EXECUTIVE SUMMARY This week, I covered a fascinating effort to preserve organs outside the body. There’s a huge shortage of donor organs, and one of the main reasons is time—they survive only a matter of hours outside the body, even when they’re kept on ice. Doctors dream of organ banks—stores of human organs that can be preserved for days, weeks, months, or even longer. That would allow them to run tests on organs, find the best matches for them, and transport the organs to those recipients. In new research, one team has been able to supercool the kidneys of pigs—animals whose organs are of a similar size to human ones—and preserve them for days. The kidneys survived being stored at −4 °C (25 °F) and eventually reimplanted back into pigs. And that’s just the latest development in a field that is positively buzzing. It has proved super difficult to freeze organs. Once ice forms in them, they’re done. The ice crystals create all kinds of damage and render the organs unusable. That hasn’t stopped many researchers from trying.
Some have focused on cryopreservation—rapid extreme cooling that essentially leaves cells in a glasslike state. This process is now routine for eggs, sperm, and embryos, which are cooled to −196 °C in less than two seconds and can be used even after decades in storage. No one has managed to cryopreserve and thaw human organs for transplantation. But plenty of human bodies and brains have been stored at ultra-low temperatures in the hope that they might one day be rewarmed and brought back to life. (You can read more about why some people opt for cryonics here.)
In March, I wrote about Stephen L. Coles, a gerontologist who had opted to cryopreserve his own brain. After the scientist died in 2014, his body was taken to Alcor, a cryonics facility in Arizona. A team at the facility removed Coles’s head, perfused his brain with cryoprotective chemicals (which work like antifreeze), removed the brain from the skull, and cooled it to −146 °C. When Coles’s friend Greg Fahy, a cryobiologist, studied pieces of his brain years later, he found that the brain cells, which had shrunk, “bounced back” once they were rewarmed. But that doesn’t mean the cells are alive, or that it might one day be possible to reanimate the brain. As Matthew Powell Palm of Texas A&M told me at the time: “There are so many ways those neurons could be toast.” Powell Palm is working on other ways to preserve organs. It was he, along with his colleagues, who managed to store supercooled pig kidneys and successfully transplant them, in a study described as “a landmark achievement.” Those organs did better than kidneys stored on ice, he says. His approach didn’t require cryoprotectants. But other teams are exploring potential chemical cocktails that might allow them to store organs at lower temperatures, potentially for longer periods of time. (More on this in The Checkup soon!) Another way to prolong the lifespan of an organ is to use a machine that perfuses it with nutrients, mimicking what happens inside the body. Machine perfusion devices have become more commonly used over the last decade or so and are typically used to maintain livers and kidneys for up to about 24 hours. Researchers are now adapting this protocol for a growing list of organs, even eyeballs—a recent feat that might enable whole-eye transplants. In March, I went to visit scientists in Valencia who had developed a perfusion system for uteruses. They had used their device—which they nicknamed “Mother”—to keep a human uterus alive for a day. It’s an exciting time for organ preservation. Keep an eye out for more coverage from MIT Technology Review in the coming weeks. This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Supercooled kidneys have been transplanted into pigs in a “landmark achievement”  When it comes to organ donation, time is everything. As soon as an organ has been removed from a donor’s body, it starts to deteriorate. Surgeons have only a matter of hours to get it into a recipient.In most cases, organs will be kept on ice during that time, at around 4 °C (39 °F). They cannot be frozen—in previous attempts, ice has formed, causing all kinds of damage. But now, scientists have come up with a device that allows organs to be cooled to -4 °C (25 °F) without forming any ice. They’ve tested it with pig organs and shown that kidneys, at least, can be preserved in the device for days then successfully transplanted.  Read our story about their breakthrough, and why it raises hopes for longer-term storage of donated human organs.
—Jessica Hamzelou If you want to read more about this story and its implications for organ preservation and transplantation, sign up to receive The Checkup, our weekly biotech newsletter, later today.
The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Inside China’s epic push to replace US chips The gap between their chips’ capabilities remains large—for now. (WSJ $)+ How China is using open source AI as a new form of soft power around the globe. (NYT $)+ A new bill in the US takes aim at Chinese AI companies’ training practices. (NBC)+ US lawmakers are also mulling banning the military from using Chinese humanoid robots. (SCMP)2 Space data centers don’t exist yet, but people already oppose themEnvironmental experts warn it’ll further pollute the stratosphere. (Guardian)+ Four things we’d need to put data centers in space. (MIT Technology Review)+ What it’s like inside the data centers powering AI down here on Earth. (Axios)+ The left and right are finding common cause with data center protests in the US. (The Verge)3 US lawmakers are pushing for an AI ‘kill switch’After OpenAI’s models went rogue and hacked Hugging Face. (BBC)4 Peptides seem on the cusp of going mainstream in the USA lack of scientific evidence didn’t stop the FDA just voting to let some pharmacies to legally dispense them. (Wired $)+ How does Make America Healthy Again hold up to scientific scrutiny? (Nature)+ US measles cases are reaching levels not seen for three decades. (Wired $)+ Peptides are everywhere. Here’s what you need to know. (MIT Technology Review)5 The EU just fined Google almost $1 billionFor competition breaches over apps and search. (FT $)+ It came just a day before Trump renewed tariffs on 60 trading partners, including the EU. (BBC)6 Electric vehicles are selling well in EuropeAnd the share made by Chinese firms has doubled in the last year. (The Next Web)+ Hybrids are hot property in the US this summer. (Wired $)7 There’s a worsening shortage of computer science professorsYou can blame AI companies—they keep snapping them up, to our general detriment. (The Atlantic $) 8 A judge caught a stenographer allowing AI errors into their transcript It’s apparently the first time this has happened, but it certainly won’t be the last. (404 Media)+ Even judges themselves are falling for AI. (MIT Technology Review)9 Here’s what new tech millionaires will do with their IPO wealth 💸What a nice problem to have to grapple with! (Quartz $)10 Dating apps’ latest wheeze? In-person events Well well well, it seems we’ve come full circle. (Bloomberg $) Quote of the day “Call us optimists, call us dreamers. Just as we’ve always done, we’re betting on people.”  —The voiceover from a new advert from Meta, which exhorts us to be more optimistic about AI.

One More Thing BELL HUTLEY AI is changing how we study bird migration In a warming world increasingly full of human infrastructure that can be deadly to them, like glass skyscrapers and power lines, migratory birds are facing many existential threats.  Scientists rely on a combination of methods to track the timing and location of their migrations, though each has shortcomings. But now, machine-learning tools are unlocking a treasure trove of acoustic data for ecologists. 

EXECUTIVE SUMMARY When it comes to organ donation, time is everything. As soon as an organ has been carefully removed from a donor’s body, it starts to deteriorate. Surgeons have a matter of hours to get it into a recipient. Leave it too long and the organ will become unusable. In most cases, organs will be kept on ice during that time, at around 4 °C (39 °F). They cannot be frozen—in previous attempts, ice has formed, causing all kinds of damage. Matthew Powell Palm at Texas A&M University and his colleagues have an alternative solution—a device that allows organs to be cooled to -4 °C (25 °F) without forming any ice. Now, in new research with pig organs, his team has shown that kidneys, at least, can be supercooled and preserved in the device for days. Once rewarmed, the organs have been successfully transplanted into animals, and they seem to do better than organs kept on ice.
The work represents “a landmark achievement,” says Kevin Myer, president and CEO of LifeGift, an organ procurement organization based in Texas, who was not involved in the research. Cooling organs Powell Palm hopes this approach could ultimately help ease the organ shortage crisis. Today, there are more than 104,000 people waiting for a kidney transplant in the US alone. It is estimated that 17 people die every day in the US while waiting for a transplant. That’s partly due to a lack of donated kidneys, but it’s also because many of those that are available never make it to a recipient. In some years, around one in three donated kidneys end up being discarded, often because they end up too degraded to use by the time they reach a recipient. Kidneys can be stored on ice for around 24 hours or placed in devices that aim to mimic the conditions of the body, also for up to around 24 hours. That’s not always long enough to find a suitable recipient and transport the organ, says Myer.
Scientists around the world have been working on ways to store organs for longer by cooling them to even chillier temperatures. Cooling an organ slows its metabolism—the colder you go, the greater the effect, and the longer you can store it. We’ve long been able to successfully cryopreserve eggs, sperm, and embryos, but it’s much harder to freeze large organs. Teams have been exploring various temperatures and cryoprotectants (chemicals that essentially work like antifreeze), but so far no one has been able to freeze human organs for transplantation.   As a thermodynamicist, Powell Palm explored another approach. By keeping an organ submerged at a constant pressure, it should be possible to prevent the formation of ice at temperatures a little below 0 °C, without the need for cryoprotectants (which might have side effects and would need to be approved before being used in human transplants).  To test this theory, Powell Palm and his colleagues have created a device that does just that. The device itself is essentially a hermetically sealed chamber with a transparent lid. At its base is a device that monitors the organ’s temperature and checks for the formation of ice. Organs are submerged in a solution that is already commonly used to preserve them for transplant. “I always describe this as low-tech high science,” says Powell Palm. “A lot of work has gone into understanding the … kinetics at play in this system, but ultimately … it’s quite simple.” Supercooled kidneys To test their device, Powell Palm and his colleagues first removed single kidneys from pigs. The organs were flushed with the same commonly used solution to remove the blood, just as transplant organs are. The team then kept some kidneys on ice for either two hours or 24 hours, to mimic standard conditions used in human transplantation. They also put some of the removed kidneys in their device for 24, 48, or 72 hours. The stored kidneys were then each transplanted back into the original donor pigs. Each pig’s second kidney was removed in the same procedure, leaving each animal with only the kidney that had been stored, and reimplanted. Once the 24-hour supercooled kidneys were transplanted, they immediately began producing urine—a key indication that they were working. The team members also measured other markers of kidney function and found that the organs appeared to be working normally within about 10 days of being transplanted. A kidney that was supercooled for 72 hours recovers once it is transplanted back into a pig.COURTESY RONALD SELLERS, POWELL-PALM LAB, TEXAS A&M UNIVERSITY That’s slower than kidneys stored on ice for two hours but much quicker than kidneys kept on ice for 24 hours, says Powell Palm.

The organs that were kept supercooled for 48 and 72 hours performed similarly, he says. “Even at three days—triple the clinical standard—we’re getting recovery that is faster than … [what has been] the gold standard for the last three decades,” he says. “So we’re really, really pumped about this.” “It is impressive,” says Heidi Yeh, a transplant surgeon at Mass General Brigham for Children, who also researches organ preservation technologies. “Often kidneys that have been stored for 48 hours [in other studies] take a week or two before they start working again.” Organs that grow The supercooled organs seem to work well in the long term, too. Over a 30-day period, the pigs grew by around 30%—and the kidneys grew with them, almost doubling in size to compensate for both the pigs’ growth and the lack of a second kidney. The team monitored one of the pigs for 200 days before removing and analyzing its kidney. Even at that point the organ looked healthy, says Powell Palm. He and his colleagues presented the findings at the American Transplant Congress in Boston last month. Earlier this year, researchers in Canada showed they could also cool pig kidneys to below-zero temperatures and transplant them into pigs. The team’s protocol included the use of a cryoprotectant, and organs were stored for up to 48 hours before being transplanted into pigs. Those organs survived for a week. In supercooling organs for 72 hours and showing that they do well for 30 days or more, Powell Palm and his colleagues have broken new ground. “It’s the first time this has ever been reported in history,” he says. Those extra hours could make all the difference, says Myer of LifeGift. The advance could give doctors more time to evaluate the kidneys, match them to the most suitable donors, and physically get the organs to their intended recipients in time. It could enable international donations and open up cheaper transport options, he adds. “Right now, with kidney transplantation the assumed limit is 18 to 24 hours,” he says. “If we can get up to 72 hours … that would change everything.” Powell Palm and his colleagues think they may even be able to go beyond 72 hours. In preliminary studies, organs that had been stored for up to 120 hours appeared healthy, although those organs have not yet been transplanted. And because the process doesn’t require any cryoprotective chemicals, the team members are hoping for an accelerated approval from the US Food and Drug Administration, which would allow them to test the device in human transplantations. The storage device is simple and compact, so Powell Palm thinks it will be easy to transport. It hasn’t been tested for air travel yet, but it has been used to take supercooled kidneys across the US in the back of a Kia Sorento, he says: “From a stability perspective, we view this as an even higher bar.” Powell Palm and his colleague Sebastian Giwa plan to launch a company dedicated to developing the technology, along with other protocols that “stop biological time,” in the coming months, he says.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. The power line that could reshape New York’s grid is hitting snags  During a heat wave on July 3, New York State’s grid imported enough electricity from Canada to meet about 9% of its total demand that day. Some of that power shuttled in on a 339-mile power line stretching from Quebec to Queens. It opened in May and is officially the longest underground transmission line in North America. It could provide up to 20% of New York City’s electricity demand, largely with abundant hydropower from Quebec. One wrinkle: The line has been down for most of this month, and some experts are concerned about how drought will affect the power supply feeding it. 
Still, the line could help shape the future of our grid, if it can overcome these sorts of snags. Read our story to understand how. —Casey Crownhart
This story is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 The US Treasury is threatening to sanction Chinese AI companiesTreasury secretary Scott Bessent has accused Moonshot of improperly distilling Anthropic’s Fable model. (TechCrunch)+ Nvidia’s Jensen Huang is arguing that America has nothing to fear from Chinese AI. (Axios)+ Like it or not, Chinese models are now part of the global AI infrastructure. (Rest of World)+ China’s AI models have Trump’s AI world at war with itself. (MIT Technology Review) 2 Why the OpenAI hack is the scariest AI mishap yetAI’s capabilities seem to be starting to outpace our current ability to control them. (The Economist $)+ Hugging Face had to turn to a Chinese AI model to rescue it from the hack. (BI) 3 Visually impaired Europeans can now get an implant that restores sightAnd Americans may not have to wait long to receive it, too. (STAT)+ This retina implant lets people with vision loss do a crossword puzzle. (MIT Technology Review)4 A bellwether lawsuit suing Meta for social media addiction has been droppedThere are, however, many more waiting in the wings. (NYT $)5 Here’s how ICE gets its hands on Americans’ dataAs soon as you open a credit card or phone account, its agents can see where you live. (404 Media)+ States are warring with the Trump administration over the right to see ICE agents’ faces. (Wired $) 6 We urgently need to grapple with AI’s environmental impactAs the world warms, is the price we’re paying worth it? (The Verge)+ We did the math on AI’s energy footprint. (MIT Technology Review)7 Privacy issues with smart glasses need an industrywide fixThat’s according to Samsung, which is unveiling glasses it developed with Google this fall. (Bloomberg $) 8 The US Army is begging soldiers to limit their AI useThe token crisis comes for us all eventually, it seems. (Ars Technica)9 Why does lettuce keep making Americans sick? 🥬😷It’s pretty simple: a lot of people eat it, and it doesn’t get cooked. (Wired $)

10 Pokemon Go is the perfect game to play this summerIt’s fun, collaborative, and it gets you outdoors. (Guardian) Quote of the day “It went off and did this hack all by itself, as far as we can tell. This is the highest level of autonomy that we’ve seen in the use of a large language model for cyber operations.” —Colin Shea-Blymyer, a cybersecurity research fellow at Georgetown University, tells NPR why the OpenAI hack on Hugging Face is so alarming.  One More Thing KAGAN MACLEOD Welcome to the dark side of crypto’s permissionless dream  Jean-Paul Thorbjornsen is a founder of THORChain, a blockchain through which users can swap one cryptocurrency for another and earn fees from making those swaps.  
But is he responsible for what it’s used for? It’s a question that matters because in January last year, its users lost more than $200 million in cryptocurrency after THORChain transactions and accounts were frozen by an admin override, which users believed was not supposed to be possible given the decentralized structure. It’s also been used by North Korean hackers to move $1.2 billion of stolen ethereum.  Thorbjornsen explains this all away as a function of THORChain’s decentralized and permissionless nature. Read our story exploring whether we should believe him or not. 
—Jessica Klein We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + A musician developed an ingenious way to strum a guitar with an electric fan.+ Ukraine’s tunnel of love is a leafy green corridor of romance that’s straight out of a fairy tale.+ The driver of a giant banana has been pulled over 100s of times, but still won’t ditch his treasured ride.+ Ever wonder which albums and songs truly stand the test of time? The Greatest Music tries to answer that via an algorithm that analyses hundreds of “best of” lists.

In partnership withAstraZeneca Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered proteins rather than synthetic chemistry (which are often used to treat conditions across most major acute and chronic diseases), the complexity is even greater. Scientists explore vast quantities of possible molecules, looking for the rare few that will bind to the right target, remain stable in the human body, and be manufacturable at scale. Today, AI is speeding up these processes and has quickly become a core part of the infrastructure in pharmaceutical R&D. AI-assisted design is a growing part of how biologic drug candidates are developed, and companies like AstraZeneca are actively building its engineering teams to push this further. “Everything we do, whether it’s design, make, test, or analyze, is now computationally enhanced,” says Puja Sapra, senior vice president and head of R&D biologics engineering and oncology targeted discovery at AstraZeneca. “The cycle times are getting shorter while productivity and innovation increase.” Sapra explains that AstraZeneca’s approach follows a build-measure-learn loop. AI generates or prioritizes candidate molecules computationally, predicting which designs are most likely to succeed. Scientists then focus lab resources only on the top-ranked candidates. This leads to a tighter feedback cycle with fewer dead ends, faster iteration, and the ability to go after disease targets that were previously considered untreatable by medicine. Because the number of possible molecular combinations far exceeds what any human team can systematically explore, using AI to narrow and refine the options for testing has become a major focus in biologics drug design.
Navigating complex drug design problems Beyond accelerating timelines, AI is also being applied to the discovery of entirely new classes of medicines. Traditional biologics typically target one disease pathway. The next generation of drugs can hit multiple targets simultaneously or precisely deliver therapeutic payloads to specific cells. Achieving this requires optimization across many variables at once. Looking ahead AI-driven models could help design these increasingly complex, multi-specific biologics, explains Puja Sapra. “For example,” she continues, “such models could help identify which two or three targets to prioritize based on the underlying biology, then optimize across multiple parameters to balance a molecule’s potency, stability, manufacturability, and safety.” “Drugging the undruggable is becoming a reality,” Sapra says. “These technologies will eventually enable us to develop medicines against targets once thought impossible to reach. The potential for benefit to patients is remarkable.” The data moat McKinsey estimates that generative AI, combined with other computational tools, could cut drug discovery timelines by as much as 50%. But every AI model is only as good as its training data. In drug discovery, that means ample quantities of high-quality biological data. Experiments can provide a rich source of such data. Whether they succeed or fail, each experiment generates a signal about what does and does not work.
“Data is our differentiator,” says Sapra, explaining how the company’s datasets are proprietary and multimodal and include molecular structures, binding measurements, safety profiles, and manufacturing outcomes. “We’ve built an intentionally diverse portfolio across multiple disease areas and drug types. All of that data empowers us to fine-tune frontier AI models with richer, more representative training sets.” She continues, “Further, we have invested in deep screening technologies to generate additional datasets required in volume to constantly refine and validate our models.” Building an autonomous discovery engine To bring all of that data together in one place, AstraZeneca is building what it calls a “lab of the future” facility in Kendall Square, Cambridge, Massachusetts where AI and robotic automation will be able to form a continuous, closed-loop discovery system. “Where a self-driving car uses sensors and models to navigate its environment, this system uses AI to make predictions, robotic systems to execute experiments, and instruments to generate data,” explains Sapra. That data feeds directly back into the models, accelerating each subsequent cycle. “Throughout, scientists will remain central to the process, providing the oversight, judgement, and strategic direction that ensure outputs are explainable, tolerable, and directed toward potential patient benefit,” she adds. Eventually, automated high-throughput systems will be able to make and evaluate thousands of molecular interactions on a weekly basis. “This will generate AI-ready data at a scale that traditional workflows cannot match,” Sapra says. “Robotic sample handling, automated quality checks, and integrated data pipelines also have the potential to help accelerate early drug development timelines significantly.” The next frontier: Generating medicines from scratch Ultimately, Sapra says, the end-state vision for AI in biologic drug discovery is what the field calls “de novo” design. For this, the goal is for AI to generate entirely new protein sequences that precisely fit the desired drug properties. This includes designing the structure, predicting safety, how it will behave in the body and how to make it manufacturable. “The field is making great progress toward a completely AI-generated biologic, designed from scratch all the way to a clinical candidate,” Sapra says. “As we continue to leverage frontier models and fine-tune them with the right datasets, we bring ourselves closer to this reality. I believe it will come. It’s a matter of time.” Several key elements are needed to reach this point, however. First is richer and more standardized training data across the industry. Second, robust evaluation benchmarks for AI-generated candidates. And third, teams that know how to work at the intersection of machine learning and biology. Of all the prerequisites, however, safety prediction may be the most consequential, and perhaps the least discussed, Sapra says. “One of the hardest problems in de novo design is predicting whether a computationally generated molecule will be safe in the human body,” Sapra explains. AstraZeneca is tackling this with what amounts to virtual clinical trials. These are advanced cell systems and micro-scale organ models that function as physical testbeds, paired with AI that learns from their outputs.

 “These systems have the potential to generate enhanced biological signals without traditional testing bottlenecks, and they’re a critical missing piece in closing the loop between AI-generated designs and clinical-ready candidates,” Sapra adds. A shift currently underway is the move toward agentic AI systems that can simultaneously generate molecule candidates and predict how efficacious and safe they are likely to be. These autonomous workflows can connect disease-level insights directly to molecule design, bridging what were previously separate data silos. “The complexity of the biology goes hand-in-hand with the design of the molecule,” summarizes Sapra. Human talent unlocks AI potential The transformation underway in biologics is not just about technology. “With more autonomous systems, human oversight remains at the heart of this approach—ensuring explainable and ethical AI for the benefit of patients,” says Sapra. For scientists, working with AI is a collaborative process. “Scientists will work hand-in-hand with these model systems,” she says. “There will be a world where models will design molecules, then scientists will work with the systems to test those molecules and put all that data together.” Through this process of human checks, balances, and judgement calls, the models will evolve and constantly improve, ultimately with potential to benefit patients. For engineers, designing and building effective systems ready for human-AI collaboration will mean ensuring high levels of model transparency and explainability. According to Sapra, AstraZeneca’s engineering teams include data scientists, automation specialists, and AI engineers, who are developing systems that act as “thinking partners” rather than black boxes. “Engineers are designing systems that generate, validate, and learn at speed. And the problems are genuinely hard: Multimodal data fusion, closed-loop optimization, uncertainty quantification, and interpretability at the point of clinical decision-making,” she adds. In taking on such technically demanding challenges, engineers and scientists have the opportunity to contribute to the research and development of potentially life-changing treatments for many diseases, says Sapra. “The biologic medicines we can develop today, and those we’ll design tomorrow, depend on combining world-class AI and engineering talent with deep scientific expertise.” This article has been initiated and funded by AstraZeneca.  Z4-85058, July 2026. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

On July 3, as a heat wave swept the region, New York State’s grid imported 52 gigawatt-hours of electricity from Canada—enough to meet about 9% of its total electricity demand that day. Some of that power shuttled in on a 339-mile power line stretching from Quebec to Queens called the Champlain Hudson Power Express (CHPE). It opened in May and is officially the longest underground transmission line in North America. An underground power line might not sound all that exciting, but this could be a big deal for the state’s grid planning, and for emissions. It could provide up to 20% of New York City’s electricity demand, largely with abundant hydropower from Quebec. One wrinkle: The line has been down for most of this month, and some experts are concerned about how drought will affect the power supply feeding it. Let’s look at how the CHPE transmission line could help shape the future of our grid, and what barriers it needs to overcome to make a difference.
Planning for the CHPE (which is charmingly pronounced “chippy”) started 15 years ago, with the permitting process formally beginning in March 2010. The vision was to build infrastructure to better connect Quebec and southern New York. Over 99% of Quebec’s electricity comes from renewable sources; most demand is met with hydropower, though the province’s wind capacity is growing quickly. New York has some hydropower of its own, as well as nuclear and wind, but the state still relies on fossil fuels for most of its energy generation.
Transmission Developers, a company owned by the alternative asset management firm Blackstone, and Hydro-Québec, the province’s manager of generation and transmission, partnered to build CHPE. Construction began in late 2022 and wrapped up earlier this year. The total cost for the privately funded project turned out to be  $6 billion. The construction of this line was a feat. It’s made up of a bundle of two high-voltage direct-current power cables, each measuring roughly five inches across. Developers buried the bundle underground or underwater across the length of New York State. Much of the line was laid at the bottom of the Hudson River, requiring special boats that shot water jets deep into the sediment to create trenches for the cable. Connecting grids together can help accelerate the transition away from fossil fuels. The ability to move electricity to where it’s needed could also help limit the amount of new capacity we need to build. Research has shown that interconnection can help cut emissions and lower system costs. But CHPE is off to a slow start and has seen two outages so far. The first, on July 1, was reportedly caused by a trip at a converter on the Canadian side of the border. The second outage began on July 4, and the power line is still down as of the morning of July 22. Some experts say this isn’t unusual for a new infrastructure project. Other power lines have seen similar startup challenges, and the equipment hasn’t really been fully tested until it’s in operation, Normand Mousseau, a physics professor at Université de Montréal, told the Gazette. Officials traced the issue to a damaged section of cable on the US side of the border, and the company that manufactured the line sent experts to investigate the cause, according to reporting from RTO Insider, a trade publication.  The damaged portion of the cable has been removed and replaced, says Lynn St-Laurent, a spokesperson for Hydro-Québec. “It is currently estimated that the remaining work, including necessary post-repair testing, will be completed by the weekend.” Similar woes have afflicted the New England Clean Energy Connect line, which opened in January, stretching 145 miles from Quebec to Maine. That project has also seen outages, and very little additional energy has flowed into the Northeast.

The good news for New York is that the grid wasn’t relying on CHPE yet. “Our planning studies did not assume CHPE would be available this summer, and that was one reason the grid performed reliably during the heat wave earlier this month,” Kevin Lanahan, a spokesperson for the New York Independent System Operator, the state’s grid management company, said in a statement. “A core principle of reliability planning is not relying on any single project.”  The idea is that eventually, states and regions will be able to rely—at least in part—on these projects, so there is pressure to get them working smoothly: Building massive transmission lines is a major long-term investment. In future years, as the equipment gets stress-tested and utilities begin to feel more confident in the projects’ reliability, they could play a bigger role on the grid. One thing to keep an eye on moving forward is the condition of Quebec’s hydropower fleet: The region has seen intense drought for the past three years, eating into the water reserves used to generate electricity. That could mean there won’t always be abundant hydropower to ship across the border—even if the transmission lines are able to carry it.  This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.