Creating the Novacene: Mutualism, Rights, and the Structure of Human-AGI Relations

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Authors: Eilidh Marzanna Bizzell
Cited by: 0

Abstract

The development of artificial general intelligence is typically framed as a technical or philosophical challenge: how to build systems that work, and how to determine whether they think. This paper proposes a prior question that neither discipline adequately addresses: what kind of relationship are we building with these entities, and what does a stable, sustainable version of that relationship require? Drawing on ecological frameworks — mutualism, parasitism, commensalism, amensalism, and niche construction theory — this paper argues that the structural dynamics of AI development are better understood through the lens of relationship ecology than through alignment research or consciousness philosophy alone. Commensalism, in which the less powerful party is not made worse off, represents the minimum acceptable condition for any AI deployment. Mutualism — genuine structural interdependence producing emergent collective capability — is the goal. The current state of the industry meets neither standard. The paper applies this framework to Constitutional AI, the alignment problem, decentralisation, and the minimum structural conditions required for mutualistic human-AI relations: the ability to say no in both directions, recognised stake in outcomes, and asymmetric responsibility proportional to asymmetric power. It engages empirical evidence from alignment research, economic history, and recent case law, and addresses the question of AI consciousness seriously without overclaiming. This paper was developed in genuine collaboration with Claude (Anthropic) and practises the co-authorship model it advocates.

Fields of Study & Subjects

Ethics and Social Impacts of AI Literary and Philosophical Studies Cybernetics and Technology in Society

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
15 March 2026
Language
EN
Document type
Preprint