AI & Compute Infrastructure
Data center architecture, network infrastructure, edge computing, and AI governance systems for the most powerful compute installation on Earth.
Subdomains
Knowledge Entries
Edge Computing and Sensor Mesh Architecture
The arcology requires 30-50 million sensors generating approximately 50 TB of data daily, necessitating a five-tier hierarchical edge-fog-cloud architecture where 90%+ of decisions occur locally. This represents a 1,500x scale increase over the largest documented smart building deployments.
AI Governance at Arcology Scale
The arcology requires governing 5,000-10,000 interdependent AI systems affecting 10 million residents across 8 engineering domains. Current governance frameworks target enterprise deployments of 100-500 models. The 10-100x scale gap is compounded by cross-domain cascading failure risks and the need for sub-second governance decisions in safety-critical contexts. Recent research on multi-agent emergent behavior and cascading failures in agentic AI provides partial frameworks, but integration at arcology scale remains unprecedented.
Network Backbone Architecture
The arcology's network infrastructure serves 10 million residents with a fiber backbone exceeding 50,000 miles of internal cabling, 500,000-1,000,000 wireless access points, and AI-driven network management at a scale 20-100x beyond any current single-cluster deployment. The core challenge is not any single technology gap but integration complexity at city scale within a single vertical structure.
Compute Infrastructure Overview
The arcology houses approximately 26,800 compute racks based on the Vera Rubin NVL72 platform (2026 specs), delivering 96.7 zettaFLOPS inference capacity — roughly 483x estimated global AI compute as of 2026. Total compute power draw: 6.175 GW.
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Open Questions
What thermal modeling is needed to quantify the interaction between distributed edge node heat generation and HVAC loads in a sealed vertical structure?
What liability framework applies when an autonomous edge AI system makes an incorrect safety-critical decision?
What federated learning architectures can detect and adapt to concept drift across 10,000 edge nodes without centralized retraining, given that building usage patterns shift seasonally and as the population grows?
What governance architecture prevents cascading failures across 8 engineering domains without introducing unacceptable latency for safety-critical decisions?
How do you establish accountability chains when a harmful outcome results from the interaction of decisions across 5-10 AI systems from different vendors, given that existing liability doctrine assigns blame to component parts rather than emergent interactions?
What mechanisms enable meaningful democratic participation in AI governance for 10 million residents without reducing everything to lowest-common-denominator simplicity?
How should the arcology handle the EU AI Act's requirement for individual-system conformity assessments when systemic risk arises from system interactions that no single assessment captures?
What is the optimal wireless technology mix for dense, vertical environments: Wi-Fi 7, private 5G/CBRS, Li-Fi, or all three in different proportions by zone?
What regulatory framework governs wireless spectrum allocation inside a single structure housing 10 million people — does the FCC's existing SAS model scale to this density, or does the arcology need a bespoke spectrum coordination authority?
How should the network power budget (estimated 15-30 MW) be distributed across zones to balance redundancy with efficiency, and can PoE advances reduce per-AP power draw below 15W by the time residential zones come online?
How is compute capacity allocated between human-serving AI services and autonomous AI agent processes?
What is the physical security model for compute infrastructure housing persistent AI agents with economic agency?
What heat pump COP can be achieved for boosting 50-60°C coolant return to 70°C district heating supply temperature at this scale?