Regulating AI use could stop its rampant energy demand

Generative AI promises to help solve everything from climate change to poverty. But behind every chatbot response lies a deep environmental cost.

Current artificial intelligence technology requires the use of large data centres stationed around the world, which altogether draw enormous amounts of power and consume millions of litres of water to stay cool.

By 2030, data centres are expected to consume as much electricity as all of Japan, according to the International Energy Agency, and AI could be responsible for 3.5 per cent of global electricity use, according to one consultancy report.

The continuous massive expansion of AI use and its rapidly growing energy demand would make it much harder for the world to cut its carbon emissions by switching fossil fuel energy sources to renewable electricity.

So, we are left with pressing questions. Can we harness the benefits of AI without accelerating environmental collapse? Can AI be made truly sustainable – and if so, how? 

We are at a critical juncture. The environmental cost of AI is accelerating and largely unreported by the firms involved. What the world does next could determine whether AI innovation aligns with our climate goals or undermines them.

At one end of the policy spectrum is the path of complacency. In this scenario, tech companies continue unchecked, expanding data centres and powering them with private nuclear microreactors, dedicated energy grids or even reviving mothballed coal plants. 

Some of this infrastructure may instead run on renewables, but there is no binding requirement that AI must avoid using fossil fuels. Even if more renewables are installed to power AI, they may compete with efforts to decarbonise other energy uses. Developers may tout efficiency gains, but these are quickly swallowed by the rebound effect: the more efficient AI becomes, the more it is used.

At the other end lies a more radical possibility: a global moratorium or outright restriction on the most harmful forms of AI, akin to international bans on landmines or ozone-depleting substances.

This is politically improbable, of course. Nations are racing to dominate the AI arms race, not to pause it. A global consensus on bans is, at least for now, a mirage.

But in between complacency and prohibition lies a window - rapidly closing - for decisive, targeted action. 

This could take many different forms:

1 Mandatory environmental disclosure

AI companies could report how much energy, water and emissions are used to train and use their models. Having a benchmark helps to measure progress while improving transparency and accountability.

While some countries have started to impose greater corporate sustainability requirements, there is significant variation. Although mandatory disclosures alone won’t reduce consumption directly, they are an essential starting point.

2 Emissions labelling for AI services

Just as carbon emissions labels on restaurant menus or supermarket produce can guide people to lower-impact options, users could be given a chance to know the footprint of their digital choices and AI providers, like efforts to measure the carbon footprint of websites. In the US, the blue Energy Star label, one of the country’s most recognisable environmental certifications, helps customers choose energy-efficient products.

Alternatively, AI providers could also temporarily reduce functionality to account for the varying levels of renewable energy available that powers them.

3 Usage-based pricing tied to impact

Existing carbon pricing aims to ensure that heavy users should pay their environmental share. Research shows that this works best when carbon is priced across the economy for all companies, rather than just specifically targeted at individual sectors.

Yet much depends on digital tech providers fully accounting for such environmental burdens in the first place.

4 Sustainability caps or ‘compute budgets’

This would especially target non-essential or commercial entertainment applications. Organisations may limit their employees’ usage similar to how they restrict heavy office printing or indeed corporate travel. As companies begin to measure and manage their indirect supply chain emissions, energy and water footprints from using AI may require new business policies. 

5 Water stewardship requirements in water-stressed regions

A simple regulation here would be to ensure no AI infrastructure depletes local aquifers unchecked.

One thing is certain: market forces alone will not solve this. Sustainability won’t emerge from goodwill or clever efficiency tricks. We need enforceable rules.

What does the AI future look like?

Awareness does help. But expecting individuals to self-regulate in a system designed for ease-of-use is naive. “Only use AI when needed” might soon be like “Don’t print this email” a decade or two ago – well-meaning, often ignored and utterly insufficient.

The world is building an AI-powered future that consumes like an industrial past. Without guardrails, we risk creating a convenience technology that accelerates environmental collapse.

Maybe AI will one day solve the problems we couldn’t, and our concerns about emissions or water will seem trivial. Or maybe we just won’t be around to worry about them.

The way we engage with AI now – blindly, cautiously, or critically – will shape whether it serves a sustainable future, or undermines it. Policymakers should treat AI as it would any other wildly profitable resource-intensive industry, with carefully thought through regulation.

This article was originally published in The Conversation.

Further reading:

Worthy suitor? How ESG can ease takeover deals 

Beyond the balance sheet: Accounting for sustainability

How will AI affect equality and inclusion?

What does AI think about sustainability? 

Frederik Dahlmann is Associate Professor of Strategy and Sustainability and teaches Business & Sustainability on the Executive MBA, Executive MBA (London), the Global Online MBA, the Global Online MBA (London) and the Accelerator MBA (London).  

Shweta Singh was formerly Assistant Professor of Information Systems and Management at Warwick Business School, working on responsible AI.

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