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.