Jan 31 2025
This quantum computer built on server racks paves the way to bigger machines

A Canadian startup called Xanadu has built a new quantum computer it says can be easily scaled up to achieve the computational power needed to tackle scientific challenges ranging from drug discovery to more energy-efficient machine learning.

Aurora is a “photonic” quantum computer, which means it crunches numbers using photonic qubits—information encoded in light. In practice, this means combining and recombining laser beams on multiple chips using lenses, fibers, and other optics according to an algorithm. Xanadu’s computer is designed in such a way that the answer to an algorithm it executes corresponds to the final number of photons in each laser beam. This approach differs from one used by Google and IBM, which involves encoding information in properties of superconducting circuits. 

Aurora has a modular design that consists of four similar units, each installed in a standard server rack that is slightly taller and wider than the average human. To make a useful quantum computer, “you copy and paste a thousand of these things and network them together,” says Christian Weedbrook, the CEO and founder of the company. 

Ultimately, Xanadu envisions a quantum computer as a specialized data center, consisting of rows upon rows of these servers. This contrasts with the industry’s earlier conception of a specialized chip within a supercomputer, much like a GPU.

But this work, which the company published last week in Nature, is just a first step toward that vision. Aurora used 35 chips to construct a total of 12 quantum bits, or qubits. Any useful applications of quantum computing proposed to date will require at least thousands of qubits, or possibly a million. By comparison, Google’s quantum computer Willow, which debuted last year, has 105 qubits (all built on a single chip), and IBM’s Condor has 1,121.

Devesh Tiwari, a quantum computing researcher at Northeastern University, describes Xanadu’s progress in an analogy with building a hotel. “They have built a room, and I’m sure they can build multiple rooms,” he says. “But I don’t know if they can build it floor by floor.”

Still, he says, the work is “very promising.” 

Xanadu’s 12 qubits may seem like a paltry number next to IBM’s 1,121, but Tiwari says this doesn’t mean that quantum computers based on photonics are running behind. In his opinion, the number of qubits reflects the amount of investment more than it does the technology’s promise. 

Photonic quantum computers offer several design advantages. The qubits are less sensitive to environmental noise, says Tiwari, which makes it easier to get them to retain information for longer. It is also relatively straightforward to connect photonic quantum computers via conventional fiber optics, because they already use light to encode information. Networking quantum computers together is key to the industry’s vision of a “quantum internet” where different quantum devices talk to each other. Aurora’s servers also don’t need to be kept as cool as superconducting quantum computers, says Weedbrook, so they don’t require as much cryogenic technology. The server racks operate at room temperature, although photon-counting detectors still need to be cryogenically cooled in another room. 

Xanadu is not the only company pursuing photonic quantum computers; others include PsiQuantum in the US and Quandela in France. Other groups are using materials like neutral atoms and ions to construct their quantum systems. 

From a technical standpoint, Tiwari suspects, no single qubit type will ever be the “winner,” but it’s likely that certain qubits will be better for specific applications. Photonic quantum computers, for example, are particularly well suited to Gaussian boson sampling, an algorithm that could be useful for quickly solving graph problems. “I really want more people to be looking at photonic quantum computers,” he says. He has studied quantum computers with multiple qubit types, including photons and superconducting qubits, and is not affiliated with a company. 

Isaac Kim, a physicist at the University of California, Davis, points out that Xanadu has not demonstrated the error correction ability many experts think a quantum computer will need in order to do any useful task, given that information stored in a quantum computer is notoriously fragile. 

Weedbrook, however, says Xanadu’s next goal is to improve the quality of the photons in the computer, which will ease the error correction requirements. “When you send lasers through a medium, whether it’s free space, chips, or fiber optics, not all the information makes it from the start to the finish,” he says. “So you’re actually losing light and therefore losing information.” The company is working to reduce this loss, which means fewer errors in the first place. 

Xanadu aims to build a quantum data center, with thousands of servers containing a million qubits, in 2029.

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Jan 31 2025
Three questions about the future of US climate tech under Trump

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

Donald Trump has officially been in office for just over a week, and the new administration has hit the ground running with a blizzard of executive orders and memos.

Some of the moves could have major effects for climate change and climate technologies—for example, one of the first orders Trump signed signaled his intention to withdraw from the Paris Agreement, the major international climate treaty.

The road map for withdrawing from the Paris agreement is clear, but not all the effects of these orders are quite so obvious. There’s a whole lot of speculation about how far these actions reach, which ones might get overturned, and generally what comes next. Here are some of the crucial threads that I’m going to be following.

Will states be able to set their own rules on electric vehicles? 

It’s clear that Donald Trump isn’t a fan of electric vehicles. One of the executive orders issued on his first day in office promised to eliminate the “electric vehicle (EV) mandate.” 

The federal government under Biden didn’t actually have an EV mandate in place—rather, Trump is targeting national support programs, including subsidies that lower the cost of EVs for drivers and support building public chargers. But that’s just the beginning, because the executive order will go after states that have set their own rules on EVs. 

While the US Environmental Protection Agency does set some rules around EVs through what are called tailpipe standards, last year California was granted a waiver that allows the state to set its own, stricter rules. The state now requires that all vehicles sold there must be zero-emissions by 2035. More than a dozen states quickly followed suit, setting a target to transition to zero-emissions vehicles within the next decade. That commitment was a major signal to automakers that there will be demand for EVs, and a lot of it, soon.

Trump appears to be coming after that waiver, and with it California’s right to set its own targets on EVs. We’ll likely see court battles over this, and experts aren’t sure how it’s going to shake out.

What will happen to wind projects?

Wind energy was one of the most explicit targets for Trump on the campaign trail and during his first few days in office. In one memo, the new administration paused all federal permits, leases, and loans for all offshore and onshore wind projects.

This doesn’t just affect projects on federal lands or waters—nearly all wind projects typically require federal permits, so this could have a wide effect.

Even if the order is temporary or doesn’t hold up in court, it could be enough to chill investment in a sector that’s already been on shaky ground. As I reported last year, rising costs and slow timelines were already throwing offshore wind projects off track in the US. Investment has slowed since I published that story, and now, with growing political opposition, things could get even rockier.

One major question is how much this will slow down existing projects, like the Lava Ridge Wind Project in Idaho, which got the green light from the Biden administration before he left office. As one source told the Washington Post, the new administration may try to go after leases and permits that have already been issued, but “there may be insufficient authority to do so.”

What about the money?

In an executive order last week, the Trump administration called for a pause on handing out the funds that are legally set aside under the Inflation Reduction Act and the Bipartisan Infrastructure Law. That includes hundreds of billions of dollars for climate research and infrastructure.

This week, a memo from the White House called for a wider pause on federal grants and loans. This goes way beyond climate spending and could affect programs like Medicaid. There’s been chaos since that was first reported; nobody seems to agree on what exactly will be affected or how long the pause was supposed to last, and as of Tuesday evening, a federal judge had blocked that order.

In any case, all these efforts to pause, slow, or stop federal spending will be a major source of fighting going forward. As for effects on climate technology, I think the biggest question is how far the new administration can and will go to block spending that’s already been designated by Congress. There could be political consequences—most funds from the Inflation Reduction Act have gone to conservative-leaning states.  

As I wrote just after the election in November, Donald Trump’s return to office means a sharp turn for the US on climate policy, and we’re seeing that start to play out very quickly. I’ll be following it all, but I’d love to hear from you. What do you most want to know more about? What questions do you have? If you work in the climate sector, how are you seeing your job affected? You can email me at casey.crownhart@technologyreview.com, message me on Bluesky, or reach me on Signal: @casey.131.

Now read the rest of The Spark

Related reading

EVs are mostly set for solid growth this year, but what happens in the US is still yet to be seen, as my colleague James Temple covered in a recent story. 

The Inflation Reduction Act set aside hundreds of billions of dollars for climate spending. Here’s how the law made a difference, two years in.

For more on Trump’s first week in office, check out this news segment from Science Friday (featuring yours truly). 

small chip rises away from large chip

STEPHANIE ARNETT/ MIT TECHNOLOGY REVIEW | RAWPIXEL

Another thing

DeepSeek has stormed onto the AI scene. The company released a new reasoning model, called DeepSeek R1, which it claims can surpass the performance of OpenAI’s ChatGPT o1. The model appears to be incredibly efficient, which upends the idea that huge amounts of computing power, and energy, are needed to drive the AI revolution. 

For more, check out this story on the company and its model from my colleague Caiwei Chen, and this look at what it means for the AI industry and its energy claims from James O’Donnell. 

Keeping up with climate

A huge surge in clean energy caused China’s carbon emissions to level off in 2024. Whether the country’s emissions peak and begin to fall for good depends on what wins in a race between clean-energy additions and growth in energy demand. (Carbon Brief)

In a bit of good news, heat pumps just keep getting hotter. The appliances outsold gas furnaces in the US last year by a bigger margin than ever. (Canary Media)
→ Here’s everything you need to know about heat pumps and how they work. (MIT Technology Review)

People are seeking refuge from floods in Kentucky’s old mountaintop mines. Decades ago, the mines were a cheap source of resources but devastated local ecosystems. Now people are moving in. (New York Times)

An Australian company just raised $20 million to use AI to search for key minerals. Earth AI has already discovered significant deposits of palladium, gold, and molybdenum. (Heatmap News)

Some research suggests a key ocean current system is slowing down, but a new study adds to the case that there’s no cause to panic … yet. The new work suggests that the Atlantic Meridional Overturning Circulation, or AMOC, hasn’t shown long-term weakening over the past 60 years. (Washington Post)
→ Efforts to observe and understand the currents have shown they’re weirder and more unpredictable than expected. (MIT Technology Review)

Floating solar panels could be a major resource in US energy. A new report finds that federal reservoirs could hold enough floating solar to produce nearly 1,500 terawatt-hours of electricity, enough to power 100 million homes each year. (Canary Media)

What sparked the LA wildfires is still a mystery, but AI is hunting for clues. Better understanding of what causes fires could be key in efforts to stop future blazes. (Grist)

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Jan 30 2025
Three reasons Meta will struggle with community fact-checking

Earlier this month, Mark Zuckerberg announced that Meta will cut back on its content moderation efforts and eliminate fact-checking in the US in favor of the more “democratic” approach that X (formerly Twitter) calls Community Notes, rolling back protections that he claimed had been developed only in response to media and government pressure.

The move is raising alarm bells, and rightly so. Meta has left a trail of moderation controversies in its wake, from overmoderating images of breastfeeding women to undermoderating hate speech in Myanmar, contributing to the genocide of Rohingya Muslims. Meanwhile, ending professional fact-checking creates the potential for misinformation and hate to spread unchecked.

Enlisting volunteers is how moderation started on the Internet, long before social media giants realized that centralized efforts were necessary. And volunteer moderation can be successful, allowing for the development of bespoke regulations aligned with the needs of particular communities. But without significant commitment and oversight from Meta, such a system cannot contend with how much content is shared across the company’s platforms, and how fast. In fact, the jury is still out on how well it works at X, which is used by 21% of Americans (Meta’s are significantly more popular—Facebook alone is used by 70% of Americans, according to Pew).  

Community Notes, which started in 2021 as Birdwatch, is a community-driven moderation system on X that allows users who sign up for the program to add context to posts. Having regular users provide public fact-checking is relatively new, and so far results are mixed. For example, researchers have found that participants are more likely to challenge content they disagree with politically and that flagging content as false does not reduce engagement, but they have also found that the notes are typically accurate and can help reduce the spread of misleading posts. 

I’m a community moderator who researches community moderation. Here’s what I’ve learned about the limitations of relying on volunteers for moderation—and what Meta needs to do to succeed: 

1. The system will miss falsehoods and could amplify hateful content

There is a real risk under this style of moderation that only posts about things that a lot of people know about will get flagged in a timely manner—or at all. Consider how a post with a picture of a death cap mushroom and the caption “Tasty” might be handled under Community Notes–style moderation. If an expert in mycology doesn’t see the post, or sees it only after it’s been widely shared, it may not get flagged as “Poisonous, do not eat”—at least not until it’s too late. Topic areas that are more esoteric will be undermoderated. This could have serious impacts on both individuals (who may eat a poisonous mushroom) and society (if a falsehood spreads widely). 

Crucially, X’s Community Notes aren’t visible to readers when they are first added. A note becomes visible to the wider user base only when enough contributors agree that it is accurate by voting for it. And not all votes count. If a note is rated only by people who tend to agree with each other, it won’t show up. X does not make a note visible until there’s agreement from people who have disagreed on previous ratings. This is an attempt to reduce bias, but it’s not foolproof. It still relies on people’s opinions about a note and not on actual facts. Often what’s needed is expertise.

I moderate a community on Reddit called r/AskHistorians. It’s a public history site with over 2 million members and is very strictly moderated. We see people get facts wrong all the time. Sometimes these are straightforward errors. But sometimes there is hateful content that takes experts to recognize. One time a question containing a Holocaust-denial dog whistle escaped review for hours and ended up amassing hundreds of upvotes before it was caught by an expert on our team. Hundreds of people—probably with very different voting patterns and very different opinions on a lot of topics—not only missed the problematic nature of the content but chose to promote it through upvotes. This happens with answers to questions, too. People who aren’t experts in history will upvote outdated, truthy-sounding answers that aren’t actually correct. Conversely, they will downvote good answers if they reflect viewpoints that are tough to swallow. 

r/AskHistorians works because most of its moderators are expert historians. If Meta wants its Community Notes–style program to work, it should  make sure that the people with the knowledge to make assessments see the posts and that expertise is accounted for in voting, especially when there’s a misalignment between common understanding and expert knowledge. 

2. It won’t work without well-supported volunteers  

Meta’s paid content moderators review the worst of the worst—including gore, sexual abuse and exploitation, and violence. As a result, many have suffered severe trauma, leading to lawsuits and unionization efforts. When Meta cuts resources from its centralized moderation efforts, it will be increasingly up to unpaid volunteers to keep the platform safe. 

Community moderators don’t have an easy job. On top of exposure to horrific content, as identifiable members of their communities, they are also often subject to harassment and abuse—something we experience daily on r/AskHistorians. However, community moderators moderate only what they can handle. For example, while I routinely manage hate speech and violent language, as a moderator of a text-based community I am rarely exposed to violent imagery. Community moderators also work as a team. If I do get exposed to something I find upsetting or if someone is being abusive, my colleagues take over and provide emotional support. I also care deeply about the community I moderate. Care for community, supportive colleagues, and self-selection all help keep volunteer moderators’ morale high(ish). 

It’s unclear how Meta’s new moderation system will be structured. If volunteers choose what content they flag, will that replicate X’s problem, where partisanship affects which posts are flagged and how? It’s also unclear what kind of support the platform will provide. If volunteers are exposed to content they find upsetting, will Meta—the company that is currently being sued for damaging the mental health of its paid content moderators—provide social and psychological aid? To be successful, the company will need to ensure that volunteers have access to such resources and are able to choose the type of content they moderate (while also ensuring that this self-selection doesn’t unduly influence the notes).    

3. It can’t work without protections and guardrails 

Online communities can thrive when they are run by people who deeply care about them. However, volunteers can’t do it all on their own. Moderation isn’t just about making decisions on what’s “true” or “false.” It’s also about identifying and responding to other kinds of harmful content. Zuckerberg’s decision is coupled with other changes to its community standards that weaken rules around hateful content in particular. Community moderation is part of a broader ecosystem, and it becomes significantly harder to do it when that ecosystem gets poisoned by toxic content. 

I started moderating r/AskHistorians in 2020 as part of a research project to learn more about the behind-the-scenes experiences of volunteer moderators. While Reddit had started addressing some of the most extreme hate on its platform by occasionally banning entire communities, many communities promoting misogyny, racism, and all other forms of bigotry were permitted to thrive and grow. As a result, my early field notes are filled with examples of extreme hate speech, as well as harassment and abuse directed at moderators. It was hard to keep up with. 

But halfway through 2020, something happened. After a milquetoast statement about racism from CEO Steve Huffman, moderators on the site shut down their communities in protest. And to its credit, the platform listened. Reddit updated its community standards to explicitly prohibit hate speech and began to enforce the policy more actively. While hate is still an issue on Reddit, I see far less now than I did in 2020 and 2021. Community moderation needs robust support because volunteers can’t do it all on their own. It’s only one tool in the box. 

If Meta wants to ensure that its users are safe from scams, exploitation, and manipulation in addition to hate, it cannot rely solely on community fact-checking. But keeping the user base safe isn’t what this decision aims to do. It’s a political move to curry favor with the new administration. Meta could create the perfect community fact-checking program, but because this decision is coupled with weakening its wider moderation practices, things are going to get worse for its users rather than better. 

Sarah Gilbert is research director for the Citizens and Technology Lab at Cornell University.

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Jan 29 2025
AI’s energy obsession just got a reality check

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

Just a week in, the AI sector has already seen its first battle of wits under the new Trump administration. The clash stems from two key pieces of news: the announcement of the Stargate project, which would spend $500 billion—more than the Apollo space program—on new AI data centers, and the release of a powerful new model from China. Together, they raise important questions the industry needs to answer about the extent to which the race for more data centers—with their heavy environmental toll—is really necessary.

A reminder about the first piece: OpenAI, Oracle, SoftBank, and an Abu Dhabi–based investment fund called MGX plan to spend up to $500 billion opening massive data centers around the US to build better AI. Much of the groundwork for this project was laid in 2024, when OpenAI increased its lobbying spending sevenfold (which we were first to report last week) and AI companies started pushing for policies that were less about controlling problems like deepfakes and misinformation, and more about securing more energy.

Still, Trump received credit for it from tech leaders when he announced the effort on his second day in office. “I think this will be the most important project of this era,” OpenAI’s Sam Altman said at the launch event, adding, “We wouldn’t be able to do this without you, Mr. President.”

It’s an incredible sum, just slightly less than the inflation-adjusted cost of building the US highway system over the course of more than 30 years. However, not everyone sees Stargate as having the same public benefit. Environmental groups say it could strain local grids and further drive up the cost of energy for the rest of us, who aren’t guzzling it to train and deploy AI models. Previous research has also shown that data centers tend to be built in areas that use much more carbon-intensive sources of energy, like coal, than the national average. It’s not clear how much, if at all, Stargate will rely on renewable energy. 

Even louder critics of Stargate, though, include Elon Musk. None of Musk’s companies are involved in the project, and he has attempted to publicly sow doubt that OpenAI and SoftBank have enough of the money needed for the plan anyway, claims that Altman disputed on X. Musk’s decision to publicly criticize the president’s initiative has irked people in Trump’s orbit, Politico reports, but it’s not clear if those people have expressed that to Musk directly. 

On to the second piece. On the day Trump was inaugurated, a Chinese startup released an AI model that started making a whole bunch of important people in Silicon Valley very worried about their competition. (This close timing is almost certainly not an accident.)

The model, called DeepSeek R1, is a reasoning model. These types of models are designed to excel at math, logic, pattern-finding, and decision-making. DeepSeek proved it could “reason” through complicated problems as well as one of OpenAI’s reasoning models, o1—and more efficiently. What’s more, DeepSeek isn’t a super-secret project kept behind lock and key like OpenAI’s. It was released for all to see.

DeepSeek was released as the US has made outcompeting China in the AI race a top priority. This goal was a driving force behind the 2022 CHIPS Act to make more chips domestically. It’s influenced the position of tech companies like OpenAI, which has embraced lending its models to national security work and has partnered with the defense-tech company Anduril to help the military take down drones. It’s led to export controls that limit what types of chips Nvidia can sell to China.

The success of DeepSeek signals that these efforts aren’t working as well as AI leaders in the US would like (though it’s worth noting that the impact of export controls for chips isn’t felt for a few years, so the policy wouldn’t be expected to have prevented a model like DeepSeek).  

Still, the model poses a threat to the bottom line of certain players in Big Tech. Why pay for an expensive model from OpenAI when you can get access to DeepSeek for free? Even other makers of open-source models, especially Meta, are panicking about the competition, according to The Information. The company has set up a number of “war rooms” to figure out how DeepSeek was made so efficient. (A couple of days after the Stargate announcement, Meta said it would increase its own capital investments by 70% to build more AI infrastructure.)

What does this all mean for the Stargate project? Let’s think about why OpenAI and its partners are willing to spend $500 billion on data centers to begin with. They believe that AI in its various forms—not just chatbots or generative video or even new AI agents, but also developments yet to be unveiled—will be the most lucrative tool humanity has ever built. They also believe that access to powerful chips inside massive data centers is the key to getting there. 

DeepSeek poked some holes in that approach. It didn’t train on yet-unreleased chips that are light-years ahead. It didn’t, to our knowledge, require the eye-watering amounts of computing power and energy behind the models from US companies that have made headlines. Its designers made clever decisions in the name of efficiency.

In theory, it could make a project like Stargate seem less urgent and less necessary. If, in dissecting DeepSeek, AI companies discover some lessons about how to make models use existing resources more effectively, perhaps constructing more and more data centers won’t be the only winning formula for better AI. That would be welcome to the many people affected by the problems data centers can bring, like lots of emissions, the loss of fresh, drinkable water used to cool them, and the strain on local power grids. 

Thus far, DeepSeek doesn’t seem to have sparked such a change in approach. OpenAI researcher Noam Brown wrote on X, “I have no doubt that with even more compute it would be an even more powerful model.”

If his logic wins out, the players with the most computing power will win, and getting it is apparently worth at least $500 billion to AI’s biggest companies. But let’s remember—announcing it is the easiest part.

Now read the rest of The Algorithm

Deeper Learning

What’s next for robots

Many of the big questions about AI–-how it learns, how well it works, and where it should be deployed—are now applicable to robotics. In the year ahead, we will see humanoid robots being put to the test in warehouses and factories, robots learning in simulated worlds, and a rapid increase in the military’s adoption of autonomous drones, submarines, and more. 

Why it matters: Jensen Huang, the highly influential CEO of the chipmaker Nvidia, stated last month that the next advancement in AI will mean giving the technology a “body” of sorts in the physical world. This will come in the form of advanced robotics. Even with the caveat that robotics is full of futuristic promises that usually aren’t fulfilled by their deadlines, the marrying of AI methods with new advancements in robots means the field is changing quickly. Read more here.

Bits and Bytes

Leaked documents expose deep ties between Israeli army and Microsoft

Since the attacks of October 7, the Israeli military has relied heavily on cloud and AI services from Microsoft and its partner OpenAI, and the tech giant’s staff has embedded with different units to support rollout, a joint investigation reveals. (+972 Magazine)

The tech arsenal that could power Trump’s immigration crackdown

The effort by federal agencies to acquire powerful technology to identify and track migrants has been unfolding for years across multiple administrations. These technologies may be called upon more directly under President Trump. (The New York Times)

OpenAI launches Operator—an agent that can use a computer for you

Operator is a web app that can carry out simple online tasks in a browser, such as booking concert tickets or making an online grocery order. (MIT Technology Review)

The second wave of AI coding is here

A string of startups are racing to build models that can produce better and better software. But it’s not only AI’s increasingly powerful ability to write code that’s impressive. They claim it’s the shortest path to superintelligent AI. (MIT Technology Review)

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Jan 29 2025
Mice with two dads have been created using CRISPR

Mice with two fathers have been born—and have survived to adulthood—following a complex set of experiments by a team in China. 

Zhi-Kun Li at the Chinese Academy of Sciences in Beijing and his colleagues used CRISPR to create the mice, using a novel approach to target genes that normally need to be inherited from both male and female parents. They hope to use the same approach to create primates with two dads. 

Humans are off limits for now, but the work does help us better understand a strange biological phenomenon known as imprinting, which causes certain genes to be expressed differently depending on which parent they came from. For these genes, animals inherit part of a “dose” from each parent, and the two must work in harmony to create a healthy embryo. Without both doses, gene expression can go awry, and the resulting embryos can end up with abnormalities.

This is what researchers have found in previous attempts to create mice with two dads. In the 1980s, scientists in the UK tried injecting the DNA-containing nucleus of a sperm cell into a fertilized egg cell. The resulting embryos had DNA from two males (as well as a small amount of DNA from a female, in the cytoplasm of the egg).

But when these embryos were transferred to the uteruses of surrogate mouse mothers, none of them resulted in a healthy birth, seemingly because imprinted genes from both paternal and maternal genomes are needed for development. 

Li and his colleagues took a different approach. The team used gene editing to knock out imprinted genes altogether.

Around 200 of a mouse’s genes are imprinted, but Li’s team focused on 20 that are known to be important for the development of the embryo.

In an attempt to create healthy mice with DNA from two male “dads,” the team undertook a complicated set of experiments. To start, the team cultured cells with sperm DNA to collect stem cells in the lab. Then they used CRISPR to disrupt the 20 imprinted genes they were targeting.

These gene-edited cells were then injected, along with other sperm cells, into egg cells that had had their own nuclei removed. The result was embryonic cells with DNA from two male mice. These cells were then injected into a type of “embryo shell” used in research, which provides the cells required to make a placenta. The resulting embryos were transferred to the uteruses of female mice.

It worked—to some degree. Some of the embryos developed into live pups, and they even survived to adulthood. The findings were published in the journal Cell Stem Cell.

“It’s exciting,” says Kotaro Sasaki, a developmental biologist at the University of Pennsylvania, who was not involved in the work. Not only have Li and his team been able to avoid a set of imprinting defects, but their approach is the second way scientists have found to create mice using DNA from two males.

The finding builds on research by Katsuhiko Hayashi, now at Osaka University in Japan, and his colleagues. A couple of years ago, that team presented evidence that they had found a way to take cells from the tails of adult male mice and turn them into immature egg cells. These could be fertilized with sperm to create bi-paternal embryos. The mice born from those embryos can reach adulthood and have their own offspring, Hayashi has said.

Li’s team’s more complicated approach was less successful. Only a small fraction of the mice survived, for a start. The team transferred 164 gene-edited embryos, but only seven live pups were born. And those that were born weren’t entirely normal, either. They grew to be bigger than untreated mice, and their organs appeared enlarged. They didn’t live as long as normal mice, and they were infertile.

It would be unethical to do such risky research with human cells and embryos. “Editing 20 imprinted genes in humans would not be acceptable, and producing individuals who could not be healthy or viable is simply not an option,” says Li.

“There are numerous issues,” says Sasaki. For a start, a lot of the technical lab procedures the team used have not been established for human cells. But even if we had those, this approach would be dangerous—knocking out human genes could have untold health consequences. 

“There’s lots and lots of hurdles,” he says. “Human applications [are] still quite far.”

Despite that, the work might shed a little more light on the mysterious phenomenon of imprinting. Previous research has shown that mice with two moms appear smaller, and live longer than expected, while the current study shows that mice with two dads are overgrown and die more quickly. Perhaps paternal imprinted genes support growth and maternal ones limit it, and animals need both to reach a healthy size, says Sasaki.

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Jan 28 2025
Useful quantum computing is inevitable—and increasingly imminent

On January 8, Nvidia CEO Jensen Huang jolted the stock market by saying that practical quantum computing is still 15 to 30 years away, at the same time suggesting those computers will need Nvidia GPUs in order to implement the necessary error correction. 

However, history shows that brilliant people are not immune to making mistakes. Huang’s predictions miss the mark, both on the timeline for useful quantum computing and on the role his company’s technology will play in that future.

I’ve been closely following developments in quantum computing as an investor, and it’s clear to me that it is rapidly converging on utility. Last year, Google’s Willow device demonstrated that there is a promising pathway to scaling up to bigger and bigger computers. It showed that errors can be reduced exponentially as the number of quantum bits, or qubits, increases. It also ran a benchmark test in under five minutes that would take one of today’s fastest supercomputers 10 septillion years. While too small to be commercially useful with known algorithms, Willow shows that quantum supremacy (executing a task that is effectively impossible for any classical computer to handle in a reasonable amount of time) and fault tolerance (correcting errors faster than they are made) are achievable.

For example, PsiQuantum, a startup my company is invested in, is set to break ground on two quantum computers that will enter commercial service before the end of this decade. The plan is for each one to be 10 thousand times the size of Willow, big enough to tackle important questions about materials, drugs, and the quantum aspects of nature. These computers will not use GPUs to implement error correction. Rather, they will have custom hardware, operating at speeds that would be impossible with Nvidia hardware.

At the same time, quantum algorithms are improving far faster than hardware. A recent collaboration between the pharmaceutical giant Boehringer Ingelheim and PsiQuantum demonstrated a more than 200x improvement in algorithms to simulate important drugs and materials. Phasecraft, another company we have invested in, has improved the simulation performance for a wide variety of crystal materials and has published a quantum-enhanced version of a widely used materials science algorithm that is tantalizingly close to beating all classical implementations on existing hardware.

Advances like these lead me to believe that useful quantum computing is inevitable and increasingly imminent. And that’s good news, because the hope is that they will be able to perform calculations that no amount of AI or classical computation could ever achieve.

We should care about the prospect of useful quantum computers because today we don’t really know how to do chemistry. We lack knowledge about the mechanisms of action for many of our most important drugs. The catalysts that drive our industries are generally poorly understood, require expensive exotic materials, or both. Despite appearances, we have significant gaps in our agency over the physical world; our achievements belie the fact that we are, in many ways, stumbling around in the dark.

Nature operates on the principles of quantum mechanics. Our classical computational methods fail to accurately capture the quantum nature of reality, even though much of our high-performance computing resources are dedicated to this pursuit. Despite all the intellectual and financial capital expended, we still don’t understand why the painkiller acetaminophen works, how type-II superconductors function, or why a simple crystal of iron and nitrogen can produce a magnet with such incredible field strength. We search for compounds in Amazonian tree bark to cure cancer and other maladies, manually rummaging through a pitifully small subset of a design space encompassing 1060 small molecules. It’s more than a little embarrassing.

We do, however, have some tools to work with. In industry, density functional theory (DFT) is the workhorse of computational chemistry and materials modeling, widely used to investigate the electronic structure of many-body systems—such as atoms, molecules, and solids. When DFT is applied to systems where electron-electron correlations are weak, it produces reasonable results. But it fails entirely on a broad class of interesting problems. 

Take, for example, the buzz in the summer of 2023 around the “room-temperature superconductor” LK-99. Many accomplished chemists turned to DFT to try to characterize the material and determine whether it was, indeed, a superconductor. Results were, to put it politely, mixed—so we abandoned our best computational methods, returning to mortar and pestle to try to make some of the stuff. Sadly, although LK-99 might have many novel characteristics, a room-temperature superconductor it isn’t. That’s unfortunate, as such a material could revolutionize energy generation, transmission, and storage, not to mention magnetic confinement for fusion reactors, particle accelerators, and more.

AI will certainly help with our understanding of materials, but it is no panacea. New AI techniques have emerged in the last few years, with some promising results. DeepMind’s Graph Networks for Materials Exploration (GNoME), for example, found 380,000 new potentially stable materials. At its core, though, GNoME depends on DFT, so its performance is only as good as DFT’s ability to produce good answers. 

The fundamental issue is that an AI model is only as good as the data it’s trained on. Training an LLM on the entire internet corpus, for instance, can yield a model that has a reasonable grasp of most human culture and can process language effectively. But if DFT fails for any non-trivially correlated quantum systems, how useful can a DFT-derived training set really be? We could also turn to synthesis and experimentation to create training data, but the number of physical samples we can realistically produce is minuscule relative to the vast design space, leaving a great deal of potential untapped. Only once we have reliable quantum simulations to produce sufficiently accurate training data will we be able to create AI models that answer quantum questions on classical hardware.

And that means that we need quantum computers. They afford us the opportunity to shift from a world of discovery to a world of design. Today’s iterative process of guessing, synthesizing, and testing materials is comically inadequate.

In a few tantalizing cases, we have stumbled on materials, like superconductors, with near-magical properties. How many more might these new tools reveal in the coming years? We will eventually have machines with millions of qubits that, when used to simulate crystalline materials, open up a vast new design space. It will be like waking up one day and finding a million new elements with fascinating properties on the periodic table.

Of course, building a million-qubit quantum computer is not for the faint of heart. Such machines will be the size of supercomputers, and require large amounts of capital, cryoplant, electricity, concrete, and steel. They also require silicon photonics components that perform well beyond anything in industry, error correction hardware that runs fast enough to chase photons, and single-photon detectors with unprecedented sensitivity. But after years of research and development, and more than a billion dollars of investment, the challenge is now moving from science and engineering to construction.

It is impossible to fully predict how quantum computing will affect our world, but a thought exercise might offer a mental model of some of the possibilities. 

Imagine our world without metal. We could have wooden houses built with stone tools, agriculture, wooden plows, movable type, printing, poetry, and even thoughtfully edited science periodicals. But we would have no inkling of phenomena like electricity or electromagnetism—no motors, generators, radio, MRI machines, silicon, or AI. We wouldn’t miss them, as we’d be oblivious to their existence. 

Today, we are living in a world without quantum materials, oblivious to the unrealized potential and abundance that lie just out of sight. With large-scale quantum computers on the horizon and advancements in quantum algorithms, we are poised to shift from discovery to design, entering an era of unprecedented dynamism in chemistry, materials science, and medicine. It will be a new age of mastery over the physical world.

Peter Barrett is a general partner at Playground Global, which invests in early-stage deep-tech companies including several in quantum computing, quantum algorithms, and quantum sensing: PsiQuantum, Phasecraft, NVision, and Ideon.

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Jan 25 2025
The US withdrawal from the WHO will hurt us all

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

On January 20, his first day in office, US president Donald Trump signed an executive order to withdraw the US from the World Health Organization. “Ooh, that’s a big one,” he said as he was handed the document.

The US is the biggest donor to the WHO, and the loss of this income is likely to have a significant impact on the organization, which develops international health guidelines, investigates disease outbreaks, and acts as an information-sharing hub for member states.

But the US will also lose out. “It’s a very tragic and sad event that could only hurt the United States in the long run,” says William Moss, an epidemiologist at Johns Hopkins Bloomberg School of Public Health in Baltimore.

Trump appears to take issue with the amount the US donates to the WHO. He points out that it makes a much bigger contribution than China, a country with a population four times that of the US. “It seems a little unfair to me,” he said as he prepared to sign the executive order.

It is true that the US is far and away the biggest financial supporter of the WHO. The US contributed $1.28 billion over the two-year period covering 2022 and 2023. By comparison, the second-largest donor, Germany, contributed $856 million in the same period. The US currently contributes 14.5% of the WHO’s total budget.

But it’s not as though the WHO sends a billion-dollar bill to the US. All member states are required to pay membership dues, which are calculated as a percentage of a country’s gross domestic product. For the US, this figure comes to $130 million. China pays $87.6 million. But the vast majority of the US’s contributions to the WHO are made on a voluntary basis—in recent years, the donations have been part of multibillion-dollar spending on global health by the US government. (Separately, the Bill and Melinda Gates Foundation contributed $830 million over 2022 and 2023.)

There’s a possibility that other member nations will increase their donations to help cover the shortfall left by the US’s withdrawal. But it is not clear who will step up—or what implications it will have to change the structure of donations.

Martin McKee, a professor of European public health at the London School of Hygiene and Tropical Medicine, thinks it is unlikely that European members will increase their contributions by much. China, India, Brazil, South Africa, and the Gulf states, on the other hand, may be more likely to pay more. But again, it isn’t clear how this will pan out, or whether any of these countries will expect greater influence over global health policy decisions as a result of increasing their donations.

WHO funds are spent on a range of global health projects—programs to eradicate polio, rapidly respond to health emergencies, improve access to vaccines and medicines, develop pandemic prevention strategies, and more. The loss of US funding is likely to have a significant impact on at least some of these programs.

“Diseases don’t stick to national boundaries, hence this decision is not only concerning for the US, but in fact for every country in the world,” says Pauline Scheelbeek at the London School of Hygiene and Tropical Medicine.“With the US no longer reporting to the WHO nor funding part of this process, the evidence on which public health interventions and solutions should be based is incomplete.”

“It’s going to hurt global health,” adds Moss. “It’s going to come back to bite us.”

There’s more on how the withdrawal could affect health programs, vaccine coverage, and pandemic preparedness in this week’s coverage.

Now read the rest of The Checkup

Read more from MIT Technology Review‘s archive

This isn’t the first time Donald Trump has signaled his desire for the US to leave the WHO. He proposed a withdrawal during his last term, in 2020. While the WHO is not perfect, it needs more power and funding, not less, Charles Kenny, director of technology and development at the Center for Global Development, argued at the time.

The move drew condemnation from those working in public health then, too. The editor in chief of the medical journal The Lancet called it “a crime against humanity,” as Charlotte Jee reported.

In 1974, the WHO launched an ambitious program to get lifesaving vaccines to all children around the world. Fifty years on, vaccines are thought to have averted 154 million deaths—including 146 million in children under the age of five. 

The WHO has also seen huge success in its efforts to eradicate polio. Today, wild forms of the virus have been eradicated in all but two countries. But vaccine-derived forms of the virus can still crop up around the world.

At the end of a round of discussions in September among WHO member states working on a pandemic agreement, director-general Tedros Adhanom Ghebreyesus remarked, “The next pandemic will not wait for us, whether from a flu virus like H5N1, another coronavirus, or another family of viruses we don’t yet know about.” The H5N1 virus has been circulating on US dairy farms for months now, and the US is preparing for potential human outbreaks.

From around the web

People with cancer paid $45,000 for an experimental blood-filtering treatment, delivered at a clinic in Antigua, after being misled about its effectiveness. Six of them have died since their treatments. (The New York Times)

The Trump administration has instructed federal health agencies to pause all external communications, such as health advisories, weekly scientific reports, updates to websites, and social media posts. (The Washington Post)

A new “virtual retina,” modeled on human retinas, has been developed to study the impact of retinal implants. The three-dimensional model simulates over 10,000 neurons. (Brain Stimulation)

Trump has signed an executive order stating that “it is the policy of the United States to recognize two sexes, male and female.” The document “defies decades of research into how human bodies grow and develop,” STAT reports, and represents “a dramatic failure to understand biology,” according to a neuroscientist who studies the development of sex. (STAT)

Attention, summer holiday planners: Biting sandflies in the Mediterranean region are transmitting Toscana virus at an increasing rate. The virus is a major cause of central nervous system disorders in the region. Italy saw a 2.6-fold increase in the number of reported infections between the 2016–21 period and 2022–23. (Eurosurveillance)

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Jan 25 2025
How a top Chinese AI model overcame US sanctions

The AI community is abuzz over DeepSeek R1, a new open-source reasoning model. 

The model was developed by the Chinese AI startup DeepSeek, which claims that R1 matches or even surpasses OpenAI’s ChatGPT o1 on multiple key benchmarks but operates at a fraction of the cost. 

“This could be a truly equalizing breakthrough that is great for researchers and developers with limited resources, especially those from the Global South,” says Hancheng Cao, an assistant professor in information systems at Emory University.

DeepSeek’s success is even more remarkable given the constraints facing Chinese AI companies in the form of increasing US export controls on cutting-edge chips. But early evidence shows that these measures are not working as intended. Rather than weakening China’s AI capabilities, the sanctions appear to be driving startups like DeepSeek to innovate in ways that prioritize efficiency, resource-pooling, and collaboration.

To create R1, DeepSeek had to rework its training process to reduce the strain on its GPUs, a variety released by Nvidia for the Chinese market that have their performance capped at half the speed of its top products, according to Zihan Wang, a former DeepSeek employee and current PhD student in computer science at Northwestern University. 

DeepSeek R1 has been praised by researchers for its ability to tackle complex reasoning tasks, particularly in mathematics and coding. The model employs a “chain of thought” approach similar to that used by ChatGPT o1, which lets it solve problems by processing queries step by step.

Dimitris Papailiopoulos, principal researcher at Microsoft’s AI Frontiers research lab, says what surprised him the most about R1 is its engineering simplicity. “DeepSeek aimed for accurate answers rather than detailing every logical step, significantly reducing computing time while maintaining a high level of effectiveness,” he says.

DeepSeek has also released six smaller versions of R1 that are small enough to  run locally on laptops. It claims that one of them even outperforms OpenAI’s o1-mini on certain benchmarks.“DeepSeek has largely replicated o1-mini and has open sourced it,” tweeted Perplexity CEO Aravind Srinivas. DeepSeek did not reply to MIT Technology Review’s request for comments.

Despite the buzz around R1, DeepSeek remains relatively unknown. Based in Hangzhou, China, it was founded in July 2023 by Liang Wenfeng, an alumnus of Zhejiang University with a background in information and electronic engineering. It was incubated by High-Flyer, a hedge fund that Liang founded in 2015. Like Sam Altman of OpenAI, Liang aims to build artificial general intelligence (AGI), a form of AI that can match or even beat humans on a range of tasks.

Training large language models (LLMs) requires a team of highly trained researchers and substantial computing power. In a recent interview with the Chinese media outlet LatePost, Kai-Fu Lee, a veteran entrepreneur and former head of Google China, said that only “front-row players” typically engage in building foundation models such as ChatGPT, as it’s so resource-intensive. The situation is further complicated by the US export controls on advanced semiconductors. High-Flyer’s decision to venture into AI is directly related to these constraints, however. Long before the anticipated sanctions, Liang acquired a substantial stockpile of Nvidia A100 chips, a type now banned from export to China. The Chinese media outlet 36Kr estimates that the company has over 10,000 units in stock, but Dylan Patel, founder of the AI research consultancy SemiAnalysis, estimates that it has at least 50,000. Recognizing the potential of this stockpile for AI training is what led Liang to establish DeepSeek, which was able to use them in combination with the lower-power chips to develop its models. 

Tech giants like Alibaba and ByteDance, as well as a handful of startups with deep-pocketed investors, dominate the Chinese AI space, making it challenging for small or medium-sized enterprises to compete. A company like DeepSeek, which has no plans to raise funds, is rare. 

Zihan Wang, the former DeepSeek employee, told MIT Technology Review that he had access to abundant computing resources and was given freedom to experiment when working at DeepSeek, “a luxury that few fresh graduates would get at any company.” 

In an interview with the Chinese media outlet 36Kr in July 2024 Liang said that an additional challenge Chinese companies face on top of chip sanctions, is that their AI engineering techniques tend to be less efficient. “We [most Chinese companies] have to consume twice the computing power to achieve the same results. Combined with data efficiency gaps, this could mean needing up to four times more computing power. Our goal is to continuously close these gaps,” he said.  

But DeepSeek found ways to reduce memory usage and speed up calculation without significantly sacrificing accuracy. “The team loves turning a hardware challenge into an opportunity for innovation,” says Wang.

Liang himself remains deeply involved in DeepSeek’s research process, running experiments alongside his team. “The whole team shares a collaborative culture and dedication to hardcore research,” Wang says.

As well as prioritizing efficiency, Chinese companies are increasingly embracing open-source principles. Alibaba Cloud has released over 100 new open-source AI models, supporting 29 languages and catering to various applications, including coding and mathematics. Similarly, startups like Minimax and 01.AI have open-sourced their models. 

According to a white paper released last year by the China Academy of Information and Communications Technology, a state-affiliated research institute, the number of AI large language models worldwide has reached 1,328, with 36% originating in China. This positions China as the second-largest contributor to AI, behind the United States. 

“This generation of young Chinese researchers identify strongly with open-source culture because they benefit so much from it,” says Thomas Qitong Cao, an assistant professor of technology policy at Tufts University.

“The US export control has essentially backed Chinese companies into a corner where they have to be far more efficient with their limited computing resources,” says Matt Sheehan, an AI researcher at the Carnegie Endowment for International Peace. “We are probably going to see a lot of consolidation in the future related to the lack of compute.”

That might already have started to happen. Two weeks ago, Alibaba Cloud announced that it has partnered with the Beijing-based startup 01.AI, founded by Kai-Fu Lee, to merge research teams and establish an “industrial large model laboratory.”

“It is energy-efficient and natural for some kind of division of labor to emerge in the AI industry,” says Cao, the Tufts professor. “The rapid evolution of AI demands agility from Chinese firms to survive.”

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Jan 24 2025
Why the next energy race is for underground hydrogen

It might sound like something straight out of the 19th century, but one of the most cutting-edge areas in energy today involves drilling deep underground to hunt for materials that can be burned for energy. The difference is that this time, instead of looking for fossil fuels, the race is on to find natural deposits of hydrogen.

Hydrogen is already a key ingredient in the chemical industry and could be used as a greener fuel in industries from aviation and transoceanic shipping to steelmaking. Today, the gas needs to be manufactured, but there’s some evidence that there are vast deposits underground.

I’ve been thinking about underground resources a lot this week, since I’ve been reporting a story about a new startup, Addis Energy. The company is looking to use subsurface rocks, and the conditions down there, to produce another useful chemical: ammonia. In an age of lab-produced breakthroughs, it feels like something of a regression to go digging for resources, but looking underground could help meet energy demand while also addressing climate change.

It’s rare that hydrogen turns up in oil and gas operations, and for decades, the conventional wisdom has been that there aren’t large deposits of the gas underground. Hydrogen molecules are tiny, after all, so even if the gas was forming there, the assumption was that it would just leak out.

However, there have been somewhat accidental discoveries of hydrogen over the decades, in abandoned mines or new well sites. There are reports of wells that spewed colorless gas, or flames that burned gold. And as people have looked more intentionally for hydrogen, they’ve started to find it.

As it turns out, hydrogen tends to build up in very different rocks from those that host oil and gas deposits. While fossil-fuel prospecting tends to focus on softer rocks, like organic-rich shale, hydrogen seems most plentiful in iron-rich rocks like olivine. The gas forms when chemical reactions at elevated temperature and pressure underground pull water apart. (There’s also likely another mechanism that forms hydrogen underground, called radiolysis, where radioactive elements emit radiation that can split water.)

Some research has put the potential amount of hydrogen available at around a trillion tons—plenty to feed our demand for centuries, even if we ramp up use of the gas.

The past few years have seen companies spring up around the world to try to locate and tap these resources. There’s an influx in Australia, especially the southern part of the country, which seems to have conditions that are good for making hydrogen. One startup, Koloma, has raised over $350 million to aid its geologic hydrogen exploration.

There are so many open questions for this industry, including how much hydrogen is actually going to be accessible and economical to extract. It’s not even clear how best to look for the gas today; researchers and companies are borrowing techniques and tools from the oil and gas industry, but there could be better ways.

It’s also unknown how this could affect climate change. Hydrogen itself may not warm the planet, but it can contribute indirectly to global warming by extending the lifetime of other greenhouse gases. It’s also often found with methane, a super-powerful greenhouse gas that could do major harm if it leaks out of operations at a significant level.

There’s also the issue of transportation: Hydrogen isn’t very dense, and it can be difficult to store and move around. Deposits that are far away from the final customers could face high costs that might make the whole endeavor uneconomical.  

But this whole area is incredibly exciting, and researchers are working to better understand it. Some are looking to expand the potential pool of resources by pumping water underground to stimulate hydrogen production from rocks that wouldn’t naturally produce the gas.

There’s something fascinating to me about using the playbook of the oil and gas industry to develop an energy source that could actually help humanity combat climate change. It could be a strategic move to address energy demand, since a lot of expertise has accumulated over the roughly 150 years that we’ve been digging up fossil fuels.

After all, it’s not digging that’s the problem—it’s emissions.

Now read the rest of The Spark

Related reading

This story from Science, published in 2023, is a great deep dive into the world of so-called “gold hydrogen.” Give it a read for more on the history and geology here.

For more on commercial efforts, specifically Koloma, give this piece from Canary Media a read.   

And for all the details on geologic ammonia and Addis Energy, check out my latest story here.

Another thing

Donald Trump officially took office on Monday and signed a flurry of executive orders. Here are a few of the most significant ones for climate:  

Trump announced his intention to once again withdraw from the Paris agreement. After a one-year waiting period, the world’s largest economy will officially leave the major international climate treaty. (New York Times)

The president also signed an order that pauses lease sales for offshore wind power projects in federal waters. It’s not clear how much the office will be able to slow projects that already have their federal permits. (Associated Press)

Another executive order, titled “Unleashing American Energy,” broadly signals a wide range of climate and energy moves. 
→ One section ends the “EV mandate.” The US government doesn’t have any mandates around EVs, but this bit is a signal of the administration’s intent to roll back policies and funding that support adoption of these vehicles. There will almost certainly be court battles. (Wired)
→ Another section pauses the disbursement of tens of billions of dollars for climate and energy. The spending was designated by Congress in two of the landmark laws from the Biden administration, the Bipartisan Infrastructure Law and the Inflation Reduction Act. Again, experts say we can likely expect legal fights. (Canary Media)

Keeping up with climate

The Chinese automaker BYD built more electric vehicles in 2024 than Tesla did. The data signals a global shift to cheaper EVs and the continued dominance of China in the EV market. (Washington Post)

A pair of nuclear reactors in South Carolina could get a second chance at life. Construction halted at the VC Summer plant in 2017, $9 billion into the project. Now the site’s owner wants to sell. (Wall Street Journal)

→ Existing reactors are more in-demand than ever, as I covered in this story about what’s next for nuclear power. (MIT Technology Review)

In California, charging depots for electric trucks are increasingly choosing to cobble together their own power rather than waiting years to connect to the grid. These solar- and wind-powered microgrids could help handle broader electricity demand. (Canary Media)

Wildfires in Southern California are challenging even wildlife that have adapted to frequent blazes. As fires become more frequent and intense, biologists worry about animals like mountain lions. (Inside Climate News)

Experts warn that ash from the California wildfires could be toxic, containing materials like lead and arsenic. (Associated Press)

Burning wood for power isn’t necessary to help the UK meet its decarbonization goals, according to a new analysis. Biomass is a controversial green power source that critics say contributes to air pollution and harms forests. (The Guardian) 

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Jan 24 2025
This is what might happen if the US withdraws from the WHO

On January 20, his first day in office, US president Donald Trump signed an executive order to withdraw the US from the World Health Organization. “Ooh, that’s a big one,” he said as he was handed the document.

The US is the biggest donor to the WHO, and the loss of this income is likely to have a significant impact on the organization, which develops international health guidelines, investigates disease outbreaks, and acts as an information-sharing hub for member states.

But the US will also lose out. “It’s a very tragic and sad event that could only hurt the United States in the long run,” says William Moss, an epidemiologist at Johns Hopkins Bloomberg School of Public Health in Baltimore.

A little unfair?

Trump appears to take issue with the amount the US donates to the WHO. He points out that it makes a much bigger contribution than China, a country with a population four times that of the US. “It seems a little unfair to me,” he said as he prepared to sign the executive order.

It is true that the US is far and away the biggest financial supporter of the WHO. The US contributed $1.28 billion over the two-year period covering 2022 and 2023. By comparison, the second-largest donor, Germany, contributed $856 million in the same period. The US currently contributes 14.5% of the WHO’s total budget.

But it’s not as though the WHO sends a billion-dollar bill to the US. All member states are required to pay membership dues, which are calculated as a percentage of a country’s gross domestic product. For the US, this figure comes to $130 million. China pays $87.6 million. But the vast majority of the US’s contributions to the WHO are made on a voluntary basis—in recent years, the donations have been part of multibillion-dollar spending on global health by the US government. (Separately, the Bill and Melinda Gates Foundation contributed $830 million over 2022 and 2023.)

There’s a possibility that other member nations will increase their donations to help cover the shortfall left by the US’s withdrawal. But it is not clear who will step up—or what implications changing the structure of donations will have.

Martin McKee, a professor of European public health at the London School of Hygiene and Tropical Medicine, thinks it is unlikely that European members will increase their contributions by much. The Gulf states, China, India, Brazil, and South Africa, on the other hand, may be more likely to pay more. But again, it isn’t clear how this will pan out, or whether any of these countries will expect greater influence over global health policy decisions as a result of increasing their donations.

Deep impacts

WHO funds are spent on a range of global health projects—programs to eradicate polio, rapidly respond to health emergencies, improve access to vaccines and medicines, develop pandemic prevention strategies, and more. The loss of US funding is likely to have a significant impact on at least some of these programs.

It is not clear which programs will lose funding, or when they will be affected. The US is required to give 12 months’ notice to withdraw its membership, but voluntary contributions might stop before that time is up. 

For the last few years, WHO member states have been negotiating a pandemic agreement designed to improve collaboration on preparing for future pandemics. The agreement is set to be finalized in 2025. But these discussions will be disrupted by the US withdrawal, says McKee. It will “create confusion about how effective any agreement will be and what it will look like,” he says.

The agreement itself won’t make as big an impact without the US as a signatory, either, says Moss, who is also a member of a WHO vaccine advisory committee. The US would not be held to information-sharing standards that other countries could benefit from, and it might not be privy to important health information from other member nations. The global community might also lose out on the US’s resources and expertise. “Having a major country like the United States not be a part of that really undermines the value of any pandemic agreement,” he says.

McKee thinks that the loss of funding will also affect efforts to eradicate polio, and to control outbreaks of mpox in the Democratic Republic of Congo, Uganda, and Burundi, which continue to report hundreds of cases per week. The virus “has the potential to spread, including to the US,” he points out.

“Diseases don’t stick to national boundaries, hence this decision is not only concerning for the US, but in fact for every country in the world,” says Pauline Scheelbeek at the London School of Hygiene and Tropical Medicine. “With the US no longer reporting to the WHO nor funding part of this process, the evidence on which public health interventions and solutions should be based is incomplete.”

Moss is concerned about the potential for outbreaks of vaccine-preventable diseases. Robert F. Kennedy Jr., Trump’s pick to lead the Department of Health and Human Services, is a prominent antivaccine advocate, and Moss worries about potential changes to vaccination-based health policies in the US. That, combined with a weakening of the WHO’s ability to control outbreaks, could be a “double whammy,” he says: “We’re setting ourselves up for large measles disease outbreaks in the United States.”

At the same time, the US is up against another growing threat to public health: the circulation of bird flu on poultry and dairy farms. The US has seen outbreaks of the H5N1 virus on poultry farms in all states, and the virus has been detected in 928 dairy herds across 16 states, according to the US Centers for Disease Control and Prevention. There have been 67 reported human cases in the US, and one person has died. While we don’t yet have evidence that the virus can spread between people, the US and other countries are already preparing for potential outbreaks.

But this preparation relies on a thorough and clear understanding of what is happening on the ground. The WHO provides an important role in information sharing—countries report early signs of outbreaks to the agency, which then shares the information with its members. This kind of information not only allows countries to develop strategies to limit the spread of disease but can also allow them to share genetic sequences of viruses and develop vaccines. Member nations need to know what’s happening in the US, and the US needs to know what’s happening globally. “Both of those channels of communication would be hindered by this,” says Moss.

As if all of that weren’t enough, the US also stands to suffer in terms of its reputation as a leader in global public health. “By saying to the world ‘We don’t care about your health,’ it sends a message that is likely to reflect badly on it,” says McKee. “It’s a classic lose-lose situation.”

“It’s going to hurt global health,” says Moss. “It’s going to come back to bite us.”

Update: this article was amended to include commentary from Pauline Scheelbeek.

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