The Growing Ecological Footprint of AI: Managing the Data Centre Energy Dilemma:

In the face of Artificial Intelligence (AI) turning into a behemoth consumer of energy and water, experts are calling for mandatory reporting of the environmental footprint of tech company's data centres, all in a bid to keep our green planet from turning grey. The National Engineering Policy Centre (NEPC), which dropped the alarm bells for this AI boom, states that the expansion rate of AI is unlike anything seen in other energy systems, putting our environment on a potential collision course.

Size, Scale, and Dangerous Trends:

The NEPC report finds that the sweeping advancements of AI services and systems have been driven by a larger-than-life pursuit of size and scale, necessitating substantial computational power. The fast-paced growth of AI systems without significant focus on resource efficiency raises environmental concerns. There's a palpable fear that the rampant development and deployment of AI could cause irreparable harm to the environment.

Water Consumption at Data Centres On the Rise:

The environmental alarm bells aren't just theoretical, after all. Google and Microsoft have seen a surge in water usage at their data centres year after year since 2020. Potable water sources form a large chunk of these withdrawals. The implications to address such consumption rates is a matter of urgency, necessitating reliable data collection about the quantity of resources used by data centres.

The Call for Transparency and Management:

The report underscores the criticality of keeping a close eye on the environmental implications of AI. By gaining access to trustworthy data pertaining to their environmental impacts, we can begin to efficiently target development, deployment, and use, while paving the path for a sustainable AI future. Specific recommendations that have been made include expanding mandatory environmental reporting, setting sustainability parameters for data centres, providing information on environmental impacts of AI systems, and reevaluating data collection, transmission, storage, and management practices.

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