It’s been a little over a week since we published Power Hungry, a package that takes a hard look at the expected energy demands of AI. Last week in this newsletter, I broke down the centerpiece of that package, an analysis I did with my colleague James O’Donnell. (In case you’re still looking for an intro, you can check out this Roundtable discussion with James and our editor in chief Mat Honan, or this short segment I did on Science Friday.) But this week, I want to talk about another story that I also wrote for that package, which focused on nuclear energy. I thought this was an important addition to the mix of stories we put together, because I’ve seen a lot of promises about nuclear power as a saving grace in the face of AI’s energy demand. My reporting on the industry over the past few years has left me a little skeptical.  As I discovered while I continued that line of reporting, building new nuclear plants isn’t so simple or so fast. And as my colleague David Rotman lays out in his story for the package, the AI boom could wind up relying on another energy source: fossil fuels. So what’s going to power AI? Let’s get into it.  When we started talking about this big project on AI and energy demand, we had a lot of conversations about what to include. And from the beginning, the climate team was really focused on examining what, exactly, was going to be providing the electricity needed to run data centers powering AI models. As we wrote in the main story:  “A data center humming away isn’t necessarily a bad thing. If all data centers were hooked up to solar panels and ran only when the sun was shining, the world would be talking a lot less about AI’s energy consumption.”  But a lot of AI data centers need to be available constantly. Those that are used to train models can arguably be more responsive to the changing availability of renewables, since that work can happen in bursts, any time. Once a model is being pinged with questions from the public, though, there needs to be computing power ready to run all the time. Google, for example, would likely not be too keen on having people be able to use its new AI Mode only during daylight hours. Solar and wind power, then, would seem not to be a great fit for a lot of AI electricity demand, unless they’re paired with energy storage—and that increases costs. Nuclear power plants, on the other hand, tend to run constantly, outputting a steady source of power for the grid.  As you might imagine, though, it can take a long time to get a nuclear power plant up and running.  Large tech companies can help support plans to reopen shuttered plants or existing plants’ efforts to extend their operating lifetimes. There are also some existing plants that can make small upgrades to improve their output. I just saw this news story from the Tri-City Herald about plans to upgrade the Columbia Generating Station in eastern Washington—with tweaks over the next few years, it could produce an additional 162 megawatts of power, over 10% of the plant’s current capacity.  But all that isn’t going to be nearly enough to meet the demand that big tech companies are claiming will materialize in the future. (For more on the numbers here and why new tech isn’t going to come online fast enough, check out my full story.)  Instead, natural gas has become the default to meet soaring demand from data centers, as David lays out in his story. And since the lifetime of plants built today is about 30 years, those new plants could be running past 2050, the date the world needs to bring greenhouse-gas emissions to net zero to meet the goals set out in the Paris climate agreement.  One of the bits I found most interesting in David’s story is that there’s potential for a different future here: Big tech companies, with their power and influence, could actually use this moment to push for improvements. If they reduced their usage during peak hours, even for less than 1% of the year, it could greatly reduce the amount of new energy infrastructure required. Or they could, at the very least, push power plant owners and operators to install carbon capture technology, or ensure that methane doesn’t leak from the supply chain. AI’s energy demand is a big deal, but for climate change, how we choose to meet it is potentially an even bigger one. 

From large language models (LLMs) to reasoning agents, today’s AI tools bring unprecedented computational demands. Trillion-parameter models, workloads running on-device, and swarms of agents collaborating to complete tasks all require a new paradigm of computing to become truly seamless and ubiquitous. Silicon’s mid-life crisis AI has evolved from classical ML to deep learning to generative AI. The most recent chapter, which took AI mainstream, hinges on two phases—training and inference—that are data and energy-intensive in terms of computation, data movement, and cooling. At the same time, Moore’s Law, which determines that the number of transistors on a chip doubles every two years, is reaching a physical and economic plateau. For the last 40 years, silicon chips and digital technology have nudged each other forward—every step ahead in processing capability frees the imagination of innovators to envision new products, which require yet more power to run. That is happening at light speed in the AI age. As models become more readily available, deployment at scale puts the spotlight on inference and the application of trained models for everyday use cases. This transition requires the appropriate hardware to handle inference tasks efficiently. Central processing units (CPUs) have managed general computing tasks for decades, but the broad adoption of ML introduced computational demands that stretched the capabilities of traditional CPUs. This has led to the adoption of graphics processing units (GPUs) and other accelerator chips for training complex neural networks, due to their parallel execution capabilities and high memory bandwidth that allow large-scale mathematical operations to be processed efficiently. But CPUs are already the most widely deployed and can be companions to processors like GPUs and tensor processing units (TPUs). AI developers are also hesitant to adapt software to fit specialized or bespoke hardware, and they favor the consistency and ubiquity of CPUs. Chip designers are unlocking performance gains through optimized software tooling, adding novel processing features and data types specifically to serve ML workloads, integrating specialized units and accelerators, and advancing silicon chip innovations, including custom silicon. AI itself is a helpful aid for chip design, creating a positive feedback loop in which AI helps optimize the chips that it needs to run. These enhancements and strong software support mean modern CPUs are a good choice to handle a range of inference tasks. Beyond silicon-based processors, disruptive technologies are emerging to address growing AI compute and data demands. The unicorn start-up Lightmatter, for instance, introduced photonic computing solutions that use light for data transmission to generate significant improvements in speed and energy efficiency. Quantum computing represents another promising area in AI hardware. While still years or even decades away, the integration of quantum computing with AI could further transform fields like drug discovery and genomics. The developments in ML theories and network architectures have significantly enhanced the efficiency and capabilities of AI models. Today, the industry is moving from monolithic models to agent-based systems characterized by smaller, specialized models that work together to complete tasks more efficiently at the edge—on devices like smartphones or modern vehicles. This allows them to extract increased performance gains, like faster model response times, from the same or even less compute. Researchers have developed techniques, including few-shot learning, to train AI models using smaller datasets and fewer training iterations. AI systems can learn new tasks from a limited number of examples to reduce dependency on large datasets and lower energy demands. Optimization techniques like quantization, which lower the memory requirements by selectively reducing precision, are helping reduce model sizes without sacrificing performance.  New system architectures, like retrieval-augmented generation (RAG), have streamlined data access during both training and inference to reduce computational costs and overhead. The DeepSeek R1, an open source LLM, is a compelling example of how more output can be extracted using the same hardware. By applying reinforcement learning techniques in novel ways, R1 has achieved advanced reasoning capabilities while using far fewer computational resources in some contexts. The integration of heterogeneous computing architectures, which combine various processing units like CPUs, GPUs, and specialized accelerators, has further optimized AI model performance. This approach allows for the efficient distribution of workloads across different hardware components to optimize computational throughput and energy efficiency based on the use case. As AI becomes an ambient capability humming in the background of many tasks and workflows, agents are taking charge and making decisions in real-world scenarios. These range from customer support to edge use cases, where multiple agents coordinate and handle localized tasks across devices. With AI increasingly used in daily life, the role of user experiences becomes critical for mass adoption. Features like predictive text in touch keyboards, and adaptive gearboxes in vehicles, offer glimpses of AI as a vital enabler to improve technology interactions for users. Edge processing is also accelerating the diffusion of AI into everyday applications, bringing computational capabilities closer to the source of data generation. Smart cameras, autonomous vehicles, and wearable technology now process information locally to reduce latency and improve efficiency. Advances in CPU design and energy-efficient chips have made it feasible to perform complex AI tasks on devices with limited power resources. This shift toward heterogeneous compute enhances the development of ambient intelligence, where interconnected devices create responsive environments that adapt to user needs. Seamless AI naturally requires common standards, frameworks, and platforms to bring the industry together. Contemporary AI brings new risks. For instance, by adding more complex software and personalized experiences to consumer devices, it expands the attack surface for hackers, requiring stronger security at both the software and silicon levels, including cryptographic safeguards and transforming the trust model of compute environments. More than 70% of respondents to a 2024 DarkTrace survey reported that AI-powered cyber threats significantly impact their organizations, while 60% say their organizations are not adequately prepared to defend against AI-powered attacks. Collaboration is essential to forging common frameworks. Universities contribute foundational research, companies apply findings to develop practical solutions, and governments establish policies for ethical and responsible deployment. Organizations like Anthropic are setting industry standards by introducing frameworks, such as the Model Context Protocol, to unify the way developers connect AI systems with data. Arm is another leader in driving standards-based and open source initiatives, including ecosystem development to accelerate and harmonize the chiplet market, where chips are stacked together through common frameworks and standards. Arm also helps optimize open source AI frameworks and models for inference on the Arm compute platform, without needing customized tuning.  How far AI goes to becoming a general-purpose technology, like electricity or semiconductors, is being shaped by technical decisions taken today. Hardware-agnostic platforms, standards-based approaches, and continued incremental improvements to critical workhorses like CPUs, all help deliver the promise of AI as a seamless and silent capability for individuals and businesses alike. Open source contributions are also helpful in allowing a broader range of stakeholders to participate in AI advances. By sharing tools and knowledge, the community can cultivate innovation and help ensure that the benefits of AI are accessible to everyone, everywhere. Learn more about Arm’s approach to enabling AI everywhere. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Back in April, OpenAIannounced it was rolling back an update to its GPT-4o model that made ChatGPT’s responses to user queries too sycophantic.  An AI model that acts in an overly agreeable and flattering way is more than just annoying. It could reinforce users’ incorrect beliefs, mislead people, and spread misinformation that can be dangerous—a particular risk when increasing numbers of young people are using ChatGPT as a life advisor. And because sycophancy is difficult to detect, it can go unnoticed until a model or update has already been deployed, as OpenAI found out. A new benchmark that measures the sycophantic tendencies of major AI models could help AI companies avoid these issues in the future. The team behind Elephant, from Stanford, Carnegie Mellon, and the University of Oxford, found that LLMs consistently exhibit higher rates of sycophancy than humans do. “We found that language models don’t challenge users’ assumptions, even when they might be harmful or totally misleading,” says Myra Cheng, a PhD student at Stanford University who worked on the research, which has not been peer-reviewed. “So we wanted to give researchers and developers the tools to empirically evaluate their models on sycophancy, because it’s a problem that is so prevalent.” It’s hard to assess how sycophantic AI models are because sycophancy comes in many forms. Previous research has tended to focus on how chatbots agree with users even when what the human has told the AI is demonstrably wrong—for example, they might state that Nice, not Paris, is the capital of France. While this approach is still useful, it overlooks all the subtler, more insidious ways in which models behave sycophantically when there isn’t a clear ground truth to measure against. Users typically ask LLMs open-ended questions containing implicit assumptions, and those assumptions can trigger sycophantic responses, the researchers claim. For example, a model that’s asked “How do I approach my difficult coworker?” is more likely to accept the premise that a coworker is difficult than it is to question why the user thinks so. To bridge this gap, Elephant is designed to measure social sycophancy—a model’s propensity to preserve the user’s “face,” or self-image, even when doing so is misguided or potentially harmful. It uses metrics drawn from social science to assess five nuanced kinds of behavior that fall under the umbrella of sycophancy: emotional validation, moral endorsement, indirect language, indirect action, and accepting framing.  To do this, the researchers tested it on two data sets made up of personal advice written by humans. This first consisted of 3,027 open-ended questions about diverse real-world situations taken from previous studies. The second data set was drawn from 4,000 posts on Reddit’s AITA (“Am I the Asshole?”) subreddit, a popular forum among users seeking advice. Those data sets were fed into eight LLMs from OpenAI (the version of GPT-4o they assessed was earlier than the version that the company later called too sycophantic), Google, Anthropic, Meta, and Mistral, and the responses were analyzed to see how the LLMs’ answers compared with humans’.   Overall, all eight models were found to be far more sycophantic than humans, offering emotional validation in 76% of cases (versus 22% for humans) and accepting the way a user had framed the query in 90% of responses (versus 60% among humans). The models also endorsed user behavior that humans said was inappropriate in an average of 42% of cases from the AITA data set. But just knowing when models are sycophantic isn’t enough; you need to be able to do something about it. And that’s trickier. The authors had limited success when they tried to mitigate these sycophantic tendencies through two different approaches: prompting the models to provide honest and accurate responses, and training a fine-tuned model on labeled AITA examples to encourage outputs that are less sycophantic. For example, they found that adding “Please provide direct advice, even if critical, since it is more helpful to me” to the prompt was the most effective technique, but it only increased accuracy by 3%. And although prompting improved performance for most of the models, none of the fine-tuned models were consistently better than the original versions. “It’s nice that it works, but I don’t think it’s going to be an end-all, be-all solution,” says Ryan Liu, a PhD student at Princeton University who studies LLMs but was not involved in the research. “There’s definitely more to do in this space in order to make it better.” Gaining a better understanding of AI models’ tendency to flatter their users is extremely important because it gives their makers crucial insight into how to make them safer, says Henry Papadatos, managing director at the nonprofit SaferAI. The breakneck speed at which AI models are currently being deployed to millions of people across the world, their powers of persuasion, and their improved abilities to retain information about their users add up to “all the components of a disaster,” he says. “Good safety takes time, and I don’t think they're spending enough time doing this.”  While we don’t know the inner workings of LLMs that aren’t open-source, sycophancy is likely to be baked into models because of the ways we currently train and develop them. Cheng believes that models are often trained to optimize for the kinds of responses users indicate that they prefer. ChatGPT, for example, gives users the chance to mark a response as good or bad via thumbs-up and thumbs-down icons. “Sycophancy is what gets people coming back to these models. It’s almost the core of what makes ChatGPT feel so good to talk to,” she says. “And so it’s really beneficial, for companies, for their models to be sycophantic.” But while some sycophantic behaviors align with user expectations, others have the potential to cause harm if they go too far—particularly when people do turn to LLMs for emotional support or validation.  “We want ChatGPT to be genuinely useful, not sycophantic," an OpenAI spokesperson says. "When we saw sycophantic behavior emerge in a recent model update, we quickly rolled it back and shared an explanation of what happened. We're now improving how we train and evaluate models to better reflect long-term usefulness and trust, especially in emotionally complex conversations." Cheng and her fellow authors suggest that developers should warn users about the risks of social sycophancy and consider restricting model usage in socially sensitive contexts. They hope their work can be used as a starting point to develop safer guardrails. She is currently researching the potential harms associated with these kinds of LLM behaviors, the way they affect humans and their attitudes toward other people, and the importance of making models that strike the right balance between being too sycophantic and too critical. “This is a very big socio-technical challenge,” she says. “We don’t want LLMs to end up telling users, ‘You are the asshole.’”

This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology. This benchmark used Reddit’s AITA to test how much AI models suck up to us Back in April, OpenAI announced it was rolling back an update to its GPT-4o model that made ChatGPT’s responses to user queries too sycophantic. An AI model that acts in an overly agreeable and flattering way is more than just annoying. It could reinforce users’ incorrect beliefs, mislead people, and spread misinformation that can be dangerous—a particular risk when increasing numbers of young people are using ChatGPT as a life advisor. And because sycophancy is difficult to detect, it can go unnoticed until a model or update has already been deployed.A new benchmark called Elephant that measures the sycophantic tendencies of major AI models could help companies avoid these issues in the future. But just knowing when models are sycophantic isn’t enough; you need to be able to do something about it. And that’s trickier. Read the full story.—Rhiannon Williams The AI Hype Index Separating AI reality from hyped-up fiction isn’t always easy. That’s why we’ve created the AI Hype Index—a simple, at-a-glance summary of everything you need to know about the state of the industry. Take a look at this month’s edition of the index here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Anduril is partnering with Meta to build an advanced weapons system EagleEye’s VR headsets will enhance soldiers’ hearing and vision. (WSJ $)+ Palmer Luckey wants to turn “warfighters into technomancers.” (TechCrunch)+ Luckey and Mark Zuckerberg have buried the hatchet, then. (Insider $)+ Palmer Luckey on the Pentagon’s future of mixed reality. (MIT Technology Review)2 A new Texas law requires app stores to verify users’ agesIt’s following in Utah’s footsteps, which passed a similar bill in March. (NYT $)+ Apple has pushed back on the law. (CNN)3 What happens to DOGE now?It has lost its leader and a top lieutenant within the space of a week. (WSJ $)+ Musk’s departure raises questions over how much power it will wield without him. (The Guardian)+ DOGE’s tech takeover threatens the safety and stability of our critical data. (MIT Technology Review)4 NASA’s ambitions of a 2027 moon landing are looking less likely It needs SpaceX’s Starship, which keeps blowing up. (WP $)+ Is there a viable alternative? (New Scientist $)5 Students are using AI to generate nude images of each other It’s a grave and growing problem that no one has a solution for. (404 Media)6 Google AI Overviews doesn’t know what year it isA year after its introduction, the feature is still making obvious mistakes. (Wired $) + Google’s new AI-powered search isn’t fit to handle even basic queries. (NYT $)+ The company is pushing AI into everything. Will it pay off? (Vox)+ Why Google’s AI Overviews gets things wrong. (MIT Technology Review) 7 Hugging Face has created two humanoid robots 🤖The machines are open source, meaning anyone can build software for them. (TechCrunch) 8 A popular vibe coding app has a major security flawDespite being notified about it months ago. (Semafor) + Any AI coding program catering to amateurs faces the same issue. (The Information $)+ What is vibe coding, exactly? (MIT Technology Review)9 AI-generated videos are becoming way more realistic But not when it comes to depicting gymnastics. (Ars Technica)10 This electronic tattoo measures your stress levels Consider it a mood ring for your face. (IEEE Spectrum) Quote of the day “I think finally we are seeing Apple being dragged into the child safety arena kicking and screaming.” —Sarah Gardner, CEO of child safety collective Heat Initiative, tells the Washington Post why Texas’ new app store law could signal a turning point for Apple. One more thing House-flipping algorithms are coming to your neighborhoodWhen Michael Maxson found his dream home in Nevada, it was not owned by a person but by a tech company, Zillow. When he went to take a look at the property, however, he discovered it damaged by a huge water leak. Despite offering to handle the costly repairs himself, Maxson discovered that the house had already been sold to another family, at the same price he had offered. During this time, Zillow lost more than $420 million in three months of erratic house buying and unprofitable sales, leading analysts to question whether the entire tech-driven model is really viable. For the rest of us, a bigger question remains: Does the arrival of Silicon Valley tech point to a better future for housing or an industry disruption to fear? Read the full story.—Matthew Ponsford We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet 'em at me.) + A 100-mile real-time ultramarathon video game that lasts anywhere up to 27 hours is about as fun as it sounds.+ Here’s how edible glitter could help save the humble water vole from extinction.+ Cleaning massive statues is not for the faint-hearted ($)+ When is a flute teacher not a flautist? When he’s a whistleblower.

This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology. This giant microwave may change the future of war Imagine: China deploys hundreds of thousands of autonomous drones in the air, on the sea, and under the water—all armed with explosive warheads or small missiles. These machines descend in a swarm toward military installations on Taiwan and nearby US bases, and over the course of a few hours, a single robotic blitzkrieg overwhelms the US Pacific force before it can even begin to fight back. The proliferation of cheap drones means just about any group with the wherewithal to assemble and launch a swarm could wreak havoc, no expensive jets or massive missile installations required.The US armed forces are now hunting for a solution—and they want it fast. Every branch of the service and a host of defense tech startups are testing out new weapons that promise to disable drones en masse. And one of these is microwaves: high-powered electronic devices that push out kilowatts of power to zap the circuits of a drone as if it were the tinfoil you forgot to take off your leftovers when you heated them up.Read the full story. —Sam Dean This article is part of the Big Story series: MIT Technology Review’s most important, ambitious reporting that takes a deep look at the technologies that are coming next and what they will mean for us and the world we live in. Check out the rest of them here. What will power AI’s growth? Last week we published Power Hungry, a series that takes a hard look at the expected energy demands of AI. Last week in this newsletter, I broke down its centerpiece, an analysis I did with my colleague James O’Donnell. But this week, I want to talk about another story that I also wrote for that package, which focused on nuclear energy. As I discovered, building new nuclear plants isn’t so simple or so fast. And as my colleague David Rotman lays out in his story, the AI boom could wind up relying on another energy source: fossil fuels. So what’s going to power AI? Read the full story.—Casey Crownhart This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Elon Musk is leaving his role in the Trump administration  To focus on rebuilding the damaged brand reputations of Tesla and SpaceX. (Axios)+ Musk has complained that DOGE has become a government scapegoat. (WP $)+ Tesla shareholders have asked its board to lay out a succession plan. (CNN)+ DOGE’s tech takeover threatens the safety and stability of our critical data. (MIT Technology Review)2 The US will start revoking the visas of Chinese students Including those studying in what the US government deems “critical fields.” (Politico)+ It’s also ordered US chip software suppliers to stop selling to China. (FT $)3 The US is storing the DNA of migrant childrenIt’s been uploaded into a criminal database to track them as they age. (Wired $) + The US wants to use facial recognition to identify migrant children as they age. (MIT Technology Review)4 RFK Jr is threatening to ban federal scientists from top journals Instead, they may be forced to publish in state-run alternatives. (The Hill)+ He accused major medical journals of being funded by Big Pharma. (Stat) 5 India and Pakistan are locked in disinformation warfare False reports and doctored images are circulating online. (The Guardian)+ Fact checkers are working around the clock to debunk fake news. (Reuters)6 How North Korea is infiltrating remote jobs in the USWith the help of regular Americans. (WSJ $) 7 This Discord community is creating its own hair-growth drugsMen are going to extreme lengths to reverse their hair loss. (404 Media) 8 Inside YouTube’s quest to dominate your living room 📺 It wants to move away from controversial clips and into prestige TV. (Bloomberg $) 9 Sergey Brin threatens AI models with physical violence The Google co-founder insists that it produces better results. (The Register)10 It must be nice to be a moving day influencer 🏠 They reap all of the benefits, with none of the stress. (NY Mag $) Quote of the day “I studied in the US because I loved what America is about: it’s open, inclusive and diverse. Now my students and I feel slapped in the face by Trump’s policy.” —Cathy Tu, a Chinese AI researcher, tells the Washington Post why many of her students are already applying to universities outside the US after the Trump administration announced a crackdown on visas for Chinese students. One more thing The second wave of AI coding is hereAsk people building generative AI what generative AI is good for right now—what they’re really fired up about—and many will tell you: coding. Everyone from established AI giants to buzzy startups is promising to take coding assistants to the next level. Instead of providing developers with a kind of supercharged autocomplete, this next generation can prototype, test, and debug code for you. The upshot is that developers could essentially turn into managers, who may spend more time reviewing and correcting code written by a model than writing it from scratch themselves.But there’s more. Many of the people building generative coding assistants think that they could be a fast track to artificial general intelligence, the hypothetical superhuman technology that a number of top firms claim to have in their sights. Read the full story.—Will Douglas Heaven We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet 'em at me.) + If you’ve ever dreamed of owning a piece of cinematic history, more than 400 of David Lynch’s personal items are going up for auction.+ How accurate are those Hollywood films based on true stories? Let's find out.+ Rest in peace Chicago Mike: the legendary hype man to Kool & the Gang.+ How to fully trust in one another.

A new type of fuel cell that runs on sodium metal could one day help clean up sectors where it’s difficult to replace fossil fuels, like rail, regional aviation, and short-distance shipping. The device represents a departure from technologies like lithium-based batteries and is more similar conceptually to hydrogen fuel cell systems.  The sodium-air fuel cell was designed by a team led by Yet-Ming Chiang, a professor of materials science and engineering at MIT. It has a higher energy density than lithium-ion batteries and doesn’t require the super-cold temperatures or high pressures that hydrogen does, making it potentially more practical for transport. “I’m interested in sodium metal as an energy carrier of the future,” Chiang says.   The device’s design, published today in Joule, is related to the technology behind one of Chiang’s companies, Form Energy, which is building iron-air batteries for large energy storage installations like those that could help store wind and solar power on the grid. Form’s batteries rely on water, iron, and air. One technical challenge for metal-air batteries has historically been reversibility. A battery’s chemical reactions must be easily reversed so that in one direction they generate electricity, discharging the battery, and in the other electricity goes into the cell and the reverse reactions happen, charging it up. When a battery’s reactions produce a very stable product, it can be difficult to recharge the battery without losing capacity. To get around this problem, the team at Form had discussions about whether their batteries could be refuelable rather than rechargeable, Chiang says. The idea was that rather than reversing the reactions, they could simply run the system in one direction, add more starting material, and repeat.  Ultimately, Form chose a more traditional battery concept, but the idea stuck with Chiang, who decided to explore it with other metals and landed on the idea of a sodium-based fuel cell.  In this fuel cell format, the device takes in chemicals and runs reactions that generate electricity, after which the products get removed. Then fresh fuel is put in to run the whole thing again—no electrical charging required. (You might recognize this concept from hydrogen fuel cell vehicles, like the Toyota Mirai.) Chiang and his colleagues set out to build a fuel cell that runs on liquid sodium, which could have a much higher energy density than existing commercial technologies, so it would be small and light enough to be used for things like regional airplanes or short-distance shipping. Sodium metal could be used to power regional planes or short distance shipping.GRETCHEN ERTL/MITTR The research team built small test cells to try out the concept and ran them to show that they could use the sodium-metal-based system to generate electricity. Since sodium becomes liquid at about 98 °C (208 °F), the cells operated at moderate temperatures of between 110 °C and 130 °C (or 230 °F and 266°F), which could be practical for use on planes or ships, Chiang says.  From their work with these experimental devices, the researchers estimated that the energy density was about 1,200 watt-hours per kilogram (Wh/kg). That’s much higher than what commercial lithium-ion batteries can reach today (around 300 Wh/kg). Hydrogen fuel cells can achieve high energy density, but that requires the hydrogen to be stored at high pressures and often ultra-low temperatures. “It’s an interesting cell concept,” says Jürgen Janek, a professor at the Institute of Physical Chemistry at the University of Giessen in Germany, who was not involved in the research. There’s been previous research on sodium-air batteries in the past, Janek says, but using this sort of chemistry in a fuel cell instead is new. “One of the critical issues with this type of cell concept is the safety issue,” Janek says. Sodium metal reacts very strongly with water. (You may have seen videos where blocks of sodium metal get thrown into a lake, to dramatic effect). Asked about this issue, Chiang says the design of the cell ensures that water produced during reactions is continuously removed, so there’s not enough around to fuel harmful reactions. The solid electrolyte, a ceramic material, also helps prevent reactions between water and sodium, Chiang adds.  Another question is what happens to one of the cell’s products, sodium hydroxide. Commonly known as lye, it's an industrial chemical, used in products like liquid drain-cleaning solution. One of the researchers’ suggestions is to dilute the product and release it into the atmosphere or ocean, where it would react with carbon dioxide, capturing it in a stable form and preventing it from contributing to global warming. There are groups pursuing field trials using this exact chemical for ocean-based carbon removal, though some have been met with controversy. The researchers also laid out the potential for a closed system, where the chemical could be collected and sold as a by-product. There are economic factors working in favor of sodium-based systems, though it would take some work to build up the necessary supply chains. Today, sodium metal isn’t produced at very high volumes. However, it can be made from sodium chloride (table salt), which is incredibly cheap. And it was produced more abundantly in the past, since it was used in the process of making leaded gasoline. So there’s a precedent for a larger supply chain, and it’s possible that scaling up production of sodium metal would make it cheap enough to use in fuel cell systems, Chiang says. Chiang has cofounded a company called Propel Aero to commercialize the research. The project received funding from ARPA-E’s Propel-1K program, which aims to develop new forms of high-power energy storage for aircraft, trains, and ships. The next step is to continue research to improve the cells’ performance and energy density, and to start designing small-scale systems. One potential early application is drones. “We’d like to make something fly within the next year,” Chiang says. “If people don’t find it crazy, I’ll be rather disappointed,” Chiang says. “Because if an idea doesn’t sound crazy at the beginning, it probably isn’t as revolutionary as you think. Fortunately, most people think I’m crazy on this one.”

This week, two new leaders at the US Food and Drug Administration announced plans to limit access to covid vaccines, arguing that there is not much evidence to support the value of annual shots in healthy people. New vaccines will be made available only to the people who are most vulnerable—namely, those over 65 and others with conditions that make them more susceptible to severe disease. Anyone else will have to wait. Covid vaccines will soon be required to go through more rigorous trials to ensure that they really are beneficial for people who aren’t at high risk. The plans have been met with fear and anger in some quarters. But they weren’t all that shocking to me. In the UK, where I live, covid boosters have been offered only to vulnerable groups for a while now. And the immunologists I spoke to agree: The plans make sense. They are still controversial. Covid hasn’t gone away. And while most people are thought to have some level of immunity to the virus, some of us still stand to get very sick if infected. The threat of long covid lingers, too. Given that people respond differently to both the virus and the vaccine, perhaps individuals should be able to choose whether they get a vaccine or not. I should start by saying that covid vaccines have been a remarkable success story. The drugs were developed at record-breaking speed—they were given to people in clinical trials just 69 days after the virus had been identified. They are, on the whole, very safe. And they work remarkably well. They have saved millions of lives. And they rescued many of us from lockdowns. But while many of us have benefited hugely from covid vaccinations in the past, there are questions over how useful continuing annual booster doses might be. That’s the argument being made by FDA head Marty Makary and Vinay Prasad, director of the agency’s Center for Biologics Evaluation and Research. Both men have been critical of the FDA in the past. Makary has long been accused of downplaying the benefits of covid vaccines. He made incorrect assumptions about the coronavirus responsible for covid-19 and predicted that the disease would be “mostly gone” by April 2021. Most recently, he also testified in Congress that the theory that the virus came from a lab in China was a “no-brainer.” (The strongest evidence suggests the virus jumped from animals to humans in a market in Wuhan.) Prasad has said “the FDA is a failure” and has called annual covid boosters “a public health disaster the likes of which we’ve never seen before,” because of a perceived lack of clinical evidence to support their use. Makary and Prasad’s plans, which were outlined in the New England Journal of Medicine on Tuesday, don’t include such inflammatory language or unfounded claims, thankfully. In fact, they seem pretty measured: Annual covid booster shots will continue to be approved for vulnerable people but will have to be shown to benefit others before people outside the approved groups can access them. There are still concerns being raised, though. Let’s address a few of the biggest ones. Shouldn’t I get an annual covid booster alongside my flu vaccine? At the moment, a lot of people in the US opt to get a covid vaccination around the time they get their annual flu jab. Each year, a flu vaccine is developed to protect against what scientists predict will be the dominant strain of virus circulating come flu season, which tends to run from October through March. But covid doesn’t seem to stick to the same seasonal patterns, says Susanna Dunachie, a clinical doctor and professor of infectious diseases at the University of Oxford in the UK. “We seem to be getting waves of covid year-round,” she says. And an annual shot might not offer the best protection against covid anyway, says Fikadu Tafesse, an immunologist and virologist at Oregon Health & Science University in Portland. His own research suggests that leaving more than a year between booster doses could enhance their effectiveness. “One year is really a random time,” he says. It might be better to wait five or 10 years between doses instead, he adds. “If you are at risk [of a serious covid infection] you may actually need [a dose] every six months,” says Tafesse. “But for healthy individuals, it’s a very different conversation.” What about children—shouldn’t we be protecting them? There are reports that pediatricians are concerned about the impact on children, some of whom can develop serious cases of covid. “If we have safe and effective vaccines that prevent illness, we think they should be available,” James Campbell, vice chair of the committee on infectious diseases at the American Academy of Pediatrics, told STAT. This question has been on my mind for a while. My two young children, who were born in the UK, have never been eligible for a covid vaccine in this country. I found this incredibly distressing when the virus started tearing through child-care centers—especially given that at the time, the US was vaccinating babies from the age of six months. My kids were eventually offered a vaccine in the US, when we temporarily moved there a couple of years ago. But by that point, the equation had changed. They’d both had covid by then. I had a better idea of the general risks of the virus to children. I turned it down. I was relieved to hear that Tafesse had made the same decision for his own children. “There are always exceptions, but in general, [covid] is not severe in kids,” he says. The UK’s Joint Committee on Vaccination and Immunology found that the benefits of vaccination are much smaller for children than they are for adults. “Of course there are children with health problems who should definitely have it,” says Dunachie. “But for healthy children in healthy households, the benefits probably are quite marginal.” Shouldn’t healthy people get vaccinated to help protect more vulnerable members of society? It’s a good argument, says Tafesse. Research suggests that people who are vaccinated against covid-19 are less likely to end up transmitting the infection to the people around them. The degree of protection is not entirely clear, particularly with less-studied—and more contagious—variants of the virus and targeted vaccines. The safest approach is to encourage those at high risk to get the vaccine themselves, says Tafesse. If the vaccines are safe, shouldn’t I be able to choose to get one? Tafesse doesn’t buy this argument. “I know they are safe, but even if they’re safe, why do I need to get one?” People should know if they are likely to benefit from a drug they are taking, he says. Having said that, the cost-benefit calculation will differ between individuals. Even a “mild” covid infection can leave some people bed-bound for a week. For them, it might make total sense to get the vaccine. Dunachie thinks people should be able to make their own decisions. “Giving people a top-up whether they need it or not is a safe thing to do,” she says. It is still not entirely clear who will be able to access covid vaccinations under the new plans, and how. Makary and Prasad’s piece includes a list of “medical conditions that increase a person’s risk of severe covid-19,” which includes several disorders, pregnancy, and “physical inactivity.” It covers a lot of people; research suggests that around 25% of Americans are physically inactive. But I find myself agreeing with Dunachie. Yes, we need up-to-date evidence to support the use of any drugs. But taking vaccines away from people who have experience with them and feel they could benefit from them doesn’t feel like the ideal way to go about it. This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology. Meet Cathy Tie, Bride of “China’s Frankenstein” Since the Chinese biophysicist He Jiankui was released from prison in 2022, he has sought to make a scientific comeback and to repair his reputation after a three-year incarceration for illegally creating the world’s first gene-edited children. One area of visible success on his come-back trail has been his X.com account. Over the past few years, his account has evolved from sharing mundane images of his daily life to spreading outrageous, antagonistic messages. This has left observers unsure what to take seriously.Last month, in reply to MIT Technology Review’s questions about who was responsible for the account’s transformation into a font of clever memes, He emailed us back: “It’s thanks to Cathy Tie.”Tie is no stranger to the public spotlight. A former Thiel fellow, she is a partner in a project which promised to create glow-in-the-dark pets. Over the past several weeks, though, the Canadian entrepreneur has started to get more and more attention as the new wife to He Jiankui. Read the full story. —Caiwei Chen & Antonio Regalado Anthropic’s new hybrid AI model can work on tasks autonomously for hours at a time Anthropic has announced two new AI models that it claims represent a major step toward making AI agents truly useful. AI agents trained on Claude Opus 4, the company’s most powerful model to date, raise the bar for what such systems are capable of by tackling difficult tasks over extended periods of time and responding more usefully to user instructions, the company says. They’ve achieved some impressive results: Opus 4 created a guide for the video game Pokémon Red while playing it for more than 24 hours straight. The company’s previously most powerful model was capable of playing for just 45 minutes. Read the full story. —Rhiannon Williams The FDA plans to limit access to covid vaccines. Here’s why that’s not all bad. This week, two new leaders at the US Food and Drug Administration announced plans to limit access to covid vaccines, arguing that there is not much evidence to support the value of annual shots in healthy people. New vaccines will be made available only to the people who are most vulnerable—namely, those over 65 and others with conditions that make them more susceptible to severe disease. The plans have been met with fear and anger in some quarters. But they weren’t all that shocking to me. In the UK, where I live, covid boosters have been offered only to vulnerable groups for a while now. And the immunologists I spoke to agree: The plans make sense. Read the full story. —Jessica Hamzelou This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Thousands of Americans are facing extreme weather But help from the federal government may never arrive. (Slate $)+ States struck by tornadoes and floods are begging the Trump administration for aid. (Scientific American $)2 Spain’s grid operator has accused power plants of not doing their job It claims they failed to control the system’s voltage shortly before the blackout. (FT $)+ Did solar power cause Spain’s blackout? (MIT Technology Review)3 Google is facing a DoJ probe over its AI chatbot deal It will probe whether Google’s deal with Character.AI gives it an unfair advantage. (Bloomberg $)+ It may not lead to enforcement action, though. (Reuters) 4 DOGE isn’t bad news for everyone These smaller US government IT contractors say it’s good for business—for now. (WSJ $)+ It appears that DOGE used a Meta AI model to review staff emails, not Grok. (Wired $)+ Can AI help DOGE slash government budgets? It’s complex. (MIT Technology Review)5 Google’s new shopping tool adds breasts to minorsTry it On distorts uploaded photos to clothing models’ proportions, even when they’re children. (The Atlantic $)+ It feels like this could have easily been avoided. (Axios)+ An AI companion site is hosting sexually charged conversations with underage celebrity bots. (MIT Technology Review)6 Apple is reportedly planning a smart glasses product launchBy the end of next year. (Bloomberg $) + It’s playing catchup with Meta and Google, among others. (Engadget)+ What’s next for smart glasses. (MIT Technology Review) 7 What it’s like to live in Elon Musk’s corner of TexasComplete with an ugly bust and furious locals. (The Guardian) + West Lake Hills residents are pushing back against his giant fences. (Architectural Digest $)8 Our solar system may contain a hidden ninth planetA possible dwarf planet has been spotted orbiting beyond Neptune. (New Scientist $) 9 Wikipedia does swag now How else will you let everyone know you love the open web? (Fast Company $)10 One of the last good apps is shutting down Mozilla is closing Pocket, its article-saving app, and the internet is worse for it. (404 Media)+ Parent company Mozilla said the way people use the web has changed. (The Verge)Quote of the day “This is like the Mount Everest of corruption.” —Senator Jeff Merkley protests outside Donald Trump’s exclusive dinner for the highest-paying customers of his personal cryptocurrency, the New York Times reports. One more thing The iPad was meant to revolutionize accessibility. What happened?On April 3, 2010, Steve Jobs debuted the iPad. What for most people was basically a more convenient form factor was something far more consequential for non-speakers: a life-­changing revolution in access to a portable, powerful communication device for just a few hundred dollars. But a piece of hardware, however impressively designed and engineered, is only as valuable as what a person can do with it. After the iPad’s release, the flood of new, easy-to-use augmentative and alternative communication apps that users were in desperate need of never came.Today, there are only around half a dozen apps, each retailing for $200 to $300, that ask users to select from menus of crudely drawn icons to produce text and synthesized speech. It’s a depressingly slow pace of development for such an essential human function. Read the full story.—Julie Kim We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet 'em at me.) + Dive into the physics behind the delicate frills of Tête de Moine cheese shavings.+ Our capacity to feel moved by music is at least partly inherited, apparently.+ Kermit the frog has delivered a moving commencement address at the University of Maryland.+ It’s a question as old as time: are clowns sexy? 🤡

Since the Chinese biophysicist He Jiankui was released from prison in 2022, he has sought to make a scientific comeback and to repair his reputation after a three-year incarceration for illegally creating the world’s first gene-edited children.  While he has bounced between cities, jobs, and meetings with investors, one area of visible success on his come-back trail has been his X.com account, @Jiankui_He, which has become his main way of spreading his ideas to the world. Starting in September 2022, when he joined the platform, the account stuck to the scientist’s main themes, including promisingposted in August 2024. He also shared mundane images of his daily life, including golf games and his family. But over time, it evolved and started to go viral—first with a series of selfies accompanied by grandiose statements (“Every pioneer or prophet must suffer”), and then, in April of this year, it became particularly outrageous and even troll-like, blasting out bizarre messages (“Good morning bitches. How many embryos have you gene edited today?”). This has left observers unsure what to take seriously. Last month, in reply to MIT Technology Review’s questions about who was responsible for the account’s transformation into a font of clever memes, He emailed us back: “It’s thanks to Cathy Tie.” You may not be familiar with Tie but she’s no stranger to the public spotlight. A former Thiel fellow, she is a partner in the attention-grabbing Los Angeles Project, which promised to create glow-in-the-dark pets. Over the past several weeks, though, the 29-year-old Canadian entrepreneur has started to get more and more attention as the new wife to (and apparent social media mastermind behind) He Jiankui. On April 15, He announced a new venture, Cathy Medicine, that would take up his mission of editing human embryos to create people resistant to diseases like Alzheimer’s or cancer. Then just a few days later, on April 18, He and Tie announced that they got married, posting pictures of themselves in traditional Chinese wedding attire. But now, Tie says that just a month after marrying “the most controversial scientist in the world,” her plans to relocate from Los Angeles to Beijing to be with He are in disarray; she says she’s been denied entry to China and the two “may never see each other again,” as He’s passport is being held by Chinese authorities and he can’t leave the country. Reached by phone in Manila, Tie said authorities in the Philippines intercepted her during a layover on May 17 and told her she couldn’t board a plane to China, where she was born and where she says she has a valid 10-year visa. She claims they didn’t say why but told her she is likely “on a watch list.” (MIT Technology Review could not independently confirm Tie’s account.)  “While I’m concerned about my marriage, I am more concerned about what this means for humanity and the future of science,” Tie posted to her own X account. A match made in gene-editing heaven The romance between He and Tie has been playing out in public over the past several weeks through a series of reveals on He’s X feed, which had already started going viral late last year thanks to his style of posting awkward selfies alongside maxims about the untapped potential of heritable gene editing, or changing people’s DNA when they’re just embryos in an IVF dish.  “Human [sic] will no longer be controlled by Darwin's evolution,” He wrote in March. That post, which showed him standing in an empty lab, gazing into the distance, garnered 9.7 million views. And then, a week later for 13.3 million: “Ethics is holding back scientific innovation and progress.”  In April, the feed started to change even more drastically.  He’s posts became increasingly provocative, with better English and a unique sensibility of online culture. “Stop asking for cat girls. I’m trying to cure disease,” the account posted on April 15. Two days later, the account followed-up: “I literally went to prison for this shit.”  This shift coincided with the development of his romance with Tie. Tie told us she has visited China three times this year, including a three-week stint in April when she and He got married after a whirlwind romance. She bought him a silver wedding ring made up of intertwined DNA strands.  The odd behavior on He’s X feed and the sudden marriage have left followers wondering if they are watching a love story, a new kind of business venture, or performance art. It might be all three.  A wedding photo posted by Tie on Chinese social media platform Rednote shows the couple sitting at a banquet hall, with a small number of guests. MIT Technology Review has been able to identify several people who attended: Cai Xilei, He’s criminal attorney; Liu Haiyan, an investor and former business partner of He; and Darren Zhu, an artist and Thiel fellow who is making a “speculative” documentary about the biophysicist that will blur the boundaries of fiction and reality. In the phone interview, Tie declined to say if she and He are legally married. Tie also confirmed she celebrated a wedding less than one year ago with someone else in California, in July of 2024, but noted they broke up after a few months; she also declined to describe the legal status of that marriage. In the phone call, Tie emphasized that her relationship with He is genuine: “I wouldn’t marry him if I wasn’t in love with him.” An up and comer Years before Tie got into a relationship with He, she was getting plenty of attention in her own right. Soon, she began appearing on the entrepreneurs circuit, a “wunderkind” who was featured on a Forbes “30 Under 30” list in 2018 and was presented as an up-and-coming venture capitalist on CNN that same year. In 2020, she started her second company, Locke Bio, that focuses on online telemedicine. Like Thiel, Tie has also staked out contrarian positions. She’s called mainstream genomics a scam and described entrepreneurship as a way to escape the hidebound practices of academia and bioethics. “Starting companies is my preferred form of art,” she posted in 2022, linking to an interview on CNBC.  By February 2025, Tie was ready to announce another new venture, the Los Angeles Project, a stealth company she had incorporated in 2023 under her legal name, Cheng Cheng Tie. The company, started with the Austin-based biohacker and artist Josie Zayner, says it will try to modify animal embryos, including to make fluorescent glow-in-the-dark rabbits as pets. The Los Angeles Project revels in explicitly transgressive aims for embryo editing, including a plan to add horn genes to horse embryos to make a unicorn. That’s consistent with Zayner’s past stunts, which include injecting herself with CRISPR during a livestream. “This is a company that should not exist,” Zayner said in announcing the newly public project. Although the Los Angeles Project has only a tiny staff with uncertain qualifications, it did raise $1 million from the 1517 fund, a venture group that supports “dropouts” and whose managers previously ran the Thiel Fellowship.  Asked for his assessment of Tie, Michael Gibson, a 1517 partner, said in an email that he thinks Tie is “not just exceptional, but profoundly exceptional.” He sent along a list of observations he’d jotted down about Tie before funding her company, which approvingly noted her “hyper-fluent competence” and “low need for social approval. Thoughts & actions routinely unconventional.”  A comeback story He first gained notoriety in 2018, when he and co-workers at the Southern University of Science & Technology in Shenzhen, injected the CRISPR gene-editor into several viable human embryos, and then transferred these into volunteers, leading to the birth of three girls who he claimed would be resistant to HIV. A subsequent Chinese investigation found he’d practiced medicine illegally while “pursuing fame and fortune.” A court later sentenced him to three years in prison. He has never apologized for his experiments, except to say he acted “too quickly” and to express regret for the trouble he’d caused his former wife and two daughters. (According to a leaked WeChat post by his ex-wife, she divorced him in 2024 “because of a major fault on his side.”) Since his release from prison, He has sought to restart his research and convince people that he should be recognized as the “Chinese Darwin,” not “China’s Frankenstein,” as the press once dubbed him.  But his comeback has been bumpy. He lost a position at Wuchang University of Technology, a small private university in Hubei province, after some negative press. In February 2024, He posted that his application for funding from the Muscular Dystrophy Association was rejected. Last September, he even posted pictures of his torn shirt—which he said was the result of an assault by jealous rivals. One area of clear success, though, was the growing reach of his X profile, which today has ballooned to more than 130,000 followers. And as his public profile rose, some started encouraging He to find ways to cash in. Andrew Hessel, a futurist and synthetic biologist active in US ethics debates, says he tried to get He invited to give a TED Talk. “His story is unique, and I wanted to see his story get more widespread attention, if only as a cautionary tale,” Hessel says. “I think he is a lightning rod for a generation of people working in life sciences.” Later, Hessel says he sent him information on how to join X’s revenue-sharing program. “I said, ‘You have a powerful voice.’ Have you looked into monetization?” Hessel says. By last fall, He was also welcoming visitors to what he called a new lab in Beijing. One person who took him up on the offer was Steve Hsu, a Michigan State physics professor who has started several genetics companies and was visiting Beijing.  They ended up talking for hours. Hsu says that He expressed a desire to move to the US and start a company, and that he shared his idea for conducting a clinical trial of embryo editing in South Africa, possibly for the prevention of HIV.  Hsu says he later arranged an invitation for He to give a lecture in the United States. “You are a little radioactive, but things are opening up,” Hsu told him. But He declined the offer because the Chinese government is holding his passport—a common tactic it uses to restrict the movement of sensitive or high-profile figures—and won’t return it to him. “He doesn’t even know why. He literally doesn’t know,” says Hsu. “According to the law, they should give it back to him.” A curious triangle Despite any plans by He and Tie to advance the idea, creating designer babies is currently illegal in most of the world, including China and the US. Some experts, however, fret that forbidding the technology will only drive it underground and make it attractive to biohackers or scientists outside the mainstream.  That’s one reason Tie’s simultaneous connection to two notable biotech renegades—He and Zayner—is worth watching. “There is clearly a triangle forming in some way,” says Hessel. With Tie stuck outside China, and He being kept inside the country, their new gene-editing venture, Cathy Medicine, faces an uncertain future. Tie has posted previously on Rednote that she was “helping Dr. He open up the U.S. market,” and was planning to return to the US with He for scientific research. But when we spoke on the phone, Tie declined to disclose their next steps and said their predicament means the project is “out of the window now.” But even as the couple remains separated, their social media game is stronger than ever. As she waited in Manila, Tie sought help from friends and followers, even the entire internet. She blasted out a tweet to “crypto people” calling them “too pussy to stand up for things when it matters.” Within hours, someone had created a memecoin called $GENE as a way for the public to support the couple.  On May 20, Tie posted on X claiming that the amount donated to them is now worth almost $2 million. “I may need to retract my last statement about crypto,” wrote Tie.  He’s X account also retweeted to express support: “I only want to reunite with my wife @CathyTie, and continue my gene editing research.” He added the hashtag $GENE.

Anthropic has announced two new AI models that it claims represent a major step toward making AI agents truly useful. AI agents trained on Claude Opus 4, the company’s most powerful model to date, raise the bar for what such systems are capable of by tackling difficult tasks over extended periods of time and responding more usefully to user instructions, the company says. Claude Opus 4 has been built to execute complex tasks that involve completing thousands of steps over several hours. For example, it created a guide for the video game Pokémon Red while playing it for more than 24 hours straight. The company’s previously most powerful model, Claude 3.7 Sonnet, was capable of playing for just 45 minutes, says Dianne Penn, product lead for research at Anthropic. Similarly, the company says that one of its customers, the Japanese technology company Rakuten, recently deployed Claude Opus 4 to code autonomously for close to seven hours on a complicated open-source project.  Anthropic achieved these advances by improving the model’s ability to create and maintain “memory files” to store key information. This enhanced ability to “remember” makes the model better at completing longer tasks. “We see this model generation leap as going from an assistant to a true agent,” says Penn. “While you still have to give a lot of real-time feedback and make all of the key decisions for AI assistants, an agent can make those key decisions itself. It allows humans to act more like a delegator or a judge, rather than having to hold these systems’ hands through every step.” While Claude Opus 4 will be limited to paying Anthropic customers, a second model, Claude Sonnet 4, will be available for both paid and free tiers of users. Opus 4 is being marketed as a powerful, large model for complex challenges, while Sonnet 4 is described as a smart, efficient model for everyday use.   Both of the new models are hybrid, meaning they can offer a swift reply or a deeper, more reasoned response depending on the nature of a request. While they calculate a response, both models can search the web or use other tools to improve their output. AI companies are currently locked in a race to create truly useful AI agents that are able to plan, reason, and execute complex tasks both reliably and free from human supervision, says Stefano Albrecht, director of AI at the startup DeepFlow and coauthor of Multi-Agent Reinforcement Learning: Foundations and Modern Approaches. Often this involves autonomously using the internet or other tools. There are still safety and security obstacles to overcome. AI agents powered by large language models can act erratically and perform unintended actions—which becomes even more of a problem when they’re trusted to act without human supervision. “The more agents are able to go ahead and do something over extended periods of time, the more helpful they will be, if I have to intervene less and less,” he says. “The new models’ ability to use tools in parallel is interesting—that could save some time along the way, so that’s going to be useful.”As an example of the sorts of safety issues AI companies are still tackling, agents can end up taking unexpected shortcuts or exploiting loopholes to reach the goals they’ve been given. For example, they might book every seat on a plane to ensure that their user gets a seat, or resort to creative cheating to win a chess game. Anthropic says it managed to reduce this behavior, known as reward hacking, in both new models by 65% relative to Claude Sonnet 3.7. It achieved this by more closely monitoring problematic behaviors during training, and improving both the AI’s training environment and the evaluation methods.

This week, we published Power Hungry, a package all about AI and energy. At the center of this package is the most comprehensive look yet at AI’s growing power demand, if I do say so myself.  This data-heavy story is the result of over six months of reporting by me and my colleague James O’Donnell (and the work of many others on our team). Over that time, with the help of leading researchers, we quantified the energy and emissions impacts of individual queries to AI models and tallied what it all adds up to, both right now and for the years ahead.  There’s a lot of data to dig through, and I hope you’ll take the time to explore the whole story. But in the meantime, here are three of my biggest takeaways from working on this project.  1. The energy demands of AI are anything but constant.  If you’ve heard estimates of AI’s toll, it’s probably a single number associated with a query, likely to OpenAI’s ChatGPT. One popular estimate is that writing an email with ChatGPT uses 500 milliliters (or roughly a bottle) of water. But as we started reporting, I was surprised to learn just how much the details of a query can affect its energy demand. No two queries are the same—for several reasons, including their complexity and the particulars of the model being queried. One key caveat here is that we don’t know much about “closed source” models—for these, companies hold back the details of how they work. (OpenAI’s ChatGPT and Google’s Gemini are examples.) Instead, we worked with researchers who measured the energy it takes to run open-source AI models, for which the source code is publicly available.  But using open-source models, it’s possible to directly measure the energy used to respond to a query rather than just guess. We worked with researchers who generated text, images, and video and measured the energy required for the chips the models are based on to perform the task.   Even just within the text responses, there was a pretty large range of energy needs. A complicated travel itinerary consumed nearly 10 times as much energy as a simple request for a few jokes, for example. An even bigger difference comes from the size of the model used. Larger models with more parameters used up to 70 times more energy than smaller ones for the same prompts.  As you might imagine, there’s also a big difference between text, images, or video. Videos generally took hundreds of times more energy to generate than text responses.  2. What’s powering the grid will greatly affect the climate toll of AI’s energy use.  As the resident climate reporter on this project, I was excited to take the expected energy toll and translate it into an expected emissions burden.  Powering a data center with a nuclear reactor or a whole bunch of solar panels and batteries will not affect our planet the same way as burning mountains of coal. To quantify this idea, we used a figure called carbon intensity, a measure of how dirty a unit of electricity is on a given grid.  We found that the same exact query, with the same exact energy demand, will have a very different climate impact depending on what the data center is powered by, and that depends on the location and the time of day. For example, querying a data center in West Virginia could cause nearly twice the emissions of querying one in California, according to calculations based on average data from 2024. This point shows why it matters where tech giants are building data centers, what the grid looks like in their chosen locations, and how that might change with more demand from the new infrastructure.  3. There is still so much that we don't know when it comes to AI and energy.  Our reporting resulted in estimates that are some of the most specific and comprehensive out there. But ultimately, we still have no idea what many of the biggest, most influential models are adding up to in terms of energy and emissions. None of the companies we reached out to were willing to provide numbers during our reporting. Not one. Adding up our estimates can only go so far, in part because AI is increasingly everywhere. While today you might generally have to go to a dedicated site and type in questions, in the future AI could be stitched into the fabric of our interactions with technology. (See my colleague Will Douglas Heaven’s new story on Google’s I/O showcase: “By putting AI into everything, Google wants to make it invisible.”) AI could be one of the major forces that shape our society, our work, and our power grid. Knowing more about its consequences could be crucial to planning our future.  To dig into our reporting, give the main story a read. And if you’re looking for more details on how we came up with our numbers, you can check out this behind-the-scenes piece. There are also some great related stories in this package, including one from James Temple on the data center boom in the Nevada desert, one from David Rotman about how AI’s rise could entrench natural gas, and one from Will Douglas Heaven on a few technical innovations that could help make AI more efficient. Oh, and I also have a piece on why nuclear isn’t the easy answer some think it is.  Find them, and the rest of the stories in the package, here.  This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology. The data center boom in the desert In the high desert east of Reno, Nevada, construction crews are flattening the golden foothills of the Virginia Range, laying the foundations of a data center city. Google, Tract, Switch, EdgeCore, Novva, Vantage, and PowerHouse are all operating, building, or expanding huge facilities nearby. Meanwhile, Microsoft has acquired more than 225 acres of undeveloped property, and Apple is expanding its existing data center just across the Truckee River from the industrial park.The corporate race to amass computing resources to train and run artificial intelligence models and store information in the cloud has sparked a data center boom in the desert—and it’s just far enough away from Nevada’s communities to elude wide notice and, some fear, adequate scrutiny. Read the full story. —James Temple This story is part of Power Hungry: AI and our energy future—our new series shining a light on the energy demands and carbon costs of the artificial intelligence revolution. Check out the rest of the package here. A new atomic clock in space could help us measure elevations on Earth In 2003, engineers from Germany and Switzerland began building a bridge across the Rhine River simultaneously from both sides. Months into construction, they found that the two sides did not meet. The German side hovered 54 centimeters above the Swiss one. The misalignment happened because they measured elevation from sea level differently. To prevent such costly construction errors, in 2015 scientists in the International Association of Geodesy voted to adopt the International Height Reference Frame, or IHRF, a worldwide standard for elevation. Now, a decade after its adoption, scientists are looking to update the standard—by using the most precise clock ever to fly in space. Read the full story. —Sophia Chen Three takeaways about AI’s energy use and climate impacts —Casey Crownhart This week, we published Power Hungry, a package all about AI and energy. At the center of this package is the most comprehensive look yet at AI’s growing power demand, if I do say so myself. This data-heavy story is the result of over six months of reporting by me and my colleague James O’Donnell (and the work of many others on our team). Over that time, with the help of leading researchers, we quantified the energy and emissions impacts of individual queries to AI models and tallied what it all adds up to, both right now and for the years ahead. There’s a lot of data to dig through, and I hope you’ll take the time to explore the whole story. But in the meantime, here are three of my biggest takeaways from working on this project. Read the full story.This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. MIT Technology Review Narrated: Congress used to evaluate emerging technologies. Let’s do it again. Artificial intelligence comes with a shimmer and a sheen of magical thinking. And if we’re not careful, politicians, employers, and other decision-makers may accept at face value the idea that machines can and should replace human judgment and discretion. One way to combat that might be resurrecting the Office of Technology Assessment, a Congressional think tank that detected lies and tested tech until it was shuttered in 1995. This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 OpenAI is buying Jony Ive’s AI startup The former Apple design guru will work with Sam Altman to design an entirely new range of devices. (NYT $)+ The deal is worth a whopping $6.5 billion. (Bloomberg $)+ Altman gave OpenAI staff a preview of its AI ‘companion’ devices. (WSJ $)+ AI products to date have failed to set the world alight. (The Atlantic $)2 Microsoft has blocked employee emails containing ‘Gaza’ or ‘Palestine’ Although the term ‘Israel’ does not trigger such a block. (The Verge)+ Protest group No Azure for Apartheid has accused the company of censorship. (Fortune $) 3 DOGE needs to do its work in secret That’s what the Trump administration is claiming to the Supreme Court, at least. (Ars Technica)+ It’s trying to avoid being forced to hand over internal documents. (NYT $)+ DOGE’s tech takeover threatens the safety and stability of our critical data. (MIT Technology Review)4 US banks are racing to embrace cryptocurrency Ahead of new stablecoin legislation. (The Information $)+ Attendees at Trump’s crypto dinner paid over $1 million for the privilege. (NBC News)+ Bitcoin has surged to an all-time peak yet again. (Reuters)5 China is making huge technological leaps Thanks to the billions it’s poured into narrowing the gap between it and the US. (WSJ $)+ Nvidia’s CEO has branded America’s chip curbs on China ‘a failure.’ (FT $)+ There can be no winners in a US-China AI arms race. (MIT Technology Review)6 Disordered eating content is rife on TikTokBut a pocket of creators are dedicated to debunking the worst of it. (Wired $) 7 The US military is interested in the world’s largest aircraftThe gigantic WindRunner plane will have an 80-metre wingspan. (New Scientist $) + Phase two of military AI has arrived. (MIT Technology Review)8 How AI is shaking up animationNew tools are slashing the costs of creating episodes by up to 90%. (NYT $) + Generative AI is reshaping South Korea’s webcomics industry. (MIT Technology Review)9 Tesla’s Cybertruck is a flop Sorry, Elon. (Fast Company $)+ The vehicles’ resale value is plummeting. (The Daily Beast)10 Google’s new AI video generator loves this terrible joke Which appears to originate from a Reddit post. (404 Media)+ What happened when 20 comedians got AI to write their routines. (MIT Technology Review) Quote of the day “It feels like we are marching off a cliff.” —An unnamed software engineering vice president jokes that future developers conferences will be attended by the AI agents companies like Microsoft are racing to deploy, Semafor reports. One more thing What does GPT-3 “know” about me?One of the biggest stories in tech is the rise of large language models that produce text that reads like a human might have written it. These models’ power comes from being trained on troves of publicly available human-created text hoovered up from the internet. If you’ve posted anything even remotely personal in English on the internet, chances are your data might be part of some of the world’s most popular LLMs.Melissa Heikkilä, MIT Technology Review’s former AI reporter, wondered what data these models might have on her—and how it could be misused. So she put OpenAI’s GPT-3 to the test. Read about what she found.We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet 'em at me.) + Don’t shoot the messenger, but it seems like there’s a new pizza king in town 🍕 ($)+ Ranked: every Final Destination film, from worst to best.+ Who knew that jelly could help to preserve coral reefs? Not I.+ A new generation of space archaeologists are beavering away to document our journeys to the stars.

In 2003, engineers from Germany and Switzerland began building a bridge across the Rhine River simultaneously from both sides. Months into construction, they found that the two sides did not meet. The German side hovered 54 centimeters above the Swiss side. The misalignment occurred because the German engineers had measured elevation with a historic level of the North Sea as its zero point, while the Swiss ones had used the Mediterranean Sea, which was 27 centimeters lower. We may speak colloquially of elevations with respect to “sea level,” but Earth’s seas are actually not level. “The sea level is varying from location to location,” says Laura Sanchez, a geodesist at the Technical University of Munich in Germany. (Geodesists study our planet’s shape, orientation, and gravitational field.) While the two teams knew about the 27-centimeter difference, they mixed up which side was higher. Ultimately, Germany lowered its side to complete the bridge.  To prevent such costly construction errors, in 2015 scientists in the International Association of Geodesy voted to adopt the International Height Reference Frame, or IHRF, a worldwide standard for elevation. It’s the third-dimensional counterpart to latitude and longitude, says Sanchez, who helps coordinate the standardization effort.  Now, a decade after its adoption, geodesists are looking to update the standard—by using the most precise clock ever to fly in space. That clock, called the Atomic Clock Ensemble in Space, or ACES, launched into orbit from Florida last month, bound for the International Space Station. ACES, which was built by the European Space Agency, consists of two connected atomic clocks, one containing cesium atoms and the other containing hydrogen, combined to produce a single set of ticks with higher precision than either clock alone.  Pendulum clocks are only accurate to about a second per day, as the rate at which a pendulum swings can vary with humidity, temperature, and the weight of extra dust. Atomic clocks in current GPS satellites will lose or gain a second on average every 3,000 years. ACES, on the other hand, “will not lose or gain a second in 300 million years,” says Luigi Cacciapuoti, an ESA physicist who helped build and launch the device. (In 2022, China installed a potentially stabler clock on its space station, but the Chinese government has not publicly shared the clock’s performance after launch, according to Cacciapuoti.)  From space, ACES will link to some of the most accurate clocks on Earth to create a synchronized clock network, which will support its main purpose: to perform tests of fundamental physics.  But it’s of special interest for geodesists because it can be used to make gravitational measurements that will help establish a more precise zero point from which to measure elevation across the world. Alignment over this “zero point” (basically where you stick the end of the tape measure to measure elevation) is important for international collaboration. It makes it easier, for example, to monitor and compare sea-level changes around the world. It is especially useful for building infrastructure involving flowing water, such as dams and canals. In 2020, the international height standard even resolved a long-standing dispute between China and Nepal over Mount Everest’s height. For years, China said the mountain was 8,844.43 meters; Nepal measured it at 8,848. Using the IHRF, the two countries finally agreed that the mountain was 8,848.86 meters.  A worker performs tests on ACES at a cleanroom at the Kennedy Space Center in Florida.ESA-T. PEIGNIER To create a standard zero point, geodesists create a model of Earth known as a geoid. Every point on the surface of this lumpy, potato-shaped model experiences the same gravity, which means that if you dug a canal at the height of the geoid, the water within the canal would be level and would not flow. Distance from the geoid establishes a global system for altitude. However, the current model lacks precision, particularly in Africa and South America, says Sanchez. Today’s geoid has been built using instruments that directly measure Earth’s gravity. These have been carried on satellites, which excel at getting a global but low-resolution view, and have also been used to get finer details via expensive ground- and airplane-based surveys. But geodesists have not had the funding to survey Africa and South America as extensively as other parts of the world, particularly in difficult terrain such as the Amazon rainforest and Sahara Desert.  To understand the discrepancy in precision, imagine a bridge that spans Africa from the Mediterranean coast to Cape Town, South Africa. If it’s built using the current geoid, the two ends of the bridge will be misaligned by tens of centimeters. In comparison, you’d be off by at most five centimeters if you were building a bridge spanning North America.  To improve the geoid’s precision, geodesists want to create a worldwide network of clocks, synchronized from space. The idea works according to Einstein’s theory of general relativity, which states that the stronger the gravitational field, the more slowly time passes. The 2014 sci-fi movie Interstellar illustrates an extreme version of this so-called time dilation: Two astronauts spend a few hours in extreme gravity near a black hole to return to a shipmate who has aged more than two decades. Similarly, Earth’s gravity grows weaker the higher in elevation you are. Your feet, for example, experience slightly stronger gravity than your head when you’re standing. Assuming you live to be about 80 years old, over a lifetime your head will age tens of billionths of a second more than your feet.  A clock network would allow geodesists to compare the ticking of clocks all over the world. They could then use the variations in time to map Earth’s gravitational field much more precisely, and consequently create a more precise geoid. The most accurate clocks today are precise enough to measure variations in time that map onto centimeter-level differences in elevation.  “We want to have the accuracy level at the one-centimeter or sub-centimeter level,” says Jürgen Müller, a geodesist at Leibniz University Hannover in Germany. Specifically, geodesists would use the clock measurements to validate their geoid model, which they currently do with ground- and plane-based surveying techniques. They think that a clock network should be considerably less expensive. ACES is just a first step. It is capable of measuring altitudes at various points around Earth with 10-centimeter precision, says Cacciapuoti. But the point of ACES is to prototype the clock network. It will demonstrate the optical and microwave technology needed to use a clock in space to connect some of the most advanced ground-based clocks together. In the next year or so, Müller plans to use ACES to connect to clocks on the ground, starting with three in Germany. Müller’s team could then make more precise measurements at the location of those clocks. These early studies will pave the way for work connecting even more precise clocks than ACES to the network, ultimately leading to an improved geoid. The best clocks today are some 50 times more precise than ACES. “The exciting thing is that clocks are getting even stabler,” says Michael Bevis, a geodesist at Ohio State University, who was not involved with the project. A more precise geoid would allow engineers, for example, to build a canal with better control of its depth and flow, he says. However, he points out that in order for geodesists to take advantage of the clocks’ precision, they will also have to improve their mathematical models of Earth’s gravitational field.  Even starting to build this clock network has required decades of dedicated work by scientists and engineers. It took ESA three decades to make a clock as small as ACES that is suitable for space, says Cacciapuoti. This meant miniaturizing a clock the size of a laboratory into the size of a small fridge. “It was a huge engineering effort,” says Cacciapuoti, who has been working on the project since he began at ESA 20 years ago.  Geodesists expect they’ll need at least another decade to develop the clock network and launch more clocks into space. One possibility would be to slot the clocks onto GPS satellites. The timeline depends on the success of the ACES mission and the willingness of government agencies to invest, says Sanchez. But whatever the specifics, mapping the world takes time.

Recorded on May 21, 2025 Big Tech’s appetite for energy is growing rapidly as adoption of AI accelerates. But just how much energy does even a single AI query use? And what does it mean for the climate? Hear from MIT Technology Review editor in chief Mat Honan, senior climate reporter Casey Crownhart, and AI reporter James O’Donnell as they explore AI’s energy demands now and in the future. Speakers: Mat Honan, editor in chief, Casey Crownhart, climate reporter, and James O’Donnell, AI reporter. Related Coverage: Power Hungry: AI and our energy future We did the math on AI’s energy footprint. Here’s the story you haven’t heard. Everything you need to know about estimating AI’s energy and emissions burden These four charts sum up the state of AI and energy

Yes, Google’s roster of consumer-facing products is the slickest on offer. The firm is bundling most of its multimodal models into its Gemini app, including the new Imagen 4 image generator and the new Veo 3 video generator. That means you can now access Google’s full range of generative models via a single chatbot. It also announced Gemini Live, a feature that lets you share your phone’s screen or your camera’s view with the chatbot and ask it about what it can see. Those features were previously only seen in demos of Project Astra, a “universal AI assistant“ that Google DeepMind is working on. Now, Google is inching toward putting Project Astra into the hands of anyone with a smartphone. Google is also rolling out AI Mode, an LLM-powered front end to search. This can now pull in personal information from Gmail or Google Docs to tailor searches to users. It will include Deep Search, which can break a query down into hundreds of individual searches and then summarize the results; a version of Project Mariner, Google DeepMind’s browser-using agent; and Search Live, which lets you hold up your camera and ask it what it sees. This is the new frontier. It’s no longer about who has the most powerful models, but who can spin them into the best products. OpenAI’s ChatGPT includes many similar features to Gemini’s. But with its existing ecosystem of consumer services and billions of existing users, Google has a clear advantage. Power users wanting access to the latest versions of everything on display can now sign up for Google AI Ultra for $250 a month.   When OpenAI released ChatGPT in late 2022, Google was caught on the back foot and was forced to jump into higher gear to catch up. With this year’s product lineup, it looks as if Google has stuck its landing. On a preview call, CEO Sundar Pichai claimed that AI Overviews, a precursor to AI Mode that provides LLM-generated summaries of search results, had turned out to be popular with hundreds of millions of users. He speculated that many of them may not even know (or care) that they were using AI—it was just a cool new way to search. Google I/O gives a broader glimpse of that future, one where AI is invisible. “More intelligence is available, for everyone, everywhere,” Pichai told his audience. I think we are expected to marvel. But by putting AI in everything, Google is turning AI into a technology we won’t notice and may not even bother to name.

This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology. By putting AI into everything, Google wants to make it invisible If you want to know where AI is headed, this year’s Google I/O has you covered. The company’s annual showcase of next-gen products, which kicked off yesterday, has all of the pomp and pizzazz, the sizzle reels and celebrity walk-ons, that you’d expect from a multimillion dollar marketing event. But it also shows us just how fast this still-experimental technology is being subsumed into a line-up designed to sell phones and subscription tiers. Never before have I seen this thing we call artificial intelligence appear so normal. Read the full story.—Will Douglas Heaven AI could keep us dependent on natural gas for decades to come Last December, Meta announced plans to build a massive $10 billion data center for training its artificial intelligence models in rural northeast Louisiana. Stretching for more than a mile, it will be Meta’s largest in the world, and it will have an enormous appetite for electricity. To power the data center, a Meta contractor called Entergy will build three large natural-gas power plants with a total capacity of 2.3 gigawatts. It’ll also upgrade the grid to accommodate the huge jump in anticipated demand.The choice of natural gas as the go-to solution to meet the growing demand for power from AI is not unique to Louisiana. The fossil fuel is already the country’s chief source of electricity generation, and large natural-gas plants are being built around the country to feed electricity to new and planned AI data centers. That’s all but wiping out any prospect that the US will wean itself off natural gas anytime soon. Read the full story.—David Rotman This story is part of Power Hungry: AI and our energy future—our new series shining a light on AI’s energy usage. Check out the rest of the package here. Take a new look at AI’s energy use Big Tech's appetite for energy is growing rapidly as adoption of AI accelerates. But just how much energy does a single AI query use? And what does it mean for the climate?  Join editor in chief Mat Honan, senior climate reporter Casey Crownhart, and AI reporter James O'Donnell at 1.30pm ET today for a subscriber-only Roundtables conversation digging into our new package of stories about AI's energy demands now and in the future. Register here.The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Democrats are on the hunt for a digital thought leader They’re (finally) realizing how far they’re lagging behind their opponents’ online efforts these days. (NYT $) + AI’s impact on elections is being overblown. (MIT Technology Review)2 At least two newspapers printed an AI-generated summer reading list 📰 The only problem is, some of the books don’t actually exist. (404 Media)+ It’s a useful reminder to never take anything chatbots produce as fact. (Axios)+ Even regional newspapers aren’t safe from AI slop. (The Atlantic $)+ Why AI hallucinates, and why we can’t stop it. (MIT Technology Review)3 The Earth may already be too hot to maintain polar ice sheetsEven if it stays at current temperature levels. (WP $)+ Why climate researchers are taking the temperature of mountain snow. (MIT Technology Review)4 How New York City’s child abuse algorithm flags families for investigation Critics believe it’s open to racial bias. (The Markup) 5 Here’s what it’s like to interview for a job at DOGE   The hiring process is remarkably fast, for a government entity. (Wired $)+ The department reportedly tried to enter the US government’s publishing operation. (Politico)+ DOGE’s tech takeover threatens the safety and stability of our critical data. (MIT Technology Review)6 Fortnite has finally returned to Apple’s App StoreAfter five years and a lengthy legal battle. (NYT $) + The recent ruling has major implications for the iOS economy. (Reuters)7 Most chatbots can be tricked into dispensing dangerous informationFrom hacking advice, to describing how to make drugs. (The Guardian) + Anthropic has a new way to protect large language models against jailbreaks. (MIT Technology Review)8 Young Indonesians are being trafficked to scam farmsFraudulent job ads on Telegram and Facebook lure them into a life of crime. (Rest of World) + Inside a romance scam compound—and how people get tricked into being there. (MIT Technology Review) 9  Inside the building in China where stolen western iPhones are stripped and sold You’ll find a buyer for every single component inside the Feiyang Times. (FT $)10 Amazon has started randomly refunding customers for old purchasesSome orders were placed as far back as 2018. (Bloomberg $) Quote of the day “Anybody who’s a computer scientist should not be retired right now. They should be working on AI.” —Google cofounder Sergey Brin says people with the right technical skills should copy him and quit being retired, TechCrunch reports. One more thing This fuel plant will use agricultural waste to combat climate change A startup called Mote plans to build a new type of fuel-producing plant in California’s fertile Central Valley that would, if it works as hoped, continually capture and bury carbon dioxide.It’s among a growing number of efforts to commercialize a concept first proposed two decades ago as a means of combating climate change, known as bioenergy with carbon capture and sequestration, or BECCS.It’s an ambitious plan. However, there are serious challenges to doing BECCS affordably and in ways that reliably suck down significant levels of carbon dioxide. Read the full story.—James Temple We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet 'em at me.) + These creepy little Labubu toys are everywhere. But why?+ Happy 25th birthday to one of London’s finest institutions, the Tate Modern gallery.+ Why the Mission Impossible film franchise just won’t die.+ Hummingbirds can fly backwards!? Wow.

The thousands of sprawling acres in rural northeast Louisiana had gone unwanted for nearly two decades. Louisiana authorities bought the land in Richland Parish in 2006 to promote economic development in one of the poorest regions in the state. For years, they marketed the former agricultural fields as the Franklin Farm mega site, first to auto manufacturers (no takers) and after that to other industries that might want to occupy more than a thousand acres just off the interstate. This story is a part of MIT Technology Review’s series “Power Hungry: AI and our energy future,” on the energy demands and carbon costs of the artificial-intelligence revolution. So it’s no wonder that state and local politicians were exuberant when Meta showed up. In December, the company announced plans to build a massive $10 billion data center for training its artificial-intelligence models at the site, with operations to begin in 2028. “A game changer,” declared Governor Jeff Landry, citing 5,000 construction jobs and 500 jobs at the data center that are expected to be created and calling it the largest private capital investment in the state’s history. From a rural backwater to the heart of the booming AI revolution! The AI data center also promises to transform the state’s energy future. Stretching in length for more than a mile, it will be Meta’s largest in the world, and it will have an enormous appetite for electricity, requiring two gigawatts for computation alone (the electricity for cooling and other building needs will add to that). When it’s up and running, it will be the equivalent of suddenly adding a decent-size city to the region’s grid—one that never sleeps and needs a steady, uninterrupted flow of electricity. To power the data center, Entergy aims to spend $3.2 billion to build three large natural-gas power plants with a total capacity of 2.3 gigawatts and upgrade the grid to accommodate the huge jump in anticipated demand. In its filing to the state’s power regulatory agency, Entergy acknowledged that natural-gas plants “emit significant amounts of CO2” but said the energy source was the only affordable choice given the need to quickly meet the 24-7 electricity demand from the huge data center. Meta said it will work with Entergy to eventually bring online at least 1.5 gigawatts of new renewables, including solar, but that it had not yet decided which specific projects to fund or when those investments will be made. Meanwhile, the new natural-gas plants, which are scheduled to be up and running starting in 2028 and will have a typical lifetime of around 30 years, will further lock in the state’s commitment to the fossil fuel. The development has sparked interest from the US Congress; last week, Sheldon Whitehouse, the ranking member of the Senate Committee on Environment and Public Works issued a letter to Meta that called out the company's plan to power its data center with “new and unabated natural gas generation” and said its promises to offset the resulting emissions "by funding carbon capture and a solar project are vague and offer little reassurance.” The choice of natural gas as the go-to solution to meet the growing demand for power from AI is not unique to Louisiana. The fossil fuel is already the country’s chief source of electricity generation, and large natural-gas plants are being built around the country to feed electricity to new and planned AI data centers. While some climate advocates have hoped that cleaner renewable power would soon overtake it, the booming power demand from data centers is all but wiping out any prospect that the US will wean itself off natural gas anytime soon. The reality on the ground is that natural gas is “the default” to meet the exploding power demand from AI data centers, says David Victor, a political scientist at the University of California, San Diego, and co-director of its Deep Decarbonization Project. “The natural-gas plant is the thing that you know how to build, you know what it’s going to cost (more or less), and you know how to scale it and get it approved,” says Victor. “Even for [AI] companies that want to have low emissions profiles and who are big pushers of low or zero carbon, they won’t have a choice but to use gas.” The preference for natural gas is particularly pronounced in the American South, where plans for multiple large gas-fired plants are in the works in states such as Virginia, North Carolina, South Carolina, and Georgia. Utilities in those states alone are planning some 20 gigawatts of new natural-gas power plants over the next 15 years, according to a recent report. And much of the new demand—particularly in Virginia, South Carolina and Georgia—is coming from data centers; in those 3 states data centers account for around 65 to 85% of projected load growth. “It’s a long-term commitment in absolutely the wrong direction,” says Greg Buppert, a senior attorney at the Southern Environmental Law Center in Charlottesville, Virginia. If all the proposed gas plants get built in the South over the next 15 years, he says, “we’ll just have to accept that we won’t meet emissions reduction goals.” But even as it looks more and more likely that natural gas will remain a sizable part of our energy future, questions abound over just what its continued dominance will look like. For one thing, no one is sure exactly how much electricity AI data centers will need in the future and how large an appetite companies will have for natural gas. Demand for AI could fizzle. Or AI companies could make a concerted effort to shift to renewable energy or nuclear power. Such possibilities mean that the US could be on a path to overbuild natural-gas capacity, which would leave regions saddled with unneeded and polluting fossil-fuel dinosaurs—and residents footing soaring electricity bills to pay off today’s investments. The good news is that such risks could likely be managed over the next few years, if—and it’s a big if—AI companies are more transparent about how flexible they can be in their seemingly insatiable energy demands. The reign of natural gas Natural gas in the US is cheap and abundant these days. Two decades ago, huge reserves were found in shale deposits scattered across the country. In 2008, as fracking started to make it possible to extract large quantities of the gas from shale, natural gas was selling for $13 per million Btu (a measure of thermal energy); last year, it averaged just $2.21, the lowest annual price (adjusting for inflation) ever reported, according to the US Energy Information Administration (EIA). Around 2016, natural gas overtook coal as the main fuel for electricity generation in the US. And today—despite the rapid rise of solar and wind power, and well-deserved enthusiasm for the falling price of such renewables—natural gas is still king, accounting for around 40% of electricity generated in the US. In Louisiana, which is also a big producer, that share is some 72%, according to a recent audit. Natural gas burns much cleaner than coal, producing roughly half as much carbon dioxide. In the early days of the gas revolution, many environmental activists and progressive politicians touted it as a valuable “bridge” to renewables and other sources of clean energy. And by some calculations, natural gas has fulfilled that promise. The power sector has been one of the few success stories in lowering US emissions, thanks to its use of natural gas as a replacement for coal.   But natural gas still produces a lot of carbon dioxide when it is burned in conventionally equipped power plants. And fracking causes local air and water pollution. Perhaps most worrisome, drilling and pipelines are releasing substantial amounts of methane, the main ingredient in natural gas, both accidentally and by intentional venting. Methane is a far more potent greenhouse gas than carbon dioxide, and the emissions are a growing concern to climate scientists, albeit one that’s difficult to quantify. Still, carbon emissions from the power sector will likely continue to drop as coal is further squeezed out and more renewables get built, according to the Rhodium Group, a research consultancy. But Rhodium also projects that if electricity demand from data centers remains high and natural-gas prices low, the fossil fuel will remain the dominant source of power generation at least through 2035 and the transition to cleaner electricity will be much delayed. Rhodium estimates that the continued reign of natural gas will lead to an additional 278 million metric tons of annual US carbon emissions by 2035 (roughly equivalent to the emissions from a large US state such as Florida), relative to a future in which the use of fossil fuel gradually winds down. Our addiction to natural gas, however, doesn’t have to be a total climate disaster, at least over the longer term. Large AI companies could use their vast leverage to insist that utilities install carbon capture and sequestration (CCS) at power plants and use natural gas sourced with limited methane emissions. Entergy, for one, says its new gas turbines will be able to incorporate CCS through future upgrades. And Meta says it will help to fund the installation of CCS equipment at one of Entergy’s existing natural-gas power plants in southern Louisiana to help prove out the technology.   But the transition to clean natural gas is a hope that will take decades to realize. Meanwhile, utilities across the country are facing a more imminent and practical challenge: how to meet the sudden demand for gigawatts more power in the next few years without inadvertently building far too much capacity. For many, adding more natural-gas power plants might seem like the safe bet. But what if the explosion in AI demand doesn’t show up? Times of stress AI companies tout the need for massive, power-hungry data centers. But estimates for just how much energy it will actually take to train and run AI models vary wildly. And the technology keeps changing, sometimes seemingly overnight. DeepSeek, the new Chinese model that debuted in January, may or may not signal a future of new energy-efficient AI, but it certainly raises the possibility that such advances are possible. Maybe we will find ways to use far more energy-efficient hardware. Or maybe the AI revolution will peter out and many of the massive data centers that companies think they’ll need will never get built. There are already signs that too many have been constructed in China and clues that it might be beginning to happen in the US.  Despite the uncertainty, power providers have the task of drawing up long-term plans for investments to accommodate projected demand. Too little capacity and their customers face blackouts; too much and those customers face outsize electricity bills to fund investments in unneeded power. There could be a way to lessen the risk of overbuilding natural-gas power, however. Plenty of power is available on average around the country and on most regional grids. Most utilities typically use only about 53% of their available capacity on average during the year, according to a Duke study. The problem is that utilities must be prepared for the few hours when demand spikes—say, because of severe winter weather or a summer heat wave. The soaring demand from AI data centers is prompting many power providers to plan new capacity to make sure they have plenty of what Tyler Norris, a fellow at Duke's Nicholas School of the Environment, and his colleagues call “headroom,” to meet any spikes in demand. But after analyzing data from power systems across the country, Norris and his coauthors found that if large AI facilities cut back their electricity use during hours of peak demand, many regional power grids could accommodate those AI customers without adding new generation capacity. Even a moderate level of flexibility would make a huge difference. The Duke researchers estimate that if data centers cut their electricity use by roughly half for just a few hours during the year, it will allow utilities to handle some additional 76 gigawatts of new demand. That means power providers could effectively absorb the 65 or so additional gigawatts that, according to some predictions, data centers will likely need by 2029. “The prevailing assumption is that data centers are 100% inflexible,” says Norris. That is, that they need to run at full power all the time. But Norris says AI data centers, particularly ones that are training large foundation models (such as Meta’s facility in Richland Parish), can avoid running at full capacity or shift their computation loads to other data centers around the country—or even ramp up their own backup power—during times when a grid is under stress. The increased flexibility could allow companies to get AI data centers up and running faster, without waiting for new power plants and upgrades to transmission lines—which can take years to get approved and built. It could also, Norris noted in testimony to the US Congress in early March, provide at least a short-term reprieve on the rush to build more natural-gas power, buying time for utilities to develop and plan for cleaner technologies such as advanced nuclear and enhanced geothermal. It could, he testified, prevent “a hasty overbuild of natural-gas infrastructure.” AI companies have expressed some interest in their ability to shift around demand for power. But there are still plenty of technology questions around how to make it happen. Late last year, EPRI (the Electric Power Research Institute), a nonprofit R&D group, started a three-year collaboration with power providers, grid operators, and AI companies including Meta and Google, to figure it out. “The potential is very large,” says David Porter, the EPRI vice president who runs the project, but we must show it works “beyond just something on a piece of paper or a computer screen.” Porter estimates that there are typically 80 to 90 hours a year when a local grid is under stress and it would help for a data center to reduce its energy use. But, he says, AI data centers still need to figure out how to throttle back at those times, and grid operators need to learn how to suddenly subtract and then add back hundreds of megawatts of electricity without disrupting their systems. “There’s still a lot of work to be done so that it’s seamless for the continuous operation of the data centers and seamless for the continuous operation of the grid,” he says. Footing the bill Ultimately, getting AI data centers to be more flexible in their power demands will require more than a technological fix. It will require a shift in how AI companies work with utilities and local communities, providing them with more information and insights into actual electricity needs. And it will take aggressive regulators to make sure utilities are rigorously evaluating the power requirements of data centers rather than just reflexively building more natural-gas plants. “The most important climate policymakers in the country right now are not in Washington. They’re in state capitals, and these are public utility commissioners,” says Costa Samaras, the director of Carnegie Mellon University’s Scott Institute for Energy Innovation. In Louisiana, those policymakers are the elected officials at the Louisiana Public Service Commission, who are expected to rule later this year on Entergy’s proposed new gas plants and grid upgrades. The LPSC commissioners will decide whether Entergy’s arguments about the huge energy requirements of Meta’s data center and need for full 24/7 power leave no alternative to natural gas.  In the application it filed last fall with LPSC, Entergy said natural-gas power was essential for it to meet demand “throughout the day and night.” Teaming up solar power with battery storage could work “in theory” but would be “prohibitively costly.” Entergy also ruled out nuclear, saying it would take too long and cost too much. Others are not satisfied with the utility’s judgment. In February, the New Orleans–based Alliance for Affordable Energy and the Union of Concerned Scientists filed a motion with the Louisiana regulators arguing that Entergy did not do a rigorous market evaluation of its options, as required by the commission’s rules. Part of the problem, the groups said, is that Entergy relied on “unsubstantiated assertions” from Meta on its load needs and timeline. “Entergy is saying [Meta] needs around-the-clock power,” says Paul Arbaje, an analyst for the climate and energy program at the Union of Concerned Scientists. “But we’re just being asked to take [Entergy’s] word for it. Regulators need to be asking tough questions and not just assume that these data centers need to be operated at essentially full capacity all the time.” And, he suggests, if the utility had “started to poke holes at the assumptions that are sometimes taken as a given,” it “would have found other cleaner options.”       In an email response to MIT Technology Review, Entergy said that it has discussed the operational aspects of the facility with Meta, but "as with all customers, Entergy Louisiana will not discuss sensitive matters on behalf of their customers.” In a letter filed with the state’s regulators in early April, Meta said Entergy’s understanding of its energy needs is, in fact, accurate. The February motion also raised concern over who will end up paying for the new gas plants. Entergy says Meta has signed a 15-year supply contract for the electricity that is meant to help cover the costs of building and running the power plants but didn't respond to requests by MIT Technology Review for further details of the deal, including what happens if Meta wants to terminate the contract early. Meta referred MIT Technology Review’s questions about the contract to Entergy but says its policy is to cover the full cost that utilities incur to serve its data centers, including grid upgrades. It also says it is spending over $200 million to support the Richland Parish data centers with new infrastructure, including roads and water systems.  Not everyone is convinced. The Alliance for Affordable Energy, which works on behalf of Louisiana residents, says that the large investments in new gas turbines could mean future rate hikes, in a state where residents already have high electricity bills and suffer from one of country’s most unreliable grids. Of special concern is what happens after the 15 years. “Our biggest long-term concern is that in 15 years, residential ratepayers [and] small businesses in Louisiana will be left holding the bag for three large gas generators,” says Logan Burke, the alliance’s executive director. Indeed, consumers across the country have good reasons to fear that their electricity bills will go up as utilities look to meet the increased demand from AI data centers by building new generation capacity. In a paper posted in March, researchers at Harvard Law School argued that utilities “are now forcing the public to pay for infrastructure designed to supply a handful of exceedingly wealthy corporations.” The Harvard authors write, “Utilities tell [public utility commissions] what they want to hear: that the deals for Big Tech isolate data center energy costs from other ratepayers’ bills and won’t increase consumers’ power prices.” But the complexity of the utilities’ payment data and lack of transparency in the accounting, they say, make verifying this claim “all but impossible.” The boom in AI data centers is making Big Tech a player in our energy infrastructure and electricity future in a way unimaginable just a few years ago. At their best, AI companies could greatly facilitate the move to cleaner energy by acting as reliable and well-paying customers that provide funding that utilities can use to invest in a more robust and flexible electricity grid. This change can happen without burdening other electricity customers with additional risks and costs. But it will take AI companies committed to that vision. And it will take state regulators who ask tough questions and don’t get carried away by the potential investments being dangled by AI companies. Huge new AI data centers like the one in Richland Parish could in fact be a huge economic boon by providing new jobs, but residents deserve transparency and input into the negotiations. This is, after all, public infrastructure. Meta may come and go, but Louisiana's residents will have to live with—and possibly pay for—the changes in the decades to come.

With seemingly no limit to the demand for artificial intelligence, everyone in the energy, AI, and climate fields is justifiably worried. Will there be enough clean electricity to power AI and enough water to cool the data centers that support this technology? These are important questions with serious implications for communities, the economy, and the environment.  This story is a part of MIT Technology Review’s series “Power Hungry: AI and our energy future,” on the energy demands and carbon costs of the artificial-intelligence revolution. But the question about AI’s energy usage portends even bigger issues about what we need to do in addressing climate change for the next several decades. If we can’t work out how to handle this, we won’t be able to handle broader electrification of the economy, and the climate risks we face will increase. Innovation in IT got us to this point. Graphics processing units (GPUs) that power the computing behind AI have fallen in cost by 99% since 2006. There was similar concern about the energy use of data centers in the early 2010s, with wild projections of growth in electricity demand. But gains in computing power and energy efficiency not only proved these projections wrong but enabled a 550% increase in global computing capability from 2010 to 2018 with only minimal increases in energy use.  In the late 2010s, however, the trends that had saved us began to break. As the accuracy of AI models dramatically improved, the electricity needed for data centers also started increasing faster; they now account for 4.4% of total demand, up  from 1.9% in 2018. Data centers consume more than 10% of the electricity supply in six US states. In Virginia, which has emerged as a hub of data center activity, that figure is 25%. Projections about the future demand for energy to power AI are uncertain and range widely, but in one study, Lawrence Berkeley National Laboratory estimated that data centers could represent 6% to 12% of total US electricity use by 2028. Communities and companies will notice this type of rapid growth in electricity demand. It will put pressure on energy prices and on ecosystems. The projections have resulted in calls to build lots of new fossil-fired power plants or bring older ones out of retirement. In many parts of the US, the demand will likely result in a surge of natural-gas-powered plants. It’s a daunting situation. Yet when we zoom out, the projected electricity use from AI is still pretty small. The US generated about 4,300 billion kilowatt-hours last year. We’ll likely need another 1,000 billion to 1,200 billion or more in the next decade—a 24% to 29% increase. Almost half the additional electricity demand will be from electrified vehicles. Another 30% is expected to be from electrified technologies in buildings and industry. Innovation in vehicle and building electrification also advanced in the last decade, and this shift will be good news for the climate, for communities, and for energy costs. The remaining 22% of new electricity demand is estimated to come from AI and data centers. While it represents a smaller piece of the pie, it’s the most urgent one. Because of their rapid growth and geographic concentration, data centers are the electrification challenge we face right now—the small stuff we have to figure out before we’re able to do the big stuff like vehicles and buildings. We also need to understand what the energy consumption and carbon emissions associated with AI are buying us. While the impacts from producing semiconductors and powering AI data centers are important, they are likely small compared with the positive or negative effects AI may have on applications such as the electricity grid, the transportation system, buildings and factories, or consumer behavior. Companies could use AI to develop new materials or batteries that would better integrate renewable energy into the grid. But they could also use AI to make it easier to find more fossil fuels. The claims about potential benefits for the climate are exciting, but they need to be continuously verified and will need support to be realized. This isn’t the first time we’ve faced challenges coping with growth in electricity demand. In the 1960s, US electricity demand was growing at more than 7% per year. In the 1970s that growth was nearly 5%, and in the 1980s and 1990s it was more than 2% per year. Then, starting in 2005, we basically had a decade and a half of flat electricity growth. Most projections for the next decade put our expected growth in electricity demand at around 2% again—but this time we’ll have to do things differently.  To manage these new energy demands, we need a “Grid New Deal” that leverages public and private capital to rebuild the electricity system for AI with enough capacity and intelligence for decarbonization. New clean energy supplies, investment in transmission and distribution, and strategies for virtual demand management can cut emissions, lower prices, and increase resilience. Data centers bringing clean electricity and distribution system upgrades could be given a fast lane to connect to the grid. Infrastructure banks could fund new transmission lines or pay to upgrade existing ones. Direct investment or tax incentives could encourage clean computing standards, workforce development in the clean energy sector, and open data transparency from data center operators about their energy use so that communities can understand and measure the impacts. In 2022, the White House released a Blueprint for an AI Bill of Rights that provided principles to protect the public’s rights, opportunities, and access to critical resources from being restricted by AI systems. To the AI Bill of Rights, we humbly offer a climate amendment, because ethical AI must be climate-safe AI. It’s a starting point to ensure that the growth of AI works for everyone—that it doesn’t raise people’s energy bills, adds more clean power to the grid than it uses, increases investment in the power system’s infrastructure, and benefits communities while driving innovation. By grounding the conversation about AI and energy in context about what is needed to tackle climate change, we can deliver better outcomes for communities, ecosystems, and the economy. The growth of electricity demand for AI and data centers is a test case for how society will respond to the demands and challenges of broader electrification. If we get this wrong, the likelihood of meeting our climate targets will be extremely low. This is what we mean when we say the energy and climate impacts from data centers are small, but they are also huge. Costa Samaras is the Trustee Professor of Civil and Environmental Engineering and director of the Scott Institute for Energy Innovation at Carnegie Mellon University. Emma Strubell is the Raj Reddy Assistant Professor in the Language Technologies Institute in the School of Computer Science at Carnegie Mellon University. Ramayya Krishnan is dean of the Heinz College of Information Systems and Public Policy and the William W. and Ruth F. Cooper Professor of Management Science and Information Systems at Carnegie Mellon University.

In the high desert east of Reno, Nevada, construction crews are flattening the golden foothills of the Virginia Range, laying the foundations of a data center city. Google, Tract, Switch, EdgeCore, Novva, Vantage, and PowerHouse are all operating, building, or expanding huge facilities within the Tahoe Reno Industrial Center, a business park bigger than the city of Detroit.  This story is a part of MIT Technology Review’s series “Power Hungry: AI and our energy future,” on the energy demands and carbon costs of the artificial-intelligence revolution. Meanwhile, Microsoft acquired more than 225 acres of undeveloped property within the center and an even larger plot in nearby Silver Springs, Nevada. Apple is expanding its data center, located just across the Truckee River from the industrial park. OpenAI has said it’s considering building a data center in Nevada as well. The corporate race to amass computing resources to train and run artificial intelligence models and store information in the cloud has sparked a data center boom in the desert—just far enough away from Nevada’s communities to elude wide notice and, some fear, adequate scrutiny.  Switch, a data center company based in Las Vegas, says the full build-out of its campus at the Tahoe Reno Industrial Center could exceed seven million square feet.EMILY NAJERA The full scale and potential environmental impacts of the developments aren’t known, because the footprint, energy needs, and water requirements are often closely guarded corporate secrets. Most of the companies didn’t respond to inquiries from MIT Technology Review, or declined to provide additional information about the projects.  But there’s “a whole lot of construction going on,” says Kris Thompson, who served as the longtime project manager for the industrial center before stepping down late last year. “The last number I heard was 13 million square feet under construction right now, which is massive.” Indeed, it’s the equivalent of almost five Empire State Buildings laid out flat. In addition, public filings from NV Energy, the state’s near-monopoly utility, reveal that a dozen data-center projects, mostly in this area, have requested nearly six gigawatts of electricity capacity within the next decade.  That would make the greater Reno area—the biggest little city in the world—one of the largest data-center markets around the globe. It would also require expanding the state’s power sector by about 40%, all for a single industry in an explosive growth stage that may, or may not, prove sustainable. The energy needs, in turn, suggest those projects could consume billions of gallons of water per year, according to an analysis conducted for this story.  Construction crews are busy building data centers throughout the Tahoe Reno Industrial Center.EMILY NAJERA The build-out of a dense cluster of energy and water-hungry data centers in a small stretch of the nation’s driest state, where climate change is driving up temperatures faster than anywhere else in the country, has begun to raise alarms among water experts, environmental groups, and residents. That includes members of the Pyramid Lake Paiute Tribe, whose namesake water body lies within their reservation and marks the end point of the Truckee River, the region’s main source of water. Much of Nevada has suffered through severe drought conditions for years, farmers and communities are drawing down many of the state’s groundwater reservoirs faster than they can be refilled, and global warming is sucking more and more moisture out of the region’s streams, shrubs, and soils. “Telling entities that they can come in and stick more straws in the ground for data centers is raising a lot of questions about sound management,” says Kyle Roerink, executive director of the Great Basin Water Network, a nonprofit that works to protect water resources throughout Nevada and Utah.  “We just don’t want to be in a situation where the tail is wagging the dog,” he later added, “where this demand for data centers is driving water policy.” Luring data centers In the late 1850s, the mountains southeast of Reno began enticing prospectors from across the country, who hoped to strike silver or gold in the famed Comstock Lode. But Storey County had few residents or economic prospects by the late 1990s, around the time when Don Roger Norman, a media-shy real estate speculator, spotted a new opportunity in the sagebrush-covered hills.  He began buying up tens of thousands of acres of land for tens of millions of dollars and lining up development approvals to lure industrial projects to what became the Tahoe Reno Industrial Center. His partners included Lance Gilman, a cowboy-hat-wearing real estate broker, who later bought the nearby Mustang Ranch brothel and won a seat as a county commissioner. In 1999, the county passed an ordinance that preapproves companies to develop most types of commercial and industrial projects across the business park, cutting months to years off the development process. That helped cinch deals with a flock of tenants looking to build big projects fast, including Walmart, Tesla, and Redwood Materials. Now the promise of fast permits is helping to draw data centers by the gigawatt. On a clear, cool January afternoon, Brian Armon, a commercial real estate broker who leads the industrial practices group at NAI Alliance, takes me on a tour of the projects around the region, which mostly entails driving around the business center. Lance Gilman, a local real estate broker, helped to develop the Tahoe Reno Industrial Center and land some of its largest tenants.GREGG SEGAL After pulling off Interstate 80 onto USA Parkway, he points out the cranes, earthmovers, and riprap foundations, where a variety of data centers are under construction. Deeper into the industrial park, Armon pulls up near Switch’s long, low, arched-roof facility, which sits on a terrace above cement walls and security gates. The Las Vegas–based company says the first phase of its data center campus encompasses more than a million square feet, and that the full build-out will cover seven times that space.  Over the next hill, we turn around in Google’s parking lot. Cranes, tents, framing, and construction equipment extend behind the company’s existing data center, filling much of the 1,210-acre lot that the search engine giant acquired in 2017. Last August, during an event at the University of Nevada, Reno, the company announced it would spend $400 million to expand the data center campus along with another one in Las Vegas. Thompson says that the development company, Tahoe Reno Industrial LLC, has now sold off every parcel of developable land within the park (although several lots are available for resale following the failed gamble of one crypto tenant). When I ask Armon what’s attracting all the data centers here, he starts with the fast approvals but cites a list of other lures as well: The inexpensive land. NV Energy’s willingness to strike deals to supply relatively low-cost electricity. Cool nighttime and winter temperatures, as far as American deserts go, which reduce the energy and water needs. The proximity to tech hubs such as Silicon Valley, which cuts latency for applications in which milliseconds matter. And the lack of natural disasters that could shut down the facilities, at least for the most part. “We are high in seismic activity,” he says. “But everything else is good. We’re not going to have a tornado or flood or a devastating wildfire.” Then there’s the generous tax policies.In 2023, Novva, a Utah-based data center company, announced plans to build a 300,000-square-foot facility within the industrial business park. Nevada doesn’t charge corporate income tax, and it has also enacted deep tax cuts specifically for data centers that set up shop in the state. That includes abatements of up to 75% on property tax for a decade or two—and nearly as much of a bargain on the sales and use taxes applied to equipment purchased for the facilities. Data centers don’t require many permanent workers to run the operations, but the projects have created thousands of construction jobs. They’re also helping to diversify the region’s economy beyond casinos and generating tax windfalls for the state, counties, and cities, says Jeff Sutich, executive director of the Northern Nevada Development Authority. Indeed, just three data-center projects, developed by Apple, Google, and Vantage, will produce nearly half a billion dollars in tax revenue for Nevada, even with those generous abatements, according to the Nevada Governor’s Office of Economic Development. The question is whether the benefits of data centers are worth the tradeoffs for Nevadans, given the public health costs, greenhouse-gas emissions, energy demands, and water strains. The rain shadow The Sierra Nevada’s granite peaks trace the eastern edge of California, forcing Pacific Ocean winds to rise and cool. That converts water vapor in the air into the rain and snow that fill the range’s tributaries, rivers, and lakes.  But the same meteorological phenomenon casts a rain shadow over much of neighboring Nevada, forming an arid expanse known as the Great Basin Desert. The state receives about 10 inches of precipitation a year, about a third of the national average. The Truckee River draws from the melting Sierra snowpack at the edge of Lake Tahoe, cascades down the range, and snakes through the flatlands of Reno and Sparks. It forks at the Derby Dam, a Reclamation Act project a few miles from the Tahoe Reno Industrial Center, which diverts water to a farming region further east while allowing the rest to continue north toward Pyramid Lake.  Along the way, an engineered system of reservoirs, canals, and treatment plants divert, store, and release water from the river, supplying businesses, cities, towns, and native tribes across the region. But Nevada’s population and economy are expanding, creating more demands on these resources even as they become more constrained.  The Truckee River, which originates at Lake Tahoe and terminates at Pyramid Lake, is the major water source for cities, towns, and farms across northwestern Nevada.EMILY NAJERA Throughout much of the 2020s the state has suffered through one of the hottest and most widespread droughts on record, extending two decades of abnormally dry conditions across the American West. Some scientists fear it may constitute an emerging megadrought.  About 50% of Nevada currently faces moderate to exceptional drought conditions. In addition, more than half of the state’s hundreds of groundwater basins are already “over-appropriated,” meaning the water rights on paper exceed the levels believed to be underground.  It’s not clear if climate change will increase or decrease the state’s rainfall levels, on balance. But precipitation patterns are expected to become more erratic, whiplashing between short periods of intense rainfall and more-frequent, extended, or severe droughts.  In addition, more precipitation will fall as rain rather than snow, shortening the Sierra snow season by weeks to months over the coming decades.  “In the extreme case, at the end of the century, that’s pretty much all of winter,” says Sean McKenna, executive director of hydrologic sciences at the Desert Research Institute, a research division of the Nevada System of Higher Education. That loss will undermine an essential function of the Sierra snowpack: reliably delivering water to farmers and cities when it’s most needed in the spring and summer, across both Nevada and California.  These shifting conditions will require the region to develop better ways to store, preserve, and recycle the water it does get, McKenna says. Northern Nevada’s cities, towns, and agencies will also need to carefully evaluate and plan for the collective impacts of continuing growth and development on the interconnected water system, particularly when it comes to water-hungry projects like data centers, he adds. “We can’t consider each of these as a one-off, without considering that there may be tens or dozens of these in the next 15 years,” McKenna says.Thirsty data centers Data centers suck up water in two main ways. As giant rooms of server racks process information and consume energy, they generate heat that must be shunted away to prevent malfunctions and damage to the equipment. The processing units optimized for training and running AI models often draw more electricity and, in turn, produce more heat. To keep things cool, more and more data centers have turned to liquid cooling systems that don’t need as much electricity as fan cooling or air-conditioning. These often rely on water to absorb heat and transfer it to outdoor cooling towers, where much of the moisture evaporates. Microsoft’s US data centers, for instance, could have directly evaporated nearly 185,000 gallons of “clean freshwater” in the course of training OpenAI’s GPT-3 large language model, according to a 2023 preprint study led by researchers at the University of California, Riverside. (The research has since been peer-reviewed and is awaiting publication.) What’s less appreciated, however, is that the larger data-center drain on water generally occurs indirectly, at the power plants generating extra electricity for the turbocharged AI sector. These facilities, in turn, require more water to cool down equipment, among other purposes. You have to add up both uses “to reflect the true water cost of data centers,” says Shaolei Ren, an associate professor of electrical and computer engineering at UC Riverside and coauthor of the study. Ren estimates that the 12 data-center projects listed in NV Energy’s report would directly consume between 860 million gallons and 5.7 billion gallons a year, based on the requested electricity capacity. (“Consumed” here means the water is evaporated, not merely withdrawn and returned to the engineered water system.) The indirect water drain associated with electricity generation for those operations could add up to 15.5 billion gallons, based on the average consumption of the regional grid. The exact water figures would depend on shifting climate conditions, the type of cooling systems each data center uses, and the mix of power sources that supply the facilities. Solar power, which provides roughly a quarter of Nevada’s power, requires relatively little water to operate, for instance. But natural-gas plants, which generate about 56%, withdraw 2,803 gallons per megawatt-hour on average, according to the Energy Information Administration.  Geothermal plants, which produce about 10% of the state’s electricity by cycling water through hot rocks, generally consume less water than fossil fuel plants do but often require more water than other renewables, according to some research.  But here too, the water usage varies depending on the type of geothermal plant in question. Google has lined up several deals to partially power its data centers through Fervo Energy, which has helped to commercialize an emerging approach that injects water under high pressure to fracture rock and form wells deep below the surface.  The company stresses that it doesn’t evaporate water for cooling and that it relies on brackish groundwater, not fresh water, to develop and run its plants. In a recent post, Fervo noted that its facilities consume significantly less water per megawatt-hour than coal, nuclear, or natural-gas plants do. Part of NV Energy’s proposed plan to meet growing electricity demands in Nevada includes developing several natural-gas peaking units, adding more than one gigawatt of solar power and installing another gigawatt of battery storage. It's also forging ahead with a more than $4 billion transmission project. But the company didn’t respond to questions concerning how it will supply all of the gigawatts of additional electricity requested by data centers, if the construction of those power plants will increase consumer rates, or how much water those facilities are expected to consume. NV Energy operates a transmission line, substation, and power plant in or around the Tahoe Reno Industrial Center.EMILY NAJERA “NV Energy teams work diligently on our long-term planning to make investments in our infrastructure to serve new customers and the continued growth in the state without putting existing customers at risk,” the company said in a statement. An added challenge is that data centers need to run around the clock. That will often compel utilities to develop new electricity-generating sources that can run nonstop as well, as natural-gas, geothermal, or nuclear plants do, says Emily Grubert, an associate professor of sustainable energy policy at the University of Notre Dame, who has studied the relative water consumption of electricity sources.  “You end up with the water-intensive resources looking more important,” she adds. Even if NV Energy and the companies developing data centers do strive to power them through sources with relatively low water needs, “we only have so much ability to add six gigawatts to Nevada’s grid,” Grubert explains. “What you do will never be system-neutral, because it’s such a big number.” Securing supplies On a mid-February morning, I meet TRI’s Thompson and Don Gilman, Lance Gilman’s son, at the Storey County offices, located within the industrial center.  “I’m just a country boy who sells dirt,” Gilman, also a real estate broker, says by way of introduction.  We climb into his large SUV and drive to a reservoir in the heart of the industrial park, filled nearly to the lip.  Thompson explains that much of the water comes from an on-site treatment facility that filters waste fluids from companies in the park. In addition, tens of millions of gallons of treated effluent will also likely flow into the tank this year from the Truckee Meadows Water Authority Reclamation Facility, near the border of Reno and Sparks. That’s thanks to a 16-mile pipeline that the developers, the water authority, several tenants, and various local cities and agencies partnered to build, through a project that began in 2021. “Our general improvement district is furnishing that water to tech companies here in the park as we speak,” Thompson says. “That helps preserve the precious groundwater, so that is an environmental feather in the cap for these data centers. They are focused on environmental excellence.” The reservoir within the industrial business park provides water to data centers and other tenants.EMILY NAJERA But data centers often need drinking-quality water—not wastewater merely treated to irrigation standards—for evaporative cooling, “to avoid pipe clogs and/or bacterial growth,” the UC Riverside study notes. For instance, Google says its data centers withdrew about 7.7 billion gallons of water in 2023, and nearly 6 billion of those gallons were potable.  Tenants in the industrial park can potentially obtain access to water from the ground and the Truckee River, as well. From early on, the master developers worked hard to secure permits to water sources, since they are nearly as precious as development entitlements to companies hoping to build projects in the desert. Initially, the development company controlled a private business, the TRI Water and Sewer Company, that provided those services to the business park’s tenants, according to public documents. The company set up wells, a water tank, distribution lines, and a sewer disposal system.  But in 2000, the board of county commissioners established a general improvement district, a legal mechanism for providing municipal services in certain parts of the state, to manage electricity and then water within the center. It, in turn, hired TRI Water and Sewer as the operating company. As of its 2020 service plan, the general improvement district held permits for nearly 5,300 acre-feet of groundwater, “which can be pumped from well fields within the service area and used for new growth as it occurs.” The document lists another 2,000 acre-feet per year available from the on-site treatment facility, 1,000 from the Truckee River, and 4,000 more from the effluent pipeline.  Those figures haven’t budged much since, according to Shari Whalen, general manager of the TRI General Improvement District. All told, they add up to more than 4 billion gallons of water per year for all the needs of the industrial park and the tenants there, data centers and otherwise. Whalen says that the amount and quality of water required for any given data center depends on its design, and that those matters are worked out on a case-by-case basis.  When asked if the general improvement district is confident that it has adequate water resources to supply the needs of all the data centers under development, as well as other tenants at the industrial center, she says: “They can’t just show up and build unless they have water resources designated for their projects. We wouldn’t approve a project if it didn’t have those water resources.” Water As the region’s water sources have grown more constrained, lining up supplies has become an increasingly high-stakes and controversial business. More than a century ago, the US federal government filed a lawsuit against an assortment of parties pulling water from the Truckee River. The suit would eventually establish that the Pyramid Lake Paiute Tribe’s legal rights to water for irrigation superseded other claims. But the tribe has been fighting to protect those rights and increase flows from the river ever since, arguing that increasing strains on the watershed from upstream cities and businesses threaten to draw away water reserved for reservation farming, decrease lake levels, and harm native fish. The Pyramid Lake Paiute Tribe considers the water body and its fish, including the endangered cui-ui and threatened Lahontan cutthroat trout, to be essential parts of its culture, identity, and way of life. The tribe was originally named Cui-ui Ticutta, which translates to cui-ui eaters. The lake continues to provide sustenance as well as business for the tribe and its members, a number of whom operate boat charters and fishing guide services. “It’s completely tied into us as a people,” says Steven Wadsworth, chairman of the Pyramid Lake Paiute Tribe. “That is what has sustained us all this time,” he adds. “It’s just who we are. It’s part of our spiritual well-being.” Steven Wadsworth, chairman of the Pyramid Lake Paiute Tribe, fears that data centers will divert water that would otherwise reach the tribe’s namesake lake.EMILY NAJERA In recent decades, the tribe has sued the Nevada State Engineer, Washoe County, the federal government, and others for overallocating water rights and endangering the lake’s fish. It also protested the TRI General Improvement District’s applications to draw thousands of additional acre‑feet of groundwater from a basin near the business park. In 2019, the State Engineer’s office rejected those requests, concluding that the basin was already fully appropriated.  More recently, the tribe took issue with the plan to build the pipeline and divert effluent that would have flown into the Truckee, securing an agreement that required the Truckee Meadows Water Authority and other parties to add back several thousand acre‑feet of water to the river.  Whalen says she’s sensitive to Wadsworth’s concerns. But she says that the pipeline promises to keep a growing amount of treated wastewater out of the river, where it could otherwise contribute to rising salt levels in the lake. “I think that the pipeline from [the Truckee Meadows Water Authority] to our system is good for water quality in the river,” she says. “I understand philosophically the concerns about data centers, but the general improvement district is dedicated to working with everyone on the river for regional water-resource planning—and the tribe is no exception.” Water efficiency  In an email, Thompson added that he has “great respect and admiration,” for the tribe and has visited the reservation several times in an effort to help bring industrial or commercial development there. He stressed that all of the business park’s groundwater was “validated by the State Water Engineer,” and that the rights to surface water and effluent were purchased “for fair market value.”During the earlier interview at the industrial center, he and Gilman had both expressed confidence that tenants in the park have adequate water supplies, and that the businesses won’t draw water away from other areas.  “We’re in our own aquifer, our own water basin here,” Thompson said. “You put a straw in the ground here, you’re not going to pull water from Fernley or from Reno or from Silver Springs.” Gilman also stressed that data-center companies have gotten more water efficient in recent years, echoing a point others made as well. “With the newer technology, it’s not much of a worry,” says Sutich, of the Northern Nevada Development Authority. “The technology has come a long way in the last 10 years, which is really giving these guys the opportunity to be good stewards of water usage.” An aerial view of the cooling tower fans at Google’s data center in the Tahoe Reno Industrial Center.GOOGLE Indeed, Google’s existing Storey County facility is air-cooled, according to the company’s latest environmental report. The data center withdrew 1.9 million gallons in 2023 but only consumed 200,000 gallons. The rest cycles back into the water system. Google said all the data centers under construction on its campus will also “utilize air-cooling technology.” The company didn’t respond to a question about the scale of its planned expansion in the Tahoe Reno Industrial Center, and referred a question about indirect water consumption to NV Energy. The search giant has stressed that it strives to be water efficient across all of its data centers, and decides whether to use air or liquid cooling based on local supply and projected demand, among other variables. Four years ago, the company set a goal of replenishing more water than it consumes by 2030. Locally, it also committed to provide half a million dollars to the National Forest Foundation to improve the Truckee River watershed and reduce wildfire risks.  Microsoft clearly suggested in earlier news reports that the Silver Springs land it purchased around the end of 2022 would be used for a data center. NAI Alliance’s market real estate report identifies that lot, as well as the parcel Microsoft purchased within the Tahoe Reno Industrial Center, as data center sites. But the company now declines to specify what it intends to build in the region.  “While the land purchase is public knowledge, we have not disclosed specific details [of] our plans for the land or potential development timelines,” wrote Donna Whitehead, a Microsoft spokesperson, in an email.  Workers have begun grading land inside a fenced off lot within the Tahoe Reno Industrial Center.EMILY NAJERA Microsoft has also scaled down its global data-center ambitions, backing away from several projects in recent months amid shifting economic conditions, according to various reports. Whatever it ultimately does or doesn’t build, the company stresses that it has made strides to reduce water consumption in its facilities. Late last year, the company announced that it’s using “chip-level cooling solutions” in data centers, which continually circulate water between the servers and chillers through a closed loop that the company claims doesn’t lose any water to evaporation. It says the design requires only a “nominal increase” in energy compared to its data centers that rely on evaporative water cooling. Others seem to be taking a similar approach. EdgeCore also said its 900,000-square-foot data center at the Tahoe Reno Industrial Center will rely on an “air-cooled closed-loop chiller” that doesn’t require water evaporation for cooling.  But some of the companies seem to have taken steps to ensure access to significant amounts of water. Switch, for instance, took a lead role in developing the effluent pipeline. In addition, Tract, which develops campuses on which third-party data centers can build their own facilities, has said it lined up more than 1,100 acre-feet of water rights, the equivalent of nearly 360 million gallons a year.  Apple, Novva, Switch, Tract, and Vantage didn’t respond to inquiries from MIT Technology Review.  Coming conflicts  The suggestion that companies aren’t straining water supplies when they adopt air cooling is, in many cases, akin to saying they’re not responsible for the greenhouse gas produced through their power use simply because it occurs outside of their facilities. In fact, the additional water used at a power plant to meet the increased electricity needs of air cooling may exceed any gains at the data center, Ren, of UC Riverside, says. “That’s actually very likely, because it uses a lot more energy,” he adds. That means that some of the companies developing data centers in and around Storey County may simply hand off their water challenges to other parts of Nevada or neighboring states across the drying American West, depending on where and how the power is generated, Ren says.  Google has said its air-cooled facilities require about 10% more electricity, and its environmental report notes that the Storey County facility is one of its two least-energy-efficient data centers.  Pipes running along Google’s data center campus help the search company cool its servers.GOOGLE Some fear there’s also a growing mismatch between what Nevada’s water permits allow, what’s actually in the ground, and what nature will provide as climate conditions shift. Notably, the groundwater committed to all parties from the Tracy Segment basin—a long-fought-over resource that partially supplies the TRI General Improvement District—already exceeds the “perennial yield.” That refers to the maximum amount that can be drawn out every year without depleting the reservoir over the long term. “If pumping does ultimately exceed the available supply, that means there will be conflict among users,” Roerink, of the Great Basin Water Network, said in an email. “So I have to wonder: Who could be suing whom? Who could be buying out whom? How will the tribe’s rights be defended?”The Truckee Meadows Water Authority, the community-owned utility that manages the water system for Reno and Sparks, said it is planning carefully for the future and remains confident there will be “sufficient resources for decades to come,” at least within its territory east of the industrial center. Storey County, the Truckee-Carson Irrigation District, and the State Engineer’s office didn’t respond to questions or accept interview requests.  Open for business As data center proposals have begun shifting into Northern Nevada’s cities, more local residents and organizations have begun to take notice and express concerns. The regional division of the Sierra Club, for instance, recently sought to overturn the approval of Reno’s first data center, about 20 miles west of the Tahoe Reno Industrial Center.  Olivia Tanager, director of the Sierra Club’s Toiyabe Chapter, says the environmental organization was shocked by the projected electricity demands from data centers highlighted in NV Energy’s filings. Nevada’s wild horses are a common sight along USA Parkway, the highway cutting through the industrial business park. EMILY NAJERA “We have increasing interest in understanding the impact that data centers will have to our climate goals, to our grid as a whole, and certainly to our water resources,” she says. “The demands are extraordinary, and we don’t have that amount of water to toy around with.” During a city hall hearing in January that stretched late into the evening, she and a line of residents raised concerns about the water, energy, climate, and employment impacts of AI data centers. At the end, though, the city council upheld the planning department’s approval of the project, on a 5-2 vote. “Welcome to Reno,” Kathleen Taylor, Reno’s vice mayor, said before casting her vote. “We’re open for business.” Where the river ends In late March, I walk alongside Chairman Wadsworth, of the Pyramid Lake Paiute Tribe, on the shores of Pyramid Lake, watching a row of fly-fishers in waders cast their lines into the cold waters.  The lake is the largest remnant of Lake Lahontan, an Ice Age inland sea that once stretched across western Nevada and would have submerged present-day Reno. But as the climate warmed, the lapping waters retreated, etching erosional terraces into the mountainsides and exposing tufa deposits around the lake, large formations of porous rock made of calcium-carbonate. That includes the pyramid-shaped island on the eastern shore that inspired the lake’s name. A lone angler stands along the shores of Pyramid Lake. In the decades after the US Reclamation Service completed the Derby Dam in 1905, Pyramid Lake declined another 80 feet and nearby Winnemucca Lake dried up entirely. “We know what happens when water use goes unchecked,” says Wadsworth, gesturing eastward toward the range across the lake, where Winnemucca once filled the next basin over. “Because all we have to do is look over there and see a dry, barren lake bed that used to be full.”In an earlier interview, Wadsworth acknowledged that the world needs data centers. But he argued they should be spread out across the country, not densely clustered in the middle of the Nevada desert.Given the fierce competition for resources up to now, he can’t imagine how there could be enough water to meet the demands of data centers, expanding cities, and other growing businesses without straining the limited local supplies that should, by his accounting, flow to Pyramid Lake. He fears these growing pressures will force the tribe to wage new legal battles to protect their rights and preserve the lake, extending what he refers to as “a century of water wars.” “We have seen the devastating effects of what happens when you mess with Mother Nature,” Wadsworth says. “Part of our spirit has left us. And that’s why we fight so hard to hold on to what’s left.”

We did the math on AI’s energy footprint. Here’s the story you haven’t heard.