sustainability

Sustainability in the Age of AI

Sustainability in the Age of AI is a 4-week hybrid cohort that explores how artificial intelligence and sustainability intersect in real-world systems. Participants learn to evaluate the environmental and social impact of AI, identify meaningful use cases, and design responsible, efficient solutions. Through case studies, tools, and a capstone project, learners build systems thinking and cross-functional skills. The course is designed for entry-level and mid-career professionals seeking to future-proof their careers at the intersection of technology, business, and sustainability.

7 hours 30 min

4 Modules

Beginner

₹25,000₹35,00029% OFF

Course Curriculum

Week 1: Sustainability as a System, Not a Slogan

12 Lessons

1.1: The Problem with Silos Breaking the Institutional Wall

1.2: Systems Mapping 101: The Causal Architecture of Innovation

1.3 : Shared Vocabulary: Building a Unified "Operating System

1.4: The Fallacy of Incrementalism Why Most ESG Efforts Fail

1.5: The Efficiency Paradox (Jevons Paradox) in AI

1.6: Measuring the Wrong Things: The Metric Trap in AI

1.7: Institutional Resistance & The "Sustainability Tax"

1.8: Understanding Leverage in Systems: The Donella Meadows Framework

1.9: Finding Leverage in the AI Lifecycle

1.10: Systems Mapping for Leverage: Tool Deep Dive (Miro/FigJam)

1.11: Institutional Strategy: Moving the Needle

1.12:Generational Value: Identifying Leverage Points Together

Week 2: The Hidden Environmental Cost of AI

13 Lessons

2.1: Forensic Analysis of AI’s Carbon, Water, and Energy Footprint

2.2: The Thermal Challenge: Evaporative Stress and the Water-Energy Nexus

2.3 :Grid Volatility: Real-time Intensity and Peak Load Management

2.4: Model Scale vs. Efficiency Trade-offs

2.5: The "Inference Economics" of 2026: Reasoning vs. Prediction

2.6: Finding the "Sweet Spot": The Value-to-Watt Ratio

2.7: Cross-Functional Application: Leading the Right-Sizing Movement

2.8: Generational Value: Navigating the Scaling Peak

2.9: The "No-AI" Framework Learning When NOT to Use AI

2.10: Case Study Failures: Analyzing the "Vapor AI" Projects

2.11: The "Zero-Base" Approach: Justifying Every Compute Cycle

2.12: Institutional Strategy: Building the "Rubric of Resistance"

2.13: Practical Tools: The "No-AI" Decision Matrix

Week 3: AI for Sustainability From Insight to Impact

12 Lessons

3.1: Precision Environmental Monitoring & Conservation

3.2: Bio-Acoustics & The Internet of Nature: Listening to the Pulse of the Wild

3.3: Natural Capital Accounting: Verifying the Value of the Standing Forest

3.4: Generational Value: Protecting the Future Together

3.5: AI-Driven Energy Transition & Smart Grids

3.6: Demand-Response Orchestration: Making the Grid "Elastic"?

3.7: The Decentralized Grid: Managing the "Prosumer" Revolution?

3.8: Generational Value: Powering the Future Together

3.9: Circular Economy & Materials Science Innovation

3.10: Waste Sorting & Optimization: Precision Recovery through Computer Vision

3.11: The "Digital Twin" of the Supply Chain: Closing the Loop

3.12: Generational Value: Closing the Loop Together

Week 4: Governance, Ethics, and the Future Roadmap

8 Lessons

4.1: The "Audit-Ready" Mandate: Transitioning to Explainable AI (XAI)

4.2: Red-Teaming for Sustainability: Stress-Testing the "Green Brain"

4.3: The Regulatory Tipping Point: Mastering the EU AI Act & Beyond

4.4: Generational Value: Strengthening the Shield Together

4.5: Ethics of the "Digital Twin" Planet Equity and Inclusion

4.6. Bias in the Biosphere: Correcting the "Data Blind Spots"?

4.7: The Workforce Transition: Managing the "Green-AI" Labor Shift?

4.8: Generational Value: Protecting the Human Pulse

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