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    Confronting the AI/energy conundrum

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    The explosive growth of AI-powered computing centers is creating an unprecedented surge in electricity demand that threatens to overwhelm power grids and derail climate goals. At the same time, artificial intelligence technologies could revolutionize energy systems, accelerating the transition to clean power.“We’re at a cusp of potentially gigantic change throughout the economy,” said William H. Green, director of the MIT Energy Initiative (MITEI) and Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering, at MITEI’s Spring Symposium, “AI and energy: Peril and promise,” held on May 13. The event brought together experts from industry, academia, and government to explore solutions to what Green described as both “local problems with electric supply and meeting our clean energy targets” while seeking to “reap the benefits of AI without some of the harms.” The challenge of data center energy demand and potential benefits of AI to the energy transition is a research priority for MITEI.AI’s startling energy demandsFrom the start, the symposium highlighted sobering statistics about AI’s appetite for electricity. After decades of flat electricity demand in the United States, computing centers now consume approximately 4 percent of the nation’s electricity. Although there is great uncertainty, some projections suggest this demand could rise to 12-15 percent by 2030, largely driven by artificial intelligence applications.Vijay Gadepally, senior scientist at MIT’s Lincoln Laboratory, emphasized the scale of AI’s consumption. “The power required for sustaining some of these large models is doubling almost every three months,” he noted. “A single ChatGPT conversation uses as much electricity as charging your phone, and generating an image consumes about a bottle of water for cooling.”Facilities requiring 50 to 100 megawatts of power are emerging rapidly across the United States and globally, driven both by casual and institutional research needs relying on large language programs such as ChatGPT and Gemini. Gadepally cited congressional testimony by Sam Altman, CEO of OpenAI, highlighting how fundamental this relationship has become: “The cost of intelligence, the cost of AI, will converge to the cost of energy.”“The energy demands of AI are a significant challenge, but we also have an opportunity to harness these vast computational capabilities to contribute to climate change solutions,” said Evelyn Wang, MIT vice president for energy and climate and the former director at the Advanced Research Projects Agency-Energy (ARPA-E) at the U.S. Department of Energy.Wang also noted that innovations developed for AI and data centers — such as efficiency, cooling technologies, and clean-power solutions — could have broad applications beyond computing facilities themselves.Strategies for clean energy solutionsThe symposium explored multiple pathways to address the AI-energy challenge. Some panelists presented models suggesting that while artificial intelligence may increase emissions in the short term, its optimization capabilities could enable substantial emissions reductions after 2030 through more efficient power systems and accelerated clean technology development.Research shows regional variations in the cost of powering computing centers with clean electricity, according to Emre Gençer, co-founder and CEO of Sesame Sustainability and former MITEI principal research scientist. Gençer’s analysis revealed that the central United States offers considerably lower costs due to complementary solar and wind resources. However, achieving zero-emission power would require massive battery deployments — five to 10 times more than moderate carbon scenarios — driving costs two to three times higher.“If we want to do zero emissions with reliable power, we need technologies other than renewables and batteries, which will be too expensive,” Gençer said. He pointed to “long-duration storage technologies, small modular reactors, geothermal, or hybrid approaches” as necessary complements.Because of data center energy demand, there is renewed interest in nuclear power, noted Kathryn Biegel, manager of R&D and corporate strategy at Constellation Energy, adding that her company is restarting the reactor at the former Three Mile Island site, now called the “Crane Clean Energy Center,” to meet this demand. “The data center space has become a major, major priority for Constellation,” she said, emphasizing how their needs for both reliability and carbon-free electricity are reshaping the power industry.Can AI accelerate the energy transition?Artificial intelligence could dramatically improve power systems, according to Priya Donti, assistant professor and the Silverman Family Career Development Professor in MIT’s Department of Electrical Engineering and Computer Science and the Laboratory for Information and Decision Systems. She showcased how AI can accelerate power grid optimization by embedding physics-based constraints into neural networks, potentially solving complex power flow problems at “10 times, or even greater, speed compared to your traditional models.”AI is already reducing carbon emissions, according to examples shared by Antonia Gawel, global director of sustainability and partnerships at Google. Google Maps’ fuel-efficient routing feature has “helped to prevent more than 2.9 million metric tons of GHG [greenhouse gas] emissions reductions since launch, which is the equivalent of taking 650,000 fuel-based cars off the road for a year,” she said. Another Google research project uses artificial intelligence to help pilots avoid creating contrails, which represent about 1 percent of global warming impact.AI’s potential to speed materials discovery for power applications was highlighted by Rafael Gómez-Bombarelli, the Paul M. Cook Career Development Associate Professor in the MIT Department of Materials Science and Engineering. “AI-supervised models can be trained to go from structure to property,” he noted, enabling the development of materials crucial for both computing and efficiency.Securing growth with sustainabilityThroughout the symposium, participants grappled with balancing rapid AI deployment against environmental impacts. While AI training receives most attention, Dustin Demetriou, senior technical staff member in sustainability and data center innovation at IBM, quoted a World Economic Forum article that suggested that “80 percent of the environmental footprint is estimated to be due to inferencing.” Demetriou emphasized the need for efficiency across all artificial intelligence applications.Jevons’ paradox, where “efficiency gains tend to increase overall resource consumption rather than decrease it” is another factor to consider, cautioned Emma Strubell, the Raj Reddy Assistant Professor in the Language Technologies Institute in the School of Computer Science at Carnegie Mellon University. Strubell advocated for viewing computing center electricity as a limited resource requiring thoughtful allocation across different applications.Several presenters discussed novel approaches for integrating renewable sources with existing grid infrastructure, including potential hybrid solutions that combine clean installations with existing natural gas plants that have valuable grid connections already in place. These approaches could provide substantial clean capacity across the United States at reasonable costs while minimizing reliability impacts.Navigating the AI-energy paradoxThe symposium highlighted MIT’s central role in developing solutions to the AI-electricity challenge.Green spoke of a new MITEI program on computing centers, power, and computation that will operate alongside the comprehensive spread of MIT Climate Project research. “We’re going to try to tackle a very complicated problem all the way from the power sources through the actual algorithms that deliver value to the customers — in a way that’s going to be acceptable to all the stakeholders and really meet all the needs,” Green said.Participants in the symposium were polled about priorities for MIT’s research by Randall Field, MITEI director of research. The real-time results ranked “data center and grid integration issues” as the top priority, followed by “AI for accelerated discovery of advanced materials for energy.”In addition, attendees revealed that most view AI’s potential regarding power as a “promise,” rather than a “peril,” although a considerable portion remain uncertain about the ultimate impact. When asked about priorities in power supply for computing facilities, half of the respondents selected carbon intensity as their top concern, with reliability and cost following. More

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      Merging AI and underwater photography to reveal hidden ocean worlds

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      In the Northeastern United States, the Gulf of Maine represents one of the most biologically diverse marine ecosystems on the planet — home to whales, sharks, jellyfish, herring, plankton, and hundreds of other species. But even as this ecosystem supports rich biodiversity, it is undergoing rapid environmental change. The Gulf of Maine is warming faster than 99 percent of the world’s oceans, with consequences that are still unfolding.A new research initiative developing at MIT Sea Grant, called LOBSTgER — short for Learning Oceanic Bioecological Systems Through Generative Representations — brings together artificial intelligence and underwater photography to document the ocean life left vulnerable to these changes and share them with the public in new visual ways. Co-led by underwater photographer and visiting artist at MIT Sea Grant Keith Ellenbogen and MIT mechanical engineering PhD student Andreas Mentzelopoulos, the project explores how generative AI can expand scientific storytelling by building on field-based photographic data.Just as the 19th-century camera transformed our ability to document and reveal the natural world — capturing life with unprecedented detail and bringing distant or hidden environments into view — generative AI marks a new frontier in visual storytelling. Like early photography, AI opens a creative and conceptual space, challenging how we define authenticity and how we communicate scientific and artistic perspectives. In the LOBSTgER project, generative models are trained exclusively on a curated library of Ellenbogen’s original underwater photographs — each image crafted with artistic intent, technical precision, accurate species identification, and clear geographic context. By building a high-quality dataset grounded in real-world observations, the project ensures that the resulting imagery maintains both visual integrity and ecological relevance. In addition, LOBSTgER’s models are built using custom code developed by Mentzelopoulos to protect the process and outputs from any potential biases from external data or models. LOBSTgER’s generative AI builds upon real photography, expanding the researchers’ visual vocabulary to deepen the public’s connection to the natural world.

      This ocean sunfish (Mola mola) image was generated by LOBSTgER’s unconditional models.

      AI-generated image: Keith Ellenbogen, Andreas Mentzelopoulos, and LOBSTgER.

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      At its heart, LOBSTgER operates at the intersection of art, science, and technology. The project draws from the visual language of photography, the observational rigor of marine science, and the computational power of generative AI. By uniting these disciplines, the team is not only developing new ways to visualize ocean life — they are also reimagining how environmental stories can be told. This integrative approach makes LOBSTgER both a research tool and a creative experiment — one that reflects MIT’s long-standing tradition of interdisciplinary innovation.Underwater photography in New England’s coastal waters is notoriously difficult. Limited visibility, swirling sediment, bubbles, and the unpredictable movement of marine life all pose constant challenges. For the past several years, Ellenbogen has navigated these challenges and is building a comprehensive record of the region’s biodiversity through the project, Space to Sea: Visualizing New England’s Ocean Wilderness. This large dataset of underwater images provides the foundation for training LOBSTgER’s generative AI models. The images span diverse angles, lighting conditions, and animal behaviors, resulting in a visual archive that is both artistically striking and biologically accurate.

      Image synthesis via reverse diffusion: This short video shows the de-noising trajectory from Gaussian latent noise to photorealistic output using LOBSTgER’s unconditional models. Iterative de-noising requires 1,000 forward passes through the trained neural network.Video: Keith Ellenbogen and Andreas Mentzelopoulos / MIT Sea Grant

      LOBSTgER’s custom diffusion models are trained to replicate not only the biodiversity Ellenbogen documents, but also the artistic style he uses to capture it. By learning from thousands of real underwater images, the models internalize fine-grained details such as natural lighting gradients, species-specific coloration, and even the atmospheric texture created by suspended particles and refracted sunlight. The result is imagery that not only appears visually accurate, but also feels immersive and moving.The models can both generate new, synthetic, but scientifically accurate images unconditionally (i.e., requiring no user input/guidance), and enhance real photographs conditionally (i.e., image-to-image generation). By integrating AI into the photographic workflow, Ellenbogen will be able to use these tools to recover detail in turbid water, adjust lighting to emphasize key subjects, or even simulate scenes that would be nearly impossible to capture in the field. The team also believes this approach may benefit other underwater photographers and image editors facing similar challenges. This hybrid method is designed to accelerate the curation process and enable storytellers to construct a more complete and coherent visual narrative of life beneath the surface.

      Left: Enhanced image of an American lobster using LOBSTgER’s image-to-image models. Right: Original image.

      Left: AI genertated image by Keith Ellenbogen, Andreas Mentzelopoulos, and LOBSTgER. Right: Keith Ellenbogen

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      In one key series, Ellenbogen captured high-resolution images of lion’s mane jellyfish, blue sharks, American lobsters, and ocean sunfish (Mola mola) while free diving in coastal waters. “Getting a high-quality dataset is not easy,” Ellenbogen says. “It requires multiple dives, missed opportunities, and unpredictable conditions. But these challenges are part of what makes underwater documentation both difficult and rewarding.”Mentzelopoulos has developed original code to train a family of latent diffusion models for LOBSTgER grounded on Ellenbogen’s images. Developing such models requires a high level of technical expertise, and training models from scratch is a complex process demanding hundreds of hours of computation and meticulous hyperparameter tuning.The project reflects a parallel process: field documentation through photography and model development through iterative training. Ellenbogen works in the field, capturing rare and fleeting encounters with marine animals; Mentzelopoulos works in the lab, translating those moments into machine-learning contexts that can extend and reinterpret the visual language of the ocean.“The goal isn’t to replace photography,” Mentzelopoulos says. “It’s to build on and complement it — making the invisible visible, and helping people see environmental complexity in a way that resonates both emotionally and intellectually. Our models aim to capture not just biological realism, but the emotional charge that can drive real-world engagement and action.”LOBSTgER points to a hybrid future that merges direct observation with technological interpretation. The team’s long-term goal is to develop a comprehensive model that can visualize a wide range of species found in the Gulf of Maine and, eventually, apply similar methods to marine ecosystems around the world.The researchers suggest that photography and generative AI form a continuum, rather than a conflict. Photography captures what is — the texture, light, and animal behavior during actual encounters — while AI extends that vision beyond what is seen, toward what could be understood, inferred, or imagined based on scientific data and artistic vision. Together, they offer a powerful framework for communicating science through image-making.In a region where ecosystems are changing rapidly, the act of visualizing becomes more than just documentation. It becomes a tool for awareness, engagement, and, ultimately, conservation. LOBSTgER is still in its infancy, and the team looks forward to sharing more discoveries, images, and insights as the project evolves.Answer from the lead image: The left image was generated using using LOBSTgER’s unconditional models and the right image is real.For more information, contact Keith Ellenbogen and Andreas Mentzelopoulos. More

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      in Environment

      AI stirs up the recipe for concrete in MIT study

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      For weeks, the whiteboard in the lab was crowded with scribbles, diagrams, and chemical formulas. A research team across the Olivetti Group and the MIT Concrete Sustainability Hub (CSHub) was working intensely on a key problem: How can we reduce the amount of cement in concrete to save on costs and emissions? The question was certainly not new; materials like fly ash, a byproduct of coal production, and slag, a byproduct of steelmaking, have long been used to replace some of the cement in concrete mixes. However, the demand for these products is outpacing supply as industry looks to reduce its climate impacts by expanding their use, making the search for alternatives urgent. The challenge that the team discovered wasn’t a lack of candidates; the problem was that there were too many to sort through.On May 17, the team, led by postdoc Soroush Mahjoubi, published an open-access paper in Nature’s Communications Materials outlining their solution. “We realized that AI was the key to moving forward,” notes Mahjoubi. “There is so much data out there on potential materials — hundreds of thousands of pages of scientific literature. Sorting through them would have taken many lifetimes of work, by which time more materials would have been discovered!”With large language models, like the chatbots many of us use daily, the team built a machine-learning framework that evaluates and sorts candidate materials based on their physical and chemical properties. “First, there is hydraulic reactivity. The reason that concrete is strong is that cement — the ‘glue’ that holds it together — hardens when exposed to water. So, if we replace this glue, we need to make sure the substitute reacts similarly,” explains Mahjoubi. “Second, there is pozzolanicity. This is when a material reacts with calcium hydroxide, a byproduct created when cement meets water, to make the concrete harder and stronger over time.  We need to balance the hydraulic and pozzolanic materials in the mix so the concrete performs at its best.”Analyzing scientific literature and over 1 million rock samples, the team used the framework to sort candidate materials into 19 types, ranging from biomass to mining byproducts to demolished construction materials. Mahjoubi and his team found that suitable materials were available globally — and, more impressively, many could be incorporated into concrete mixes just by grinding them. This means it’s possible to extract emissions and cost savings without much additional processing. “Some of the most interesting materials that could replace a portion of cement are ceramics,” notes Mahjoubi. “Old tiles, bricks, pottery — all these materials may have high reactivity. That’s something we’ve observed in ancient Roman concrete, where ceramics were added to help waterproof structures. I’ve had many interesting conversations on this with Professor Admir Masic, who leads a lot of the ancient concrete studies here at MIT.”The potential of everyday materials like ceramics and industrial materials like mine tailings is an example of how materials like concrete can help enable a circular economy. By identifying and repurposing materials that would otherwise end up in landfills, researchers and industry can help to give these materials a second life as part of our buildings and infrastructure.Looking ahead, the research team is planning to upgrade the framework to be capable of assessing even more materials, while experimentally validating some of the best candidates. “AI tools have gotten this research far in a short time, and we are excited to see how the latest developments in large language models enable the next steps,” says Professor Elsa Olivetti, senior author on the work and member of the MIT Department of Materials Science and Engineering. She serves as an MIT Climate Project mission director, a CSHub principal investigator, and the leader of the Olivetti Group.“Concrete is the backbone of the built environment,” says Randolph Kirchain, co-author and CSHub director. “By applying data science and AI tools to material design, we hope to support industry efforts to build more sustainably, without compromising on strength, safety, or durability.In addition to Mahjoubi, Olivetti, and Kirchain, co-authors on the work include MIT postdoc Vineeth Venugopal, Ipek Bensu Manav SM ’21, PhD ’24; and CSHub Deputy Director Hessam AzariJafari. More

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      in Energy

      The MIT-Portugal Program enters Phase 4

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      Since its founding 19 years ago as a pioneering collaboration with Portuguese universities, research institutions and corporations, the MIT-Portugal Program (MPP) has achieved a slew of successes — from enabling 47 entrepreneurial spinoffs and funding over 220 joint projects between MIT and Portuguese researchers to training a generation of exceptional researchers on both sides of the Atlantic.In March, with nearly two decades of collaboration under their belts, MIT and the Portuguese Science and Technology Foundation (FCT) signed an agreement that officially launches the program’s next chapter. Running through 2030, MPP’s Phase 4 will support continued exploration of innovative ideas and solutions in fields ranging from artificial intelligence and nanotechnology to climate change — both on the MIT campus and with partners throughout Portugal.  “One of the advantages of having a program that has gone on so long is that we are pretty well familiar with each other at this point. Over the years, we’ve learned each other’s systems, strengths and weaknesses and we’ve been able to create a synergy that would not have existed if we worked together for a short period of time,” says Douglas Hart, MIT mechanical engineering professor and MPP co-director.Hart and John Hansman, the T. Wilson Professor of Aeronautics and Astronautics at MIT and MPP co-director, are eager to take the program’s existing research projects further, while adding new areas of focus identified by MIT and FCT. Known as the Fundação para a Ciência e Tecnologia in Portugal, FCT is the national public agency supporting research in science, technology and innovation under Portugal’s Ministry of Education, Science and Innovation.“Over the past two decades, the partnership with MIT has built a foundation of trust that has fostered collaboration among researchers and the development of projects with significant scientific impact and contributions to the Portuguese economy,” Fernando Alexandre, Portugal’s minister for education, science, and innovation, says. “In this new phase of the partnership, running from 2025 to 2030, we expect even greater ambition and impact — raising Portuguese science and its capacity to transform the economy and improve our society to even higher levels, while helping to address the challenges we face in areas such as climate change and the oceans, digitalization, and space.”“International collaborations like the MIT-Portugal Program are absolutely vital to MIT’s mission of research, education and service. I’m thrilled to see the program move into its next phase,” says MIT President Sally Kornbluth. “MPP offers our faculty and students opportunities to work in unique research environments where they not only make new findings and learn new methods but also contribute to solving urgent local and global problems. MPP’s work in the realm of ocean science and climate is a prime example of how international partnerships like this can help solve important human problems.”Sharing MIT’s commitment to academic independence and excellence, Kornbluth adds, “the institutions and researchers we partner with through MPP enhance MIT’s ability to achieve its mission, enabling us to pursue the exacting standards of intellectual and creative distinction that make MIT a cradle of innovation and world leader in scientific discovery.”The epitome of an effective international collaboration, MPP has stayed true to its mission and continued to deliver results here in the U.S. and in Portugal for nearly two decades — prevailing amid myriad shifts in the political, social, and economic landscape. The multifaceted program encompasses an annual research conference and educational summits such as an Innovation Workshop at MIT each June and a Marine Robotics Summer School in the Azores in July, as well as student and faculty exchanges that facilitate collaborative research. During the third phase of the program alone, 59 MIT students and 53 faculty and researchers visited Portugal, and MIT hosted 131 students and 49 faculty and researchers from Portuguese universities and other institutions.In each roughly five-year phase, MPP researchers focus on a handful of core research areas. For Phase 3, MPP advanced cutting-edge research in four strategic areas: climate science and climate change; Earth systems: oceans to near space; digital transformation in manufacturing; and sustainable cities. Within these broad areas, MIT and FCT researchers worked together on numerous small-scale projects and several large “flagship” ones, including development of Portugal’s CubeSat satellite, a collaboration between MPP and several Portuguese universities and companies that marked the country’s second satellite launch and the first in 30 years.While work in the Phase 3 fields will continue during Phase 4, researchers will also turn their attention to four more areas: chips/nanotechnology, energy (a previous focus in Phase 2), artificial intelligence, and space.“We are opening up the aperture for additional collaboration areas,” Hansman says.In addition to focusing on distinct subject areas, each phase has emphasized the various parts of MPP’s mission to differing degrees. While Phase 3 accentuated collaborative research more than educational exchanges and entrepreneurship, those two aspects will be given more weight under the Phase 4 agreement, Hart said.“We have approval in Phase 4 to bring a number of Portuguese students over, and our principal investigators will benefit from close collaborations with Portuguese researchers,” he says.The longevity of MPP and the recent launch of Phase 4 are evidence of the program’s value. The program has played a role in the educational, technological and economic progress Portugal has achieved over the past two decades, as well.  “The Portugal of today is remarkably stronger than the Portugal of 20 years ago, and many of the places where they are stronger have been impacted by the program,” says Hansman, pointing to sustainable cities and “green” energy, in particular. “We can’t take direct credit, but we’ve been part of Portugal’s journey forward.”Since MPP began, Hart adds, “Portugal has become much more entrepreneurial. Many, many, many more start-up companies are coming out of Portuguese universities than there used to be.”  A recent analysis of MPP and FCT’s other U.S. collaborations highlighted a number of positive outcomes. The report noted that collaborations with MIT and other US universities have enhanced Portuguese research capacities and promoted organizational upgrades in the national R&D ecosystem, while providing Portuguese universities and companies with opportunities to engage in complex projects that would have been difficult to undertake on their own.Regarding MIT in particular, the report found that MPP’s long-term collaboration has spawned the establishment of sustained doctoral programs and pointed to a marked shift within Portugal’s educational ecosystem toward globally aligned standards. MPP, it reported, has facilitated the education of 198 Portuguese PhDs.Portugal’s universities, students and companies are not alone in benefitting from the research, networks, and economic activity MPP has spawned. MPP also delivers unique value to MIT, as well as to the broader US science and research community. Among the program’s consistent themes over the years, for example, is “joint interest in the Atlantic,” Hansman says.This summer, Faial Island in the Azores will host MPP’s fifth annual Marine Robotics Summer School, a two-week course open to 12 Portuguese Master’s and first year PhD students and 12 MIT upper-level undergraduates and graduate students. The course, which includes lectures by MIT and Portuguese faculty and other researchers, workshops, labs and hands-on experiences, “is always my favorite,” said Hart.“I get to work with some of the best researchers in the world there, and some of the top students coming out of Woods Hole Oceanographic Institution, MIT, and Portugal,” he says, adding that some of his previous Marine Robotics Summer School students have come to study at MIT and then gone on to become professors in ocean science.“So, it’s been exciting to see the growth of students coming out of that program, certainly a positive impact,” Hart says.MPP provides one-of-a-kind opportunities for ocean research due to the unique marine facilities available in Portugal, including not only open ocean off the Azores but also Lisbon’s deep-water port and a Portuguese Naval facility just south of Lisbon that is available for collaborative research by international scientists. Like MIT, Portuguese universities are also strongly invested in climate change research — a field of study keenly related to ocean systems.“The international collaboration has allowed us to test and further develop our research prototypes in different aquaculture environments both in the US and in Portugal, while building on the unique expertise of our Portuguese faculty collaborator Dr. Ricardo Calado from the University of Aveiro and our industry collaborators,” says Stefanie Mueller, the TIBCO Career Development Associate Professor in MIT’s departments of Electrical Engineering and Computer Science and Mechanical Engineering and leader of the Human-Computer Interaction Group at the MIT Computer Science and Artificial Intelligence Lab.Mueller points to the work of MIT mechanical engineering PhD student Charlene Xia, a Marine Robotics Summer School participant, whose research is aimed at developing an economical system to monitor the microbiome of seaweed farms and halt the spread of harmful bacteria associated with ocean warming. In addition to participating in the summer school as a student, Xia returned to the Azores for two subsequent years as a teaching assistant.“The MIT-Portugal Program has been a key enabler of our research on monitoring the aquatic microbiome for potential disease outbreaks,” Mueller says.As MPP enters its next phase, Hart and Hansman are optimistic about the program’s continuing success on both sides of the Atlantic and envision broadening its impact going forward.“I think, at this point, the research is going really well, and we’ve got a lot of connections. I think one of our goals is to expand not the science of the program necessarily, but the groups involved,” Hart says, noting that MPP could have a bigger presence in technical fields such as AI and micro-nano manufacturing, as well as in social sciences and humanities.“We’d like to involve many more people and new people here at MIT, as well as in Portugal,” he says, “so that we can reach a larger slice of the population.”  More

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      in Energy

      Taking the “training wheels” off clean energy

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      Renewable power sources have seen unprecedented levels of investment in recent years. But with political uncertainty clouding the future of subsidies for green energy, these technologies must begin to compete with fossil fuels on equal footing, said participants at the 2025 MIT Energy Conference.“What these technologies need less is training wheels, and more of a level playing field,” said Brian Deese, an MIT Institute Innovation Fellow, during a conference-opening keynote panel.The theme of the two-day conference, which is organized each year by MIT students, was “Breakthrough to deployment: Driving climate innovation to market.” Speakers largely expressed optimism about advancements in green technology, balanced by occasional notes of alarm about a rapidly changing regulatory and political environment.Deese defined what he called “the good, the bad, and the ugly” of the current energy landscape. The good: Clean energy investment in the United States hit an all-time high of $272 billion in 2024. The bad: Announcements of future investments have tailed off. And the ugly: Macro conditions are making it more difficult for utilities and private enterprise to build out the clean energy infrastructure needed to meet growing energy demands.“We need to build massive amounts of energy capacity in the United States,” Deese said. “And the three things that are the most allergic to building are high uncertainty, high interest rates, and high tariff rates. So that’s kind of ugly. But the question … is how, and in what ways, that underlying commercial momentum can drive through this period of uncertainty.”A shifting clean energy landscapeDuring a panel on artificial intelligence and growth in electricity demand, speakers said that the technology may serve as a catalyst for green energy breakthroughs, in addition to putting strain on existing infrastructure. “Google is committed to building digital infrastructure responsibly, and part of that means catalyzing the development of clean energy infrastructure that is not only meeting the AI need, but also benefiting the grid as a whole,” said Lucia Tian, head of clean energy and decarbonization technologies at Google.Across the two days, speakers emphasized that the cost-per-unit and scalability of clean energy technologies will ultimately determine their fate. But they also acknowledged the impact of public policy, as well as the need for government investment to tackle large-scale issues like grid modernization.Vanessa Chan, a former U.S. Department of Energy (DoE) official and current vice dean of innovation and entrepreneurship at the University of Pennsylvania School of Engineering and Applied Sciences, warned of the “knock-on” effects of the move to slash National Institutes of Health (NIH) funding for indirect research costs, for example. “In reality, what you’re doing is undercutting every single academic institution that does research across the nation,” she said.During a panel titled “No clean energy transition without transmission,” Maria Robinson, former director of the DoE’s Grid Deployment Office, said that ratepayers alone will likely not be able to fund the grid upgrades needed to meet growing power demand. “The amount of investment we’re going to need over the next couple of years is going to be significant,” she said. “That’s where the federal government is going to have to play a role.”David Cohen-Tanugi, a clean energy venture builder at MIT, noted that extreme weather events have changed the climate change conversation in recent years. “There was a narrative 10 years ago that said … if we start talking about resilience and adaptation to climate change, we’re kind of throwing in the towel or giving up,” he said. “I’ve noticed a very big shift in the investor narrative, the startup narrative, and more generally, the public consciousness. There’s a realization that the effects of climate change are already upon us.”“Everything on the table”The conference featured panels and keynote addresses on a range of emerging clean energy technologies, including hydrogen power, geothermal energy, and nuclear fusion, as well as a session on carbon capture.Alex Creely, a chief engineer at Commonwealth Fusion Systems, explained that fusion (the combining of small atoms into larger atoms, which is the same process that fuels stars) is safer and potentially more economical than traditional nuclear power. Fusion facilities, he said, can be powered down instantaneously, and companies like his are developing new, less-expensive magnet technology to contain the extreme heat produced by fusion reactors.By the early 2030s, Creely said, his company hopes to be operating 400-megawatt power plants that use only 50 kilograms of fuel per year. “If you can get fusion working, it turns energy into a manufacturing product, not a natural resource,” he said.Quinn Woodard Jr., senior director of power generation and surface facilities at geothermal energy supplier Fervo Energy, said his company is making the geothermal energy more economical through standardization, innovation, and economies of scale. Traditionally, he said, drilling is the largest cost in producing geothermal power. Fervo has “completely flipped the cost structure” with advances in drilling, Woodard said, and now the company is focused on bringing down its power plant costs.“We have to continuously be focused on cost, and achieving that is paramount for the success of the geothermal industry,” he said.One common theme across the conference: a number of approaches are making rapid advancements, but experts aren’t sure when — or, in some cases, if — each specific technology will reach a tipping point where it is capable of transforming energy markets.“I don’t want to get caught in a place where we often descend in this climate solution situation, where it’s either-or,” said Peter Ellis, global director of nature climate solutions at The Nature Conservancy. “We’re talking about the greatest challenge civilization has ever faced. We need everything on the table.”The road aheadSeveral speakers stressed the need for academia, industry, and government to collaborate in pursuit of climate and energy goals. Amy Luers, senior global director of sustainability for Microsoft, compared the challenge to the Apollo spaceflight program, and she said that academic institutions need to focus more on how to scale and spur investments in green energy.“The challenge is that academic institutions are not currently set up to be able to learn the how, in driving both bottom-up and top-down shifts over time,” Luers said. “If the world is going to succeed in our road to net zero, the mindset of academia needs to shift. And fortunately, it’s starting to.”During a panel called “From lab to grid: Scaling first-of-a-kind energy technologies,” Hannan Happi, CEO of renewable energy company Exowatt, stressed that electricity is ultimately a commodity. “Electrons are all the same,” he said. “The only thing [customers] care about with regards to electrons is that they are available when they need them, and that they’re very cheap.”Melissa Zhang, principal at Azimuth Capital Management, noted that energy infrastructure development cycles typically take at least five to 10 years — longer than a U.S. political cycle. However, she warned that green energy technologies are unlikely to receive significant support at the federal level in the near future. “If you’re in something that’s a little too dependent on subsidies … there is reason to be concerned over this administration,” she said.World Energy CEO Gene Gebolys, the moderator of the lab-to-grid panel, listed off a number of companies founded at MIT. “They all have one thing in common,” he said. “They all went from somebody’s idea, to a lab, to proof-of-concept, to scale. It’s not like any of this stuff ever ends. It’s an ongoing process.” More

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      in Environment

      MIT Maritime Consortium sets sail

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      Around 11 billion tons of goods, or about 1.5 tons per person worldwide, are transported by sea each year, representing about 90 percent of global trade by volume. Internationally, the merchant shipping fleet numbers around 110,000 vessels. These ships, and the ports that service them, are significant contributors to the local and global economy — and they’re significant contributors to greenhouse gas emissions.A new consortium, formalized in a signing ceremony at MIT last week, aims to address climate-harming emissions in the maritime shipping industry, while supporting efforts for environmentally friendly operation in compliance with the decarbonization goals set by the International Maritime Organization.“This is a timely collaboration with key stakeholders from the maritime industry with a very bold and interdisciplinary research agenda that will establish new technologies and evidence-based standards,” says Themis Sapsis, the William Koch Professor of Marine Technology at MIT and the director of MIT’s Center for Ocean Engineering. “It aims to bring the best from MIT in key areas for commercial shipping, such as nuclear technology for commercial settings, autonomous operation and AI methods, improved hydrodynamics and ship design, cybersecurity, and manufacturing.” Co-led by Sapsis and Fotini Christia, the Ford International Professor of the Social Sciences; director of the Institute for Data, Systems, and Society (IDSS); and director of the MIT Sociotechnical Systems Research Center, the newly-launched MIT Maritime Consortium (MC) brings together MIT collaborators from across campus, including the Center for Ocean Engineering, which is housed in the Department of Mechanical Engineering; IDSS, which is housed in the MIT Schwarzman College of Computing; the departments of Nuclear Science and Engineering and Civil and Environmental Engineering; MIT Sea Grant; and others, with a national and an international community of industry experts.The Maritime Consortium’s founding members are the American Bureau of Shipping (ABS), Capital Clean Energy Carriers Corp., and HD Korea Shipbuilding and Offshore Engineering. Innovation members are Foresight-Group, Navios Maritime Partners L.P., Singapore Maritime Institute, and Dorian LPG.“The challenges the maritime industry faces are challenges that no individual company or organization can address alone,” says Christia. “The solution involves almost every discipline from the School of Engineering, as well as AI and data-driven algorithms, and policy and regulation — it’s a true MIT problem.”Researchers will explore new designs for nuclear systems consistent with the techno-economic needs and constraints of commercial shipping, economic and environmental feasibility of alternative fuels, new data-driven algorithms and rigorous evaluation criteria for autonomous platforms in the maritime space, cyber-physical situational awareness and anomaly detection, as well as 3D printing technologies for onboard manufacturing. Collaborators will also advise on research priorities toward evidence-based standards related to MIT presidential priorities around climate, sustainability, and AI.MIT has been a leading center of ship research and design for over a century, and is widely recognized for contributions to hydrodynamics, ship structural mechanics and dynamics, propeller design, and overall ship design, and its unique educational program for U.S. Navy Officers, the Naval Construction and Engineering Program. Research today is at the forefront of ocean science and engineering, with significant efforts in fluid mechanics and hydrodynamics, acoustics, offshore mechanics, marine robotics and sensors, and ocean sensing and forecasting. The consortium’s academic home at MIT also opens the door to cross-departmental collaboration across the Institute.The MC will launch multiple research projects designed to tackle challenges from a variety of angles, all united by cutting-edge data analysis and computation techniques. Collaborators will research new designs and methods that improve efficiency and reduce greenhouse gas emissions, explore feasibility of alternative fuels, and advance data-driven decision-making, manufacturing and materials, hydrodynamic performance, and cybersecurity.“This consortium brings a powerful collection of significant companies that, together, has the potential to be a global shipping shaper in itself,” says Christopher J. Wiernicki SM ’85, chair and chief executive officer of ABS. “The strength and uniqueness of this consortium is the members, which are all world-class organizations and real difference makers. The ability to harness the members’ experience and know-how, along with MIT’s technology reach, creates real jet fuel to drive progress,” Wiernicki says. “As well as researching key barriers, bottlenecks, and knowledge gaps in the emissions challenge, the consortium looks to enable development of the novel technology and policy innovation that will be key. Long term, the consortium hopes to provide the gravity we will need to bend the curve.” More

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      in Energy

      Puzzling out climate change

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      Shreyaa Raghavan’s journey into solving some of the world’s toughest challenges started with a simple love for puzzles. By high school, her knack for problem-solving naturally drew her to computer science. Through her participation in an entrepreneurship and leadership program, she built apps and twice made it to the semifinals of the program’s global competition.Her early successes made a computer science career seem like an obvious choice, but Raghavan says a significant competing interest left her torn.“Computer science sparks that puzzle-, problem-solving part of my brain,” says Raghavan ’24, an Accenture Fellow and a PhD candidate in MIT’s Institute for Data, Systems, and Society. “But while I always felt like building mobile apps was a fun little hobby, it didn’t feel like I was directly solving societal challenges.”Her perspective shifted when, as an MIT undergraduate, Raghavan participated in an Undergraduate Research Opportunity in the Photovoltaic Research Laboratory, now known as the Accelerated Materials Laboratory for Sustainability. There, she discovered how computational techniques like machine learning could optimize materials for solar panels — a direct application of her skills toward mitigating climate change.“This lab had a very diverse group of people, some from a computer science background, some from a chemistry background, some who were hardcore engineers. All of them were communicating effectively and working toward one unified goal — building better renewable energy systems,” Raghavan says. “It opened my eyes to the fact that I could use very technical tools that I enjoy building and find fulfillment in that by helping solve major climate challenges.”With her sights set on applying machine learning and optimization to energy and climate, Raghavan joined Cathy Wu’s lab when she started her PhD in 2023. The lab focuses on building more sustainable transportation systems, a field that resonated with Raghavan due to its universal impact and its outsized role in climate change — transportation accounts for roughly 30 percent of greenhouse gas emissions.“If we were to throw all of the intelligent systems we are exploring into the transportation networks, by how much could we reduce emissions?” she asks, summarizing a core question of her research.Wu, an associate professor in the Department of Civil and Environmental Engineering, stresses the value of Raghavan’s work.“Transportation is a critical element of both the economy and climate change, so potential changes to transportation must be carefully studied,” Wu says. “Shreyaa’s research into smart congestion management is important because it takes a data-driven approach to add rigor to the broader research supporting sustainability.”Raghavan’s contributions have been recognized with the Accenture Fellowship, a cornerstone of the MIT-Accenture Convergence Initiative for Industry and Technology. As an Accenture Fellow, she is exploring the potential impact of technologies for avoiding stop-and-go traffic and its emissions, using systems such as networked autonomous vehicles and digital speed limits that vary according to traffic conditions — solutions that could advance decarbonization in the transportation section at relatively low cost and in the near term.Raghavan says she appreciates the Accenture Fellowship not only for the support it provides, but also because it demonstrates industry involvement in sustainable transportation solutions.“It’s important for the field of transportation, and also energy and climate as a whole, to synergize with all of the different stakeholders,” she says. “I think it’s important for industry to be involved in this issue of incorporating smarter transportation systems to decarbonize transportation.”Raghavan has also received a fellowship supporting her research from the U.S. Department of Transportation.“I think it’s really exciting that there’s interest from the policy side with the Department of Transportation and from the industry side with Accenture,” she says.Raghavan believes that addressing climate change requires collaboration across disciplines. “I think with climate change, no one industry or field is going to solve it on its own. It’s really got to be each field stepping up and trying to make a difference,” she says. “I don’t think there’s any silver-bullet solution to this problem. It’s going to take many different solutions from different people, different angles, different disciplines.”With that in mind, Raghavan has been very active in the MIT Energy and Climate Club since joining about three years ago, which, she says, “was a really cool way to meet lots of people who were working toward the same goal, the same climate goals, the same passions, but from completely different angles.”This year, Raghavan is on the community and education team, which works to build the community at MIT that is working on climate and energy issues. As part of that work, Raghavan is launching a mentorship program for undergraduates, pairing them with graduate students who help the undergrads develop ideas about how they can work on climate using their unique expertise.“I didn’t foresee myself using my computer science skills in energy and climate,” Raghavan says, “so I really want to give other students a clear pathway, or a clear sense of how they can get involved.”Raghavan has embraced her area of study even in terms of where she likes to think.“I love working on trains, on buses, on airplanes,” she says. “It’s really fun to be in transit and working on transportation problems.”Anticipating a trip to New York to visit a cousin, she holds no dread for the long train trip.“I know I’m going to do some of my best work during those hours,” she says. “Four hours there. Four hours back.” More

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      in Environment

      Streamlining data collection for improved salmon population management

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      Sara Beery came to MIT as an assistant professor in MIT’s Department of Electrical Engineering and Computer Science (EECS) eager to focus on ecological challenges. She has fashioned her research career around the opportunity to apply her expertise in computer vision, machine learning, and data science to tackle real-world issues in conservation and sustainability. Beery was drawn to the Institute’s commitment to “computing for the planet,” and set out to bring her methods to global-scale environmental and biodiversity monitoring.In the Pacific Northwest, salmon have a disproportionate impact on the health of their ecosystems, and their complex reproductive needs have attracted Beery’s attention. Each year, millions of salmon embark on a migration to spawn. Their journey begins in freshwater stream beds where the eggs hatch. Young salmon fry (newly hatched salmon) make their way to the ocean, where they spend several years maturing to adulthood. As adults, the salmon return to the streams where they were born in order to spawn, ensuring the continuation of their species by depositing their eggs in the gravel of the stream beds. Both male and female salmon die shortly after supplying the river habitat with the next generation of salmon. Throughout their migration, salmon support a wide range of organisms in the ecosystems they pass through. For example, salmon bring nutrients like carbon and nitrogen from the ocean upriver, enhancing their availability to those ecosystems. In addition, salmon are key to many predator-prey relationships: They serve as a food source for various predators, such as bears, wolves, and birds, while helping to control other populations, like insects, through predation. After they die from spawning, the decomposing salmon carcasses also replenish valuable nutrients to the surrounding ecosystem. The migration of salmon not only sustains their own species but plays a critical role in the overall health of the rivers and oceans they inhabit. At the same time, salmon populations play an important role both economically and culturally in the region. Commercial and recreational salmon fisheries contribute significantly to the local economy. And for many Indigenous peoples in the Pacific northwest, salmon hold notable cultural value, as they have been central to their diets, traditions, and ceremonies. Monitoring salmon migrationIncreased human activity, including overfishing and hydropower development, together with habitat loss and climate change, have had a significant impact on salmon populations in the region. As a result, effective monitoring and management of salmon fisheries is important to ensure balance among competing ecological, cultural, and human interests. Accurately counting salmon during their seasonal migration to their natal river to spawn is essential in order to track threatened populations, assess the success of recovery strategies, guide fishing season regulations, and support the management of both commercial and recreational fisheries. Precise population data help decision-makers employ the best strategies to safeguard the health of the ecosystem while accommodating human needs. Monitoring salmon migration is a labor-intensive and inefficient undertaking.Beery is currently leading a research project that aims to streamline salmon monitoring using cutting-edge computer vision methods. This project fits within Beery’s broader research interest, which focuses on the interdisciplinary space between artificial intelligence, the natural world, and sustainability. Its relevance to fisheries management made it a good fit for funding from MIT’s Abdul Latif Jameel Water and Food Systems Lab (J-WAFS). Beery’s 2023 J-WAFS seed grant was the first research funding she was awarded since joining the MIT faculty.  Historically, monitoring efforts relied on humans to manually count salmon from riverbanks using eyesight. In the past few decades, underwater sonar systems have been implemented to aid in counting the salmon. These sonar systems are essentially underwater video cameras, but they differ in that they use acoustics instead of light sensors to capture the presence of a fish. Use of this method requires people to set up a tent alongside the river to count salmon based on the output of a sonar camera that is hooked up to a laptop. While this system is an improvement to the original method of monitoring salmon by eyesight, it still relies significantly on human effort and is an arduous and time-consuming process. Automating salmon monitoring is necessary for better management of salmon fisheries. “We need these technological tools,” says Beery. “We can’t keep up with the demand of monitoring and understanding and studying these really complex ecosystems that we work in without some form of automation.”In order to automate counting of migrating salmon populations in the Pacific Northwest, the project team, including Justin Kay, a PhD student in EECS, has been collecting data in the form of videos from sonar cameras at different rivers. The team annotates a subset of the data to train the computer vision system to autonomously detect and count the fish as they migrate. Kay describes the process of how the model counts each migrating fish: “The computer vision algorithm is designed to locate a fish in the frame, draw a box around it, and then track it over time. If a fish is detected on one side of the screen and leaves on the other side of the screen, then we count it as moving upstream.” On rivers where the team has created training data for the system, it has produced strong results, with only 3 to 5 percent counting error. This is well below the target that the team and partnering stakeholders set of no more than a 10 percent counting error. Testing and deployment: Balancing human effort and use of automationThe researchers’ technology is being deployed to monitor the migration of salmon on the newly restored Klamath River. Four dams on the river were recently demolished, making it the largest dam removal project in U.S. history. The dams came down after a more than 20-year-long campaign to remove them, which was led by Klamath tribes, in collaboration with scientists, environmental organizations, and commercial fishermen. After the removal of the dams, 240 miles of the river now flow freely and nearly 800 square miles of habitat are accessible to salmon. Beery notes the almost immediate regeneration of salmon populations in the Klamath River: “I think it was within eight days of the dam coming down, they started seeing salmon actually migrate upriver beyond the dam.” In a collaboration with California Trout, the team is currently processing new data to adapt and create a customized model that can then be deployed to help count the newly migrating salmon.One challenge with the system revolves around training the model to accurately count the fish in unfamiliar environments with variations such as riverbed features, water clarity, and lighting conditions. These factors can significantly alter how the fish appear on the output of a sonar camera and confuse the computer model. When deployed in new rivers where no data have been collected before, like the Klamath, the performance of the system degrades and the margin of error increases substantially to 15-20 percent. The researchers constructed an automatic adaptation algorithm within the system to overcome this challenge and create a scalable system that can be deployed to any site without human intervention. This self-initializing technology works to automatically calibrate to the new conditions and environment to accurately count the migrating fish. In testing, the automatic adaptation algorithm was able to reduce the counting error down to the 10 to 15 percent range. The improvement in counting error with the self-initializing function means that the technology is closer to being deployable to new locations without much additional human effort. Enabling real-time management with the “Fishbox”Another challenge faced by the research team was the development of an efficient data infrastructure. In order to run the computer vision system, the video produced by sonar cameras must be delivered via the cloud or by manually mailing hard drives from a river site to the lab. These methods have notable drawbacks: a cloud-based approach is limited due to lack of internet connectivity in remote river site locations, and shipping the data introduces problems of delay. Instead of relying on these methods, the team has implemented a power-efficient computer, coined the “Fishbox,” that can be used in the field to perform the processing. The Fishbox consists of a small, lightweight computer with optimized software that fishery managers can plug into their existing laptops and sonar cameras. The system is then capable of running salmon counting models directly at the sonar sites without the need for internet connectivity. This allows managers to make hour-by-hour decisions, supporting more responsive, real-time management of salmon populations.Community developmentThe team is also working to bring a community together around monitoring for salmon fisheries management in the Pacific Northwest. “It’s just pretty exciting to have stakeholders who are enthusiastic about getting access to [our technology] as we get it to work and having a tighter integration and collaboration with them,” says Beery. “I think particularly when you’re working on food and water systems, you need direct collaboration to help facilitate impact, because you’re ensuring that what you develop is actually serving the needs of the people and organizations that you are helping to support.”This past June, Beery’s lab organized a workshop in Seattle that convened nongovernmental organizations, tribes, and state and federal departments of fish and wildlife to discuss the use of automated sonar systems to monitor and manage salmon populations. Kay notes that the workshop was an “awesome opportunity to have everybody sharing different ways that they’re using sonar and thinking about how the automated methods that we’re building could fit into that workflow.” The discussion continues now via a shared Slack channel created by the team, with over 50 participants. Convening this group is a significant achievement, as many of these organizations would not otherwise have had an opportunity to come together and collaborate. Looking forwardAs the team continues to tune the computer vision system, refine their technology, and engage with diverse stakeholders — from Indigenous communities to fishery managers — the project is poised to make significant improvements to the efficiency and accuracy of salmon monitoring and management in the region. And as Beery advances the work of her MIT group, the J-WAFS seed grant is helping to keep challenges such as fisheries management in her sights.  “The fact that the J-WAFS seed grant existed here at MIT enabled us to continue to work on this project when we moved here,” comments Beery, adding “it also expanded the scope of the project and allowed us to maintain active collaboration on what I think is a really important and impactful project.” As J-WAFS marks its 10th anniversary this year, the program aims to continue supporting and encouraging MIT faculty to pursue innovative projects that aim to advance knowledge and create practical solutions with real-world impacts on global water and food system challenges.  More

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      in Energy

      The multifaceted challenge of powering AI

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      Artificial intelligence has become vital in business and financial dealings, medical care, technology development, research, and much more. Without realizing it, consumers rely on AI when they stream a video, do online banking, or perform an online search. Behind these capabilities are more than 10,000 data centers globally, each one a huge warehouse containing thousands of computer servers and other infrastructure for storing, managing, and processing data. There are now over 5,000 data centers in the United States, and new ones are being built every day — in the U.S. and worldwide. Often dozens are clustered together right near where people live, attracted by policies that provide tax breaks and other incentives, and by what looks like abundant electricity.And data centers do consume huge amounts of electricity. U.S. data centers consumed more than 4 percent of the country’s total electricity in 2023, and by 2030 that fraction could rise to 9 percent, according to the Electric Power Research Institute. A single large data center can consume as much electricity as 50,000 homes.The sudden need for so many data centers presents a massive challenge to the technology and energy industries, government policymakers, and everyday consumers. Research scientists and faculty members at the MIT Energy Initiative (MITEI) are exploring multiple facets of this problem — from sourcing power to grid improvement to analytical tools that increase efficiency, and more. Data centers have quickly become the energy issue of our day.Unexpected demand brings unexpected solutionsSeveral companies that use data centers to provide cloud computing and data management services are announcing some surprising steps to deliver all that electricity. Proposals include building their own small nuclear plants near their data centers and even restarting one of the undamaged nuclear reactors at Three Mile Island, which has been shuttered since 2019. (A different reactor at that plant partially melted down in 1979, causing the nation’s worst nuclear power accident.) Already the need to power AI is causing delays in the planned shutdown of some coal-fired power plants and raising prices for residential consumers. Meeting the needs of data centers is not only stressing power grids, but also setting back the transition to clean energy needed to stop climate change.There are many aspects to the data center problem from a power perspective. Here are some that MIT researchers are focusing on, and why they’re important.An unprecedented surge in the demand for electricity“In the past, computing was not a significant user of electricity,” says William H. Green, director of MITEI and the Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering. “Electricity was used for running industrial processes and powering household devices such as air conditioners and lights, and more recently for powering heat pumps and charging electric cars. But now all of a sudden, electricity used for computing in general, and by data centers in particular, is becoming a gigantic new demand that no one anticipated.”Why the lack of foresight? Usually, demand for electric power increases by roughly half-a-percent per year, and utilities bring in new power generators and make other investments as needed to meet the expected new demand. But the data centers now coming online are creating unprecedented leaps in demand that operators didn’t see coming. In addition, the new demand is constant. It’s critical that a data center provides its services all day, every day. There can be no interruptions in processing large datasets, accessing stored data, and running the cooling equipment needed to keep all the packed-together computers churning away without overheating.Moreover, even if enough electricity is generated, getting it to where it’s needed may be a problem, explains Deepjyoti Deka, a MITEI research scientist. “A grid is a network-wide operation, and the grid operator may have sufficient generation at another location or even elsewhere in the country, but the wires may not have sufficient capacity to carry the electricity to where it’s wanted.” So transmission capacity must be expanded — and, says Deka, that’s a slow process.Then there’s the “interconnection queue.” Sometimes, adding either a new user (a “load”) or a new generator to an existing grid can cause instabilities or other problems for everyone else already on the grid. In that situation, bringing a new data center online may be delayed. Enough delays can result in new loads or generators having to stand in line and wait for their turn. Right now, much of the interconnection queue is already filled up with new solar and wind projects. The delay is now about five years. Meeting the demand from newly installed data centers while ensuring that the quality of service elsewhere is not hampered is a problem that needs to be addressed.Finding clean electricity sourcesTo further complicate the challenge, many companies — including so-called “hyperscalers” such as Google, Microsoft, and Amazon — have made public commitments to having net-zero carbon emissions within the next 10 years. Many have been making strides toward achieving their clean-energy goals by buying “power purchase agreements.” They sign a contract to buy electricity from, say, a solar or wind facility, sometimes providing funding for the facility to be built. But that approach to accessing clean energy has its limits when faced with the extreme electricity demand of a data center.Meanwhile, soaring power consumption is delaying coal plant closures in many states. There are simply not enough sources of renewable energy to serve both the hyperscalers and the existing users, including individual consumers. As a result, conventional plants fired by fossil fuels such as coal are needed more than ever.As the hyperscalers look for sources of clean energy for their data centers, one option could be to build their own wind and solar installations. But such facilities would generate electricity only intermittently. Given the need for uninterrupted power, the data center would have to maintain energy storage units, which are expensive. They could instead rely on natural gas or diesel generators for backup power — but those devices would need to be coupled with equipment to capture the carbon emissions, plus a nearby site for permanently disposing of the captured carbon.Because of such complications, several of the hyperscalers are turning to nuclear power. As Green notes, “Nuclear energy is well matched to the demand of data centers, because nuclear plants can generate lots of power reliably, without interruption.”In a much-publicized move in September, Microsoft signed a deal to buy power for 20 years after Constellation Energy reopens one of the undamaged reactors at its now-shuttered nuclear plant at Three Mile Island, the site of the much-publicized nuclear accident in 1979. If approved by regulators, Constellation will bring that reactor online by 2028, with Microsoft buying all of the power it produces. Amazon also reached a deal to purchase power produced by another nuclear plant threatened with closure due to financial troubles. And in early December, Meta released a request for proposals to identify nuclear energy developers to help the company meet their AI needs and their sustainability goals.Other nuclear news focuses on small modular nuclear reactors (SMRs), factory-built, modular power plants that could be installed near data centers, potentially without the cost overruns and delays often experienced in building large plants. Google recently ordered a fleet of SMRs to generate the power needed by its data centers. The first one will be completed by 2030 and the remainder by 2035.Some hyperscalers are betting on new technologies. For example, Google is pursuing next-generation geothermal projects, and Microsoft has signed a contract to purchase electricity from a startup’s fusion power plant beginning in 2028 — even though the fusion technology hasn’t yet been demonstrated.Reducing electricity demandOther approaches to providing sufficient clean electricity focus on making the data center and the operations it houses more energy efficient so as to perform the same computing tasks using less power. Using faster computer chips and optimizing algorithms that use less energy are already helping to reduce the load, and also the heat generated.Another idea being tried involves shifting computing tasks to times and places where carbon-free energy is available on the grid. Deka explains: “If a task doesn’t have to be completed immediately, but rather by a certain deadline, can it be delayed or moved to a data center elsewhere in the U.S. or overseas where electricity is more abundant, cheaper, and/or cleaner? This approach is known as ‘carbon-aware computing.’” We’re not yet sure whether every task can be moved or delayed easily, says Deka. “If you think of a generative AI-based task, can it easily be separated into small tasks that can be taken to different parts of the country, solved using clean energy, and then be brought back together? What is the cost of doing this kind of division of tasks?”That approach is, of course, limited by the problem of the interconnection queue. It’s difficult to access clean energy in another region or state. But efforts are under way to ease the regulatory framework to make sure that critical interconnections can be developed more quickly and easily.What about the neighbors?A major concern running through all the options for powering data centers is the impact on residential energy consumers. When a data center comes into a neighborhood, there are not only aesthetic concerns but also more practical worries. Will the local electricity service become less reliable? Where will the new transmission lines be located? And who will pay for the new generators, upgrades to existing equipment, and so on? When new manufacturing facilities or industrial plants go into a neighborhood, the downsides are generally offset by the availability of new jobs. Not so with a data center, which may require just a couple dozen employees.There are standard rules about how maintenance and upgrade costs are shared and allocated. But the situation is totally changed by the presence of a new data center. As a result, utilities now need to rethink their traditional rate structures so as not to place an undue burden on residents to pay for the infrastructure changes needed to host data centers.MIT’s contributionsAt MIT, researchers are thinking about and exploring a range of options for tackling the problem of providing clean power to data centers. For example, they are investigating architectural designs that will use natural ventilation to facilitate cooling, equipment layouts that will permit better airflow and power distribution, and highly energy-efficient air conditioning systems based on novel materials. They are creating new analytical tools for evaluating the impact of data center deployments on the U.S. power system and for finding the most efficient ways to provide the facilities with clean energy. Other work looks at how to match the output of small nuclear reactors to the needs of a data center, and how to speed up the construction of such reactors.MIT teams also focus on determining the best sources of backup power and long-duration storage, and on developing decision support systems for locating proposed new data centers, taking into account the availability of electric power and water and also regulatory considerations, and even the potential for using what can be significant waste heat, for example, for heating nearby buildings. Technology development projects include designing faster, more efficient computer chips and more energy-efficient computing algorithms.In addition to providing leadership and funding for many research projects, MITEI is acting as a convenor, bringing together companies and stakeholders to address this issue. At MITEI’s 2024 Annual Research Conference, a panel of representatives from two hyperscalers and two companies that design and construct data centers together discussed their challenges, possible solutions, and where MIT research could be most beneficial.As data centers continue to be built, and computing continues to create an unprecedented increase in demand for electricity, Green says, scientists and engineers are in a race to provide the ideas, innovations, and technologies that can meet this need, and at the same time continue to advance the transition to a decarbonized energy system. More