Problem
Research questions and friction points this paper is trying to address.
Artificial General Intelligence
socio-technical pathways
generative AI
foundation models
critical software studies
Pathways to AGI
🤖 AI Summary
This study critically examines the sociotechnical origins of the dominant trajectory in generative AI development, interrogating the conceptual validity of artificial general intelligence (AGI) and its entanglement with prevailing political-economic structures. Drawing on sociology of technology, path dependency analysis, and comparative case studies, it systematically traces the evolution of closed-source large models, open-weight models, and domain-specific architectures to identify pivotal decision points and marginalized alternative pathways. Moving beyond technological determinism, the work proposes a normative framework for developing “AGI-proximate capabilities” oriented toward transparency, human well-being, and sustainability. This approach seeks to balance ethical imperatives, governance requirements, and commercial viability, offering conditional pathways to guide the responsible evolution of artificial intelligence.
📝 Abstract
Our focus are five related questions that stem from a critical software studies perspective. Underpinning this view is the acknowledged need to avoid assumptions regarding the inevitability of the current situation relating to AI. What we need to see is the closeness of the linkage between current commercial AI development and our prevailing social, political and economic circumstances. This does mean that the perspectives presented here are done so critically and conditionally. Most importantly, Artificial General Intelligence (AGI) is seen as being problematic both conceptually and definitionally. This conditioning of any view regarding AGI does lead the discussion in specific directions and to certain conclusions regarding the future. However, adopting this perspective enables the work to offer some final recommendations. We set out to ask the following questions, 1. What are the critical pathways that produced the current dominant generative AI tools (capabilities, product forms, adoption patterns)? 2. Which decision points acted as leverage nodes (small changes that had large downstream effects), and which dead ends reveal alternative possibilities that did not become dominant? 3. How do pathways differ across three foundational-model trajectories such as the frontier proprietary models, open-weight models or specific domain and sovereign models? 4. Which alternative projects branched from key leverage nodes, what is their current state, and why did some succeed, stall, fail or become absorbed? 5. Based on this analysis, what socio-technical development programmes could plausibly move toward AGI-adjacent capability while meeting requirements for transparency, moderation, wellbeing and sustainable business models?
Innovation
Methods, ideas, or system contributions that make the work stand out.
Artificial General Intelligence
socio-technical pathways
critical software studies
foundation models
leverage nodes
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