Heliox, A Siemens Business, a leader in EV charging solutions, is proud to highlight its VersiCharge Blue 80A, engineered for the most demanding fleet and commercial vehicle charging environments. Designed to deliver up to 80A AC (19.2 kW) power output, the VersiCharge Blue 80A ensures that fleet operators can keep vehicles moving efficiently and reduce operational downtime. With Level 2 charging capability via a J1772 connector and a 24-foot cable, this solution is compatible with most standard EVs, E-Trucks and School Buses, and streamlines installation and daily operation for maximum flexibility and reach.
This charger exemplifies robust quality, featuring Buy America compliance to meet government procurement requirements and ENERGY STAR certification to support lower operational costs and high energy efficiency. Safety remains paramount, as the VersiCharge Blue 80A holds multiple UL listings and carries a NEMA 4 and IK10 rating to ensure exceptional resilience against extreme temperatures, humidity, and physical impact. Backed by a 3-year warranty, customers gain peace of mind knowing their investment is safeguarded for the long haul.
Connectivity is central to the VersiCharge Blue 80A’s design, with cellular and Wi-Fi networking providing easy remote monitoring and flexible network-sharing in commercial deployments. Site safety and aesthetics are prioritized thanks to retractable cable management, reducing trip hazards and maintaining a clean, professional appearance. State-of-the-art smart charging features, including ISO15118-2 hardware readiness and OCPP 1.6J support, enable advanced load management, authentication, security, and future compatibility, while Sifinity Setup mobile app configuration simplifies multi-charger installations.
Precise energy tracking is guaranteed by embedded metering, helping operators optimize usage and manage costs. Built for resilient operation, the unit withstands wide temperature swings from -40°C to 50°C (>50°C with derating) and functions reliably in up to 98% humidity, making it ideal for harsh climates and challenging locations. Wall or post mounting options offer flexible installation for any site layout, and over-the-air (OTA) software upgrades future-proof investments by delivering remote updates and new capabilities.
Engineered for versatility, VersiCharge Blue 80A features rated current settings from 12A to 80A to easily accommodate varying power needs across fleet and facility applications. Its recommended wire cross section of 3 AWG with a 90°C minimum ensures safe, high-capacity wiring and consistent performance even under heavy usage. Built-in ground fault and overvoltage protection shield both users and vehicles against electrical risks, while multicolor LED indicators provide instant feedback on charging status, connectivity, and fault diagnostics to streamline site management.
Advanced OCPP and ISO15118-2 user authentication deliver enterprise-grade security and fleet management capability. The charger operates at altitudes up to 6,562 feet, expanding site possibilities in high-elevation regions, and customizable mounting options ensure seamless integration in diverse venues.
With VersiCharge Blue 80A, Heliox, A Siemens Business, is bringing a powerful blend of reliability, safety, and intelligent connectivity to the heart of fleet and commercial EV operations, enabling customers to scale with confidence as electrification demands grow.
About Heliox, A Siemens Business
Heliox, A Siemens Business, delivers world class EV charging equipment, EV charger maintenance and support services, and robust solutions for a broad range of EV fleets. Our portfolio encompasses all aspects of smart and efficient AC and DC charging infrastructure, including IoT-connected hardware, software, and a comprehensive service offering. Heliox manufactures UL compliant products that meet Buy America Act (BAA) and Build America Buy America (BABA) standards. Heliox’s high-quality, field-proven charging products are now backed by Siemens’ financial strength, global reach, and long-term stability—delivering the best of both worlds.
The Trump administration on Wednesday issued a presidential permit for Enbridge’s Line 5 pipeline crossing at the St. Clair River, renewing federal authorization for the decades-old infrastructure as part of a broader push to bolster cross-border oil transport.
The permit replaces a 1991 authorization for the Michigan crossing near Marysville in St. Clair County and allows the Canadian company to continue operating and maintaining the existing pipeline facilities at the international boundary. It applies only to the St. Clair River border crossing and does not apply to Enbridge’s separate proposal to build a tunnel beneath the Straits of Mackinac, which remains under review by state and federal regulators.
Similar permits issued the same day by Trump also cover several Enbridge pipeline operations in North Dakota, part of a wider effort to streamline energy infrastructure between the U.S. and Canada.
In the White House order, the administration said the permit authorizes the transport of crude oil and petroleum products across the border and requires the company to comply with all applicable federal, state and local regulations. It also mandates that the pipeline be maintained in “good repair” and holds the company responsible for environmental damages tied to its operation.
Sean McBrearty, coordinator for the environmental advocacy group Oil & Water Don’t Mix, said the move benefits Enbridge without addressing consumer costs or environmental risks.
“Calling this ‘energy relief’ is a smokescreen. This permit won’t lower prices by a single cent. It’s a subsidy for Enbridge and paid for with continued Great Lakes risk,” McBrearty said.
Enbridge, meanwhile, welcomed the permit authorization.
“This important action enables Enbridge’s existing cross-border pipeline network, moving more than 3 million barrels a day, to continue safely and reliably delivering the energy that is foundational to both U.S. and Canadian economic competitiveness and security,” Enbridge spokesperson Ryan Duffy said.
The Line 5 pipeline, built in 1953, stretches from northwestern Wisconsin through Michigan into Sarnia, Ontario, carrying 540,000 barrels of light crude oil, light synthetic crude and natural gas liquids a day.
The permit comes as legal disputes continue over Michigan’s attempt to shut down the pipeline and as tribal nations press treaty-based claims against the project.
McBrearty argues the administration’s action is part of a broader strategy to expand fossil fuel infrastructure.
“This is a political decision, not an energy solution,” McBrearty said. “Trump’s pipeline won’t lower gas prices. It won’t protect the Great Lakes. It will pad Enbridge’s bottom line and leave Michigan holding the risk.”
This story was originally produced by Michigan Advance, which is part of States Newsroom, a nonprofit news network which includes Wisconsin Examiner, and is supported by grants and a coalition of donors as a 501c(3) public charity.
REDWOOD CITY, Calif., -Zūm, a leader in student mobility, today announced unaudited 2025 financial highlights, reflecting sustained growth at scale as the company expands in the $50 billion student mobility market, the largest segment of the mass mobility industry, and one of the last segments still underserved by AI and cloud technology. While the industry has historically been defined by fragmented, analog transportation services, Zum is pioneering a mobility experience that is replacing traditional approaches to operating yellow buses with a modern, fully integrated mass mobility ecosystem.
2025 Financial Highlights:
Revenue of $333 million, up 35% year-over-year.
Four-year revenue CAGR of greater than 40%.
Over $2 billion in Total Contract Value (TCV).
Adjusted EBITDA1 breakeven and steadily improving contribution margin.
“Every weekday, 26 million American students ride the school bus — three times more passengers than U.S. airlines carry — making it the largest mass transportation system in the country and one of the last to undergo technological transformation,” said Ritu Narayan, Zum Founder and CEO. “For too long, families have faced morning anxiety, wondering if the bus will arrive, if it is safe, and when their kids will get home. We are redefining mobility by moving far beyond legacy models to create a connected, intelligent system for the communities we serve. In student transportation, that means replacing legacy infrastructure with a dynamic, technology-enabled platform — transforming what was once a standalone service into a responsive ecosystem that anticipates and adapts to the needs of districts and families.”
Durable Business Model Driving Scalable Growth
Across the United States, forward-thinking school districts have moved away from the status quo and adopted Zum as their transportation provider. In these districts, Zum has transformed their transportation systems into state-of-the-art operations characterized by enhanced visibility, improved safety standards, real-time data that provides full transparency, and measurable performance outcomes.
Today Zum serves more than 4,000 schools across 15 states, including major districts such as Branford (CT), Kansas City (MO), Los Angeles (CA), Oakland (CA), Omaha (NE), Roanoke City (VA), San Francisco (CA), and Seattle (WA). Zum’s business model is built on structural advantages to drive predictable, profitable growth:
Long-term contracted revenue: 5-10 year agreements with school districts, delivering predictable cash flow, with an established track record of improving profitability.
Exceptional retention: Strong customer loyalty, with growing revenue as existing customers expand their utilization of Zum services.
“Our strong unit economics and long-term contracted revenue demonstrate the scalability of our platform,” said Daniel Berenbaum, Zum’s Chief Financial Officer. “We achieved Adjusted EBITDA breakeven while growing revenue 35% year-over-year, validating our disciplined approach to expansion. Student mobility is one of the last major undigitized sectors, representing a $50 billion segment of the mass mobility industry. While legacy competitors manage physical assets, Zum is deploying a modern, scalable system driven by technology, operational excellence, and safety – using real-time data to connect people, vehicles, and the energy grid, delivering better outcomes for all.”
AI-Powered Technology Driving Reliability and Safety
Zum uses AI and advanced technologies to ensure drivers take the most efficient, logical routes, a significant departure from traditional routing methods that have been used for decades. The system is designed specifically for the unique requirements of student mobility, from complex routing algorithms that account for tiered bell schedules and custom needs, to safety protocols that meet and exceed both bus company and ride-share standards. Zum’s platform also includes mobile apps and web dashboards for students, parents, drivers, dispatchers, and school administrators, enabling enhanced visibility, greater trust, streamlined communication, and incident-tracking capabilities, all powered by real-time data in a single integrated platform.
Modernizing Infrastructure and Powering the Grid
Zum’s electrification strategy represents yet another step in modernization, turning an underutilized asset into an energy resource to power the grid. Zum’s groundbreaking vehicle-to-grid (V2G) technology transforms school bus parking yards into virtual power plants, storing and distributing energy to support grid resilience. Zum made history in the 2024–25 school year by deploying the nation’s first fully electric school bus fleet in the Oakland Unified School District in California, and recently announced plans to launch a fully electric fleet with V2G capabilities for Branford Public Schools in Connecticut beginning in the 2026–27 school year.
Delivering Measurable Impact at Scale
Zum’s impact is measurable and significant:
Reliability: On average, 98% on-time performance.
Cost Savings: San Francisco Unified has cut annual transportation costs by up to 10% using Zum’s technology-driven platform to optimize routes, fleet utilization, and daily operations. That budget flexibility can enable districts to reinvest directly in classroom priorities such as instructional resources, staffing, and student support, strengthening both academic outcomes and long-term sustainability.
Transformational Customer Impact: With Zum, school districts use up to 25% fewer assets by utilizing a multi-size vehicle fleet, spend up to 20% less time through optimized routes, and report 30% higher asset utilization throughout each day.
Efficiency: Through intelligent routing, Oakland Unified has cut one‑hour or longer commutes from 70% to under 10%, and San Francisco Unified has reduced average bus stop time from 3 minutes to just 8 seconds.
Student Experience: Parents have rated Zum with a 4.9 out of 5-star rating in student experience across 1.5 million reviews.
Attendance: After partnering with Zum, Kansas City Public Schools saw an 89% increase in ridership driven by improved reliability and reduced transportation-related absences in secondary schools from 25% to 5.6% during the 2024–25 school year.
Growth: Safely completed 68.5 million student rides in 2025, up 120% over 2024.
About Zum:
Zum is revolutionizing mass mobility with a fully integrated platform that connects and coordinates people, vehicles, and operations in real time. In the $50 billion student mobility market – the largest segment of the mass mobility industry – Zum is designed to deliver a predictable, safe, and seamless experience for students and families. Today, more than 4,000 schools across 15 states rely on Zum’s advanced platform, with customers now deploying its groundbreaking vehicle-to-grid (V2G) technology to modernize vehicles and strengthen grid resilience. Recognized globally for its innovative approach and operational execution, Zum has been named to Fast Company’s World’s Most Innovative Companies, CNBC Disruptor 50 and Changemakers, the World Economic Forum, and the Financial Times Fastest Growing Companies lists. Zum is backed by leading investors including Sequoia Capital, GIC, and SoftBank. Learn more at www.ridezum.com.
Pupil transportation is one of the most visible ways a nation demonstrates its commitment to education. Every school day, millions of students travel from home to classroom using systems designed not only for efficiency, but for safety and equity. While Australia and the U.S. share similarities as large, developed, federal nations, their approaches to pupil transportation reflect important structural and cultural differences.
By examining governance, fleet design, funding models, rural challenges, and safety standards, it becomes clear that both countries aim for the same goal—safe and reliable access to education—but achieve it through different methods.
Both Australia and the U.S. operate under federal systems of government but differently distribute the responsibility for pupil transportation. In the U.S., pupil transportation is primarily managed at the local school district level. States establish regulatory frameworks, and federal safety standards govern vehicle manufacturing. However, day-to-day operations—routing, hiring drivers, maintaining fleets—are typically handled by individual districts or contracted providers. This creates a highly localized system, where policies can vary significantly from one district to another.
In Australia, pupil transportation is largely administered at the state and territory level rather than by individual school districts. States such as New South Wales, Queensland, Victoria, and Western Australia design and oversee their own school transport assistance schemes. The federal government plays a minimal operational role. This state-centered approach results in more centralized control within each state, even though policies differ between states.
What’s Different with Pupil Transportation?
The key difference is the scale of control. U.S. decisions are often made at the district level. Australian decisions are typically made at the state level. Both models allow flexibility, but Australia’s approach tends to create more uniformity within each state.
Perhaps the most recognizable feature of American pupil transportation is the yellow school bus. The U.S. yellow bus is a national symbol. Nearly every public school district operates dedicated fleets painted in a standardized shade of yellow. Strict federal safety standards regulate construction, and compartmentalized seating design has been central to American school bus safety philosophy for decades.
Australia does not have the same universal yellow bus requirement. School buses in Australia may be white, yellow, or another color depending on the contractor or region. While clearly marked as school services, they do not carry the same nationally standardized appearance as American buses. This reflects a difference in cultural identity. In the U.S., the yellow bus represents childhood and public education. In Australia, school transportation is more functionally defined than symbolically branded.
Another major difference involves seatbelt policies. In Australia, seatbelts are common in school buses and often required in newer vehicles. In contrast, large American school buses traditionally rely on compartmentalization rather than seatbelts, although seatbelt requirements are expanding in some states. These differing design philosophies reflect variations in regulatory priorities and historical safety research.
One of the clearest contrasts between the two systems is how they interact with public transit. In the U.S., pupil transportation is generally separate from public transportation systems. School buses are dedicated vehicles serving only students. Even in large cities, districts often operate independent fleets rather than relying on municipal transit systems, though some districts do provide older students with transit passes.
In Australia, especially in urban areas, students frequently use public bus, train, or tram systems. Discounted or free student travel passes are common. Rather than maintaining fully separate fleets in metropolitan areas, Australia often integrates students into existing public transport networks.
This integrated approach can increase efficiency and reduce duplication of services. However, it also means that student riders share space with the general public. The American model, by contrast, prioritizes separation and controlled environments for school-aged passengers.
What’s Similar with Pupil Transportation?
Both nations face significant rural transportation challenges due to their size and geography. In the U.S., rural districts may cover hundreds of square miles, with students traveling long distances on highways and country roads. In states such as Montana or Texas long travel times are common.
Australia faces similar challenges, especially in remote outback regions. In some parts of Western Australia or Queensland, students may travel extremely long distances to reach school. However, Australia often applies strict distance-based eligibility rules. Students must live beyond a minimum distance from their nearest appropriate school to qualify for subsidized transportation. Families living closer may be responsible for arranging their own transport.
In contrast, many American districts provide transportation to all eligible students within the district, even if they live relatively close to school. The U.S. model often prioritizes broader access, while Australia’s system focuses on distance-based need.
In extremely remote parts of Australia, boarding schools are sometimes used as a practical solution due to travel distances. While boarding options exist in the U.S., they are far less central to the public education system.
Funding structures also reveal differences. In the U.S., transportation funding varies by state and is often supported by local tax revenue. This can lead to disparities in fleet age and service quality between wealthier and less affluent districts.
Australia typically funds pupil transportation at the state level. Many routes are operated by private contractors under government agreements. Rather than school districts owning large fleets, governments often contract services to private bus companies. This contractor-based system requires strong oversight to ensure compliance and safety standards.
The American system uses a mix of district-owned fleets and contracted providers. However, district ownership remains more common in the U.S. than in Australia.
Both countries prioritize safety, but enforcement structures differ. In the U.S., strict stop-arm laws require motorists to stop when a school bus is loading or unloading students. Violations can result in significant fines. This legal framework reinforces the protective environment surrounding the school bus.
Australia does not use the same stop-arm system in most regions. Instead, safety relies more heavily on general road rules, bus signage and public awareness. The American stop-arm system creates a highly visible and enforceable protective zone around students.
Despite these differences, Australia and the U.S. share core principles. Both aim to provide safe, reliable transportation that supports equal access to education. Both must manage long distances, rural isolation and funding constraints. Both rely on regulated driver accreditation and vehicle inspection systems.
The primary differences lie in structure and philosophy. The U.S. emphasizes a distinct, symbolic and highly regulated dedicated school bus system. Australia emphasizes state-level coordination, contractor delivery and integration with public transit.
In the end, both systems reflect national priorities and geography. Whether through the iconic yellow bus traveling down an American suburban street or a state-contracted bus crossing the wide landscapes of the rural Australian Outback, pupil transportation remains a vital link between home and classroom. Each country has developed a model suited to its environment, but both share a common mission: ensuring that distance does not prevent opportunity.
Watch for the next article in this series as we travel to another continent-sized country – Brazil.
Bret E. Brooks is the chief operating officer for Gray Ram Tactical, LLC, a Missouri-based international consulting and training firm specializing in transportation safety and security. He is a keynote speaker, author of multiple books and articles, and has trained audiences around the world. He can be reached at BretBrooks@GrayRamTacticalTraining.com.
There is growing attention on the links between artificial intelligence and increased energy demands. But while the power-hungry data centers being built to support AI could potentially stress electricity grids, increase customer prices and service interruptions, and generally slow the transition to clean energy, the use of artificial intelligence can also help the energy transition.
For example, use of AI is reducing energy consumption and associated emissions in buildings, transportation, and industrial processes. In addition, AI is helping to optimize the design and siting of new wind and solar installations and energy storage facilities.
On electric power grids, using AI algorithms to control operations is helping to increase efficiency and reduce costs, integrate the growing share of renewables, and even predict when key equipment needs servicing to prevent failure and possible blackouts. AI can help grid planners schedule investments in generation, energy storage, and other infrastructure that will be needed in the future. AI is also helping researchers discover or design novel materials for nuclear reactors, batteries, and electrolyzers.
Researchers at MIT and elsewhere are actively investigating aspects of those and other opportunities for AI to support the clean energy transition. At its 2025 research conference, MITEI announced the Data Center Power Forum, a targeted research effort for MITEI member companies interested in addressing the challenges of data center power demand.
Controlling real-time operations
Customers generally rely on receiving a continuous supply of electricity, and grid operators get help from AI to make that happen — while optimizing the storage and distribution of energy from renewable sources at the same time.
But with more installation of solar and wind farms — both of which provide power in smaller amounts, and intermittently — and the growing threat of weather events and cyberattacks, ensuring reliability is getting more complicated. “That’s exactly where AI can come into the picture,” explains Anuradha Annaswamy, a senior research scientist in MIT’s Department of Mechanical Engineering and director of MIT’s Active-Adaptive Control Laboratory. “Essentially, you need to introduce a whole information infrastructure to supplement and complement the physical infrastructure.”
The electricity grid is a complex system that requires meticulous control on time scales ranging from decades all the way down to microseconds. The challenge can be traced to the basic laws of power physics: electricity supply must equal electricity demand at every instant, or generation can be interrupted. In past decades, grid operators generally assumed that generation was fixed — they could count on how much electricity each large power plant would produce — while demand varied over time in a fairly predictable way. As a result, operators could commission specific power plants to run as needed to meet demand the next day. If some outages occurred, specially designated units would start up as needed to make up the shortfall.
Today and in the future, that matching of supply and demand must still happen, even as the number of small, intermittent sources of generation grows and weather disturbances and other threats to the grid increase. AI algorithms provide a means of achieving the complex management of information needed to forecast within just a few hours which plants should run while also ensuring that the frequency, voltage, and other characteristics of the incoming power are as required for the grid to operate properly.
Moreover, AI can make possible new ways of increasing supply or decreasing demand at times when supplies on the grid run short. As Annaswamy points out, the battery in your electric vehicle (EV), as well as the one charged up by solar panels or wind turbines, can — when needed — serve as a source of extra power to be fed into the grid. And given real-time price signals, EV owners can choose to shift charging from a time when demand is peaking and prices are high to a time when demand and therefore prices are both lower. In addition, new smart thermostats can be set to allow the indoor temperature to drop or rise — a range defined by the customer — when demand on the grid is peaking. And data centers themselves can be a source of demand flexibility: selected AI calculations could be delayed as needed to smooth out peaks in demand. Thus, AI can provide many opportunities to fine-tune both supply and demand as needed.
In addition, AI makes possible “predictive maintenance.” Any downtime is costly for the company and threatens shortages for the customers served. AI algorithms can collect key performance data during normal operation and, when readings veer off from that normal, the system can alert operators that something might be going wrong, giving them a chance to intervene. That capability prevents equipment failures, reduces the need for routine inspections, increases worker productivity, and extends the lifetime of key equipment.
Annaswamy stresses that“figuring out how to architect this new power grid with these AI components will require many different experts to come together.” She notes that electrical engineers, computer scientists, and energy economists “will have to rub shoulders with enlightened regulators and policymakers to make sure that this is not just an academic exercise, but will actually get implemented. All the different stakeholders have to learn from each other. And you need guarantees that nothing is going to fail. You can’t have blackouts.”
Using AI to help plan investments in infrastructure for the future
Grid companies constantly need to plan for expanding generation, transmission, storage, and more, and getting all the necessary infrastructure built and operating may take many years, in some cases more than a decade. So, they need to predict what infrastructure they’ll need to ensure reliability in the future. “It’s complicated because you have to forecast over a decade ahead of time what to build and where to build it,” says Deepjyoti Deka, a research scientist in MITEI.
One challenge with anticipating what will be needed is predicting how the future system will operate. “That’s becoming increasingly difficult,” says Deka, because more renewables are coming online and displacing traditional generators. In the past, operators could rely on “spinning reserves,” that is, generating capacity that’s not currently in use but could come online in a matter of minutes to meet any shortfall on the system. The presence of so many intermittent generators — wind and solar — means there’s now less stability and inertia built into the grid. Adding to the complication is that those intermittent generators can be built by various vendors, and grid planners may not have access to the physics-based equations that govern the operation of each piece of equipment at sufficiently fine time scales. “So, you probably don’t know exactly how it’s going to run,” says Deka.
And then there’s the weather. Determining the reliability of a proposed future energy system requires knowing what it’ll be up against in terms of weather. The future grid has to be reliable not only in everyday weather, but also during low-probability but high-risk events such as hurricanes, floods, and wildfires, all of which are becoming more and more frequent, notes Deka. AI can help by predicting such events and even tracking changes in weather patterns due to climate change.
Deka points out another, less-obvious benefit of the speed of AI analysis. Any infrastructure development plan must be reviewed and approved, often by several regulatory and other bodies. Traditionally, an applicant would develop a plan, analyze its impacts, and submit the plan to one set of reviewers. After making any requested changes and repeating the analysis, the applicant would resubmit a revised version to the reviewers to see if the new version was acceptable. AI tools can speed up the required analysis so the process moves along more quickly. Planners can even reduce the number of times a proposal is rejected by using large language models to search regulatory publications and summarize what’s important for a proposed infrastructure installation.
Harnessing AI to discover and exploit advanced materials needed for the energy transition
“Use of AI for materials development is booming right now,” says Ju Li, MIT’s Carl Richard Soderberg Professor of Power Engineering. He notes two main directions.
First, AI makes possible faster physics-based simulations at the atomic scale. The result is a better atomic-level understanding of how composition, processing, structure, and chemical reactivity relate to the performance of materials. That understanding provides design rules to help guide the development and discovery of novel materials for energy generation, storage, and conversion needed for a sustainable future energy system.
And second, AI can help guide experiments in real time as they take place in the lab. Li explains: “AI assists us in choosing the best experiment to do based on our previous experiments and — based on literature searches — makes hypotheses and suggests new experiments.”
He describes what happens in his own lab. Human scientists interact with a large language model, which then makes suggestions about what specific experiments to do next. The human researcher accepts or modifies the suggestion, and a robotic arm responds by setting up and performing the next step in the experimental sequence, synthesizing the material, testing the performance, and taking images of samples when appropriate. Based on a mix of literature knowledge, human intuition, and previous experimental results, AI thus coordinates active learning that balances the goals of reducing uncertainty with improving performance. And, as Li points out, “AI has read many more books and papers than any human can, and is thus naturally more interdisciplinary.”
The outcome, says Li, is both better design of experiments and speeding up the “work flow.” Traditionally, the process of developing new materials has required synthesizing the precursors, making the material, testing its performance and characterizing the structure, making adjustments, and repeating the same series of steps. AI guidance speeds up that process, “helping us to design critical, cheap experiments that can give us the maximum amount of information feedback,” says Li.
“Having this capability certainly will accelerate material discovery, and this may be the thing that can really help us in the clean energy transition,” he concludes. “AI [has the potential to] lubricate the material-discovery and optimization process, perhaps shortening it from decades, as in the past, to just a few years.”
MITEI’s contributions
At MIT, researchers are working on various aspects of the opportunities described above. In projects supported by MITEI, teams are using AI to better model and predict disruptions in plasma flows inside fusion reactors — a necessity in achieving practical fusion power generation. Other MITEI-supported teams are using AI-powered tools to interpret regulations, climate data, and infrastructure maps in order to achieve faster, more adaptive electric grid planning. AI-guided development of advanced materials continues, with one MITEI project using AI to optimize solar cells and thermoelectric materials.
Other MITEI researchers are developing robots that can learn maintenance tasks based on human feedback, including physical intervention and verbal instructions. The goal is to reduce costs, improve safety, and accelerate the deployment of the renewable energy infrastructure. And MITEI-funded work continues on ways to reduce the energy demand of data centers, from designing more efficient computer chips and computing algorithms to rethinking the architectural design of the buildings, for example, to increase airflow so as to reduce the need for air conditioning.
In addition to providing leadership and funding for many research projects, MITEI acts as a convenor, bringing together interested parties to consider common problems and potential solutions. In May 2025, MITEI’s annual spring symposium — titled “AI and energy: Peril and promise” — brought together AI and energy experts from across academia, industry, government, and nonprofit organizations to explore AI as both a problem and a potential solution for the clean energy transition. At the close of the symposium, William H. Green, director of MITEI and Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering, noted, “The challenge of meeting data center energy demand and of unlocking the potential benefits of AI to the energy transition is now a research priority for MITEI.”
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