AI’s environmental cost could outweigh sustainability benefits

The potential of artificial intelligence (AI) to help companies measure and optimise their sustainability efforts could be outweighed by the huge environmental impacts of the technology itself.

During the AI Summit London, sustainability experts said although the technology can be deployed in a number of ways to help companies become more environmentally sustainable, there must be a recognition of the clearly negative impacts it is currently having on the planet.

On the positive side, speakers outlined, for example, how the data analysis capabilities of AI can assist companies with decarbonisation and other environmental initiatives by capturing, connecting and mapping currently disparate data sets; automatically pin point harmful emissions to specific sites in supply chains; as well as predict and manage the demand and supply of energy in specific areas.

They also said it could help companies better manage their Scope 3 emissions (which refers to indirect greenhouse gas emissions that occur outside of a company’s operations, but that are still a result of their activities) by linking up data sources and making them more legible.

They added that while scope 3 accounts for roughly 80 to 90% of a given companies emissions, they can be difficult to track given how differently organisations collect, manage and share their data.

Giving more specific examples, David Pugh, director of sustainable industry at Digital Catapult, said that AI has been deployed in HS2’s construction to help figure out new use cases for all the concrete being dug up, to ensure none of the material is not going to waste; and could also be deployed throughout the energy system to feed real-time information about the supply of gas, wind or hydro to decision-makers planning how to best distribute power.

Giving the example of AI-powered image recognition, Jarmo Eskelinen, executive director of data-driven innovation at the University of Edinburgh, added that the tech can also be applied to satellite imagery and data to rapidly analyse and deal with methane emissions from specific factories or pipelines

The both noted that while all of this is entirely possible to do manually without AI, using the technology speeds up these processes massively.

“It would probably take a lifetime [to go through all of the satellite imagery],” said Eskelinen. “But using AI, those patterns can be detected in a flash.”

What are the environmental impacts of AI?

However, despite the potential sustainability benefits of AI, speakers were clear that the technology itself is having huge environmental impacts around the world, and that AI itself will come to be a major part of many organisations Scope 3 emissions.

Harvey Lewis – a partner in Ernst & Young’s AI division who was chairing the panel on how to leverage the tech for sustainability purposes – said that while there are a number of estimates out there, “broadly speaking, [training GPT] required around 25,000 Nvidia A100 GPUs running for about 100 days”. This is equal to the amount of energy required to power every single household in the UK for an hour, or 2,000 homes for 18 months.

“It’s a staggering amount of energy, and that’s just scratching the surface,” he said, adding that this does not take into account things such as the amount of energy it takes to make a single inference; the sheer and growing number of users such AI systems have; and the substantial increases happening in model sizes.

Pugh added that if the rate of AI usage continues on its current trajectory without any form of intervention, then half of the world’s total energy supply will be used on AI by 2040.

Pointing to the example of some AWS datacentres in Dublin having to turn off their AI services because Ireland’s grid could no longer meet the energy burden of the technology, Pugh said: “We’re already seeing that [AI] demand is outstripping [energy] supply.”

Eskelinen also pointed out that, at a time when billions of people are struggling with access to water, AI-providing companies are using huge amounts of water to cool their datacentres.

However, he added that while running AI systems is clearly extremely energy intensive, there is also the potential to build a degree of circularity into its operation by, for example, using the excess energy from datacentres to power surrounding buildings or infrastructure.

In another example, he said that datacentres in Finland are using sea water as a natural coolant, but further noted that it would likely require some sort of regulatory interventions from governments to make these kind of alternative approaches viable.

“There are ways to do this, but it will probably require regulatory intervention to tax water usage, which would create a financial incentive to be better at this,” he said.

Eskelinen added that it was also key for people in the tech sector to “internalise” thinking about the socio-economic and environmental impacts of AI, so that it is thought about from a much earlier stage in a system’s lifecycle.

On practical steps companies can take now to manage the environmental impact of their AI use, Pugh said the first step is working with IT teams and suppliers throughout the chain to understand where data is stored (for instance, to figure out if there are any renewable energy sources that can be used or if diesel generators are deployed), before then working on a plan of action for how to make the operation as clean as possible.

Part of this, he added, would also be assessing what applications are in use throughout an organisation, and whether any pre-trained algorithms can instead be used to minimise the emissions in model training.

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