From Stacks to Circuits: A Regenerative Socio-Technical Roadmap for AI Infrastructure within Planetary Boundaries
- URL: http://arxiv.org/abs/2606.10544v1
- Date: Tue, 09 Jun 2026 08:11:25 GMT
- Title: From Stacks to Circuits: A Regenerative Socio-Technical Roadmap for AI Infrastructure within Planetary Boundaries
- Abstract summary: Current scaling trajectories for Generative AI, typified by linear supply-side "stacks," prioritize performance density while externalizing significant thermodynamic and material costs.<n>This study proposes a Regenerative Socio-Technical roadmap that reframes artificial intelligence infrastructure as a system-of-systems governed ultimately by planetary limits.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
Related papers
- Scoping Review of AI, Metrology, and ESG in the Semiconductor Sector: Implications for Safe and Sustainable by Design (SSbD) [0.0]
This paper presents a scoping review of 1,465 documents indexed in Web of Science and Scopus.<n>Network analysis reveals a highly fragmented "core-periphery" knowledge structure.<n>To close these gaps, this study proposes a 6-layer Safe and Sustainable by Design architecture.
arXiv Detail & Related papers (2026-07-25T07:08:44Z) - A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design [71.32556809242666]
The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains.<n>It has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability.<n>This survey aims to offer a holistic roadmap toward sustainable, scalable, and socially responsible development of large models.
arXiv Detail & Related papers (2026-07-10T04:02:22Z) - Born-Qualified: An Autonomous Framework for Deploying Advanced Energy and Electronic Materials [2.568084337313714]
"born-qualified" autonomous development embeds manufacturability, cost, and durability constraints from the outset.<n>This vision will require sustained, community-wide commitment, but the potential return on that investment is commensurate with the scale of the challenge.
arXiv Detail & Related papers (2026-05-01T13:21:25Z) - Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey [64.20429885919701]
We propose an energy accounting framework identifying computational and communication costs across the Perception-Reasoning-Action cycle.<n>We establish a unified taxonomy spanning model simplification, computation control, input and attention optimization, and hardware-aware inference.<n>We identify open challenges of federated green learning, carbon-aware agency, 6th generation mobile communication (6G)-native Agentic AI, and self-sustaining systems.
arXiv Detail & Related papers (2026-04-09T06:13:59Z) - Toward Large-Scale Photonics-Empowered AI Systems: From Physical Design Automation to System-Algorithm Co-Exploration [5.036634263468385]
SimPhony provides implementation-aware modeling and rapid cross-layer evaluation.<n>ADEPT and ADEPT-Z enable end-to-end circuit and topology exploration.<n>Apollo and LiDAR provide scalable photonic physical design automation.
arXiv Detail & Related papers (2025-12-31T22:21:42Z) - Infrastructure-based Autonomous Mobile Robots for Internal Logistics -- Challenges and Future Perspectives [7.090184152982336]
This paper presents a comprehensive overview of infrastructure-based Autonomous Mobile Robots (AMRs)<n>We introduce a reference architecture combining infrastructure-based sensing, on-premise cloud computing, and onboard autonomy.<n>We demonstrate the approach in a real-world deployment in a heavy-vehicle manufacturing environment.
arXiv Detail & Related papers (2025-12-17T09:10:43Z) - Futurity as Infrastructure: A Techno-Philosophical Interpretation of the AI Lifecycle [0.0]
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.<n>We introduce a conceptual tool to frame the AI pipeline, spanning data, training regimes, architectures, feature stores, and transfer learning.
arXiv Detail & Related papers (2025-08-21T16:00:13Z) - Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion [51.198001060683296]
Networked urban systems facilitate the flow of people, resources, and services.<n>Current models such as graph neural networks have shown promise but face a trade-off between efficacy and efficiency.<n>This paper addresses this trade-off by drawing inspiration from physical laws to inform essential model designs.
arXiv Detail & Related papers (2025-07-31T01:24:01Z) - Generative AI for Autonomous Driving: Frontiers and Opportunities [145.6465312554513]
This survey delivers a comprehensive synthesis of the emerging role of GenAI across the autonomous driving stack.<n>We begin by distilling the principles and trade-offs of modern generative modeling, encompassing VAEs, GANs, Diffusion Models, and Large Language Models.<n>We categorize practical applications, such as synthetic data generalization, end-to-end driving strategies, high-fidelity digital twin systems, smart transportation networks, and cross-domain transfer to embodied AI.
arXiv Detail & Related papers (2025-05-13T17:59:20Z) - Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries.<n>By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures.<n>GenAI can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing.
arXiv Detail & Related papers (2024-08-02T10:47:10Z) - Green Edge AI: A Contemporary Survey [46.11332733210337]
The transformative power of AI is derived from the utilization of deep neural networks (DNNs)
Deep learning (DL) is increasingly being transitioned to wireless edge networks in proximity to end-user devices (EUDs)
Despite its potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of DL.
arXiv Detail & Related papers (2023-12-01T04:04:37Z) - Evaluating Distribution System Reliability with Hyperstructures Graph
Convolutional Nets [74.51865676466056]
We show how graph convolutional networks and hyperstructures representation learning framework can be employed for accurate, reliable, and computationally efficient distribution grid planning.
Our numerical experiments show that the proposed Hyper-GCNNs approach yields substantial gains in computational efficiency.
arXiv Detail & Related papers (2022-11-14T01:29:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.