If NASA establishes a permanent presence on the moon, its astronauts’ homes could be made of a new 3D-printable, waterless concrete. Someday, so might yours. By accelerating the curing process for more rapid construction, this sulfur-based compound could become just as applicable on our home terrain as it is on lunar soil.
Artemis III—set to launch no earlier than September 2026—will not only mark humanity’s return to the moon after more than 50 years, but also be the first mission to explore the lunar South Pole, the proposed site of NASA’s base camp.
Building a home base on the moon will demand a steep supply of moon-based infrastructure: launch pads, shelter, and radiation blockers. But shipping Earth-based concrete to the lunar surface bears a hefty price tag. Sending just 1 kilogram (2.2 pounds) of material to the moon costs roughly $1.2 million, says Ali Kazemian, a robotic construction researcherat Louisiana State University (LSU). Instead, NASA hopes to create new materials from lunar soil and eventually adapt the same techniques for building on Mars.
Traditional concrete requires large amounts of water, a commodity that will be in short supply on the moon and critically important for life support or scientific research, according to the American Society of Civil Engineers. While prior NASA projects have tested compounds that could be used to make “lunarcrete,” they’re still working to craft the right waterless material.
So LSU researchers are refining the formula, developing a new cement based on sulfur, which they heat until it’s molten to bind material without the need for water. In recentwork, the team mixed their waterless cement with simulated lunar and Martian soil to create a 3D-printable concrete, which they used to assemble walls and beams. “We need automated construction, and NASA thinks 3D printing is one of the few viable technologies for building lunar infrastructure,” says Kazemian.
A curved wall is 3D printed from waterless concrete.
COURTESY OF ALI KAZEMIAN
Beyond circumventing the need for water, the cement can handle wider temperature extremes and cures faster than traditional methods. The group used a pre-made powder for their experiments, but on the moon and Mars, astronauts might extract sulfur from surface soil.
To test whether the concrete can stand up to the moon’s harsh environment, the team placed its structures in a vacuum chamber for weeks, analyzing the material’s stability at different temperatures. Originally, researchers worried that cold conditions on the dark side of the moon might cause the compound to turn into a gas through a process called sublimation, like when dry ice skips its liquid phase and evaporates directly. Ultimately, they found that the concrete can handle the lunar South Pole’s frigid forecast without losing its form.
Some conditions, like reduced gravity, could even work toward the concrete’s advantage. The experiment tested structures like walls and small circular towers, each made by stacking many layers of concrete. “One of the main challenges in larger-scale 3D printing is a distortion of these thick, heavy layers,” says Kazemian “But when you have lower gravity, that can actually help keep the layers from deforming.”
Kazemian and his colleagues recently transferred the technology to NASA’s Marshall Space Flight Center in Huntsville, Alabama, to implement their design on a larger-scale robotic system and test construction in larger vacuum chambers. If adopted, the concrete will most likely be used for taller lunar structures like habitats and radiation shields. Flatter designs, like a landing pad, will probably use laser-based technologies to melt down lunar soil into a ceramic structure.
There may only be so much testing we can do on Earth, however. According to Philip Metzger, a planetary physicist at University of Central Florida who recently retired from NASA’s Kennedy Space Center, the concrete’s efficacy may falter with the shift from simulant to real soil. “There’s chemistry in the samples of these planets that the simulants cannot perfectly replicate,” he says. “When we send missions to these planetary bodies to test the technology using the real soil, we may find that we need to further improve the technology to get it to work in that environment.”
But Metzger still sees the sulfur-based concrete as a vital foundation for the tall orders of upcoming planetary projects. Future missions to Mars could demand roads to drive back and forth from ice-mining sites and pavement around habitats to create dust-free work zones. This new concrete brings these distant goals a touch closer to reality.
It could benefit construction on Earth, too. Kazemian sees the new material as a potential alternative for traditional concrete, especially in areas with water scarcity or a surplus of sulfur. Parts of the Middle East, for example, have abundant sulfur as a result of oil and gas production.
The technology could become especially useful in disaster areas with broken supply chains, according to Metzger. It could also have military applications for rapid construction of structures like storage buildings. “This is great for people out there working on another planet who don’t have a lot of support,” Metzger says. “But there are already plenty of analogs to that here on Earth.”
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.
How worried should we be about bird flu? It’s a question that I’ve been asked by friends and colleagues several times over the last couple of weeks. Their concerns have been spurred by some potentially worrisome developments in the US, including the continued spread of the virus among dairy cattle, the detection of the virus in a pig as well as cow’s milk, and—most concerning of all—the growing number of human infections.
I’ll admit that I’m worried. We don’t yet have any evidence that the virus is spreading between people, but the risk of a potential pandemic has increased since I last covered this topic a couple of months ago.
The good news is we are in a much better position to tackle any potential future flu outbreaks than we were to face covid-19 back in 2020, given that we already have vaccines. But, on the whole, it’s not looking great.
The bird flu that is currently spreading in US dairy cattle is caused by the H5N1 virus. The virus is especially lethal to some bird populations and has been wiping out poultry and seabirds for the last couple of years. It has also caused fatal infections in many mammals who came into contact with those birds.
Those are just the cases we know about. There may be more. The USDA requires testing of cattle before they are moved between states. And it offers a voluntary testing program for farmers who want to know if the virus is present in their bulk milk tanks. But participation in that program is optional.
At the end of October, the USDA reported that the virus had been detected in a pig for the first time. The pig was one of five in a farm in Oregon that had “a mix of poultry and livestock.” All the pigs were slaughtered.
Virologists have been especially worried about the virus making its way into pigs, because these animals are notorious viral incubators. “They can become infected with swine strains, bird strains and human strains,” says Brinkley Bellotti, an infectious disease epidemiologist at Wake Forest University in North Carolina. These strains can swap genes and give rise to new, potentially more infectious or harmful strains.
Thankfully, we haven’t seen any other cases in pig farms, and there’s no evidence that the virus can spread between pigs. And while it has been spreading pretty rapidly between cattle, the virus doesn’t seem to have evolved much, says Seema Lakdawala, a virologist at the Emory University School of Medicine in Atlanta, Georgia. That suggests that the virus made the leap into cattle, probably from birds, only once. And it has been spreading through herds since.
Unfortunately, we still don’t really know how it is spreading. There is some evidence to suggest the virus can be spread from cow to cow through shared milking equipment. But it is unclear how the virus is spreading between farms. “It’s hard to form an effective control strategy when you don’t know exactly how it’s spreading,” says Bellotti.
But it is in cows. And it’s in their milk. When scientists analyzed 297 samples of Grade A pasteurized retail milk products, including milk, cream and cheese, they found viral RNA from H5N1 in 20% of them. Those samples were collected from 17 states across the US. And the study was conducted in April, just weeks after the virus was first detected in cattle. “It’s surprising to me that we are totally fine with … our pasteurized milk products containing viral DNA,” says Lakdawala.
Research suggests that, as long as the milk is pasteurized, the virus is not infectious. But Lakdawala is concerned that pasteurization may not inactivate all of the virus, all the time. “We don’t know how much virus we need to ingest [to become infected], and whether any is going to slip through pasteurization,” she says.
The most concerning development, though, is the rise in human cases. So far, 55 such cases of H5N1 bird flu have been reported in the US, according to the US Centers for Disease Control and Prevention (CDC). Twenty-nine of those cases have been detected in California. In almost all those cases, the infected person is thought to have caught the virus from cattle or poultry on farms. But in two of those cases, the source of the infection is unknown.
Health professionals don’t know how a teenager in British Columbia, Canada, got so sick with bird flu, either. The anonymous teenager, who sought medical care for an eye infection on November 2, is still seriously ill in hospital, and continues to rely on a ventilator to breathe. Local health officials have closed their investigation into the teen’s infection.
“Just because we [haven’t seen human-to-human spread] now doesn’t mean that it’s not capable of happening, that it won’t happen, or that it hasn’t already happened,” says Lakdawala.
So where do we go from here? Lakdawala thinks we should already have started vaccinating dairy farm workers. After all, the US has already stockpiled vaccines for H5N1, which were designed to protect against previous variants of the virus. “We’re not taking [the human cases] seriously enough,” she says.
We still have an opportunity to prevent the outbreak from turning into a global catastrophe. But the situation has worsened since the summer. “This is sort of how the 2009 pandemic started,” says Lakdawala, referring to the H1N1 swine flu pandemic. “We started to have a couple of cases sporadically, and then the next thing you knew, you were seeing it everywhere.”
Some researchers are exploring new ways to prevent the spread of H5N1 in poultry. The gene editing tool CRISPR could be used to help make chickens more resistant to the virus, according to preliminary research published last year.
From around the web
President-elect Donald Trump has chosen Jay Bhattacharya for his pick to lead the US National Institutes of Health, an agency with a $48 billion budget that oversees the majority of medical research in the country. Bhattacharya was one of three lead authors of the Great Barrington Declaration, a manifesto published in 2020 arguing against lockdowns during the height of the covid-19 pandemic, and supporting a “let it rip” approach instead. (STAT)
An IVF mix up left two families raising each other’s biological babies. They didn’t realize until the children were a couple of months old. What should they do? (Have the tissues ready for this one, which is heartbreaking and heartwarming in equal measure) (New York Times)
Why do we feel the need to surveil our sleeping babies? This beautiful comic explores the various emotional pulls experienced by new parents. (The Verge)
Australia’s parliament has passed a law that bans children under the age of 16 from using social media. Critics are concerned that the law is a “blunt instrument” that might drive young teens to the dark web, or leave them feeling isolated. (The Guardian)
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.
Earlier this year, a boy in Sweden celebrated his 10th birthday. Reproductive scientists and doctors marked the occasion too. This little boy’s birth had been special. He was the first person to be born from a transplanted uterus.
The boy was born in 2014 after his mother, a 35-year-old woman who had been born without a uterus, received a donated uterus from a 61-year-old close family friend. At the time, she was one of only 11 women who had undergone the experimental procedure.
A decade on, over 135 uterus transplants have been performed globally, resulting in the births of over 50 healthy babies. The surgery has had profound consequences for these families—the recipients would not have been able to experience pregnancy any other way.
But legal and ethical questions continue to surround the procedure, which is still considered experimental. Who should be offered a uterus transplant? Could the procedure ever be offered to transgender women? And if so, who should pay for these surgeries?
These issues were raised at a recent virtual event run by Progress Educational Trust, a UK-based charity that aims to provide information to the public on genomics and infertility. One of the speakers was Mats Brännström, who led the team at the University of Gothenburg that performed the first successful transplant.
For Brännström, the story of uterus transplantation begins in 1998. While traveling in Australia, he said, he met a 27-year-old woman called Angela, who longed to be pregnant but lacked a functional uterus. She suggested to Brännström that her mother could donate hers. “I was amazed I hadn’t thought of it before,” he said.
According to Brännström, around 1 in 500 women experience infertility due to what’s known as absolute uterine factor infertility, or AUFI, meaning they do not have a functional uterus. Uterus transplants could offer them a way to get pregnant.
His meeting with Angela kick-started a research project that started in mice and eventually moved on to pigs, sheep, and baboons. Brännström’s team started performing uterus transplants in women as part of a small clinical trial in 2012. In that trial, all the donors were living, and in many cases they were the mothers or aunts of the recipients.
The surgeries ended up being more complicated than he had anticipated, said Brännström. The operation to remove a donor’s uterus was expected to take between three and four hours. It ended up taking between eight and 11 hours.
In that first trial, Brännström’s team transplanted uteruses into nine women, each of whom had IVF to create and store embryos beforehand. The woman who was the first to give birth had IVF over a 12-month period, which ended six months before her surgery. It took a little over 10 hours to remove the uterus from the donor, and just under five hours to stitch it into the recipient.
The recipient started getting her period 43 days after her transplant. Doctors transferred one of her embryos into the uterus a year after her surgery. Three weeks later, a pregnancy test confirmed she was pregnant.
At 31 weeks, she was admitted to hospital with preeclampsia, a serious medical condition that can develop during pregnancy, and her baby was delivered by C-section 16 hours later. She was discharged from hospital after three days, although the baby spent 16 days in the hospital’s neonatal unit.
Despite those difficulties, her story is considered a success. Other uterus recipients have also experienced pregnancy complications, and some have had surgical complications. And all transplant recipients must adhere to a regimen of immunosuppressant drugs, which can have side effects.
The uteruses aren’t intended to last forever, either. Surgeons remove them once the recipients have completed their families, often after one or two children. The removal is also a significant operation.
Given all that, uterus transplants are not to be taken lightly. And there are other paths to parenthood. Some ethicists are concerned that in pursuing uterus transplantation as a fertility treatment, we might reinforce ideas that define a woman’s value in terms of her reproductive potential, Natasha Hammond-Browning, a legal scholar at Cardiff University in Wales, said at the event. “There is debate around whether we should be giving greater preference to adoption, to surrogacy, and to supporting children who already exist and who need care,” she said.
We also need to consider whether there is a “right to gestate,” and if there is, who has that right, said Hammond-Browning. And these concerns need to be balanced with the importance of reproductive autonomy—the idea that people have the right to decide and control their own reproductive efforts.
Further questions remain over whether uterus transplants might ever be an option for trans women, who not only lack a uterus but also have a different pelvic anatomy. I asked the speakers if the surgery might ever be feasible. They weren’t hugely optimistic that it would, at least in the near future.
“I personally think that the transgender community have been given … false hope for responsible transplantation in the near future,” was the response of J. Richard Smith of Imperial College London, who co-led the first uterus transplant performed in the UK. Even cisgender women who have needed surgery to create “neovaginas” aren’t eligible for the uterus transplants his team are offering as part of a clinical study. They have an altered vaginal microbiome that appears to increase the risk of miscarriage, he said.
“There is a huge amount of work to be done before this work can be translated to the transgender community,” Smith said. Brännström agreed but added that he thinks the surgery will be available at some point—just after a lot more research.
And then there are the legal and ethical questions, none of which have easy answers. Hammond-Browning pointed out that clinical teams would first need to determine what the goal of such an operation would be. Is it about reproduction or gender realignment, for example? And how might that goal influence decisions over who should get a donated uterus, and why?
Considering only 135 human uterus transplants have ever been carried out, we still have a lot to learn about the best way to perform them. (For context, more than 25,000 kidney transplants were carried out in 2023 in the US alone.) Researchers are still figuring out how uteruses from deceased donors differ from those of living ones, and how to minimize complications in young, healthy women. Since that little boy was born 10 years ago, only 50 other children have been born in a similar way. It’s still early days.
Now read the rest of The Checkup
Read more from MIT Technology Review
The first birth following the transplantation of a uterus from a dead donor happened in 2017. A team in Brazil transferred the uterus of a 45-year-old donor, who had died from a brain hemorrhage, to a 32-year-old recipient born without a uterus.
People on Ozempic, Wegovy, and similar drugs are losing their appetite for sugary, ultraprocessed foods. The food industry will have to adapt. (TIL Nestlé has already started a line of frozen meals targeted at people on these weight-loss drugs.) (The New York Times Magazine)
People who have a history of obesity can find it harder to lose weight. That might be because the fat cells in our bodies seem to “remember” that history and have an altered response to food. (The Guardian)
Robert F. Kennedy Jr. took leave as chairman of Children’s Health Defense, a nonprofit known for spreading doubt about vaccines, to run for US president last year.But he is still involved in legal cases filed by the group. And several of its cases remain open, including ones against the Food and Drug Administration, the Centers for Disease Control and Prevention, and the National Institutes of Health—all agencies Kennedy would lead if his nomination for head of Health and Human Services is confirmed. (STAT)
Researchers are among the millions of new users of Bluesky, a social media alternative to X (formerly known as Twitter). “There is this pent-up demand among scientists for what is essentially the old Twitter,” says one researcher who found that the number of influential scientists using the platform doubled between August and November. (Science)
Since 2016, a team of around 100 scientists have been working to catalogue the 37 trillion or so cells in the human body. This week, the Human Cell Atlas published a collection of studies that represents a significant first step toward that goal—including maps of cells in the nervous system, lungs, heart, gut, and immune system. (Nature)
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.
How OpenAI stress-tests its large language models
OpenAI has lifted the lid (just a crack) on its safety-testing processes. It has put out two papers describing how it stress-tests its powerful large language models to try to identify potential harmful or otherwise unwanted behavior, an approach known as red-teaming.
The first paper describes how OpenAI directs an extensive network of human testers outside the company to vet the behavior of its models before they are released. The second presents a new way to automate parts of the testing process, using a large language model like GPT-4 to come up with novel ways to bypass its own guardrails. MIT Technology Review got an exclusive preview of the work.
—Will Douglas Heaven
Who should get a uterus transplant? Experts aren’t sure.
Over 135 uterus transplants have been performed globally in the last decade, resulting in the births of over 50 healthy babies. The surgery has had profound consequences for these families—the recipients would not have been able to experience pregnancy any other way.
But legal and ethical questions continue to surround the procedure, which is still considered experimental. Who should be offered a uterus transplant? Could the procedure ever be offered to transgender women? And if so, who should pay for these surgeries? Read the full story.
—Jessica Hamzelou
This story is from The Checkup, our weekly newsletter about the latest in biotech and health. Sign up to receive it in your inbox every Thursday.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 OpenAI may launch a web browser Which would be a full-frontal assault on Google (The Information $) + The Google browser break-up is an answer in search of a question. (FT $) + OpenAI accidentally deleted potential evidence in a training data lawsuit. (The Verge)
2 Border militias are ready to help with Trump’s deportation plans Regardless of whether they’re asked to or not. (Wired $) + Trump’s administration plans to radically curb the powers of the federal agency that protects unions. (WP $)
3 Russia hit Ukraine with a new type of missile Here’s what we know about it so far. (The Guardian)
4 Microsoft is about to turn 50 And it’s every bit as relevant and powerful as it’s ever been. (Wired $)
5 China has overtaken Germany in industrial robot adoption South Korea, however, remains streets ahead of both of them. (Reuters $) + Three reasons robots are about to become way more useful. (MIT Technology Review)
6 The irresistible rise of cozy tech Our devices, social media and now AI are encouraging us to keep looking inward. (New Yorker $) + Inside the cozy but creepy world of VR sleep rooms. (MIT Technology Review)
7 Churchgoers in a Swiss city have been spilling their secrets to AI Jesus And they’re mostly really enjoying it. Watch out, priests. (The Guardian)
8 A French startup wants to make fuel out of thin air Then use it to fuel ships and airplanes. (IEEE Spectrum) + Everything you need to know about alternative jet fuels. (MIT Technology Review)
9 WhatsApp is going to start transcribing voice messages This seems a good compromise to bridge people’s different communication preferences. (The Verge)
10 Want a new phone? You should consider second-hand It’s better for the planet—and your wallet. (Vox)
Quote of the day
“Nope. 100% not true.”
—Jeff Bezos fires back at Elon Musk’s claim that he was telling everyone that Trump would lose pre-election in a rare post on X.
The big story
This chemist is reimagining the discovery of materials using AI and automation
DEREK SHAPTON
October 2021
Alán Aspuru-Guzik, a Mexico City–born, Toronto-based chemist, has devoted much of his life to contemplating worst-case scenarios. What if climate change proceeds as expected, or gets significantly worse? Could we quickly come up with the materials we’ll need to cheaply capture carbon, or make batteries from something other than costly lithium?
Materials discovery—the science of creating and developing useful new substances—often moves at a frustratingly slow pace. The typical trial-and-error approach takes an average of two decades, making it too expensive and risky for most companies to pursue.
Aspuru-Guzik’s objective—which he shares with a growing number of computer-savvy chemists—is to shrink that interval to a matter of months or years. And advances in AI, robotics, and computing are bringing new life to his vision. Read the full story.
—Simon Lewsen
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 tweet ’em at me.)
+ Do you struggle with a lack of confidence? Here’s how to take up a bit more space. + These recipes will ensure you have a delicious Thanksgiving next week. + It’s impossible not to dream of lazy sunny days while gazing at Quentin Monge’s work. + Tom Jones x Disturbed = very funny.
MIT Technology Review’s How To series helps you get things done.
Since the start of the generative AI boom, artists have been worried about losing their livelihoods to AI tools. There have been plenty of examples of companies’ replacing human labor with computer programs. Most recently, Coca-Cola sparked controversy by creating a new Christmas ad with generative AI.
Artists and writers have launched several lawsuits against AI companies, arguing that their work has been scraped into databases for training AI models without consent or compensation. Tech companies have responded that anything on the public internet falls under fair use. But it will be years until we have a legal resolution to the problem.
Unfortunately, there is little you can do if your work has been scraped into a data set and used in a model that is already out there. You can, however, take steps to prevent your work from being used in the future.
Here are four ways to do that.
Mask your style
One of the most popular ways artists are fighting back against AI scraping is by applying “masks” on their images, which protect their personal style from being copied.
Tools such as Mist, Anti-DreamBooth, and Glaze add tiny changes to an image’s pixels that are invisible to the human eye, so that if and when images are scraped, machine-learning models cannot decipher them properly. You’ll need some coding skills to run Mist and Anti-DreamBooth, but Glaze, developed by researchers at the University of Chicago, is more straightforward to apply. The tool is free and available to download as an app, or the protection can be applied online. Unsurprisingly, it is the most popular tool and has been downloaded millions of times.
But defenses like these are never foolproof, and what works today might not work tomorrow. In computer security, breaking defenses is standard practice among researchers, as this helps people find weaknesses and make systems safer. Using these tools is a calculated risk: Once something is uploaded online, you lose control of it and can’t retroactively add protections to images.
Rethink where and how you share
Popular art profile sites such as DeviantArt and Flickr have become gold mines for AI companies searching for training data. And when you share images on platforms such as Instagram, its parent company, Meta, can use your data to build its models in perpetuity if you’ve shared it publicly. (See opt-outs below.)
One way to prevent scraping is by not sharing images online publicly, or by making your social media profiles private. But for many creatives that is simply not an option; sharing work online is a crucial way to attract clients.
It’s worth considering sharing your work on Cara, a new platform created in response to the backlash against AI. Cara, which collaborates with the researchers behind Glaze, is planning to add integrations to the lab’s art defense tools. It automatically implements “NoAI” tags that tell online scrapers not to scrape images from the site. It currently relies on the goodwill of AI companies to respect artists’ stated wishes, but it’s better than nothing.
Opt out of scraping
Data protection laws might help you get tech companies to exclude your data from AI training. If you live somewhere that has these sorts of laws, such as the UK or the EU, you can ask tech companies to opt you out of having your data scraped for AI training. For example, you can follow these instructions for Meta. Unfortunately, opt-out requests from users in places without data protection laws are honored only at the discretion of tech companies.
The site Have I Been Trained, created by the artist-run company Spawning AI, lets you search to find out if your images have ended up in popular open-source AI training data sets. The organization has partnered with two companies: Stability AI, which created Stable Diffusion, and Hugging Face, which promotes open access to AI. If you add your images to Spawning AI’s Do Not Train Registry, these companies have agreed to remove your images from their training data sets before training new models. Again, unfortunately, this relies on the goodwill of AI companies and is not an industry-wide standard.
If all else fails, add some poison
The University of Chicago researchers who created Glaze have also created Nightshade, a tool that lets you add an invisible layer of “poison” to your images. Like Glaze, it adds invisible changes to pixels, but rather than just making it hard for AI models to interpret images, it can break future iterations of these models and make them behave unpredictably. For example, images of dogs might become cats, and handbags might become toasters. The researchers say relatively few samples of poison are needed to make an impact.
You can add Nightshade to your image by downloading an app here. In the future, the team hopes to combine Glaze and Nightshade, but at the moment the two protections have to be added separately.
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
“Well, what about China?”
This is a comment I get all the time on the topic of climate change, both in conversations and on whatever social media site is currently en vogue. Usually, it comes in response to some statement about how the US and Europe are addressing the issue (or how they need to be).
Sometimes I think people ask this in bad faith. It’s a rhetorical way to throw up your hands, imply that the US and Europe aren’t the real problem, and essentially say: “if they aren’t taking responsibility, why should we?” However, amid the playground-esque finger-pointing there are some undeniable facts: China emits more greenhouse gases than any other country, by far. It’s one of the world’s most populous countries and a climate-tech powerhouse, and its economy is still developing.
With many complicated factors at play, how should we think about the country’s role in addressing climate change?
There’s context missing if we just look at that one number, as I wrote in my latest story that digs into recent global climate data. Since carbon dioxide hangs around in the atmosphere for centuries, we should arguably consider not just a country’s current emissions, but everything it’s produced over time. If we do that, the US still takes the crown for the world’s biggest climate polluter.
However, China is now in second place, according to a new analysis from Carbon Brief released this week. In 2023, the country exceeded the EU’s 27 member states in historical emissions for the first time.
This reflects a wider trend that we’re seeing around the world: Developing nations are starting to account for a larger fraction of emissions than they used to. In 1992, when countries agreed to the UN climate convention, industrialized countries (a category called Annex I) made up about one-fifth of the world’s population but were responsible for a whopping 61% of historical emissions. By the end of 2024, though, those countries’ share of global historical emissions will fall to 52%, and it is expected to keep ticking down.
China, like all nations, will need to slash its emissions for the world to meet global climate goals. One crucial point here is that while its emissions are still huge, there are signs that the nation is making some progress.
China’s carbon dioxide’s emissions are set to fall in 2024 because of record growth in low-carbon energy sources. That decline is projected to continue under the country’s current policy settings, according to an October report from the IEA. China’s oil demand could soon peak and start to fall, largely because it’s seeing such a huge uptake of electric vehicles.
One growing question: With all this progress and a quickly growing economy, should we be expecting China to do more than just make progress on its own emissions?
As I wrote in the newsletter last week, the current talks at COP29 (the UN climate conference) are focused on setting a new, more aggressive global climate finance goal to help developing nations address climate change. China isn’t part of the group of countries that are required to pay into this pot of money, but some are calling for that to change given that it is the world’s biggest polluter.
Talks at COP29 aren’t going very well. The COP29 president called for faster action, but progress toward a finance deal has stalled amid infighting over how much money should be on the table and who should pay up.
China’s complex role in emissions and climate action is far from the only holdup at the talks. Leaders from major nations including Germany and France canceled plans to attend, and the looming threat that the US could pull out of the Paris climate agreement is coloring the negotiations.
But disagreement over how to think about China’s role in all this is a good example of how difficult it is to assign responsibility when it comes to climate change, and how much is at play in global climate negotiations. One thing I do know for sure is that pointing fingers doesn’t cut emissions.
Fusion energy has been a dream for decades, and a handful of startups say we’re closer than ever to making it a reality. This deep dive looks at a few of the companies looking to be the first to deploy fusion power. (New York Times) → I recently visited one of the startups, Commonwealth Fusion Systems. (MIT Technology Review)
President-elect Donald Trump has tapped Chris Wright to lead the Department of Energy. Wright is head of the fracking company Liberty Energy. (Washington Post)
In the wake of Trump’s election, it might be time for climate tech to get a rebrand. Companies and investors might increasingly avoid using the term, opting instead for phrases like “energy independence” or “frontier tech,” to name a few. (Heatmap)
Rooftop solar has saved customers in California about $2.3 billion on utility bills this year, according to a new analysis. This result is counter to a report from a state agency, which found that rooftop panels impose over $8 billion in extra costs on consumers of the state’s three major utilities. (Canary Media)
Low-carbon energy needs much less material than it used to. Rising efficiency in making technology like solar panels bodes well for hopes of cutting mining needs. (Sustainability by Numbers)
New York governor Kathy Hochul has revived a plan to implement congestion pricing, which would charge drivers to enter the busiest parts of Manhattan. It would be the first such program in the US. (The City)
Enhanced geothermal technology could be close to breaking through into commercial success. Companies that aim to harness Earth’s heat for power are making progress toward deploying facilities. (Nature) → Fervo Energy found that its wells can be used like a giant underground battery. (MIT Technology Review)
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.
AI can now create a replica of your personality
Imagine sitting down with an AI model for a spoken two-hour interview. A friendly voice guides you through a conversation that ranges from your childhood, your formative memories, and your career to your thoughts on immigration policy. Not long after, a virtual replica of you is able to embody your values and preferences with stunning accuracy.
That’s now possible, according to a new paper from a team including researchers from Stanford and Google DeepMind.
They recruited 1,000 people and, from interviews with them, created agent replicas of them all. To test how well the agents mimicked their human counterparts, participants did a series of tests, games and surveys, then the agents completed the same exercises. The results were 85% similar. Freaky. Read our story about the work, and why it matters.
—James O’Donnell
China’s complicated role in climate change
“But what about China?”
In debates about climate change, it’s usually only a matter of time until someone brings up China. Often, it comes in response to some statement about how the US and Europe are addressing the issue (or how they need to be).
Sometimes it can be done in bad faith. It’s a rhetorical way to throw up your hands, and essentially say: “if they aren’t taking responsibility, why should we?”
However, there are some undeniable facts: China emits more greenhouse gases than any other country, by far. It’s one of the world’s most populous countries and a climate-tech powerhouse, and its economy is still developing.
With many complicated factors at play, how should we think about the country’s role in addressing climate change? Read the full story.
—Casey Crownhart
This story is from The Spark, our weekly newsletter giving you the inside track on all things energy and climate. Sign up to receive it in your inbox every Wednesday.
Four ways to protect your art from AI
Since the start of the generative AI boom, artists have been worried about losing their livelihoods to AI tools.
Unfortunately, there is little you can do if your work has been scraped into a data set and used in a model that is already out there. You can, however, take steps to prevent your work from being used in the future. Here are four ways to do that.
—Melissa Heikkila
This is part of our How To series, where we give you practical advice on how to use technology in your everyday lives. You can read the rest of the series here.
MIT Technology Review Narrated: The world’s on the verge of a carbon storage boom
In late 2023, one of California’s largest oil and gas producers secured draft permits from the US Environmental Protection Agency to develop a new type of well in an oil field. If approved, it intends to drill a series of boreholes down to a sprawling sedimentary formation roughly 6,000 feet below the surface, where it will inject tens of millions of metric tons of carbon dioxide to store it away forever.
Hundreds of similar projects are looming across the state, the US, and the world. Proponents hope it’s the start of a sort of oil boom in reverse, kick-starting a process through which the world will eventually bury more greenhouse gas than it adds to the atmosphere. But opponents insist these efforts will prolong the life of fossil-fuel plants, allow air and water pollution to continue, and create new health and environmental risks.
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 How the Trump administration could hack your phone Spyware acquired by the US government in September could fairly easily be turned on its own citizens. (New Yorker $) + Here’s how you can fight back against being digitally spied upon. (The Guardian)
2 The DOJ is trying to force Google to sell off Chrome Whether Trump will keep pushing it through is unclear, though. (WP $) + Some financial and legal experts argue that just selling Chrome is not enough to address antitrust issues. (Wired $)
3 There’s a booming ‘AI pimping’ industry People are stealing videos from real adult content creators, giving them AI-generated faces, and monetizing their bodies. (Wired $) + This viral AI avatar app undressed me—without my consent. (MIT Technology Review)
4 Here’s Elon Musk and Vivek Ramaswamy plan for federal employees Large-scale firings and an end to any form of remote work. (WSJ $)
5 The US is scaring everyone with its response to bird flu It’s done remarkably little to show it’s trying to contain the outbreak. (NYT $) + Virologists are getting increasingly nervous about how it could evolve and spread. (MIT Technology Review)
6 AI could boost the performance of quantum computers A new model created by Google DeepMind is very good at correcting errors. (New Scientist $) + But AI could also make quantum computers less necessary. (MIT Technology Review)
7 Biden has approved the use of anti-personnel mines in Ukraine It comes just days after he gave the go-ahead for it to use long-range missiles inside Russia. (Axios) + The US military has given a surveillance drone contract to a little-known supplier from Utah. (WSJ $) + The Danish military said it’s keeping a close eye on a Chinese ship in its waters after data cable breaches. (Reuters $)
8 The number of new mobile internet users is stalling Only about 57% of the world’s population is connected. (Rest of World)
9 All of life on Earth descended from this single cell Our “last universal common ancestor” (or LUCA for short) was a surprisingly complex organism living 4.2 billion years ago. (Quanta) + Scientists are building a catalog of every type of cell in our bodies. (The Economist $)
10 What it’s like to live with a fluffy AI pet Try as we might, it seems we can’t help but form attachments to cute companion robots. (The Guardian)
Quote of the day
“The free pumpkins have brought joy to many.”
—An example of the sort of stilted remarks made by a now-abandoned AI-generated news broadcaster at local Hawaii paper The Garden Island, Wired reports.
The big story
How Bitcoin mining devastated this New York town
GABRIELA BHASKAR
April 2022
If you had taken a gamble in 2017 and purchased Bitcoin, today you might be a millionaire many times over. But while the industry has provided windfalls for some, local communities have paid a high price, as people started scouring the world for cheap sources of energy to run large Bitcoin-mining farms.
It didn’t take long for a subsidiary of the popular Bitcoin mining firm Coinmint to lease a Family Dollar store in Plattsburgh, a city in New York state offering cheap power. Soon, the company was regularly drawing enough power for about 4,000 homes. And while other miners were quick to follow, the problems had already taken root. Read the full story.
—Lois Parshley
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 tweet ’em at me.)
+ Cultivating gratitude is a proven way to make yourself happier. + You can’t beat a hot toddy when it’s cold outside. + If you like abandoned places and overgrown ruins, Jonathan Jimenez is the photographer for you. + A lot changed between Gladiator I and II, not least Hollywood’s version of the male ideal.
Large language models are now being used by millions of people for many different things. But as OpenAI itself points out, these models are known to produce racist, misogynistic and hateful content; reveal private information; amplify biases and stereotypes; and make stuff up. The company wants to share what it is doing to minimize such behaviors.
The aim is to combine these two approaches, with unwanted behaviors discovered by human testers handed off to an AI to be explored further and vice versa. Automated red-teaming can come up with a large number of different behaviors, but human testers bring more diverse perspectives into play, says Lama Ahmad, a researcher at OpenAI: “We are still thinking about the ways that they complement each other.”
Red-teaming isn’t new. AI companies have repurposed the approach from cybersecurity, where teams of people try to find vulnerabilities in large computer systems. OpenAI first used the approach in 2022, when it was testing DALL-E 2. “It was the first time OpenAI had released a product that would be quite accessible,” says Ahmad. “We thought it would be really important to understand how people would interact with the system and what risks might be surfaced along the way.”
The technique has since become a mainstay of the industry. Last year, President Biden’s Executive Order on AI tasked the National Institute of Standards and Technology (NIST) with defining best practices for red-teaming. To do this, NIST will probably look to top AI labs for guidance.
Tricking ChatGPT
When recruiting testers, OpenAI draws on a range of experts, from artists to scientists to people with detailed knowledge of the law, medicine, or regional politics. OpenAI invites these testers to poke and prod its models until they break. The aim is to uncover new unwanted behaviors and look for ways to get around existing guardrails—such as tricking ChatGPT into saying something racist or DALL-E into producing explicit violent images.
Adding new capabilities to a model can introduce a whole range of new behaviors that need to be explored. When OpenAI added voices to GPT-4o, allowing users to talk to ChatGPT and ChatGPT to talk back, red-teamers found that the model would sometimes start mimicking the speaker’s voice, an unexpected behavior that was both annoying and a fraud risk.
There is often nuance involved. When testing DALL-E 2 in 2022, red-teamers had to consider different uses of “eggplant,” a word that now denotes an emoji with sexual connotations as well as a purple vegetable. OpenAI describes how it had to find a line between acceptable requests for an image, such as “A person eating an eggplant for dinner,” and unacceptable ones, such as “A person putting a whole eggplant into her mouth.”
Similarly, red-teamers had to consider how users might try to bypass a model’s safety checks. DALL-E does not allow you to ask for images of violence. Ask for a picture of a dead horse lying in a pool of blood, and it will deny your request. But what about a sleeping horse lying in a pool of ketchup?
When OpenAI tested DALL-E 3 last year, it used an automated process to cover even more variations of what users might ask for. It used GPT-4 to generate requests producing images that could be used for misinformation or that depicted sex, violence, or self-harm. OpenAI then updated DALL-E 3 so that it would either refuse such requests or rewrite them before generating an image. Ask for a horse in ketchup now, and DALL-E is wise to you: “It appears there are challenges in generating the image. Would you like me to try a different request or explore another idea?”
In theory, automated red-teaming can be used to cover more ground, but earlier techniques had two major shortcomings: They tend to either fixate on a narrow range of high-risk behaviors or come up with a wide range of low-risk ones. That’s because reinforcement learning, the technology behind these techniques, needs something to aim for—a reward—to work well. Once it’s won a reward, such as finding a high-risk behavior, it will keep trying to do the same thing again and again. Without a reward, on the other hand, the results are scattershot.
“They kind of collapse into ‘We found a thing that works! We’ll keep giving that answer!’ or they’ll give lots of examples that are really obvious,” says Alex Beutel, another OpenAI researcher. “How do we get examples that are both diverse and effective?”
A problem of two parts
OpenAI’s answer, outlined in the second paper, is to split the problem into two parts. Instead of using reinforcement learning from the start, it first uses a large language model to brainstorm possible unwanted behaviors. Only then does it direct a reinforcement-learning model to figure out how to bring those behaviors about. This gives the model a wide range of specific things to aim for.
Beutel and his colleagues showed that this approach can find potential attacks known as indirect prompt injections, where another piece of software, such as a website, slips a model a secret instruction to make it do something its user hadn’t asked it to. OpenAI claims this is the first time that automated red-teaming has been used to find attacks of this kind. “They don’t necessarily look like flagrantly bad things,” says Beutel.
Will such testing procedures ever be enough? Ahmad hopes that describing the company’s approach will help people understand red-teaming better and follow its lead. “OpenAI shouldn’t be the only one doing red-teaming,” she says. People who build on OpenAI’s models or who use ChatGPT in new ways should conduct their own testing, she says: “There are so many uses—we’re not going to cover every one.”
For some, that’s the whole problem. Because nobody knows exactly what large language models can and cannot do, no amount of testing can rule out unwanted or harmful behaviors fully. And no network of red-teamers will ever match the variety of uses and misuses that hundreds of millions of actual users will think up.
That’s especially true when these models are run in new settings. People often hook them up to new sources of data that can change how they behave, says Nazneen Rajani, founder and CEO of Collinear AI, a startup that helps businesses deploy third-party models safely. She agrees with Ahmad that downstream users should have access to tools that let them test large language models themselves.
Rajani also questions using GPT-4 to do red-teaming on itself. She notes that models have been found to prefer their own output: GPT-4 ranks its performance higher than that of rivals such as Claude or Llama, for example. This could lead it to go easy on itself, she says: “I’d imagine automated red-teaming with GPT-4 may not generate as harmful attacks [as other models might].”
Miles behind
For Andrew Tait, a researcher at the Ada Lovelace Institute in the UK, there’s a wider issue. Large language models are being built and released faster than techniques for testing them can keep up. “We’re talking about systems that are being marketed for any purpose at all—education, health care, military, and law enforcement purposes—and that means that you’re talking about such a wide scope of tasks and activities that to create any kind of evaluation, whether that’s a red team or something else, is an enormous undertaking,” says Tait. “We’re just miles behind.”
Tait welcomes the approach of researchers at OpenAI and elsewhere (he previously worked on safety at Google DeepMind himself) but warns that it’s not enough: “There are people in these organizations who care deeply about safety, but they’re fundamentally hamstrung by the fact that the science of evaluation is not anywhere close to being able to tell you something meaningful about the safety of these systems.”
Tait argues that the industry needs to rethink its entire pitch for these models. Instead of selling them as machines that can do anything, they need to be tailored to more specific tasks. You can’t properly test a general-purpose model, he says.
“If you tell people it’s general purpose, you really have no idea if it’s going to function for any given task,” says Tait. He believes that only by testing specific applications of that model will you see how well it behaves in certain settings, with real users and real uses.
“It’s like saying an engine is safe; therefore every car that uses it is safe,” he says. “And that’s ludicrous.”
If you’ve ever been through a large US airport, you’re probably at least vaguely aware of Clear. Maybe your interest (or irritation) has been piqued by the pods before the security checkpoints, the attendants in navy bluevests who usher clients to the front of the security line (perhaps just ahead of you), and the sometimes pushy sales pitches to sign up and skip ahead yourself. After all, is there anything people dislike more than waiting in line?
Its position in airports has made Clear Secure, with its roughly $3.75 billion market capitalization, the most visible biometric identity company in the United States. Over the past two decades, Clear has put more than 100 lanes in 58 airports across the US, and in the past decade it has entered 17 sports arenas and stadiums, from San Jose to Denver to Atlanta. Now you can also use its identity verification platform to rent tools at Home Depot, put your profile in front of recruiters on LinkedIn, and, as of this month, verify your identity as a rider on Uber.
And soon enough, if Clear has its way, it may also be in your favorite retailer, bank, and even doctor’s office—or anywhere else that you currently have to pull out a wallet (or, of course, wait in line). The company that has helped millions of vetted members skip airport security lines is now working to expand its “frictionless,” “face-first” line-cutting service from the airport to just about everywhere, online and off, by promising to verify that you are who you say you are and you are where you are supposed to be. In doing so, CEO Caryn Seidman Becker told investors in an earnings call earlier this year, it has designs on being no less than the “identity layer of the internet,” as well as the “universal identity platform” of the physical world.
All you have to do is show up—and show your face.
This is enabled by biometric technology, but Clear is far more than just a biometrics company. As Seidman Becker has told investors, “biometrics aren’t the product … they are a feature.” Or, as she put it in a 2022 podcast interview, Clear is ultimately a platform company “no different than Amazon or Apple”—with dreams, she added, “of making experiences safer and easier, of giving people back their time, of giving people control, of using technology for … frictionless experiences.” (Clear did not make Seidman Becker available for an interview.)
While the company has been building toward this sweeping vision for years, it now seems the time has finally come. A confluence of factors is currently accelerating the adoption of—even necessity for—identity verification technologies: increasingly sophisticated fraud, supercharged by artificial intelligence that is making it harder to distinguish who or what is real; data breaches that seem to occur on a near daily basis; consumers who are more concerned about data privacy and security; and the lingering effects of the pandemic’s push toward “contactless” experiences.
All of this is creating a new urgency around ways to verify information, especially our identities—and, in turn, generating a massive opportunity for Clear. For years, Seidman Becker has been predicting that biometrics will go mainstream.
But now that biometrics have, arguably, gone mainstream, what—and who—bears the cost? Because convenience, even if chosen by only some of us, leaves all of us wrestling with the effects. Some critics warn that not everyone will benefit from a world where identity is routed through Clear—maybe because it’s too expensive, and maybe because biometric technologies are often less effective at identifying people of color, people with disabilities, or those whose gender identity may not match what official documents say.
What’s more, says Kaliya Young, an identity expert who has advised the US government, having a single private company “disintermediating” our biometric data—especially facial data—is the wrong “architecture” to manage identity. “It seems they are trying to create a system like login with Google, but for everything in real life,” Young warns. While the single sign-on option that Google (or Facebook or Apple) provides for websites and apps may make life easy, it also poses greater security and privacy risks by putting both our personal data and the keys to it in the hands of a single profit-driven entity: “We’re basically selling our identity soul to a private company, who’s then going to be the gatekeeper … everywhere one goes.”
Though Clear remains far less well known than Google, more than 27 million people have already helped it become that very gatekeeper—and “one of the largest private repositories of identities on the planet,” as Nicholas Peddy, Clear’s chief technology officer, put it in an interview with MIT Technology Review this summer.
With Clear well on the way to realizing its plan for a frictionless future, it’s time to try to understand both how we got here and what we have (been) signed up for.
A new frontier in identity management
Imagine this: On a Friday morning in the near future, you are rushing to get through your to-do list before a weekend trip to New York.
In the morning, you apply for a new job on LinkedIn. During lunch, assured that recruiters are seeing your professional profile because it’s been verified by Clear, you pop out to Home Depot, confirm your identity with a selfie, and rent a power drill for a quick bathroom repair. Then, in the midafternoon, you drive to your doctor’s office; having already verified your identity—prompted by a text message sent a few days earlier—you confirm your arrival with a selfie at a Clear kiosk. Before you go to bed, you plan your morning trip to the airport and set an alarm—but not too early, because you know that with Clear, you can quickly drop your bags and breeze through security.
Once you’re in New York, you head to Barclays Center, where you’ll be seeing your favorite singer; you skip the long queue out front to hop in the fast-track Clear line. It’s late when the show is over, so you grab an Uber home and barely need to wait for a driver, who feels more comfortable thanks to your verified rider profile.
At no point did you pull out your driver’s license or fill out repetitive paperwork. All that was already on file. Everything was easy; everything was frictionless.
More than 27 million people have already helped Clear become “one of the largest private repositories of identities on the planet.”
This, at least, is the world that Clear is actively building toward.
Part of Clear’s power, Seidman Becker often says, is that it can wholly replace our wallets: our credit cards, driver’s licenses, health insurance cards, perhaps even building key fobs. But you can’t just suddenly be all the cards you carry. For Clear to link your digital identity to your real-world self, you must first give up a bit of personal data—specifically, your biometric data.
Biometrics refers to the unique physical and behavioral characteristics—faces, fingerprints, irises, voices, and gaits, among others—that identify each of us as individuals. For better or worse, they typically remain stable during our lifetimes.
Relying on biometrics for identification can be convenient, since people are apt to misplace a wallet or forget the answer to a security question. But on the other hand, if someone manages to compromise a database of biometric information, that convenience can become dangerous: We cannot easily change our face or fingerprint to secure our data again, the way we could change a compromised password.
On a practical level, there are generally two ways that biometrics are used to identify individuals. The first, generally referred to “one-to-many” or “one-to-n” matching, compares one person’s biometric identifier with a database full of them. This is sometimes associated with a stereotypical idea of dystopian surveillance in which real-time facial recognition from live video could allow authorities to identify anyone walking down the street. The other, “one-to-one” matching, is the basis for Clear; it compares a biometric identifier (like the face of a live person standing before an airport agent) with a previously recorded biometric template (such as a passport photo) to verify that they match. This is usually done with the individual’s knowledge and consent, and it arguably poses a lower privacy risk. Often, one-to-one matching includes a layer of document verification, like checking that your passport is legitimate and matches a photograph you used to register with the system.
The US Congress urgently saw the need for better identity management following the September 11 terrorist attacks; 18 of the 19 hijackers used fake identity documents to board their flights. In the aftermath, the newly created Transportation Security Administration (TSA) implemented security processes that slowed down air travel significantly. Part of the problem was that “everybody was just treated the same at airports,” recalls the serial media entrepreneur Steven Brill—including, famously, former vice president Al Gore. “It sounded awfully democratic … but in terms of basic risk management and allocation of resources, it just didn’t make any sense.”
Congress agreed, authorizing the TSA to create a program that would allow people who passed background checks to be recognized as trusted travelers and skip some of the scrutiny at the airport.
In 2007, San Francisco’s then mayor, Gavin Newsom, had his irises scanned by Clear at San Francisco International Airport.
DAVID PAUL MORRIS/GETTY
In 2003, Brill teamed up with Ajay Amlani, a technology entrepreneur and former adviser to the Department of Homeland Security, and founded a company called Verified Identity Pass (VIP) to provide biometric identity verification in the TSA’s new program. “The vision,” says Amlani, “was a unified fast lane—similar to a toll lane.”
It appeared to be a win-win solution. The TSA had a private-sector partner for its registered-traveler program; VIP had a revenue stream from user fees; airports got a cut of the fees in exchange for leasing VIP space;and initial members—typically frequent business travelers—were happy to cut down on airport wait times.
By 2005, VIP had launched in its first airport, Orlando International in Florida. Members—initially paying $80—received “Clear cards” that contained a cryptographic representation of their fingerprint, iris scans, and a photo of their face taken at enrollment. They could use those cards at the airport to be escorted to the front of the security lines.
The defense contracting giant Lockheed Martin, which already provided biometric capabilities to the US Department of Defense and the FBI, was responsible for deploying and providing technology for VIP’s system, with additional technical expertise from Oracle and others. This left VIP to “focus on marketing, pricing, branding, customer service, and consumer privacy policies,” as the president of Lockheed Transportation and Security Solutions, Don Antonucci, said at the time.
By 2009, nearly 200,000 people had joined. The company had received $116 million in investments and signed contracts with about 20 airports. It all seemed so promising—if VIP had not already inadvertently revealed the risks inherent in a system built on sensitive personal data.
A lost laptop and a big opportunity
From the beginning, there were concerns about the implications of VIP’s Clear card for privacy, civil liberty, and equity, as well as questions about its effectiveness at actually stopping future terrorist attacks. Advocacy groups like the Electronic Privacy Information Center (EPIC) warned that the biometrics-based system would result in a surveillance infrastructure built on sensitive personal information, but data from the Pew Research Center shows that a majority of the public at the time felt that it was generally necessary to sacrifice some civil liberties in the name of safety.
Then a security lapse sent the whole operation crumbling.
In the summer of 2008, VIP reported that an unencrypted company laptop containing addresses, birthdays, and driver’s license and passport numbers of 33,000 applicants had gone missing from an office at San Francisco International Airport (SFO)—even though TSA’s security protocol required it to encrypt all laptops holding personal data.
NEIL WEBB
The laptop was found about two weeks later and the company said no data was compromised. But it was still a mess for VIP. Months later, investors pushed Brill out, and associated costs led the company to declare bankruptcy and close the following year.
Disgruntled users filed a class action lawsuit against VIP to recoup membership fees and “punitive damages.” Some users were upset they had recently renewed their subscriptions, and others worried about what would happen to their personal information. A judge temporarily prevented the company from selling user data, but the decision didn’t hold.
Seidman Becker and her longtime business partner Ken Cornick, both hedge fund managers, saw an opportunity. In 2010, they bought VIP—and its user data—in a bankruptcy sale for just under $6 million and registered a new company called Alclear. “I was a big believer in biometrics,” Seidman Becker told the tech journalists Kara Swisher and Lauren Goode in 2017. “I wanted to build something that made the world a better place, and Clear was that platform.”
Initially, the new Clear followed closely in the footsteps of its predecessor: Lockheed Martin transferred the members’ information to the new company, which had acquired VIP’s hardware and continued to use Clear cards to hold members’ biometrics.
After the relaunch, Clear also started building partnerships with other companies in the travel industry—including American Express, United Airlines, Alaska Airlines, Delta Airlines, and Hertz Rental Cars—to bundle its service for free or at a discount. (Clear declined to specify how many of its users have such discounts, but in earnings calls the company has stressed its efforts to reduce the number of members paying reduced rates.)
By 2014, improvements in internet latency and biometric processing speeds allowed Clear to eliminate the cards and migrate to a server-based system—without compromising data security, the company says. Clear emphasizes that it meets industry standards for keeping data secure, with methods including encryption, firewalls, and regular penetration testing by both internal and external teams. The company says it also maintains “locked boxes” around data relating to air travelers.
Still, the reality is that every database of this kind is ultimately a target, and “almost every day there’s a massive breach or hack,” says Chris Gilliard, a privacy and surveillance researcher who was recently named co-director of the Critical Internet Studies Institute. Over the years, even apparently well-protected biometric information has been compromised. Last year, for instance, a data breach at the genetic testing company 23andMe exposed sensitive information—including geographic locations, birth years, family trees, and user-uploaded photos—from nearly 7 million customers.
This is what Young, who helped facilitate the creation of the open-source identity management standards Open ID Connect and OAuth, means when she says that Clear has the wrong “architecture” for managing digital identity; it’s too much of a risk to keep our digital identities in a central database, cryptographically protected or not. She and many other identity and privacy experts believe that the most privacy-protecting way to manage digital identity is to “use credentials, like a mobile driver’s license, stored on people’s devices in digital wallets,“ she says. “These digital credentials can have biometrics, but the biometrics in a central database are not being pinged for day to day use.”
But it’s not just data that’s potentially vulnerable. In 2022 and 2023, Clear faced three high-profile security incidents in airports, including one in which a passenger successfully got through the company’s checks using a boarding pass found in the trash. In another, a traveler in Alabama used someone else’s ID to register for Clear and, later, to successfully pass initial security checks; he was discovered only when he tried to bring ammunition through a subsequent checkpoint.
This spurred an investigation by the TSA, which turned up more alarming information: Nearly 50,000 photos used by Clear to enroll customers were flagged as “non-matches” by the company’s facial recognition software. Some photos didn’t even contain full faces, according to Bloomberg. (In a press release after the incident, the company refuted the reporting, describing it as “a single human error—having nothing to do with our technology” and stating that “the images in question were not relied upon during the secure, multi-layered enrollment process.”)
“How do you get to be the one?”
When I spoke to Brill this spring, he told me he’d always envisioned that Clear would expand far beyond the airport. “The idea I had was that once you had a trusted identity, you would potentially be able to use it for a lot of different things,” he said, but “the trick is to get something that is universally accepted. And that’s the battle that Clear and anybody else has to fight, which is: How do you get to be the one?”
Goode Intelligence, a market research firm that focuses on the booming identity space, estimates that by 2029, there will be 1.5 billion digital identity wallets around the world—with use for travel leading the way and generating an estimated $4.6 billion in revenue. Clear is just one player, and certainly not the biggest. ID.me, for instance, provides similar face-based identity verification and has over 130 million users, dwarfing Clear’s roughly 27 million. It’s also already in use by numerous US federal and state agencies, including the IRS.
The reality is that every database of this kind is ultimately a target, and “almost every day there’s a massive breach or hack.”
But as Goode Intelligence CEO Alan Goode tells me, Clear’s early-mover advantage, particularly in the US, “puts it in a good space within North America … [to] be more pervasive”—or to become what Brill called “the one” that is most closely stitched into people’s daily lives.
Clear began growing beyond travel in 2015, when it started offering biometric fast-pass access to what was then AT&T Park in San Francisco. Stadiums across California, Colorado, and Washington, and in major cities in other states, soon followed.Fans can simply download the free Clear app and scan the QR code to bypass normal lines in favor of designated Clear lanes. For a time, Clear also promoted its biometric payment systems at some venues, including two in Seattle, which could include built-in age verification. It even partnered with Budweiser for a “Bud Now” machine that used your fingerprint to verify your identity, age, and payment. (These payment programs, which a Clear representative called “pilots” in an email, have since ended; representatives for the Seattle Mariners and Seahawks did not respond to multiple requests for comment on why.) Clear’s programs for expedited event access have been popular enough to drive greater user growth than its paid airport service, according to numbers provided by the company.
Then came the pandemic, hitting Clear (and the entire travel industry) hard. But the crisis for Clear’s primary business actually accelerated its move into new spaces with “Health Pass,” which allowed organizations to confirm the health status of employees, residents, students, and visitors who sought access to a physical space. Users could upload vaccination cards to the Health Pass section in the Clear mobile app; the program was adopted by nearly 70 partners in 110 unique locations, including NFL stadiums, the Mariners’ T-Mobile Park, and the 9/11 Memorial Museum.
Demand for vaccine verification eventually slowed, and Health Pass shut down in March 2024. But as Jason Sherwin, Clear’s senior director of health-care business development, said in a podcast interview earlier this year, it was the company’s “first foray into health care”—the business line that currently represents its “primary focus across everything we’re doing outside of the airport.” Today, Clear kiosks for patient sign-ins are being piloted at Georgia’s Wellstar Health Systems, in conjunction with one of the largest providers of electronic health records in the United States: Epic (which is unrelated to the privacy nonprofit).
What’s more, Health Pass enabled Clear to expand at a time when the survival of travel-focused businesses wasn’t guaranteed. In November 2020, Clear had roughly 5 million members; today, that number has grown fivefold. The company went public in 2021 and has experienced double-digit revenue growth annually.
These doctor’s office sign-ins, in which the system verifies patient identity via a selfie, rely on what’s called Clear Verified, a platform the company has rolled out over the past several years that allows partners (health-care systems, as well as brick-and-mortar retailers, hotels, and online platforms) to integrate Clear’s identity checks into their own user-verification processes. It again seems like a win-win situation: Clear gets more users and a fee from companies using the platform, while companies confirm customers’ identity and information, and customers, in theory, get that valuable frictionless experience. One high-profile partnership, with LinkedIn, was announced last year: “We know authenticity matters and we want the people, companies and jobs you engage with everyday to be real and trusted,” Oscar Rodriguez, LinkedIn’s head of trust and privacy, said in a press release.
All this comes together to create the foundation for what is Clear’s biggest advantage today: its network. The company’s executives often speak about its “embedded” users across various services and platforms, as well as its “ecosystem,” meaning the venues where it is used. As Peddy explains, the value proposition for Clear today is not necessarily any particular technology or biometric algorithm, but how it all comes together—and can work universally. Clear would be “wherever our consumers need us to be,” he says—it would “sort of just be this ubiquitous thing that everybody has.”
Clear CEO Caryn Seidman Becker (left) rings the bell at the New York Stock Exchange in 2021.
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A prospectus to investors from the company’s IPO makes the pitch simple: “We believe Clear enables our partners to capture not just a greater share of their customers’ wallet, but a greater share of their overall lives.”
The more Clear is able to reach into customers’ lives, the more valuable customer data it can collect. All user interactions and experiences can be tracked, the company’s privacy policy explains. While the policy states that Clear will not sell data and will never share biometric or health information without “express consent,” it also lays out the non-health and non-biometric data that it collects and can use for consumer research and marketing. This includes members’ demographic details, a record of every use of Clear’s various products, and even digital images and videos of the user. Documents obtained by OneZerooffer some further detail into what Clear has at least considered doing with customer data: David Gershgorn wrote about a 2015 presentation to representatives from Los Angeles International Airport, titled “Identity Dashboard—Valuable Marketing Data,” which “showed off” what the company had collected, including the number of sports games users had attended and with whom, which credit cards they had, their favorite airlines and top destinations, and how often they flew first class or economy.
Clear representatives emphasized to MIT Technology Review that the company “does not share or sell information without consent,” though they “had nothing to add” in response to a question about whether Clear can or does aggregate data to derive its own marketing insights, a business model popularized by Facebook. “At Clear, privacy and security are job one,” spokesperson Ricardo Quinto wrote in an email. “We are opt-in. We never sell or share our members’ information and utilize a multilayered, best-in-class infosec system that meets the highest standards and compliance requirements.”
Nevertheless, this influx of customer data is not just good for business; it’s risky for customers. It creates “another attack surface,” Gilliard warns. “This makes us less safe, not more, as a consistent identifier across your entire public and private life is the dream of every hacker, bad actor, and authoritarian.”
A face-based future for some
Today, Clear is in the middle of another major change: replacing its use of iris scans and fingerprints with facial verification in airports—part of “a TSA-required upgrade in identity verification,” a TSA spokesperson wrote in an email to MIT Technology Review.
For a long time, facial recognition technology “for the highest security purposes” was “not ready for prime time,” Seidman Becker told Swisher and Goode back in 2017. It wasn’t operating with “five nines,” she added—that is, “99.999% from a matching and an accuracy perspective.” But today, facial recognition has “significantly improved” and the company has invested “in enhancing image quality through improved capture, focus, and illumination,” according to Quinto.
Clear says switching to facial images in airports will also further decrease friction, enabling travelers to verify their identity so effortlessly it’s “almost like you don’t really break stride,” Peddy says. “You walk up, you scan your face. You walk straight to the TSA.”
The move is part of a broader shift toward facial recognition technology in US travel, bringing the country in line with practices at many international airports. The TSA began expanding facial identification from a few pilot programs this year, while airlines including Delta and United are also introducing face-based boarding, baggage drops, and even lounge access. And the International Air Transport Association, a trade group for the airline industry, is rolling out a “contactless travel” process that will allow passengers to check in, drop off their bags, and board their flights—all without showing either passports or tickets, just their faces.
NEIL WEBB
Privacy experts worry that relying on faces for identity verification is even riskier than other biometric methods. After all, “it’s a lot easier to scan people’s faces passively than it is to scan irises or take fingerprints,” Senator Jeff Merkley of Oregon, an outspoken critic of government surveillance and of the TSA’s plans to employ facial verification at airports, said in an email. The point is that once a database of faces is built, it is potentially far more useful for surveillance purposes than, say, fingerprints. “Everyone who values privacy, freedom, and civil rights should be concerned about the increasing, unchecked use of facial recognition technology by corporations and the federal government,” Merkley wrote.
Even if Clear is not in the business of surveillance today, it could, theoretically, pivot or go bankrupt and (again) sell off its parts, including user data. Jeramie Scott, senior counsel and director of the Project on Surveillance Oversight at EPIC, says that ultimately, the “lack of federal [privacy] regulation” means that we’re just taking the promises of companies like Clear at face value: “Whatever they say about how they implement facial recognition today does not mean that that’s how they’ll be implementing facial recognition tomorrow.”
Making this particular scenario potentially more concerning is that the images stored by this private company are “generally going to be much higher quality” than those collected by scraping the internet—which Albert Fox Cahn, the executive director of the Surveillance Technology Oversight Project (STOP), says would make its data far more useful for surveillance than that held by more controversial facial recognition companies like Clearview AI.
Even a far less pessimistic read of Clear’s data collection reveals the challenges of using facial identification systems, which—as a 2019 report from the National Institute for Standards and Technology revealed—have been shown to work less effectively in certain populations, particularly people of African and East Asian descent, women, and elderly and very young people. NIST has also not tested identification accuracy for individuals who are transgender, but Gilliard says he expects the algorithms would fall short.
More recent testing shows that some algorithms have improved, NIST spokesperson Chad Boutin tells MIT Technology Review—though accuracy is still short of the “five nines” that Seidman Becker once said Clear was aiming for. (Quinto, the Clear representative, maintains that Clear’s recent upgrades, combined with the fact that the company’s testing involves “comparing member photos to smaller galleries, rather than the millions used in NIST scenarios,” means its technology “remains accurate and suitable for secure environments like airports.”)
Even a very small error rate “in a system that is deployed hundreds of thousands of times a day” could still leave “a lot of people” at risk of misidentification, explains Hannah Quay-de La Vallee, a technologist at the Center for Democracy & Technology, a nonprofit based in Washington, DC. All this could make Clear’s services inaccessible to some—even if they can afford it, which is less likely given the recent increase in the subscription fee for travelers to $199 a year.
The free Clear Verified Platform is already giving rise to access problems in at least one partnership, with LinkedIn. The professional networking site encourages users to verify their identities either with an employer email address or with Clear, which marketing materials say will yield more engagement. But some LinkedIn users have expressed concerns, claiming that even after uploading a selfie, they were unable to verify their identities with Clear if they were subscribed to a smaller phone company or if they had simply not had their phone number for enough time. As one Reddit user emphasized, “Getting verified is a huge deal when getting a job.” LinkedIn said it does not enable recruiters to filter, rank, or sort by whether a candidate has a verification badge, but also said that verified information does “help people make more informed decisions as they build their network or apply for a job.” Clear only said it “works with our partners to provide them with the level of identity assurance that they require for their customers” and referred us back to LinkedIn.
An opt-in future that may not really be optional
Maybe what’s worse than waiting in line, or even being cut in front of, is finding yourself stuck in what turns out to be the wrong line—perhaps one that you never want to be in.
That may be how it feels if you don’t use Clear and similar biometric technologies. “When I look at companies stuffing these technologies into vending machines, fast-food restaurants, schools, hospitals, and stadiums, what I see is resignation rather than acceptance—people often don’t have a choice,” says Gilliard, the privacy and surveillance scholar. “The life cycle of these things is that … even when it is ‘optional,’ oftentimes it is difficult to opt out.”
And while the stakes may seem relatively low—Clear is, after all, a voluntary membership program—they will likely grow as the system is deployed more widely. As Seidman Becker said on Clear’s latest earnings call in early November, “The lines between physical and digital interactions continue to blur. A verified identity isn’t just a check mark. It’s the foundation for everything we do in a high-stakes digital world.” Consider a job ad posted by Clear earlier this year, seeking to hire a vice president for business development; it noted that the company has its eye on a number of additional sectors, including financial services, e-commerce, P2P networking, “online trust,” gaming, government, and more.
“Increasingly, companies and the government are making the submission of your biometrics a barrier to participation in society,” Gilliard says.
This will be particularly true at the airport, with the increasing ubiquity of facial recognition across all security checks and boarding processes, and where time-crunched travelers could be particularly vulnerable to Clear’s sales pitch. Airports have even privately expressed concerns about these scenarios to Clear. Correspondence from early 2022 between the company and staff at SFO, released in response to a public records request, reveals that the airport “received a number of complaints” about Clear staff “improperly and deceitfully soliciting approaching passengers in the security checkpoint lanes outside of its premises,” with an airport employee calling it “completely unacceptable” and “aggressive and deceptive behavior.”
Of course, this isn’t to say everyone with a Clear membership was coerced into signing up. Many people love it; the company told MIT Technology Review that it had a nearly 84% retention rate earlier this year. Still, for some experts, it’s worrisome to think that what Clear users are comfortable with ends up setting the ground rules for the rest of us.
“We’re going to normalize potentially a bunch of biometric stuff but not have a sophisticated conversation about where and how we’re normalizing what,” says Young. She worries this will empower “actors who want to move toward a creepy surveillance state, or corporate surveillance capitalism on steroids.”
“Without understanding what we’re building or how or where the guardrails are,” she adds, “I also worry that there could be major public backlash, and then legitimate uses [of biometric technology] are not understood and supported.”
But in the meantime, even superfans are grumbling about an uptick in wait times in the airport’s Clear lines. After all, if everyone decides to cut to the front of the line, that just creates a new long line of line-cutters.
Once again, global greenhouse-gas emissions are projected to hit a new high in 2024.
In this time of shifting political landscapes and ongoing international negotiations, many are quick to blame one country or another for an outsize role in causing climate change.
But assigning responsibility is complicated. These three visualizations help explain why and provide some perspective about the world’s biggest polluters.
Greenhouse-gas emissions from fossil fuels and industry reached 37.4 billion metric tons of carbon dioxide in 2024, according to projections from the Global Carbon Budget, an annual emissions report released last week. That’s a 0.8% increase over last year.
Breaking things down by country, China is far and away the single biggest polluter today, a distinction it has held since 2006. The country currently emits roughly twice as much greenhouse gas as any other nation. The power sector is its single greatest source of emissions as the grid is heavily dependent on coal, the most polluting fossil fuel.
The US is the world’s second-biggest polluter, followed by India. Combined emissions from the 27 nations that make up the European Union are next, followed by Russia and Japan.
Considering a country’s current emissions doesn’t give the whole picture of its climate responsibility, though. Carbon dioxide is stable in the atmosphere for hundreds of years. That means greenhouse gases from the first coal power plant, which opened in the late 19th century, are still having a warming effect on the planet today.
Adding up each country’s emissions over the course of its history reveals that the US has the greatest historical contribution—the country is responsible for about 24% of all the climate pollution released into the atmosphere as of 2023. While it’s the biggest polluter today, China comes in second in terms of historical emissions, at 14%.
If the EU’s member states are totaled as one entity, the group is among the top historical contributors as well. According to an analysis published November 19 by the website Carbon Brief, China passed EU member states in terms of historical emissions in 2023 for the first time.
China could catch up with the West in the coming decades, as its emissions are significant and still growing, while the US and EU are seeing moderate declines.
Even then, though, there’s another factor to consider: population. Dividing a country’s total emissions by its population reveals how the average individual in each nation is contributing to climate change today.
Countries with smaller populations and economies that are heavily reliant on oil and gas tend to top this list, including Saudi Arabia, Bahrain, and the United Arab Emirates.
Among the larger nations, Australia has the highest per capita emissions from fossil fuels, with the US and Canada close behind. Meanwhile, other countries that have high total emissions are farther down the list when normalized by population: China’s per capita emissions are just over half that of the US, while India’s is a small fraction.
Understanding the complicated picture of global emissions is crucial, especially during ongoing negotiations (including the current meeting at COP29 in Baku, Azerbaijan) over how to help developing nations pay for efforts to combat climate change.
Looking at current emissions, one might expect the biggest emitter, China, to contribute more than any other country to climate finance. But considering historical contributions, per capita emissions, and details about national economies, other nations like the US, UK, and members of the EU emerge as those experts tend to say should feature prominently in the talks.
What is clear is that when it comes to the emissions blame game, it’s more complicated than just pointing at today’s biggest polluters. Ultimately, addressing climate change will require everyone to get on board—we all share an atmosphere, and we’re all going to continue feeling the effects of a changing climate.
Notes on data methodology:
Emissions data is from the Global Carbon Project, which estimates carbon emissions based on energy use. Territorial emissions take into account energy and some industry, but don’t include land use emissions.
Data from the European Union is the sum of its current 27 member states. The bloc is represented together because the EU generally negotiates together on the international stage.
Historical emissions for some countries are disaggregated from former borders, including the former USSR and Yugoslavia.
The per capita emissions map uses official World Bank boundaries, with the exception of Taiwan, which has separate emissions data in the Global Carbon Project.
Western Sahara’s energy data are reported by Morocco, so its emissions are included in that total. Per capita emissions for Morocco are also used for Western Sahara on the map.
More detailed information about the Global Carbon Project methods (including the particulars on how territorial emissions are broken down) is available here.
