AI’s thirst for energy is ballooning into a monster of a challenge. And it’s not just about the electricity bills. The environmental fallout is serious, stretching to guzzling precious water resources, creating mountains of electronic waste, and, yes, adding to those greenhouse gas emissions we’re all trying to cut.

As AI models get ever more complex and weave themselves into yet more parts of our lives, a massive question mark hangs in the air: can we power this revolution without costing the Earth?

The numbers don’t lie: AI’s energy demand is escalating fast

The sheer computing power needed for the smartest AI out there is on an almost unbelievable upward curve – some say it’s doubling roughly every few months. This isn’t a gentle slope; it’s a vertical climb that’s threatening to leave even our most optimistic energy plans in the dust.

To give you a sense of scale, AI’s future energy needs could soon gulp down as much electricity as entire countries like Japan or the Netherlands, or even large US states like California. When you hear stats like that, you start to see the potential squeeze AI could put on the power grids we all rely on.

2024 saw a record 4.3% surge in global electricity demand, and AI’s expansion was a big reason why, alongside the boom in electric cars and factories working harder. 

Wind back to 2022, and data centres, AI, and even cryptocurrency mining were already accounting for nearly 2% of all the electricity used worldwide – that’s about 460 terawatt-hours (TWh).

Jump to 2024, and data centres on their own use around 415 TWh, which is roughly 1.5% of the global total, and growing at 12% a year. AI’s direct share of that slice is still relatively small – about 20 TWh, or 0.02% of global energy use – but hold onto your hats, because that number is set to rocket upwards.

The forecasts? Well, they’re pretty eye-opening. By the end of 2025, AI data centres around the world could demand an extra 10 gigawatts (GW) of power. That’s more than the entire power capacity of a place like Utah.

Roll on to 2026, and global data centre electricity use could hit 1,000 TWh – similar to what Japan uses right now. And, by 2027, the global power hunger of AI data centres is tipped to reach 68 GW, which is almost what California had in total power capacity back in 2022. 

Towards the end of this decade, the figures get even more jaw-dropping. Global data centre electricity consumption is predicted to double to around 945 TWh by 2030, which is just shy of 3% of all the electricity used on the planet.

OPEC reckons data centre electricity use could even triple to 1,500 TWh by then. And Goldman Sachs? They’re saying global power demand from data centres could leap by as much as 165% compared to 2023, with those data centres specifically kitted out for AI seeing their demand shoot up by more than four times.

There are even suggestions that data centres could be responsible for up to 21% of all global energy demand by 2030 if you count the energy it takes to get AI services to us, the users.

When we talk about AI’s energy use, it mainly splits into two big chunks: training the AI, and then actually using it.

Training enormous models, like GPT-4, takes a colossal amount of energy. Just to train GPT-3, for example, it’s estimated they used 1,287 megawatt-hours (MWh) of electricity, and GPT-4 is thought to have needed a whopping 50 times more than that. 

While training is a power hog, it’s the day-to-day running of these trained models that can chew through over 80% of AI’s total energy. It’s reported that asking ChatGPT a single question uses about ten times more energy than a Google search (we’re talking roughly 2.9 Wh versus 0.3 Wh).

With everyone jumping on the generative AI bandwagon, the race is on to build ever more powerful – and therefore more energy-guzzling – data centres.

So, can we supply energy for AI – and for ourselves?

This is the million-dollar question, isn’t it? Can our planet’s energy systems cope with this new demand? We’re already juggling a mix of fossil fuels, nuclear power, and renewables. If we’re going to feed AI’s growing appetite sustainably, we need to ramp up and diversify how we generate energy, and fast.

Naturally, renewable energy – solar, wind, hydro, geothermal – is a huge piece of the puzzle. In the US, for instance, renewables are set to go from 23% of power generation in 2024 to 27% by 2026. 

The tech giants are making some big promises; Microsoft, for example, is planning to buy 10.5 GW of renewable energy between 2026 and 2030 just for its data centres. AI itself could actually help us use renewable energy more efficiently, perhaps cutting energy use by up to 60% in some areas by making energy storage smarter and managing power grids better.

But let’s not get carried away. Renewables have their own headaches. The sun doesn’t always shine, and the wind doesn’t always blow, which is a real problem for data centres that need power around the clock, every single day. The batteries we have now to smooth out these bumps are often expensive and take up a lot of room. Plus, plugging massive new renewable projects into our existing power grids can be a slow and complicated business.

This is where nuclear power is starting to look more appealing to some, especially as a steady, low-carbon way to power AI’s massive energy needs. It delivers that crucial 24/7 power, which is exactly what data centres crave. There’s a lot of buzz around Small Modular Reactors (SMRs) too, because they’re potentially more flexible and have beefed-up safety features. And it’s not just talk; big names like Microsoft, Amazon, and Google are seriously looking into nuclear options.

Matt Garman, who heads up AWS, recently put it plainly to the BBC, calling nuclear a “great solution” for data centres. He said it’s “an excellent source of zero carbon, 24/7 power.” He also stressed that planning for future energy is a massive part of what AWS does.

“It’s something we plan many years out,” Garman mentioned. “We invest ahead. I think the world is going to have to build new technologies. I believe nuclear is a big part of that, particularly as we look 10 years out.”

Still, nuclear power isn’t a magic wand. Building new reactors takes a notoriously long time, costs a fortune, and involves wading through complex red tape. And let’s be frank, public opinion on nuclear power is still a bit shaky, often because of past accidents, even though modern reactors are much safer.

The sheer speed at which AI is developing also creates a bit of a mismatch with how long it takes to get a new nuclear plant up and running. This could mean we end up leaning even more heavily on fossil fuels in the short term, which isn’t great for our green ambitions. Plus, the idea of sticking data centres right next to nuclear plants has got some people worried about what that might do to electricity prices and reliability for everyone else.

Not just kilowatts: Wider environmental shadow of AI looms

AI’s impact on the planet goes way beyond just the electricity it uses. Those data centres get hot, and cooling them down uses vast amounts of water. Your average data centre sips about 1.7 litres of water for every kilowatt-hour of energy it burns through.

Back in 2022, Google’s data centres reportedly drank their way through about 5 billion gallons of fresh water – that’s a 20% jump from the year before. Some estimates suggest that for every kWh a data centre uses, it might need up to two litres of water just for cooling. Put it another way, global AI infrastructure could soon be chugging six times more water than the entirety of Denmark.

And then there’s the ever-growing mountain of electronic waste, or e-waste. Because AI tech – especially specialised hardware like GPUs and TPUs – moves so fast, old kit gets thrown out more often. We could be looking at AI contributing to an e-waste pile-up from data centres hitting five million tons every year by 2030. 

Even making the AI chips and all the other bits for data centres takes a toll on our natural resources and the environment. It means mining for critical minerals like lithium and cobalt, often using methods that aren’t exactly kind to the planet.

Just to make one AI chip can take over 1,400 litres of water and 3,000 kWh of electricity. This hunger for new hardware is also pushing for more semiconductor factories, which, guess what, often leads to more gas-powered energy plants being built.

And, of course, we can’t forget the carbon emissions. When AI is powered by electricity generated from burning fossil fuels, it adds to the climate change problem we’re all facing. It’s estimated that training just one big AI model can pump out as much CO2 as hundreds of US homes do in a year.

If you look at the environmental reports from the big tech companies, you can see AI’s growing carbon footprint. Microsoft’s yearly emissions, for example, went up by about 40% between 2020 and 2023, mostly because they were building more data centres for AI. Google also reported that its total greenhouse gas emissions have shot up by nearly 50% over the last five years, with the power demands of its AI data centres being a major culprit.

Can we innovate our way out?

It might sound like all doom and gloom, but a combination of new ideas could help.

A big focus is on making AI algorithms themselves more energy-efficient. Researchers are coming up with clever tricks like “model pruning” (stripping out unnecessary bits of an AI model), “quantisation” (using less precise numbers, which saves energy), and “knowledge distillation” (where a smaller, thriftier AI model learns from a big, complex one). Designing smaller, more specialised AI models that do specific jobs with less power is also a priority.

Inside data centres, things like “power capping” (putting a lid on how much power hardware can draw) and “dynamic resource allocation” (shifting computing power around based on real-time needs and when renewable energy is plentiful) can make a real difference. Software that’s “AI-aware” can even shift less urgent AI jobs to times when energy is cleaner or demand on the grid is lower. AI can even be used to make the cooling systems in data centres more efficient.

On-device AI could also help to reduce power consumption. Instead of sending data off to massive, power-hungry cloud data centres, the AI processing happens right there on your phone or device. This could slash energy use, as the chips designed for this prioritise being efficient over raw power.

And we can’t forget about rules and regulations. Governments are starting to wake up to the need to make AI accountable for its energy use and wider environmental impact.

Having clear, standard ways to measure and report AI’s footprint is a crucial first step. We also need policies that encourage companies to make hardware that lasts longer and is easier to recycle, to help tackle that e-waste mountain. Things like energy credit trading systems could even give companies a financial reason to choose greener AI tech.

It’s worth noting that the United Arab Emirates and the United States shook hands this week on a deal to build the biggest AI campus outside the US in the Gulf. While this shows just how important AI is becoming globally, it also throws a spotlight on why all these energy and environmental concerns need to be front and centre for such huge projects.

Finding a sustainable future for AI

AI has the power to do some amazing things, but its ferocious appetite for energy is a serious hurdle. The predictions for its future power demands are genuinely startling, potentially matching what whole countries use.

If we’re going to meet this demand, we need a smart mix of energy sources. Renewables are fantastic for the long run, but they have their wobbles when it comes to consistent supply and scaling up quickly. Nuclear power – including those newer SMRs – offers a reliable, low-carbon option that’s definitely catching the eye of big tech companies. But we still need to get our heads around the safety, cost, and how long they take to build.

And remember, it’s not just about electricity. AI’s broader environmental impact – from the water it drinks to cool data centres, to the growing piles of e-waste from its hardware, and the resources it uses up during manufacturing – is huge. We need to look at the whole picture if we’re serious about lessening AI’s ecological footprint.

The good news? There are plenty of promising ideas and innovations bubbling up. 

Energy-saving AI algorithms, clever power management in data centres, AI-aware software that can manage workloads intelligently, and the shift towards on-device AI all offer ways to cut down on energy use. Plus, the fact that we’re even talking about AI’s environmental impact more means that discussions around policies and rules to push for sustainability are finally happening.

Dealing with AI’s energy and environmental challenges needs everyone – researchers, the tech industry, and policymakers – to roll up their sleeves and work together, and fast.

If we make energy efficiency a top priority in how AI is developed, invest properly in sustainable energy, manage hardware responsibly from cradle to grave, and put supportive policies in place, we can aim for a future where AI’s incredible potential is unlocked in a way that doesn’t break our planet.

The race to lead in AI has to be a race for sustainable AI too.

(Photo by Nejc Soklič)

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