Google is rolling out an updated AI weather model that’s supposed to be more accurate, especially when it comes to predicting rain and snowfall.

In the announcement today, the company says it’s now able to make forecasts with “unprecedented resolution” using its new WeatherNext 3 AI model. It can produce a global picture that’s five times sharper than Google’s previous model by learning from real-time weather observations, according to the company.

“One of the main developments is for [WeatherNext 3] to go beyond what data most global AI models train on,” says Samier Merchant, a research engineer at Google Research. “We’re able to leverage fresher and richer observational data sets.”

Weather forecasting has traditionally relied primarily on supercomputers that simulate the physics of the atmosphere. That involves solving complex equations, and ultimately comes with a time-lag. AI weather models created by Google and other developers, in contrast, can make faster predictions by recognizing patterns in historical weather data. 

Google is going a step further by incorporating live satellite data in its new model. WeatherNext 3 is able to produce a forecast each hour based on the most recent satellite observations. That allows for faster predictions than its previous AI models, as well as higher spatial and temporal resolution. 

For comparison, the previous model, WeatherNext 2, produced forecasts every 6 hours on a 25-kilometer grid. The newer model can visualize certain variables, including temperature and moisture, at up to a 5-kilometer resolution. 

That speed and resolution is particularly helpful when it comes to predicting rain and snow stemming from fast-moving weather systems, Google says. Using satellite data to make predictions also fills in gaps left in locations where there are fewer rain gauges on the ground. These are places — primarily outside of the US and Europe — where Google says WeatherNext 3 can provide the biggest improvements to weather forecasts. Google says users will see precipitation forecasts that are up to 50 percent more accurate when looking at least a day in advance.

Google also designed its latest AI weather model to produce forecasts for renewable energy generation. That includes predictions on wind speed at 100 meters, for example, at about the height of a turbine. Google, like other tech companies developing generative AI tools, is demanding more energy for data centers. 

“As the energy needs of Google, but [also] entire humanity, is increasing its energy needs, to make sure that we make renewable a very appealing opportunity is very important for us,” Ferran Alet, a research scientist at Google DeepMind tells The Verge.

WeatherNext 3 is now incorporated into Search, Maps, Gemini, and other Google products. Google has also worked with the US National Hurricane Center and other agencies in Asia to improve weather forecasting using its AI models.

While AI models are becoming more accurate, they’re still expected to work in tandem with traditional physics-based models. WeatherNext 3 is still trained on data from physics-based models and weather agencies typically look at a range of predictions to issue warnings.

How does AI make you feel? Are you excited to “vibe-code” your smart home? Or anxious about all the added pollution and billions of gallons of water used by data centers? Dig a little deeper and you’ll start to question the actual value of the GPUs that underpin all the leaps and promises of generative AI.  

Right now, GPUs, hundreds of thousands of them, are being crammed into data centers around the world to power the AI boom. These chips are also found in everything from smartphones to cars to gaming PCs. Nvidia — once a niche chipmaker that has become the world’s most valuable company — still brags about releasing what it calls “the world’s first GPU” and a “gaming breakthrough” in 1999, although some trace the origins of the GPU back to at least the ’70s with graphics hardware used in arcade games. 

“There are massive hardware developments that start because of games,” says Catherine Flick, a professor of ethics and games technology at University of Staffordshire. And whether it’s a testbed for new graphics processing units or the development of virtual reality and AI, “all these sorts of things that have quite significant ethical issues, a lot of these start with games,” Flick says. So since its inception, the GPU has been at the root of some of the biggest ethical questions new technologies pose. What impact do games, and now AI chatbots, have on how we interact with the world around us?

Whether mining for raw materials or exposing workers to toxic chemicals in semiconductor factories, GPU manufacturing can leave behind a big mess. Collectively, GPUs warehoused in data centers burn through an enormous amount of water and energy, which can lead to more air pollution and greenhouse gas emissions causing climate change. And at the end of its life, a GPU can do even more damage in the form of e-waste.  

What’s worth taking those risks? Do potential AI-driven advances in weather forecasting or wildlife conservation justify the environmental footprint of a data center? And what about the GPUs in a gaming PC or iPhone — do they deserve just as much scrutiny? 

Hitting close to home 

While GPUs have been around for a long time, AI has thrust them into the spotlight in a way that, quite literally, hits close to home for many Americans. The US has far more data centers than any other country and has plans to build many more. As tech companies race to expand a new generation of hyperscale data centers for AI, communities are grappling with the prospect of having these hulking warehouses full of servers as their neighbors. 

As an environmental journalist, I sometimes get comments on my stories about whether these data centers or AI models are being unfairly portrayed as the menacing new bogeyman, especially when other industries — like fast fashion, for example — exacerbate climate change and pollute the environment. And as for GPUs, they’re not just found in data centers, but also in consumer electronics. So why single out AI for its sustainability issues? 

Some venture capitalists have blamed AI’s slow consumer uptake on the bad press surrounding its environmental impact. To that, Flick can’t help but chuckle: “Isn’t it nice to have the environment as a scapegoat?” 

A similar sentiment is bubbling up on social media. Ashley Striblet, who works in product strategy and consumer AI, recently published a video on TikTok pushing back on those VCs for “blaming consumers” for their own failures to invest in products people actually want. Many people understand that fast fashion or consumer technology can be bad for the environment, Striblet says, but they still buy these things because they feel like they’re getting value from the products.

“I think most people know that ordering clothes from Shein or Amazon is not the most environmentally conscious thing to do,” Striblet tells me. “We also know Gen Z at some point were big consumers of these brands that provide really cheap clothing.” The cost savings from buying cheap clothes turns out to be enough of a tradeoff to justify the purchase for many consumers, Flick adds. 

On the other hand, many people just aren’t yet seeing the benefits to their lives that VCs and CEOs are promising with AI, Flick and Striblet each tell me. “It’s just rubbish what they’re saying that their technology can do,” Flick says. “It’d be nice if we all had personalized butlers or whatever it is. But how do you get from point A to point B? It’s not with what we’ve got.” 

And while the average American has yet to see the kinds of big leaps forward that are being promised, they’re increasingly likely to see the costs of a new AI data center moving next door. For years, hubs for fast fashion or semiconductor manufacturing have been concentrated in Asia. Now, an explosion of data centers springing up in the US is making headlines for raising utility bills and creating more pollution. It’s not happening in some faraway country, but in the same places where big American tech companies are courting more affluent consumers.  

To be sure, even in the US, data centers are landing in some places where low-income neighborhoods and communities of color have long had to fight against the environmental injustice of polluters setting up shop at their doorsteps. The NAACP, for example, has sued xAI (now doing business as SpaceXAI) over air pollution from gas generators the company installed on-site to power its massive data centers. (SpaceXAI didn’t respond to emails from The Verge.) The civil rights group had already warned tech companies to “be on alert” as it helps local groups across the nation mount their campaigns. 

Growing globally, impacting locally

Shaolei Ren has witnessed the costs communities often pay in the name of progress. He’s a researcher who has broadened the study of data centers’ environmental impact beyond climate change to focus on air pollution and water scarcity near the facilities. Having grown up in a coal-mining region of northern China in the 1980s, Ren remembers keeping the windows shut 24/7 to keep out the black carbon accumulating on the streets outside. And with limited water infrastructure in the province, his family stored water in tanks to ration throughout the day.

Ren, who is now an associate professor of electrical and computer engineering at the University of California, Riverside (UCR), leads research into the impact data centers have on air quality and water resources for nearby communities — costs that are often invisible each time a user enters a query into a chatbot. 

Sitting in his office at UCR on a rare gloomy Southern California afternoon just before a rainstorm, Ren tells me he’s optimistic about the benefits AI can ultimately bring, including aiding scientific discovery. But those gains shouldn’t be made at the expense of local communities, he says. The whiteboard behind him is covered with equations scrawled in blue marker for his research exploring how to tweak data center operations in order to use fewer resources and minimize pollution. Ren has a vision of what he calls a “community-integrated data center approach” to prevent these facilities from harming nearby residents. “It’s definitely doable,” he says. 

But for now, that’s not what’s typically happening. More powerful GPUs are demanding ever more energy, creating air pollution and greenhouse gas emissions in the process. Running them around the clock while keeping the hardware from overheating requires lots of water. It all adds up over the lifespan of a GPU, which might only be several years in a data center. 

The energy needed to train a model as large as Meta’s Llama 3.1 can lead to as much air pollution as 10,000 round trips by car between Los Angeles and New York City, Ren and colleagues at UCR and Caltech estimated in a 2024 preprint study. (Meta declined to comment on the record and instead referred The Verge to its sustainability report and webpage on data centers.) Public health costs associated with growing adoption of AI could reach more than $20 billion by 2028 and 1,300 premature deaths annually from air pollution by 2030, the study found. 

AI is quickly surpassing the energy use and climate impact of other applications for GPUs. Gaming used roughly 34 terawatt-hours (TWh) per year in the US and led to carbon dioxide emissions equivalent to about 5 million cars on the road (about 24 million tons of CO2), according to a comprehensive study published in 2019. Since then, newer consoles have become more energy-intensive, although their climate impact depends a lot on user behavior and how dirty the electricity grid is wherever they’re playing.

For a rough comparison, the energy use of GPU-accelerated AI servers in data centers grew from 2TWh in 2017 to more than 40TWh in 2023 in the US, according to a 2024 study by the Lawrence Berkeley National Laboratory. Now, with all the hype, AI servers’ annual power consumption could grow to between 165 and 326TWh by 2028, the study predicted. The lower estimate would be roughly equivalent to the energy more than 8.7 million homes in the US might use in a year.

In 2025, AI likely exceeded the power consumption of Bitcoin mining, accounting for nearly half of all the electricity data centers used around the world, according to a study by Alex de Vries-Gao, a PhD candidate at Vrije Universiteit Amsterdam Institute for Environmental Studies. The resulting carbon emissions likely reached between 32.6 million and 79.7 million tons annually, he estimates. For comparison, New York City’s climate pollution reaches around 50 million tons of CO2 annually.

In Southern California, where both Ren and I live and work, massive warehouses storing and sorting Americans’ e-commerce orders increasingly define the region. Goods from Asia arrive by ship in the nearby Port of Los Angeles before making their way inland to the “dry ports” made up of Amazon fulfillment centers and similar facilities buzzing with big rigs around the clock. 

The boom in new data centers reminds me of some of the same challenges these warehouses brought. National environmental groups rally around the climate impact of fast fashion and online shopping, while residents mount campaigns over local air pollution. Retirees suddenly see their quiet neighborhoods transformed by blocks of noisy industrial complexes. The pollution and noise complaints just come from the whirring of generators and cooling systems at data centers rather than truck traffic surrounding warehouses. But data centers raise an added concern, especially in dry regions out west. 

Powering AI is an incredibly thirsty business, requiring water for electricity generation and cooling systems at data centers. De Vries-Gao estimates that AI could have used between 312.5 billion and 764.6 billion liters of water in 2025, in the range of how much people consume globally in water bottles each year. It could be an even bigger jump in water demand than Ren predicted in a 2023 study, when he and his colleagues estimated that water use could climb to as high as 600 billion liters in 2027.

As eye-popping as those numbers can be, looking at an annual total can still be an incomplete picture, Ren asserts. That’s because the amount of water a facility uses is “spikey.” They tend to use the most water for cooling when temperatures rise — a demand spike that can suddenly put a ton of stress on a local water district at a time when the community might also be more prone to drought and water scarcity. 

During water demand peaks, a home might use 1.5 to 2.5 times more water than it typically would. A data center, on the other hand, might use 6 to 10 times as much — with some massive new data center projects potentially needing 30 times more water, Ren says. If these trends continue, US data centers could require up to 1,451 million gallons per day of new peak water capacity through 2030, according to a recent preprint paper Ren coauthored. Meeting that demand would cost as much as $10 billion. Those are difficult costs to swallow for small community water systems that are often underfunded and may struggle to upgrade their infrastructure without additional support.

Even tech companies’ pledges to recycle water at data centers or replenish water sources nearby miss the mark, Ren argues. The public needs more transparency about water usage during peak demand so they can plan ahead. Communities should be able to work with tech companies to build up the infrastructure needed to increase capacity, he says, making sure residents aren’t left with the tab. 

From the cradle

In order to better understand the environmental impact of GPUs, Sophia Falk, a PhD candidate at Bonn University, David Ekchajzer, a PhD student at the Université Paris-Saclay, and their colleagues have thrown tens of thousands of dollars’ worth of the chips into industrial blenders. They’re still hoping for more donated graphics cards to study what they’re made of and the footprint they have on the planet.

While data centers have been in the spotlight lately, they’re far from the only way GPUs can take an environmental toll. There are the metals and chemicals used to manufacture them, and pollution across the entire supply chain. 

“When we use AI we tend to forget that this has an impact on the real world around us,” Falk says. “This goes beyond carbon [emissions], and it’s kind of out of sight, out of mind.” 

Falk has studied the material footprint of the Nvidia A100, 90 percent of which she and her coauthors found to be composed of heavy metals — primarily copper, iron, tin, and nickel — as well as silicon. Copper, a material that conducts electricity well and is widely used in power lines and electronics, makes up the biggest chunk. All the hype around generative AI and the wave of new data centers being built is exacerbating a global shortfall in copper supply (also fueled by the electrification of homes, buildings, and transportation). 

Demand for the material could grow by 24 percent over the next decade, and new mines would have to open twice as fast as they did a decade ago, Wood Mackenzie estimates. One of the biggest risks with large-scale metal mines, including copper, is acid drainage that can contaminate nearby water sources. Sulfides exposed during the mining process react with water and air to form sulfuric acid.

While a single A100 GPU might require about 1.4 kilograms of copper, the impacts multiply when used to train or run a generative AI model. Training GPT-4, for example, could have required between 1,174 and 8,800 A100 GPUs. That much hardware could ultimately mean extracting and eventually dumping up to 7 tons of toxic elements, according to a recent preprint study by Falk and other researchers.

Falk and Ekchajzer also coauthored a study along with a group of other researchers assessing the environmental costs of training GPT-4 using Nvidia’s A100 GPU. They considered 16 different environmental impacts training can have — ranging from high-profile issues like data centers’ energy use and carbon emissions to chemicals that escape into the environment from producing and discarding chips. The GPU chip itself dominated 10 categories, with manufacturing accounting for a majority of public health risks posed by toxic chemicals and nearly all of the cancer risk. 

Since the very first computer chips were manufactured, they’ve left behind a toxic legacy. It’s why Santa Clara County in the heart of Silicon Valley has more Superfund sites than any other in the US. These are places so polluted by the chemicals used in manufacturing — some of which leaked out of underground storage tanks at chip factories to contaminate drinking water — that the federal government has put them on the National Priorities List for cleanup. 

Some of the chemicals used in chipmaking linked to miscarriages among workers were phased out in the US in the 1990s as the harms came to light. Around the same time, chip manufacturing was moving to Asia, where some of the same health risks showed up decades later. Now, generative AI is driving a rush to develop ever more powerful chips. New semiconductor factories are cropping up as a result, turning growing manufacturing hubs like Phoenix into the next silicon valley. 

TSMC started producing Nvidia’s coveted Blackwell chips at its new factory (called a fab in industry speak) in Arizona last year. The facility rises suddenly from the desert landscape around Phoenix when I drive by in October. It’s still expanding, flanked by red cranes that loom taller over the construction site and surrounding brush than anything other than power lines that connect it to the rest of the region.

Electricity demand is climbing in the US after more than a decade of flatlining — not only because of energy-hungry data centers, but also because of a resurgence of domestic manufacturing. In places like the US where fossil fuels still dominate the electricity mix — and where President Donald Trump is on a crusade to prop up coal and gas over wind and solar — soaring electricity demand also means more air pollutants and planet-heating emissions. 

Globally, the amount of electricity that AI chipmaking consumed jumped 350 percent from 2023 to 2024, according to a report de Vries-Gao coauthored with Greenpeace last year. That resulted in a fourfold rise in carbon pollution, reaching about 453,600 metric tons, more than the annual emissions from a gas-fired power plant. Most of the GPUs are still manufactured in East Asia, where power grids are similarly heavily reliant on fossil fuels.  

The energy crunch was top of mind at Semicon West, the annual trade conference that was held in the new chip manufacturing hub of Phoenix last October. “Do we have enough energy to go around? My answer is no,” Ajit Manocha, president and CEO of the industry group SEMI that organized the conference, said during his keynote talk at the event. A diagram of “unprecedented obstacles” the industry faces includes two resources crucial to making semiconductors: energy, and per- and polyfluoroalkyl substances (PFAS), also known as “forever chemicals” linked to kidney and testicular cancer and other health risks. 

The risk of forever chemicals escaping from industrial facilities sprouting up to support chip manufacturing in Arizona was a big worry for some of the residents I met there last fall. The prospect of chemicals contaminating already drought-vulnerable water sources in the region only raises the stakes. 

“It’s terrifying,” Cheryl Orosco tells me in October, chunky turquoise and silver jewelry dangling from her ears and neck. She’s retired and joined neighbors in pushing developers to build a chip packaging plant farther away from homes and schools in their community. “This is our forever home,” she says, placing her hands over her heart. 

To the grave 

De Vries-Gao, who gained notoriety tracking the energy consumption and carbon pollution of crypto mining on the website Digiconomist, thought his work studying the environmental impact of data centers was coming to a close. 

He started the website thinking, “There’s no nutrition label on digital applications,” he says. “There’s real-world impact for sure, but it’s not tangible and I think it’s really important that people are at least informed that, hold up, you know, when you’re using these applications, there is a cost.” But research by de Vries-Gao and others had already called a lot of attention to energy-hungry data farms used for crypto mining. The largest cryptocurrency after Bitcoin, Ethereum, had already switched to a vastly less energy-intensive way of operating — showing that it was possible to slash carbon emissions if crypto networks were willing. 

Then as the hype around generative AI grew, his focus shifted. “One thing that crypto mining and AI have in common is the ‘bigger is better’ dynamic,” he says. Data centers for AI posed many similar environmental challenges to crypto mines. But housing more powerful hardware, those risks can be even greater. Whereas specialized chips used for crypto mining perform the same repetitive task — solving puzzles to validate transactions — GPUs used for AI perform far more complex computing. 

One unseen consequence is even more e-waste, including larger and more complex components used in AI data centers. De Vries-Gao’s latest study, published in February, estimates that AI servers could create between 0.131 million and 0.225 million tons of e-waste each year by 2030. He says that’s comparable to all the e-waste produced by a country the size of Denmark, Norway, or Austria. It’s also a much more conservative estimate compared to a 2024 study by other researchers, which predicted that e-waste from AI could reach between 1.2 million and 5 million tons by the end of the decade. 

De Vries-Gao estimates a smaller amount of waste after factoring in supply chain constrictions, and because he sees the lifespan of a server growing beyond the three years that other researchers have used as a benchmark. But the amount of e-waste he projects will build up as a result of AI is still a lot, he tells me. “The total impact is significant,” he says. “Whether you’re talking about crypto miners or whether you’re talking about the big tech companies, they don’t take responsibility.”

Only about 22 percent of the 68.3 million tons of e-waste accumulating annually worldwide is collected and recycled, according to the last comprehensive count published in 2024. That rate is higher, 30 percent, in the Americas, where there are the most data centers. The rest of that e-waste enters the shadowy “informal” sector, often in lower-income countries where waste management facilities are less likely to meet high environmental, health, and safety standards. Sometimes called “backyard recycling,” people — and potentially child laborers — manually dismantle discarded electronics with little or no personal protective equipment. They might burn or melt devices down to extract raw materials that they can resell. Servers used for large language models hold valuable materials like gold, silver, and platinum, but also toxic materials including lead and the carcinogen chromium. Burning or burying those toxic substances can release harmful pollutants into the air or allow them to seep into groundwater.

Around 58 percent of e-waste coming from AI data centers is generated in North America, where there is a higher formal collection rate. And experts tell me that data center operators often work with subcontractors to toss out old GPUs. But the US has not ratified the Basel Convention that limits the international trade of hazardous wastes, including e-waste. Investigations have found US recyclers sending e-waste abroad, where it might fall into the black hole of backyard recycling.

Gaming out the way forward 

Some of the researchers I talked to for this story say they also encounter questions about the need to focus on the environmental impact of AI when data centers globally only made up 1.5 percent of global electricity consumption in 2024.

“Of course on a global scale, it’s not that much,” Falk says. But it’s important to scrutinize AI, she says, because of “the exponential growth in energy consumption of data centers,” especially in comparison to other sectors.

Ekchajzer points to the ways in which AI is also changing the environmental footprints of other industries. “For instance, if we want to electrify the transport system, now it is being jeopardized by the fact that we are using additional electricity for AI rather than for transport,” he says. The environmental consultancy Hubblo, which Ekchajzer cofounded, recently launched a new online tool that compares the environmental impacts of different graphics cards, starting with nine different Nvidia designs.

The question to ask with generative AI, as with other emerging technologies, is whether it’s providing benefits that outweigh the costs to society. “Generative AI is totally one of those classic solutions looking for a problem,” says Flick, the ethics and games technology professor. That mentality feeds the risky hype cycle of companies pushing for any way to justify and continue scaling up the investments they’ve made in the technology.

“The way we kind of pull this back a little bit is to say, ‘Okay, well, what is usable from this and how can we scope it back down again so that it’s going to be worth the [societal] cost?’” she says. 

Using a GPU longer before throwing it out, dismantling and reassembling an obsolete graphics card so it can be downcycled for other uses, and similar waste-cutting measures could reduce e-waste from generative AI by up to 86 percent, according to the 2024 study.

Tech companies can also design chips and AI models to be more energy-efficient. Nvidia points out in an email to The Verge that its Blackwell Ultra is 50 times more efficient than its previous Hopper GPU architecture, and that the company has improved inference throughput per megawatt by 1,000,000x over the past six architecture generations. 

“If the average fuel efficiency of a car had improved as swiftly as chips over a similar time period, one gallon of gas would suffice for a trip to the moon and back,” Nvidia head of sustainability Josh Parker says in the email.

But increased efficiency can become a double-edged sword that incentivizes companies to burn through even more resources overall. That’s where it gets important to challenge the “bigger is better” mentality within the tech industry, some of the researchers I spoke with say.

An AI model might double in size just to achieve a 2 percent gain in capability — is that worth the added environmental toll, Falk posits during our Zoom call. “I have a question of sufficiency, that we should say like, ‘OK, that’s enough, the model is good enough.’ Where do we stop?” Falk says.  

Microsoft is building out a network of new AI data centers it’s calling Fairwater sites, each able to scale to “hundreds of thousands of NVIDIA Blackwell GPUs.” The company declined to comment on the record for this story, instead pointing to blogs it’s published on Microsoft’s research into techniques like using superconducting materials and liquid cooling to make data centers more energy-efficient.

Environmental groups want more accountability. Since other companies manufacture the chips Nvidia designs, Greenpeace East Asia is pushing Nvidia to clean up its supply chain by investing in more renewable energy capacity where they operate. And it doesn’t discriminate between GPUs made for AI or gaming or any other purpose. The organization and other local groups have called attention to the health and environmental impacts semiconductor manufacturing has had in Taiwan and Korea for years. 

All of the hype around AI is just bringing the issue to the forefront. “The whole AI boom accelerates and intensifies this kind of phenomenon in East Asia,” says Katrin Wu, Greenpeace East Asia’s supply chain project lead. 

And even though the GPU is the “backbone” of that boom, she says, and Nvidia has a market cap of $4 trillion, the chip designer doesn’t get the same pushback that more consumer-facing companies like Microsoft or Google might have over their environmental footprint. “Somehow they can avoid the public and social scrutiny,” she says, adding, “We’re not, like, completely against AI development.” Greenpeace just wants it to happen more sustainably.

TSMC, which makes chips for Nvidia, points to investments it’s made in renewable energy, water reclamation, and waste reduction in an email to The Verge. In Arizona, for example, TSMC is building an industrial water reclamation plant at its manufacturing site “to achieve near-zero liquid discharge, substantially reducing dependence on local water sources,” according to TSMC deputy spokesperson Nina Kao. 

Intel, which is also expanding its fabs in Arizona to produce powerful GPUs for AI, points to commitments the company has made to achieve zero waste to landfill, replenish more water than it uses, and match electricity use with renewable energy in an email to The Verge. “We believe that corporate responsibility and innovation go hand in hand. This belief has guided our work for decades – from setting ambitious sustainability aspirations to embedding responsible practices across our global operations, supply chain, and product design,” Madison West, head of sustainability at Intel, said in the email. 

Sure, environmental concerns don’t often influence consumers’ purchases. And much of the pushback against AI stems from other moral quandaries. But consumers can play a role in advocating for more accountability from tech companies. Some game developers have already faced fierce criticism over their use of generative AI, albeit largely over the prospect of AI replacing people’s jobs and artistry. “What do we do when we make games? It’s a human story that we tell, right?” Flick says. “Games are very powerful tools for narrative and telling stories, and shaping how people think about things.”

A 2024 study in the US found that gamers were more likely to want to take collective action on climate change than people who don’t play video games. Marina Psaros, one of the authors of the study, wasn’t exactly surprised. After all, gaming isn’t a passive form of entertainment, she says. 

“You’re actually directly responsible for the experience that you have with that game. Often, that’s the joy of it, is that you’re driving the narrative and you’re discovering how to do new things,” Psaros says. In that way, games — and the GPUs powering them — offer people a way to envision a different, better world. 

“That’s one that I think is exciting,” she says. “[It’s] the opportunity for us to be able to use the imagination and the visualization aspect of games to think about how we want our communities to function differently.”

And when it comes to facing the issues that hold up consumer adoption of AI, “really the ideal solution for this is to actually just directly address and be empathetic to consumers’ concerns,” says Striblet. “So that you’re able to actually develop the solutions.”

This reporting was supported by a grant from the Tarbell Center for AI Journalism.

Additional reporting by Sean Hollister.