🌍 Environmental Impact
Understanding and reducing AI's carbon footprint
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0 / 5 completedIntroduction to AI's Environmental Impact
🌱 The Hidden Cost of AI
While AI promises transformative benefits, training and deploying large models consumes significant energy and generates substantial carbon emissions. Understanding this impact is crucial for building sustainable AI systems.
Training GPT-3 emitted ~500 tons CO₂ - equivalent to driving 1.2M miles
📊 Environmental Metrics
Energy Consumption
Measured in kilowatt-hours (kWh) for training and inference
Carbon Emissions
CO₂ equivalent (CO₂e) from electricity generation
Water Usage
Data center cooling consumes millions of gallons
🔍 Why This Matters
Climate Crisis
ICT sector accounts for 2-4% of global emissions, AI contributing growing share
Resource Scarcity
Limited renewable energy and freshwater in data center regions
Corporate Responsibility
Companies face pressure to meet net-zero commitments
Regulatory Requirements
Emerging mandates for carbon reporting and reduction
📈 Growth Trajectory
🎯 Key Challenges
Scaling Models
Bigger models = exponentially more energy
Experimentation
Multiple training runs waste resources
Inference Load
Billions of queries daily from deployed models
Hardware Limits
GPU/TPU manufacturing has environmental cost