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.

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 them
Environmental 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 US
A 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 billion
For 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 Europe
And 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 professors
You 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. 

Read our story about the technology that’s making it easier to detect and identify birds and their movements.

—Christian Elliott

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.)

+ It’s never too late to live your dreams. Here’s how these late bloomers did it.
+ Limbs feeling a bit tight? Try these stretches!
+ These English football fans went to the 1986 World Cup—and loved it so much they never came home.
+ How the invention of the humble paint tube revolutionized the world of art.

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.

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.

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 companies
Treasury 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 yet
AI’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 sight
And 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 dropped
There are, however, many more waiting in the wings. (NYT $)

5 Here’s how ICE gets its hands on Americans’ data
As 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 impact
As 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 fix
That’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 use
The 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 summer
It’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.

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.

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. 

Shape-shifting mirrors on NASA’s new space telescope could unveil Jupiters like our own

When NASA’s Nancy Grace Roman Space Telescope launches, as early as the end of next month, it will attempt one of astronomy’s most precise disappearing acts to date. It will carry the first space-bound “active” coronagraph, an instrument that effectively erases most of the light from a star during photography.

The technology will allow astronomers to take the first pictures of planets orbiting other stars that are similar to those in our solar system. Ultimately, it could pave the way for a future mission that could snap the first photos of Earth-like worlds.

“I hope it’s remembered for it being that critical stepping stone for … finding Earth 2.0,” says Brandon Creager, the instrument’s lead mechanical engineer.

Read the full story on the space telescope that could transform the search for distant planets.

—Eshan Raul

MIT Technology Review Narrated: PsiQuantum has a plan to make a massive quantum computer out of light

The machine that could change the world will be housed in a room that looks like a data center crossed with an ice cream factory. 

Inside, some 100 stainless-steel cabinets each hold hundreds of chips. On those chips, thousands of light particles will fly through a maze of optical switches and beam splitters. Each photon must be accounted for, because precisely measuring where it ends up will help answer questions that current computers might take millions of years to solve.

This computer, as described, does not exist. It’s the brainchild of a company called PsiQuantum, founded in 2016 by four physicists from UK universities. In a crowded field of deep-pocketed competitors with similarly fantastical visions, the company aims to be the first to build a useful quantum machine.

—James O’Donnell

This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we publish each week on Spotifyand Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 OpenAI says one of its models carried out an autonomous hack
It escaped its testing sandbox and breached AI research platform Hugging Face. (Reuters $)
+ OpenAI described it as a cybersecurity test that went badly wrong. (WSJ $)
+ The hack is among the first known cyberattacks by an AI acting on its own. (FT $)
+ Even simple AI attacks are cause for alarm, though. (MIT Technology Review)

2 France has become the first EU country to ban social media for under-15s
Its parliament approved the ban, which President Macron championed. (NYT $)
+ He pledged to enforce it by September, the start of the school year. (Guardian)
+ But critics say it’s unconstitutional and impossible to enforce. (NPR)

3 The US and China will hold talks over AI in September
Treasury Secretary Scott Bessent will lead the US side. (Reuters $)
+ Chinese models have Trump’s AI world at war with itself. (MIT Technology Review)

4 Publishers are considering cutting Google off as AI reshapes search
News outlets are weighing lost traffic against AI exposure. (WSJ $)

5 Samsung is in talks to invest €1 billion in Mistral
The French AI firm is positioning itself as an alternative to US models. (FT $)
+ It’s Europe’s leading AI firm, but US peers dwarf its $20 billion valuation. (Reuters $)

6 Amazon pushed up rivals’ prices, leaked records allege
Internal emails reveal tactics that allegedly reshaped online pricing. (Guardian)

7 Trump has tapped a Big Tech critic to lead the DOJ’s antitrust division
Adam Candeub has called for tougher federal competition enforcement. (FT $)

8 New drilling methods could unlock geothermal energy almost anywhere
They aim to unlock Earth’s enormous heat reserves. (New Scientist $)
+ AI is uncovering hidden geothermal energy resources. (MIT Technology Review)

9 AI researchers have proposed a “Genie coefficient” for measuring AI risks
It would track the gap between intent and action. (IEEE Spectrum)
+ We need better ways to evaluate AI. (MIT Technology Review)

10 Japan’s AI boom has two unlikely winners: a toilet maker and an MSG giant
They’re supplying critical chipmaking materials. (CNBC)

Quote of the day

“He’s an analog man in a digital AI world, and I think that’s incredibly appealing.”

—Paul Dergarabedian, a movie industry analyst at Comscore, tells Fortune that Christopher Nolan’s commitment to human filmmaking provides an attractive counterweight to Hollywood’s embrace of AI.

One More Thing

DANA SMITH

Taiwan’s “silicon shield” could be weakening

Taiwan produces the majority of the world’s semiconductors and more than 90% of the most advanced chips needed for AI applications. Many believe that’s helped deter China from invading the island. But now some Taiwan specialists and citizens are worried that this “silicon shield” is cracking.

Facing pressure from Washington, TSMC—the world’s largest chipmaker—is expanding manufacturing abroad. In Taiwan, there are worries that this will dilute the company’s power at home, making the US and other countries less inclined to defend the island.

Find out why Taiwan’s chipmaking dominance could be key to its future security.

—Johanna M. Costigan

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.)

+ Thrifty filmmakers have masterfully recreated Star Wars on a $10 budget.
+ A man discovered squirrels hug and kiss their loved ones in the privacy of their homes.
+ Toronto’s floating waterfront store is reimagining one of the most familiar spaces across cultures.
+ These animations of Sesame Street characters performing classic tracks like Underworld’s “Born Slippy” will brighten up your day.

When NASA’s Nancy Grace Roman Space Telescope launches, as early as the end of next month, it will attempt one of astronomy’s most precise disappearing acts to date. The telescope will carry the first space-bound “active” coronagraph, an instrument that effectively erases most of the light from a star during photography.

It will allow astronomers to take the first pictures of planets orbiting other stars that are similar to those in our solar system. Ultimately, it could pave the way for a future mission that could snap the first photos of Earth-like worlds.

“I hope it’s remembered for it being that critical stepping stone for … finding Earth 2.0,” says Brandon Creager, the instrument’s lead mechanical engineer at NASA’s Jet Propulsion Laboratory (JPL).

Named after Nancy Grace Roman, NASA’s first chief of astronomy, this new telescope will carry a roughly 300-megapixel wide-field camera that will enable it to capture images about 100 times larger than the Hubble Space Telescope’s widest exposures at a similar resolution.

These capabilities will help astronomers unpack the mysterious identities of dark matter and dark energy—and to detect around 100,000 new exoplanets, planets outside our solar system, whose presence can be inferred from the way they distort the starlight of more distant stars. Javier Viaña, a research scientist at Harvard who has had two projects selected for Roman’s highly competitive first year of observing, compares the leap to moving from “interviewing a handful of people” to “conducting a global census.”

Another camera will use the coronagraph, blocking out a star’s light as it observes one stellar system at a time. The instrument will allow astronomers an unprecedented look at the space around stars, enabling them to see smaller, dimmer, and more close-in exoplanets. “It’s giving us the ability to see planets that we haven’t been able to physically see before,” says Creager.

The anatomy of a vanishing trick

Coronagraphs in space aren’t new. But earlier incarnations, such as those currently aboard Hubble and the James Webb Space Telescope, use a stationary system to block a star’s blinding light. The approach does help, but it’s a bit like putting your thumb over a flashlight while searching a dark room for a firefly. Though the bulb vanishes, stray glare can still escape and overwhelm the light of the insect. Inside a telescope, that glare can come from light leaking around the edges of machinery or from minuscule imperfections in mirrors and coatings that can scatter starlight into speckles. All this can hide, or even impersonate, a planet.

Roman’s coronagraph, however, will attempt something completely unseen in space telescopes until this year: Before each observation, it will measure that leftover light and try to suppress it, a technique known as active wavefront control.

The telescope is able to do this because it contains two deformable mirrors. Each has a 48-by-48 checkerboard of actuators (tiny pistons) beneath a thin, deformable sheet of glass. Applying a small amount of voltage makes the actuators contract and tug their patches of mirror slightly backward, like thousands of microscopic fingers delicately sculpting a surface.

The effect is very subtle: Each patch of mirror can deform by up to 0.5 micrometers, or about one-fourth the size of an E. coli bacterium, and in increments as small as approximately 10 picometers. That’s about a tenth the diameter of a hydrogen atom, says Ilya Poberezhskiy, the instrument’s project systems engineer at JPL.

The actuators allow the mirrors to create an “active wavefront,” where each component is moved to the perfect position to cancel out incoming waves of unwanted light—a bit like a pair of noise-canceling headphones, but for light instead of sound. The “canceled-out” light creates a “doughnut-shaped region around the star where we suppress starlight and where we’re hoping to see exoplanets,” says Poberezhskiy.

Compared with current space-based coronagraphs, the system is expected to improve sensitivity to exoplanets against the glare of their host stars by a factor of up to 1,000, revealing planets that would have been far too faint to detect before.

Like Hubble and JWST, Roman also uses masks, patterned plates placed in the path of the light that are designed to block the photons that run into them. One tool in Roman’s mask arsenal is “silicon grass,” a thicket of microscopic spikes on some masks that can be used in certain configurations to absorb photons so they don’t bounce around the telescope and accidentally reach a detector.

Light entering the forest bounces deeper and deeper between the blades and gets trapped instead of reflecting back toward the camera. “Once the light gets into there, it never gets out,” Poberezhskiy says. The mirrors and masks form a succession of gates and hedges to guide as much of the preserved planetary light as possible toward the final detector.

Alien Jupiters

This elaborate setup could open a new chapter in the direct imaging of exoplanets. Nearly all exoplanets photographed so far are oversize youngsters that are nothing like the residents of our solar system: several times the mass of Jupiter, still glowing with the heat left over from their birth, and orbiting tens or hundreds of times farther from their star than the Earth is from the sun. This is because they are relatively easy to see. Their size, warmth, and distance from their parent star makes them shine brightly in infrared light, far away from the worst of the stellar glare.

Roman, however, could directly image a true Jupiter analogue—a planet similar to Jupiter in mass and circling a sunlike star a few times farther out than Earth is from our sun. Unlike the hot Jupiters we can see now, this one would be a much more mature gas giant like ours, primarily reflecting its parent star’s light after billions of years of cooling instead of heavily emitting its own.

Astronomers have been able to infer the existence of such planets from the gravitational wobble they impart to the star. Roman instead will collect starlight reflected from the planet itself. “We’re not looking at the star. We’re not looking at the effect of the planet on the star,” says Meredith MacGregor, a professor of astronomy at Johns Hopkins who has also secured an observing program. “We are actually looking at the planet, and that is super powerful.”

Once this instrument becomes available, it will become the scientists’ turn to do their jobs. “I’m honestly a little terrified about how we’re all going to deal with it, because I think it’s just so much data,” MacGregor says. “I think people will legitimately still be working on Roman data for decades.”

But don’t expect to see a 4K photo of an alien Jupiter in the coming months. Roman will not be able to resolve such a planet into a solid globe—at best, it will likely resemble a smattering of pixels. Still, that will be enough, MacGregor says, as Roman can then use the coronagraph to get information on the various wavelengths of light from the planet, which can tell astronomers about its atmospheric chemistry.

“You’re taking something that’s a point of light and turning it into an actual world,” she says, “because if you know that about its atmosphere, now you know something about the surface of the planet and the possibility of life being on that planet, right? So that’s a big step.”

During its first observations, scientists and engineers will see whether they can hold a star at the very center of the coronagraph’s masks, shape the mirrors, “dig” the dark doughnut (as Poberezhskiy describes it), and then maintain everything as the spacecraft moves through space and actively changes temperature.

The results will inform NASA’s proposed Habitable Worlds Observatory, the daydream of many an exoplanet astronomer, which will in theory be able to separate the light of an Earthlike planet from that of a sunlike star, over 10 billion times brighter.

Creager, who has worked on the instrument since 2018, is proud of the achievement: “Not too many people get to say, ‘I built something and it’s taking a picture of a planet that’s at a star that’s 50 light-years away or 100 light-years away.’” He imagines the moment he and his team will be able to look at the first image as it arrives: “Yes, we did that.” While the planet may show up only as a tiny dot, Roman’s achievement will be the darkness engineered around it.

China’s AI models have Trump’s AI world at war with itself

Last weekend, several current and former advisers to President Donald Trump on AI publicly lobbed insults at the country’s leading AI companies. David Sacks branded Anthropic’s models “lobotomized” and “woke.” Emil Michael, a top Pentagon official, called OpenAI’s new head of strategic futures a “supreme village idiot.”

It began because no one can agree on what to do about Kimi, a free, open-source model that Chinese AI company Moonshot launched last week. It appears to rival the intelligence of models from OpenAI and Anthropic, which are very much not free. 

Every time a new smart, free model from China gets released, US companies see less reason to fork out money for models from Anthropic or OpenAI. That’s creating economic and political problems for the president—and dividing the top AI strategists in his orbit. 

Read the full story on why no one can agree what to do about Kimi.

—James O’Donnell

This article is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Anthropic’s record $1.5 billion copyright settlement has been approved
The plaintiffs said Anthropic used pirated works to train Claude. (Reuters $)
+ And won the largest known copyright payout in history. (Engadget)
+ Yet many authors and creators still don’t view it as a win. (TechCrunch)
+ But AI copyright anxiety could limit creativity. (MIT Technology Review)
 
2 The Trump administration is weighing a ban on Chinese AI models
The launch of Kimi K3 has revived calls for restrictions. (Axios)
+ But officials are divided on the proposals. (Fast Company)
+ China’s bet on open-source is paying off. (MIT Technology Review)
 
3 China is mulling tighter export controls on AI models and chips
It wants to stop the West from acquiring its tech and startups. (FT $)
+ Beijing has held talks with tech firms about potential restrictions. (Reuters $)
 
4 Trump’s AI safety head has resigned after just three months
Chris Fall had led CAISI, the federal AI Safety Institute, since April. (Axios)
+ No reason was given for his exit. (CNBC)
 
5 Google is working on a new chip to run Gemini models more efficiently 
The chip, called “Frozen V2,” may be deployed in 2028. (Information $)
+ Alphabet stock popped on the report. (CNBC)

6 New Orleans police have explored arming drones with weapons
A draft drone manual paves the way for weaponised quadcopters. (404 Media)
+ Shoplifters could soon be chased by drones. (MIT Technology Review)
 
7 The EU has handed AliExpress a record fine over unsafe product sales
The €550 million fine is the largest-ever under the Digital Services Act. (BBC)
+ Alibaba has vowed to appeal the fine. (SCMP)

8 Election advice from AI chatbots is “inaccurate and unreliable”
That’s the conclusion from tests in Hungary earlier this year. (Guardian)

9 Red light therapy is showing promise for healing and healthy aging
Better skin and reduced vision loss are also on the cards. (Economist $)

10 Neill Blomkamp’s new horror clip is all AI-generated—and it sucks
The acclaimed director wants to make “a full feature in this format.” (Gizmodo)

Quote of the day

“This would be a terribly self-defeating form of intervention if it were to happen.” 

—Tech investor Chamath Palihapitiya slams plans to restrict Chinese AI models in a post on X.

One More Thing

AKILAH TOWNSEND

Inside Chicago’s surveillance panopticon

Early on the morning of September 2, 2024, four people were shot and killed on a westbound train in Chicago. Police swiftly activated a digital dragnet—a surveillance network that connects thousands of cameras across the city—and arrested the suspect just 90 minutes later.

Law enforcement and security advocates say this vast monitoring system protects public safety and works well. But activists and many residents say it’s a surveillance panopticon that creates a chilling effect on behavior and violates guarantees of privacy and free speech.

Go inside the surveillance network that’s dividing Chicago.

—Rod McCullom

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.)

+ NASA has shared a stunning timelapse video of the Psyche spacecraft’s view of Mars.
+ This comparison of American and European Urbanism shows good city design is a choice.
+ Musician Luca Stricagnoli recently performed a marvellous acoustic guitar medley of Prodigy songs.
+ Two Australian paddleboarders saved a stranded wallaby after it was swept out to sea—and caught the whole rescue on video.

Every new generation of AI technology demands more processing power, more memory, greater energy efficiency, and higher reliability. Every increase in computing performance increases the physical demands placed on the systems that make and run AI.

Delivering these gains depends not only on advances in chip design and system architecture, but on advances in the materials that enable them to perform under extreme conditions.

As AI continues to push the physical limits of semiconductors and data center infrastructure, advanced materials are no longer simply supporting innovation in this area; they are defining the limits of what is possible.

Performance first

Advanced materials exist to solve performance challenges. As AI raises the bar, these challenges are becoming more demanding.

Manufacturing a semiconductor chip today requires thousands of tightly controlled process steps, with almost no room for error. Tiny variations in temperature or chemical instability can create defects that reduce yield and drive up manufacturing costs. With every new generation of semiconductor chips, manufacturers seek advanced materials that can deliver greater purity, higher chemical and plasma resistance, and better stability under increasingly harsh operating conditions.

These are familiar engineering challenges being pushed to new extremes. And it’s here that materials innovation makes the difference with continuous advances in polymers, elastomers, specialty fluids, and other advanced materials that make each new generation of technology possible.

For materials companies, it’s not about reinventing semiconductor manufacturing but about ensuring the materials supporting the industry continue to evolve alongside it. This same principle applies beyond the semiconductor fabrication floor. As AI workloads become more demanding, the physical infrastructure that powers them is evolving rapidly.

Increasing computing density is transforming data center design, driving the need for more sophisticated thermal management, higher-voltage power architectures, increased data storage, and faster, more reliable data transmission. Every part of the system is under greater pressure, from cooling and power management to critical electronic components, such as connectors, capacitors, and hard disk drives.

At Syensqo, we’re building on our expertise in electronic and electrical components, along with insights from other markets, to meet these emerging needs.

For example, as data centers shift to higher-voltage architectures and greater power density, many of the materials challenges we face closely mirror those of electric vehicles. Fluid-circulation know-how from semiconductor and automotive coolant systems, for instance, can be adapted to direct liquid-cooling designs for AI servers. By transferring knowledge across markets, we can accelerate new power and thermal management solutions while supporting the reliability required by next-generation AI infrastructure.

Whether we’re talking about semiconductor fabrication or hyperscale server farms, the challenge for materials science companies is the same: enabling greater performance without compromising reliability.

A new definition of what performance means

While performance remains the first priority, the way performance is defined is changing.

In addition to meeting the increasingly demanding technical requirements of next-generation semiconductors and data centers, there is now an expectation that these materials are developed and manufactured more responsibly.

Perfluoroelastomers, for example, are used to seal semiconductor manufacturing equipment. These materials operate under extreme temperatures, aggressive plasma, and highly reactive chemicals.

To make the process more sustainable, at Syensqo, our next generation of perfluoroelastomers use a fluorosurfactant-free manufacturing process. Our goal was to make a better-performing material, produced in a better way, ensuring manufacturers no longer have to choose between higher performance and a more responsible way of producing the materials that enable it.

This approach reflects a broader reality across the industry.

New materials aren’t adopted simply because they are new. Qualification can take years, and manufacturers only make changes when a material solves a genuine engineering challenge or enables new technology.

Performance remains the price of entry. The difference today is that the definition of performance has expanded. Success increasingly depends on delivering technical excellence through more responsible manufacturing from the outset.

Accelerating the pace of discovery

As the performance bar rises, the way we innovate must evolve with it.

Developing advanced materials has traditionally involved a lengthy process of hypothesis, synthesis, testing, and iteration. While this process remains unchanged, new digital tools are helping researchers move through these cycles faster. By helping researchers identify the most promising candidates earlier, AI can reduce the number of physical experiments required and accelerate the earliest stages of materials discovery.

AI isn’t replacing scientific expertise. It’s helping scientists apply that expertise more effectively, allowing them to spend less time searching for answers and more time solving the industry’s toughest challenges.

At Syensqo, we’re putting this approach into practice through use of several AI tools, including the Microsoft Discovery platform, which are helping researchers identify and evaluate promising molecular candidates for next-generation heat transfer fluids, used in semiconductor manufacturing and data centers.

AI helps our researchers rapidly identify and evaluate promising molecular candidates based on the properties they need to achieve. This allows us to focus laboratory work where it has the greatest potential to deliver results, accelerating discovery and reducing the time needed to turn promising materials into solutions customers can qualify and deploy.

The journey from laboratory discovery to a qualified material will always require scientific expertise, rigorous testing, and close collaboration with customers. But by accelerating the earliest stages of discovery, AI can help materials innovation keep pace with the evolving needs of industries such as semiconductors, electronics, and data centers.

Progress is earned

The future of artificial intelligence will depend on better algorithms, more powerful chips, and larger computing infrastructure. But sustaining that progress will also require advances in the materials that make those technologies possible.

Whether in semiconductor manufacturing or AI infrastructure, progress is earned. Every new generation of technologies raises the bar, and every new material must prove it can deliver the performance, reliability, and efficiency needed before it earns its place.

For materials companies, that remains both the challenge and the opportunity.

This content was produced by Syensqo. It was not written by MIT Technology Review’s editorial staff.