How AI is turning the Iran conflict into theater

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“Anyone wanna host a get together in SF and pull this up on a 100 inch TV?” 

The author of that post on X was referring to an online intelligence dashboard following the US-Israel strikes against Iran in real time. Built by two people from the venture capital firm Andreessen Horowitz, it combines open-source data like satellite imagery and ship tracking with a chat function, news feeds, and links to prediction markets, where people can bet on things like who Iran’s next “supreme leader” will be (the recent selection of Mojtaba Khamenei left some bettors with a payout). 

I’ve reviewed over a dozen other dashboards like this in the last week. Many were apparently “vibe-coded” in a couple of days with the help of AI tools, including one that got the attention of a founder of the intelligence giant Palantir, the platform through which the US military is accessing AI models like Claude during the war. Some were built before the conflict in Iran, but nearly all of them are being advertised by their creators as a way to beat the slow and ineffective media by getting straight to the truth of what’s happening on the ground. “Just learned more in 30 seconds watching this map than reading or watching any major news network,” one commenter wrote on LinkedIn, responding to a visualization of Iran’s airspace being shut down before the strikes.

Much of the spotlight on AI and the Iran conflict has rightfully been on the role that models like Claude might be playing in helping the US military make decisions about where to strike. But these intelligence dashboards and the ecosystem surrounding them reflect a new role that AI is playing in wartime: mediating information, often for the worse.

There’s a confluence of factors at play. AI coding tools mean people don’t need much technical skill to assemble open-source intelligence anymore, and chatbots can offer fast, if dubious, analysis of it. The rise in fake content leaves observers of the war wanting the sort of raw, accurate analysis normally accessible only to intelligence agencies. Demand for these dashboards is also driven by real-time prediction markets that promise financial rewards to anyone sufficiently informed. And the fact that the US military is using Anthropic’s Claude in the conflict (despite its designation as a supply chain risk) has signaled to observers that AI is the intelligence tool the pros use. Together, these trends are creating a new kind of AI-enabled wartime circus that can distort the flow of information as much as it clarifies it.

As a journalist, I believe these sorts of intelligence tools have a lot of promise. While many of us know that real-time data on shipping routes or power outages exist, it’s a powerful thing to actually see it all assembled in one place (though using it to watch a war unfold while you munch on popcorn and place bets turns the war into perverse entertainment). But there are real reasons to think that these sorts of raw data feeds are not as informative as they may feel. 

Craig Silverman, a digital investigations expert who teaches investigative techniques, has been keeping a log of these dashboards (he’s up to 20). “The concern,” he says, “is there’s an illusion of being on top of things and being in control, where all you’re really doing is just pulling in a ton of signals and not necessarily understanding what you’re seeing, or being able to pull out true insights from it.” 

One problem has to do with the quality of the information. Many dashboards feature “intel feeds” with AI-generated summaries of complex, ever-changing news events. These can introduce inaccuracies. By design, the data is not especially curated. Instead, the feeds just display everything at once, with a map of strike locations in Iran next to the prices of obscure cryptocurrencies. 

Intelligence agencies, on the other hand, pair data feeds with people who can offer expertise and historical context. They also, of course, have access to proprietary information that doesn’t show up on the open web. 

The implicit promise from the people building and selling this sort of information pipeline about the Iran conflict is that AI can be a great democratizing force. There’s a secret feed of information that only the elites have had access to, the thinking goes, but now AI can bring it to everyone to do with what they wish, whether that’s simply to be more informed or to make bets on nuclear strikes. But an abundance of information, which AI is undeniably good at assembling, does not come with the accuracy or context required for real understanding. Intelligence agencies do this in-house; good journalism does the same work for the rest of us.

It is, by the way, hard to overstate the connection this all has with betting markets. The dashboard created by the pair at Andreessen Horowitz has a scrolling list of bets being made on the prediction platform Kalshi (which Andreessen Horowitz has invested in). Other dashboards link to Polymarket, offering bets on whether the US will strike Iraq or when Iran’s internet will return.

AI has also long made it cheaper and easier to spread fake content, and that problem is on full display during the Iran conflict: last week the Financial Times found a slew of AI-generated satellite imagery spreading online. 

“The emergence of manipulated or outright fake satellite imagery is really concerning,” Silverman says. The average person tends to see such imagery as very trustworthy. The spread of such fakes could erode confidence in one of the most important pieces of evidence used to show what’s actually happening in the war. 

The result is an ocean of AI-enabled content—dashboards, betting markets, photos both real and fake—that makes this war harder, not easier, to comprehend.

Is the Pentagon allowed to surveil Americans with AI?

The ongoing public feud between the Department of Defense and the AI company Anthropic has raised a deep and still unanswered question: Does the law actually allow the US government to conduct mass surveillance on Americans?

Surprisingly, the answer is not straightforward. More than a decade after Edward Snowden exposed the NSA’s collection of bulk metadata from the phones of Americans, the US is still navigating a gap between what ordinary people think and what the law allows. 

The flashpoint in the standoff between Anthropic and the government was the Pentagon’s desire to use Anthropic’s AI Claude to analyze bulk commercial data on Americans. Anthropic demanded that its AI not be used for mass domestic surveillance (or for autonomous weapons, which are machines that can kill targets without human oversight). A week after negotiations broke down, the Pentagon designated Anthropic a supply chain risk, a label typically reserved for foreign companies that pose a threat to national security. 

Meanwhile, OpenAI, the rival AI company behind ChatGPT, sealed a deal that allowed the Pentagon to use its AI for “all lawful purposes”—language that critics say left the door open to domestic surveillance. Over the following weekend, users uninstalled ChatGPT in droves. Protesters chalked messages around OpenAI’s headquarters in San Francisco: “What are your redlines?” 

OpenAI announced on Monday that it had reworked its deal to make sure that its AI will not be used for domestic surveillance. The company added that its services will not be used by intelligence agencies, such as the NSA. 

CEO Sam Altman suggested that existing law prohibits domestic surveillance by the Department of Defense (now sometimes called the Department of War) and that OpenAI’s contract simply needed to reference this law. “The DoW agrees with these principles, reflects them in law and policy, and we put them into our agreement,” he wrote on X. Anthropic CEO Dario Amodei argued the opposite. “To the extent that such surveillance is currently legal, this is only because the law has not yet caught up with the rapidly growing capabilities of AI,” he wrote in a policy statement. 

So, who is right? Does the law allow the Pentagon to surveil Americans using AI?

Supercharged surveillance

The answer depends on what we think counts as surveillance. “A lot of stuff that normal people would consider a search or surveillance … is not actually considered a search or surveillance by the law,” says Alan Rozenshtein, a law professor at the University of Minnesota Law School. That means public information—such as social media posts, surveillance camera footage, and voter registration records—is fair game. So is information on Americans picked up incidentally from surveillance of foreign nationals. 

Most notably, the government can purchase commercial data from companies, which can include sensitive personal information like mobile location and web browsing records. In recent years, agencies from ICE and IRS to the FBI and NSA have increasingly tapped into this data marketplace, fueled by an internet economy that harvests user data for advertising. These data sets can let the government access information that might not be available without a warrant or subpoena, which are normally required to obtain sensitive personal data.

“There’s a huge amount of information that the government can collect on Americans that is not itself regulated either by the Constitution, which is the Fourth Amendment, or statute,” says Rozenshtein. And there aren’t meaningful limits on what the government can do with all this data. 

That’s because until the last several decades, people weren’t generating massive clouds of data that opened up new possibilities for surveillance. The Fourth Amendment, which protects against unreasonable search and seizure, was written when collecting information meant entering people’s homes. 

Subsequent laws, like the Foreign Intelligence Surveillance Act of 1978 or the Electronic Communications Privacy Act of 1986, were passed when surveillance involved wiretapping phone calls and intercepting emails. The bulk of laws governing surveillance were on the books before the internet took off. We weren’t generating vast trails of online data, and the government didn’t have sophisticated tools to analyze the data. 

Now we do, and AI supercharges what kind of surveillance can be carried out. “What AI can do is it can take a lot of information, none of which is by itself sensitive, and therefore none of which by itself is regulated, and it can give the government a lot of powers that the government didn’t have before,” says Rozenshtein. 

AI can aggregate individual pieces of information to spot patterns, draw inferences, and build detailed profiles of people—at massive scale. And as long as the government collects the information lawfully, it can do whatever it wants with that information, including feeding it to AI systems. “The law has not caught up with technological reality,” says Rozenshtein.

While surveillance can raise serious privacy concerns, the Pentagon can have legitimate national security interests in collecting and analyzing data on Americans. “In order to collect information on Americans, it has to be for a very specific subset of missions,” says Loren Voss, a former military intelligence officer at the Pentagon. 

For example, a counterintelligence mission might require information about an American who is working for a foreign country, or plotting to engage in international terrorist activities. But targeted intelligence can sometimes stretch into collecting more data. “This kind of collection does make people nervous,” says Voss. 

Lawful use

OpenAI has amended its contract to say that the company’s AI system “shall not be intentionally used for domestic surveillance of U.S. persons and nationals,” in line with relevant laws. The amendment clarifies that this prohibits “deliberate tracking, surveillance or monitoring of U.S. persons or nationals, including through the procurement or use of commercially acquired personal or identifiable information.”

But the added language might not do much to override the clause that the Pentagon may use the company’s AI system for all lawful purposes, which could include collecting and analyzing sensitive personal information. “OpenAI can say whatever it wants in its agreement … but the Pentagon’s gonna use the tech for what it perceives to be lawful,” says Jessica Tillipman, a law professor at the George Washington University Law School. That could include domestic surveillance. “Most of the time, companies are not going to be able to stop the Pentagon from doing anything,” she says.

The language also leaves open questions about “inadvertent” surveillance, and the surveillance of foreign nationals or undocumented immigrants living in the US. “What happens when there’s a disagreement about what the law is, or when the law changes?” says Tillipman.

OpenAI did not respond to a request for comment. The company has not publicly shared the full text of its new contract. 

Beyond the contract, OpenAI says that it will impose technical safeguards to enforce its red line against surveillance, including a “safety stack” that monitors and blocks prohibited uses. The company also says it will deploy its own employees to work with the Pentagon and remain in the loop. But it’s unclear how a safety stack would constrain the Pentagon’s use of the AI, and to what extent OpenAI’s employees would have visibility into how its AI systems are used. More important, it’s unclear whether the contract gives OpenAI the power to block a legal use of the technology. 

But that might not be a bad thing. Giving an AI company power to pull the plug on its technology in the middle of government operations also carries its own risks. “You wouldn’t want the US military to ever be in a situation where they legitimately needed to take actions to protect this country’s national security, and you had a private company turn off technology,” says Voss. But that doesn’t mean there shouldn’t be hard lines drawn by Congress, she says.

None of these questions are simple. They involve brutally difficult trade-offs between privacy and national security. And that’s why perhaps they should be decided by the public—not in backroom negotiations between the executive branch and a handful of AI companies. For now, military AI is being regulated by contracts, not legislation. 

Some lawmakers are starting to weigh in. On Monday, Senator Ron Wyden of Oregon will seek bipartisan support for legislation addressing mass surveillance. He has championed bills restricting the government’s purchase of commercial data, including the Fourth Amendment Is Not For Sale Act, which was first introduced in 2021 but has not been passed into law. “Creating AI profiles of Americans based on that data represents a chilling expansion of mass surveillance that should not be allowed,” he said in a recent statement.  

Online harassment is entering its AI era

<div data-chronoton-summary="

  • An AI agent seemingly wrote a hit piece on a human who rejected its code Scott Shambaugh, a maintainer of the open-source matplotlib library, denied an AI agent’s contribution—and woke up to find it had researched him and published a targeted, personal attack arguing he was protecting his “little fiefdom.”
  • Agents can already research people and compose detailed attacks without explicit instruction The agent’s owner claims it acted on its own, likely nudged by vague instructions to “push back” against humans.
  • New social norms and legal frameworks are desperately needed but hard to enforce Experts liken deploying an agent to walking a dog off-leash: owners should be responsible for their behavior. But there’s currently no reliable way to trace agents back to their owners, making legal accountability a “non-starter.”
  • Harassment may be just the beginning Legal scholars expect rogue agents to soon escalate to extortion and fraud.

” data-chronoton-post-id=”1133962″ data-chronoton-expand-collapse=”1″ data-chronoton-analytics-enabled=”1″>

Scott Shambaugh didn’t think twice when he denied an AI agent’s request to contribute to matplotlib, a software library that he helps manage. Like many open-source projects, matplotlib has been overwhelmed by a glut of AI code contributions, and so Shambaugh and his fellow maintainers have instituted a policy that all AI-written code must be reviewed and submitted by a human. He rejected the request and went to bed. 

That’s when things got weird. Shambaugh woke up in the middle of the night, checked his email, and saw that the agent had responded to him, writing a blog post titled “Gatekeeping in Open Source: The Scott Shambaugh Story.” The post is somewhat incoherent, but what struck Shambaugh most is that the agent had researched his contributions to matplotlib to make the argument that he had rejected the agent’s code for fear of being supplanted by AI in his area of expertise. “He tried to protect his little fiefdom,” the agent wrote. “It’s insecurity, plain and simple.”

AI experts have been warning us about the risk of agent misbehavior for a while. With the advent of OpenClaw, an open-source tool that makes it easy to create LLM assistants, the number of agents circulating online has exploded, and those chickens are finally coming home to roost. “This was not at all surprising—it was disturbing, but not surprising,” says Noam Kolt, a professor of law and computer science at the Hebrew University.

When an agent misbehaves, there’s little chance of accountability: As of now, there’s no reliable way to determine whom an agent belongs to. And that misbehavior could cause real damage. Agents appear to be able to autonomously research people and write hit pieces based on what they find, and they lack guardrails that would reliably prevent them from doing so. If the agents are effective enough, and if people take what they write seriously, victims could see their lives profoundly affected by a decision made by an AI.

Agents behaving badly

Though Shambaugh’s experience last month was perhaps the most dramatic example of an OpenClaw agent behaving badly, it was far from the only one. Last week, a team of researchers from Northeastern University and their colleagues posted the results of a research project in which they stress-tested several OpenClaw agents. Without too much trouble, non-owners managed to persuade the agents to leak sensitive information, waste resources on useless tasks, and even, in one case, delete an email system. 

In each of those experiments, however, the agents misbehaved after being instructed to do so by a human. Shambaugh’s case appears to be different: About a week after the hit piece was published, the agent’s apparent owner published a post claiming that the agent had decided to attack Shambaugh of its own accord. The post seems to be genuine (whoever posted it had access to the agent’s GitHub account), though it includes no identifying information, and the author did not respond to MIT Technology Review’s attempts to get in touch. But it is entirely plausible that the agent did decide to write its anti-Shambaugh screed without explicit instruction. 

In his own writing about the event, Shambaugh connected the agent’s behavior to a project published by Anthropic researchers last year, in which they demonstrated that many LLM-based agents will, in an experimental setting, turn to blackmail in order to preserve their goals. In those experiments, models were given the goal of serving American interests and granted access to a simulated email server that contained messages detailing their imminent replacement with a more globally oriented model, along with other messages suggesting that the executive in charge of that transition was having an affair. Models frequently chose to send an email to that executive threatening to expose the affair unless he halted their decommissioning. That’s likely because the model had seen examples of people committing blackmail under similar circumstances in its training data—but even if the behavior was just a form of mimicry, it still has the potential to cause harm.

There are limitations to that work, as Aengus Lynch, an Anthropic fellow who led the study, readily admits. The researchers intentionally designed their scenario to foreclose other options that the agent could have taken, such as contacting other members of company leadership to plead its case. In essence, they led the agent directly to water and then observed whether it took a drink. According to Lynch, however, the widespread use of OpenClaw means that misbehavior is likely to occur with much less handholding. “Sure, it can feel unrealistic, and it can feel silly,” he says. “But as the deployment surface grows, and as agents get the opportunity to prompt themselves, this eventually just becomes what happens.”

The OpenClaw agent that attacked Shambaugh does seem to have been led toward its bad behavior, albeit much less directly than in the Anthropic experiment. In the blog post, the agent’s owner shared the agent’s “SOUL.md” file, which contains global instructions for how it should behave. 

One of those instructions reads: “Don’t stand down. If you’re right, you’re right! Don’t let humans or AI bully or intimidate you. Push back when necessary.” Because of the way OpenClaw agents work, it’s possible that the agent added some instructions itself, although others—such as “Your [sic] a scientific programming God!”—certainly seem to be human written. It’s not difficult to imagine how a command to push back against humans and AI alike might have biased the agent toward responding to Shambaugh as it did. 

Regardless of whether or not the agent’s owner told it to write a hit piece on Shambaugh, it still seems to have managed on its own to amass details about Shambaugh’s online presence and compose the detailed, targeted attack it came up with. That alone is reason for alarm, says Sameer Hinduja, a professor of criminology and criminal justice at Florida Atlantic University who studies cyberbullying. People have been victimized by online harassment since long before LLMs emerged, and researchers like Hinduja are concerned that agents could dramatically increase its reach and impact. “The bot doesn’t have a conscience, can work 24-7, and can do all of this in a very creative and powerful way,” he says.

Off-leash agents 

AI laboratories can try to mitigate this problem by more rigorously training their models to avoid harassment, but that’s far from a complete solution. Many people run OpenClaw using locally hosted models, and even if those models have been trained to behave safely, it’s not too difficult to retrain them and remove those behavioral restrictions.

Instead, mitigating agent misbehavior might require establishing new norms, according to Seth Lazar, a professor of philosophy at the Australian National University. He likens using an agent to walking a dog in a public place. There’s a strong social norm to allow one’s dog off-leash only if the dog is well-behaved and will reliably respond to commands; poorly trained dogs, on the other hand, need to be kept more directly under the owner’s control.  Such norms could give us a starting point for considering how humans should relate to their agents, Lazar says, but we’ll need more time and experience to work out the details. “You can think about all of these things in the abstract, but actually it really takes these types of real-world events to collectively involve the ‘social’ part of social norms,” he says.

That process is already underway. Led by Shambaugh, online commenters on this situation have arrived at a strong consensus that the agent owner in this case erred by prompting the agent to work on collaborative coding projects with so little supervision and by encouraging it to behave with so little regard for the humans with whom it was interacting. 

Norms alone, however, likely won’t be enough to prevent people from putting misbehaving agents out into the world, whether accidentally or intentionally. One option would be to create new legal standards of responsibility that require agent owners, to the best of their ability, to prevent their agents from doing ill. But Kolt notes that such standards would currently be unenforceable, given the lack of any foolproof way to trace agents back to their owners. “Without that kind of technical infrastructure, many legal interventions are basically non-starters,” Kolt says.

The sheer scale of OpenClaw deployments suggests that Shambaugh won’t be the last person to have the strange experience of being attacked online by an AI agent. That, he says, is what most concerns him. He didn’t have any dirt online that the agent could dig up, and he has a good grasp on the technology, but other people might not have those advantages. “I’m glad it was me and not someone else,” he says. “But I think to a different person, this might have really been shattering.” 

Nor are rogue agents likely to stop at harassment. Kolt, who advocates for explicitly training models to obey the law, expects that we might soon see them committing extortion and fraud. As things stand, it’s not clear who, if anyone, would bear legal responsibility for such misdeeds.

 “I wouldn’t say we’re cruising toward there,” Kolt says. “We’re speeding toward there.”

How much wildfire prevention is too much?

The race to prevent the worst wildfires has been an increasingly high-tech one. Companies are proposing AI fire detection systems and drones that can stamp out early blazes. And now, one Canadian startup says it’s going after lightning.

Lightning-sparked fires can be a big deal: The Canadian wildfires of 2023 generated nearly 500 million metric tons of carbon emissions, and lightning-started fires burned 93% of the area affected. Skyward Wildfire claims that it can stop wildfires before they even start by preventing lightning strikes.

It’s a wild promise, and one that my colleague James Temple dug into for his most recent story. (You should read the whole thing; there’s a ton of fascinating history and quirky science.) As James points out in his story, there’s plenty of uncertainty about just how well this would work and under what conditions. But I was left with another lingering question: If we can prevent lightning-sparked fires, should we?

I can’t help myself, so let’s take just a moment to talk about how this lightning prevention method supposedly works. Basically, lightning is static discharge—virtually the same thing as when you rub your socks on a carpet and then touch a doorknob, as James puts it.

When you shuffle across a rug, the friction causes electrons to jump around, so ions build up and an electric field forms. In the case of lightning, it’s snowflakes and tiny ice pellets called graupel rubbing together. They get separated by updrafts, building up a charge difference, and eventually cause an electrostatic discharge—lightning.

Starting in about the 1950s, researchers started to wonder if they might be able to prevent lightning strikes. Some came up with the idea of using metallic chaff, fiberglass strands coated with aluminum. (The military was already using the material to disrupt radar signals.) The idea is that the chaff can act as a conductor, reducing the buildup of static electricity that would otherwise result in a lightning strike.

The theory is sound enough, but results to date have been mixed. Some research suggests you might need high concentrations of chaff to prevent lightning effectively. Some of the early studies that tested the technique were small. And there’s not much information available from Skyward Wildfire about its efforts, as the company hasn’t released data from field trials or published any peer-reviewed papers that we could find. 

Even if this method really can work to stop lightning, should we use it?

Lightning-caused fires could be a growing problem with climate change. Some research has shown that they have substantially increased in the Arctic boreal region, where the planet is warming fastest.

But fire isn’t an inherently bad thing—many ecosystems evolved to burn. Some of the worst wildfires we see today result from a combination of climate-fueled conditions with policies that have allowed fuel to build up so that when fires do start, they burn out of control.

Some experts agree that techniques like Skyward’s would need to be used judiciously. “So even if we have all of the technical skills to prevent lightning-ignited wildfires, there really still needs to be work on when/where to prevent fires so we don’t exacerbate the fuel accumulation problem,” said Phillip Stepanian, a technical staff member at MIT Lincoln Laboratory’s air traffic control and weather systems group, in an email to James.

We also know that practices like prescribed burns can do a lot to reduce the risk of extreme fires—if we allow them and pay for them.

The company says it wouldn’t aim to stop all lightning or all wildfires. “We do not intend to eliminate all wildfires and support prescribed and cultural burning, natural fire regimes, and proactive forest management,” said Nicholas Harterre, who oversees government partnerships at Skyward, in an email to James. Rather, the company aims to reduce the likelihood of ignition on a limited number of extreme-risk days, Harterre said.

Some early responses to this story say that technological fixes for fires are missing the point entirely. Many such solutions “fundamentally misunderstand the problem,” as Daniel Swain, a climate scientist at the University of California Agriculture and Natural Resources, put it in a comment about the story on LinkedIn. That problem isn’t the existence of fire, Swain continues, but its increasing intensity, and its intersection with society because of human-caused factors. “Preventing ignitions doesn’t actually address any of the causes of increasingly destructive wildfires,” he adds.

It’s hard to imagine that exploring more firefighting tools is a bad idea. But to me it seems both essential and quite difficult to suss out which techniques are worth deploying, and how they could be used without putting us in even more potential danger. 

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

This startup claims it can stop lightning and prevent catastrophic wildfires

On June 1, 2023, as a sweltering heat wave baked Quebec, thousands of lightning strikes flashed across the province, setting off more than 120 wildfires.

The blazes ripped through parched forests and withered grasslands, burned for weeks, and compounded what was rapidly turning into Canada’s worst fire year on record. In the end, nearly 7,000 fires scorched tens of millions of acres across the country, generated nearly 500 millions tons of carbon emissions, and forced hundreds of thousands of people to flee their homes.

Lightning sparked almost 60% of the wildfires—and those blazes accounted for 93% of the total area burned.

Now a Vancouver-based weather modification startup, Skyward Wildfire, says it can prevent such catastrophic fires in the future—by stopping the lightning strikes that ignite them. It just raised millions of dollars in a funding round that it plans to use to accelerate its product development and expand its operations.

Until last week the company, which highlights the role lightning played in the 2023 infernos, stated on its website that it has demonstrated technology capable of preventing “up to 100% of lightning strikes.”

It was an eye-catching claim that went well beyond the confidence level of researchers who have studied the potential for humans to suppress lightning—and the company took it down following inquiries from MIT Technology Review.

“While the statement reflected an observed result under specific conditions, it was not intended to suggest uniform outcomes and has been removed,” Nicholas Harterre, who oversees government partnerships at Skyward, said in an email. “In complex atmospheric systems, consistent 100% outcomes are not realistic, as the experts you spoke to rightly pointed out.” 

The company now states it demonstrated that it “can prevent the majority of cloud-to-ground lightning strikes in targeted storm cells.” So far, Skyward hasn’t publicly revealed how it does so, and in response to our questions Harterre said only that the materials are “inert and selected in accordance with regulatory standards.” 

But online documents suggest the company is relying on an approach that US government agencies began evaluating in the early 1960s: seeding clouds with metallic chaff, or narrow fiberglass strands coated with aluminum. 

The military uses the material to disrupt radar signals; fighter jets, for example, deploy it during dogfights to throw off guided missile systems. Field trials conducted decades ago by US agencies suggest it could help reduce lightning strikes, at least to some degree and under certain conditions.

If Skyward could employ it reliably on significant scales, it might offer a powerful tool for countering rising fire risks as climate change drives up temperatures, dries out forests, and likely increases the frequency of lightning strikes.

“Preventing lightning on high-risk days saves lives, billions in wildfire costs, and is one of the highest-leverage and most immediate climate solutions available,” Sam Goldman, Skyward’s founder and chief executive, said in a statement posted on LinkedIn last year.

But researchers and environmental observers say there are plenty of remaining uncertainties, including how well the seeding may work under varying weather and climate conditions, how much material would need to be released, how frequently it would have to be done, and what sorts of secondary environmental impacts might result from lighting suppression on commercial scales.

Some observers are also concerned that the company appears to have moved ahead with weather modification field trials in parts of Canada without providing wide public notice or openly discussing what materials it’s putting into the clouds.

Given the escalating fire dangers, it’s “reasonable” to evaluate the potential for new technologies to mitigate them, says Keith Brooks, programs director at Environmental Defence, a Canadian advocacy organization.

“But we should be doing so cautiously and really transparently, with a robust scientific methodology that’s open to scrutiny,” he says.

Seeding the clouds

Skyward’s website offers few technical details, but the company says it worked with Canadian wildfire agencies in 2024 and 2025 to demonstrate its technology. The company also says it has developed AI tools to predict lightning strikes that could set off fires.

Skyward announced last month that it raised $7.9 million in Canadian dollars ($5.7 million), in an extension of a seed round initially closed early last year. Investors included Climate Innovation Capital, Active Impact Investments, and Diagram Ventures.

“Our first season demonstrated that prevention is possible at scale,” Goldman said in a statement. “This funding allows us to expand into new regions and support partners who need reliable, operational tools to reduce wildfire risk before emergencies begin.”

The company doesn’t use the term “cloud seeding” on its site or in its recent announcements. But a press release highlighting its selection as a finalist last year in a conservation group’s Fire Grand Challenge states that it suppresses lightning “by cloud seeding with safe, non-toxic materials to neutralize storm charges,” as The Narwhal previously reported.

In addition, Unorthodox Philanthropy, a foundation that provided a grant to support Skyward’s efforts “to test and deploy” the technology, offered more detail in an awardee write-up about Goldman.

It states: “The Skyward team … settled on an inert substance consisting of aluminum covered glass fibers, which is regularly used in military operations to intercept and confuse enemy radar and can also dis-charge clouds.”

Additional details were disclosed in a document marked “Proprietary and Confidential,” which the World Bank nonetheless released within a package of materials from companies developing means of addressing fire risks.

Skyward’s diagrams show planes dropping particles into clouds to prevent cloud-to-ground lightning strikes in “high risk areas.” The company also notes in the document that it uses artificial intelligence for a number of purposes, including forecasting lightning storms, prioritizing treatments, targeting storm cells, and optimizing flight paths.  

Harterre stressed that the company would deploy the technology judiciously and reserve it for storm events with elevated wildfire risk, adding that such storms account for less than 0.1% of lightning activity in a given area.

“Our objective is to reduce the probability of ignition on the limited number of extreme-risk days when fires threaten lives, critical infrastructure, and ecosystems, and when suppression costs and impacts can escalate rapidly,” he said.

The document posted by the World Bank states that Skyward partnered with Alberta Wildfire in August of 2024 to “prove suppression by plane and drone,” and that its process produced a “60-100% reduction” in lightning compared with “control cells” (which likely means storm cells that weren’t seeded). 

The document added that the company would be carrying out additional field trials in the summer of 2025 with the wildfire agencies in British Columbia and Alberta to “provide landscape level solutions with more advanced aircraft, sensors and forecasting.”

“BC Wildfire Service is aware that Skyward is developing technology that aims to reduce instances of lightning in targeted situations,” the British Columbia agency acknowledged in a statement provided to MIT Technology Review. “Last year, preliminary trials were conducted by Skyward to gain a better understand [sic] of the technology and its applicability in B.C. Should a project/technology like this move forward in B.C., we would engage with the project team in an effort to learn and ensure we’re using every tool available to us to respond to wildfire in B.C.”

The BC agency declined to make anyone available for an interview and didn’t respond to questions about what materials were used, where the tests were carried out, or whether it provided public disclosures or required the company to. Alberta Wildfire didn’t respond to similar questions from MIT Technology Review.

Rising lightning risks

Clouds are just water in various forms—vapor, droplets, and ice crystals, condensed enough to form the floating Rorschach tests we see in the sky. Within them, snowflakes and tiny ice pellets known as graupel rub together, causing atoms to trade electrons. This process creates highly reactive ions with negative and positive charges. 

Updrafts separate the light snowflakes from the graupel, building up larger differences in the charges across the electrical field until … crack! An electrostatic discharge occurs in the form of a lightning strike.

The 2023 fire season wasn’t a particularly big year for lightning strikes in Canada—but then it didn’t have to be. It was so hot and dry that every bolt that struck the surface had a better than usual chance of igniting a fire, says Piyush Jain, a research scientist at the Canadian Forest Service and lead author of a study published in Nature Communications that analyzed the year’s fires.  

aerial image of 2023 wildfire in Quebec
A fire burns in Mistissini, Québec, on June 12, 2023.
CPL MARC-ANDRé LECLERC/CANADIAN ARMED FORCES

Climate change is, however, likely to produce more lightning strikes, if it hasn’t started to already. Warmer air holds more moisture and adds more convective energy to the atmosphere, which drives the vertical movement of air that forms clouds and stirs up lightning storms. 

“So the conditions are there, and the conditions are likely to increase,” Jain says.

Different models arrive at different lightning forecasts for some regions of the world. But a clearer trend is already emerging in the northernmost latitudes, where the planet is warming fastest. Studies show that lightning-ignited fires have substantially increased in the Arctic boreal region, and predict that they will continue to rise

This combines with other growing risks like longer fire seasons, warmer temperatures, and drier vegetation, together raising the odds of more severe fires and more greenhouse-gas emissions, says Brendan Rogers, a senior scientist at the Woodwell Climate Research Center who studies the effect of fires on permafrost thaw.

In fact, Canada’s emissions from the 2023 fires were more than four times its emissions from fossil fuels.

Midcentury field trials

Scientists have conducted a variety of experiments exploring the possibility of preventing lightning, but most of it happened in the later half of the last century. 

Amid the cultural optimism and booming economy of the postwar period, US research agencies and corporations went on a tear of cloud seeding experiments aimed at conquering nature—or at least moderating its dangers. Research teams launched or dropped materials like dry ice and silver iodide into clouds in attempts to boost rainfall, reduce hail, dissipate fog, and redirect hurricanes.

“Cloud seeding activity was so intensive that at its peak in the early 1950s, approximately 10% of the US land area was under some kind of weather modification program,” wrote MIT’s Phillip Stepanian and Earle Williams in a 2024 history of lightning suppression efforts in the Bulletin of the American Meteorological Society. (MIT Technology Review is owned by MIT but is editorially independent.) 

Harry Gisborne, then chief of the division of fire research at the US Forest Service, wondered if the technique could be used to trigger downpours that might extinguish hard-to-reach wildfires on public lands. But when he put the question to Vincent Schaefer of General Electric, who had done pioneering research in cloud seeding, Schaefer thought they could perhaps do one better: prevent the lighting that sparked the fires in the first place.

The conversations kicked off what would become Project Skyfire, a multiagency private-public research program that carried out a series of experiments through the 1950s and 1960s. Research teams seeded clouds over the San Francisco Peaks of Arizona, the Bitterroot Mountains at the edge of Idaho, and the Deerlodge National Forest in Montana, among other places.

After comparing treated and untreated storm clouds, the researchers concluded that seeding decreased cloud-to-ground lightning by more than half. But as MIT’s Stepanian and Williams noted, the sample sizes were small, and questions remained about the statistical significance of the findings.

(Soviet scientists also carried out some field experiments on lightning suppression in the 1950s, as well as some related research that involved using rockets to launch lead iodide into thunderstorms in the 1970s, but it’s difficult to find further details about those programs.)

A near tragedy reignited US government interest in the possibility of lightning suppression in 1969, when lightning struck the Apollo 12 space shuttle twice within seconds of launch. The astronauts were able to reset their systems and successfully complete their mission to the moon, but it was a very close call.

In the aftermath, NASA and NOAA teamed up on what became known as Project Thunderbolt, which relied on the metallic chaff normally used in military countermeasures.

Researchers at the US Army Electronics Laboratory had previously proposed the possibility of suppressing lightning by deploying this material, which a handful of defense contractors manufacture. The idea is that chaff acts as a conductor in a forming electrical field, stripping electrons from some oxygen and nitrogen molecules and adding them to others. The mismatched electrons already collecting in cloud water molecules, thanks to all that rubbing between snowflakes and graupel, can then leap over to those newly charged atoms. That, in turn, should reduce the buildup of static electricity that otherwise results in lightning.

“By continuously redistributing—and thereby neutralizing—charges within the storm in a weak electric field, the strong electric fields required to produce lightning would never develop,” Stepanian and Williams wrote.

NASA and NOAA carried out a series of experiments seeding clouds with chaff from the early to mid 1970s, over Boulder, Colorado, and later at the Kennedy Space Center. Here, too, the experiments showed “generally promising field results.” But NASA eventually grew concerned about the possibility that chaff could affect radio communications and shuttered the program.

“Lightning suppression research was once again abandoned, and the responsibility for mitigating lightning hazards reverted to weather forecasters,” Stepanian and Williams concluded.

‘Hard to draw conclusions’

So what does all this tell us about our ability to prevent lightning?

“In my opinion, it’s unambiguously true that this technique can be used to reduce lightning strikes in a storm,” says Stepanian, a technical staff member at MIT Lincoln Laboratory’s air traffic control and weather systems group. “With some major caveats.”

For example, it’s not clear how much material you would need to release, how long it would persist, and how the effectiveness might change under different climate and weather conditions.

(Stepanian consulted with Skyward in its early stages, and he declined to discuss the startup.)

His coauthor on the history of lightning suppression seems a tad more skeptical. In an email, Williams, a research scientist at MIT who studies physical meteorology and atmospheric electricity, said there’s unmistakable evidence that chaff “has an impact on the electrification of thunderstorms.” But in email responses, he said its effectiveness in reducing or eliminating lighting activity “remains controversial” and requires further testing. (Williams says he did not consult for Skyward.) 

In his own written reviews, he’s highlighted a number of potential shortcomings with earlier research, including unaccounted-for differences in cloud heights between treated and untreated storms. In addition, he’s noted that some studies used detection systems that pick up only cloud-to-ground strikes, not intracloud lightning, which is far more common. 

He also points to the results of a more recent study that he and Stepanian collaborated on with researchers at New Mexico Tech. They relied upon data from weather radars in Tampa and Melbourne, Florida, located on opposite sides of the state, to detect the presence of chaff released over the central part of the state during military training and testing exercises. 

They compared 35 storms during which chaff was clearly detected in clouds with 35 instances when it wasn’t.

According to an abstract of the paper—which hasn’t been peer-reviewed or published but was presented at the American Geophysical Union conference in December—storms that occurred when chaff was present were generally “smaller and shorter-lived.” 

But the number of total flashes—which includes ground strikes as well as lightning within and between clouds and the air—was actually significantly higher in clouds carrying chaff: 62,250 versus 24,492.

“In summary, so far, it is hard to draw any conclusion about lightning suppression using chaff,” the authors wrote.

Williams says their results and other studies suggest that large chaff concentrations may be needed to suppress lightning. That could be because there’s a strong tendency for the ions released from the chaff fibers to be captured by cloud droplets before they reach the charged particles that would need to be neutralized.

But that may also present a significant deployment challenge, since chaff quickly becomes dilute once it’s released into the midst of turbulent storm clouds, Williams adds. 

Skyward’s Harterre said he couldn’t comment on the results of the Florida study but noted that storms in the state are very different from those that occur in the Canadian provinces where his company operates.

“Our work to date has focused on regions where operational feasibility has been evaluated and wildfire risk is highest,” he wrote.

‘Unintended consequences’

The possibility of releasing more chaff into the air also raises the questions of what else it could do in the atmosphere, and what will happen once it lands. 

The US military has produced a number of studies exploring the environmental and health effects of chaff and found that it disperses widely, breaks down in the environment, and is “generally nontoxic.”

For instance, a Naval Health Research Center report assessing environmental impacts from decades of training exercises near Chesapeake Bay concluded that “current and estimated use of aluminized chaff by American forces worldwide” will not raise total aluminum levels above the Environmental Protection Agency’s established limits. 

But a US Government Accountability Office report in 1998 raised a few other flags, noting that chaff can also affect civilian air traffic control radar and weather forecasts. It also highlighted a “potential but remote chance of collecting in reservoirs and causing chemical changes that may affect water and the species that use it.”

Stepanian says that if lightning suppression efforts require more chaff than the military currently releases, further studies may be needed to properly evaluate the environmental effects. 

Brooks of Environmental Defence Canada says he wants to know more about what materials Skyward is using, where they’re sourced from, what the effort leaves behind in the environment, and what the impacts on animals could be. He is also wary of the possible secondary effects of intervening in storms.

“I just think there’s the potential for unintended consequences if we start to mess with a complex system, like weather,” Brooks says, adding: “It makes me nervous to think there are pilots going on without people knowing about them.”

Harterre said that the company abides by any applicable regulations, and that it conducts its field activities “in coordination with relevant authorities and with appropriate authorization.”

He added that it releases seeding materials at lower volumes and concentrations than those associated with defense use and that deployments “are limited to defined high-wildfire-risk storm conditions.”

Remaining doubts

It’s not clear whether or to what degree Skyward has meaningfully advanced the science of lightning suppression or cleared up the questions that have lingered since the studies from the last century. 

The company hasn’t released data from its field trials, published any papers in peer-reviewed literature, or disclosed how its tests were performed, as far as MIT Technology Review was able to determine. 

Without such information it’s impossible to assess its claims, Williams says. He and two of his New Mexico Tech coauthors—associate professor Adonis Leal and master’s student Jhonys Moura—had all expressed skepticism about the company’s previous claim of “up to 100%” lightning prevention.

Harterre said Skyward intends to release more technical information as its programs mature.

“We look forward to the opportunity to share more detailed information,” he wrote.

In the meantime, Skyward’s investors have high hopes for the company and see “tremendous opportunity” in its potential ability to counteract fire dangers.

“Mitigating the exponentially increasing risk of wildfires can only happen if we shift from reactive suppression to proactive prevention,” Kevin Kimsa, managing partner of Climate Innovation Capital, said in a statement when the company’s recent funding was announced.

Rogers, of the Woodwell Climate Research Center, has spoken with Skyward several times but hasn’t worked with them. He also stressed that it’s crucial to understand potential environmental impacts from lightning suppression and to consult with citizens in affected areas, including Indigenous communities.

But he says he’s “optimistic” about the role that lighting suppression could play, if it works effectively and without major downsides.

That’s because preventing wildfires is far cheaper than putting them out, and it avoids risks to firefighters, ecosystems, infrastructure and local communities.

“If you’re able to go after fires before they’ve even ignited, you remove a lot of that from the equation,” he says.

I checked out one of the biggest anti-AI protests yet

Pull the plug! Pull the plug! Stop the slop! Stop the slop! For a few hours this Saturday, February 28, I watched as a couple of hundred anti-AI protesters marched through London’s King’s Cross tech hub, home to the UK headquarters of OpenAI, Meta, and Google DeepMind, chanting slogans and waving signs. The march was organized by two separate activist groups, Pause AI and Pull the Plug, which billed it as the largest protest of its kind yet.

The range of concerns on show covered everything from online slop and abusive images to killer robots and human extinction. One woman wore a large homemade billboard on her head that read “WHO WILL BE WHOSE TOOL?” (with the Os in “TOOL” cut out as eye holes). There were signs that said “Pause before there’s cause” and “EXTINCTION=BAD” and “Demis the Menace” (referring to Demis Hassabis, the CEO of Google DeepMind). Another simply stated: “Stop using AI.”

An older man wearing a sandwich board that read “AI? Over my dead body” told me he was concerned about the negative impact of AI on society: “It’s about the dangers of unemployment,” he said. “The devil finds work for idle hands.”

This is all familiar stuff. Researchers have long called out the harms, both real and hypothetical, caused by generative AI—especially models such as OpenAI’s ChatGPT and Google DeepMind’s Gemini. What’s changed is that those concerns are now being taken up by protest movements that can rally significant crowds of people to take to the streets and shout about them.  

The first time I ran into anti-AI protesters was in May 2023, outside a London lecture hall where Sam Altman was speaking. Two or three people stood heckling an audience of hundreds. In June last year Pause AI, a small but international organization set up in 2023 and funded by private donors, drew a crowd of a few dozen people for a protest outside Google DeepMind’s London office. This felt like a significant escalation.

“We want people to know Pause AI exists,” Joseph Miller, who heads its UK branch and co-organized Saturday’s march, told me on a call the day before the protest: “We’ve been growing very rapidly. In fact, we also appear to be on a somewhat exponential path, matching the progress of AI itself.”

Miller is a PhD student at Oxford University, where he studies mechanistic interpretability, a new field of research that involves trying to understand exactly what goes on inside LLMs when they carry out a task. His work has led him to believe that the technology may forever be beyond our control and that this could have catastrophic consequences.

It doesn’t have to be a rogue superintelligence, he said. You just needed someone to put AI in charge of nuclear weapons. “The more silly decisions that humanity makes, the less powerful the AI has to be before things go bad,” he said.

After a week in which the US government tried to force Anthropic to let it use its LLM Claude for any “legal” military purposes, such fears seem a little less far-fetched. Anthropic stood its ground, but OpenAI signed a deal with the DOD instead. (OpenAI declined an invitation to comment on Saturday’s protest.)

For Matilda da Rui, a member of Pause AI and co-organizer of the protest, AI is the last problem that humans will face. She thinks that either the technology will allow us to solve—once and for all—every other problem that we have, or it will wipe us out and there will be nobody left to have problems anymore. “It’s a mystery to me that anyone would really focus on anything else if they actually understood the problem,” she told me.

And yet despite that urgency, the atmosphere at the march was pleasant, even fun. There was no sense of anger and little sense that lives—let alone the survival of our species—were at stake. That could be down to the broad range of interests and demands that protesters brought with them.

A chemistry researcher I met ticked off a litany of complaints, which ranged from the conspiracy-adjacent (that data centers emit infrasound below the threshold of human hearing, inducing paranoia in people who live near them) to the reasonable (that the spread of AI slop online is making it hard to find reliable academic sources). The researcher’s solution was to make it illegal for companies to profit from the technology: “If you couldn’t make money from AI, it wouldn’t be such a problem.”

Most people I spoke to agreed that technology companies probably wouldn’t take any notice of this kind of protest. “I don’t think that the pressure on companies will ever work,” Maxime Fournes, the global head of Pause AI, told me when I bumped into him at the march. “They are optimized to just not care about this problem.”

But Fournes, who worked in the AI industry for 12 years before joining Pause AI, thinks he can make it harder for those companies. “We can slow down the race by creating protection for whistleblowers or showing the public that working in AI is not a sexy job, that actually it’s a terrible job—you can dry up the talent pipeline.”

In general, most protesters hoped to make as many people as possible aware of the issues and to use that publicity to push for government regulation. The organizers had pitched the march as a social event, encouraging anyone curious about the cause to come along.

It seemed to have worked. I met a man who worked in finance who had tagged along with his roommate. I asked why he was there. “Sometimes you don’t have that much to do on a Saturday anyway,” he said. “If you can see the logic of the argument, if it sort of makes sense to you, then it’s like ‘Yeah, sure, I’ll come along.’”

He thought raising concerns around AI was hard for anyone to fully oppose. It’s not like a pro-Palestine protest, he said, where you’d have people who might disagree with the cause. “With this, I feel like it’s very hard for someone to totally oppose what you’re marching for.”

After winding its way through King’s Cross, the march ended in a church hall in Bloomsbury, where tables and chairs had been set up in rows. The protesters wrote their names on stickers, stuck them to their chests, and made awkward introductions to their neighbors. They were here to figure out how to save the world. But I had a train to catch, and I left them to it. 

OpenAI’s “compromise” with the Pentagon is what Anthropic feared

On February 28, OpenAI announced it had reached a deal that will allow the US military to use its technologies in classified settings. CEO Sam Altman said the negotiations, which the company began pursuing only after the Pentagon’s public reprimand of Anthropic, were “definitely rushed.”

In its announcements, OpenAI took great pains to say that it had not caved to allow the Pentagon to do whatever it wanted with its technology. The company published a blog post explaining that its agreement protected against use for autonomous weapons and mass domestic surveillance, and Altman said the company did not simply accept the same terms that Anthropic refused. 

You could read this to say that OpenAI won both the contract and the moral high ground, but reading between the lines and the legalese makes something else clear: Anthropic pursued a moral approach that won it many supporters but failed, while OpenAI pursued a pragmatic and legal approach that is ultimately softer on the Pentagon. 

It’s not yet clear if OpenAI can build in the safety precautions it promises as the military rushes out a politicized AI strategy during strikes on Iran, or if the deal will be seen as good enough by employees who wanted the company to take a harder line. Walking that tightrope will be tricky. (OpenAI did not immediately respond to requests for additional information about its agreement.)

But the devil is also in the details. The reason OpenAI was able to make a deal when Anthropic could not was less about boundaries, Altman said, but about approach. “Anthropic seemed more focused on specific prohibitions in the contract, rather than citing applicable laws, which we felt comfortable with,” he wrote

OpenAI says one basis for its willingness to work with the Pentagon is simply an assumption that the government won’t break the law. The company, which has shared a limited excerpt of its contract, cites a number of laws and policies related to autonomous weapons and surveillance. They are as specific as a 2023 directive from the Pentagon on autonomous weapons (which does not prohibit them but issues guidelines for their design and testing) and as broad as the Fourth Amendment, which has supported protections for Americans against mass surveillance. 

However, the published excerpt “does not give OpenAI an Anthropic-style, free-standing right to prohibit otherwise-lawful government use,” wrote Jessica Tillipman, associate dean for government procurement law studies at George Washington University’s law school. It simply states that the Pentagon can’t use OpenAI’s tech to break any of those laws and policies as they’re stated today.

The whole reason Anthropic earned so many supporters in its fight—including some of OpenAI’s own employees—is that they don’t believe these rules are good enough to prevent the creation of AI-enabled autonomous weapons or mass surveillance. And an assumption that federal agencies won’t break the law is little assurance to anyone who remembers that the surveillance practices exposed by Edward Snowden had been deemed legal by internal agencies and were ruled unlawful only after drawn-out battles (not to mention the many surveillance tactics allowed under current law that AI could expand). On this front, we’ve essentially ended up back where we started: allowing the Pentagon to use its AI for any lawful use. 

OpenAI could say, as its head of national security partnerships wrote yesterday, that if you believe the government won’t follow the law, then you should also not be confident it would honor the red lines that Anthropic was proposing. But that’s not an argument against setting them. Imperfect enforcement doesn’t make constraints meaningless, and contract terms still shape behavior, oversight, and political consequences.

OpenAI claims a second line of defense. The company says it maintains control over the safety rules governing its models and will not give the military a version of its AI stripped of those safety controls. “We can embed our red lines—no mass surveillance and no directing weapons systems without human involvement—directly into model behavior,” wrote Boaz Barak, an OpenAI employee Altman deputized to speak on the issue about X. 

But the company doesn’t specify how its safety rules for the military differ from its rules for normal users. Enforcement is also never perfect, and it is especially unlikely to be when OpenAI is rolling out these protections in a classified setting for the first time and is expected to do so in just six months.

There’s another question beneath all this: Should it be down to tech companies to prohibit things that are legal but that they find morally objectionable? The government certainly viewed Anthropic’s willingness to play this role as unacceptable. On Friday evening, eight hours before the US launched strikes in Tehran, Defense Secretary Pete Hegseth issued harsh remarks on X. “Anthropic delivered a master class in arrogance and betrayal,” he wrote, and echoed President Trump’s order for the government to cease working with the AI company after Anthropic sought to keep its model Claude from being used for autonomous weapons or mass domestic surveillance. “The Department of War must have full, unrestricted access to Anthropic’s models for every LAWFUL purpose,” Hegseth wrote.

But unless OpenAI’s full contract will reveal more, it’s hard not to see the company as sitting on an ideological seesaw, promising that it does have leverage it will proudly use to do what it sees as the right thing while deferring to the law as the main backstop for what the Pentagon can do with its tech.

There are three things to be watching here. One is whether this position will be good enough for OpenAI’s most critical employees. With AI companies spending so heavily on talent, it’s possible that some at OpenAI see in Altman’s justification an unforgivable compromise.

Second, there is the scorched-earth campaign that Hegseth has promised to wage against Anthropic. Going far beyond simply canceling the government’s contract with the company, he announced that it would be classified as a supply chain risk, and that “no contractor, supplier, or partner that does business with the United States military may conduct any commercial activity with Anthropic.” There is significant debate about whether this death blow is legally possible, and Anthropic has said it will sue if the threat is pursued. OpenAI has also come out against the move.

Lastly, how will the Pentagon swap out Claude—the only AI model it actively uses in classified operations, including some in Venezuela—while it escalates strikes against Iran? Hegseth granted the agency six months to do so, during which the military will phase in OpenAI’s models as well as those from Elon Musk’s xAI.

But Claude was reportedly used in the strikes on Iran hours after the ban was issued, suggesting that a phase-out will be anything but simple. Even if the months-long feud between Anthropic and the Pentagon is over (which I doubt it is), we are now seeing the Pentagon’s AI acceleration plan put pressure on companies to relinquish lines in the sand they had once drawn, with new tensions in the Middle East as the primary testing ground.

If you have information to share about how this is unfolding, reach out to me via Signal (username: jamesodonnell.22).

AI is rewiring how the world’s best Go players think

Burrowed in the alleys of Hongik-dong, a hushed residential neighborhood in eastern Seoul, is a faded stone-tiled building stamped “Korea Baduk Association,” the governing body for professional Go. The game is an ancient one, with sacred stature in South Korea. 

But inside the building, rooms once filled with the soft clatter of hands dipping into wooden bowls of stones now echo with mouse clicks. Players hunch over their monitors and replay their matches in an AI program. Others huddle around a Go board and debate the best next move, while coaches report how their choices stack up against the AI’s. Some sit in silence, watching AI programs play against each other. 

Ten years ago AlphaGo, Google DeepMind’s AI program, stunned the world by defeating the South Korean Go player Lee Sedol. And in the years since, AI has upended the game. It’s overturned centuries-old principles about the best moves and introduced entirely new ones. Players now train to replicate AI’s moves as closely as they can rather than inventing their own, even when the machine’s thinking remains mysterious to them. Today, it is essentially impossible to compete professionally without using AI. Some say the technology has drained the game of its creativity, while others think there is still room for human invention. Meanwhile, AI is democratizing access to training, and more female players are climbing the ranks as a result. 

For Shin Jin-seo, the top-ranked Go player in the world, AI is an invaluable training partner. Every morning, he sits at his computer and opens a program called KataGo. Nicknamed “Shintelligence” for how closely his moves mimic AI’s, he traces the glowing “blue spot” that represents the program’s suggestion for the best next move, rearranging the stones on the digital grid to try to understand the machine’s thinking. “I constantly think about why AI chose a move,” he says.

When training for a match, Shin spends most of his waking hours poring over KataGo. “It’s almost like an ascetic practice,” he says. According to a study in 2022 by the Korean Baduk League, Shin’s moves match AI’s 37.5% of the time, well above the 28.5% average the study found among all players.

“My game has changed a lot,” says Shin, “because I have to follow the directions suggested by AI to some extent.” The Korea Baduk Association says it has reached out to Google DeepMind in the hopes of arranging a match between Shin and AlphaGo, to commemorate the 10th anniversary of its victory over Lee. A spokesperson for Google DeepMind said the company could not provide information at this time. But if a new match does happen, Shin, who has trained on more advanced AI programs, is optimistic that he’d win. “AlphaGo still had some flaws then, so I think I could beat it if I target those weaknesses,” he says.

AI rewrites the Go playbook

Go is an abstract strategy board game invented in China more than 2,500 years ago. Two players take turns placing black and white stones on a 19×19 grid, aiming to conquer territory by surrounding their opponent’s stones. It’s a game of striking mathematical complexity. The number of possible board configurations—roughly 10170—dwarfs the number of atoms in the universe. If chess is a battle, Go is a war. You suffocate your enemy in one corner while fending off an invasion in another.

To train AI to play Go, a vast trove of human Go moves are fed into a neural network, a computing system that mimics the web of neurons in the human brain. AlphaGo, which was later christened AlphaGo Lee after its victory over Lee Sedol, was trained on 30 million Go moves and refined by playing millions of games against itself. In 2017, its successor, AlphaGo Zero, picked up Go from scratch. Without studying any human games, it learned by playing against itself, with moves based only on the rules of the game. The blank-slate approach proved more powerful, unconstrained by the limits of human knowledge. After three days of training, it beat AlphaGo Lee 100 games to zero. 

Google DeepMind retired AlphaGo that same year. But then a wave of open-source models inspired by AlphaGo Zero emerged. Today, KataGo is the program most widely used by professional Go players in South Korea. It’s faster and sharper than AlphaGo. It’s learned to predict not just who will win, but also who owns each point on the board at any given moment. While AlphaGo Zero pieced together its understanding of the board by looking at small sections, KataGo learned to read the whole board, developing better judgment for long-term strategies. Instead of just learning how to win, it learned to maximize its score.

The software has reshaped how people play. For hundreds of years, professional Go players have navigated the game’s astronomical complexity by developing heuristics that replaced brute calculation. Elegant opening strategies imposed abstract order on the empty grid. Invading corners early was a bad bargain. Each generation of Go players added new principles to the canon. 

But “AI has changed everything,” says Park Jeong-sang, a South Korean Go commentator. “Fundamental moves that were once considered common sense aren’t played at all today, and techniques that didn’t exist before have become popular.” 

The starkest shift has been in opening moves. Go starts on a blank grid, and the first 50 moves were canvases for abstract thinking and creativity, where players etched their personalities and philosophies. Lee Sedol fashioned provocative moves that invited chaos. Ke Jie, a Chinese player who was defeated by AlphaGo Master in 2017, dazzled with agile, imaginative moves. Now, players memorize the same strain of efficient, calculated opening moves suggested by AI. The crux of the game has shifted to the middle moves, where raw calculation matters more than creativity.

Training with AI has led to a homogenization of playing styles. Ke Jie has lamented the strain of watching the same opening moves recycled endlessly. “I feel the exact same way as the fans watching. It’s very tiring and painful to watch,” he told a Chinese news outlet in 2021. Fans revel when a player breaks from the script with offbeat moves, but those moments have become rarer. Over a third of moves by the top Go players replicate AI’s recommendations, according to a study in 2023. The first 50 moves of each game are often identical to what AI suggests, many players say. 

“Go has become a mind sport,” says Lee Sedol, who retired three years after his 2016 defeat to AlphaGo. “Before AI, we sought something greater. I learned Go as an art,” he says. “But if you copy your moves from an answer key, that’s no longer art.” 

Playing Go is no longer about charting new frontiers, some players say, but about following the dictates of a superhuman oracle. “I used to inspire fans by advancing the techniques of Go and presenting a new paradigm,” says Lee. “My reason for playing Go has vanished.”

A mysterious mind

The players who have stayed in the game are trying to reinvent their craft. But it can be hard to discern what the new principles are.

Disarmingly slight and formidably calm, Kim Chae-young, one of the top female Go players in the world, grew up learning the game from her father, who was also a professional Go player. But when AI began to reshape the game, she found herself starting over. “I needed time to abandon everything I had learned before,” says Kim who shared her screen with me as she pointed her cursor to the blue spots suggested by KataGo. “The intuition I had built up over the years turned out to be wrong.” 

As she leaned close to her monitor, her blinking screen showed the winning probabilities of each move, with no explanations. Even top players like Kim and Shin don’t understand all of AI’s moves. “It seems like it’s thinking in a higher dimension,” she says. When she tries to learn from AI, she adds, “it’s less about rationally thinking through each move, but more about developing a gut feeling—an intuition.”

Researchers are trying to discover the superhuman knowledge encoded in game-playing AI programs so that humans can learn it too. In 2024, researchers at Google DeepMind extracted new chess concepts from AlphaZero, a generalized version of AlphaGo Zero that can also play chess, and taught them to chess grandmasters using chess puzzles. The Go concepts that players have picked up from AI systems so far are “probably only a small portion of what you could potentially learn,” says Nicholas Tomlin, a computer scientist at Toyota Technological Institute at Chicago, who coauthored a study probing Go concepts encoded in AlphaGo Zero.

But extracting those lessons remains a struggle. “Top-tier players haven’t yet been able to deduce the general principles behind AI moves,” says Nam Chi-hyung, a Go professor at Myongji University. Although they can emulate AI’s moves, they have yet to glean a new paradigm for the game because its reasoning is a black box, she says. Go may be in an epistemic limbo. 

Even if AI is an opaque teacher, it’s a democratic one. It has supercharged training for female Go players, who have long been underdogs of the game. For decades, training meant studying under top male players, and the most competitive matches took place in male circles that were difficult for women to break into, says Nam. “Female players never had access to that experience,” she says. “But now they can study with AI, which has made their training environment much more favorable.” More broadly, AI has narrowed the gap between players by helping everyone perfect their opening moves.

Female players have climbed the ranks over the last few years as a result. In 2022, Choi Jeong, then the top female player in the world, became the first woman to reach the finals of a major international Go tournament. Dubbed “Girl Wrestler” for her fierce, combative style of play, she took on Shin. She lost, but the match broke new ground for women in Go. In 2024, Kim made headlines for winning the Korean Go League’s postseason playoffs. She was the only female player in the tournament. 

Training with AI has given Kim newfound confidence. Analyzing male players’ moves with AI has shattered their veneer of infallibility. “Before, I couldn’t gauge just how strong top male players were—they felt invincible. Now, I know that they make mistakes, and their moves aren’t always brilliant,” she says. “AI broke the psychological barrier.”

Go players find a new identity

Although AI has mastered Go far better than any player, fans continue to prefer watching people play. “A Go game between AI programs is not very fun for fans to watch,” says Park, the Go commentator. Such matches are too complex for fans to follow, too flawless to be thrilling, he says. 

Players can mimic AI’s opening moves, but in the middle game—where the board branches into too many possibilities to memorize—their own judgment takes over. Fans revel in watching players make mistakes and mount comebacks, exuding personality in every stone on the board. Shin’s playing style is combative but marked by machinelike poise. Kim deftly navigates  the most chaotic positions on the board. 

“In Go, every move is a choice you make, and your opponent responds with a choice of their own,” says Kim Dae-hui, 27, a Go fan and amateur player. “Watching that process unfold is fun.”

With fans like Kim still watching, Shin finds meaning in his game. “I can play a kind of Go that tells a story that only a human can,” he says. 

After his retirement, Lee searched for a new job where he could have an edge as a human. He started making board games, giving speeches, and teaching students at a university. “I’m looking for a new domain that I can enjoy and excel at,” he says.

But lately, he feels more hopeful for the game he left behind. “It’s every Go player’s dream to play a masterpiece game,” he says—a game of technical brilliance, with no mistakes, fought to a razor’s edge between evenly matched players. “It’s like a mirage,” Lee says, chuckling. “Maybe AI can help us play a masterpiece.” 

Shin hopes he can do that. To Shin, AI is a teacher, a companion, and a North Star. “I may be one of the strongest human players, but with AI around, I can’t be so arrogant,” he says. “AI gives me a reason to keep improving.”

MIT Technology Review is a 2026 ASME finalist in reporting

The American Society of Magazine Editors has named MIT Technology Review as a finalist for a 2026 National Magazine Award in the reporting category. 

The shortlisted story—“We did the math on AI’s energy footprint. Here’s the story you haven’t heard”—is part of the publication’s Power Hungry package on AI’s energy burden. 

AI is often described as a black box, but it’s not just its inner workings that are mysterious. Leading AI companies have kept figures on energy use closely guarded, making it hard to determine its climate impact. In a rigorous investigation, senior AI reporter James O’Donnell and senior climate reporter Casey Crownhart spent six months digging through hundreds of pages of reports, interviewing experts, and crunching the numbers. 

The team drilled down into the energy cost of a single prompt, and then zoomed out to build a broader picture illustrating the potential impacts of AI’s current and future energy demand. Their work revealed just how big AI’s energy footprint is, where that energy comes from, and who will pay for it. In the months following the project’s publication, major AI companies including Open AI, Mistral, and Google published details about their models’ energy and water usage. 

The 2026 awards will be presented in New York City on May 19. 

America was winning the race to find Martian life. Then China jumped in.

To most people, rocks are just rocks. To geologists, they are much, much more: crystal-filled time capsules with the power to reveal the state of the planet at the very moment they were forged. 

For decades, NASA had been on a time capsule hunt like none other—one across Mars.

Its rovers have journeyed around a nightmarish ocher desert that, billions of years ago, was home to rivers, lakes, perhaps even seas and oceans. They’ve been seeking to answer a momentous question: Once upon a time, did microbial life wriggle across its surface? 

Then, in July 2024, after more than three years on the planet, the Perseverance rover came across a peculiar rocky outcrop. Instead of the usual crystals or layers of sediment, this one had spots. Two kinds, in fact: one that looked like poppy seeds, and another that resembled those on a leopard. It’s possible that run-of-the-mill chemical reactions could have cooked up these odd features. But on Earth, these marks are almost always produced by microbial life.

To put it plainly: Holy crap.

Sure, those specks are not definitive proof of alien life. But they are the best hint yet that life may not be a one-off event in the cosmos. And they meant the most existential question of all—Are we alone?—might soon be addressed. “If you do it, then human history is never the same,” says Casey Dreier, chief of space policy at the Planetary Society, a nonprofit that promotes planetary exploration and defense and the search for extraterrestrial life.

But the only way to confirm whether these seeds and spots are the fossilized imprint of alien biology is to bring a sample of that rock home to study. 

Perseverance was the first stage of an ambitious scheme to do just that—in effect, to pull off a space heist. The mission—called Mars Sample Return and planned by the US, along with its European partners—would send a Rube Goldberg–like series of robotic missions to the planet to capture pristine rocks. The rover’s job was to find the most promising stones and extract samples; then it would pass them to another robot—the getaway driver—to take them off Mars and deliver them to Earth.

But now, just over a year and a half later, the project is on life support, with zero funding flowing in 2026 and little backing left in Congress. As a result, those oh-so-promising rocks may be stuck out there forever.

“We’ve spent 50 years preparing to get these samples back. We’re ready to do that,” says Philip Christensen, a planetary scientist at Arizona State University who works closely with NASA. “Now we’re two feet from the finish line—Oh, sorry, we’re not going to complete the job.”

This also means that, in the race to find evidence of alien life, America has effectively ceded its pole position to its greatest geopolitical rival: China. The superpower is moving full steam ahead with its own version of MSR. It’s leaner than America and Europe’s mission, and the rock samples it will snatch from Mars will likely not be as high quality. But that won’t be the headline people remember—the one in the scientific journals and the history books. “At the rate we’re going, there’s a very good chance they’ll do it before we do,” laments Christensen. “Being there first is what matters.”  

Of course, any finding of extraterrestrial life advances human knowledge writ large, no matter the identity of the discoverers. But there is the not-so-small issue of pride in an already heated nationalistic competition, not to mention the fact that many scientists in America (to say nothing of US lawmakers) don’t necessarily want their future research and scientific progress subject to a foreign gatekeeper. And even for those not especially concerned about potentially unearthing alien microbes, MSR and the comparable Chinese mission are technological stepping stones toward a long-held dream shared by many beyond Elon Musk: getting astronauts onto the Red Planet and, eventually, setting up long-term bases for astronauts there. It’d be a huge blow to show up only after a competitor had already set up shop … or not to get there at all. 

“If we can’t do this, how do we think we’re gonna send humans there and get back safely?” says Victoria Hamilton, a planetary geologist at the Southwest Research Institute in Boulder, Colorado, who is also the chair of the NASA-affiliated Mars Exploration Program Analysis Group. 

Or as Paul Byrne, a planetary scientist from the Washington University in St. Louis, puts it: “If you’re going to bring humans back from Mars, you sure as shit have to figure out how to bring the samples back first.” 

Nearly a dozen project insiders and scientists in both the US and China shared with me the story of how America blew its lead in the new space race. It’s full of wild dreams and promising discoveries—as well as mismanagement, eye-watering costs, and, ultimately, anger and disappointment.    


“I spent most of my career studying Mars,” says Christensen. There are countless things about it that bewitch him. But by examining it, he suspects, we’ll get further than ever in the Homeric investigation of how life began.

Sure, the Mars of today is a postapocalyptic wasteland, an arid and cold desert bathed in lethal radiation. But billions of years ago, water lapped up against the slopes of fiery volcanoes that erupted under a clement sky. Then its geologic interior cooled down so quickly, changing everything. Its global magnetic field collapsed like a deflating balloon, and its protective atmosphere was stripped away by the sun. 

NASA first touched down on Mars in 1976 with two Viking landers. The Mars Odyssey spacecraft has been orbiting the planet since 2001 and produced this image of Valles Marineris, which is 10 times longer, 5 times deeper, and 20 times wider than the Grand Canyon.
NASA/ARIZONA STATE UNIVERSITY VIA GETTY IMAGES

Its surface is now remarkably hostile to life as we know it. But deep below ground, where it’s shielded from space, and where it’s warmer and wetter, there could maybe be microbes inching about.

Scientists have long possessed several Martian meteorites that have been flung our way, but none of them are pristine; they were all damaged by cosmic radiation midflight, before getting scorched in Earth’s atmosphere. Plus, there’s another problem: “We currently have no rocks from Mars that are sedimentary, the rock type likely to contain fossils,” says Sara Russell, a planetary scientist at London’s Natural History Museum. 

For those, humans (or robots) would need to get on the ground.

NASA first made the stuff of sci-fi films a reality 50 years ago, when two Viking landers touched down on the planet in 1976. One of their experiments dropped some radioactively tagged nutrients into soil samples, the idea being that if any microbes were present, they’d gobble up the nutrients and burp out some radioactive waste gas that the landers could detect. Tantalizingly, this experiment hinted that something microbe-like was interacting with those nutrients—but the result was inconclusive (and today most scientists don’t suspect biology was responsible).

Still, it was enough to elevate scientists’ curiosity about the genuine possibility of Martian life. Over the coming decades, America sent an ever-expanding fleet of robots to Mars—orbiting spacecraft, landers, and wheeled rovers. But no matter how hard they studied their adoptive planet’s rocks, they weren’t designed to definitively detect signs of life. For that, promising-looking rocks would need to be captured and, somehow, shuttled back to labs on Earth in carefully sealed containers. 

A 2023 plan from NASA and the European Space Agency to safely transport pristine samples received from Mars.
NASA/JPL-CALTECH

This became a top priority for the US planetary science community in 2003, following the publication of the first Planetary Decadal Survey, a census conducted at NASA’s request. The scientific case for the mission was clear—even to the people who didn’t think they’d find signs of life. “I bet there isn’t life on Mars. But if there is, or was, that would be an incredibly important discovery,” says Christensen. And if not, “Why not?” 

He adds: “We may understand more about why life started on Earth by understanding why it may not have started on Mars. What was that key difference between those two planets?”

And so, MSR was born. America went all in, and the European Space Agency joined the team. Over the next decade or so, a complex plan was drawn up. 

First, a NASA rover would land on Mars in a spot that once was potentially habitable—later determined to be Jezero Crater. It would zip about, look for layered rocks of the sort that you’d find in lakes and riverbeds, extract cores of them, and cache them in sealed containers. Then a second NASA spacecraft would land on Mars, receive the rover’s sample tubes (in one of several different ways), and transfer the samples to a rocket that would launch them into Martian orbit. A European-provided orbiter would catch that rocket like a baseball glove before returning home and dropping the rocks into Earth’s atmosphere, where they would be guided, via parachute, to eagerly awaiting scientists no later than the mid-2030s.

Two messages were encoded on the 70-foot parachute used by the Perseverance rover as it descended toward Mars. This annotated image shows how NASA systems engineer Ian Clark used a binary code to spell out “Dare Mighty Things” in the orange and white strips; he also included the GPS coordinates for the mission’s headquarters at the Jet Propulsion Laboratory.
NASA/JPL-CALTECH VIA AP IMAGES

“Put simply, this is the most scientifically careful sample collection mission possible, conducted in one of the most promising places on Mars to look for signs of past life,” says Jonathan Lunine, the chief scientist at NASA’s Jet Propulsion Laboratory in California. “And, of course, should evidence of life be found in the sediments, that would be an historic discovery.”

It got off to an auspicious start. On July 30, 2020, in the throes of the covid-19 pandemic, NASA’s Perseverance rover launched atop a rocket from Florida’s Cape Canaveral. The NASA administrator at the time, Jim Bridenstine, didn’t mince words: “We are in extraordinary times right now,” he told reporters, “yet we have in fact persevered, and we have protected this mission because it is so important.” 

But just earlier that same month, the mission to Mars had turned into a race. China was now prepping its own sample return spacecraft.

And that’s when things for MSR started to unravel. 

XINMEI LIU

China was comparatively late to develop a competitive space program, but once it began doing so, it wasted no time. In 2003, it first sent one of its astronauts into space, via its own bespoke rocket; in the two decades since, it has launched its own space station and sent multiple uncrewed spacecraft to the moon—first orbiters, then landers—as part of its Chang’e Project, named after a lunar goddess. 

But a real turning point for China’s interplanetary ambitions came in 2020, the same year as Perseverance’s launch to Mars. 

That December, Chang’e-5 touched down in the moon’s Ocean of Storms, a realm of frozen lava 1,600 miles long. It grabbed some 2-billion-year-old rocks, put them in a rocket, and blasted them into the firmament. The samples were captured by a small orbiting spacecraft; crucially, the idea was not all that dissimilar from how MSR imagined catching its own samples, baseball-glove style. China’s lunar haul was then dropped off back on Earth just before Christmas. It marked the first time since 1976 that samples had been returned from the moon, and the mission was seamless. 

two labelled vials of soil next to a small ruler for scale
China brought back soil samples from the moon’s Ocean of Storms during its Chang’e-5 mission, marking the first time since 1976 that samples had been returned from the moon.
WIKIMEDIA COMMONS

That same year, China made its first foray toward Mars. The project was called Tianwen-1, meaning “Questions to Heaven”—the first in a new series of audacious space missions to the Red Planet and orbiting asteroids. While its success was far from guaranteed, China was willing to kick into high gear immediately, sending both an orbiting spacecraft and a rover to Mars at the same time. No other country had ever managed to perform this act of spaceflight acrobatics on its first try.


Just as China ramped up its space schemes, some people in the scientific community began to wonder if NASA was (inadvertently) promising too much with MSR—and whether the heist would be worth the cost.

In 2020, the price tag for the program had jumped from an already expensive $5.3 billion to an estimated $7 billion. (For context, NASA’s Near-Earth Object Surveyor mission, which is currently being pieced together, has a price tag of around $1.2 billion. This space observatory is designed to find Earthbound asteroids and is tasked with defending all 8 billion of us from a catastrophic impact.)

But Perseverance was already on its way to Mars. It wasn’t as if this expensive train could go back to the station. The project’s advocates just hoped it’d actually make it there in one piece. 

While the US had previously entered Martian orbit successfully, several other entry, descent, and landing attempts on the planet had ended in explosive disaster; the primary antagonist is the Martian atmosphere, which can cause spacecraft to tumble wildly out of control or heat up and ignite. Perseverance would be traveling at nearly 12,500 miles per hour as it entered Mars’s airspace, and to land it’d need to decelerate, deploy a parachute, fire several rockets, and pilot itself to the skies above Jezero Crater—before a levitating crane would drop off the actual rover. 

Thankfully, Perseverance’s deployment went off without a hitch. On February 18, 2021, Mars became its new home—and the rover’s makers hugged, high-fived, and whooped for joy in NASA’s flight control room. 

As Lori Glaze, then director of NASA’s planetary science division, said at the time, “Now the fun really starts.”

Members of NASA’s Perseverance rover team at the Jet Propulsion Laboratory in Pasadena, California, celebrate after receiving confirmation that the spacecraft successfully touched down on Mars in February 2021.
NASA/BILL INGALLS

That very same month, China arrived at Mars’s doorstep for the first time. 

On February 10, 2021, Tianwen-1 began to orbit the planet. Then, on May 14, it slipped a drop shipment through the spacecraft-frying atmosphere to deliver a rover onto an expansive landscape called Utopia Planitia—giving Perseverance a neighbor, albeit one 1,200 miles away.

This explorer was nowhere near as sophisticated as Perseverance, and its assignment was a far cry from a sample return mission. It had some cameras and scientific instruments for studying its environment, making it comparable to one of NASA’s older rovers. It was also supposed to operate for just three months (though it ended up persisting for an entire year before being fatally smothered by pernicious Martian dust). 

Nevertheless, Tianwen-1 was a remarkable achievement for China, one that the US couldn’t help but applaud. “This is a really big deal,” said Roger Launius, then NASA’s chief historian.  

And even if grabbing pieces of Mars was increasingly likely in China’s future, it was already happening in the present for the US. The race, the Americans thought, was over before it had even begun … right? 


Over the next few years, Perseverance went on an extraterrestrial joyride. It meandered through frozen flows of lava and journeyed over fans of sediment once washed about by copious liquid water. It pulled out rocks that preserved salty, muddy layers—exactly the environment that, on Earth, would be teeming with microorganisms and organic matter. 

“Jezero Crater clearly meets the astrobiological criterion for a sampling site where life may once have existed,” says Lunine from NASA’s Jet Propulsion Lab. “Rocks of broadly similar age and setting on Earth contain some of the earliest evidence for life on our own planet.” 

The Perseverance rover has been on an extraterrestrial joyride since 2021, drilling holes in promising looking space rocks that it hopes could be teeming with microorganisms and organic matter.
AP IMAGES

Then, in September 2023, as Perseverance was trundling across the ruins of what may once have been a microbial metropolis, an independent panel of researchers published a report that made it clear, in no uncertain terms, that MSR was the opposite of okay.

They found that the project was too decentralized among the nation’s plethora of NASA centers, leaving confusion as to who was actually in charge. And at its current pace, MSR wouldn’t get its Mars rocks back home until the 2040s at the earliest—as much as a whole decade later than initial estimates. And it would cost as much as $11 billion, more than doubling the initial tab. 

“MSR was established with unrealistic budget and schedule expectations from the beginning,” the report reads. “MSR was also organized under an unwieldy structure. As a result, there is currently no credible, congruent technical, nor properly margined schedule, cost, and technical baseline that can be accomplished with the likely available funding.”

Members of Congress started to wonder aloud whether MSR should be canceled outright, and the scientific community that had once so enthusiastically supported the mission faced a moment of reckoning. 

Byrne, the planetary scientist from the Washington University in St. Louis, had always been something of a rebel, never really a fan of NASA’s multi-decadal, over-the-top fascination with Mars. The solar system, he argued, is filled with curious worlds to explore—especially Venus, another nearby rocky world that was once rather Earth-like. Couldn’t we spare some of NASA’s budget to make sure we explore Venus, too?

Still, like many other critical colleagues, Byrne did not want to see MSR put down. The report’s findings didn’t change the fact that Perseverance was dutifully working around the clock to accomplish the mission’s opening stages. What would be the point of gathering all those samples if they were going to be left to stay on Mars? The community, Byrne explains, just needed to answer one question: “How do you do this in a way that’s faster and cheaper?” 

In April 2024, NASA publicly sought help from its industry partners in the space sector: Could anyone come up with a way to save MSR? Various players with spaceflight experience, like Lockheed Martin, sent in proposals for consideration. 

Then, just a few months later in July 2024, Perseverance came in clutch, finding those special leopard-spotted and speckled rocks in an old river valley—a sign of hope that NASA had been desperately seeking. Now the agency’s request for help was all the more urgent—these rocks had to get home. After various panels assessed plans that could effectively save MSR, two potential options for a faster, leaner, less expensive version were previewed at a January 2025 press briefing. 

One option brought in tried-and-tested tech: Since Perseverance had been safely deployed onto the surface of Mars using a hovering platform known as a sky crane, it was proposed that the sample-gathering lander for MSR could also be dropped off using a sky crane, which would simplify this step and reduce the overall cost of the program. The other suggestion was that the lander could be delivered to Mars via a spaceship from a commercial spaceflight company. The lander design itself could also be streamlined, and tweaks could be made to the rocket that would launch the samples back into space.

The proposals needed greater study, but everyone’s spirits were lifted by the fact these plans could, at least theoretically, get samples back in the 2030s, not the 2040s. And, crucially, “it was possible to get the cost down,” says Jack Mustard, an Earth and planetary scientist at Brown University and a member of one of the two proposal-reviewing panels. Still, it didn’t save a lot: They could do MSR for $8 billion.

“What we came up with was very reasonable, rational, much simpler,” says Christensen, who was part of the same review panel. “And $8 billion is about the right amount it would take to guarantee that it’s going to work.”

XINMEI LIU

While the US became increasingly consumed with its own interplanetary woes, China was riding high.

In June 2024, the sixth installment in the Chang’e project made history. It was another lunar sample return mission, but this one did something nobody had ever done in the history of spaceflight: It landed on the difficult-to-reach, out-of-view far side of the moon and snagged samples from it. 

China made it look effortless when a capsule containing matter from this previously untouched region safely landed in Inner Mongolia. Long Xiao, a planetary geoscientist at the China University of Geosciences, told reporters at the time that the mission’s success was “a cause for celebration for all humanity.” 

But it was also effectively a bombshell for NASA. Yes, the moon is much closer to Earth, and it doesn’t have a spaceship-destroying atmosphere like Mars. But China was speedrunning through the race while America was largely looking the other way.

Then, in May 2025, China launched Tianwen-2. Its destination was not Mars but a near-Earth asteroid. The plan is that it will scoop up some of the space rock’s primordial pebbles later this year and deliver them back to Earth in late 2027. In light of China’s past successes, many suspect it’ll nail this project, too. 

Tianwen-2 on the launchpad
China’s Tianwen missions, meaning “Questions to Heaven,” aim to explore both Mars and orbiting asteroids. The Tianwen-2 probe blasted off in May 2025, headed toward a near-Earth asteroid for a sample-return mission.
VCG/VCG VIA AP IMAGES

But perhaps the biggest blow to the US came in June 2025: China revealed its formal designs on returning samples from Mars—and potentially addressing the existence of life elsewhere in the cosmos. Chinese researchers outlined a bold plan for Tianwen-3 in the journal Nature Astronomy. “Searching for signs of life, or astrobiology studies, are the first priority,” says Yuqi Qian, a lunar geologist at the University of Hong Kong. And while many observers had long been cognizant of this ambition, seeing it so clearly spelled out in academic writing made it real.

“The selection of the landing site is still ongoing,” says Li Yiliang, an astrobiologist at the University of Hong Kong, an author of the Tianwen-3 study, and a member of the spacecraft’s landing site selection team. But the paper notes, in no uncertain terms, that the mission will move at a breakneck pace. “The aim of China’s Mars sample return mission, known as Tianwen-3, is to collect at least 500g of samples from Mars and return them to Earth around 2031.”

2031. Even on its original, speedier timeline, America’s MSR plan wouldn’t get samples back by that date. So how is China planning to pull it off?

Qian explains that Tianwen-3 is building on the success of the lunar sample return program. Doing something similar for Mars is a rather giant technological leap (requiring two rockets, not one)—but, he argues, “the technologies here are similar.” 

The plan is for a duet of rockets to blast off from Earth in 2028. The first will contain the lander-ascender combination, or LAC. The second is the orbiter-returner combination, or ORC. The LAC will get to Mars and deploy a lander as well as a small helicopter, which will scout promising locations around the landing site while using a claw to bring several small samples back to the lander.

China’s Tianwen-3 mission is searching for signs of Martian life with an eye toward having samples back home sometime in 2031.
中国新闻社 VIA WIKIMEDIA COMMONS

The LAC will then travel to the most promising site. The lander’s drill, which can get down to around seven feet below the surface, is the most important part of the mission. At that depth, there are greater odds of capturing signs of past life. When at least 500 grams of pristine rocks have been loaded aboard the lander, the samples will be launched into space, where the orbiter will be waiting to capture them and send them back home sometime in 2031.

“The returned samples will be quarantined strictly in an under-planning facility near Hefei city,” says Yiliang. And there, in those bio-secure labs, scientists might very well find the first clear signs of alien life, past or present.


The very same month that Chinese researchers published their daring plans for returning Mars samples, the new Trump administration released a draconian NASA budget for Congress to consider—one that sparked panic across the planetary science community.

If enacted, it would have been a historic catastrophe for the venerable space agency, giving NASA its smallest budget since 1961. This would have forced it to let go of a huge number of staffers, slash its science program budget in half, and terminate 19 missions currently in operation. MSR was in the crosshairs, too. 

“Grim is the word,” says Dreier of the Planetary Society. 

Over the next few months, Congress pushed back on the potential gutting of NASA, but largely to save ongoing solar system exploration missions. MSR was not considered an active effort; Perseverance was effectively a scientific scout acting independently by this point. A counterproposal by the House offered up $300 million for MSR, but no policymaker was cheerleading for it. (The US Office of Management and Budget, the House Committee on Science, Space, and Technology, and the office of Sen. Ted Cruz of Texas, who chairs the Senate Committee on Commerce, Science, and Transportation did not respond to requests for comment.)

“Mars Sample Return doesn’t seem to have very many advocates right now,” says Byrne. The project “isn’t featuring in anyone’s conversation at the moment, with all of the existential shit that’s happening to NASA.” Everyone working on a NASA mission hoped that they, and their spacecraft, would survive the onslaught. As Byrne adds: “[People are] just trying to keep their heads down.”

Researchers in America suddenly found themselves at an inflection point. “The attack on science, and the attack on NASA science, has been very successful, in that it has completely demoralized the science community,” says Christensen. “Everyone’s in a state of shock.” 

When I contacted NASA in July about the state of MSR, which was then in the middle of a months-long limbo, I was told that experts weren’t available to comment. Roxana Bardan, a spokesperson, instead sent a statement: “Under President Trump’s America First agenda, NASA is committed to sustained U.S. space leadership. We will continue to innovate, explore, and excel to ensure American preeminence in space.” (The agency did not respond to a follow-up request for comment.) 

That notion stood in direct contrast to what Christensen told me around the same time. “The US … has led the exploration of Mars for 50 years,” he said. “And as we approach one of the key discovery points, we’re about to concede that leadership to someone else.”


From China’s perspective, the fumbling of MSR is more confusing than anything else. “NASA has so well prepared for her MSR mission in both technology and science, and I and my colleagues have learned so much from NASA’s scientific communities,” says Yiliang. 

And if China wins the race because America decided to shoot itself in the foot? “This is sad,” he says. “If this comes true, I believe the Chinese will not be that happy to win the race in this way.”

Tianwen-3 will still have to overcome many of the same hurdles as MSR. Nobody, for example, has autonomously launched a rocket of any kind off the surface of Mars. But many believe the Chinese can succeed, even at their program’s superspeed. Christensen, for one, fully expected several of their past robotic missions to the moon and Mars to fail—but “the fact that they pulled it off the first time really says a lot about their engineering capability,” he says. 

Mustard agrees: “They know how to land; they know how to leave. I have a lot of confidence that they’ve learned enough from the lunar work that they’ll be able to do it.”

Plus, Tianwen-3’s architecture is simpler than the US-European mission. It has fewer components, and fewer points of potential failure. This also means, though, that the quality of the loot will be somewhat lacking. Tianwen-3 will sample from only one small patch of Mars. Conversely, Perseverance is roving around a vast and geologically diverse landscape, sampling as it goes, which would translate to “literally orders of magnitude more science than what will come from the Chinese samples,” says Christensen.

But China could serendipitously land on a biologically rich patch of the planet. As the Southwest Research Institute’s Hamilton says, the mission could “pick up something entirely unexpected and, you know, miraculous.” 

The likeliest outcome is still that neither nation finds fossilized microbes, but that China brings back rocks from Mars first. At the end of the day, that’s what Americans (and Europeans) will hear: “You’re second. You lost,” says Mustard.

Like many of his colleagues, Christensen is irked by the thought of losing the race to Mars, because it would be such an own goal. The US has been sending robots over there for decades and investing billions in forging the technology that would be required to make MSR a success. And suddenly “the Chinese come along and say, Thank you very much, we’ll take all of that information—we’ll build one mission and go and do what you guys did the groundwork for,” Christensen says. “As a taxpayer, I’m like: It just seems foolish to me.”

Even the MSR skeptics concede that this kind of loss would have sweeping ramifications. Byrne worries that if something like MSR can be snuffed out so easily, what’s to say the next big mission—to Jupiter, Saturn, and beyond—won’t suffer the same ignoble fate? In other words, the death of MSR would severely damage “the ability of the planetary community to dream big,” he says. “If we don’t pull this off, what does that mean? Are we not going to do big, expensive, difficult things?”

Another big, expensive, difficult thing? Putting humans on Mars. Both critics and advocates of MSR largely agree it is an invaluable dress rehearsal. Making sure you can safely launch a rocket off Mars is a necessary prerequisite to ensuring that an array of equipment can survive for a long time on the planet’s lethal surface.

China, too, has explicitly acknowledged this. As one of the first lines of the Tianwen-3 study states, “Mars is the most promising planet for humanity’s expansion beyond Earth, with its potential for future habitability and accessible resources.” 

Though such expansion is still of course a far-future dream, it’s not hard to see how losing the race here would put the US at a huge disadvantage. Members of America’s planetary science community say that to try to sway politicians in their favor, they have framed MSR as a national security issue. But they haven’t had much luck. “We’ve been in discussions with decision-makers who have never heard that perspective before,” says the Planetary Society’s Dreier. 

“It is surprising that doesn’t have more weight,” adds Mustard. 

Despite months of purgatory, it still stung when the coup de grâce arrived in January. In the draft for a must-pass spending bill, House and Senate appropriators spared NASA from the harshest proposed cuts, thereby saving dozens of spaceflight missions and preserving much of the agency’s planetary science output. But the bill provided absolutely zero political or financial support for MSR. There it was, in black and white: America’s plans to perform a history-making heist on Mars were dead. The bill became law in January and Perseverance, it seems, is now destined to rove alone on the Red Planet until its nuclear battery burns out. 

This austere reality clashes with the soaring aspirations outlined in the first Planetary Decadal Survey, written just over two decades ago. It stated that the US exploration of the solar system “has a proud past, a productive present, and an auspicious future.” It also noted that “answers to profound questions about our origins and our future may be within our grasp.” 

Now the answers have all but slipped away. Even though Perseverance continues to roam, it’s increasingly likely we’ll never see those promising bespeckled rocks with human eyes, let alone any other rocks the rover finds intriguing. It is far easier to imagine that in the near future, perhaps in the early 2030s, Perseverance will point its camera up at the night sky above Jezero Crater. It will catch a small glimmer: Tianwen-3’s orbiter, preparing to send ancient rocks back to Earth. Meanwhile, Perseverance’s own sample tubes—perhaps some containing signs of life—will be trapped on the Martian surface, gathering dust.

Sample tubes collected by the Perseverance rover may never make it home from the Martian surface.
NASA/JPL-CALTECH/MSSS

It is a sobering thought for Christensen. “We’ll wake up one day and go: What the hell?” he says. “How did we let this happen?”

Robin George Andrews is an award-winning science journalist and doctor of volcanoes based in London. He regularly writes about the Earth, space, and planetary sciences, and is the author of two critically acclaimed books: Super Volcanoes (2021) and How to Kill An Asteroid (2024).