
Crypto executives have tipped Ether to rise as high as 200% by the end of the year, led by corporate Ether purchases, ETF accumulation and Ether locked in staking.


Crypto executives have tipped Ether to rise as high as 200% by the end of the year, led by corporate Ether purchases, ETF accumulation and Ether locked in staking.
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.
Big Tech’s big bet on a controversial carbon removal tactic
Microsoft, JP MorganChase, and a tech company consortium that includes Alphabet, Meta, Shopify, and Stripe have all recently struck multimillion-dollar deals to pay paper mill owners to capture at least hundreds of thousands of tons of this greenhouse gas by installing carbon scrubbing equipment in their facilities.
The captured carbon dioxide will then be piped down into saline aquifers more than a mile underground, where it should be sequestered permanently.
Big Tech is suddenly betting big on this form of carbon removal, known as bioenergy with carbon capture and storage, or BECCS. But experts have raised a number of concerns. Read the full story.
—James Temple
2025 climate tech companies to watch: Kairos Power and its next-generation nuclear reactors
Like many new nuclear startups, Kairos promises a path to reliable, 24/7 decarbonized power. Unlike most, it already has prototypes under construction and permits for several reactors.
The company uses molten salt to cool its reactions and transfer heat, rather than the high-pressure water that’s used in existing fission reactors. It hopes its technology will enable commercial reactors that are cost-competitive with natural gas plants and boast safer operation than conventional reactors, even in the event of complete power loss. Read the full story.
—Mark Harris
Kairos Power is one of our 10 climate tech companies to watch—our annual list of some of the most promising climate tech firms on the planet. Check out the rest of the list here.
MIT Technology Review Narrated: Inside the strange limbo facing millions of IVF embryos
Millions of embryos created through IVF sit frozen in time, stored in tanks around the world. The number is only growing thanks to advances in technology, the rising popularity of IVF, and improvements in its success rates.
At a basic level, an embryo is simply a tiny ball of a hundred or so cells. But unlike other types of body tissue, it holds the potential for life. Many argue that this endows embryos with a special moral status, one that requires special protections.
The problem is that no one can really agree on what that status is. While these embryos persist in suspended animation, patients, clinicians, embryologists, and legislators must grapple with the essential question of what we should do with them. What do these embryos mean to us? Who should be responsible for them?
This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 ChatGPT will start talking dirty to verified adults
The chatbot is getting a new erotica function as part of OpenAI’s bid to “safely relax” its restrictions. (The Verge)
+ The company has created its own wellness council to inform its decisions. (Ars Technica)
+ It’s surprisingly easy to stumble into a relationship with an AI chatbot. (MIT Technology Review)
2 A secret surveillance empire tracked thousands of people across the world
The European-led First Wap has operated covertly for more than two decades. (Mother Jones)
+ The group ran at least 10 scam compounds across the country. (Wired $)
+ Inside a romance scam compound—and how people get tricked into being there. (MIT Technology Review)
3 YouTube ran Israel-funded ads claiming there was food in famine-struck Gaza
And allowed them to remain online even after complaints from multiple government authorities. (WP $)
+ Companies have denied they’re involved in rebuilding Gaza. (Wired $)
4 Instagram wants to become a more teen-friendly space
It’s bringing in new age-gating measures inspired by the PG-13 movie rating. (NBC News)
+ The policy will also extend to its chatbots. (NYT $)
5 A massive Cambodia-based pig butchering scheme has been foiled
It’s the biggest forfeiture action the US Department of Justice has ever pursued. (CNBC)
6 Waymo’s driverless taxis are coming to London
From next year, it says pedestrians will be able to hail its robotaxis. (WSJ $)
7 Black patients were failed by a race-based medical calculation
It delayed their access to life-saving kidney transplants. (The Markup)
+ A woman in the US is the third person to receive a gene-edited pig kidney. (MIT Technology Review)
8 AI flood forecasting is helping farmers across the world
Nonprofits are using it to deliver early aid. (Rest of World)
9 A man with paralysis can feel objects through another person’s hand
Thanks to a new brain implant. (New Scientist $)
+ Meet the other companies developing brain-computer interfaces. (MIT Technology Review)
10 Tech internships are alive and well
Despite all the AI angst. (Insider $)
Quote of the day
“You made ChatGPT “pretty restrictive”? Really. Is that why it has been recommending kids harm and kill themselves?”
—Josh Hawley, US Senator for Missouri, reacts to the news OpenAI is planning to loosen its restrictions in a post on X.
One more thing

Why we should thank pigeons for our AI breakthroughs
People looking for precursors to artificial intelligence often point to science fiction by authors like Isaac Asimov or thought experiments like the Turing test. But an equally important, if surprising and less appreciated, forerunner is American psychologist B.F. Skinner’s research with pigeons in the middle of the 20th century.
Skinner believed that association—learning, through trial and error, to link an action with a punishment or reward—was the building block of every behavior, not just in pigeons but in all living organisms, including human beings.
His “behaviorist” theories fell out of favor with psychologists and animal researchers in the 1960s but were taken up by computer scientists who eventually provided the foundation for many of the artificial-intelligence tools from leading firms like Google and OpenAI. Read the full story.
—Ben Crair
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)
+ I love the sound of Grateful Fishing TV—starring two fishermen who just love hanging out and frying some fish. Truly wholesome stuff (thanks to Chino Moreno via Perfectly Imperfect for the recommendation!)
+ Rest in power D’Angelo, your timeless tunes will live on.
+ If you’re into stress-watches, this list is full of anxiety-inducing classics.
+ One of the world’s longest dinosaur superhighways has been uncovered in a sleepy part of England.
Artificial intelligence has always promised speed, efficiency, and new ways of solving problems. But what’s changed in the past few years is how quickly those promises are becoming reality. From oil and gas to retail, logistics to law, AI is no longer confined to pilot projects or speculative labs. It is being deployed in critical workflows, reducing processes that once took hours to just minutes, and freeing up employees to focus on higher-value work.

“Business process automation has been around a long while. What GenAI and AI agents are allowing us to do is really give superpowers, so to speak, to business process automation.” says chief AI architect at Cloudera, Manasi Vartak.
Much of the momentum is being driven by two related forces: the rise of AI agents and the rapid democratization of AI tools. AI agents, whether designed for automation or assistance, are proving especially powerful at speeding up response times and removing friction from complex workflows. Instead of waiting on humans to interpret a claim form, read a contract, or process a delivery driver’s query, AI agents can now do it in seconds, and at scale.
At the same time, advances in usability are putting AI into the hands of nontechnical staff, making it easier for employees across various functions to experiment, adopt and adapt these tools for their own needs.
That doesn’t mean the road is without obstacles. Concerns about privacy, security, and the accuracy of LLMs remain pressing. Enterprises are also grappling with the realities of cost management, data quality, and how to build AI systems that are sustainable over the long term. And as companies explore what comes next—including autonomous agents, domain-specific models, and even steps toward artificial general intelligence—questions about trust, governance, and responsible deployment loom large.
“Your leadership is especially critical in making sure that your business has an AI strategy that addresses both the opportunity and the risk while giving the workforce some ability to upskill such that there’s a path to become fluent with these AI tools,” says principal advisor of AI and modern data strategy at Amazon Web Services, Eddie Kim.
Still, the case studies are compelling. A global energy company cutting threat detection times from over an hour to just seven minutes. A Fortune 100 legal team saving millions by automating contract reviews. A humanitarian aid group harnessing AI to respond faster to crises. Long gone are the days of incremental steps forward. These examples illustrate that when data, infrastructure, and AI expertise come together, the impact is transformative.
The future of enterprise AI will be defined by how effectively organizations can marry innovation with scale, security, and strategy. That’s where the real race is happening.
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. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
It’s the 25th of June and I’m shivering in my lab-issued underwear in Fort Worth, Texas. Libby Cowgill, an anthropologist in a furry parka, has wheeled me and my cot into a metal-walled room set to 40 °F. A loud fan pummels me from above and siphons the dregs of my body heat through the cot’s mesh from below. A large respirator fits snug over my nose and mouth. The device tracks carbon dioxide in my exhales—a proxy for how my metabolism speeds up or slows down throughout the experiment. Eventually Cowgill will remove my respirator to slip a wire-thin metal temperature probe several pointy inches into my nose.
Cowgill and a graduate student quietly observe me from the corner of their so-called “climate chamber.” Just a few hours earlier I’d sat beside them to observe as another volunteer, a 24-year-old personal trainer, endured the cold. Every few minutes, they measured his skin temperature with a thermal camera, his core temperature with a wireless pill, and his blood pressure and other metrics that hinted at how his body handles extreme cold. He lasted almost an hour without shivering; when my turn comes, I shiver aggressively on the cot for nearly an hour straight.
I’m visiting Texas to learn about this experiment on how different bodies respond to extreme climates. “What’s the record for fastest to shiver so far?” I jokingly ask Cowgill as she tapes biosensing devices to my chest and legs. After I exit the cold, she surprises me: “You, believe it or not, were not the worst person we’ve ever seen.”
Climate change forces us to reckon with the knotty science of how our bodies interact with the environment.
Cowgill is a 40-something anthropologist at the University of Missouri who powerlifts and teaches CrossFit in her spare time. She’s small and strong, with dark bangs and geometric tattoos. Since 2022, she’s spent the summers at the University of North Texas Health Science Center tending to these uncomfortable experiments. Her team hopes to revamp the science of thermoregulation.
While we know in broad strokes how people thermoregulate, the science of keeping warm or cool is mottled with blind spots. “We have the general picture. We don’t have a lot of the specifics for vulnerable groups,” says Kristie Ebi, an epidemiologist with the University of Washington who has studied heat and health for over 30 years. “How does thermoregulation work if you’ve got heart disease?”
“Epidemiologists have particular tools that they’re applying for this question,” Ebi continues. “But we do need more answers from other disciplines.”
Climate change is subjecting vulnerable people to temperatures that push their limits. In 2023, about 47,000 heat-related deaths are believed to have occurred in Europe. Researchers estimate that climate change could add an extra 2.3 million European heat deaths this century. That’s heightened the stakes for solving the mystery of just what happens to bodies in extreme conditions.
Extreme temperatures already threaten large stretches of the world. Populations across the Middle East, Asia, and sub-Saharan Africa regularly face highs beyond widely accepted levels of human heat tolerance. Swaths of the southern US, northern Europe, and Asia now also endure unprecedented lows: The 2021 Texas freeze killed at least 246 people, and a 2023 polar vortex sank temperatures in China’s northernmost city to a hypothermic record of –63.4 °F.
This change is here, and more is coming. Climate scientists predict that limiting emissions can prevent lethal extremes from encroaching elsewhere. But if emissions keep course, fierce heat and even cold will reach deeper into every continent. About 2.5 billion people in the world’s hottest places don’t have air-conditioning. When people do, it can make outdoor temperatures even worse, intensifying the heat island effect in dense cities. And neither AC nor radiators are much help when heat waves and cold snaps capsize the power grid.
“You, believe it or not, were not the worst person we’ve ever seen,” the author was told after enduring Cowgill’s “climate chamber.”
Through experiments like Cowgill’s, researchers around the world are revising rules about when extremes veer from uncomfortable to deadly. Their findings change how we should think about the limits of hot and cold—and how to survive in a new world.
Archaeologists have known for some time that we once braved colder temperatures than anyone previously imagined. Humans pushed into Eurasia and North America well before the last glacial period ended about 11,700 years ago. We were the only hominins to make it out of this era. Neanderthals, Denisovans, and Homo floresiensis all went extinct. We don’t know for certain what killed those species. But we do know that humans survived thanks to protection from clothing, large social networks, and physiological flexibility. Human resilience to extreme temperature is baked into our bodies, behavior, and genetic code. We wouldn’t be here without it.
“Our bodies are constantly in communication with the environment,” says Cara Ocobock, an anthropologist at the University of Notre Dame who studies how we expend energy in extreme conditions. She has worked closely with Finnish reindeer herders and Wyoming mountaineers.
But the relationship between bodies and temperature is surprisingly still a mystery to scientists. In 1847, the anatomist Carl Bergmann observed that animal species grow larger in cold climates. The zoologist Joel Asaph Allen noted in 1877 that cold-dwellers had shorter appendages. Then there’s the nose thing: In the 1920s, the British anthropologist Arthur Thomson theorized that people in cold places have relatively long, narrow noses, the better to heat and humidify the air they take in. These theories stemmed from observations of animals like bears and foxes, and others that followed stemmed from studies comparing the bodies of cold-accustomed Indigenous populations with white male control groups. Some, like those having to do with optimization of surface area, do make sense: It seems reasonable that a tall, thin body increases the amount of skin available to dump excess heat. The problem is, scientists have never actually tested this stuff in humans.
“Our bodies are constantly in communication with the environment.”
Cara Ocobock, anthropologist, University of Notre Dame
Some of what we know about temperature tolerance thus far comes from century-old race science or assumptions that anatomy controls everything. But science has evolved. Biology has matured. Childhood experiences, lifestyles, fat cells, and wonky biochemical feedback loops can contribute to a picture of the body as more malleable than anything imagined before. And that’s prompting researchers to change how they study it.
“If you take someone who’s super long and lanky and lean and put them in a cold climate, are they gonna burn more calories to stay warm than somebody who’s short and broad?” Ocobock says. “No one’s looked at that.”
Ocobock and Cowgill teamed up with Scott Maddux and Elizabeth Cho at the Center for Anatomical Sciences at the University of North Texas Health Fort Worth. All four are biological anthropologists who have also puzzled over whether the rules Bergmann, Allen, and Thomson proposed are actually true.
For the past four years, the team has been studying how factors like metabolism, fat, sweat, blood flow, and personal history control thermoregulation.
Your native climate, for example, may influence how you handle temperature extremes. In a unique study of mortality statistics from 1980s Milan, Italians raised in warm southern Italy were more likely to survive heat waves in the northern part of the country.
Similar trends have appeared in cold climes. Researchers often measure cold tolerance by a person’s “brown adipose,” a type of fat that is specialized for generating heat (unlike white fat, which primarily stores energy). Brown fat is a cold adaptation because it delivers heat without the mechanism of shivering. Studies have linked it to living in cold climates, particularly at young ages. Wouter van Marken Lichtenbelt, the physiologist at Maastricht University who with colleagues discovered brown fat in adults, has shown that this tissue can further activate with cold exposure and even help regulate blood sugar and influence how the body burns other fat.
That adaptability served as an early clue for the Texas team. They want to know how a person’s response to hot and cold correlates with height, weight, and body shape. What is the difference, Maddux asks, between “a male who’s 6 foot 6 and weighs 240 pounds” and someone else in the same environment “who’s 4 foot 10 and weighs 89 pounds”? But the team also wondered if shape was only part of the story.
Their multi-year experiment uses tools that anthropologists couldn’t have imagined a century ago—devices that track metabolism in real time and analyze genetics. Each participant gets a CT scan (measuring body shape), a DEXA scan (estimating percentages of fat and muscle), high-resolution 3D scans, and DNA analysis from saliva to examine ancestry genetically.
Volunteers lie on a cot in underwear, as I did, for about 45 minutes in each climate condition, all on separate days. There’s dry cold, around 40 °F, akin to braving a walk-in refrigerator. Then dry heat and humid heat: 112 °F with 15% humidity and 98 °F with 85% humidity. They call it “going to Vegas” and “going to Houston,” says Cowgill. The chamber session is long enough to measure an effect, but short enough to be safe.
Before I traveled to Texas, Cowgill told me she suspected the old rules would fall. Studies linking temperature tolerance to race and ethnicity, for example, seemed tenuous because biological anthropologists today reject the concept of distinct races. It’s a false premise, she told me: “No one in biological anthropology would argue that human beings do not vary across the globe—that’s obvious to anyone with eyes. [But] you can’t draw sharp borders around populations.”
She added, “I think there’s a substantial possibility that we spend four years testing this and find out that really, limb length, body mass, surface area […] are not the primary things that are predicting how well you do in cold and heat.”
In July 1995, a week-long heat wave pushed Chicago above 100 °F, killing roughly 500 people. Thirty years later, Ollie Jay, a physiologist at the University of Sydney, can duplicate the conditions of that exceptionally humid heat wave in a climate chamber at his laboratory.
“We can simulate the Chicago heat wave of ’95. The Paris heat wave of 2003. The heat wave [in early July of this year] in Europe,” Jay says. “As long as we’ve got the temperature and humidity information, we can re-create those conditions.”
“Everybody has quite an intimate experience of feeling hot, so we’ve got 8 billion experts on how to keep cool,” he says. Yet our internal sense of when heat turns deadly is unreliable. Even professional athletes overseen by experienced medics have died after missing dangerous warning signs. And little research has been done to explore how vulnerable populations such as elderly people, those with heart disease, and low-income communities with limited access to cooling respond to extreme heat.
Jay’s team researches the most effective strategies for surviving it. He lambastes air-conditioning, saying it demands so much energy that it can aggravate climate change in “a vicious cycle.” Instead, he has monitored people’s vital signs while they use fans and skin mists to endure three hours in humid and dry heat. In results published last year, his research found that fans reduced cardiovascular strain by 86% for people with heart disease in the type of humid heat familiar in Chicago.
Dry heat was a different story. In that simulation, fans not only didn’t help but actually doubled the rate at which core temperatures rose in healthy older people.
Heat kills. But not without a fight. Your body must keep its internal temperature in a narrow window flanking 98 °F by less than two degrees. The simple fact that you’re alive means you are producing heat. Your body needs to export that heat without amassing much more. The nervous system relaxes narrow blood vessels along your skin. Your heart rate increases, propelling more warm blood to your extremities and away from your organs. You sweat. And when that sweat evaporates, it carries a torrent of body heat away with it.
This thermoregulatory response can be trained. Studies by van Marken Lichtenbelt have shown that exposure to mild heat increases sweat capacity, decreases blood pressure, and drops resting heart rate. Long-term studies based on Finnish saunas suggest similar correlations.
The body may adapt protectively to cold, too. In this case, body heat is your lifeline. Shivering and exercise help keep bodies warm. So can clothing. Cardiovascular deaths are thought to spike in cold weather. But people more adapted to cold seem better able to reroute their blood flow in ways that keep their organs warm without dropping their temperature too many degrees in their extremities.
Earlier this year, the biological anthropologist Stephanie B. Levy (no relation) reported that New Yorkers who experienced lower average temperatures had more productive brown fat, adding evidence for the idea that the inner workings of our bodies adjust to the climate throughout the year and perhaps even throughout our lives. “Do our bodies hold a biological memory of past seasons?” Levy wonders. “That’s still an open question. There’s some work in rodent models to suggest that that’s the case.”
Although people clearly acclimatize with enough strenuous exposures to either cold or heat, Jay says, “you reach a ceiling.” Consider sweat: Heat exposure can increase the amount you sweat only until your skin is completely saturated. It’s a nonnegotiable physical limit. Any additional sweat just means leaking water without carrying away any more heat. “I’ve heard people say we’ll just find a way of evolving out of this—we’ll biologically adapt,” Jay says. “Unless we’re completely changing our body shape, then that’s not going to happen.”
And body shape may not even sway thermoregulation as much as previously believed. The subject I observed, a personal trainer, appeared outwardly adapted for cold: his broad shoulders didn’t even fit in a single CT scan image. Cowgill supposed that this muscle mass insulated him. When he emerged from his session in the 40 °F environment, though, he had finally started shivering—intensely. The researchers covered him in a heated blanket. He continued shivering. Driving to lunch over an hour later in a hot car, he still mentioned feeling cold. An hour after that, a finger prick drew no blood, a sign that blood vessels in his extremities remained constricted. His body temperature fell about half a degree C in the cold session—a significant drop—and his wider build did not appear to shield him from the cold as well as my involuntary shivering protected me.
I asked Cowgill if perhaps there is no such thing as being uniquely predisposed to hot or cold. “Absolutely,” she said.
So if body shape doesn’t tell us much about how a person maintains body temperature, and acclimation also runs into limits, then how do we determine how hot is too hot?
In 2010 two climate change researchers, Steven Sherwood and Matthew Huber, argued that regions around the world become uninhabitable at wet-bulb temperatures of 35 °C, or 95 °F. (Wet-bulb measurements are a way to combine air temperature and relative humidity.) Above 35 °C, a person simply wouldn’t be able to dissipate heat quickly enough. But it turns out that their estimate was too optimistic.
Researchers “ran with” that number for a decade, says Daniel Vecellio, a bioclimatologist at the University of Nebraska, Omaha. “But the number had never been actually empirically tested.” In 2021 a Pennsylvania State University physiologist, W. Larry Kenney, worked with Vecellio and others to test wet-bulb limits in a climate chamber. Kenney’s lab investigates which combinations of temperature, humidity, and time push a person’s body over the edge.
Not long after, the researchers came up with their own wet-bulb limit of human tolerance: below 31 °C in warm, humid conditions for the youngest cohort, people in their thermoregulatory prime. Their research suggests that a day reaching 98 °F and 65% humidity, for example, poses danger in a matter of hours, even for healthy people.
Cowgill and her colleagues Elizabeth Cho (top) and Scott Maddux prepare graduate student Joanna Bui for a “room-temperature test.”
In 2023, Vecellio and Huber teamed up, combining the growing arsenal of lab data with state-of-the-art climate simulations to predict where heat and humidity most threatened global populations: first the Middle East and South Asia, then sub-Saharan Africa and eastern China. And assuming that warming reaches 3 to 4 °C over preindustrial levels this century, as predicted, parts of North America, South America, and northern and central Australia will be next.
Last June, Vecellio, Huber, and Kenney co-published an article revising the limits that Huber had proposed in 2010. “Why not 35 °C?” explained why the human limits have turned out to be lower than expected. Those initial estimates overlooked the fact that our skin temperature can quickly jump above 101 °F in hot weather, for example, making it harder to dump internal heat.
The Penn State team has published deep dives on how heat tolerance changes with sex and age. Older participants’ wet-bulb limits wound up being even lower—between 27 and 28 °C in warm, humid conditions—and varied more from person to person than they did in young people. “The conditions that we experience now—especially here in North America and Europe, places like that—are well below the limits that we found in our research,” Vecellio says. “We know that heat kills now.”
What this fast-growing body of research suggests, Vecellio stresses, is that you can’t define heat risk by just one or two numbers. Last year, he and researchers at Arizona State University pulled up the hottest 10% of hours between 2005 and 2020 for each of 96 US cities. They wanted to compare recent heat-health research with historical weather data for a new perspective: How frequently is it so hot that people’s bodies can’t compensate for it? Over 88% of those “hot hours” met that criterion for people in full sun. In the shade, most of those heat waves became meaningfully less dangerous.
“There’s really almost no one who ‘needs’ to die in a heat wave,” says Ebi, the epidemiologist. “We have the tools. We have the understanding. Essentially all [those] deaths are preventable.”
A year after visiting Texas, I called Cowgill to hear what she was thinking after four summers of chamber experiments. She told me that the only rule about hot and cold she currently stands behind is … well, none.
She recalled a recent participant—the smallest man in the study, weighing 114 pounds. “He shivered like a leaf on a tree,” Cowgill says. Normally, a strong shiverer warms up quickly. Core temperature may even climb a little. “This [guy] was just shivering and shivering and shivering and not getting any warmer,” she says. She doesn’t know why this happened. “Every time I think I get a picture of what’s going on in there, we’ll have one person come in and just kind of be a complete exception to the rule,” she says, adding that you can’t just gloss over how much human bodies vary inside and out.
The same messiness complicates physiology studies.
Jay looks to embrace bodily complexities by improving physiological simulations of heat and the human strain it causes. He’s piloted studies that input a person’s activity level and type of clothing to predict core temperature, dehydration, and cardiovascular strain based on the particular level of heat. One can then estimate the person’s risk on the basis of factors like age and health. He’s also working on physiological models to identify vulnerable groups, inform early-warning systems ahead of heat waves, and possibly advise cities on whether interventions like fans and mists can help protect residents. “Heat is an all-of-society issue,” Ebi says. Officials could better prepare the public for cold snaps this way too.
“Death is not the only thing we’re concerned about,” Jay adds. Extreme temperatures bring morbidity and sickness and strain hospital systems: “There’s all these community-level impacts that we’re just completely missing.”
Climate change forces us to reckon with the knotty science of how our bodies interact with the environment. Predicting the health effects is a big and messy matter.
The first wave of answers from Fort Worth will materialize next year. The researchers will analyze thermal images to crunch data on brown fat. They’ll resolve whether, as Cowgill suspects, your body shape may not sway temperature tolerance as much as previously assumed. “Human variation is the rule,” she says, “not the exception.”
Max G. Levy is an independent journalist who writes about chemistry, public health, and the environment.
For years at Orchard Care Homes, a 23‑facility dementia-care chain in northern England, Cheryl Baird watched nurses fill out the Abbey Pain Scale, an observational methodology used to evaluate pain in those who can’t communicate verbally. Baird, a former nurse who was then the facility’s director of quality, describes it as “a tick‑box exercise where people weren’t truly considering pain indicators.”
As a result, agitated residents were assumed to have behavioral issues, since the scale does not always differentiate well between pain and other forms of suffering or distress. They were often prescribed psychotropic sedatives, while the pain itself went untreated.
Then, in January 2021, Orchard Care Homes began a trial of PainChek, a smartphone app that scans a resident’s face for microscopic muscle movements and uses artificial intelligence to output an expected pain score. Within weeks, the pilot unit saw fewer prescriptions and had calmer corridors. “We immediately saw the benefits: ease of use, accuracy, and identifying pain that wouldn’t have been spotted using the old scale,” Baird recalls.
In nursing homes, neonatal units, and ICU wards, researchers are racing to turn pain into something a camera or sensor can score as reliably as blood pressure.
This kind of technology-assisted diagnosis hints at a bigger trend. In nursing homes, neonatal units, and ICU wards, researchers are racing to turn pain—medicine’s most subjective vital sign—into something a camera or sensor can score as reliably as blood pressure. The push has already produced PainChek, which has been cleared by regulators on three continents and has logged more than 10 million pain assessments. Other startups are beginning to make similar inroads in care settings.
The way we assess pain may finally be shifting, but when algorithms measure our suffering, does that change the way we understand and treat it?
Science already understands certain aspects of pain. We know that when you stub your toe, for example, microscopic alarm bells called nociceptors send electrical impulses toward your spinal cord on “express” wires, delivering the first stab of pain, while a slower convoy follows with the dull throb that lingers. At the spinal cord, the signal meets a microscopic switchboard scientists call the gate. Flood that gate with friendly touches—say, by rubbing the bruise—or let the brain return an instruction born of panic or calm, and the gate might muffle or magnify the message before you even become aware of it.
The gate can either let pain signals pass through or block them, depending on other nerve activity and instructions from your brain. Only the signals that succeed in getting past this gate travel up to your brain’s sensory map to help locate the damage, while others branch out to emotion centers that decide how bad it feels. Within milliseconds, those same hubs in the brain shoot fresh orders back down the line, releasing built-in painkillers or stoking the alarm. In other words, pain isn’t a straightforward translation of damage or sensation but a live negotiation between the body and the brain.
But much of how that negotiation plays out is still a mystery. For instance, scientists cannot predict what causes someone to slip from a routine injury into years-long hypersensitivity; the molecular shift from acute to chronic pain is still largely unknown. Phantom-limb pain remains equally puzzling: About two-thirds of amputees feel agony in a part of their body that no longer exists, yet competing theories—cortical remapping, peripheral neuromas, body-schema mismatch—do not explain why they suffer while the other third feel nothing.
The first serious attempt at a system for quantifying pain was introduced in 1921. Patients marked their degree of pain as a point on a blank 10‑centimeter line and clinicians scored the distance in millimeters, converting lived experience into a 0–100 ladder. By 1975, psychologist Ronald Melzack’s McGill Pain Questionnaire offered 78 adjectives like “burning,” “stabbing,” and “throbbing,” so that pain’s texture could join intensity in the chart. Over the past few decades, hospitals have ultimately settled on the 0–10 Numeric Rating Scale.
Yet pain is stubbornly subjective. Feedback from the brain in the form of your reaction can send instructions back down the spinal cord, meaning that expectation and emotion can change how much the same injury hurts. In one trial, volunteers who believed they had received a pain relief cream reported a stimulus as 22% less painful than those who knew the cream was inactive—and a functional magnetic resonance image of their brains showed that the drop corresponded with decreased activity in the parts of the brain that report pain, meaning they really did feel less hurt.
What’s more, pain can also be affected by a slew of external factors. In one study, experimenters applied the same calibrated electrical stimulus to volunteers from Italy, Sweden, and Saudi Arabia, and the ratings varied dramatically. Italian women recorded the highest scores on the 0–10 scale, while Swedish and Saudi participants judged the identical burn several points lower, implying that culture can amplify or dampen the felt intensity of the same experience.
Bias inside the clinic can drive different responses even to the same pain score. A 2024 analysis of discharge notes found that women’s scores were recorded 10% less often than men’s. At a large pediatric emergency department, Black children presenting with limb fractures were roughly 39% less likely to receive an opioid analgesic than their white non-Hispanic peers, even after the researchers controlled for pain score and other clinical factors. Together these studies make clear that an “8 out of 10” does not always result in the same reaction or treatment. And many patients cannot self-report their pain at all—for example, a review of bedside studies concludes that about 70% of intensive-care patients have pain that goes unrecognized or undertreated, a problem the authors link to their impaired communication due to sedation or intubation.
These issues have prompted a search for a better, more objective way to understand and assess pain. Progress in artificial intelligence has brought a new dimension to that hunt.
Research groups are pursuing two broad routes. The first listens underneath the skin. Electrophysiologists strap electrode nets to volunteers and look for neural signatures that rise and fall with administered stimuli. A 2024 machine-learning study reported that one such algorithm could tell with over 80% accuracy, using a few minutes of resting-state EEG, which subjects experienced chronic pain and which were pain-free control participants. Other researchers combine EEG with galvanic skin response and heart-rate variability, hoping a multisignal “pain fingerprint” will provide more robust measurements.
One example of this method is the PMD-200 patient monitor from Medasense, which uses AI-based tools to output pain scores. The device uses physiological patterns like heart rate, sweating, or peripheral temperature changes as the input and focuses on surgical patients, with the goal of helping anesthesiologists adjust doses during operations. In a 2022 study of 75 patients undergoing major abdominal surgery, use of the monitor resulted in lower self-reported pain scores after the operation—a median score of 3 out of 10, versus 5 out of 10 in controls—without an increase in opioid use. The device is authorized by the US Food and Drug Administration and is in use in the United States, the European Union, Canada, and elsewhere.
The second path is behavioral. A grimace, a guarded posture, or a sharp intake of breath correlates with various levels of pain. Computer-vision teams have fed high-speed video of patients’ changing expressions into neural networks trained on the Face Action Coding System (FACS), which was introduced in the late 1970s with the goal of creating an objective and universal system to analyze such expressions—it’s the Rosetta stone of 44 facial micro-movements. In lab tests, those models can flag frames indicating pain from the data set with over 90% accuracy, edging close to the consistency of expert human assessors. Similar approaches mine posture and even sentence fragments in clinical notes, using natural-language processing, to spot phrases like “curling knees to chest” that often correlate with high pain.
PainChek is one of these behavioral models, and it acts like a camera‑based thermometer, but for pain: A care worker opens the app and holds a phone 30 centimeters from a person’s face. For three seconds, a neural network looks for nine particular microscopic movements—upper‑lip raise, brow pinch, cheek tension, and so on—that research has linked most strongly to pain. Then the screen flashes a score of 0 to 42. “There’s a catalogue of ‘action‑unit codes’—facial expressions common to all humans. Nine of those are associated with pain,” explains Kreshnik Hoti, a senior research scientist with PainChek and a co-inventor of the device. This system is built directly on the foundation of FACS. After the scan, the app walks the user through a yes‑or‑no checklist of other signs, like groaning, “guarding,” and sleep disruption, and stores the result on a cloud dashboard that can show trends.
Linking the scan to a human‑filled checklist was, Hoti admits, a late design choice. “Initially, we thought AI should automate everything, but now we see [that] hybrid use—AI plus human input—is our major strength,” he says. Care aides, not nurses, complete most assessments, freeing clinicians to act on the data rather than gather it.
PainChek was cleared by Australia’s Therapeutic Goods Administration in 2017, and national rollout funding from Canberra helped embed it in hundreds of nursing homes in the country. The system has also won authorization in the UK—where expansion began just before covid-19 started spreading and resumed as lockdowns eased—and in Canada and New Zealand, which are running pilot programs. In the US, it’s currently awaiting an FDA decision. Company‑wide data show “about a 25% drop in antipsychotic use and, in Scotland, a 42% reduction in falls,” Hoti says.

Orchard Care Homes is one of its early adopters. Baird, then the facility’s director of quality, remembers the pre‑AI routine that was largely done “to prove compliance,” she says.
PainChek added an algorithm to that workflow, and the hybrid approach has paid off. Orchard’s internal study of four care homes tracked monthly pain scores, behavioral incidents, and prescriptions. Within weeks, psychotropic scripts fell and residents’ behavior calmed. The ripple effects went beyond pharmacy tallies. Residents who had skipped meals because of undetected dental pain “began eating again,” Baird notes, and “those who were isolated due to pain began socializing.”
Inside Orchard facilities, a cultural shift is underway. When Baird trained new staff, she likened pain “to measuring blood pressure or oxygen,” she says. “We wouldn’t guess those, so why guess pain?” The analogy lands, but getting people fully on board is still a slog. Some nurses insist their clinical judgment is enough; others balk at another login and audit trail. “The sector has been slow to adopt technology, but it’s changing,” Baird says. That’s helped by the fact that administering a full Abbey Pain Scale takes 20 minutes, while a PainChek scan and checklist take less than five.
Engineers at PainChek are now adapting the code for the very youngest patients. PainChek Infant targets babies under one year, whose grimaces flicker faster than adults’. The algorithm, retrained on neonatal faces, detects six validated facial action units based on the well-established Baby Facial Action Coding System. PainChek Infant is starting limited testing in Australia while the company pursues a separate regulatory pathway.
Skeptics raise familiar red flags about these devices. Facial‑analysis AI has a history of skin‑tone bias, for example. Facial analysis may also misread grimaces stemming from nausea or fear. The tool is only as good as the yes‑or‑no answers that follow the scan; sloppy data entry can skew results in either direction. Results lack the broader clinical and interpersonal context a caregiver is likely to have from interacting with individual patients regularly and understanding their medical history. It’s also possible that clinicians might defer too strongly to the algorithm, over-relying on outside judgment and eroding their own.
If PainChek is approved by the FDA this fall, it will be part of a broader effort to create a system of new pain measurement technology. Other startups are pitching EEG headbands for neuropathic pain, galvanic skin sensors that flag breakthrough cancer pain, and even language models that comb nursing notes for evidence of hidden distress. Still, quantifying pain with an external device could be rife with hidden issues, like bias or inaccuracies, that we will uncover only after significant use.
For Baird, the issue is fairly straightforward nonetheless. “I’ve lived with chronic pain and had a hard time getting people to believe me. [PainChek] would have made a huge difference,” she says. If artificial intelligence can give silent sufferers a numerical voice—and make clinicians listen—then adding one more line to the vital‑sign chart might be worth the screen time.
Deena Mousa is a researcher, grantmaker, and journalist focused on global health, economic development, and scientific and technological progress.
Mousa is employed as lead researcher by Open Philanthropy, a funder and adviser focused on high-impact causes, including global health and the potential risks posed by AI. The research team investigates new causes of focus and is not involved in work related to pain management. Mousa has not been involved with any grants related to pain management, although Open Philanthropy has funded research in this area in the past.