Continuing their discussion on the future of AI, David and Kate explore how advances in large language models could enable a new generation of smaller, more specialised AI systems. They discuss why the next wave of innovation may come from building tools that are more efficient, focused, and responsive to real-world needs rather than simply pursuing ever-larger models.

David and Kate explore the historical divide between Symbolist and Connectionist approaches to AI, reflecting on how today’s dominant AI narratives emerged and what may have been lost along the way. They discuss the difference between expert systems built on structured human knowledge and data-driven learning systems based on neural networks, and consider the implications of each for governance, traceability, social impact, and responsible technology development. The conversation highlights how alternative approaches to AI may offer more practical and trustworthy

In the second part of their discussion, David and Kate reflect more deeply on the Earthkeepers versus AI Empires convening in Zambia, exploring the diverse perspectives and tensions that emerged during the event. They discuss questions of power, governance, indigenous knowledge, and technological futures, as well as the growing recognition that current AI trajectories are not inevitable. The conversation highlights alternative visions for AI and digital technologies built around community ownership, trusted data, local governance, and smaller-scale systems designed to

In the first of a two-part discussion, David and Kate reflect on a recent convening in Zambia that brought together activists, technologists, researchers, and civil society groups concerned with the impacts of AI infrastructure and large-scale data centres. They discuss the influence of Karen Hao’s book Empire of AI, the emergence of global resistance movements around extractive AI development, and the distinction between AI as a useful tool and the broader systems of power shaping its deployment. The conversation highlights

Lily and David discuss the challenges of working with rainfall and climate data, exploring ideas of data quality, data rescue, and data accreditation. They reflect on different sources of climate data—from weather stations and satellites to reanalysis products—and examine how these can be evaluated for specific applications such as agriculture. The conversation also highlights ongoing research into rainfall intensity, satellite validation, and the importance of building evidence around which climate products are appropriate for different contexts and uses.

Lucie and David continue their discussion on Farmer Research Networks (FRNs), focusing on the idea of embedded scaling and its implications. They explore how scaling out, scaling up, and scaling deep each change the nature of the data and the research itself, and reflect on the challenge of designing systems where farmers collect and use data for their own benefit while also contributing to wider learning and research.

Lucie and David discuss the origins and evolution of Farmer Research Networks (FRNs) within the work of the Global Collaboration for Resilient Food Systems. They explore how FRNs were conceived as a way to combine participatory research with large-scale data and reflect on ongoing debates around embedded scaling, participation, and the distinction between FRNs and approaches such as Participatory Action Research (PAR). The conversation highlights both the promise and the practical challenges of building research systems that are deeply contextual

Lily and David discuss recent work on the ePICSA system, focusing on the development of a structured summaries database to support climate information for agriculture. They explore how moving from file-based systems to a database approach creates new opportunities for versioning, quality control, decentralised workflows, and accreditation of climate products. The conversation also reflects on the broader challenges of climate data quality, data rescue, and building sustainable systems that can support national meteorological services.

Santiago and David discuss how a specialised AI assistant is supporting their work authoring and reviewing STACK questions. They explore the balance between human expertise and AI support, reflecting on how the assistant improves efficiency, enhances question quality, and helps navigate complex documentation and legacy code. The conversation also highlights the broader potential of specialised AI assistants as collaborative tools that augment, rather than replace, expert human work.

Michele and David explore the ideas and design principles behind the Open App Builder, a system developed through IDEMS’ collaboration with Parenting for Lifelong Health (PLH). They discuss the challenges of creating flexible, reusable app infrastructures that support collaboration across technical and non-technical teams, and reflect on the long-term vision of enabling local organisations to build and adapt their own digital tools. The conversation highlights both the complexity of the system and the growing opportunities emerging as it matures and

Michele and David discuss the development of facilitator apps within the Parenting for Lifelong Health (PLH) ecosystem, exploring how these tools support facilitators delivering parenting programmes in diverse contexts. They reflect on the growing role of adaptable digital tools for reporting, monitoring, and programme delivery, and discuss the wider opportunities these technologies create for scalable implementations, embedded research, and locally owned digital ecosystems that could support many different types of interventions in the future.

Santiago and David discuss the emergence of STACK question authoring as a growing area of work within IDEMS. They reflect on the value of cross-institution collaboration, the role of formative assessment and feedback in mathematics education, and how expertise developed across diverse educational contexts can support universities internationally.