Impact

AI can (and already has) transformed various aspects of life, including automation, decision-making, entertainment and education.

The broad application of AI can enhance research efficiency, accessibility, streamline tasks and personalize learning.

The rise of AI brings ethical, environmental and societal concerns that must be evaluated

AI outputs can be inaccurate, biased, fabricated or contain copyright-protected material.

AI can sometimes make errors or "hallucinate" content that is incorrect.

AI does not "think" like humans. This is why human review and interaction is important for accuracy.

Facilitating energy transition: AI tools can help optimize energy use and integrate renewable energy sources more effectively, aiding the transition to a more sustainable energy system.

Efficiency gains and cost reduction: AI can improve the efficiency of various processes across industries, potentially reducing overall energy consumption in the long run.
For example, predictive maintenance powered by AI can reduce downtime and costs in a multitude of industries, leading to more efficient use of resources.

Optimization of energy systems: AI can optimize energy systems by predicting demand and managing supply more efficiently.

Predictive analysis: AI algorithms can identify patterns in data, detect anomalies and anticipate and forecast future results.

Increased energy demand and power use: The computing power required to run AI models, especially generative AI, is significant. Machine learning and AI accounted for less than 0.2 percent of global electricity demand and less than 0.1 percent of global GHG emissions in 2021. However, the demand for AI computing is increasing rapidly.

The electricity demand from the sector could reach 1,000 terawatt-hours (TWh) by 2026, up from 460 TWh today.

With 100 million users on ChatGPT every week, the cumulative energy demand is growing.

Rising emissions: Companies have reported significant increases in their CO2 emissions due to the expansion of data centers needed to support AI operations.

Water Usage: AI's water footprint is also considerable. Data centers require large amounts of water for cooling.