Sustainable AI isn't greenwashing — it's strategic resilience . Model choice impacts energy costs, carbon footprint, regulatory compliance, and social trust over years, not quarters. Key sustainability lenses: Inference efficiency (smaller models = lower operational carbon) Training footprint (pre-trained vs. from-scratch) Responsible agency (bias detection, explainability, moral alignment) Scalability (infra costs as usage 10x's) Real-world proof: Google's DeepMind opti

A common trap: labeling models "good" or "bad" in isolation. Performance is model + dataset + objective . Like a Ferrari excelling on racetracks but flopping off-road — context dictates everything. Key dimensions to benchmark: Customization level (prompt tuning vs. full retraining) Model size (parameter count vs. inference efficiency) Context window (how much history it retains) Latency (critical for real-time apps) Licensing (commercial restrictions) Deployment (API vs

Point 1: Sharper Focus, Smarter AI — Why Defining Use Cases Narrowly Wins AI teams often jump into model selection with vague goals like "face recognition" or "customer shopping assistant." That's not a use case—it's a technology trap that leads to under-tuned models and wasted cycles. Narrow it down: A gallery retrieval system for finding missing persons favors recall to surface every possible lead, even with noise—as seen in AI surveillance systems achieving 94% accuracy