The problem with thinking you’re part Neanderthal

You’ve probably heard some version of this idea before: that many of us have an “inner Neanderthal.” That is to say, around 45,000 years ago, when Homo sapiens first arrived in Europe, they met members of a cousin species—the broad-browed, heavier-set Neanderthals—and, well, one thing led to another, which is why some people now carry a small amount of Neanderthal DNA. 

This DNA is arguably the 21st century’s most celebrated discovery in human evolution. It has been connected to all kinds of traits and health conditions, and it helped win the Swedish geneticist Svante Pääbo a Nobel Prize.

But in 2024, a pair of French population geneticists called into question the foundation of the popular and pervasive theory. 

Lounès Chikhi and Rémi Tournebize, then colleagues at the Université de Toulouse, proposed an alternative explanation for the very same genomic patterns. The problem, they said, was that the original evidence for the inner Neanderthal was based on a statistical assumption: that humans, Neanderthals, and their ancestors all mated randomly in huge, continent-size populations. That meant a person in South Africa was just as likely to reproduce with a person in West Africa or East Africa as with someone from their own community. 

Archaeological, genetic, and fossil evidence all shows, though, that Homo ­sapiens evolved in Africa in smaller groups, cut off from one another by deserts, mountains, and cultural divides. People sometimes crossed those barriers, but more often they partnered up within them. 

In the terminology of the field, this dynamic is called population structure. Because of structure, genes do not spread evenly through a population but can concentrate in some places and be totally absent from others. The human gene pool is not so much an Olympic-size swimming pool as a complex network of tidal pools whose connectivity ebbs and flows over time.

This dynamic greatly complicates the math at the heart of evolutionary biology, which long relied on assumptions like randomly mating populations to extract general principles from limited data. If you take structure into account, Chikhi told me recently, then there are other ways to explain the DNA that some living people share with Neanderthals—ways that don’t require any interspecies sex at all.

“I believe most species are spatially organized and structured in different, complex ways,” says Chikhi, who has researched population structure for more than two decades and has also studied lemurs, orangutans, and island birds. “It’s a general failure of our field that we do not compare our results in a clear way with alternative scenarios.” (Pääbo did not respond to multiple requests for comment.)

The inner Neanderthal became a story we could tell ourselves about our flaws and genetic destiny: Don’t blame me; blame the prognathic caveman hiding in my cells.

Chikhi and Tournebize’s argument is about population structure, yes, but at heart, it is actually one about methods—how modern evolutionary science deploys computer models and statistical techniques to make sense of mountains upon mountains of genetic data. 

They’re not the only scientists who are worried. “People think we really understand how genomes evolve and can write sophisticated algorithms for saying what happened,” says William Amos, a University of Cambridge population geneticist who has been critical of the “inner Neanderthal” theory. But, he adds, those models are “based on simple assumptions that are often wrong.” 

And if they’re wrong, what’s at stake is far more than a single evolutionary mystery. 

A captivating story of interspecies passion

Back in 2010, Pääbo’s lab pulled off something of a miracle. The researchers were able to extract DNA from nuclei in the cells of 40,000-year-old Neanderthal bones. DNA breaks down quickly after death, but the group got enough of it from three different individuals to produce a draft sequence of the entire Neanderthal genome, with 4 billion base pairs. 

As part of their study, they performed a statistical test comparing their Neanderthal genome with the genomes of five present-day people from different parts of the world. That’s how they discovered that modern humans of non-African ancestry had a small amount of DNA in common with Neanderthals, a species that diverged from the Homo sapiens line more than 400,000 years ago, that they did not share with either modern humans of African ancestry or our closest living relative, the chimpanzee. 

Neanderthal front and profile view
This model of a Neanderthal man was exhibited in the “Prehistory Gallery” at London’s Wellcome Historical Medical Museum in the 1930s.
WELLCOME COLLECTION

Pääbo’s team interpreted this as evidence of sexual reproduction between ancient Homo sapiens and the Neanderthals they encountered after they expanded out of Africa. “Neanderthals are not totally extinct,” Pääbo said to the BBC in 2010. “In some of us, they live on a little bit.”

The discovery was monumental on its own—but even more so because it reversed a previous consensus. More than a decade earlier, in 1997, Pääbo had sequenced a much smaller amount of Neanderthal DNA, in that case from a cell structure called a mitochondrion. It was different enough from Homo sapiens mitochondrial DNA for his team to cautiously conclude there had been “little or no interbreeding” between the two species. 

After 2010, though, the idea of hybridization, also called admixture, effectively became canon. Top journals like Science and Nature published study after study on the inner Neanderthal. Some scientists have argued that Homo sapiens would never have adapted to colder habitats in Europe and Asia without an infusion of Neanderthal DNA. Other research teams used Pääbo’s techniques to find genetic traces of interbreeding with an extinct group of hominins in Asia, called the Denisovans, and a mysterious “ghost lineage” in Africa. Biologists used similar tests to find evidence of interbreeding between chimpanzees and bonobos, polar and brown bears, and all kinds of other animals. 

The inner-Neanderthal hypothesis also took a turn for the personal. Various studies linked Neanderthal DNA to a head-spinning range of conditions: alcoholism, asthma, autism, ADHD, depression, diabetes, heart disease, skin cancer, and severe covid-19. Some researchers suggested that Neanderthal DNA had an impact on hair and skin color, while others assigned individuals a “NeanderScore” that was correlated with skull shape and prevalence of schizophrenia markers. Commercial genetic testing companies like 23andMe started offering customers Neanderthal ancestry reports. 

The inner Neanderthal became a story we could tell ourselves about our flaws and genetic destiny: Don’t blame me; blame the prognathic caveman hiding in my cells. Or as Latif Nasser, a host of the popular-science program Radiolab, put it when he was hospitalized with Crohn’s disease, another Neanderthal-associated condition: “I just keep imagining these tiny Neanderthals … just, like, stabbing me and drawing these little droplets of blood out of me.”

“These things become meaningful to people,” Chikhi says. “What we say will be important to how people view themselves.” 

The pitfalls of simplistic solutions 

When population geneticists built the theoretical framework for evolutionary biology in the early 20th century, genes were only abstract units of heredity inferred from experiments with peas and fruit flies. Population genetics developed theory far more quickly than it accumulated data. As a result, many data-driven scientists dismissed the study of evolution as a form of storytelling based on unexamined assumptions and preconceived ideas.

By the ’90s, though, genes were no longer abstractions but sequenced segments of DNA. Genomic sequencing grounded evolutionary studies in the kind of hard data that a chemist or physicist could respect. 

Yet biologists could not simply read evolutionary history from genomes as though they were books. They were trying to determine which of a nearly infinite number of plausible histories was the most likely to have created the patterns they observed in a small sample of genomes. For that, they needed simplified, algorithmic models of evolution. The study of evolution shifted from storytelling to statistics, and from biology to computer science. 

That suited Chikhi, who as a child was drawn to the predictable laws and numerical precision of math and science. He entered the field in the mid-’90s just as the first big studies of human DNA were settling old debates about human origins. DNA showed that Africa harbored far more genetic diversity than the entire rest of the planet. The new evidence supported the idea that modern humans evolved for hundreds of thousands of years in Africa and expanded to the other continents only in the last 100,000 years. For Chikhi, whose parents were Algerian immigrants, this discovery was a powerful challenge to the way some archaeologists and biologists talked about race. DNA could be used to deconstruct rather than encourage the pernicious idea that human races had deep-seated evolutionary differences based on their places of origin. 

At the same time, though, he was wary of the tendency to treat DNA as the final verdict on open questions in evolution. Chikhi had been surprised when, back in 1997, Pääbo and his team used that small amount of mitochondrial DNA to rule out hybridization between Homo sapiens and Neanderthals. He didn’t think that the absence of Neanderthal DNA there necessarily meant it wouldn’t be found elsewhere in the Homo sapiens genome.

Chikhi’s own research in the aughts opened his eyes to the gaps between historical reality and models of evolution. For one, despite the assumption of random mating, none of the animals Chikhi studied actually mated randomly. Orangutans lived in highly fragmented habitats, which restricted their pool of potential mates, and female birds were often extremely picky about their male partners. 

These factors could confound an evolutionary biologist’s traditional statistical tool kit. Scientists were starting to apply a mathematical technique to estimate historical population sizes for a species from the genome of just a single individual. This method showed sharp population declines in the histories of many different species. Chikhi realized, though, that the apparent declines could be an artifact of treating a structured population as one that evolved with random mating; in that case, the technique could indicate a bottleneck even if all the subgroups were actually growing in size. “This is completely counterintuitive,” he says. 

That’s at least partly why, when Pääbo’s 2010 Neanderthal genome came out, Chikhi was impressed with the sheer technical accomplishment but also leery of the findings about hybridization. “It was the type of thing we conclude too quickly based on genetic data,” he says. Pääbo’s work mentioned population structure as a possible alternative explanation—but didn’t follow up.

Just a couple of years later, a pair of independent scientists named Anders Eriksson and Andrea Manica picked up the idea, building a model with simple population structure that explicitly excluded admixture. They simulated human evolution starting from 500,000 years ago and found that their model produced the same genomic patterns Pääbo’s group had interpreted as evidence of hybridization.

“Working with structured models is really out of the comfort zone of a lot of population geneticists,” says Eriksson, now a professor at the University of Tartu in Estonia.

Their research impressed Chikhi. “At the time, I thought people would focus on population structure in the evolution of humans,” he says. Instead, he watched as the inner-Neanderthal hypothesis took on a life of its own. Scientists produced new methods to quantify hybridization but rarely examined whether population structure would yield the same results. To Chikhi, this wasn’t science; it was storytelling, like some of the old narratives about the evolution of racial differences. 

Chikhi and Tournebize decided to take a crack at the problem themselves. “I’ve always been very skeptical about science, and population genetics in particular,” says Tournebize, now a researcher at the French National Research Institute for Sustainable Development. “We make a lot of assumptions, and the models we use are very simplistic.” As detailed in a 2024 paper published in Nature Ecology & Evolution, they built a model of human evolution that replaced randomly mating continent-wide populations with many smaller populations linked by occasional migration. Then they let it run—a million times.

At the end of the simulation, they kept the 20 scenarios that produced genomes most similar to the ones in a sample of actual Homo sapiens and Neanderthals. Many of these scenarios produced long segments of DNA like the ones their peers argued could only have been inherited from Neanderthals. They showed that several statistics, which other scientists had proposed as measurements of Neanderthal DNA, couldn’t actually distinguish between hybridization and population structure. What’s more, they showed that many of the models that supported hybridization failed to accurately predict other known features of human evolution.

“A model will say there was admixture but then predict diversity that is totally incompatible with what we actually know of human diversity,” Chikhi says. “Nobody seems to care.”

So how did Neanderthal DNA wind up in living people if not via interspecies passion? Chikhi and Tournebize think it’s more likely that it was inherited by both Neanderthals and some sapiens groups in Africa from a common ancestor living at least half a million years ago. If the sapiens groups carrying those genetic variants included the people who migrated out of Africa, then the two human species would have already had the DNA in common when they came into contact in Europe and Asia—no sex required. 

“The interpretation of genetic data is not straightforward,” Chikhi says. “We always have to make assumptions. Nobody takes data and magically comes up with a solution.” 

Embracing the uncertainty 

Most of the half-dozen population geneticists I spoke with praised Chikhi and Tournebize’s ingenuity and appreciated the spirit of their critique. “Their paper forces us to think more critically about the model we use for inference and consider alternatives,” says Aaron Ragsdale, a population geneticist at the University of Wisconsin–Madison. His own work likewise suggests that the earliest Homo sapiens populations in Africa were probably structured—and that this is the likely reason for genomic patterns that other research groups had attributed to hybridization with a mysterious “ghost lineage” of hominins in Africa.

Yet most researchers still believe that modern humans and Neanderthals did probably have children with each other tens of thousands of years ago. Several pointed to the fact that fossil DNA of Homo sapiens who died thousands of years ago had longer chunks of apparent Neanderthal DNA than living people, which is exactly what you would expect if they had a more recent Neanderthal ancestor. (To address this possibility, Chikhi and Tournebize included DNA from 10 ancient humans in their study and found that most of them fit the structured model.) And while the Harvard population geneticist David Reich, who helped design the statistical test from Pääbo’s 2010 study, declined an interview, he did say he thought Chikhi and Tournebize’s model was “weak” and “very contrived,” adding that “there are multiple lines of evidence for Neanderthal admixture into modern humans that make the evidence for this overwhelming.” (Two other authors of that study, Richard Green and Nick Patterson, did not respond to requests for comment.) 

Nevertheless, most scientists these days welcome the development of structured, or “spatially explicit,” models that account for the fact that any given member of a population is usually more closely related to individuals living nearby than to those living far away. 

Loosening our attachment to certain narratives of evolution can create space for wonder at the sheer complexity of life’s history.

Other scientists also say that random mating isn’t the only assumption in population genetics that merits scrutiny. Models rarely factor in natural selection, which can also create genetic patterns that look like hybridization. Another common assumption is that everyone’s DNA mutates at the same, constant rate. “All the theory says the mutation rate is fixed,” says Amos, the Cambridge population geneticist. But he thinks that rate would have slowed drastically in the group of Homo sapiens that expanded to Europe around 45,000 years ago. This, too, could have created genomic patterns that other scientists interpret as evidence of interbreeding with Neanderthals. 

Commercial genetic testing companies like 23andMe started offering customers Neanderthal ancestry reports.
COURTESY OF 23ANDME

The point here isn’t that a complex model of evolution with many moving pieces is necessarily better than a simple one. Scientists need to reduce complexity in order to see the underlying processes more clearly. But simple models require assumptions, and scientists need to reevaluate those assumptions in light of what they learn. “As you get more data, you can justify more complex models of the world,” says Mark Thomas, a population geneticist at University College London, who wrote a history of random mating in population genetics that highlighted how the field was starting to see it as “a limiting assumption as opposed to a simplifying one.” 

It can feel discouraging to couch conversations about the past in confusing terms like “population structure” and “mutation rates.” It seems almost antithetical to the spirit of science to talk more about uncertainty at the same time we are developing powerful technologies and enormous data sets for analyzing evolution. These tools often yield novel answers, but they can also limit the questions we ask. The French archaeologist Ludovic Slimak, for example, has complained that the idea of the inner Neanderthal has domesticated our image of Neanderthals and made it difficult to imagine their humanity as distinct from our own. Investigating Neanderthal DNA is sexier to many young researchers than searching for archaeological and fossil evidence of how Neanderthals actually lived. 

Loosening our attachment to certain narratives of evolution can create space for wonder at the sheer complexity of life’s history. Ultimately, that’s what Chikhi and Tournebize hope to do. After all, they don’t believe the question of population structure versus hybridization is either-or. It’s possible, and even likely, that both played a role in human evolution. “Our structured model does not necessarily mean that no admixture ever took place,” Chikhi and Tournebize wrote in their study. “What our results suggest is that, if admixture ever occurred, it is currently hard to identify using existing methods.” 

Future methods might disentangle the different factors, but it’s just as important, Chikhi says, for scientists to be up-front about their assumptions and test alternatives. “There’s still so much uncertainty on so many aspects of the demographic history of Neanderthals and Homo sapiens,” he notes. 

Keep that in mind the next time you read about your inner Neanderthal. The association between this DNA and some diseases may be real, of course—but would journals publish these studies without the additional claim that the DNA is from Neanderthals? Any good storyteller knows that sex sells, even in science. 

Ben Crair is a science and travel writer based in Berlin.

Coming soon: 10 Things That Matter in AI Right Now

Each year we compile our 10 Breakthrough Technologies list, featuring our educated predictions for which technologies will have the biggest impact on how we live and work.

This year, however, we had a dilemma. While our final picks encompass all our core coverage areas (energy, AI, and biotech, plus a few more), our 2026 list was harder to wrangle than normal. Why? We had so many worthy AI candidates we couldn’t fit them all in! (The ones that made it were AI companions, mechanistic interpretability, generative coding, and hyperscale data centers.) Many great ideas fell by the wayside to keep the list as wide-ranging as possible.

Well, that got us thinking: What if we made an entirely new list that was all about AI? We got excited about that idea—and before we knew it we had the beginnings of what we’re calling 10 Things That Matter in AI Right Now. It’s an entirely new annual list that we’re proud to be publishing for the first time on April 21, 2026. We’ll unveil it on stage for attendees at our signature AI conference, EmTech AI, held on MIT’s campus (it’s not too late to get tickets), and then publish the list online later that day.

The process for coming up with the list was similar to the way we pick our 10 Breakthrough Technologies. We petitioned our AI team of reporters and editors to propose ideas, put them all in a document, and engaged in some robust discussion. Eventually, we voted for our favorites and whittled the long list down to a final 10.

But there’s a slight difference between this list and our 10 Breakthrough Technologies. AI is already such a big part of our lives that we didn’t want to restrict ourselves to nominating only technologies. Instead, we wanted to put together a definitive annual list that highlights what we believe are the biggest ideas, topics, and research directions in AI right now. So yes, it will include cutting-edge AI technologies, but it will also feature other trends and developments in AI that we want to bring to our subscribers’ attention.

Think of it as a sneak peek inside the collective brain of our crack AI reporting team: These are the things that our reporters will be watching this year. We intend to follow the items on this list really closely, and you will see it reflected in the news and feature stories we publish in 2026.

For us, 10 Things That Matter in AI Right Now is a guide to how we view the current AI landscape. It will be a source of discussion, debate, and maybe some arguments! We are so excited to share it with you on April 21. If you want to be among the first to see it—join us at EmTech AI or become a subscriber to livestream the announcement.

NASA is building the first nuclear reactor-powered interplanetary spacecraft. How will it work?

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  • A US nuclear-powered spacecraft may head to Mars: NASA has announced SR-1, the first-ever nuclear-reactor-powered interplanetary spacecraft, with a planned Mars launch before the end of 2028—a timeline experts call aggressive but exciting.
  • Nuclear could beat chemical and solar power: Unlike traditional propulsion, nuclear electric propulsion is orders of magnitude more efficient and doesn’t depend on sunlight, making it better suited for long, fast journeys through the solar system.
  • The design is already taking shape: SR-1 will resemble a giant fletched arrow, with a recycled Gateway space station propulsion unit at the rear and a 20-kilowatt uranium reactor up front, cooled by enormous fins that vent excess heat into space.
  • The stakes go beyond engineering: With China and Russia pursuing their own deep-space nuclear programs, SR-1 is as much a geopolitical gambit as a scientific one—and success could put the US ahead in the race to land humans on Mars.

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MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

Just before Artemis II began its historic slingshot around the moon, Jared Isaacman, the recently confirmed NASA administrator, made a flurry of announcements from the agency’s headquarters in Washington, DC. He said the US would soon undertake far more regular moon missions and establish the foundations for a base at the lunar south pole before the end of the decade. He also affirmed the space agency’s commitment to putting a nuclear reactor on the lunar surface.

These goals were largely expected—but there was still one surprise. Isaacman also said NASA would build the first-ever nuclear reactor-powered interplanetary spacecraft and fly it to Mars by the end of 2028. It’s called the Space Reactor-1 Freedom, or SR-1 for short. “After decades of study, and billions spent on concepts that have never left Earth, America will finally get underway on nuclear power in space,” he said at the event. “We will launch the first-of-its-kind interplanetary mission.”

A successful mission would herald a new era in spaceflight, one in which traveling between Earth, the moon, and Mars would—according to a range of experts—be faster and easier than ever. And it might just give the US the edge in the race against China—allowing the country to beat its greatest geopolitical rival to landing astronauts on another planet.

While experts agree the timeline is extremely tight, they’re excited to see if America’s space agency and its industry partners can deliver an engineering miracle. “You wake up to that announcement, and it puts a big smile on your face,” says Simon Middleburgh, co-director of the Nuclear Futures Institute at Bangor University in Wales.

Little detail on SR-1 is publicly available, and NASA’s own spaceflight researchers did not respond to requests for comment. But MIT Technology Review spoke to several nuclear power and propulsion experts to find out how the new nuclear-powered spacecraft might work.

Nuclear propulsion 101

Traditionally, spaceflight has been powered by chemical propulsion. Liquefied hydrogen and liquefied oxygen are mixed, and then ignited, within a rocket; the searingly hot exhaust from this explosion is ejected through a nozzle, which propels the rocket forth.

Chemical propulsion offers a significant amount of thrust and will, for the foreseeable future, still be used to launch spacecraft from Earth. But nuclear propulsion would enable spacecraft to fly through the solar system for far longer, and faster, than is currently possible. 

“You get more bang per kilogram,” says Middleburgh. A nuclear fuel source is far more energy-dense than its conventional cousin, which means it’s orders of magnitude more efficient. “It’s really, really, really high efficiency,” says Lindsey Holmes, an expert in space nuclear technology and the vice president of advanced projects at Analytical Mechanics Associates, an aerospace company in Virginia. 

The approach also removes one other element of the traditional power equation: solar. Spacecraft, including the Artemis II mission’s Orion space capsule, often rely on the sun for power. But this can be a problem, since it doesn’t always shine in space, particularly when a planet or moon gets in its way—and as you head toward the outer solar system, beyond Mars, there’s just less sunlight available. 

To circumvent this issue, nuclear energy sources have been used in spacecraft plenty of times before—including on both Voyager missions and the Saturn-interrogating Cassini probe. Known as radioisotope thermoelectric generators, or RTGs, these use plutonium, which radioactively decays and generates heat in the process. That heat is then converted into electricity for the spacecraft to use. RTGs, however, aren’t the same as nuclear reactors; they are more akin to radioactive batteries—more rudimentary and considerably less powerful.

So how will a nuclear-reactor-powered spacecraft work? 

Despite operational differences, the fundamentals of running a nuclear reactor in space are much the same as they are on Earth. First, get some uranium fuel; then bombard it with neutrons. This ruptures the uranium’s unstable atomic nuclei, which expel a torrent of extra neutrons—and that rapidly escalates into a self-sustaining, roasting-hot nuclear fission reaction. Its prodigious heat output can then be used to produce electricity.

Doing this in space may sound like an act of lunacy, but it’s not: The idea, and even a lot of the basic technology, has been around for decades. The Soviet Union sent dozens of nuclear reactors into orbit (often to power spy satellites), while the US deployed just one, known as SNAP-10A, back in 1965—a technological demonstration to see if it would operate normally in space. The aim was for the reactor to generate electricity for at least a year, but it ran for just over a month before a high-voltage failure in the spacecraft caused it to malfunction and shut down. 

Now, more than half a century later, the US wants its second-ever space-based nuclear reactor to do something totally different: power an interplanetary spacecraft.

To be clear, the US has started, and terminated, myriad programs looking into nuclear propulsion. The latest casualty was DRACO, a collaboration between NASA and the Department of Defense, which ended in 2025. Like several previous efforts, DRACO was canceled because of a mix of high experimentation costs, lower prices for conventional rocket propulsion, and the difficulty of ensuring that ground tests could be performed safely and effectively (they are creating an incredibly powerful nuclear reaction, after all).

But now external considerations may be changing the calculus. The Artemis program has jump-started America’s return to the moon, and the new space race has palpable momentum behind it. The first nation to deploy nuclear propulsion would have a serious advantage navigating through deep space. 

“I think it’s a very doable technology,” says Philip Metzger, a spaceflight engineering researcher at the Florida Space Institute. “I’m happy to see them finally doing this.”

One version of this technology is known as nuclear thermal propulsion, or NTP. You start with a nuclear reactor, one that’s cooking at around 5,000°F. Then “you’ve got a cold gas, and you squirt cold gas over the hot reactor,” says Middleburgh. “The gas expands, you shoot it out the back of a nozzle, and you have an impulse. And that impulse drives you forward.” 

Because the thrust depends on the speed of the gas being ejected, the propellant gas needs to be light, making hydrogen a popular choice. But hydrogen is a corrosive and explosive substance, so using it in NTP engines can make them precarious to operate. On top of this, NTP doesn’t necessarily have a very long operating life.

Alternatively, there’s nuclear electric propulsion, or NEP, which “is very low thrust, but very efficient, so you can use it for a long period of time,” says Sebastian Corbisiero, the US Department of Energy’s national technical director of space reactor programs. This method uses heat from a fission reactor to generate power. That power is used to electrify a gas and then  blast it out of the spacecraft, generating thrust.  

Both NTP and NEP have been investigated by US researchers, because both have the added benefit of making it easier and safer for human beings to explore the solar system. Astronauts in space are exposed to harmful cosmic radiation, but because nuclear propulsion makes spacecraft speedier and more agile, they’d spend less time in it. “It solves the radiation problem,” says Metzger. “That’s one of the main motivations for inventing better propulsion to and from Mars.”

How to build a nuclear-powered spaceship

For SR-1, NASA has opted for nuclear electric propulsion. NEP is “a much simpler affair” than its thermal counterpart, says Middleburgh. Essentially, you just need to plug a nuclear reactor into a power-and-propulsion system. Luckily for NASA, it’s already got one.

For many years, NASA—along with its space agency partners in Canada, Europe, Japan, and the Middle East—was preparing for Gateway, meant to be humanity’s first space station to orbit around the moon. Isaacman canceled the project in March, but that doesn’t mean its technology will go to waste; the power-and-propulsion element of the nixed space station will be used in SR-1 instead. This contraption was going to be powered by solar energy. It’ll now be attached to an in-development nuclear reactor custom built to survive in space.

What might the SR-1 look like? MIT Technology Review saw a presentation by Steve Sinacore, program executive of NASA’s Space Reactor Office, that offers some clues. So far, the concept art makes it look like a colossal fletched arrow. At the back will be the power-and-propulsion system, while its tip will hold a 20-kilowatt-or-greater uranium-filled nuclear reactor. (For context, a typical nuclear plant on Earth is 50,000 times more powerful, producing a gigawatt of power.) 

NASA

The “fletches” on SR-1 are large fins that allow the reactor to cool down. “You have to have really large radiators,” says Holmes, since the nuclear fission process produces so much heat that much of it has to be vented into space—otherwise, the reactor and spacecraft will melt.

According to that presentation, the spacecraft’s hardware development is due to start this June. By January 2028, SR-1’s systems should be ready for assembly and testing. And by that October, the spacecraft will arrive at the launch site, ready for liftoff before the year’s end. Will the nuclear reactor manage to hold itself together? “Going through the launch safely is going to be a challenge,” says Middleburgh. “You are being shaken, rattled, and rolled.” 

Then, he says, “once you’re up in space, once you’ve got through that few minutes of hell in getting there, it’s zero-gravity considerations you have to worry about.” The question then becomes: Will the mechanics of the reactor, built on terra firma, still work? 

For safety reasons, the nuclear reactor will be switched on around two days post-launch, when it’s comfortably in space. Uranium isn’t tremendously dangerous by itself, but that can’t be said of the nuclear waste products that emerge when the reactor is activated, so you don’t want any of that to fall back to Earth. 

If this schedule is adhered to, and SR-1 works as planned, it’s expected to reach Mars about a year after launch. “It’s an aggressive timeline,” says Holmes, something she suspects is being driven partly by China’s and Russia’s own deep-space nuclear ambitions. The two countries aim to place their own nuclear reactor on the moon’s surface to power the planned International Lunar Research Station—a jointly operated lunar base—by 2035. 

Whether it flies or fails in space, SR-1’s operations should help NASA with putting a nuclear reactor on the moon soon after. “All of the things we’d be learning about how that system operates in space [are] very helpful for a surface application, because basically it’s the same,” says Corbisiero. “There’s still no air on the moon.”

And if SR-1 does triumph, it will be a game-changing victory for NASA. It will also be “a massive win for the human race, frankly,” says Middleburgh. “It will be a marvel of engineering, and it will move the dial in humans potentially taking a step on Mars.” Like many of his colleagues, including Holmes, he remains thrilled by the prospect of the first-ever nuclear-powered interplanetary spacecraft—even with the incredibly ambitious timeline. 

“These are the things that get us up in the morning,” he says. “These are the sorts of things we will remember when we’re old.”

Job titles of the future: Wildlife first responder

Grizzly bears have made such a comeback across eastern Montana that in 2017, the state hired its first-ever prairie-based grizzly manager: wildlife biologist Wesley Sarmento. 

For some seven years, Sarmento worked to keep both the bears, which are still listed as threatened under the Endangered Species Act, and the humans, who are sprawling into once-wild spaces, out of trouble. Based in the small city of Conrad, population 2,553, he acted sort of like a first responder, trying to defuse potentially dangerous situations. He even got caught in some himself—which is why, before he left the role to pursue a PhD, he turned to drones to get the job done. 

The bear necessities

Sarmento was studying mountain goats in Glacier National Park when he first started working with bears. To better understand how goats responded to the apex predator, he dressed up in a bear costume once a week for over three years. 

When he later started as grizzly manager, he often drove long distances to push bears away from farms. Bears are drawn to spilled or leaking grains, and an open silo quickly turns into a buffet. Sarmento would typically arrive armed with a shotgun, cracker shells, and bear spray, but after he narrowly escaped getting mauled one day, he knew he had to pivot.

“In that moment,” he says, “I was like, I am gonna get myself killed.”

A bird’s-eye view

Sarmento first turned to two Airedale dogs, a breed known for deterring bears on farms, but the dogs were easily sidetracked. Meanwhile, drones were slowly becoming more common tools for biologists in a range of activities, including counting birds and mapping habitats.

He first took one into the field in 2022, when a grizzly mom and two cubs were found rummaging around in a silo outside of town. The drone’s infrared sensors helped him quickly find their location, and he used the aircraft’s sound to drive them away from the property. (Researchers suspect bears instinctively dislike the whir of blades because it sounds like a swarm of bees.) “The whole thing was so clean and controlled,” he says. “And I did it all from the safety of my truck.”

Since then, the flying machine that Sarmento bought for $4,000—a fairly simple model with a thermal camera and 30 minutes of battery life—has shown its potential for detecting grizzlies in perilous terrain he’d otherwise have to approach on foot, like dense brush or hard-to-reach river bottoms.

A new technological foundation

Now studying wildlife ecology at the University of Montana, Sarmento is hoping to design a drone campus police can use to deter black bears from school grounds. In the future, he hopes, AI image recognition might be broadly integrated into his wildlife management work—maybe even helping drones identify bears and autonomously divert them from high-traffic areas.

All this helps keep bears from learning behaviors that lead to conflict with people—which typically ends badly for the bear and is occasionally fatal for humans.

“The out-of-the-box technology doesn’t exist yet, but the hope is to keep exploring applications,” he says. “Drones are the next frontier.” 

Emily Senkosky is a writer with a master’s degree in environmental science journalism from the University of Montana.

You have no choice in reading this article—maybe

Uri Maoz loved doing his human research, back when he was getting his PhD. He was studying a very specific topic in computational neuroscience: how the brain instructs our arms to move and how our gray matter in turn perceives that motion. 

Then his professor asked him to deliver an undergrad lecture. Maoz assumed his boss was going to tell him exactly what to do, or at least throw some PowerPoint slides his way. But no. Maoz had free rein to teach anything, as long as it was relevant to the students. “I could have gone to human brain augmentation,” he says. “Cyborgs or whatever.”

Yet that admittedly fun and borderline sci-fi topic wasn’t what popped, unbidden, into his mind. His idea, he recalls with excitement: “What neuroscience has to say about the question of free will!” 

How—or whether—humans make decisions (like, say, about what to discuss in an undergrad lecture) had been on his mind since he’d read an article in his early twenties suggesting that … maybe they didn’t. This question might naturally beget others: Had he even had a choice about whether to read that article in the first place? How would he ever know if he was responsible for making decisions in his life or if he just had the illusion of control?

“After that, there was no turning back,” says Maoz, now a professor at Chapman University, in California. He finished his PhD work in human movement, but afterward he scooted further up the neural chain to find out how desires and beliefs turn into actions—from raising an arm to choosing someone to ask out to dinner on a Friday night.

Today, Maoz is a central figure in the attempt to (sort of, maybe) answer how that neural chain functions. His research has since overturned and reinter­preted canonical neuroscience studies and united the straight-scientific and philosophical sides of the free-will question. More than anything, though, he’s succeeded in uncovering new wrinkles in the debate.

Machines and magic tricks

The concept of free will seems straightforward, but it doesn’t have a universally accepted definition. One intuitive notion is that it’s the ability to make our own decisions and take our own actions on purpose—that we control our lives. But physicists might ask if the universe is deterministic, following a preordained path, and if human choices can still happen in such a universe. 

That’s a question for them, Maoz says. What neuroscientists can do is figure out what’s going on in the brain when people make decisions. “And that’s what we’re trying to do: to understand how our wishes, desires, beliefs, turn into actions,” he says.

By the time Maoz had finished his PhD, in 2008, neuroscientific research into the question had been going on for decades. One foundational study from the 1960s showed that a hand movement—something a person seemingly decides to do—was preceded by the appearance in the brain of an electrical signal called the “readiness potential.” 

Building on that result, in the 1980s a neuroscientist named Benjamin Libet did the experiment that had first piqued Maoz’s interest in the topic—one that many, until recently, interpreted as a death knell for the concept of free will.

An electrical impulse in our brains can shed only so much light on whether we truly are the architects of our own fates.

“He just had people sit there, and whenever they feel like it, they would go like this,” says Maoz, wiggling his wrist. Libet would then ask where a rotating dot was on a screen when they first had the urge to flick. He found that the readiness potential appeared not only before they moved their hand but before they reported having the urge to move—or, in Libet’s interpretation, before they knew they were going to move. 

Studies since have confirmed the observation and shown that the readiness potential appears a second or two—and maybe, fMRI implies, up to 10 seconds—before participants report making a conscious decision. “It suggests we are essentially passengers in a self-driving car,” says Maoz. “The unconscious biological machine does all the steering, but our conscious mind sits in the driver’s seat and takes the credit.” 

Maoz initially approached his own research with variations on Libet’s experiments. He worked with epilepsy patients who already had electrodes in their brains, for clinical purposes, and was able to predict which hand they would raise before they raised it. 

Still, some of the Libet-inspired studies people were doing nagged at him. “All these results were about completely arbitrary decisions. Raise your hand whenever you feel like it,” he says. “Why? No reason.” A decision like that is quite different from, say, choosing to break up with your partner. Try telling someone they weren’t in the driver’s seat for that

The field wasn’t looking at meaningful decisions, he says—the ones that actually set the course of lives. 

Maoz began pulling in philosophers to help guide his approach. They would challenge him to confront the semantic differences between things like intention, desire, and urge. Neuroscientists have tended to lump those concepts together, but philosophers tease them apart: Desire is a want that doesn’t necessarily progress toward an action; urge carries implications of immediacy and compulsion; and intention involves committing to a plan. (Maoz has come to focus specifically on intention—including, recently, the potential intentions of AI.)

In 2017, he organized his first in a series of free-will conferences, drawing many autonomy-interested philosophers. “Thank you so much for coming,” he recalls saying at the opening of the meeting. “As if you had a choice.” One day, the crew took an excursion out on a lake. As the group munched on shrimp, someone joked that they hoped the boat didn’t sink, because everybody in the field would die. 

The comment didn’t make Maoz feel existential dread. Instead, he figured that if the whole field was already there, why not lasso them all into writing a research grant? “He just thinks what should be the next step and just has a very good ability to just make it happen,” says Liad Mudrik, a neuroscientist at Tel Aviv University and a frequent collaborator.

That ability is special among scientists, says Chapman colleague Aaron Schurger, with whom Maoz co-directs the Laboratory for Understanding Consciousness, Intentions, and Decision-Making (LUCID, appropriately). “I really think that Uri is kind of at the nexus of this field right now because he’s really, really good at bringing people together around these big ideas,” he says.

Donations and interruptions

Maoz has recently been making progress on one of the big ideas that have consistently occupied his working hours: how trivial and significant decisions play out differently in the brain. In collaborations with Mudrik, he’s parsed the neural difference between picking and choosing—their terms for arbitrary decisions and those that change your life and tug on your emotions. 

Readiness potential? Their measurements didn’t clock it ahead of choices. In 2019, Maoz and a crew published a paper measuring the electrical activity in people’s brains as they pressed a key to choose one of two nonprofits to donate $1,000 to—for real, with actual dollars. Then the researchers compared that activity with what they saw when the same group pressed a key at random to donate $500 each to two nonprofits. The team saw the readiness potential in the arbitrary decision, but not for the $1,000 question. 

Libet’s result, they concluded, doesn’t apply to the important stuff, which means readiness potential might not actually be a sign that your brain is making a choice before you’re aware of it. “If Libet would have chosen to focus on deliberate decisions, then maybe the entire debate about neuroscience proving free will to be an illusion would have been spared from us,” Mudrik says. 

Maoz’s research has spurred others to reinterpret Libet’s work. It’s “enriched my thought process a great deal,” says Bianca Ivanof, a psychologist whose dissertation scrutinized Libet’s methods. They turn out to identify readiness potential at different times depending on how the rotating-dot setup is designed, complicating the ability to compare and interpret results.

Maoz has also continued to gather data on the subject. Last year, for example, he used an EEG to measure electrical signals in people’s brains as they got ready to press a keyboard space bar. At random moments, he interrupted their preparations with an audible tone and asked them about their intentions. He saw no connection between the readiness potential and whether or not they were planning to tap the key—evidence that the potential doesn’t represent the buildup of either conscious or unconscious plans. The team did see a signal, though, in a different part of the brain when people said they were preparing to move.

So … that’s free will? Sadly, Maoz would be compelled to say Well, not exactly. An electrical impulse in our brains can shed only so much light on whether we truly are the architects of our own fates. And maybe the confusing data from neurons is actually the point. “I don’t think it is a yes-or-no question,” Maoz says. Maybe our less meaningful choices aren’t mindfully made but big ones are; maybe we have the conscious power to change an intended action, but only if our brains are in a particular state. 

Neuroscientists likely can’t figure out, on their own, if free will exists. But they can, Maoz says, parse how semantically distinct decision-making forces—desires, urges, intentions, wishes, beliefs—manifest in our brains and become actions. “That is something that we are making progress on,” he says, “and I think that that’s going to help us understand what we do control.” And perhaps also help us make peace with what we do not. 

Sarah Scoles is a freelance science journalist and author based in southern Colorado.

Want to understand the current state of AI? Check out these charts.

<div data-chronoton-summary="

  • The US-China AI race is closer than you think: Chinese models from DeepSeek and Alibaba now trail American ones by razor-thin margins. Meanwhile, the US has more data centers and capital, while China leads in research publications and robotics.
  • AI benchmarks are badly broken: One popular math benchmark has a 42% error rate, and models can game tests by training on the answers. Strong test scores increasingly fail to predict how AI actually performs in the real world.
  • Jobs and anxiety are both rising: Software developer employment for workers aged 22–25 has dropped nearly 20% since 2022, with AI likely a factor. Globally, 59% of people think AI will do more good than harm—but 52% say it still makes them nervous.
  • Regulation is losing the race: The EU banned predictive policing AI, and US states passed a record 150 AI-related bills, but experts say lawmakers don’t yet understand the technology well enough to govern it effectively.

” data-chronoton-post-id=”1135675″ data-chronoton-expand-collapse=”1″ data-chronoton-analytics-enabled=”1″>

If you’re following AI news, you’re probably getting whiplash. AI is a gold rush. AI is a bubble. AI is taking your job. AI can’t even read a clock. The 2026 AI Index from Stanford University’s Institute for Human-Centered Artificial Intelligence, AI’s annual report card, comes out today and cuts through some of that noise. 

Despite predictions that AI development may hit a wall, the report says that the top models just keep getting better. People are adopting AI faster than they picked up the personal computer or the internet. AI companies are generating revenue faster than companies in any previous technology boom, but they’re also spending hundreds of billions of dollars on data centers and chips. The benchmarks designed to measure AI, the policies meant to govern it, and the job market are struggling to keep up. AI is sprinting, and the rest of us are trying to find our shoes.

All that speed comes at a cost. AI data centers around the world can now draw 29.6 gigawatts of power, enough to run the entire state of New York at peak demand. Annual water use from running OpenAI’s GPT-4o alone may exceed the drinking water needs of 12 million people. At the same time, the supply chain for chips is alarmingly fragile. The US hosts most of the world’s AI data centers, and one company in Taiwan, TSMC, fabricates almost every leading AI chip. 

The data reveals a technology evolving faster than we can manage. Here’s a look at some of the key points from this year’s report. 

The US and China are nearly tied

In a long, heated race with immense geopolitical stakes, the US and China are almost neck and neck on AI model performance, according to Arena, a community-driven ranking platform that allows users to compare the outputs of large language models on identical prompts. In early 2023, OpenAI had a lead with ChatGPT, but this gap narrowed in 2024 as Google and Anthropic released their own models. In February 2025, R1, an AI model built by the Chinese lab DeepSeek, briefly matched the top US model, ChatGPT. As of March 2026, Anthropic leads, trailed closely by xAI, Google, and OpenAI. Chinese models like DeepSeek and Alibaba lag only modestly. With the best AI models separated in the rankings by razor-thin margins, they’re now competing on cost, reliability, and real-world usefulness. 

Chart of the performance of top models on the Arena by select providers, showing the Arena score from May 2023 to Jan 2026 with the models all trending upward.  The scores are tightly packed by US based Anthropic, xAI, Google and OpenAI lead Alibaba, DeepSeek and Mistral (in that order.) Meta trails the pack.

The index notes that the US and China have different AI advantages. While the US has more powerful AI models, more capital, and an estimated 5,427 data centers (more than 10 times as many as any other country), China leads in AI research publications, patents, and robotics. 

As competition intensifies, companies like OpenAI, Anthropic, and Google no longer disclose their training code, parameter counts, or data-set sizes. “We don’t know a lot of things about predicting model behaviors,” says Yolanda Gil, a computer scientist at the University of Southern California who coauthored the report. This lack of transparency makes it difficult for independent researchers to study how to make AI models safer, she says.

AI models are advancing super fast

Despite predictions that development will plateau, AI models keep getting better and better. By some measures, they now meet or exceed the performance of human experts on tests that aim to measure PhD-level science, math, and language understanding. SWE-bench Verified, a software engineering benchmark for AI models, saw top scores jump from around 60% in 2024 to almost 100% in 2025. In 2025, an AI system produced a weather forecast on its own.  

“I am stunned that this technology continues to improve, and it’s just not plateauing in any way,” says Gil.

line chart of Select AI Index technical performance benchmarks vs human performance, showing that skills such as image classification, English language understanding, multitask language understanding, visual reasoning, medium level reading comprehension, multimodal understanding and reasoning have surpassed the human baseline at or before 2025, with autonomous software engineering, mathmatical reasoning and agent multimodal computer use trending towards meeting the human baseline by 2026.

However, AI still struggles in plenty of other areas. Because the models learn by processing enormous amounts of text and images rather than by experiencing the physical world, AI exhibits “jagged intelligence.” Robots are still in their early days and succeed in only 12% of household tasks. Self-driving cars are farther along: Waymos are now roaming across five US cities, and Baidu’s Apollo Go vehicles are shuttling riders around in China. AI is also expanding into professional domains like law and finance, but no model dominates the field yet. 

But the way we test AI is broken

These reports of progress should be taken with a grain of salt. The benchmarks designed to track AI progress are struggling to keep up as models quickly blow past their ceilings, the Stanford report says. Some are poorly constructed—a popular benchmark that tests a model’s math abilities has a 42% error rate. Others can be gamed: when models are trained on benchmark test data, for example, they can learn to score well without getting smarter. 

Because AI is rarely used the same way it’s tested, strong benchmark performance doesn’t always translate to real-world performance. And for complex, interactive technologies such as AI agents and robots, benchmarks barely exist yet. 

AI companies are also sharing less about how their models are trained, and independent testing sometimes tells a different story from what they report. “A lot of companies are not releasing how their models do in certain benchmarks, particularly the responsible-AI benchmarks,” says Gil. “The absence of how your model is doing on a benchmark maybe says something.” 

AI is starting to affect jobs

Within three years of going mainstream, AI is now used by more than half of people around the world, a rate of adoption faster than the personal computer or the internet. An estimated 88% of organizations now use AI, and four in five university students use it. 

It’s early days for deployment, and AI’s impact on jobs is hard to measure. Still, some studies suggest AI is beginning to affect young workers in certain professions. According to a 2025 study by economists at Stanford, employment for software developers aged 22 to 25 has fallen nearly 20% since 2022. The decline might not be pinned on AI alone, as broader macroeconomic conditions could be to blame, but AI appears to be playing a part.

two line charts showing the normalized headcount trends by age group from 2021 through 2025. On the left for software developers the early career (age 22-25) cohort drops rapidly after a peak in September 2022, with other ages still rising albeit less steeply.  On the right, customer support agents see a similar trend, although the decline for the early career group is less steep than for software developers.

Employers say that hiring may continue to tighten. According to a 2025 survey conducted by McKinsey & Company, a third of organizations expect AI to shrink their workforce in the coming year, particularly in service and supply chain operations and software engineering. AI is boosting productivity by 14% in customer service and 26% in software development, according to research cited by the index, but such gains are not seen in tasks requiring more judgment. Overall, it’s still too early to understand the bigger economic impact of AI. 

People have complicated feelings about AI 

Around the world, people feel both optimistic and anxious about AI: 59% of people think that it will provide more benefits than drawbacks, while 52% say that it makes them nervous, according to an Ipsos survey cited in the index. 

Notably, experts and the public see the future of AI very differently, according to a Pew survey. The biggest gap is around the future of work: While 73% of experts think that AI will have a positive impact on how people do their jobs, only 23% of the American public thinks so. Experts are also more optimistic than the public about AI’s impact on education and medical care, but they agree that AI will hurt elections and personal relationships.

Bar chart of US perceptions of AI's societal impact contrasting US adults with AI experts, with the percentage of AI experts saying that AI will have a positive impact in the next 20 years is 2-3 times higher than the US adults.  The most optimistic AI experts are in the field of medical care with 84% predicting a positive outcome (versus 44% of US adults.) The greatest difference is for jobs with experts polling at 73% and US adults  polling at 23%.  Both groups have a similar (11% for experts and 9% of adults.) expectation for a positive outcome for AI in elections.

Among all countries surveyed, Americans trust their government least to regulate AI appropriately, according to another Ipsos survey. More Americans worry federal AI regulation won’t go far enough than worry it will go too far. 

Governments are struggling to regulate AI

Governments around the world are struggling to regulate AI, but there were some minor successes last year. The EU AI Act’s first prohibitions, which ban the use of AI in predictive policing and emotion recognition, took effect. Japan, South Korea, and Italy also passed national AI laws. Meanwhile, the US federal government moved toward deregulation, with President Trump issuing an executive order seeking to handcuff states from regulating AI. 

Despite this federal action, state legislatures in the US passed a record 150 AI-related bills. California enacted landmark legislation, including SB 53, which mandates safety disclosures and whistleblower protections for developers of AI models. New York passed the RAISE Act, requiring AI companies to publish safety protocols and report critical safety incidents.

line chart showing the number of AI-related bills passed into law by all US states from 2016-2025, which increases sharply in 2023 and peaks with 150 bills in 2025.

But for all the legislative activity, Gil says, regulation is running behind the technology because we don’t really understand how it works. “Governments are cautious to regulate AI because … we don’t understand many things very well,” she says. “We don’t have a good handle on those systems.”

Why opinion on AI is so divided

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

In an industry that doesn’t stand still, Stanford’s AI Index, an annual roundup of key results and trends, is a chance to take a breath. (It’s a marathon, not a sprint, after all.)

This year’s report, which dropped today, is full of striking stats. A lot of the value comes from having numbers to back up gut feelings you might already have, such as the sense that the US is gunning harder for AI than everyone else: It hosts 5,427 data centers (and counting). That’s more than 10 times as many as any other country.  

There’s also a reminder that the hardware supply chain the AI industry relies on has some major choke points. Here’s perhaps the most remarkable fact: “A single company, TSMC, fabricates almost every leading AI chip, making the global AI hardware supply chain dependent on one foundry in Taiwan.” One foundry! That’s just wild.

But the main takeaway I have from the 2026 AI Index is that the state of AI right now is shot through with inconsistencies. As my colleague Michelle Kim put it today in her piece about the report: “If you’re following AI news, you’re probably getting whiplash. AI is a gold rush. AI is a bubble. AI is taking your job. AI can’t even read a clock.” (The Stanford report notes that Google DeepMind’s top reasoning model, Gemini Deep Think, scored a gold medal in the International Math Olympiad but is unable to read analog clocks half the time.)

Michelle does a great job covering the report’s highlights. But I wanted to dwell on a question that I can’t shake. Why is it so hard to know exactly what’s going on in AI right now?  

The widest gap seems to be between experts and non-experts. “AI experts and the general public view the technology’s trajectory very differently,” the authors of the AI Index write. “Assessing AI’s impact on jobs, 73% of U.S. experts are positive, compared with only 23% of the public, a 50 percentage point gap. Similar divides emerge with respect to the economy and medical care.”

That’s a huge gap. What’s going on? What do experts know that the public doesn’t? (“Experts” here means US-based researchers who took part in AI conferences in 2023 and 2024.)

I suspect part of what’s going on is that experts and non-experts base their views on very different experiences. “The degree to which you are awed by AI is perfectly correlated with how much you use AI to code,” a software developer posted on X the other day. Maybe that’s tongue-in-cheek, but there’s definitely something to it.

The latest models from the top labs are now better than ever at producing code. Because technical tasks like coding have right or wrong results, it is easier to train models to do them, compared with tasks that are more open-ended. What’s more, models that can code are proving to be profitable, so model makers are throwing resources at improving them.

This means that people who use those tools for coding or other technical work are experiencing this technology at its best. Outside of those use cases, you get more of a mixed bag. LLMs still make dumb mistakes. This phenomenon has become known as the “jagged frontier”: Models are very good at doing some things and less good at others.

The influential AI researcher Andrej Karpathy also had some thoughts. “Judging by my [timeline] there is a growing gap in understanding of AI capability,” he wrote in reply to that X post. He noted that power users (read: people who use LLMs for coding, math, or research) not only keep up to date with the latest models but will often pay $200 a month for the best versions. “The recent improvements in these domains as of this year have been nothing short of staggering,” he continued.

Because LLMs are still improving fast, someone who pays to use Claude Code will in effect be using a different technology from someone who tried using the free version of Claude to plan a wedding six months ago. Those two groups are speaking past each other.

Where does that leave us? I think there are two realities. Yes, AI is far better than a lot of people realize. And yes, it is still pretty bad at a lot of stuff that a lot of people care about (and it may stay that way). Anyone making bets about the future on either side should bear that in mind.

Constellations

I.

We had crash-landed on the planet. We were far from home. The spaceship could not be repaired, and the rescue beacon had failed. Besides me, only the astrogator, part of the captain, and the ship’s AI mind were left. 

Outside, the atmosphere registered as hostile to most organisms. We huddled in the lifeboat, which was inoperable but still held air. Vast storms buffeted our cockleshell shelter, although we knew from prior readings that other areas remained calm. All that remained to us was to explore, if we wanted to live. The captain gave me the sole weapon. She tasked the astrogator with carrying some tools that would not unduly weigh him down.

Little existed on the planet except deserts of snow. But alien artifacts lay in an area near us. We were an exploration team, so this discovery had oddly comforted us, even though we had been on our way elsewhere. The massive systems failure had no discernible source, and the planet had been our only choice for landfall.

The artifacts took the form of 13 domes, spread out over that hostile terrain. The domes had been linked by cables just below shoulder level, threaded through the tops of metal posts at irregular intervals. Whether intended or not, these cables and rods formed a series of paths between the domes. 

Before our instruments failed, the AI had reported that the domes appeared to have a heat signature. The cables pulsed under our grip in a way that teased promised warmth far ahead. It took some time to get used to the feeling.

The shortest path between domes was a thousand miles long. The longest path was 10 thousand miles long. Our suit technology was good: A suit could recycle water, generate food, create oxygen. It could push us into various states of near hibernation while motors in the legs drove us forward. For the captain, the suit would compensate for having lost her legs and ease her pain. We estimated we could reach the nearest path and follow it to the nearest dome … and that was it. If the dome had life support capabilities, or even just a way to replenish our suits, we would live. Otherwise, we would probably die.

We revised the estimate of our survival downward when we reached the path and soon encountered the skeletons of dead astronauts littering the way. In all shapes and sizes, cocooned within their suits. Their huddled forms under the snow displayed a serenity at odds with their fate. But when I wiped the frost from face plates, we saw the extremity of their suffering.

It is difficult to explain how we felt walking among so many fatalities. So many dead first contacts. 

We no longer had to puzzle over the systems failure. Spaceships came here to crash, and intelligent entities came here to die, for whatever reason. We could not presume our fate would be any different, and adjusted our expectations accordingly. The AI’s platitudes about courage did not raise morale. There were too many lost there in the frozen wastes. 

Here were the ghastly emissaries of hundreds of spacefaring species we had never before encountered.

The number of the bodies and their haphazard positioning hampered our ability to make progress to the dome. The AI estimated our chances of survival at below 50% for the first time. We would starve in our suits as the motors propelled us forward. We would become desiccated and exist in an elongation of our thoughts that made us weak and stupid until the light winked out. But still, we had no choice. So even in places where the dead in their suits were piled high, we would simply plunge forward, over and through them, headed for the dome. 

What we would find there, as I have said, we did not know. But we were in an area of the galaxy where ancient civilizations had died out millions of years ago. We had been on our way to a major site, an ancient city on a moon with no atmosphere in a wilderness of stars. 

Although our emotions fluctuated, a professional awe and curiosity about the dead eventually came over us. This created much debate over the comms. We had made a discovery for the ages, but our satisfaction was bittersweet. Even if we lived longer than expected, we would never return home, never see our friends or family again. The AI might continue on after we were dead, but I doubt it envied being the one to report on our discovery centuries hence. And to who?

Here were the ghastly emissaries of hundreds of spacefaring species we had never before encountered. Their suits displayed an extraordinary range, although our examination was cursory. Some even appeared to be made out of scales and other biological substances from their home worlds, giving us further clues as to their origins. 

The burial of the suits by snow and the lack of access to anything other than a screaming face or faces, often distorted by time and ice, worked against recording much usable data. This issue was compounded in those cases where the suit was part of the organism and they had not needed any “artificial skin,” as the AI put it, to survive harsh conditions. That many had died despite appearing well-­prepared for the planet’s environment sobered us up even before our own suits dispensed drugs to help our mental states. 

After a time, each face seemed to express some aspect of our own stress and terror at the seriousness of our situation. After a time, the sheer welter of detail defeated us and caused us extreme distress. The captain made the observation that even one instance of alien contact might cause physiological and mental conditions, including anxiety, stress, fatigue. Here, we were constantly encountering the alien dead of what seemed at times an infinite number of civilizations. 

We stopped recording. We recommitted ourselves to the slog toward the nearest dome. 

The captain’s drugs unit had failed, but the AI found a way to help her by turning off the heating element in select panels of her suit. Some parts of her would soon be lost to the cold, but the system would allow her to live on with some measure of comfort.

I must admit, we were just glad the screaming had stopped and welcomed her counsel.


II.

For a long time, as we labored in our spacesuits on that planet—following the path, beleaguered by snowstorms—we could not understand why we found so many dead astronauts, of so many unknown alien types, and yet no spaceships. During good visibility, our line of sight reached, unbroken, for 500 miles. Where were the crash sites? 

But one day we chanced upon an antenna sticking up out of the ground. Clumsy attempts at excavation soon revealed that below this antenna lay a vast dead spaceship of a kind we had never seen before. The gash that had opened it to the elements had laid bare its unique architecture, but also gave the illusion that the snow had spilled out of it to create the world around us rather than having infiltrated and accumulated inside over time.

Aspects of the spaceship’s texture gave the startling suggestion that it had been made of some ultra-hard wood or wood equivalent. Clambering partway up to stare at the inner compartments, we all felt the strangeness of the dimensions and proportions of the living quarters. There was no sign of the occupants. Perhaps, I suggested, they had headed for the domes. Perhaps they had even made it to the domes. I tried and failed to keep hope from my voice.

But the captain had ordered the AI to perform a materials analysis. The “snow” in this region had been contaminated by ash and tiny particles of bone. The AI estimated that more than 70% of the white surrounding us was made of the remains of vertebrate sentient life and the remnants of suits. Of invertebrates there was no telling. A thaw might bring not just the drip, drip of water but a shushing sound indicative of bone particulate in the mixture. I imagined there might even be the clink of small objects not rendered down by whatever intense heat had created the ash.

The astrogator had insisted on digging deeper into the ship, with the idea that some recognizable commonality between technologies might yield a part or parts with which he could fix our ship. The rest of us allowed this delusion for the obvious reasons. But upon his return, he held in his hands ovals of snow not much larger than the space formed by the circle between a thumb and finger. Many of them had soft indentations, as one might find in the afterbirth of reptiles from eggs. A kind of ghostly cilia-like tread appeared along the bottoms of these objects.

The astrogator did not find any technology of use to us. Instead, he discovered that the species piloting the spaceship had been so different from us as to be safely encapsuled in suits the size of eggs. Much of what had spilled into or spilled out of the gash constituted the bodies of the crew, in their hundreds of thousands. Their suits had been inadequate to the conditions. They had died en masse attempting to escape their own ship.

The AI speculated that it had been a generation ship, perhaps fleeing a planet with a dying star. If we wondered how the AI had reached this conclusion, it was because we did not want it to be true.

The captain became silent upon receiving this further news and did not speak to us for more than 100 miles of further progress. 

As we left that site, unsure exactly what we stepped upon, we also knew that since the spaceship was entirely covered by snow, it had been falling into the sediment for days or months or years. We knew then that our ship might not be visible against the horizon should we retrace our steps. The already bleak probability of rescue through visual identification of a crash site from above would be lost to us in time, even as the line of cables remained perpetually visible to the horizon. We now thought of the planet as a trap. But of what sort? 


III.

We could not be sure, but in the absence of the captain’s voice, it may have been the AI that put forward the idea of the planet’s being “duplicitous.” The phrasing concerned us, for there was a duplicity in using the planet as the subject of the spoken sentence. A sphere rotating around a sun in deep space could not exhibit forethought or premeditation or other qualities of sentience. 

The AI meant whoever or whatever had created the conditions on the planet that allowed spacecraft to be trapped and then the occupants placed in a perilous situation with no recourse. But I distinctly recall the AI using the words “the planet.” In addition to being inaccurate, this also let us know that the AI did not have any analysis available that might help us understand the agency and motivations acting upon us. 

But in a sense, the AI only voiced something I had felt for several miles: that there existed an overlay to the planet’s surface, an area or space or different landscape unavailable to us. This overlay had also not been available to any of the prior astronauts who had died here. In this area or space or different landscape existed a wealth of the usual hoped-for things: a breathable atmosphere and abundant food and water. 

While we struggled with the line through the snow and through the storms that welled up, others could see us but chose to ignore us for reasons or perhaps just for their own well-being. For hundreds, possibly thousands of years, as explorers had died here in merciless and terrible ways, there raged a sumptuous feast for the senses, as excessive as it was ancient and unending.

I cannot tell you how powerfully the AI’s words struck us, so that our mouths watered at the thought of real food and of clean, unrecycled water, of a freedom unencumbered by suits and breathing apparatus. Even at our intended destination, we would have spent most of our days aboard a small space station. This tedium would have been broken only by the arduous process of reaching the unbreathable surface and its ancient ruins of jagged black stone. 

This vision that overtook us functioned not just as tantalizing delusion. It scared us so much that we could not compartmentalize it in our thoughts. It continued to overwhelm us like a wave.

We fought for the first time, with the astrogator expressing the wish to return to the ruined spacecraft and explore nearby areas for parts, while the captain broke silence to order us to continue to make progress toward the nearest dome. The AI, which had brought us to this point, stole the captain’s silence and said no more.

For each of us, those endless white plains with no real elevation, just the metal rope and the metal posts, had become a kind of repetition that hurt the brain, and the mind with it.

As I looked out across the white, I could not help seeing the impression of shapes in the wind, as if invisible entities fled by, carried there by gusts, unable to get purchase, swept up for hundreds and hundreds of miles before being dashed to the ground.

We did not give up, however.


IV.

About halfway to the nearest dome, amid a storm that reduced our progress incrementally and our line of sight to nothing, we came upon a peculiar tableau. 

Six astronaut suits had fallen across and around the metal rope. With the flurries of snow, it took us, even with our powerful headlamps, some minutes to determine the nature of the obstruction. The six suits had been created for a humanoid species that must have had torsos like nine-foot-long slabs, attached to six limbs, three for walking. Their heads had flared out like thick fans. All the helmets were cracked open, and curled inside were the skeletons of some other intelligent species no larger than 40 or 50 pounds, possibly warm-blooded. With no sign of the original occupants. 

After a brief analysis cut short by the conditions, we postulated that the warm-blooded species had worn breathable skin suits that, as they failed, required these intruders to seek shelter. All they could find were these six dead astronauts. Because we could discover no trace of the original occupants, the AI put forward the theory that this smaller species had eaten every scrap of the remains within the suits. 

Then they too had perished, and in time, the AI suggested, something smaller would take up residence inside those bodies, then smaller still within those, and smaller still—

At this point, the captain attempted a soft reboot of the AI using a coded question. We could hear the concern in her voice.

Yet the AI continued undeterred, suggesting that we might find this to be a common situation. It might be replicated across the planet, depending on a system’s ability to break down and process meat that had not evolved alongside the devourer for millions of years. In all likelihood, most who attempted to eat in this way died soon after, poisoned by alien flesh.

The astrogator had taken to muttering inside his suit, off comms, as if he no longer thought we functioned as a team. No amount of castigation from the captain served to change his mind.

In the terse harshness of the captain’s reprimand, I recognized that her pain levels had spiked once again.


V.

The AI began to talk to us in strange alien voices at mile 700, as we labored through the snowstorm to hold onto the cables and thus the path. The AI warbled and chirped and howled and hummed and clucked. The AI spoke in voices like fossilized choruses of beasts, vast and harmonious. And in voices like dry grass spun to fire by the sun. And in voices like the dissolution of all things, darkness in the blinding white that scared me. 

At first we thought the AI was deranged. Then that the AI channeled voices from the dome 300 miles ahead. But finally, the AI managed to make known to us that these were the voices of the dead astronauts we had come across from time to time. Huddled frozen. The suits in so many shapes and sizes. That the voices of the dead were channeled through the AI, and nothing could stop them.

We chose to believe that the AI had begun to malfunction. We did not waste time with a response. The captain asked the AI to perform self-shutdown and whispered the numbers in the correct sequence. We knew what we lost with this act, and yet we knew if we did not shut down the AI it might become harmful to us beyond the mental distress of what it had just conveyed to us.

Soon after, the AI gave up its own voice, and all that came from it were the sounds of the others. 

A little later, the AI no longer spoke at all.


VI.

The snow began to betray us, as the storms created different forms of ice. Often, our arms became weary, our legs cramping, and we had to rest with greater frequency. We came to accept the solid crunch that could support our weight. We came to reject the feather-light freshness that felt effortless underfoot but could give way just as easily as if it were air. In some places, slick purple-hued ice welled up in sluggish layers as if something half-alive. In others, we discovered strange islands of elevation, with brutal curls and curves that suggested two continental shelves had clashed in that space.

As we adapted to these conditions, and as conditions worsened and still we adapted, we came to feel an illusion of competency, one that made even the astrogator temporarily cheerful. The sounds through the comms of our efforts, the deeper breathing, the occasional muffled curse, seduced us in this regard. We felt that we were becoming adroit at handling the snow. We began to believe if we could only make it to the dome, we would be saved.

Yet this uptick in morale ran parallel to, rather than intersected with, the idea of our ultimate survival.


VII.

We lost track of the distance left to us without the AI to tell us. Or the captain, in her pain, no longer thought to issue updates. But across the distance left to us came sights beyond reckoning: three giant astronauts spaced 50 miles apart. Larger than most starships, each body lay sprawled across an area larger than several fields and in very different conditions.

The first had been badly burned and was thus unrecoverable, even in terms of salvage. The astronaut had crawled or pulled itself along for some distance. It had left a long smudge of black and red across that expanse. The alien species was, as ever, unknown to us, but the five arms were sunk in the ground as if in agony. The skull had once held three eyes, and the face plate had been cracked by force so strong it resembled a meteor strike. The body was bloated, the fabric of the suit gray with a shimmer of green that came and went, linked to photosensitive skin cells. The way the flesh took up space, and how it exhibited aspects more plant than animal, made it impossible to study further.

The second was a sprawl of limbs, with the suggestion of a defensive posture. The debris of conflict flared out to the side in an incomprehensible display. The suit had an intactness that surprised us, but a similar crack in the face plate without any trace of body within. The rest of the suit had become inhabited by a wealth of other dead astronauts of varying sizes and shapes, who had sought shelter or sustenance and then become trapped or simply … given up. As the AI had predicted, we had once again encountered bodies providing other bodies with temporary sustenance and shelter.

I felt like a parasite who beheld a god. Or was the scale even more ludicrous?

But this condition was not at first evident to us, becoming apparent only after we had clambered for an hour to reach the cracked face plate and the entry hole extended like a broken archway before us.

Despite the number of remains within, and the difficulty in moving through them to explore, the captain ordered an exhaustive recon. Her pulse in the readings had a thready quality. Sometimes I felt, and the astrogator too when we took private comms, that the captain had begun to say things similar to the AI’s delusions. Yet we obeyed the order, on the chance that some internal calculation on the captain’s part meant she believed this was the only way we would survive. 

What did we expect to find in the dead body of a once-­intelligent giant? Food? Oxygen? Some cause of death? To put off the thought of our own death by seeking shelter with a death so large we could not comprehend it?

I felt like a parasite who beheld a god. Or was the scale even more ludicrous? I had trouble envisioning the way the body must have twisted as it pitched forward into that icy ground. I had trouble holding onto my own thoughts.

More and more pressure moved through my skull as I contemplated that scene. We were in the midst of something none of my kind had ever known. We might be the only ones, ever. I better understood the unraveling of the AI and of the captain. My sharpness had dulled, taking my calm with it.

It was impossible to tell how long the astronaut had taken to die. Unless somewhere within that fallen figure some hint of life hid that we would never find.

The storms fell away, rose, then fell away again. 


VIII.

The third huge astronaut was full of light and life and shone out across the wasteland of snow like a beacon. For a moment, I thought we had pierced the invisible layer and could see what lay beyond the veil. We would have comforts beyond anything found on our ruined spaceship even when it had been fit to cross galactic space. There would not be recycled urine for our water. There would not be the faint stink of sweat creeping into our suits as the ventilation system began to fail. Our liquid food would not taste stale and moldy. 

As we approached, the suit extended almost to the horizon in that foreshortened perspective created by the left foot. We noted through our remaining instrumentation that the suit remained intact. The pressure told us a kind of air circulated within its sealed surfaces. 

We climbed with a renewed energy, the promise of sanctuary so close making us giddy. We each exhorted the others on with such exuberance that it made me a little afraid. What lay on the other side of this state of mind but a fall?

When we reached the helmet plate, we could see inside not a face or a skull, but instead such a richness of healthy growth that we fell silent before it. None of us could, I believe, understand exactly what we saw, except that it equaled ecosystem—resplendent with vibrant greens and blues, stippled with other colors. There might be some parallel to a terrarium full of moss and exotic plants. There might be some sense of life moving amongst those plants, as of jewel-like amphibians or even tiny shy sapphire birds. We could not smell or taste or hear what lay behind the face plate. We could not experience it in that way, but somehow we each imagined enough to be calmed and comforted by it. 

The astrogator said he might be able to create a hole in the plate or elsewhere on the body to let us in, and then patch the surface such that not too much air or vitality would spill out. This workaround might take an hour or two, due to the delicate nature of what we saw within. But it was possible.

The captain considered the astrogator’s proposal and then agreed. The weather had begun to turn dangerous again. That we should begin immediately did not need to be said. With the proper pressure brought to bear, we would have some measure of sanctuary from which to recover for a final push to the dome. It could be the difference between life and death, the astrogator said. If the atmosphere was breathable, we might even be able to give the captain some better solution to her pain.

I unclipped the astrogator’s equipment from his waist and threw it off the mountain that was the astronaut and watched it sail through the air and into the snow. Then I used my weapon to fry it where it lay. Then I threw my weapon into the snow, too, in a place where the featheriness would cover it and hide it forever. 

We were a team and I had helped my team while showing them I posed no threat—although I knew the astrogator and the captain would not see it that way. I stood there on the face plate that we could no longer open with the diminished tools at our disposal as they both yelled at me through the comms. It’s unimportant what they said to me. They were admonishing me for something that had already happened and that they had no power to stop. I did not bother to explain, but began to make the descent to the ground so we could once again take up the metal rope and make for the dome.

Will you follow, I asked them from the ground, when I saw they still stood on the heights. There came no reply, but when they saw me take up the rope, they climbed down to take up the rope too.

I waited then, and let them catch up.


IX.

The captain died not long after. The pain was too great or the wounds she had suffered too damaging. I had known for some time she would never make it to the dome, but there was no point in emphasizing that to her. Nothing she had done until the end had required her to be removed from command. Her last words were the name of our ship and giving her love to someone who would be dead of old age even if we found a way to escape this place and return home. But the astrogator told her he would carry those words forward. 

Then we left her by the marker that meant we had 100 miles left to the dome. We knew the snow would cover her for burial. It had done so faithfully for all the rest.

That in that frozen hellscape, the persistence of life in that manner, an oasis in the midst of nothing, could be categorized as a miracle.

As the astrogator followed me down the rope line, he cried out for explanation. The captain’s death required it for some reason, in his mind. The captain had not deserved my betrayal. The captain would not rest easy until I told him why. 

You must believe in ghosts, I replied.

ROGAN BROWN

This reply incensed him and he castigated me in words not used among members of a team that respect each other. Once more, I ignored him, but told him if our oxygen got low, he could have mine if we calculated he could make it to the base. I meant this, as I knew the odds were low anyway. I had hurt my knee taking the equipment from the astrogator and then making my way so rapidly down from the dead astronaut.

The astrogator did not reply, by which I knew he did not accept my answer.

The reason I took the tools and destroyed them is because the wind had told me something it had not whispered to the captain or the astrogator. The wind had not spoken to me before, so I believed what it told me. That the astronaut within the suit lived on, if unable to move. That what we saw on the outside and registered as ecosystem, as separate “plants” and “animals,” instead formed a composite life-form and that to crack open the suit or cut through the suit at a leg would have been a violation.

That in that frozen hellscape, the persistence of life in that manner, an oasis in the midst of nothing, could be categorized as a miracle. 

I would not snuff that out. I could not allow that to be snuffed out. But I remembered too how I felt looking at that vast and alien country behind the face plate. So calm, so comforted, overcome by the depths of an emotion I could not place. Would I replace that feeling with the feeling of seeing all those explorers dead within the other vast suit? Even as I become one of them? 

Because the planet had already told us the rules, the consequences, and the ultimate outcome. There are no odds so terrible that they could not be experienced, and in dozens of ways, in this place. 

So I trudged on and the astrogator cursed me and cursed me and called out my childhood and how badly I must have been brought up and how I must have cheated to pass the psych exams, and yet I had thought the same of him at various points during our journey.

See how beautiful the snow is, falling now, I said to him over the comms. See how precise and geometric this line we follow across this expanse. 

He did not reply, but a little later he told me he no longer believed in the line at all, and by his calculations he would get to the dome faster if he abandoned it and struck out on his own.

I could not stop the astrogator and did not want to, so I watched him become a smaller and smaller figure against the white until the white ate him up and I was alone.


X.

I have been walking a long time, visiting with the dead. Here, against an arch of heaven that appears no different than what I see directly in front of me. 

Jeff VanderMeer is the author of the critically acclaimed, bestselling Southern Reach series, translated into 38 languages. His short fiction has appeared in Vulture, Slate, New York Magazine, Black Clock, Interzone, American Fantastic Tales (Library of America), and many others.

What’s in a name? Moderna’s “vaccine” vs. “therapy” dilemma

Is it the Department of Defense or the Department of War? The Gulf of Mexico or the Gulf of America? A vaccine—or an “individualized neoantigen treatment”?

That’s the Trump-era vocabulary paradox facing Moderna, the covid-19 shot maker whose plans for next-generation mRNA vaccines against flus and emerging pathogens have been dashed by vaccine skeptics in the federal government. Canceled contracts and unfriendly regulators have pushed the Massachusetts-based biotech firm to a breaking point. Last year, Robert F. Kennedy Jr., head of the Department of Health and Human Services, zeroed in on mRNA, unwinding support for dozens of projects—including a $776 million award to Moderna for a bird flu vaccine. By January, the company was warning it might have to stop late-stage programs to develop vaccines against infections altogether.

That raises the stakes for a second area of Moderna’s research. In a partnership with Merck, it’s been using its mRNA technology to destroy tumors through a very, very promising technique known as a cancer vacc—

“It’s not a vaccine,” a spokesperson for Merck jumped in before the V-word could leave my mouth. “It’s an individualized neoantigen therapy.”

Oh, but it is a vaccine. And here’s how it works. Moderna sequences a patient’s cancer cells to find the ugliest, most peculiar molecules on their surface. Then it packages the genetic code for those same molecules, called neoantigens, into a shot. The patient’s immune system has its orders: Kill any cells with those yucky surface markers.

Mechanistically, it’s similar to the covid-19 vaccines. What’s different, of course, is that the patient is being immunized against a cancer, not a virus.

And it looks like a possible breakthrough. This year, Moderna and Merck showed that such shots halved the chance that patients with the deadliest form of skin cancer would die from a recurrence after surgery.

In its formal communications, like regulatory filings, Moderna hasn’t called the shot a cancer vaccine since 2023. That’s when it partnered up with Merck and rebranded the tech as individualized neoantigen therapy, or INT. Moderna’s CEO said at the time that the renaming was to “better describe the goal of the program.” (BioNTech, the European vaccine maker that’s also working in cancer, has shifted its language too, moving from “neoantigen vaccine” in 2021 to “mRNA cancer immunotherapies” in its latest report.)

The logic of casting it as a therapy is that patients already have cancer—so it’s a treatment as opposed to a preventive measure. But it’s no secret what the other goal is: to distance important innovation from vaccine fearmongering, which has been inflamed by high-ranking US officials. “Vaccines are maybe a dirty word nowadays, but we still believe in the science and harnessing our immune system to not only fight infections, but hopefully to also fight … cancers,” Kyle Holen, head of Moderna’s cancer program, said last summer during BIO 2025, a big biotech event in Boston.

Not everyone is happy with the word games. Take Ryan Sullivan, a physician at Massachusetts General Hospital who has enrolled patients in Moderna’s trials. He says the change raises questions over whether trial volunteers are being properly informed. “There is some concern that there will be patients who decline to treat their cancer because it is a vaccine,” Sullivan told me. “But I also felt it was important, as many of my colleagues did, that you have to call it what it is.”

But is it worth going to the mat for a word? Lillian Siu, a medical oncologist at the Princess Margaret Cancer Centre, in Toronto, who has played a role in safety testing for the new shots, watches US politics from a distance. She believes name change is acceptable “if it allows the research to continue.”

Holen told me the doctors complaining to Moderna were basically motivated by a desire to defend vaccines—which are, of course, among the greatest public health interventions of all time. They wanted the company to stand strong. 

But that’s not what’s happening. When Moderna’s latest results were published in February, the paper’s main text didn’t use the word “vaccine” at all. It was only in the footnotes that you could see the term—in the titles of old papers and patents.

All this could be a sign that Kennedy’s strategy is working. His agencies often appear to make mRNA vaccines a focus of people’s worries, impede their reach, devalue them for companies, and sideline their defenders. 

Still, Moderna’s strategy may be working too. So far, at least, the government hasn’t had much to say about the company’s cancer vacc— I mean, its individualized neoantigen therapy.

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.

Is fake grass a bad idea? The AstroTurf wars are far from over.

A rare warm spell in January melted enough snow to uncover Cornell University’s newest athletic field, built for field hockey. Months before, it was a meadow teeming with birds and bugs; now it’s more than an acre of synthetic turf roughly the color of the felt on a pool table, almost digital in its saturation. The day I walked up the hill from a nearby creek to take a look, the metal fence around the field was locked, but someone had left a hallway-size piece of the new simulated grass outside the perimeter. It was bristly and tough, but springy and squeaky under my booted feet. I could imagine running around on it, but it would definitely take some getting used to.

My companion on this walk seemed even less favorably disposed to the thought. Yayoi Koizumi, a local environmental advocate, has been fighting synthetic-turf projects at Cornell since 2023. A petite woman dressed that day in a faded plum coat over a teal vest, with a scarf the colors of salmon, slate, and sunflowers, Koizumi compulsively picked up plastic trash as we walked: a red Solo cup, a polyethylene Dunkin’ container, a five-foot vinyl panel. She couldn’t bear to leave this stuff behind to fragment into microplastic bits—as she believes the new field will. “They’ve covered the living ground in plastic,” she said. “It’s really maddening.” 

The new pitch is one part of a $70 million plan to build more recreational space at the university. As of this spring, Cornell plans to install something like a quarter million square feet of synthetic grass—what people have colloquially called “astroturf” since the middle of the last century. University PR says it will be an important part of a “health-promoting campus” that is “supportive of holistic individual, social, and ecological well-being.” Koizumi runs an anti-plastic environmental group called Zero Waste Ithaca, which says that’s mostly nonsense.

This fight is more than just the usual town-versus-gown tension. Synthetic turf used to be the stuff of professional sports arenas and maybe a suburban yard or two; today communities across the United States are debating whether to lay it down on playgrounds, parks, and dog runs. Proponents say it’s cheaper and hardier than grass, requiring less water, fertilizer, and maintenance—and that it offers a uniform surface for more hours and more days of the year than grass fields, a competitive advantage for athletes and schools hoping for a more robust athletic program.

But while new generations of synthetic turf look and feel better than that mid-century stuff, it’s still just plastic. Some evidence suggests it sheds bits that endanger users and the environment, and that it contains PFAS “forever chemicals”—per- and polyfluoroalkyl substances, which are linked to a host of health issues. The padding within the plastic grass is usually made from shredded tires, which might also pose health risks. And plastic fields need to be replaced about once a decade, creating lots of waste.

Yet people are buying a lot of the stuff. In 2001, Americans installed just over 7 million square meters of synthetic turf, just shy of 11,000 metric tons. By 2024, that number was 79 million square meters—enough to carpet all of Manhattan and then some, almost 120,000 metric tons. Synthetic turf covers 20,000 athletic fields and tens of thousands of parks, playgrounds, and backyards. And the US is just 20% of the global market. 

Where real estate is limited and demand for athletic facilities is high, artificial turf is tempting. “It all comes down to land and demand.”

Frank Rossi, professor of turf science, Cornell

Those increases worry folks who study microplastics and environmental pollution. Any actual risk is hard to parse; the plastic-making industry insists that synthetic fields are safe if properly installed, but lots of researchers think that isn’t so. “They’re very expensive, they contain toxic chemicals, and they put kids at unnecessary risk,” says Philip Landrigan, a Boston College epidemiologist who has studied environmental toxins like lead and microplastics.

But at Cornell, where real estate is limited and demand for athletic facilities is high, synthetic turf was a tempting option. As Frank Rossi, a professor of turf science at Cornell, told me: “It all comes down to land and demand.”


In 1965, Houston’s new, domed base­ball stadium was an icon of space-age design. But the Astrodome had a problem: the sun. Deep in the heart of Texas, it shined brightly through the Astrodome’s skylights—so much so that players kept missing fly balls. So the club painted over the skylights. Denied sunlight, the grass in the outfield withered and died.

A replacement was already in the works. In the late 1950s a Ford Foundation–funded educational laboratory determined that a soft, grasslike surface material would give city kids more places to play outside and had prevailed upon the Monsanto corporation to invent one. The result was clipped blades of nylon stuck to a rubber base, which the company called ChemGrass. Down it went into Houston’s outfield, where it got a new, buzzier name: AstroTurf.

Workers lay artificial turf at the Astrodome in Houston on July 13, 1966. Developed by Monsanto, the material was originally known as ChemGrass but was later renamed AstroTurf after the stadium.
AP PHOTO/ED KOLENOVSKY, FILE

That first generation of simulated lawn was brittle and hard, but quality has improved. Today, there are a few competing products, but they’re all made by extruding a petroleum-based polymer—that’s plastic—through tiny holes and then stitching or fusing the resulting fibers to a carpetlike bottom. That gets attached to some kind of padding, also plastic. In the 1970s the industry started layering that over infill, usually sand; by the 1990s, “third generation” synthetic turf had switched to softer fibers made of polyethylene. Beneath that, they added infill that combined sand and a soft, cheap shredded rubber made from discarded automobile tires, which pile up by the hundreds of millions every year. This “crumb rubber” provides padding and fills spaces between the blades and the backing.

In the early 1980s, nearly half the professional baseball and football fields in the US had synthetic turf. But many players didn’t like it. It got hotter than real grass, gave the ball different action, and seemed to be increasing the rate of injuries among athletes. Since the 1990s, most pro sports have shifted back toward grass—water and maintenance costs pale in comparison to the importance of keeping players happy or sparing them the risk of injury. 

But at the same time, more universities and high schools are buying the artificial stuff. The advantages are clear, especially in places where it rains either too much or not enough. A natural-grass field is usable for a little more than 800 hours a year at the most, spread across just eight months in the cooler, wetter northern US. An artificial-turf field can see 3,000 hours of activity per year. For sports like lacrosse, which begins in late winter, this makes artificial turf more appealing. Most lacrosse pitches are now synthetic. So are almost all field hockey pitches; players like the way the even, springy turf makes the ball bounce.

Furthermore, supporters say synthetic turf needs less maintenance than grass, saving money and resources. That’s not always true; workers still have to decompact the playing surface and hose it off to remove bird poop or cool it down. Sometimes the infill needs topping up. But real grass allows less playing time, and because grass athletic fields often need to be rotated to avoid damage, synthetic ground cover can require less space. Hence the market’s explosive growth in the 21st century.


The city and town of Ithaca—two separate political entities with overlapping jurisdiction over Cornell construction projects—held multiple public meetings about the university’s new synthetic fields: the field hockey pitch and a complex called the Meinig Fieldhouse. Koizumi’s group turned up in force, and a few folks who worked at Cornell came to oppose the idea too—submitting pages of citations and studies on the risks of synthetic grass.

At two of those meetings, dozens of Cornell athletes turned out to support the turf. Representatives of the university and the athletic department declined to speak with me for this story, citing an ongoing lawsuit from Zero Waste Ithaca. But before that, Nicki Moore, Cornell’s director of athletics, told a local newspaper that demand from campus groups and sports teams meant the fields were constantly overcrowded. “Activities get bumped later and later, and sometimes varsity teams won’t start practicing until 10 at night, you know?” Moore told the paper. “Availability of all-weather space should normalize scheduling a great deal.”

That argument wasn’t universally convincing. “It’s a bad idea, but that’s from the environmental perspective,” says Marianne Krasny, director of Cornell’s Civic Ecology Lab and one of the speakers at those hearings. “Obviously the athletic department thinks it’s a great idea.”

square patch of artificial turf

GETTY IMAGES

Members of Cornell on Fire, a climate action group with members from both the university and the town, joined in opposing the use of artificial turf, citing the fossil-fuel origins of the stuff. They described the nominal support of the project from student athletes as inauthentic, representing not grassroots support but, yes, an astroturf campaign. 

Sorting out the actual science here isn’t simple. Over time, the plastic that synthetic turf is made of sheds bits of itself into the environment. In one study, published in 2023 in the journal Environmental Pollution, researchers found that 15% of the medium-­size and microplastic particles in a river and the Mediterranean Sea outside Barcelona, Spain, came from artificial turf, mostly in the form of tiny green fibers. Back in 2020, the European Chemicals Agency estimated that infill material from artificial-­turf fields in the European Union was contributing 16,000 metric tons of microplastics to the environment each year—38% of all annual microplastic pollution. Most of that came from the crumb rubber infill, which Europe now plans to ban by 2031. 

This pollution worries the Cornell activists. Ithaca is famous for scenic gorges and waterways. The new field hockey pitch is uphill from a local creek that empties into Cayuga Lake, the longest of the Finger Lakes and the source of drinking water for over 40,000 people.

And it’s not just the plastic bits. When newer generations of synthetic turf switched to durable high-density polyethylene, the new material gunked up the extruders used in the manufacturing process. So turf makers started adding fluorinated polymers—a type of PFAS. Some of these environmentally persistent “forever chemicals” cause cancer, disrupt the endocrine system, or lead to other health problems. Research in several different labs has found PFAS in many types of plastic grass.

But the key to assessing the threat here is exposure. Heather Whitehead, an analytical chemist then at the University of Notre Dame, found PFAS in synthetic turf at levels around five parts per billion—but estimated it’d be in water running off the fields at three parts per trillion; for context, the US Environmental Protection Agency’s legal drinking-water limit on one of the most widespread and dangerous PFAS chemicals is four parts per trillion. “These chemicals will wash off in small amounts for long periods of time,” says Graham Peaslee, Whitehead’s advisor and an emeritus nuclear physicist who studies PFAS concentrations. “I think it’s reason enough not to have artificial turf.”

This gets confusing, though. There are over 16,000 different types of PFAS, few have been well studied, and different ­companies use different manufacturing techniques. Companies represented by the Synthetic Turf Council now “use zero intentionally added PFAS,” says Melanie Taylor, the group’s president. “This means that as the field rolls off the assembly line, there are zero PFAS-formulated materials present.”

Some researchers are skeptical of the industry’s assurances. They’re hard to confirm, especially because there are a lot of ways to test for PFAS. The type of synthetic turf going onto the new field hockey pitch at Cornell is called GreenFields TX; the university had a sample tested using an EPA method that looks for 40 different PFAS compounds. It came back negative for all of them. The local activists countered that the test doesn’t detect the specific types they’re most concerned about, and in 2025 they paid for three more tests on newly purchased synthetic turf. Two clearly found fluorine—the F in “PFAS”—and one identified two distinct PFAS compounds. (The company that makes GreenFields TX, TenCate, declined to comment, citing ongoing litigation.)

PFAS isn’t the only potential problem. There’s also the crumb rubber made from tires. A billion tires get thrown out every year worldwide, and if they aren’t recycled they sit in giant piles that make great habitats for rats and mosquitoes; they also occasionally catch fire. Lots of the tires that go into turf are made of styrene-­butadiene rubber, or SBR. In bulk, that’s bad. Butadiene is a carcinogen that causes leukemia, and fumes from styrene can cause nervous system damage. SBR also contains high levels of lead.

But how much of that comes out of synthetic-­turf infill? Again, that’s hotly debated. Researchers around the world have published suggestive studies finding potentially dangerous levels of heavy metals like zinc and lead in synthetic turf, with possible health risks to people using the fields. But a review of many of the relevant studies on turf and crumb rubber from Canada’s National Collaborating Centre for Environmental Health determined that most well-conducted health risk assessments over the last decade found exposures below levels of concern for cancer and certain other diseases. A 2017 report by the European Chemicals Agency—the same people who found all those microplastics in the environment—“found no reason to advise people against playing sports on synthetic turf containing recycled rubber granules as infill material.” And a multiyear study from the EPA, published in 2024, found much the same thing—although the researchers said that levels of certain synthetic chemicals were elevated inside places that used indoor artificial turf. They also stressed that the paper was not a risk assessment. 

The problem is, the kinds of cancers these chemicals can cause may take decades to show up. Long-term studies haven’t been done yet. All the evidence available so far is anecdotal—like a series for the Philadelphia Inquirer that linked the deaths of six former Phillies players from a rare type of brain cancer called glioblastoma to years spent playing on PFAS-containing artificial turf. That’d be about three times the usual rate of glioblastoma among adult men, but the report comes with a lot of cautions—small sample size, lots of other potential causes, no way to establish causation.

Synthetic turf has one negative that no one really disputes: It gets very hot in the sun—as hot as 150 °F (66 °C). This can actually burn players, so they often want to avoid using a field on very hot days.

A field hockey player from Cornell University passes the ball during a game played on artificial turf at Bryant University in 2025. Cornell’s own turf field will be ready for the 2026 season.
GETTY IMAGES

Athletes playing on artificial turf also have a higher rate of foot and ankle injuries, and elite-level football players seem to be more predisposed to knee injuries on those surfaces. But other studies have found rates of knee and hip injury to be roughly comparable on artificial and natural turf—a point the landscape architect working on the Cornell project made in the information packet the university sent to the city. Athletic departments and city parks departments say that the material’s upsides make it worthwhile, given that there’s no conclusive proof of harm.

Back in Ithaca, Cornell hired an environmental consulting firm called Haley & Aldrich to assess the evidence. The company concluded that none of the university’s proposed installations of artificial turf would have a negative environmental impact. People from Cornell on Fire and Zero Waste Ithaca told me they didn’t trust the firm’s findings; representatives from Haley & Aldrich declined to comment.

Longtime activists say that as global consumption of fossil fuels declines, petrochemical companies are desperate to find other markets. That means plastics. “There’s a big push to shift more petrochemicals into plastic products for an end market,” says Jeff Gearhart, a consumer product researcher at the Ecology Center. “Industry people, with a vested interest in petrochemicals, are looking to expand and build out alternative markets for this stuff.”

All that and more went before the decision-­makers in Ithaca. In September 2024, the City of Ithaca Planning Board unanimously issued a judgment that the Meinig Fieldhouse would not have a significant environmental impact and thus would not need to complete a full environmental impact assessment. Six months later, the town made the same determination for the field hockey pitch.

Zero Waste Ithaca sued in New York’s supreme court, which ruled against the group. Koizumi and lawyers from Pace University’s Environmental Litigation Clinic have appealed. She says she’s still hopeful the court might agree that Ithaca authorities made a mistake by not requiring an environmental impact statement from the college. “We have the science on our side,” she says.


Ithaca is a pretty rarefied place, an Ivy League university town. But these same tensions—potential long-term environmental and public health consequences versus the financial and maintenance concerns of the now—are pitting worried citizens against their representatives and city agencies around the country. 

New York City has 286 municipal synthetic-­turf fields, with more under construction. In Inwood, the northernmost neighborhood in Manhattan, two fields were approved via Zoom meetings during the pandemic, and Massimo Strino, a local artist who makes kaleidoscopes, says he found out only when he saw signs announcing the work on one of his daily walks in Inwood Hill Park, along the Hudson River. He joined a campaign against the plan, gathering more than 4,300 signatures. “I was canvassing every weekend,” Strino says. “You can count on one hand, literally, the number of people who said they were in favor.” 

But that doesn’t include the group that pushed for one of those fields in the first place: Uptown Soccer, which offers free and low-cost lessons and games to 1,000 kids a year, mostly from underserved immigrant families. “It was turning an unused community space into a usable space,” says David Sykes, the group’s executive director. “That trumped the sort of abstract concerns about the environmental impacts. I’m not an expert in artificial turf, but the parks department assured me that there was no risk of health effects.”

Artificial turf doesn’t go away. “You’re going to be paying to get rid of it. Somebody will have to take it to a dump, where it will sit for a thousand years.”

Graham Peaslee, emeritus nuclear physicist studying PFAS concentrations, University of Notre Dame

New York City councilmember Christopher Marte disagrees. He has introduced a bill to ban new artificial turf from being installed in parks, and he hopes the proposal will be taken up by the Parks Committee this spring. Last session, the bill had 10 cosponsors—that’s a lot. Marte says he expects resistance from lobbyists, but there’s precedent. The city of Boston banned artificial turf in 2022.  

Upstate, in a Rochester suburb called Brighton, the school district included synthetic-­turf baseball and softball diamonds in a wide-ranging February 2024 capital improvement proposition. The measure passed. In a public meeting in November 2025, the school board acknowledged the intent to use synthetic grass—or, as concerned parents had it, “to rip up a quarter ­million square feet of this open space and replace it with artificial turf,” says David Masur, executive director of the environmental group PennEnvironment, whose kids attend school in Brighton. Parents and community members mobilized against the plan, further angered when contractors also cut down a beloved 200-year-old tree. School superintendent Kevin McGowan says it’s too late to change course. Masur has been working to oppose the plan nevertheless—he says school boards are making consequential decisions about turf without sharing information or getting input, even though these fields can cost millions of dollars of taxpayer money.

In short, the fights can get tense. On Martha’s Vineyard, in Massachusetts, a meeting about plans to install an artificial field at a local high school had to be ended early amid verbal abuse. A staffer for the local board of health who voiced concern about PFAS in the turf quit the board after discovering bullet casings in her tote bag, she said, which she perceived as a death threat. After an eight-year fight, the board eventually banned artificial turf altogether. 


What happens next? Well, outdoor artificial turf lasts only eight to 12 years before it needs to be taken up and replaced. The Synthetic Turf Council says it’s at least partially recyclable and cites a company called BestPLUS Plastic Lumber as a purveyor of products made from recycled turf. The company says one of its products, a liner called GreenBoard that artificial turf can be nailed into, is at least 40% recycled from fake grass. Joseph Sadlier, vice president and general manager of plastics recycling at BestPLUS, says the company recycles over 10 million pounds annually. 

Yet the material is piling up. In 2021, a Danish company called Re-Match announced plans to open a recycling plant in Pennsylvania and began amassing thousands of tons of used plastic turf in three locations. The company filed for bankruptcy in 2025.

In Ithaca, university representatives told planning boards that it would be possible to recycle the old artificial turf they ripped out to make way for the Meinig Fieldhouse. That didn’t happen. An anonymous local activist tracked the old rolls to a hauling company a half-hour’s drive south of campus and shared pictures of them sitting on the lot, where they stayed for months. It’s unclear what their ultimate fate will be.

That’s the real problem: Artificial turf just doesn’t go away. “You’re going to be paying to get rid of it,” says Peaslee, the PFAS expert. “Somebody will have to take it to a dump, where it will sit for a thousand years.” At minimum, real grass is a net carbon sink, even including installation and maintenance. Synthetic turf releases greenhouse gases. One life-­cycle analysis of a 2.2-acre synthetic field in Toronto determined that it would emit 55 metric tons of carbon dioxide over a decade. Plastic fields need less water to maintain, but it takes water to make plastic, and natural grass lets rainwater seep into the ground. Synthetic turf sends most of it away as runoff.

It’s a boggling set of issues to factor into a decision. Rossi, the Cornell turf scientist, says he can understand why a school in the northern United States might go plastic, even when it cares about its students’ health. “It was the best bad option,” he says. Concerns about microplastics and PFAS are “significant issues we have not fully addressed.” And they need to be. 

Douglas Main is a journalist and former senior editor and writer at National Geographic.