing across diverse languages and contexts.

Key Discussion Points & Insights

1. The Acceleration and Changing Nature of AI Developments

• Rapid Model Releases: AI models evolve extremely quickly, with multiple major releases in just a few months, rendering previous assumptions and limitations outdated.
  • "It's still so hard to predict because things are moving quickly... I was surprised to see how much things have shifted just in the last four or five months." – Brian (02:45)
• Shift From Consumer to Enterprise: While foundational models (from Google, Apple, Microsoft, etc.) are rapidly being integrated into consumer products, Dimagi’s focus is on ensuring these advances translate into meaningful impacts for low-resource and high-need contexts.

2. Equity as a Guiding Principle

• Dimagi’s Unique Role: Rather than aiming to compete at the level of foundational models, Dimagi positions itself as an intermediary—experimenting, testing, and deploying AI in ways that prioritize equity and practical impact for underserved populations.
  • "Technology is going to keep getting better and markets are going to keep driving it into profitable use cases... We see huge potential and with AI potentially transformative potential to make it also work for the impact use cases that we care about." – Jonathan (10:39)

3. Three Buckets of AI Use Cases at Dimagi

• Direct to Client: Using AI to interact directly with end users, often focused on language accessibility and information dissemination.
• Coaching and Support for Health Workers: AI as a tool to augment, not replace, frontline workers—including coaching, supervision, and program management assistance tools.
• Ecosystem/Platform (Open Chat Studio): Building infrastructure to allow both internal and external developers (including non-technical users) to easily create, test, and deploy chatbots—ensuring broad, inclusive participation in AI innovation.

4. Innovations in Language and Context

• Handling Low-Resource Languages: Proactive efforts are made to ensure AI tools work across diverse linguistic contexts. The team tested dozens of languages within hours, leveraging Dimagi's internal diversity.
  • "In a literally like a four hour period... we tried out over a dozen different languages on a model and got some early, early understanding of how well the model could communicate." – Brian (13:01)
• Localizing for Cultural Relevance: Special attention to local dialects, slang, and context-specific needs (e.g., Shang in Kenya), with a recognition that colloquial language is crucial for accessibility and engagement.

5. Open Chat Studio: Democratizing AI Development

• Accessibility: Designed for both developers and non-technical users, lowering the barrier to entry for organizations to test and build chatbots tailored to their contexts.
• Iterative Improvement: Emphasis on the “last 10%” of the user experience, such as language support, context-specific guardrails, and the integration of multimodal channels (e.g., WhatsApp, SMS).
  • "Adding the safety layer... making it easy to test multiple instances... all these little things add up... it's solving so many annoying edge cases for you that it's really an accelerant for developers and organizations." – Jonathan (23:11)

6. Navigating Market Uncertainties and Product Integration

• AI as Table Stakes vs. Add-on: Debate whether AI features will become expected (and thus free) or if they remain sellable premium features.
• Voice Interfaces & Future Opportunities: Exploration into AI-driven voice data entry and coaching, with the prediction that these capabilities may soon be essential, not optional.
  • "You could easily imagine replacing that with just voice conversation... and the AI just interprets all the data out of that conversation." – Jonathan (28:32)
• Balancing AI Hype with User Needs: The team stresses the continuing importance of focusing on end-user problems, not just on using AI for its own sake.

7. Guarding Against Dystopia: The 70% Problem

• Risks of “Good Enough”: Concerns that settling for AI that’s 70% effective could entrench inequities if only wealthy communities get full human support.
  • "What if it never gets better than 70%?... For coaching health workers or for being empathetic... we shouldn't settle for 70%." – Jonathan (38:07)
• Continuous Equity Lens: Commitment to ensuring AI progress does not inadvertently reinforce a “two-tier” system of care and support.

8. Optimism, Caution, and the Road Ahead

• Sense of Overwhelm and Opportunity: Hosts and guests express both excitement and apprehension about the pace, breadth, and decision-paralysis induced by AI’s rapid progress.
  • "I feel like there's so much potential... and that process of choosing is really tricky." – Amie (34:55)
• Testing, Evidence Generation, and Iteration: Focus on rigorous evaluation of impact, not just deployment.

Notable Quotes & Memorable Moments

• On AI’s Progress Pace & Internal Experience

  • “If you’ve been on the inside of this, we’ve been seeing really impressive step changes for years… For consumers who just got exposed, saw this huge jump between 3.5 and 4, now it feels weird that you haven’t seen that next jump, but they’re all like, no, nobody inside this world is worried about the progress.” – Jonathan (09:44)

• On Language Diversity Testing

  • “When the dust settled, we had this big spreadsheet that kind of gave us some initial understanding of how well a few different models were working across these languages. And it was really a fun moment where we got to leverage the incredible diversity… at Dimagi.” – Brian (13:01)

• On Real-World Application Flaws

  • “One of our colleagues was like, oh, I’m running low on money, I don’t know what to do… And the chatbot responded, oh, you should go to a soup kitchen. And she was like, yeah, that’s a great answer if you live in New York, but maybe doesn’t apply to the context where we’re working in Kenya.” – Brian (25:18)

• On Democratization of AI

  • “Large language models in some ways are democratizing AI because it’s more accessible. And then Open Chat Studio is taking that a layer further… Democratizing the ability to be building these chatbots in a safe environment…” – Amie (27:45)

• On Balancing Optimism and Dystopia

  • “Avoiding the dystopia where only the rich people get to see the real live health provider… and everybody else is left with that 70% as good AI version… We’re actively working to increase the equity and exposure and access to these tools.” – Brian (40:58)

Timestamps for Key Segments

• [02:45] – Brian’s story illustrates rapid AI progress.
• [03:48] – Jonathan on the explosion of foundational models and Dimagi’s equity-focused approach.
• [10:39] – Discussion of Dimagi’s unique equity-enabling role in AI.
• [13:01] – Brian’s account of internal language diversity testing.
• [15:04] – Amie introduces the three focus areas for AI at Dimagi.
• [17:31] – Brian elaborates on direct-to-client, health worker support, and ecosystem projects.
• [23:11] – Jonathan and Brian on the aims and surprises of building Open Chat Studio.
• [28:32] – Jonathan unpacks the challenges of adding AI to core products.
• [34:55] – Hosts discuss optimism, caution, and overwhelm regarding AI’s trajectory.
• [38:07] – Jonathan on the limits and risks of “70%” effective AI support.

Language & Tone

The episode maintains a candid, thoughtful, and occasionally playful tone, balancing optimism about AI’s potential with rigorous attention to both evidence and unintended consequences.

Summary

High-Impact Growth’s latest AI episode captures Dimagi’s commitment to bending the arc of AI towards equity. The discussion reveals the vibrant internal experimentation, the technical and human challenges of broadening access (especially across languages and contexts), and the practical, sometimes philosophical, dilemmas facing mission-driven tech organizations. Whether discussing real-world stories or macro-level trends, the conversation circles back to a central theme: moving fast with AI is essential—but only if guided by inclusive stewardship, transparency, and a relentless focus on closing—not widening—the world’s equity gaps.