Forces of neural production: the infrastructural geography of artificial intelligence

Thesis

It is commonly argued that only a handful of technology firms control the infrastructure that underpins contemporary forms of artificial intelligence (AI). However, little is known about how this concentration of computational resources manifests in new production geographies. This thesis asks: What forces enact, and are enacted by, AI's geographies of production? In answering this question, it develops the original framework of neural production networks: geographically dispersed but computationally enveloped production arrangements powered by artificial neural networks. Due to the impacts of AI on economic, political, and cultural interactions, neural production networks already infiltrate infrastructural life. Drawing on a paradigmatic case study design that is primarily informed by document analysis, the thesis applies this new framework to answer three research questions: First, how do technology giants act as lead firms within AI’s geographies of production? Second, how does state action shape, and is shaped by, AI’s geographies of production? Third, what do AI’s geographies of production imply for platformised cultural production? The thesis answers these questions by disassembling neural production networks into three of their mutually conditioning forces. The first force is envelopment: Acting as industry-dominating lead firms, Amazon, Google, and Microsoft decentralise the production of AI and centralise its provision, thereby enclosing users into their proprietary ecosystems. The second force is sovereignty: Although the EU’s Digital Single Market Strategy attempts to push back against an infrastructural dependency on the computational resources of American lead firms, its agency to reconfigure power relations in neural production networks remains limited. The third force is hyperproduction: Underlying the penetration of cultural life with AI-generated media, new revenue streams emerge that purport to destabilise the boundary between reality and simulation. By opening out a conceptual space that probes how those forces relate to each other, the thesis expounds a new understanding of production geographies. This conceptual space contributes to cross-disciplinary scholarship at the intersection of platform studies and economic geography. Additionally, the arguments and insights of this thesis are relevant to policymakers and regulators facing the task of governing the diffusion of neural production networks into ever-more societal spheres.

Authors: Ferrari, F

• DOI: 10.5287/ora-kood4wn0b
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: platform studies; artificial intelligence; global production networks; neural production networks; infrastructure studies; economic geography
• Subjects: Artificial intelligence; Economic geography; Political economy; Multi-sided platform businesses