AI's Hidden Environmental Cost Challenges ESG Frameworks

Datacenters represent 2% of all global electricity usage, but this figure is set to double by 2026 as AI adoption accelerates

The artificial intelligence revolution sweeping through corporate Australia carries a hidden environmental cost that sustainability executives can no longer ignore, according to Jon Whittle, Director of CSIRO Data61.

While organisations rush to deploy AI across their operations, the technology's soaring energy consumption and resource demands are creating blind spots in ESG reporting that could undermine climate commitments and stakeholder trust.

The Scale of AI's Environmental Impact

The numbers are staggering. A single generative AI query consumes approximately 10 times more electricity than a traditional Google search, according to analysis by the International Energy Agency and Goldman Sachs. With billions of AI interactions occurring daily, this multiplier effect is rapidly reshaping global energy consumption patterns.

"We don't yet have good data on all of this. It's probably fair to say that it's a bit of a closely guarded secret on environmental data of AI from the people developing these systems," said Whittle, addressing the Impact X Summit for ESG Reporting & Disclosure.

Data centres consumed 460TWh globally in 2022, representing two percent of all global electricity usage, but this figure is set to double by 2026 as AI adoption accelerates. The infrastructure requirements are equally intensive – modern AI data centres can span the equivalent of 25 football fields, housing thousands of GPU racks that generate enormous heat loads requiring constant cooling.

"If you do about 10 to 50 prompts with ChatGPT, then you're probably consuming about a 500 millilitre bottle of water doing that," Whittle noted, highlighting the often-overlooked water consumption from cooling systems that frequently draw from water-scarce regions.

The Double-Edged Sword of AI Development

Yet the environmental story isn't entirely negative. Whittle's research demonstrates AI's potential as a powerful tool for environmental problem-solving, from predicting bushfire paths in real-time to detecting invasive species threatening biodiversity. CSIRO's Spark system, now deployed nationally, enables more efficient firefighting resource allocation, while underwater AI gliders can identify crown-of-thorns starfish on the Great Barrier Reef with order-of-magnitude better accuracy than human divers.

"AI can have a lot of very positive benefits for the environment. Our challenge is given everything I've said about the negative parts of AI and now the positive parts of AI, how can we get that balance right?" Whittle asked.

The complexity extends beyond simple energy calculations. While one study suggests AI-generated content has a carbon footprint 1,500 times lower than traditional methods – thanks to automated information synthesis – the exponential growth in AI model parameters tells a different story. GPT-4's 1.75 trillion parameters dwarf GPT-3.5's 175 billion, representing an exponential increase in computational requirements.

"The tech industry consensus is you need bigger and bigger models and just keep putting more data into the system and you'll get better results. So it's all been about size without really thinking through what the consequences are," Whittle observed.

ESG Reporting Blind Spots

The implications for ESG reporting are profound. Most organisations deploying AI across operations lack frameworks to account for its environmental impact, creating potential gaps in sustainability disclosures. The challenge is compounded by the fact that 90% of AI system costs come from inference – actual usage by employees and customers – rather than model training.

"There are many organisations now that are really interested in AI adoption. They're rolling that out across their business but they're not necessarily thinking through what are the environmental social impacts of this," Whittle warned.

CSIRO has partnered with boutique investor Alfinity to develop practical guidance for integrating AI considerations into ESG reporting, including free downloadable templates for organisations beginning this journey.

Strategic Implications for Leaders

For sustainability executives, the AI dilemma represents both a disclosure challenge and a strategic opportunity. Traditional ESG frameworks weren't designed for technologies that scale exponentially in both benefit and environmental cost. Leaders must now navigate reporting requirements while the underlying data remains largely proprietary to tech giants.

The solution lies in distinguishing between AI types. While generative AI demands massive data centre resources, smaller AI applications – like those used in environmental monitoring – can deliver significant benefits with minimal environmental impact.

"Not everything has to be big AI. A lot of things can be small AI that by definition have a much lower impact but you also get some of the benefits as well," Whittle suggested.

A Path Forward

The AI-ESG intersection demands immediate attention from corporate leaders. As regulatory scrutiny intensifies and stakeholder expectations evolve, organisations must develop comprehensive frameworks that account for AI's environmental footprint while harnessing its potential for sustainability solutions.

The question isn't whether to embrace AI, but how to do so responsibly. For sustainability executives, the time to act is now – before AI's hidden environmental costs become tomorrow's compliance crisis.