Alternatives to AI Empires
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Karen Hao’s Empire of AI had been on my reading list for a while, but it wasn’t until David attended an Earthkeepers vs. AI Empires event in Zambia in the spring of 2026 that our engagement with it really kicked off. We both read the book, and discovered that Karen has such a deep, nuanced understanding, and is such a brilliant communicator about the technology — and the human decisions that have shaped existing AI and the consequences. It just made sense for us to be in conversation with her work, and to speak to how IDEMS’ current work, and the vision for where we’re going, is so relevant.
More than anything, I like this series because it presents a pro-active, buildable alternative to existing AI. I get really frustrated when people say, If this is what AI looks like, it’s just bad, we just shouldn’t have it. No, that’s not true. AI is just technology, a tool. It has the power to transform society, introduce breakthrough innovation that’s incredibly positive. But we have to design it toward those ends — and that can’t happen if we cede responsibility to the same handful of AI companies that are more focused on profits than public good.
We, the people, can build (with aligned funding, policy, teams, of course) better AI that isn’t exploitative or harmful. But we need to understand how it works and how it can work differently. We need to see the forces — the profit-centric funding incentives, the design choices, the teams and governance structures, the policy capture — that have created the current problematic direction of travel to move in a different direction, and create an alternative paradigm and practice of human-centered AI that benefits everyone.
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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.
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.
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.
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.
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 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 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 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 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.
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.
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