🔬 Deep Dive
AI Infrastructure & Sustainability
Running AI at scale is a physical infrastructure problem. Data centers, power grids, cooling systems, and nuclear reactors are now part of the AI story. This section covers the physical layer that makes AI possible - and the environmental and economic consequences of that infrastructure.
AI's Energy Footprint
Training vs inference energy, absolute numbers, and why inference now dominates total consumption.
Data Center Architecture
Power, cooling, networking - the physical infrastructure stack that runs AI at scale.
Cooling Technologies
Air, liquid, and immersion cooling - why dense GPU racks have made cooling the new bottleneck.
The Nuclear-AI Connection
Microsoft, Google, Amazon, and Meta are all investing in nuclear power for AI data centers.
Efficiency Strategies for Builders
Model routing, caching, quantization, batching - how your design decisions affect energy use.
Water & Environmental Impact
Water consumption, carbon footprint, and the environmental cost of AI inference at scale.
Carbon Footprint Measurement
Scope 2 emissions, cloud carbon tools, and how to measure and report your AI workload's footprint.
Infrastructure Economics
Cost breakdown of AI data centers, inference cost trends, and the economics of the GPU supply chain.