Energy used to power artificial intelligence (AI) could jump to 3% of global electricity demand by 2030, guzzling as much water as the 1.3 billion people in sub-Saharan Africa consume in one year to meet their domestic water needs.

Those are the conclusions of a recent United Nations report that estimated the land use, water consumption and greenhouse gas emissions associated with AI's breakneck expansion. If the data centers that underpin AI formed a country, they would rank 11th in the world for energy use due to their high infrastructure and electricity needs to train ever more complicated models and satisfy users, the report found.

By 2030, data centers could rise to sixth in the world for energy consumption, which would have a land footprint the size of Connecticut and release emissions comparable to those of the U.K. in 2025, depending on how much renewable energy is in the mix.

The findings highlight how much additional pressure AI and the infrastructure that supports it could put on the environment and the climate within the next few years. But why does AI have such a huge footprint, who is benefiting or being left out from the opportunities linked to AI's growth, and what can be done to limit the damage?

To find out more, we spoke with Kaveh Madani, lead investigator for the U.N. report; director of the United Nations University Institute for Water, Environment and Health; and the recipient of this year's Stockholm Water Prize.

Sascha Pare: What would you say is the main takeaway from the report?

Kaveh Madani: The main takeaway of this report is that although in the general discourse AI is perceived as something virtual, or digital, or up in the clouds, there is [a] massive physicality to AI and the supply chains and infrastructure that back it up. And that's one thing that this report has tried to do: to remind people that behind every prompt, every use, every interaction, there is some level of impact on the environment. This is because from the top of the supply chain, where the extraction of critical minerals happens, to the point of manufacturing the hardware, the construction of the data centers, then the operation of data centers, and then dealing with the e-waste, there are major environmental impacts. If we take all of those into account, then we realize that what's digital is not necessarily free of impact. There is always some footprint associated with it, and we have to remember that.

SP: Why does AI have such massive land and water footprints, specifically?

KM: The report outlines the carbon, water and land footprints of AI's energy use. All along the supply chain, from the extraction of critical minerals to the point of disposing and dealing with the electronic waste, we have actions and interventions that require water, require land, and are associated with carbon emissions. So, if you think about, for example, the extraction of critical minerals, we know that during the process, a lot of water is being used and a lot of water is being polluted and poisoned. We published a report in April about the water injustice implications of the critical minerals, showing exactly what is happening where we have the extraction of critical minerals.

But let's not forget that the [new] report is focused on AI's energy use, and then tries to argue that the energy production process itself requires also a lot of water and land. If you are using hydropower to provide energy to your data center, you're using a lot of land and a lot of water. This applies to all sorts of energy sources, regardless of being clean or not, or if you consider them renewable or not — they all require water and land. On top of this, of course, you need to build data centers on land, but also you need water for cooling. That's why, throughout the supply chain, throughout the life cycle of AI, we have a lot of water and land use, in addition to carbon emissions.

SP: The report is packed with jaw-dropping statistics about how big AI's environmental footprint could get by 2030. But how significant are the impacts?

KM: First of all, it is very hard to estimate exactly how much energy AI is currently using, but we know that roughly 20% of the current load of data centers can be attributed to AI. We are expecting that to be 40% within a few years. And by then, the data centers that back AI's operations are expected to have an energy demand that is about 3% of the total energy demand of the world. This is equivalent to being the sixth-most-energy-intensive country in the world. The water demand of that is also huge; the water footprint associated with that is enough to satisfy the domestic water needs of 1.3 billion people in sub-Saharan Africa.

SP: Can the environment and communities cope with the projected levels of energy and water consumption needed for AI?

KM: There would be places in the world where big decisions must be made, meaning that you have to decide if you want to continue using your water for agriculture or if you want to put it into data centers. Those would be decisions for the communities — and if the communities are not involved, then the most vulnerable, the poor, will be dealing with the consequences.

At the same time, we know that the world's electricity consumption keeps increasing. That's a major problem, because although we are trying to add more and more renewables to the energy supply systems, the renewables cannot keep up with the increasing electricity demand. This means that not only can we not retire the old systems, but we might also need to use more fossil energy to satisfy this growing demand. And of course, that means more pressure on the fragile environment.

We know some of the data centers are being placed in locations that are already dry or suffering from what we refer to as "water bankruptcy," based on the report we published in January. These are major issues. More pressure on the environment [puts] more pressure on humans, and this recipe means [we could have] a kind of reinforcing degradation loop that would jeopardize both nature and human society.

SP: Who is benefiting the most from AI's expansion, and who is being excluded?

KM: AI expansion is benefiting humanity as a whole. It has changed our lifestyle; it has provided a lot of opportunities and improvements. But at the same time, it has some consequences. The issue that we see right now is that the richer communities and countries of the world are the ones that are benefiting from it the most, and within those communities and countries, it's the rich who are also profiting more from the expansion. If you look at the investment landscape of AI, you can see that there is a lot of push from a number of strong players and private investors. And they don't bear the costs when it comes to pollution, water bankruptcy, land degradation and so on.

If you think about the emissions, they are contributing to global warming, and everybody would suffer from it. Even the countries that don't have AI infrastructure are affected: If you think about where the critical minerals come from, you see a lot of poor communities, poor countries and poor regions in Africa, South America, parts of Asia, where people don't have basic infrastructure — they don't even have clean drinking water and energy infrastructure. They don't benefit from this expansion and the profits and utilities it provides. It's the most vulnerable communities and the poor economies that are going to suffer the consequences, while the other ones will benefit more.

SP: How did you estimate AI's growth by 2030, and how likely is it that your numbers will come true, given the fears that AI is a bubble that's about to catastrophically burst?

KM: We were looking at the data centers, and we still think that our projections are conservative. There is a lot of push from the private sector to further growth. Countries are also seeing investment in AI and data centers as an investment in security, sovereignty and other matters, so there's also a competition there. Some of the investments — some of the decisions about expanding AI — are not necessarily based on comprehensive assessments. Investments remain a bid to stay in the race, and that means more and more push. So we think that what we have projected is probably very conservative.

SP: China is scaling up its energy capacity together with data center buildout, and it is putting data centers in the ocean to try to solve the hardware cooling issue. What do you make of this strategy, and should other countries learn from it?

KM: China's more centralized decision-making system provides advantages, but I think we need to be careful about generalizing the information of one or two projects highlighted by the media to the overall strategy.

We know that China has been expanding its renewable energy production capacity, and that's definitely a good thing. We have to make sure that the additional load of AI would not mean more fossil energy and would not compromise the decarbonization process. But at the same time, we should note that just scaling up renewables is not sufficient if you're thinking about decarbonization. We need a massive addition of renewables if we're going to reverse climate change, and we are not seeing strong enough signs of that around the world. So that's something that we have to be worried about.

That has been the challenge created for the world because of the expansion of AI. When it comes to putting things under the ocean, I think we do not yet have enough information and enough experience to judge if those things come with less environmental impact. What we hide would not be impact-free; there are also other impacts to worry about.

SP: What are some other solutions to the pressures AI is putting on the environment and people? How should we approach the rapid expansion to ensure it is fair?

KM: We offer a framework based on a number of principles about making the AI governance system more fair and transparent and sustainable. So, those are the principles suggested, and they bring responsibility to all stakeholders, including the developers and service providers — those who provide the technology and have responsibilities of ensuring that their systems are more transparent and efficient.

Then, we have the governments that have the responsibility of ensuring that information becomes available, that footprints are properly monitored and disclosed and regulated. They can use a range of incentives, mechanisms or penalties to ensure that footprints are reduced across the supply chain — and I insist on that — from the mines to the landfill. So, that can be done; pollution taxes can be charged and so on. [Governments should ensure] that those who have to deal with the consequences also benefit from the profits and the opportunities that data centers bring to their communities. Decisions must be made based on resource availability and the environmental consequences taken into account.

Users also can do a better job of making smarter choices by using AI more responsibly and only when it's absolutely necessary. When using AI, choose the right models, and be mindful of what is happening behind the scenes. Is it really necessary to generate another image? Is it really necessary to generate a video? Is it necessary to use the model in the "thinking mode"? Together, all the stakeholders can make a difference, and users can also call for more transparency and force governments to take action to force the service providers to provide more information and be more transparent.

Editor's note: This interview has been condensed and lightly edited for clarity.

A giant blob of abnormally cold water in the North Atlantic Ocean is shifting the Indian summer monsoon, threatening the livelihoods of more than 1 billion people, new research suggests.

The link between these two systems highlights a previously unrecognized connection that could inform weather forecasts in South Asia and shed light on climatic events elsewhere, scientists say.

The Indian summer monsoon is a rainfall pattern that lasts from June to September and is driven by temperature differences between the warm northern Indian Ocean and cooler seawater below the equator. Historically, the monsoon triggered heavy rainfall along the west coast of India and a huge region of northern India called the Indo-Gangetic Plain. But since 1999, this pattern has changed dramatically, the researchers reported in a new study.

Northwest India now receives about 25% more rain during the monsoon season than it did before 1999, while the Indo-Gangetic Plain gets roughly 4% less, the team found. This is disastrous for farmers, in particular, because their soils and crops are adapted to the old rainfall pattern, said study first author Mahendra Nimmakanti, a climate scientist at the Indian Institute of Science's Centre for Atmospheric and Oceanic Sciences.

"India is largely dependent on agriculture," Nimmakanti told Live Science in a joint interview with study co-author Matthew Huber, a professor in the Department of Earth, Atmospheric, and Planetary Sciences at Purdue University in Indiana. Higher-than-normal precipitation in northwest India is causing flash floods and crop losses, because agriculture in this region is adapted to dry conditions. Meanwhile, the Indo-Gangetic Plain has seen periods of drought that also caused crop declines and impacted farmers' livelihoods, Nimmakanti said.

Previous studies have linked shifts in the Indian summer monsoon to changes in the Atlantic Meridional Overturning Circulation (AMOC), a giant web of ocean currents in the Atlantic that regulates the global climate and carries heat to the Northern Hemisphere. Data suggests the AMOC is slowing due to climate change and releasing less heat in the North Atlantic Ocean than it did before. This may be causing a southward shift of the Intertropical Convergence Zone, a band of low-pressure atmospheric conditions around the equator that drives tropical monsoons, including the Indian summer monsoon.

But these studies didn't specify how the Indian monsoon might shift or explain the underlying mechanism in detail, Nimmakanti said. "They generally explain that if there is a weakening of AMOC, that suppresses the Indian summer monsoon," he said.

One part of the problem is that current climate models do not show the changes in the Indian monsoon that have happened in real life, possibly because they also don't fully capture shifts in North Atlantic sea surface temperatures. This is especially true for a region southeast of Greenland known as the "cold blob," where the water was colder between 1901 and 2021 than it was in the late 1800s, even as the ocean around it heated up.

The cold blob suggests the AMOC is weakening because it points to a reduction in the amount of heat reaching the North Atlantic.

To pinpoint how and why the Indian monsoon has changed, the researchers fed precipitation data, sea surface temperature records and other real-life observations into dozens of climate models. This reproduced the shifts observed over the past 27 years. However, the results only implied a correlation between shifts in the North Atlantic and changes in the monsoon, not that the former directly caused the latter.

To figure out if North Atlantic sea surface temperatures caused the Indian monsoon to behave strangely, the team added and removed the cold blob in a simulation. The results showed that the cold blob has shifted the Indian monsoon by creating a strong temperature gradient over the North Atlantic, which, in turn, affects jet stream winds and pressure systems in the atmosphere above Eurasia.

Specifically, the jet stream above the North Atlantic has intensified, and a "blocking" system over the Ural Mountains in western Russia has strengthened, Huber said. As a result, weather systems in India have changed, sucking moist air toward the country's northwest and away from other regions.

"It's a shifting of the high- and low-pressure systems," Huber said. "Realizing that these two systems were directly linked through this wave train coming off the North Atlantic, that was novel."

More than 1 billion people in India and other parts of South Asia depend on the monsoon for food security and economic stability. The new results, published April 27 in the journal AGU Advances, could help forecasters predict extreme rainfall and drought events in India and neighboring countries like Pakistan during the monsoon season.

Some researchers think the Indian monsoon is a key tipping point within the global climate, and the findings suggest the system crossed a threshold in 1999. Since then, the cold blob has caused a "persistent jet stream reorganization," leading to abrupt shifts in the monsoon, according to the study.

It's unclear how the Indian monsoon will evolve under intensifying climate change, because climate models produce different predictions of what will happen in the North Atlantic, and other drivers may come into play as the world changes, Huber said.

"What we do now know is that this is one of the key building blocks of formulating a theory for what will happen in the future," he said.

The San Andreas and San Jacinto fault systems are at their highest levels of tectonic stress in 1,000 years, raising the threat of a major, imminent earthquake that could devastate Southern California, a new study finds.

The faults could rupture separately or together, thanks to an "earthquake gate" between them at Cajon Pass, where the San Jacinto fault splits from the main trace of the San Andreas fault. Researchers discovered that Cajon Pass can prevent or facilitate earthquakes moving between the faults, depending on how similar their stress levels are at the time of rupture.

And right now, the San Andreas and San Jacinto faults appear to have comparable, extremely elevated stress levels, potentially spelling trouble for Los Angeles, San Bernardino, Riverside and the Coachella Valley, the team warned.

"Our results show that stress levels on multiple fault segments are now at or above the highest values seen in the past millennium and that the region may be capable of a large through-going rupture involving both fault systems," study first author Liliane Burkhard, a planetary geologist at the University of Bern in Switzerland and at the University of Hawaii at Manoa, said in a statement.

The San Andreas and San Jacinto faults have caused 36 earthquakes with magnitudes of 6.4 or above in the past 1,000 years. Southern California's last "big one" was a magnitude 7.9 event in 1857, when a 205-mile (330 kilometers) segment of the San Andreas fault slipped horizontally between Parkfield and Cajon Pass. That rupture did not propagate through Cajon Pass, but a similar megaquake in 1812 did, suggesting this could happen again in what is now a much more built-up and densely populated environment, according to the study.

Almost 170 years have passed since the 1857 megaquake, raising fears that another huge earthquake could be due to hit soon.

To estimate this risk, Burkhard and her colleagues built a model replicating the last 1,000 years of major earthquake activity along the southern San Andreas and San Jacinto fault systems.

The researchers used observations such as tree-ring records and age data from sediments that have been displaced to reconstruct Southern California's earthquake history. They fed this information into the model, which simulated the accumulation, release and propagation of tectonic stress in the San Andreas and San Jacinto faults.

The results, published June 3 in the Journal of Geophysical Research: Solid Earth, suggest the San Andreas and San Jacinto faults are primed for an Earth-shattering rupture, which may involve the earthquake gate at Cajon Pass opening to unleash more destruction than a single-fault event would on its own.

If a rupture were to occur along the two branches of the San Andreas fault that connect at Cajon Pass, it would be a joint rupture, according to the study. If both branches of the San Andreas fault and the San Jacinto fault were involved, this would constitute a tripartite rupture.

The chance of each event happening and the timing of a potential rupture are unknown, but understanding how much stress is building up inside the system could help planners and policymakers prepare for whatever comes next, Burkhard said.

"What we can say is that the system is critically stressed, and that physics-based models like this one give us a clearer picture of the range of scenarios we should be prepared for," she said. "That information matters for hazard assessment, infrastructure planning, and emergency preparedness."

The researchers say their model could apply to other fault junctions and be used as a tool for hazard assessment globally. "We are using rigorous, quantitative science to better understand the risk facing millions of people," Burkhard said.