Current scaling trajectories for Generative AI, typified by linear supply-side "stacks," prioritize performance density while externalizing significant thermodynamic and material costs. As the "Twin Transition" of green and digital transformation accelerates, the industry faces technology gaps - including Scope 3 emissions and e-waste recycling - that impede sustainable scaling and lead to social tensions. This study proposes a Regenerative Socio-Technical roadmap that repurposes the Sustainable Production and Consumption system map to reframe artificial intelligence infrastructure as a system-of-systems governed ultimately by planetary limits. By integrating the Institute of Electrical and Electronics Engineers International Roadmap for Devices and Systems (IEEE IRDS) sustainability considerations for semiconductor facilities, the study proposes a metabolic circuit framework that centers "Values and Needs" within production and consumption relationship loops. This study identifies critical gaps in current Nvidia-centric roadmaps and proposes a competing reference architecture. It demonstrates how a spontaneous order of resource parsimony and planetary accountability can provide an actionable pathway for regulatory compliance and industrial resilience in the digital circular economy.

翻译：当前生成式人工智能的规模化发展轨迹，以线性供给侧"堆栈"为典型特征，在优先追求性能密度的同时，将大量热力学与材料成本外部化。随着绿色与数字化转型的"双转型"加速，该行业面临阻碍可持续规模化并导致社会紧张的技术鸿沟——包括范围三排放与电子废弃物回收问题。本研究提出一项再生式社会-技术路线图，将可持续生产与消费系统地图重新应用于人工智能基础设施，将其重构为最终受制于行星边界的系统之系统。通过整合电气电子工程师学会国际器件与系统路线图（IEEE IRDS）中针对半导体设施的可持续性考量，本研究提出一个代谢回路框架，将"价值观与需求"置于生产与消费关系回路的核心。研究识别出当前以英伟达为中心的路线图中的关键缺陷，并提出了一个竞争性参考架构。它展示了资源节约与行星问责的自发秩序如何为数字循环经济中的监管合规与产业韧性提供可操作路径。