Ecologically Compatible Autonomous AI Production

Increasingly autonomous AI-mediated production can expand task-level capabilities while leaving the renewal of machinery, energy provision, knowledge, labour, and material supply dependent on wider productive relations. This exploratory paper examines the conditions under which those capacities can be reproduced without undermining affected ecological processes. Historical-materialist analysis and a provisional generative-relational (GR) representation are used to distinguish operational autonomy, continued productive capacity, changes in critical dependencies, ecological viability, and institutional authority over feasible alternatives. Domain-specific reviews assess documented capabilities and limits in robotics, automated laboratories, production control, computing, power and cooling, supply chains, repair, and materials recovery. A typed relational model makes physical flows, enabling conditions, evidentiary provenance, and decision rights jointly inspectable without treating them as interchangeable quantities. Its incremental value relative to life-cycle assessment, material-flow analysis, reliability engineering, conventional production networks, and established provenance methods remains to be tested. Conditional formal constructions are connected to proposed counterfactual comparisons, empirical measurement protocols, and explicit identification and falsification criteria. The paper distinguishes technical feasibility, ecological effects, distributional consequences, and normative justification, allowing incompatibility or indeterminate results. It reports no original production experiment, causal ecological estimate, or demonstrated self-reproducing AI factory and establishes no general ranking of ownership forms. Its contribution is a bounded research architecture and staged empirical programme for investigating the historically constituted conditions of ecologically compatible autonomous production.