A world model: On the political logics of generative AI

Amoore, Louise; Campolo, Alexander; Jacobsen, Benjamin; Rella, Ludovico

Abstract

The computational logics of large language models (LLMs) or generative AI – from the early models of CLIP and BERT to the explosion of text and image generation via ChatGPT and DALL-E − are increasingly penetrating the social and political world. Not merely in the direct sense that generative AI models are being deployed to govern difficult problems, whether decisions on the battlefield or responses to pandemic, but also because generative AI is shaping and delimiting the political parameters of what can be known and actioned in the world. Contra the promise of a generalizable “world model” in computer science, the article addresses how and why generative AI gives rise to a model of the world, and with it a set of political logics and governing rationalities that have profound and enduring effects on how we live today. The article traces the genealogies of generative AI models, how they have come into being, and why some concepts and techniques that animate these models become durable forms of knowledge that actively shape the world, even long after a specific material commercial GPT model has moved on to a new iteration. Though generative AI retains significant traces of former scientific and computational regimes – in statistical practices, probabilistic knowledge, and so on – it is also dislocating epistemological arrangements and opening them to novel ways of perceiving, characterising, classifying, and knowing the world. Four defining aspects of the political logic of generative AI are elaborated: i) generativity as something more than the capacity to generate image or text outputs, so that a generative logic acts upon the world understood as estimates of “underlying distributions” in data; ii) latency as a political logic of compression in which (by contrast with claims to reduction or distortion) the thing that is hidden, unknown or latent becomes surfaced and amenable to being governed; iii) broken and parallelized sequences as the ordering device of the political logic of generative AI, where attention frameworks radically change the possibilities for governing non-linear problems; iv) pre-training and fine-tuning as a computational logic of generative AI that simultaneously shapes a “zero shot politics” oriented towards unencountered data and new tasks. Across each of the four aspects, the article maps the emerging contemporary political logic of generative AI.

Citation

Amoore, L., Campolo, A., Jacobsen, B., & Rella, L. (2024). A world model: On the political logics of generative AI. Political Geography, 113, Article 103134. https://doi.org/10.1016/j.polgeo.2024.103134

• Journal Article Type: Article
• Acceptance Date: May 14, 2024
• Online Publication Date: May 24, 2024
• Publication Date: Aug 1, 2024
• Deposit Date: May 29, 2024
• Publicly Available Date: May 29, 2024
• Journal: Political Geography
• Print ISSN: 0962-6298
• Electronic ISSN: 1873-5096
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 113
• Article Number: 103134
• DOI: https://doi.org/10.1016/j.polgeo.2024.103134
• Public URL: https://durham-repository.worktribe.com/output/2467333