All exams GA
In one line
In order to remain sustainable into the future, AI should be developed in an environmentally sustainable way. By ensuring that green requi
AI’s Expanding Influence
- AI is redefining industries, with a with present day valuation of $200 billion, it is expected to contribute $15.7 trillion to the global economy by 2030.
- Some major investments
- Stargate Project in the USA classic, which has an established $500 billion worth AI infrastructure investment.
- Reliance Nvidia alliance, where world’s largest data center is being set up in India.
- An LLM project in India rivals ChatGPT and DeepSeek.
- But with advancement in AI also comes significant environmental cost to AI.
Environmental Impact of AI
- Data Centers & Energy Consumption
- AI requires energy hungry infrastructure.
- It has been said that data centers emit 1% of the total global CO₂ emissions, and the numbers are expected to double by 2026 (International Energy Agency).
- Generative AI needs100x more computing power, meaning greater carbon footprints.
- AI Training & E Waste
- Training for GPT-3 emits 552 tonnes of CO₂, which is equivalent to the annual emission of dozens of cars.
- The demand for high performance chips (GPUs, TPUs) is also raising e waste concerns.
Global Response & Gap in Policy
- Urgency of sustainable AI practice has been emphasized by ITU & COP29.
- Over 190 countries have adopted non binding AI ethics for sustainability.
- Regulatory measures concerning AI in the EU and the U.S., on the whole, are lacking in accordance with sustainable AI principles.
- National AI strategies tend to neglect sustainability, particularly regarding private sector roles.
Sustainable AI: The Way Forward
- Transition to Clean Energy
- Switching data centers to renewable sources of energy.
- Google’s DeepMind’s use of AI to enhance the forecasting of wind energy.
- Energy Efficient AI Infrastructure
- Deploying optimized hardware with regular maintenance to cut emissions.
- Domain specific models require much less power, yet perform comparably.
- Pre shot AI models have a lower demand for computing resources than building one from scratch.
- Transparency & Accountability
- The AI industry should measure and announce emissions.
- Industry wide accountability is supported by standardized carbon tracking frameworks.
