Futurity as Infrastructure: A Techno-Philosophical Interpretation of the AI Lifecycle

Abstract

This paper argues that a techno-philosophical reading of the EU AI Act provides insight into the long-term dynamics of data in AI systems, specifically, how the lifecycle from ingestion to deployment generates recursive value chains that challenge existing frameworks for Responsible AI. We introduce a conceptual tool to frame the AI pipeline, spanning data, training regimes, architectures, feature stores, and transfer learning. Using cross-disciplinary methods, we develop a technically grounded and philosophically coherent analysis of regulatory blind spots. Our central claim is that what remains absent from policymaking is an account of the dynamic of becoming that underpins both the technical operation and economic logic of AI. To address this, we advance a formal reading of AI inspired by Simondonian philosophy of technology, reworking his concept of individuation to model the AI lifecycle, including the pre-individual milieu, individuation, and individuated AI. To translate these ideas, we introduce futurity: the self-reinforcing lifecycle of AI, where more data enhances performance, deepens personalisation, and expands application domains. Futurity highlights the recursively generative, non-rivalrous nature of data, underpinned by infrastructures like feature stores that enable feedback, adaptation, and temporal recursion. Our intervention foregrounds escalating power asymmetries, particularly the tech oligarchy whose infrastructures of capture, training, and deployment concentrate value and decision-making. We argue that effective regulation must address these infrastructural and temporal dynamics, and propose measures including lifecycle audits, temporal traceability, feedback accountability, recursion transparency, and a right to contest recursive reuse.