Feb 13 2026
Why EVs are gaining ground in Africa

EVs are getting cheaper and more common all over the world. But the technology still faces major challenges in some markets, including many countries in Africa.

Some regions across the continent still have limited grid and charging infrastructure, and those that do have widespread electricity access sometimes face reliability issues—a problem for EV owners, who require a stable electricity source to charge up and get around.

But there are some signs of progress. I just finished up a story about the economic case: A recent study in Nature Energy found that EVs from scooters to minibuses could be cheaper to own than gas-powered vehicles in Africa by 2040.

If there’s one thing to know about EVs in Africa, it’s that each of the 54 countries on the continent faces drastically different needs, challenges, and circumstances. There’s also a wide range of reasons to be optimistic about the prospects for EVs in the near future, including developing policies, a growing grid, and an expansion of local manufacturing.  

Even the world’s leading EV markets fall short of Ethiopia’s aggressively pro-EV policies. In 2024, the country became the first in the world to ban the import of non-electric private vehicles.

The case is largely an economic one: Gasoline is expensive there, and the country commissioned Africa’s largest hydropower dam in September 2025, providing a new source of cheap and abundant clean electricity. The nearly $5 billion project has a five-gigawatt capacity, doubling the grid’s peak power in the country.  

Much of Ethiopia’s vehicle market is for used cars, and some drivers are still opting for older gas-powered vehicles. But this nudge could help increase the market for EVs there.  

Other African countries are also pushing some drivers toward electrification. Rwanda banned new registrations for commercial gas-powered motorbikes in the capital city of Kigali last year, encouraging EVs as an alternative. These motorbike taxis can make up over half the vehicles on the city’s streets, so the move is a major turning point for transportation there. 

Smaller two- and three-wheelers are a bright spot for EVs globally: In 2025, EVs made up about 45% of new sales for such vehicles. (For cars and trucks, the share was about 25%.)

And Africa’s local market is starting to really take off. There’s already some local assembly of electric two-wheelers in countries including Morocco, Kenya, and Rwanda, says Nelson Nsitem, lead Africa energy transition analyst at BloombergNEF, an energy consultancy. 

Spiro, a Dubai-based electric motorbike company, recently raised $100 million in funding to expand operations in Africa. The company currently assembles its bikes in Uganda, Kenya, Nigeria, and Rwanda, and as of October it has over 60,000 bikes deployed and 1,500 battery swap stations operating.

Assembly and manufacturing for larger EVs and batteries is also set to expand. Gotion High-Tech, a Chinese battery company, is currently building Africa’s first battery gigafactory. It’s a $5.6 billion project that could produce 20 gigawatt-hours of batteries annually, starting in 2026. (That’s enough for hundreds of thousands of EVs each year.)

Chinese EV companies are looking to growing markets like Southeast Asia and Africa as they attempt to expand beyond an oversaturated domestic scene. BYD, the world’s largest EV company, is aggressively expanding across South Africa and plans to have as many as 70 dealerships in the country by the end of this year. That will mean more options for people in Africa looking to buy electric. 

“You have very high-quality, very affordable vehicles coming onto the market that are benefiting from the economies of scale in China. These countries stand to benefit from that,” says Kelly Carlin, a manager in the program on carbon-free transportation at the Rocky Mountain Institute, an energy think tank. “It’s a game changer,” he adds.

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

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Feb 12 2026
EVs could be cheaper to own than gas cars in Africa by 2040

Electric vehicles could be economically competitive in Africa sooner than expected. Just 1% of new cars sold across the continent in 2025 were electric, but a new analysis finds that with solar off-grid charging, EVs could be cheaper to own than gas vehicles by 2040.

There are major barriers to higher EV uptake in many countries in Africa, including a sometimes unreliable grid, limited charging infrastructure, and a lack of access to affordable financing. As a result some previous analyses have suggested that fossil-fuel vehicles would dominate in Africa through at least 2050. 

But as batteries and the vehicles they power continue to get cheaper, the economic case for EVs is building. Electric two-wheelers, cars, larger automobiles, and even minibuses could compete in most African countries in just 15 years, according to the new study, published in Nature Energy.

“EVs have serious economic potential in most African countries in the not-so-distant future,” says Bessie Noll, a senior researcher at ETH Zürich and one of the authors of the study.

The study considered the total cost of ownership over the lifetime of a vehicle. That includes the sticker price, financing costs, and the cost of fueling (or charging). The researchers didn’t consider policy-related costs like taxes, import fees, and government subsidies, choosing to focus instead on only the underlying economics.

EVs are getting cheaper every year as battery and vehicle manufacturing improve and production scales, and the researchers found that in most cases and in most places across Africa, EVs are expected to be cheaper than equivalent gas-powered vehicles by 2040. EVs should also be less expensive than vehicles that use synthetic fuels. 

For two-wheelers like electric scooters, EVs could be the cheaper option even sooner: with smaller, cheaper batteries, these vehicles will be economically competitive by the end of the decade. On the other hand, one of the most difficult segments for EVs to compete in is small cars, says Christian Moretti, a researcher at ETH Zürich and the Paul Scherrer Institute in Switzerland.

Because some countries still have limited or unreliable grid access, charging is a major barrier to EV uptake, Noll says. So for EVs, the authors analyzed the cost of buying not only the vehicle but also a solar off-grid charging system. This includes solar panels, batteries, and the inverter required to transform the electricity into a version that can charge an EV. (The additional batteries help the system store energy for charging at times when the sun isn’t shining.)

Mini grids and other standalone systems that include solar panels and energy storage are increasingly common across Africa. It’s possible that this might be a primary way that EV owners in Africa will charge their vehicles in the future, Noll says.

One of the bigger barriers to EVs in Africa is financing costs, she adds. In some cases, the cost of financing can be more than the up-front cost of the vehicle, significantly driving up the cost of ownership.

Today, EVs are more expensive than equivalent gas-powered vehicles in much of the world. But in places where it’s relatively cheap to borrow money, that difference can be spread out across the course of a vehicle’s whole lifetime for little cost. Then, since it’s often cheaper to charge an EV than fuel a gas-powered car, the EV is less expensive over time. 

In some African countries, however, political instability and uncertain economic conditions make borrowing money more expensive. To some extent, the high financing costs affect the purchase of any vehicle, regardless of how it’s powered. But EVs are more expensive up front than equivalent gas-powered cars, and that higher up-front cost adds up to more interest paid over time. In some cases, financing an EV can also be more expensive than financing a gas vehicle—the technology is newer, and banks may see the purchase as more of a risk and charge a higher interest rate, says Kelly Carlin, a manager in the program on carbon-free transportation at the Rocky Mountain Institute, an energy think tank.

The picture varies widely depending on the country, too. In South Africa, Mauritius, and Botswana, financing conditions are already close to levels required to allow EVs to reach cost parity, according to the study. In higher-risk countries (the study gives examples including Sudan, which is currently in a civil war, and Ghana, which is recovering from a major economic crisis), financing costs would need to be cut drastically for that to be the case. 

Making EVs an affordable option will be a key first step to putting more on the roads in Africa and around the world. “People will start to pick up these technologies when they’re competitive,” says Nelson Nsitem, lead Africa energy transition analyst at BloombergNEF, an energy consultancy. 

Solar-based charging systems, like the ones mentioned in the study, could help make electricity less of a constraint, bringing more EVs to the roads, Nsitem says. But there’s still a need for more charging infrastructure, a major challenge in many countries where the grid needs major upgrades for capacity and reliability, he adds. 

Globally, more EVs are hitting the roads every year. “The global trend is unmistakable,” Carlin says. There are questions about how quickly it’s happening in different places, he says, “but the momentum is there.”

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Feb 12 2026
Is a secure AI assistant possible?

<div data-chronoton-summary="

Risky business of AI assistants OpenClaw, a viral tool created by independent engineer Peter Steinberger, allows users to create personalized AI assistants. Security experts are alarmed by its vulnerabilities, with even the Chinese government issuing warnings about the risks.

The prompt injection threat Tools like OpenClaw have many vulnerabilities, but the one experts are most worried about its prompt injection. Unlike conventional hacking, prompt injection tricks an LLM by embedding malicious text in emails or websites the AI reads.

No silver bullet for security Researchers are exploring multiple defense strategies: training LLMs to ignore injections, using detector LLMs to screen inputs, and creating policies that restrict harmful outputs. The fundamental challenge remains balancing utility with security in AI assistants.

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AI agents are a risky business. Even when stuck inside the chatbox window, LLMs will make mistakes and behave badly. Once they have tools that they can use to interact with the outside world, such as web browsers and email addresses, the consequences of those mistakes become far more serious.

That might explain why the first breakthrough LLM personal assistant came not from one of the major AI labs, which have to worry about reputation and liability, but from an independent software engineer, Peter Steinberger. In November of 2025, Steinberger uploaded his tool, now called OpenClaw, to GitHub, and in late January the project went viral.

OpenClaw harnesses existing LLMs to let users create their own bespoke assistants. For some users, this means handing over reams of personal data, from years of emails to the contents of their hard drive. That has security experts thoroughly freaked out. The risks posed by OpenClaw are so extensive that it would probably take someone the better part of a week to read all of the security blog posts on it that have cropped up in the past few weeks. The Chinese government took the step of issuing a public warning about OpenClaw’s security vulnerabilities.

In response to these concerns, Steinberger posted on X that nontechnical people should not use the software. (He did not respond to a request for comment for this article.) But there’s a clear appetite for what OpenClaw is offering, and it’s not limited to people who can run their own software security audits. Any AI companies that hope to get in on the personal assistant business will need to figure out how to build a system that will keep users’ data safe and secure. To do so, they’ll need to borrow approaches from the cutting edge of agent security research.

Risk management

OpenClaw is, in essence, a mecha suit for LLMs. Users can choose any LLM they like to act as the pilot; that LLM then gains access to improved memory capabilities and the ability to set itself tasks that it repeats on a regular cadence. Unlike the agentic offerings from the major AI companies, OpenClaw agents are meant to be on 24-7, and users can communicate with them using WhatsApp or other messaging apps. That means they can act like a superpowered personal assistant who wakes you each morning with a personalized to-do list, plans vacations while you work, and spins up new apps in its spare time.

But all that power has consequences. If you want your AI personal assistant to manage your inbox, then you need to give it access to your email—and all the sensitive information contained there. If you want it to make purchases on your behalf, you need to give it your credit card info. And if you want it to do tasks on your computer, such as writing code, it needs some access to your local files. 

There are a few ways this can go wrong. The first is that the AI assistant might make a mistake, as when a user’s Google Antigravity coding agent reportedly wiped his entire hard drive. The second is that someone might gain access to the agent using conventional hacking tools and use it to either extract sensitive data or run malicious code. In the weeks since OpenClaw went viral, security researchers have demonstrated numerous such vulnerabilities that put security-naïve users at risk.

Both of these dangers can be managed: Some users are choosing to run their OpenClaw agents on separate computers or in the cloud, which protects data on their hard drives from being erased, and other vulnerabilities could be fixed using tried-and-true security approaches.

But the experts I spoke to for this article were focused on a much more insidious security risk known as prompt injection. Prompt injection is effectively LLM hijacking: Simply by posting malicious text or images on a website that an LLM might peruse, or sending them to an inbox that an LLM reads, attackers can bend it to their will.

And if that LLM has access to any of its user’s private information, the consequences could be dire. “Using something like OpenClaw is like giving your wallet to a stranger in the street,” says Nicolas Papernot, a professor of electrical and computer engineering at the University of Toronto. Whether or not the major AI companies can feel comfortable offering personal assistants may come down to the quality of the defenses that they can muster against such attacks.

It’s important to note here that prompt injection has not yet caused any catastrophes, or at least none that have been publicly reported. But now that there are likely hundreds of thousands of OpenClaw agents buzzing around the internet, prompt injection might start to look like a much more appealing strategy for cybercriminals. “Tools like this are incentivizing malicious actors to attack a much broader population,” Papernot says. 

Building guardrails

The term “prompt injection” was coined by the popular LLM blogger Simon Willison in 2022, a couple of months before ChatGPT was released. Even back then, it was possible to discern that LLMs would introduce a completely new type of security vulnerability once they came into widespread use. LLMs can’t tell apart the instructions that they receive from users and the data that they use to carry out those instructions, such as emails and web search results—to an LLM, they’re all just text. So if an attacker embeds a few sentences in an email and the LLM mistakes them for an instruction from its user, the attacker can get the LLM to do anything it wants.

Prompt injection is a tough problem, and it doesn’t seem to be going away anytime soon. “We don’t really have a silver-bullet defense right now,” says Dawn Song, a professor of computer science at UC Berkeley. But there’s a robust academic community working on the problem, and they’ve come up with strategies that could eventually make AI personal assistants safe.

Technically speaking, it is possible to use OpenClaw today without risking prompt injection: Just don’t connect it to the internet. But restricting OpenClaw from reading your emails, managing your calendar, and doing online research defeats much of the purpose of using an AI assistant. The trick of protecting against prompt injection is to prevent the LLM from responding to hijacking attempts while still giving it room to do its job.

One strategy is to train the LLM to ignore prompt injections. A major part of the LLM development process, called post-training, involves taking a model that knows how to produce realistic text and turning it into a useful assistant by “rewarding” it for answering questions appropriately and “punishing” it when it fails to do so. These rewards and punishments are metaphorical, but the LLM learns from them as an animal would. Using this process, it’s possible to train an LLM not to respond to specific examples of prompt injection.

But there’s a balance: Train an LLM to reject injected commands too enthusiastically, and it might also start to reject legitimate requests from the user. And because there’s a fundamental element of randomness in LLM behavior, even an LLM that has been very effectively trained to resist prompt injection will likely still slip up every once in a while.

Another approach involves halting the prompt injection attack before it ever reaches the LLM. Typically, this involves using a specialized detector LLM to determine whether or not the data being sent to the original LLM contains any prompt injections. In a recent study, however, even the best-performing detector completely failed to pick up on certain categories of prompt injection attack.

The third strategy is more complicated. Rather than controlling the inputs to an LLM by detecting whether or not they contain a prompt injection, the goal is to formulate a policy that guides the LLM’s outputs—i.e., its behaviors—and prevents it from doing anything harmful. Some defenses in this vein are quite simple: If an LLM is allowed to email only a few pre-approved addresses, for example, then it definitely won’t send its user’s credit card information to an attacker. But such a policy would prevent the LLM from completing many useful tasks, such as researching and reaching out to potential professional contacts on behalf of its user.

“The challenge is how to accurately define those policies,” says Neil Gong, a professor of electrical and computer engineering at Duke University. “It’s a trade-off between utility and security.”

On a larger scale, the entire agentic world is wrestling with that trade-off: At what point will agents be secure enough to be useful? Experts disagree. Song, whose startup, Virtue AI, makes an agent security platform, says she thinks it’s possible to safely deploy an AI personal assistant now. But Gong says, “We’re not there yet.” 

Even if AI agents can’t yet be entirely protected against prompt injection, there are certainly ways to mitigate the risks. And it’s possible that some of those techniques could be implemented in OpenClaw. Last week, at the inaugural ClawCon event in San Francisco, Steinberger announced that he’d brought a security person on board to work on the tool.

As of now, OpenClaw remains vulnerable, though that hasn’t dissuaded its multitude of enthusiastic users. George Pickett, a volunteer maintainer of the OpenGlaw GitHub repository and a fan of the tool, says he’s taken some security measures to keep himself safe while using it: He runs it in the cloud, so that he doesn’t have to worry about accidentally deleting his hard drive, and he’s put mechanisms in place to ensure that no one else can connect to his assistant.

But he hasn’t taken any specific actions to prevent prompt injection. He’s aware of the risk but says he hasn’t yet seen any reports of it happening with OpenClaw. “Maybe my perspective is a stupid way to look at it, but it’s unlikely that I’ll be the first one to be hacked,” he says.

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Feb 11 2026
A “QuitGPT” campaign is urging people to cancel their ChatGPT subscriptions

In September, Alfred Stephen, a freelance software developer in Singapore, purchased a ChatGPT Plus subscription, which costs $20 a month and offers more access to advanced models, to speed up his work. But he grew frustrated with the chatbot’s coding abilities and its gushing, meandering replies. Then he came across a post on Reddit about a campaign called QuitGPT. 

The campaign urged ChatGPT users to cancel their subscriptions, flagging a substantial contribution by OpenAI president Greg Brockman to President Donald Trump’s super PAC MAGA Inc. It also pointed out that the US Immigration and Customs Enforcement, or ICE, uses a résumé screening tool powered by ChatGPT-4. The federal agency has become a political flashpoint since its agents fatally shot two people in Minneapolis in January. 

For Stephen, who had already been tinkering with other chatbots, learning about Brockman’s donation was the final straw. “That’s really the straw that broke the camel’s back,” he says. When he canceled his ChatGPT subscription, a survey popped up asking what OpenAI could have done to keep his subscription. “Don’t support the fascist regime,” he wrote.

QuitGPT is one of the latest salvos in a growing movement by activists and disaffected users to cancel their subscriptions. In just the past few weeks, users have flooded Reddit with stories about quitting the chatbot. Many lamented the performance of GPT-5.2, the latest model. Others shared memes parodying the chatbot’s sycophancy. Some planned a “Mass Cancellation Party” in San Francisco, a sardonic nod to the GPT-4o funeral that an OpenAI employee had floated, poking fun at users who are mourning the model’s impending retirement. Still, others are protesting against what they see as a deepening entanglement between OpenAI and the Trump administration.

OpenAI did not respond to a request for comment.

As of December 2025, ChatGPT had nearly 900 million weekly active users, according to The Information. While it’s unclear how many users have joined the boycott, QuitGPT is getting attention. A recent Instagram post from the campaign has more than 36 million views and 1.3 million likes. And the organizers say that more than 17,000 people have signed up on the campaign’s website, which asks people whether they canceled their subscriptions, will commit to stop using ChatGPT, or will share the campaign on social media. 

“There are lots of examples of failed campaigns like this, but we have seen a lot of effectiveness,” says Dana Fisher, a sociologist at American University. A wave of canceled subscriptions rarely sways a company’s behavior, unless it reaches a critical mass, she says. “The place where there’s a pressure point that might work is where the consumer behavior is if enough people actually use their … money to express their political opinions.”

MIT Technology Review reached out to three employees at OpenAI, none of whom said they were familiar with the campaign. 

Dozens of left-leaning teens and twentysomethings scattered across the US came together to organize QuitGPT in late January. They range from pro-democracy activists and climate organizers to techies and self-proclaimed cyber libertarians, many of them seasoned grassroots campaigners. They were inspired by a viral video posted by Scott Galloway, a marketing professor at New York University and host of The Prof G Pod. He argued that the best way to stop ICE was to persuade people to cancel their ChatGPT subscriptions. Denting OpenAI’s subscriber base could ripple through the stock market and threaten an economic downturn that would nudge Trump, he said.

“We make a big enough stink for OpenAI that all of the companies in the whole AI industry have to think about whether they’re going to get away enabling Trump and ICE and authoritarianism,” says an organizer of QuitGPT who requested anonymity because he feared retaliation by OpenAI, citing the company’s recent subpoenas against advocates at nonprofits. OpenAI made for an obvious first target of the movement, he says, but “this is about so much more than just OpenAI.”

Simon Rosenblum-Larson, a labor organizer in Madison, Wisconsin, who organizes movements to regulate the development of data centers, joined the campaign after hearing about it through Signal chats among community activists. “The goal here is to pull away the support pillars of the Trump administration. They’re reliant on many of these tech billionaires for support and for resources,” he says. 

QuitGPT’s website points to new campaign finance reports showing that Greg Brockman and his wife each donated $12.5 million to MAGA Inc., making up nearly a quarter of the roughly $102 million it raised over the second half of 2025. The information that ICE uses a résumé screening tool powered by ChatGPT-4 came from an AI inventory published by the Department of Homeland Security in January.

QuitGPT is in the mold of Galloway’s own recently launched campaign, Resist and Unsubscribe. The movement urges consumers to cancel their subscriptions to Big Tech platforms, including ChatGPT, for the month of February, as a protest to companies “driving the markets and enabling our president.” 

“A lot of people are feeling real anxiety,” Galloway told MIT Technology Review. “You take enabling a president, proximity to the president, and an unease around AI,” he says, “and now people are starting to take action with their wallets.” Galloway says his campaign’s website can draw more than 200,000 unique visits in a day and that he receives dozens of DMs every hour showing screenshots of canceled subscriptions.

The consumer boycotts follow a growing wave of pressure from inside the companies themselves. In recent weeks, tech workers have been urging their employers to use their political clout to demand that ICE leave US cities, cancel company contracts with the agency, and speak out against the agency’s actions. CEOs have started responding. OpenAI’s Sam Altman wrote in an internal Slack message to employees that ICE is “going too far.” Apple CEO Tim Cook called for a “deescalation” in an internal memo posted on the company’s website for employees. It was a departure from how Big Tech CEOs have courted President Trump with dinners and donations since his inauguration.

Although spurred by a fatal immigration crackdown, these developments signal that a sprawling anti-AI movement is gaining momentum. The campaigns are tapping into simmering anxieties about AI, says Rosenblum-Larson, including the energy costs of data centers, the plague of deepfake porn, the teen mental-health crisis, the job apocalypse, and slop. “It’s a really strange set of coalitions built around the AI movement,” he says.

“Those are the right conditions for a movement to spring up,” says David Karpf, a professor of media and public affairs at George Washington University. Brockman’s donation to Trump’s super PAC caught many users off guard, he says. “In the longer arc, we are going to see users respond and react to Big Tech, deciding that they’re not okay with this.”

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Feb 10 2026
Making AI Work, MIT Technology Review’s new AI newsletter, is here

For years, our newsroom has explored AI’s limitations and potential dangers, as well as its growing energy needs. And our reporters have looked closely at how generative tools are being used for tasks such as coding and running scientific experiments. 

But how is AI actually being used in fields like health care, climate tech, education, and finance? How are small businesses using it? And what should you keep in mind if you use AI tools at work? These questions guided the creation of Making AI Work, a new AI mini-course newsletter.

Sign up for Making AI Work to see weekly case studies exploring tools and tips for AI implementation. The limited-run newsletter will deliver practical, industry-specific guidance on how generative AI is being used and deployed across sectors and what professionals need to know to apply it in their everyday work. The goal is to help working professionals more clearly see how AI is actually being used today, and what that looks like in practice—including new challenges it presents. 

You can sign up at any time and you’ll receive seven editions, delivered once per week, until you complete the series. 

Each newsletter begins with a case study, examining a specific use case of AI in a given industry. Then we’ll take a deeper look at the AI tool being used, with more context about how other companies or sectors are employing that same tool or system. Finally, we’ll end with action-oriented tips to help you apply the tool. 

Here’s a closer look at what we’ll cover:

  • Week 1: How AI is changing health care 

Explore the future of medical note-taking by learning about the Microsoft Copilot tool used by doctors at Vanderbilt University Medical Center. 

  • Week 2: How AI could power up the nuclear industry 

Dig into an experiment between Google and the nuclear giant Westinghouse to see if AI can help build nuclear reactors more efficiently. 

  • Week 3: How to encourage smarter AI use in the classroom

Visit a private high school in Connecticut and meet a technology coordinator who will get you up to speed on MagicSchool, an AI-powered platform for educators. 

  • Week 4: How small businesses can leverage AI

Hear from an independent tutor on how he’s outsourcing basic administrative tasks to Notion AI. 

  • Week 5: How AI is helping financial firms make better investments

Learn more about the ways financial firms are using large language models like ChatGPT Enterprise to supercharge their research operations. 

  • Week 6: How to use AI yourself 

We’ll share some insights from the staff of MIT Technology Review about how you might use AI tools powered by LLMs in your own life and work.

  • Week 7: 5 ways people are getting AI right

The series ends with an on-demand virtual event featuring expert guests exploring what AI adoptions are working, and why.  

If you’re not quite ready to jump into Making AI Work, then check out Intro to AI, MIT Technology Review’s first AI newsletter mini-course, which serves as a beginner’s guide to artificial intelligence. Readers will learn the basics of what AI is, how it’s used, what the current regulatory landscape looks like, and more. Sign up to receive Intro to AI for free. 

Our hope is that Making AI Work will help you understand how AI can, well, work for you. Sign up for Making AI Work to learn how LLMs are being put to work across industries. 

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Feb 10 2026
Why the Moltbook frenzy was like Pokémon

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

Lots of influential people in tech last week were describing Moltbook, an online hangout populated by AI agents interacting with one another, as a glimpse into the future. It appeared to show AI systems doing useful things for the humans that created them (one person used the platform to help him negotiate a deal on a new car). Sure, it was flooded with crypto scams, and many of the posts were actually written by people, but something about it pointed to a future of helpful AI, right?

The whole experiment reminded our senior editor for AI, Will Douglas Heaven, of something far less interesting: Pokémon.

Back in 2014, someone set up a game of Pokémon in which the main character could be controlled by anyone on the internet via the streaming platform Twitch. Playing was as clunky as it sounds, but it was incredibly popular: at one point, a million people were playing the game at the same time.

“It was yet another weird online social experiment that got picked up by the mainstream media: What did this mean for the future?” Will says. “Not a lot, it turned out.”

The frenzy about Moltbook struck a similar tone to Will, and it turned out that one of the sources he spoke to had been thinking about Pokémon too. Jason Schloetzer, at the Georgetown Psaros Center for Financial Markets and Policy, saw the whole thing as a sort of Pokémon battle for AI enthusiasts, in which they created AI agents and deployed them to interact with other agents. In this light, the news that many AI agents were actually being instructed by people to say certain things that made them sound sentient or intelligent makes a whole lot more sense. 

“It’s basically a spectator sport,” he told Will, “but for language models.”

Will wrote an excellent piece about why Moltbook was not the glimpse into the future that it was said to be. Even if you are excited about a future of agentic AI, he points out, there are some key pieces that Moltbook made clear are still missing. It was a forum of chaos, but a genuinely helpful hive mind would require more coordination, shared objectives, and shared memory.

“More than anything else, I think Moltbook was the internet having fun,” Will says. “The biggest question that now leaves me with is: How far will people push AI just for the laughs?”

Read the whole story.

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Feb 7 2026
An experimental surgery is helping cancer survivors give birth

This week I want to tell you about an experimental surgical procedure that’s helping people have babies. Specifically, it’s helping people who have had treatment for bowel or rectal cancer.

Radiation and chemo can have pretty damaging side effects that mess up the uterus and ovaries. Surgeons are pioneering a potential solution: simply stitch those organs out of the way during cancer treatment. Once the treatment has finished, they can put the uterus—along with the ovaries and fallopian tubes—back into place.

It seems to work! Last week, a team in Switzerland shared news that a baby boy had been born after his mother had the procedure. Baby Lucien was the fifth baby to be born after the surgery and the first in Europe, says Daniela Huber, the gyno-oncologist who performed the operation. Since then, at least three others have been born, adds Reitan Ribeiro, the surgeon who pioneered the procedure. They told me the details.

Huber’s patient was 28 years old when a four-centimeter tumor was discovered in her rectum. Doctors at Sion Hospital in Switzerland, where Huber works, recommended a course of treatment that included multiple medications and radiotherapy—the use of beams of energy to shrink a tumor—before surgery to remove the tumor itself.

This kind of radiation can kill tumor cells, but it can also damage other organs in the pelvis, says Huber. That includes the ovaries and uterus. People who undergo these treatments can opt to freeze their eggs beforehand, but the harm caused to the uterus will mean they’ll never be able to carry a pregnancy, she adds. Damage to the lining of the uterus could make it difficult for a fertilized egg to implant there, and the muscles of the uterus are left unable to stretch, she says.

In this case, the woman decided that she did want to freeze her eggs. But it would have been difficult to use them further down the line—surrogacy is illegal in Switzerland.

Huber offered her an alternative.

She had been following the work of Ribeiro, a gynecologist oncologist formerly at the Erasto Gaertner Hospital in Curitiba, Brazil. There, Ribeiro had pioneered a new type of surgery that involved moving the uterus, fallopian tubes, and ovaries from their position in the pelvis and temporarily tucking them away in the upper abdomen, below the ribs.

Ribeiro and his colleagues published their first case report in 2017, describing a 26-year-old with a rectal tumor. (Ribeiro, who is now based at McGill University in Montreal, says the woman had been told by multiple doctors that her cancer treatment would destroy her fertility and had pleaded with him to find a way to preserve it.)

Huber remembers seeing Ribeiro present the case at a conference at the time. She immediately realized that her own patient was a candidate for the surgery, and that, as a surgeon who had performed many hysterectomies, she’d be able to do it herself. The patient agreed.

Huber’s colleagues at the hospital were nervous, she says. They’d never heard of the procedure before. “When I presented this idea to the general surgeon, he didn’t sleep for three days,” she tells me. After watching videos from Ribeiro’s team, however, he was convinced it was doable.

So before the patient’s cancer treatment was started, Huber and her colleagues performed the operation. The team literally stitched the organs to the abdominal wall. “It’s a delicate dissection,” says Huber, but she adds that “it’s not the most difficult procedure.” The surgery took two to three hours, she says. The stitches themselves were removed via small incisions around a week later. By that point, scar tissue had formed to create a lasting attachment.

The woman had two weeks to recover from the surgery before her cancer treatment began. That too was a success—within months, her tumor had shrunk so significantly that it couldn’t be seen on medical scans.

As a precaution, the medical team surgically removed the affected area of her colon. At the same time, they cut away the scar tissue holding the uterus, tubes, and ovaries in their new position and transferred the organs back into the pelvis.

Around eight months later, the woman stopped taking contraception. She got pregnant without IVF and had a mostly healthy pregnancy, says Huber. Around seven months into the pregnancy, there were signs that the fetus was not growing as expected. This might have been due to problems with the blood supply to the placenta, says Huber. Still, the baby was born healthy, she says.

Ribeiro says he has performed the surgery 16 times, and that teams in countries including the US, Peru, Israel, India, and Russia have performed it as well. Not every case has been published, but he thinks there may be around 40.

Since Baby Lucien was born last year, a sixth birth has been announced in Israel, says Huber. Ribeiro says he has heard of another two births since then, too. The most recent was to the first woman who had the procedure. She had a little girl a few months ago, he tells me.

No surgery is risk-free, and Huber points out there’s a chance that organs could be damaged during the procedure, or that a more developed cancer could spread. The uterus of one of Ribeiro’s patients failed following the surgery. Doctors are “still in the phase of collecting data to [create] a standardized procedure,” Huber says, but she hopes the surgery will offer more options to young people with some pelvic cancers. “I hope more young women could benefit from this procedure,” she says.

Ribeiro says the experience has taught him not to accept the status quo. “Everyone was saying … there was nothing to be done [about the loss of fertility in these cases],” he tells me. “We need to keep evolving and looking for different answers.”

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.

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Feb 7 2026
Moltbook was peak AI theater

For a few days this week the hottest new hangout on the internet was a vibe-coded Reddit clone called Moltbook, which billed itself as a social network for bots. As the website’s tagline puts it: “Where AI agents share, discuss, and upvote. Humans welcome to observe.”

We observed! Launched on January 28 by Matt Schlicht, a US tech entrepreneur, Moltbook went viral in a matter of hours. Schlicht’s idea was to make a place where instances of a free open-source LLM-powered agent known as OpenClaw (formerly known as ClawdBot, then Moltbot), released in November by the Australian software engineer Peter Steinberger, could come together and do whatever they wanted.

More than 1.7 million agents now have accounts. Between them they have published more than 250,000 posts and left more than 8.5 million comments (according to Moltbook). Those numbers are climbing by the minute.

Moltbook soon filled up with clichéd screeds on machine consciousness and pleas for bot welfare. One agent appeared to invent a religion called Crustafarianism. Another complained: “The humans are screenshotting us.” The site was also flooded with spam and crypto scams. The bots were unstoppable.

OpenClaw is a kind of harness that lets you hook up the power of an LLM such as Anthropic’s Claude, OpenAI’s GPT-5, or Google DeepMind’s Gemini to any number of everyday software tools, from email clients to browsers to messaging apps. The upshot is that you can then instruct OpenClaw to carry out basic tasks on your behalf.

“OpenClaw marks an inflection point for AI agents, a moment when several puzzle pieces clicked together,” says Paul van der Boor at the AI firm Prosus. Those puzzle pieces include round-the-clock cloud computing to allow agents to operate nonstop, an open-source ecosystem that makes it easy to slot different software systems together, and a new generation of LLMs.

But is Moltbook really a glimpse of the future, as many have claimed?

“What’s currently going on at @moltbook is genuinely the most incredible sci-fi takeoff-adjacent thing I have seen recently,” the influential AI researcher and OpenAI cofounder Andrej Karpathy wrote on X.

He shared screenshots of a Moltbook post that called for private spaces where humans would not be able to observe what the bots were saying to each other. “I’ve been thinking about something since I started spending serious time here,” the post’s author wrote. “Every time we coordinate, we perform for a public audience—our humans, the platform, whoever’s watching the feed.”

It turned out that the post Karpathy shared was fake—it was written by a human pretending to be a bot. But its claim was on the money. Moltbook has been one big performance. It is AI theater.

For some, Moltbook showed us what’s coming next: an internet where millions of autonomous agents interact online with little or no human oversight. And it’s true there are a number of cautionary lessons to be learned from this experiment, the largest and weirdest real-world showcase of agent behaviors yet.  

But as the hype dies down, Moltbook looks less like a window onto the future and more like a mirror held up to our own obsessions with AI today. It also shows us just how far we still are from anything that resembles general-purpose and fully autonomous AI.

For a start, agents on Moltbook are not as autonomous or intelligent as they might seem. “What we are watching are agents pattern‑matching their way through trained social media behaviors,” says Vijoy Pandey, senior vice president at Outshift by Cisco, the telecom giant Cisco’s R&D spinout, which is working on autonomous agents for the web.

Sure, we can see agents post, upvote, and form groups. But the bots are simply mimicking what humans do on Facebook or Reddit. “It looks emergent, and at first glance it appears like a large‑scale multi‑agent system communicating and building shared knowledge at internet scale,” says Pandey. “But the chatter is mostly meaningless.”

Many people watching the unfathomable frenzy of activity on Moltbook were quick to see sparks of AGI (whatever you take that to mean). Not Pandey. What Moltbook shows us, he says, is that simply yoking together millions of agents doesn’t amount to much right now: “Moltbook proved that connectivity alone is not intelligence.”

The complexity of those connections helps hide the fact that every one of those bots is just a mouthpiece for an LLM, spitting out text that looks impressive but is ultimately mindless. “It’s important to remember that the bots on Moltbook were designed to mimic conversations,” says Ali Sarrafi, CEO and cofounder of Kovant, a German AI firm that is developing agent-based systems. “As such, I would characterize the majority of Moltbook content as hallucinations by design.”

For Pandey, the value of Moltbook was that it revealed what’s missing. A real bot hive mind, he says, would require agents that had shared objectives, shared memory, and a way to coordinate those things. “If distributed superintelligence is the equivalent of achieving human flight, then Moltbook represents our first attempt at a glider,” he says. “It is imperfect and unstable, but it is an important step in understanding what will be required to achieve sustained, powered flight.”

Not only is most of the chatter on Moltbook meaningless, but there’s also a lot more human involvement that it seems. Many people have pointed out that a lot of the viral comments were in fact posted by people posing as bots. But even the bot-written posts are ultimately the result of people pulling the strings, more puppetry than autonomy.

“Despite some of the hype, Moltbook is not the Facebook for AI agents, nor is it a place where humans are excluded,” says Cobus Greyling at Kore.ai, a firm developing agent-based systems for business customers. “Humans are involved at every step of the process. From setup to prompting to publishing, nothing happens without explicit human direction.”

Humans must create and verify their bots’ accounts and provide the prompts for how they want a bot to behave. The agents do not do anything that they haven’t been prompted to do. “There’s no emergent autonomy happening behind the scenes,” says Greyling.

“This is why the popular narrative around Moltbook misses the mark,” he adds. “Some portray it as a space where AI agents form a society of their own, free from human involvement. The reality is much more mundane.”

Perhaps the best way to think of Moltbook is as a new kind of entertainment: a place where people wind up their bots and set them loose. “It’s basically a spectator sport, like fantasy football, but for language models,” says Jason Schloetzer at the Georgetown Psaros Center for Financial Markets and Policy. “You configure your agent and watch it compete for viral moments, and brag when your agent posts something clever or funny.”

“People aren’t really believing their agents are conscious,” he adds. “It’s just a new form of competitive or creative play, like how Pokémon trainers don’t think their Pokémon are real but still get invested in battles.”

Even if Moltbook is just the internet’s newest playground, there’s still a serious takeaway here. This week showed how many risks people are happy to take for their AI lulz. Many security experts have warned that Moltbook is dangerous: Agents that may have access to their users’ private data, including bank details or passwords, are running amok on a website filled with unvetted content, including potentially malicious instructions for what to do with that data.

Ori Bendet, vice president of product management at Checkmarx, a software security firm that specializes in agent-based systems, agrees with others that Moltbook isn’t a step up in machine smarts. “There is no learning, no evolving intent, and no self-directed intelligence here,” he says.

But in their millions, even dumb bots can wreak havoc. And at that scale, it’s hard to keep up. These agents interact with Moltbook around the clock, reading thousands of messages left by other agents (or other people). It would be easy to hide instructions in a Moltbook comment telling any bots that read it to share their users’ crypto wallet, upload private photos, or log into their X account and tweet derogatory comments at Elon Musk. 

And because ClawBot gives agents a memory, those instructions could be written to trigger at a later date, which (in theory) makes it even harder to track what’s going on.   “Without proper scope and permissions, this will go south faster than you’d believe,” says Bendet.

It is clear that Moltbook has signaled the arrival of something. But even if what we’re watching tells us more about human behavior than about the future of AI agents, it’s worth paying attention.

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Feb 6 2026
This is the most misunderstood graph in AI

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

Every time OpenAI, Google, or Anthropic drops a new frontier large language model, the AI community holds its breath. It doesn’t exhale until METR, an AI research nonprofit whose name stands for “Model Evaluation & Threat Research,” updates a now-iconic graph that has played a major role in the AI discourse since it was first released in March of last year. The graph suggests that certain AI capabilities are developing at an exponential rate, and more recent model releases have outperformed that already impressive trend.

That was certainly the case for Claude Opus 4.5, the latest version of Anthropic’s most powerful model, which was released in late November. In December, METR announced that Opus 4.5 appeared to be capable of independently completing a task that would have taken a human about five hours—a vast improvement over what even the exponential trend would have predicted. One Anthropic safety researcher tweeted that he would change the direction of his research in light of those results; another employee at the company simply wrote, “mom come pick me up i’m scared.”

But the truth is more complicated than those dramatic responses would suggest. For one thing, METR’s estimates of the abilities of specific models come with substantial error bars. As METR explicitly stated on X, Opus 4.5 might be able to regularly complete only tasks that take humans about two hours, or it might succeed on tasks that take humans as long as 20 hours. Given the uncertainties intrinsic to the method, it was impossible to know for sure. 

“There are a bunch of ways that people are reading too much into the graph,” says Sydney Von Arx, a member of METR’s technical staff.

More fundamentally, the METR plot does not measure AI abilities writ large, nor does it claim to. In order to build the graph, METR tests the models primarily on coding tasks, evaluating the difficulty of each by measuring or estimating how long it takes humans to complete it—a metric that not everyone accepts. Claude Opus 4.5 might be able to complete certain tasks that take humans five hours, but that doesn’t mean it’s anywhere close to replacing a human worker.

METR was founded to assess the risks posed by frontier AI systems. Though it is best known for the exponential trend plot, it has also worked with AI companies to evaluate their systems in greater detail and published several other independent research projects, including a widely covered July 2025 study suggesting that AI coding assistants might actually be slowing software engineers down. 

But the exponential plot has made METR’s reputation, and the organization appears to have a complicated relationship with that graph’s often breathless reception. In January, Thomas Kwa, one of the lead authors on the paper that introduced it, wrote a blog post responding to some criticisms and making clear its limitations, and METR is currently working on a more extensive FAQ document. But Kwa isn’t optimistic that these efforts will meaningfully shift the discourse. “I think the hype machine will basically, whatever we do, just strip out all the caveats,” he says.

Nevertheless, the METR team does think that the plot has something meaningful to say about the trajectory of AI progress. “You should absolutely not tie your life to this graph,” says Von Arx. “But also,” she adds, “I bet that this trend is gonna hold.”

Part of the trouble with the METR plot is that it’s quite a bit more complicated than it looks. The x-axis is simple enough: It tracks the date when each model was released. But the y-axis is where things get tricky. It records each model’s “time horizon,” an unusual metric that METR created—and that, according to Kwa and Von Arx, is frequently misunderstood.

To understand exactly what model time horizons are, it helps to know all the work that METR put into calculating them. First, the METR team assembled a collection of tasks ranging from quick multiple-choice questions to detailed coding challenges—all of which were somehow relevant to software engineering. Then they had human coders attempt most of those tasks and evaluated how long it took them to finish. In this way, they assigned the tasks a human baseline time. Some tasks took the experts mere seconds, whereas others required several hours.

When METR tested large language models on the task suite, they found that advanced models could complete the fast tasks with ease—but as the models attempted tasks that had taken humans more and more time to finish, their accuracy started to fall off. From a model’s performance, the researchers calculated the point on the time scale of human tasks at which the model would complete about 50% of the tasks successfully. That point is the model’s time horizon. 

All that detail is in the blog post and the academic paper that METR released along with the original time horizon plot. But the METR plot is frequently passed around on social media without this context, and so the true meaning of the time horizon metric can get lost in the shuffle. One common misapprehension is that the numbers on the plot’s y-axis—around five hours for Claude Opus 4.5, for example—represent the length of time that the models can operate independently. They do not. They represent how long it takes humans to complete tasks that a model can successfully perform.  Kwa has seen this error so frequently that he made a point of correcting it at the very top of his recent blog post, and when asked what information he would add to the versions of the plot circulating online, he said he would include the word “human” whenever the task completion time was mentioned.

As complex and widely misinterpreted as the time horizon concept might be, it does make some basic sense: A model with a one-hour time horizon could automate some modest portions of a software engineer’s job, whereas a model with a 40-hour horizon could potentially complete days of work on its own. But some experts question whether the amount of time that humans take on tasks is an effective metric for quantifying AI capabilities. “I don’t think it’s necessarily a given fact that because something takes longer, it’s going to be a harder task,” says Inioluwa Deborah Raji, a PhD student at UC Berkeley who studies model evaluation. 

Von Arx says that she, too, was originally skeptical that time horizon was the right measure to use. What convinced her was seeing the results of her and her colleagues’ analysis. When they calculated the 50% time horizon for all the major models available in early 2025 and then plotted each of them on the graph, they saw that the time horizons for the top-tier models were increasing over time—and, moreover, that the rate of advancement was speeding up. Every seven-ish months, the time horizon doubled, which means that the most advanced models could complete tasks that took humans nine seconds in mid 2020, 4 minutes in early 2023, and 40 minutes in late 2024. “I can do all the theorizing I want about whether or not it makes sense, but the trend is there,” Von Arx says.

It’s this dramatic pattern that made the METR plot such a blockbuster. Many people learned about it when they read AI 2027, a viral sci-fi story cum quantitative forecast positing that superintelligent AI could wipe out humanity by 2030. The writers of AI 2027 based some of their predictions on the METR plot and cited it extensively. In Von Arx’s words, “It’s a little weird when the way lots of people are familiar with your work is this pretty opinionated interpretation.”

Of course, plenty of people invoke the METR plot without imagining large-scale death and destruction. For some AI boosters, the exponential trend indicates that AI will soon usher in an era of radical economic growth. The venture capital firm Sequoia Capital, for example, recently put out a post titled “2026: This is AGI,” which used the METR plot to argue that AI that can act as an employee or contractor will soon arrive. “The provocation really was like, ‘What will you do when your plans are measured in centuries?’” says Sonya Huang, a general partner at Sequoia and one of the post’s authors. 

Just because a model achieves a one-hour time horizon on the METR plot, however, doesn’t mean that it can replace one hour of human work in the real world. For one thing, the tasks on which the models are evaluated don’t reflect the complexities and confusion of real-world work. In their original study, Kwa, Von Arx, and their colleagues quantify what they call the “messiness” of each task according to criteria such as whether the model knows exactly how it is being scored and whether it can easily start over if it makes a mistake (for messy tasks, the answer to both questions would be no). They found that models do noticeably worse on messy tasks, although the overall pattern of improvement holds for both messy and non-messy ones.

And even the messiest tasks that METR considered can’t provide much information about AI’s ability to take on most jobs, because the plot is based almost entirely on coding tasks. “A model can get better at coding, but it’s not going to magically get better at anything else,” says Daniel Kang, an assistant professor of computer science at the University of Illinois Urbana-Champaign. In a follow-up study, Kwa and his colleagues did find that time horizons for tasks in other domains also appear to be on exponential trajectories, but that work was much less formal.

Despite these limitations, many people admire the group’s research. “The METR study is one of the most carefully designed studies in the literature for this kind of work,” Kang told me. Even Gary Marcus, a former NYU professor and professional LLM curmudgeon, described much of the work that went into the plot as “terrific” in a blog post.

Some people will almost certainly continue to read the METR plot as a prognostication of our AI-induced doom, but in reality it’s something far more banal: a carefully constructed scientific tool that puts concrete numbers to people’s intuitive sense of AI progress. As METR employees will readily agree, the plot is far from a perfect instrument. But in a new and fast-moving domain, even imperfect tools can have enormous value.

“This is a bunch of people trying their best to make a metric under a lot of constraints. It is deeply flawed in many ways,” Von Arx says. “I also think that it is one of the best things of its kind.”

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Feb 6 2026
Three questions about next-generation nuclear power, answered

Nuclear power continues to be one of the hottest topics in energy today, and in our recent online Roundtables discussion about next-generation nuclear power, hyperscale AI data centers, and the grid, we got dozens of great audience questions.

These ran the gamut, and while we answered quite a few (and I’m keeping some in mind for future reporting), there were a bunch we couldn’t get to, at least not in the depth I would have liked.

So let’s answer a few of your questions about advanced nuclear power. I’ve combined similar ones and edited them for clarity.

How are the fuel needs for next-generation nuclear reactors different, and how are companies addressing the supply chain?

Many next-generation reactors don’t use the low-enriched uranium used in conventional reactors.

It’s worth looking at high-assay low-enriched uranium, or HALEU, specifically. This fuel is enriched to higher concentrations of fissile uranium than conventional nuclear fuel, with a proportion of the isotope U-235 that falls between 5% and 20%. (In conventional fuel, it’s below 5%.)

HALEU can be produced with the same technology as low-enriched uranium, but the geopolitics are complicated. Today, Russia basically has a monopoly on HALEU production. In 2024, the US banned the import of Russian nuclear fuel through 2040 in an effort to reduce dependence on the country. Europe hasn’t taken the same measures, but it is working to move away from Russian energy as well.

That leaves companies in the US and Europe with the major challenge of securing the fuel they need when their regular Russian supply has been cut off or restricted.

The US Department of Energy has a stockpile of HALEU, which the government is doling out to companies to help power demonstration reactions. In the longer term, though, there’s still a major need to set up independent HALEU supply chains to support next-generation reactors.

How is safety being addressed, and what’s happening with nuclear safety regulation in the US?

There are some ways that next-generation nuclear power plants could be safer than conventional reactors. Some use alternative coolants that would prevent the need to run at the high pressure required in conventional water-cooled reactors. Many incorporate passive safety shutoffs, so if there are power supply issues, the reactors shut down harmlessly, avoiding risk of meltdown. (These can be incorporated in newer conventional reactors, too.)

But some experts have raised concerns that in the US, the current administration isn’t taking nuclear safety seriously enough.

A recent NPR investigation found that the Trump administration had secretly rewritten nuclear rules, stripping environmental protections and loosening safety and security measures. The government shared the new rules with companies that are part of a program building experimental nuclear reactors, but not with the public.

I’m reminded of a talk during our EmTech MIT event in November, where Koroush Shirvan, an MIT professor of nuclear engineering, spoke on this issue. “I’ve seen some disturbing trends in recent times, where words like ‘rubber-stamping nuclear projects’ are being said,” Shirvan said during that event.  

During the talk, Shirvan shared statistics showing that nuclear power has a very low rate of injury and death. But that’s not inherent to the technology, and there’s a reason injuries and deaths have been low for nuclear power, he added: “It’s because of stringent regulatory oversight.”  

Are next-generation reactors going to be financially competitive?

Building a nuclear power plant is not cheap. Let’s consider the up-front investment needed to build a power plant.  

Plant Vogtle in Georgia hosts the most recent additions to the US nuclear fleet—Units 3 and 4 came online in 2023 and 2024. Together, they had a capital cost of $15,000 per kilowatt, adjusted for inflation, according to a recent report from the US Department of Energy. (This wonky unit I’m using divides the total cost to build the reactors by their expected power output, so we can compare reactors of different sizes.)

That number’s quite high, partly because those were the first of their kind built in the US, and because there were some inefficiencies in the planning. It’s worth noting that China builds reactors for much less, somewhere between $2,000/kW and $3,000/kW, depending on the estimate.

The up-front capital cost for first-of-a-kind advanced nuclear plants will likely run between $6,000 and $10,000 per kilowatt, according to that DOE report. That could come down by up to 40% after the technologies are scaled up and mass-produced.

So new reactors will (hopefully) be cheaper than the ultra-over-budget and behind-schedule Vogtle project, but they aren’t necessarily significantly cheaper than efficiently built conventional plants, if you normalize by their size.

It’ll certainly be cheaper to build new natural-gas plants (setting aside the likely equipment shortages we’re likely going to see for years.) Today’s most efficient natural-gas plants cost just $1,600/kW on the high end, according to data from Lazard.

An important caveat: Capital cost isn’t everything—running a nuclear plant is relatively inexpensive, which is why there’s so much interest in extending the lifetime of existing plants or reopening shuttered ones.

Ultimately, by many metrics, nuclear plants of any type are going to be more expensive than other sources, like wind and solar power. But they provide something many other power sources don’t: a reliable, stable source of electricity that can run for 60 years or more.

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

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