Welcome to The State of AI, a new collaboration between the Financial Times and MIT Technology Review. Every Monday for the next six weeks, writers from both publications will debate one aspect of the generative AI revolution reshaping global power.

This week, Casey Crownhart, senior reporter for energy at MIT Technology Review and Pilita Clark, FT’s columnist, consider how China’s rapid renewables buildout could help it leapfrog on AI progress.

Casey Crownhart writes:

In the age of AI, the biggest barrier to progress isn’t money but energy. That should be particularly worrying here in the US, where massive data centers are waiting to come online, and it doesn’t look as if the country will build the steady power supply or infrastructure needed to serve them all.

It wasn’t always like this. For about a decade before 2020, data centers were able to offset increased demand with efficiency improvements. Now, though, electricity demand is ticking up in the US, with billions of queries to popular AI models each day—and efficiency gains aren’t keeping pace. With too little new power capacity coming online, the strain is starting to show: Electricity bills are ballooning for people who live in places where data centers place a growing load on the grid.

If we want AI to have the chance to deliver on big promises without driving electricity prices sky-high for the rest of us, the US needs to learn some lessons from the rest of the world on energy abundance. Just look at China.

China installed 429 GW of new power generation capacity in 2024, more than six times the net capacity added in the US during that time.

China still generates much of its electricity with coal, but that makes up a declining share of the mix. Rather, the country is focused on installing solar, wind, nuclear, and gas at record rates.

The US, meanwhile, is focused on reviving its ailing coal industry. Coal-fired power plants are polluting and, crucially, expensive to run. Aging plants in the US are also less reliable than they used to be, generating electricity just 42% of the time, compared with a 61% capacity factor in 2014.

It’s not a great situation. And unless the US changes something, we risk becoming consumers as opposed to innovators in both energy and AI tech. Already, China earns more from exporting renewables than the US does from oil and gas exports. 

Building and permitting new renewable power plants would certainly help, since they’re currently the cheapest and fastest to bring online. But wind and solar are politically unpopular with the current administration. Natural gas is an obvious candidate, though there are concerns about delays with key equipment.

One quick fix would be for data centers to be more flexible. If they agreed not to suck electricity from the grid during times of stress, new AI infrastructure might be able to come online without any new energy infrastructure.

One study from Duke University found that if data centers agree to curtail their consumption just 0.25% of the time (roughly 22 hours over the course of the year), the grid could provide power for about 76 GW of new demand. That’s like adding about 5% of the entire grid’s capacity without needing to build anything new.

But flexibility wouldn’t be enough to truly meet the swell in AI electricity demand. What do you think, Pilita? What would get the US out of these energy constraints? Is there anything else we should be thinking about when it comes to AI and its energy use? 

Pilita Clark responds:

I agree. Data centers that can cut their power use at times of grid stress should be the norm, not the exception. Likewise, we need more deals like those giving cheaper electricity to data centers that let power utilities access their backup generators. Both reduce the need to build more power plants, which makes sense regardless of how much electricity AI ends up using.

This is a critical point for countries across the world, because we still don’t know exactly how much power AI is going to consume. 

Forecasts for what data centers will need in as little as five years’ time vary wildly, from less than twice today’s rates to four times as much.

This is partly because there’s a lack of public data about AI systems’ energy needs. It’s also because we don’t know how much more efficient these systems will become. The US chip designer Nvidia said last year that its specialized chips had become 45,000 times more energy efficient over the previous eight years. 

Moreover, we have been very wrong about tech energy needs before. At the height of the dot-com boom in 1999, it was erroneously claimed that the internet would need half the US’s electricity within a decade—necessitating a lot more coal power.

Still, some countries are clearly feeling the pressure already. In Ireland, data centers chew up so much power that new connections have been restricted around Dublin to avoid straining the grid.

Some regulators are eyeing new rules forcing tech companies to provide enough power generation to match their demand. I hope such efforts grow. I also hope AI itself helps boost power abundance and, crucially, accelerates the global energy transition needed to combat climate change. OpenAI’s Sam Altman said in 2023 that “once we have a really powerful super intelligence, addressing climate change will not be particularly difficult.” 

The evidence so far is not promising, especially in the US, where renewable projects are being axed. Still, the US may end up being an outlier in a world where ever cheaper renewables made up more than 90% of new power capacity added globally last year. 

Europe is aiming to power one of its biggest data centers predominantly with renewables and batteries. But the country leading the green energy expansion is clearly China.

The 20th century was dominated by countries rich in the fossil fuels whose reign the US now wants to prolong. China, in contrast, may become the world’s first green electrostate. If it does this in a way that helps it win an AI race the US has so far controlled, it will mark a striking chapter in economic, technological, and geopolitical history.

Casey Crownhart replies:

I share your skepticism of tech executives’ claims that AI will be a groundbreaking help in the race to address climate change. To be fair, AI is progressing rapidly. But we don’t have time to wait for technologies standing on big claims with nothing to back them up. 

When it comes to the grid, for example, experts say there’s potential for AI to help with planning and even operating, but these efforts are still experimental.  

Meanwhile, much of the world is making measurable progress on transitioning to newer, greener forms of energy. How that will affect the AI boom remains to be seen. What is clear is that AI is changing our grid and our world, and we need to be clear-eyed about the consequences. 

Further reading 

MIT Technology Review reporters did the math on the energy needs of an AI query.

There are still a few reasons to be optimistic about AI’s energy demands.  

The FT’s visual data team take a look inside the relentless race for AI capacity.

And global FT reporters ask whether data centers can ever truly be green.