Stories from a social enterprise that uses mathematical sciences in impact-oriented work around the world. Our experiences range from helping some of the world's poorest farmers get value from data, to enabling academics to use AI responsibly in their teaching. We never know what our next task will be but the last 6 years have shown that it is likely to lead to a story.
David and Michele discuss how recent advances in AI are opening new possibilities for the Open App Builder and the authoring of digital tools for social impact. Drawing on IDEMS’ work with Parenting for Lifelong Health, they explore how AI agents can help reduce barriers to authoring while keeping people in control of the decisions that matter, enabling more adaptable and community-owned digital technologies.
David and Michele discuss how advances in AI are opening new possibilities for STACK question authoring. Using this practical example, they reflect on the design of multi-agent systems, the role of specialised models, and why building efficient, interoperable AI tools may be just as important as building ever more powerful ones.
Following David’s participation in the Global Smart Farming Conference hosted by the Food and Agriculture Organization of the United Nations (FAO), he and Santiago reflect on some of the key themes and debates that emerged. Their discussion explores competing visions of smart farming, the role of AI and digital technologies in agriculture, and the importance of ensuring that innovation remains centred on farmers and the communities they serve.
Following David’s participation in a World Food Programme forum on artificial intelligence, digitisation, data governance, and food security, he and Santiago reflect on some of the key themes and debates that emerged. Their discussion explores the opportunities and risks AI presents for food systems, and the role of governance, local knowledge, and technological alternatives in shaping more equitable and sustainable futures.
Continuing their discussion of The Agency Fund’s proposed technology stack for the social sector, David and Santiago explore the three frontend approaches outlined in the article: frontline worker tools, chatbots, and custom applications. Drawing on their own experience developing digital tools for social impact, they reflect on the strengths, limitations, and future potential of each approach. https://theagencyfund.substack.com/p/a-default-tech-stack-for-the-social
"David and Santiago discuss The Agency Fund’s recent article on a technology stack for the social sector, exploring the backend systems that make modern AI-enabled tools possible. From LLM gateways and agent builders to data pipelines, monitoring, and experimentation, they examine the growing ecosystem of open tools that can help small organisations access capabilities once reserved for large technical teams. Along the way, they reflect on how these developments relate to IDEMS’ own work and why this is an exciting moment for organisations seeking to build impactful technology with limited resources. https://theagencyfund.substack.com/p/a-default-tech-stack-for-the-social"
In the final episode of the series, David and Kate explore the power of narratives in shaping the future of AI. Inspired by Karen Hao’s *Empire of AI*, they discuss how messaging, lobbying, and financial influence have created a sense of inevitability around one particular vision of AI, while alternative approaches struggle to gain visibility. They reflect on the importance of articulating and championing alternative visions for AI, concluding with a call for the liberation of AI from AI empires.
Continuing their exploration of alternatives to today’s dominant AI paradigm, David and Kate reflect on what the future of work could look like beyond AI empires. They discuss the role of education, apprenticeships, and human expertise in a world increasingly shaped by AI, and consider how technology might free people to focus on more creative, collaborative, and meaningful work. The conversation highlights the importance of building alternative visions for the future and asks how societies can create systems that value human development, collaboration, and social impact alongside technological progress.
Building on their exploration of alternatives to today’s dominant AI paradigm, David and Kate discuss what a community-centred approach to AI might look like. They explore the importance of collaboration, deep interoperability, distributed ownership, and adaptability, arguing that effective AI systems should strengthen communities rather than replace them. The conversation considers how communities can retain agency over their data, tools, and knowledge while remaining connected to wider networks of learning and innovation.
Continuing their examination of the assumptions underlying today’s dominant AI narrative, David and Kate reflect on why AI has become such an important topic within IDEMS. They discuss how years of work on community ownership, trust, interoperability, and complex social systems have shaped their thinking, and why recent advances in AI may finally make it possible to build technologies that support rather than constrain local agency. The conversation explores the relationship between technology, governance, and social impact, and considers what kinds of foundations are needed for more distributed and community-centred approaches to AI.
Continuing their examination of the assumptions underlying today’s dominant AI narrative, David and Kate explore the role of human expertise in building effective AI systems. They discuss the often-overlooked human work that underpins current AI, from reinforcement learning and quality assurance to research, teaching, and domain expertise. The conversation highlights how diverse forms of human capital, collaboration, and innovation may be far more important to the future of AI than simply increasing data and compute.
Continuing their examination of the assumptions underlying today’s dominant AI narrative, David and Kate explore what makes data useful, trustworthy, and meaningful. They discuss the limitations of extraction-based approaches to AI, the importance of local context and data ownership, and the challenges of building systems that can learn across diverse communities without centralising control. The conversation highlights why better data—not just more data—may be key to building more effective and trustworthy AI systems.
Continuing their examination of the assumptions underlying today’s dominant AI narrative, David and Kate explore the distinction between AI as a product and AI as a sociotechnical system. They reflect on the often-invisible infrastructure, labour, resources, and governance structures that sit behind AI technologies, and discuss why understanding these systems is essential for making informed choices about technology, impact, and innovation. The conversation highlights how different assumptions about ownership, trust, and accountability shape the technologies we build and the societies they serve.
Continuing their discussion on the future of AI, David and Kate explore the economic and institutional forces shaping today’s dominant AI models. They discuss the roles of investment, monopoly power, research funding, and commercial incentives in driving ever-larger AI systems, and consider how these pressures influence both technological development and public narratives around AI. The conversation highlights why the current trajectory of AI is not inevitable and what alternative paths might look like.
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 pathways for addressing real-world challenges.
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 serve real social needs rather than concentrated power.
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 growing concerns around the resource demands and extractive dynamics associated with large-scale AI infrastructure.
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.
