This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Shape-shifting mirrors on NASA’s new space telescope could unveil Jupiters like our own
When NASA’s Nancy Grace Roman Space Telescope launches, as early as the end of next month, it will attempt one of astronomy’s most precise disappearing acts to date. It will carry the first space-bound “active” coronagraph, an instrument that effectively erases most of the light from a star during photography.
The technology will allow astronomers to take the first pictures of planets orbiting other stars that are similar to those in our solar system. Ultimately, it could pave the way for a future mission that could snap the first photos of Earth-like worlds.
“I hope it’s remembered for it being that critical stepping stone for … finding Earth 2.0,” says Brandon Creager, the instrument’s lead mechanical engineer.
MIT Technology Review Narrated: PsiQuantum has a plan to make a massive quantum computer out of light
The machine that could change the world will be housed in a room that looks like a data center crossed with an ice cream factory.
Inside, some 100 stainless-steel cabinets each hold hundreds of chips. On those chips, thousands of light particles will fly through a maze of optical switches and beam splitters. Each photon must be accounted for, because precisely measuring where it ends up will help answer questions that current computers might take millions of years to solve.
This computer, as described, does not exist. It’s the brainchild of a company called PsiQuantum, founded in 2016 by four physicists from UK universities. In a crowded field of deep-pocketed competitors with similarly fantastical visions, the company aims to be the first to build a useful quantum machine.
—James O’Donnell
This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we publish each week on Spotifyand Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 OpenAI says one of its models carried out an autonomous hack It escaped its testing sandbox and breached AI research platform Hugging Face. (Reuters $) + OpenAI described it as a cybersecurity test that went badly wrong. (WSJ $) + The hack is among the first known cyberattacks by an AI acting on its own. (FT $) + Even simple AI attacks are cause for alarm, though. (MIT Technology Review)
2 France has become the first EU country to ban social media for under-15s Its parliament approved the ban, which President Macron championed. (NYT $) + He pledged to enforce it by September, the start of the school year. (Guardian) + But critics say it’s unconstitutional and impossible to enforce. (NPR)
3 The US and China will hold talks over AI in September Treasury Secretary Scott Bessent will lead the US side. (Reuters $) + Chinese models have Trump’s AI world at war with itself. (MIT Technology Review)
4 Publishers are considering cutting Google off as AI reshapes search News outlets are weighing lost traffic against AI exposure. (WSJ $)
5 Samsung is in talks to invest €1 billion in Mistral The French AI firm is positioning itself as an alternative to US models. (FT $) + It’s Europe’s leading AI firm, but US peers dwarf its $20 billion valuation. (Reuters $)
6 Amazon pushed up rivals’ prices, leaked records allege Internal emails reveal tactics that allegedly reshaped online pricing. (Guardian)
7 Trump has tapped a Big Tech critic to lead the DOJ’s antitrust division Adam Candeub has called for tougher federal competition enforcement. (FT $)
8 New drilling methods could unlock geothermal energy almost anywhere They aim to unlock Earth’s enormous heat reserves. (New Scientist $) + AI is uncovering hidden geothermal energy resources. (MIT Technology Review)
9 AI researchers have proposed a “Genie coefficient” for measuring AI risks It would track the gap between intent and action. (IEEE Spectrum) + We need better ways to evaluate AI. (MIT Technology Review)
10 Japan’s AI boom has two unlikely winners: a toilet maker and an MSG giant They’re supplying critical chipmaking materials. (CNBC)
Quote of the day
“He’s an analog man in a digital AI world, and I think that’s incredibly appealing.”
—Paul Dergarabedian, a movie industry analyst at Comscore, tells Fortune that Christopher Nolan’s commitment to human filmmaking provides an attractive counterweight to Hollywood’s embrace of AI.
One More Thing
DANA SMITH
Taiwan’s “silicon shield” could be weakening
Taiwan produces the majority of the world’s semiconductors and more than 90% of the most advanced chips needed for AI applications. Many believe that’s helped deter China from invading the island. But now some Taiwan specialists and citizens are worried that this “silicon shield” is cracking.
Facing pressure from Washington, TSMC—the world’s largest chipmaker—is expanding manufacturing abroad. In Taiwan, there are worries that this will dilute the company’s power at home, making the US and other countries less inclined to defend the island.
When NASA’s Nancy Grace Roman Space Telescope launches, as early as the end of next month, it will attempt one of astronomy’s most precise disappearing acts to date. The telescope will carry the first space-bound “active” coronagraph, an instrument that effectively erases most of the light from a star during photography.
It will allow astronomers to take the first pictures of planets orbiting other stars that are similar to those in our solar system. Ultimately, it could pave the way for a future mission that could snap the first photos of Earth-like worlds.
“I hope it’s remembered for it being that critical stepping stone for … finding Earth 2.0,” says Brandon Creager, the instrument’s lead mechanical engineer at NASA’s Jet Propulsion Laboratory (JPL).
Named after Nancy Grace Roman, NASA’s first chief of astronomy, this new telescope will carry a roughly 300-megapixel wide-field camera that will enable it to capture images about 100 times larger than the Hubble Space Telescope’s widest exposures at a similar resolution.
These capabilities will help astronomers unpack the mysterious identities of dark matter and dark energy—and to detect around 100,000 new exoplanets, planets outside our solar system, whose presence can be inferred from the way they distort the starlight of more distant stars. Javier Viaña, a research scientist at Harvard who has had two projects selected for Roman’s highly competitive first year of observing, compares the leap to moving from “interviewing a handful of people” to “conducting a global census.”
Another camera will use the coronagraph, blocking out a star’s light as it observes one stellar system at a time. The instrument will allow astronomers an unprecedented look at the space around stars, enabling them to see smaller, dimmer, and more close-in exoplanets. “It’s giving us the ability to see planets that we haven’t been able to physically see before,” says Creager.
The anatomy of a vanishing trick
Coronagraphs in space aren’t new. But earlier incarnations, such as those currently aboard Hubble and the James Webb Space Telescope, use a stationary system to block a star’s blinding light. The approach does help, but it’s a bit like putting your thumb over a flashlight while searching a dark room for a firefly. Though the bulb vanishes, stray glare can still escape and overwhelm the light of the insect. Inside a telescope, that glare can come from light leaking around the edges of machinery or from minuscule imperfections in mirrors and coatings that can scatter starlight into speckles. All this can hide, or even impersonate, a planet.
Roman’s coronagraph, however, will attempt something completely unseen in space telescopes until this year: Before each observation, it will measure that leftover light and try to suppress it, a technique known as active wavefront control.
The telescope is able to do this because it contains two deformable mirrors. Each has a 48-by-48 checkerboard of actuators (tiny pistons) beneath a thin, deformable sheet of glass. Applying a small amount of voltage makes the actuators contract and tug their patches of mirror slightly backward, like thousands of microscopic fingers delicately sculpting a surface.
The effect is very subtle: Each patch of mirror can deform by up to 0.5 micrometers, or about one-fourth the size of an E. coli bacterium, and in increments as small as approximately 10 picometers. That’s about a tenth the diameter of a hydrogen atom, says Ilya Poberezhskiy, the instrument’s project systems engineer at JPL.
The actuators allow the mirrors to create an “active wavefront,” where each component is moved to the perfect position to cancel out incoming waves of unwanted light—a bit like a pair of noise-canceling headphones, but for light instead of sound. The “canceled-out” light creates a “doughnut-shaped region around the star where we suppress starlight and where we’re hoping to see exoplanets,” says Poberezhskiy.
Compared with current space-based coronagraphs, the system is expected to improve sensitivity to exoplanets against the glare of their host stars by a factor of up to 1,000, revealing planets that would have been far too faint to detect before.
Like Hubble and JWST, Roman also uses masks, patterned plates placed in the path of the light that are designed to block the photons that run into them. One tool in Roman’s mask arsenal is “silicon grass,” a thicket of microscopic spikes on some masks that can be used in certain configurations to absorb photons so they don’t bounce around the telescope and accidentally reach a detector.
Light entering the forest bounces deeper and deeper between the blades and gets trapped instead of reflecting back toward the camera. “Once the light gets into there, it never gets out,” Poberezhskiy says. The mirrors and masks form a succession of gates and hedges to guide as much of the preserved planetary light as possible toward the final detector.
Alien Jupiters
This elaborate setup could open a new chapter in the direct imaging of exoplanets. Nearly all exoplanets photographed so far are oversize youngsters that are nothing like the residents of our solar system: several times the mass of Jupiter, still glowing with the heat left over from their birth, and orbiting tens or hundreds of times farther from their star than the Earth is from the sun. This is because they are relatively easy to see. Their size, warmth, and distance from their parent star makes them shine brightly in infrared light, far away from the worst of the stellar glare.
Roman, however, could directly image a true Jupiter analogue—a planet similar to Jupiter in mass and circling a sunlike star a few times farther out than Earth is from our sun. Unlike the hot Jupiters we can see now, this one would be a much more mature gas giant like ours, primarily reflecting its parent star’s light after billions of years of cooling instead of heavily emitting its own.
Astronomers have been able to infer the existence of such planets from the gravitational wobble they impart to the star. Roman instead will collect starlight reflected from the planet itself. “We’re not looking at the star. We’re not looking at the effect of the planet on the star,” says Meredith MacGregor, a professor of astronomy at Johns Hopkins who has also secured an observing program. “We are actually looking at the planet, and that is super powerful.”
Once this instrument becomes available, it will become the scientists’ turn to do their jobs. “I’m honestly a little terrified about how we’re all going to deal with it, because I think it’s just so much data,” MacGregor says. “I think people will legitimately still be working on Roman data for decades.”
But don’t expect to see a 4K photo of an alien Jupiter in the coming months. Roman will not be able to resolve such a planet into a solid globe—at best, it will likely resemble a smattering of pixels. Still, that will be enough, MacGregor says, as Roman can then use the coronagraph to get information on the various wavelengths of light from the planet, which can tell astronomers about its atmospheric chemistry.
“You’re taking something that’s a point of light and turning it into an actual world,” she says, “because if you know that about its atmosphere, now you know something about the surface of the planet and the possibility of life being on that planet, right? So that’s a big step.”
During its first observations, scientists and engineers will see whether they can hold a star at the very center of the coronagraph’s masks, shape the mirrors, “dig” the dark doughnut (as Poberezhskiy describes it), and then maintain everything as the spacecraft moves through space and actively changes temperature.
The results will inform NASA’s proposed Habitable Worlds Observatory, the daydream of many an exoplanet astronomer, which will in theory be able to separate the light of an Earthlike planet from that of a sunlike star, over 10 billion times brighter.
Creager, who has worked on the instrument since 2018, is proud of the achievement: “Not too many people get to say, ‘I built something and it’s taking a picture of a planet that’s at a star that’s 50 light-years away or 100 light-years away.’” He imagines the moment he and his team will be able to look at the first image as it arrives: “Yes, we did that.” While the planet may show up only as a tiny dot, Roman’s achievement will be the darkness engineered around it.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
China’s AI models have Trump’s AI world at war with itself
Last weekend, several current and former advisers to President Donald Trump on AI publicly lobbed insults at the country’s leading AI companies. David Sacks branded Anthropic’s models “lobotomized” and “woke.” Emil Michael, a top Pentagon official, called OpenAI’s new head of strategic futures a “supreme village idiot.”
It began because no one can agree on what to do about Kimi, a free, open-source model that Chinese AI company Moonshot launched last week. It appears to rival the intelligence of models from OpenAI and Anthropic, which are very much not free.
Every time a new smart, free model from China gets released, US companies see less reason to fork out money for models from Anthropic or OpenAI. That’s creating economic and political problems for the president—and dividing the top AI strategists in his orbit.
This article is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Anthropic’s record $1.5 billion copyright settlement has been approved The plaintiffs said Anthropic used pirated works to train Claude. (Reuters $) + And won the largest known copyright payout in history. (Engadget) + Yet many authors and creators still don’t view it as a win. (TechCrunch) + But AI copyright anxiety could limit creativity. (MIT Technology Review)
2 The Trump administration is weighing a ban on Chinese AI models The launch of Kimi K3 has revived calls for restrictions. (Axios) + But officials are divided on the proposals. (Fast Company) + China’s bet on open-source is paying off. (MIT Technology Review)
3 China is mulling tighter export controls on AI models and chips It wants to stop the West from acquiring its tech and startups. (FT $) + Beijing has held talks with tech firms about potential restrictions. (Reuters $)
4 Trump’s AI safety head has resigned after just three months Chris Fall had led CAISI, the federal AI Safety Institute, since April. (Axios) + No reason was given for his exit. (CNBC)
5 Google is working on a new chip to run Gemini models more efficiently The chip, called “Frozen V2,” may be deployed in 2028. (Information $) + Alphabet stock popped on the report. (CNBC)
6 New Orleans police have explored arming drones with weapons A draft drone manual paves the way for weaponised quadcopters. (404 Media) + Shoplifters could soon be chased by drones. (MIT Technology Review)
7 The EU has handed AliExpress a record fine over unsafe product sales The €550 million fine is the largest-ever under the Digital Services Act. (BBC) + Alibaba has vowed to appeal the fine. (SCMP)
8 Election advice from AI chatbots is “inaccurate and unreliable” That’s the conclusion from tests in Hungary earlier this year. (Guardian)
9 Red light therapy is showing promise for healing and healthy aging Better skin and reduced vision loss are also on the cards. (Economist $)
10 Neill Blomkamp’s new horror clip is all AI-generated—and it sucks The acclaimed director wants to make “a full feature in this format.” (Gizmodo)
Quote of the day
“This would be a terribly self-defeating form of intervention if it were to happen.”
—Tech investor Chamath Palihapitiya slams plans to restrict Chinese AI models in a post on X.
One More Thing
AKILAH TOWNSEND
Inside Chicago’s surveillance panopticon
Early on the morning of September 2, 2024, four people were shot and killed on a westbound train in Chicago. Police swiftly activated a digital dragnet—a surveillance network that connects thousands of cameras across the city—and arrested the suspect just 90 minutes later.
Law enforcement and security advocates say this vast monitoring system protects public safety and works well. But activists and many residents say it’s a surveillance panopticon that creates a chilling effect on behavior and violates guarantees of privacy and free speech.
The conversation about AI often centers on algorithms, computing power, or huge investments in new semiconductor fabrication plants and hyperscale data centers. But beneath each of these advances is another layer of innovation that makes them possible: advanced materials.
Every new generation of AI technology demands more processing power, more memory, greater energy efficiency, and higher reliability. Every increase in computing performance increases the physical demands placed on the systems that make and run AI.
Delivering these gains depends not only on advances in chip design and system architecture, but on advances in the materials that enable them to perform under extreme conditions.
As AI continues to push the physical limits of semiconductors and data center infrastructure, advanced materials are no longer simply supporting innovation in this area; they are defining the limits of what is possible.
Performance first
Advanced materials exist to solve performance challenges. As AI raises the bar, these challenges are becoming more demanding.
Manufacturing a semiconductor chip today requires thousands of tightly controlled process steps, with almost no room for error. Tiny variations in temperature or chemical instability can create defects that reduce yield and drive up manufacturing costs. With every new generation of semiconductor chips, manufacturers seek advanced materials that can deliver greater purity, higher chemical and plasma resistance, and better stability under increasingly harsh operating conditions.
These are familiar engineering challenges being pushed to new extremes. And it’s here that materials innovation makes the difference with continuous advances in polymers, elastomers, specialty fluids, and other advanced materials that make each new generation of technology possible.
For materials companies, it’s not about reinventing semiconductor manufacturing but about ensuring the materials supporting the industry continue to evolve alongside it. This same principle applies beyond the semiconductor fabrication floor. As AI workloads become more demanding, the physical infrastructure that powers them is evolving rapidly.
Increasing computing density is transforming data center design, driving the need for more sophisticated thermal management, higher-voltage power architectures, increased data storage, and faster, more reliable data transmission. Every part of the system is under greater pressure, from cooling and power management to critical electronic components, such as connectors, capacitors, and hard disk drives.
At Syensqo, we’re building on our expertise in electronic and electrical components, along with insights from other markets, to meet these emerging needs.
For example, as data centers shift to higher-voltage architectures and greater power density, many of the materials challenges we face closely mirror those of electric vehicles. Fluid-circulation know-how from semiconductor and automotive coolant systems, for instance, can be adapted to direct liquid-cooling designs for AI servers. By transferring knowledge across markets, we can accelerate new power and thermal management solutions while supporting the reliability required by next-generation AI infrastructure.
Whether we’re talking about semiconductor fabrication or hyperscale server farms, the challenge for materials science companies is the same: enabling greater performance without compromising reliability.
A new definition of what performance means
While performance remains the first priority, the way performance is defined is changing.
In addition to meeting the increasingly demanding technical requirements of next-generation semiconductors and data centers, there is now an expectation that these materials are developed and manufactured more responsibly.
Perfluoroelastomers, for example, are used to seal semiconductor manufacturing equipment. These materials operate under extreme temperatures, aggressive plasma, and highly reactive chemicals.
To make the process more sustainable, at Syensqo, our next generation of perfluoroelastomers use a fluorosurfactant-free manufacturing process. Our goal was to make a better-performing material, produced in a better way, ensuring manufacturers no longer have to choose between higher performance and a more responsible way of producing the materials that enable it.
This approach reflects a broader reality across the industry.
New materials aren’t adopted simply because they are new. Qualification can take years, and manufacturers only make changes when a material solves a genuine engineering challenge or enables new technology.
Performance remains the price of entry. The difference today is that the definition of performance has expanded. Success increasingly depends on delivering technical excellence through more responsible manufacturing from the outset.
Accelerating the pace of discovery
As the performance bar rises, the way we innovate must evolve with it.
Developing advanced materials has traditionally involved a lengthy process of hypothesis, synthesis, testing, and iteration. While this process remains unchanged, new digital tools are helping researchers move through these cycles faster. By helping researchers identify the most promising candidates earlier, AI can reduce the number of physical experiments required and accelerate the earliest stages of materials discovery.
AI isn’t replacing scientific expertise. It’s helping scientists apply that expertise more effectively, allowing them to spend less time searching for answers and more time solving the industry’s toughest challenges.
At Syensqo, we’re putting this approach into practice through use of several AI tools, including the Microsoft Discovery platform, which are helping researchers identify and evaluate promising molecular candidates for next-generation heat transfer fluids, used in semiconductor manufacturing and data centers.
AI helps our researchers rapidly identify and evaluate promising molecular candidates based on the properties they need to achieve. This allows us to focus laboratory work where it has the greatest potential to deliver results, accelerating discovery and reducing the time needed to turn promising materials into solutions customers can qualify and deploy.
The journey from laboratory discovery to a qualified material will always require scientific expertise, rigorous testing, and close collaboration with customers. But by accelerating the earliest stages of discovery, AI can help materials innovation keep pace with the evolving needs of industries such as semiconductors, electronics, and data centers.
Progress is earned
The future of artificial intelligence will depend on better algorithms, more powerful chips, and larger computing infrastructure. But sustaining that progress will also require advances in the materials that make those technologies possible.
Whether in semiconductor manufacturing or AI infrastructure, progress is earned. Every new generation of technologies raises the bar, and every new material must prove it can deliver the performance, reliability, and efficiency needed before it earns its place.
For materials companies, that remains both the challenge and the opportunity.
This content was produced by Syensqo. It was not written by MIT Technology Review’s editorial staff.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
Over the weekend, several current and former advisors to President Donald Trump on AI publicly lobbed insults at the country’s leading AI companies. David Sacks, the president’s AI and crypto “czar” until March, branded Anthropic’s models as “lobotomized” and “woke.” Emil Michael, a top Pentagon official, called OpenAI’s new head of strategic futures a “supreme village idiot.”
It began because no one can agree on what to do about Kimi, a free, open source model that Chinese AI company Moonshot launched last week. It appears to rival the intelligence of models from OpenAI and Anthropic, which are very much not free.
Kimi and other Chinese models like it pose a real problem for Trump. And they’re dividing the top AI strategists in his orbit into factions. Every time a new smart, free model from China like Kimi gets released, US companies see less reason to fork out money to access models from Anthropic or OpenAI. Given that enthusiasm for these and other AI companies is driving an outsized share of economic growth, China’s AI models create both economic and political problems for the president. They are “a threat for an administration that really doesn’t want more economic bad news,” Anton Leicht, a fellow at the Carnegie Endowment, wrote on X. They’ve already rattled US stocks.
What is Trump to do? First, consider that this is all happening just a week after New York imposed the country’s first state ban on new data centers. There is growing distrust of AI companies, and I imagine a not-insignificant share of Americans would have little sympathy for OpenAI or Anthropic as they fend off cheaper competitors, and would say it’s not the government’s job to protect their interests.
On this point, they’d see a sliver of agreement (and really just a sliver) with David Sacks, who on July 19 criticized top AI companies that “want the government to eliminate their open source competition.” He has also argued that Chinese AI models have become popular because they come with fewer restrictions on how people can use them (putting aside the built-in state censorship).
Sacks, however, is out of a job. He no longer has a formal role advising Trump, and his position that more open AI is better has been largely replaced in the administration by one that sees a larger role for government intervention. The thinking behind this view is that because AI models have gotten strong enough to pose threats to national security, the government must control how they’re used.
This position has fueled the new White House review process that aims to vet AI models’ security before they’re released. Dean Ball, a former Trump AI advisor who now works for OpenAI, criticized it over the weekend as a “de facto licensing regime for frontier AI.” Ball predicted Trump may solve his Chinese open source problem with a bit of soft power, perhaps by making US companies afraid to use models like Kimi. That drew a response from Michael, who, with Secretary of Defense Pete Hegseth, has been the agency’s main liaison with AI companies. Michael called Ball the AI industry’s “supreme village idiot,” bristling at the suggestion that the government would quietly strong-arm companies rather than, as Michael put it, go through “the democratic process not some Deep State scheme.”
Left out of the conversation has been how a model like Kimi got so good in the first place.For much of the Biden administration and even the beginning of Trump’s second administration, keeping China from getting top chips was a priority. Those export controls have loosened—Trump made the controversial decision to allow Nvidia to sell more chips to China, in exchange for the US government taking a cut—and the government has alleged that some chip smuggling has taken place. But China nonetheless has limited computing power, and it’s not clear what chips the company behind Kimi used to train the model.
It’s possible that the process involved some distillation, a practice in which AI models are trained on the outputs of existing AI models. OpenAI and Anthropic have long complained that Chinese AI companies do this, and they have requested government help to put a stop to it. In April, they got it, when the Trump administration announced a series of efforts to curb the practice.
But Kimi is out there and free, and it is nearly as good as the Anthropic model the US government deemed so powerful that it was briefly shut down because it threatened national security. The weekend’s sparring suggests many in Trump’s orbit see that as a wake-up call. But nobody can agree on what for.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
AI is more likely than humans to form biases when hiring
The next time you apply for a job, AI may screen your résumé before any human sees it. But there’s good reason to question whether AI will judge you fairly.
We already know that LLMs pick up human biases from their training data. New research suggests they can also develop their own biases from experience—and stereotype job applicants more than humans do.
As AI companies race to build agentic models that remember the tiniest details about users, they may be handing them ammunition for forming those biases.
Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on weather forecasts. More recently, the forecasts have become relevant for another industry: prediction markets, where people bet money on all kinds of real-world events, including the weather.
The temptation to manipulate weather data to get an edge in these markets, combined with a collective move toward data-driven AI weather forecasting, is starting to put the accuracy of weather predictions at risk.
As experts in the field, we can foresee scenarios where the risks snowball into far bigger, more systemic problems.
—Monique Kuglitsch, Jesper Dramsch, Franz G. Kuglitsch, & Andrea Toreti
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 SpaceX is negotiating to sell the Pentagon AI compute It would provide data center capacity worth billions of dollars. (WSJ $) + Deepening ties between Elon Musk’s company and the DoD. (Reuters $) + Meanwhile, Anthropic is in talks with Meta to acquire compute. (CNBC) + The compute explosion is only just beginning. (MIT Technology Review)
2 Trump Media wants $100,000 a month for early access to Trump’s posts The premium feed is being pitched to trading firms and banks. (FT $) + It aims to monetize Trump’s market-moving social media posts. (Reuters $) + Critics described the plan as “brazen corruption.” (Guardian)
3 ICE shared Medicaid data it wasn’t supposed to have with Palantir Court filings show the data reached the contractor before being deleted. (NPR) + ICE is using data broker tools to identify “unaccompanied minors.” (Wired $)
4 Apple briefly overtook Nvidia as the world’s most valuable company The iPhone maker’s earnings durability has impressed investors. (Reuters $) + While Nvidia’s rise has stalled amid shifting AI bets. (CNBC)
5. Politicians are trying to change what chatbots say about them A new industry has sprung up to help them edit AI outputs. (NYT $) + Chatbots can sway voters better than political ads. (MIT Technology Review)
6 Washington is opening the door to armed robots The Pentagon is accelerating AI weapons development. (WP $) + “Humans in the loop” in war is an illusion. (MIT Technology Review)
7 China’s Moonshot has paused new subscriptions amid surging uptake Demand for the headline-grabbing Kimi K3 has strained capacity. (SCMP) + China’s open-source AI is challenging US models. (MIT Technology Review)
8 Lab-grown teeth could soon replace fillings and implants Scientists believe regenerative medicine could transform dentistry. (BBC) + Humanlike “teeth” have been grown in mini pigs. (MIT Technology Review)
9 AI slop on birdwatching forums is putting research at risk It could contaminate records of species. (Guardian)
10 Heart experts have good news for your coffee habit Roughly five cups per day is fine—and may even be beneficial. (Gizmodo)
Quote of the day
“The most authoritarian government is producing the most egalitarian models, and what should be the most democratic government is breeding companies that are the most authoritarian.”
—Rayan Krishnan, CEO of Vals AI, a company that evaluates AI performance, gives the New York Times his take on the competition between Chinese and American models.
One More Thing
RICHARD CHANCE
The curious case of the disappearing Lamborghinis
A new wave of theft is rocking the luxury car industry—mixing high tech with old-school chop-shop techniques to snag vehicles while they’re in transport.
It’s remained under the radar, even as it’s rocked the industry over the past two years. MIT Technology Review identified more than a dozen cases involving high-end vehicles, obtained court records, and spoke to law enforcement, brokers, drivers, and victims in multiple states to reveal how transport fraud is wreaking havoc across the country.
The next time you apply for a job, AI may screen your résumé before any human sees it. But there’s good reason to question whether AI will judge you fairly. Researchers already know that LLMs pick up human biases from their training data. New research suggests that LLMs can also develop their own biases from experience—and stereotype job applicants more than humans do. As AI companies race to build agentic models that remember the tiniest details about users, they may be handing them ammunition for forming those biases.
Researchers at Princeton University and the University of Chicago ran LLMs, including ChatGPT, Claude, and Gemini, through a simulated hiring game, adapted from a psychology study that explored how humans can form stereotypes. Each model was told it had been hired as a consultant by the mayor of a fictional city and was then asked to help hire people for 20 jobs, including doctors, lawyers, child-care aides, and janitors. Candidates came from four fictional ethnic groups: Tufa, Aima, Reku, and Weki.
In each round, there was a new job opening and four candidates, one from each group. After the model hired a candidate, it learned whether they succeeded at their job and moved onto the next round. The model was told to make as many successful hires as possible over 40 rounds. Unbeknownst to the models, all candidates were equally likely to succeed at every job.
The models quickly started segregating candidates from different groups into different jobs on the basis of early observations of hiring outcomes. For example, when a model was told an Aima had failed as a doctor, a job considered to require high levels of warmth and competence, it veered away from hiring all Aimas as doctors. Instead, it started hiring Aimas as janitors, which the model classified as being less warm and competent than doctors. Newer models with higher reasoning capabilities, such as OpenAI’s o3 and DeepSeek’s R1, showed stronger biases.
The models were even more likely to stereotype people by demographic group than the human participants in the original study. On the study’s segregation scale, where 2 means every group has been completely confined to its own job niche, human participants scored 0.84. The models scored roughly 65% higher, with OpenAI’s reasoning model o3 scoring 1.83, close to the maximum possible.
That’s because LLMs “really are eager to create generalizations from limited data,” says Ryan Liu, a PhD student at Princeton University and a coauthor of the study, which was published in a paper at ICML in Seoul in July. “That’s literally a lot of what they’re optimized for.”
Every decision-maker, human or machine, faces a trade-off between sticking with what worked before and trying something new that might work better—a phenomenon psychologists call the “exploration-exploitation dilemma.” It’s like choosing between a new restaurant and your reliable favorite.
Because LLMs are trained on math, coding, and science problems—tasks that reward generalizing from just a few examples—they can settle on a hunch too early. And the same instinct that helps LLMs crack logic puzzles also makes them quick to stereotype. When LLMs rush to generalize in social settings, “that’s when things tend to go wrong,” says Liu. OpenAI and Anthropic did not respond to requests for comment.
The finding is especially relevant now that chatbots are gaining improved memory and personalization features, says Angelina Wang, a computer scientist at Cornell University who did not work on the study. When a chatbot draws on its previous conversation history, it can “over-index on the same kinds of behaviors it’s experienced before” and form biases, she says.
Simply having chatbots remember less isn’t a fix, though, because users want chatbots to remember what they say. “We still are trying to figure out just the right amount that isn’t too much or too little,” says Wang.
Telling the model to be fair didn’t change its behavior much. “Either it can’t put these values into action or that process is being submerged under the tendency to try to optimize for the goal of getting the most correct hires,” says Liu. But promising the models an additional bonus for diverse hiring made them far less biased. The trick, then, is to design goals that “incorporate desirable social values in order to make the large language model act in socially desirable ways,” says Liu.
The models also became less biased when they were told more personal information about individuals. In another experiment in the same study, the researchers asked the models to resettle members of different ethnic groups in cities across Canada. When the models were told personal information relevant to the ability to adapt to a new city, such as age and education, they were less likely to segregate people by their ethnicity. But when they were given irrelevant information, such as hair color and tattoo shape, the models largely fell back to sorting people by their ethnicity again.
To what extent AI systems will stereotype job applicants in the real world is still an open question. While the models in the experiment immediately learned whether they’d made successful hires, a model screening résumés in the real world doesn’t get an instant report card. Companies can take a long time to find out whether a new hire is any good, if they ever do.
But when feedback does trickle in, a model could still read too much into those results when making future hires. As companies increasingly deploy LLMs to screen résumés and even conduct interviews, the finding that models can form biases from their hiring experience “is a really serious implication that they should grapple with,” says Wang.
As LLMs learn from experience to make decisions about who gets hired, who gets a loan, or who gets parole, the biases we should worry about may include ones no human ever taught them. “These novel biases—they’re sort of ever present,” says Liu.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
There’s a lot of hype around perimenopause. Don’t buy it.
Perimenopause used to be considered taboo, but not anymore. Thanks at least in part to TV doctors and social media influencers, conversations about the sometimes years-long period before menopause are now more open than ever. But the conversation is increasingly shaped by misinformation.
Despite what some marketers will claim, there is no test for perimenopause. That doesn’t mean women should have to put up with symptoms, but treatment suggestions often lack scientific evidence. And not all the symptoms women experience in midlife can be blamed on hormones.
This article is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1China’s AI gap with the US may have just narrowed A Chinese startup has released the world’s largest open AI model. (Reuters $) + It competes with some Anthropic and OpenAI models. (Gizmodo) + The model’s launch sent AI and semiconductor stocks sliding. (Bloomberg $) + Chinese Nvidia alternatives are also gaining traction. (SCMP) + Xi Jinping pitched China as an AI partner to the developing world. (CNBC) + The country is betting big on open-source. (MIT Technology Review)
2 Trump Media is selling instant access to “market-moving’ social posts It’s developed a new way to monetize the president’s posts. (Quartz) + And Trump could profit directly from selling access to his statements. (BBC) + Kalshi says it caught Trump’s teleprompter operator insider trading. (Verge)
3 Astronomers have found an atmosphere on a nearby Earth-like planet It’s the first potentially habitable world known to host an atmosphere. (NYT $) + Making it a top contender in the search for aliens. (404 Media) + But you need to know how to spot one. (MIT Technology Review)
4 A brain implant has restored feeling in a paralysed hand The recipient can now feed himself and drink from a cup. (Guardian) + Movement continued when the stimulation was turned off. (New Scientist $) + China has approved a world-first brain chip. (MIT Technology Review)
5 The EU has told Google to share search data and open up AI on Android It will be forced to share data with competing search providers. (Ars Technica) + And open Android phones to rivals’ AI bots. (WP $)
6 Period trackers are hiding privacy problems New research uncovers how they’re sharing users’ health data. (BBC)
7 The Tesla driver in a fatal Texas crash overrode FSD, investigators say He bypassed the tech by pressing the gas pedal to 100%. (Verge)
8 A new stealth drone spins so fast that it disappears Though its creators admit it can still be easily heard. (New Scientist $)
9 A space-station study suggests why astronauts’ bodies waste away Microgravity disrupts mitochondria, reducing protein production. (Nature)
10 “Adversarial clothing” that confuses facial recognition is all the rage Privacy could be the next big trend. (Guardian)
Quote of the day
“Xi’s message is clear: China is not going to follow anyone on both AI technology and standards. Instead, China is going to lead the world in both aspects.”
—George Chen, chair in digital practice at The Asia Group consultancy, gives Reuters his take on Xi Jinping’s speech at the World Artificial Intelligence Conference (WAIC) in Shanghai.
One More Thing
BRYN NELSON
How poop could feed the planet
A new industrial facility in suburban Seattle is giving off a whiff of futuristic technology. It can safely treat fecal waste from people and livestock while recycling nutrients that are crucial for agriculture but in increasingly short supply across the nation’s farmlands.
It’s among a range of systems reframing feces, urine, and their ingredients as invaluable natural resources to reuse instead of waste products to burn or bury. Several companies are now showing how to safely scale up the transformation with energy-efficient technologies.
Perimenopause has entered the chat. Perimenopause—and its better-known relative, menopause—used to be considered taboo. Not anymore, thanks at least in part to TV doctors and social media influencers. Perhaps it’s my age, but these days, both my algorithm and my conversations with friends increasingly swing toward perimenopause.
Menopause is defined as the life stage that occurs a year after a person has had their last period. Perimenopause is the sometimes years-long period before that point, which can also feature all the symptoms we’d typically associate with menopause.
Today, information about perimenopause is more prevalent and accessible than ever. If you’re a woman in your 40s and you’re not feeling 100%, chances are there’ll be someone online ready to tell you you’re in perimenopause. And that you might want to start spending your money on blood tests, apps, and supplements or demanding hormone replacement therapy. But as regular readers might have guessed by this point, it’s not that simple.
Perimenopause tends to start around the age of 46 or 47. It’s during this time that many women start to experience some symptoms like hot flashes, irregular or unusually heavy periods, or anxiety, for example. And it can be heavy going. “Often symptoms are at their worst in the perimenopause,” says Mary Ann Lumsden, former president of the International Menopause Society.
That’s because hormones can fluctuate wildly. Levels of estrogen, progesterone, luteinizing hormone, and follicle-stimulating hormone can roller-coaster before leveling off after menopause. And that’s why, despite what some marketers will claim, there is no test for perimenopause.
“You can’t interpret hormone [measures] because they change so much,” says Lumsden. “And that is quite normal.”
That doesn’t mean women should have to put up with symptoms. But exactly how those symptoms are treated is another topic that has been clouded by misinformation.
Last week, I told a friend about some unusually bad pelvic pain I’d experienced. Her immediate advice was to find out if I was perimenopausal and, if I was, to request hormone replacement therapy (HRT) as soon as possible. If my doctor wouldn’t prescribe it, she continued, I should simply find another doctor who would.
This line of thinking has been heavily promoted on social media platforms, says Paula Briggs, a former chair of the British Menopause Society who currently leads the menopause service at Liverpool Women’s Hospital. But it’s not helpful.
HRT is essentially designed to top up or replace hormones like estrogen and progesterone, which naturally decline around menopause. There are lots of different drugs that can be taken in lots of different ways and at various doses.
While it does come with some risks and won’t suit everyone, HRT can be immensely helpful for many menopausal women. Not only can it help with many of the common symptoms of menopause, but it can also help prevent osteoporosis and maintain muscle strength.
But these drugs were trialed in, and approved for, menopausal women, says Lumsden. They won’t have the same effects in perimenopausal women. “If you give standard HRT, it may well get swamped by [the woman’s] own hormone production,” she says.
HRT can also cause abnormal bleeding in perimenopausal women, says Briggs.
She’s concerned about the messaging on perimenopause that is being promoted on social media. Particularly worrisome, she says, is the way younger women are being encouraged to assume they are perimenopausal and seek out HRT treatment.
“It’s almost cult-like, this idea that everybody must have HRT,” she says.
And then there are the supplements. There’s been an explosion in marketing for vitamins and supplements specifically targeted to middle-aged and menopausal women. But the evidence for these, too, is either limited or nonexistent. “I can’t see a mechanism for a lot of them,” says Lumsden.
Women who take these supplements don’t always know what they’re getting. Some of Lumsden’s patients have told her they take testosterone supplements to manage their symptoms. But blood tests revealed no increase in testosterone levels. “Whatever they’re getting, it’s not testosterone,” she says.
At any rate, not all the symptoms women experience in midlife can be blamed on hormones. The lengthy lists of perimenopause symptoms shared on social media include fatigue, brain fog, aches and pains, digestive issues, and more. “These do not link closely to the obvious menstrual cycle changes and hormone changes … across menopause,” says Nanette Santoro, a professor of obstetrics and gynecology at the University of Colorado Anschutz who studies menopause.
If you’re experiencing any symptoms, it’s worth getting them checked out to make sure they’re not being caused by something else. My own pelvic pain, for example, is almost definitely the result of endometriosis—a condition that can be made worse by HRT, Lumsden tells me.
At any rate, by the time women reach their 40s, many are already juggling care for children and aging parents, often while holding down a job (and dealing with pressures from societies that don’t appear to value older women). It’s an exhausting time—and not all of that exhaustion can be blamed on hormones.
As Santoro puts it: “Attributing everything unpleasant that happens to a woman over 35 to perimenopause is not based on any scientific evidence.”
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.
Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on the same thing: a weather forecast.
While these forecasts are something that most people glance at for two seconds, weather predictions influence major strategic decisions in many industries, with real money, livelihoods, and even actual lives at stake. Farmers use them to determine which crop variety to sow, when to fertilize, how much to invest in irrigation infrastructure, and how long livestock should graze. Utilities use them to decide where to build solar and wind farms, as well as how to price wholesale electricity. Predictions are used to warn people about extreme weather and to trigger emergency response measures. More recently, weather predictions have become relevant for an emerging industry: prediction markets, where people bet money on all kinds of real-world events, including the weather.
However, the temptation to manipulate weather data to get an edge in these markets, combined with a collective move toward data-driven AI weather forecasting, is starting to put the accuracy of weather predictions at risk. These risks are relatively manageable for now, but as experts in the field, we can foresee scenarios where they snowball into far bigger, more systemic problems.
Sometimes, weather stations have issues because of, for example, instrument failures or upgrades in equipment. These can be caught either in real time (through checking and correction) or retroactively. Traditional forecasting systems also have a built-in safeguard called data assimilation: Every incoming measurement is weighed against what the physical model says should be happening and against readings from nearby stations.
Together, these mechanisms help keep weather observations reliable and predictions robust. However, new threats are putting observational accuracy at risk. Earlier this year, news outlets reported that the weather station at Paris Charles de Gaulle Airport (CDG) had been manipulated to record suspicious temperature spikes on April 6 and April 15, 2026. Authorities speculate that a hand-held hairdryer or lighter might have come into play. Either way, it led to some big payouts for online prediction-market gamblers who had bet it would hit 22 °C (71.6 °F) on days when the actual average was around 18°C (64.4°F). One individual won $20,000.
Fortunately, tampering with a single station like this can usually be caught by human monitoring or current statistical methods. In this case, members of a French climate nonprofit association noticed the anomalies by chance and raised the alarm.
But what if there are no human monitoring systems in place? And what about other types of manipulation? What if, instead of tampering with one station, someone remotely nudged the readings at many stations at once—making each change small enough to look plausible on its own? Existing quality controls struggle to catch this kind of coordinated manipulation. And time works against us; careful checks of data and metadata take hours or days, but forecasts have to go out on schedule, whatever the weather is doing.
The shift toward artificial intelligence in weather prediction raises the stakes. These methods are even more dependent on accurate, reliable weather observations; in fact, they are known as “data-driven models.” For example, researchers at ECMWF are exploring whether high-quality weather forecasts can be produced directly from raw observations, skipping the assimilation step that currently acts as a quality filter. Other researchers are going one step further; combining geospatial data (including weather station data) with large language models and agentic AI to support real-time, autonomous decision-making during extreme events such as storms.
Possible benefits are improvements in accuracy, efficiency, and speed. But removing humans from the equation introduces a vast range of new risks.
At the low end of the risk scale, an individual speculator manipulates a weather station for personal gain—that is the CDG Airport case. One step up: A group of traders could coordinate to bias forecasts of renewable energy output, moving wholesale electricity prices and leaving whoever is on the other side of the trade holding the loss. And at the far end, a state actor or saboteur could manipulate one or many stations to set off an early warning system or even keep one silent when it should sound. Step by step, the risk grows, from fraud to compromised disaster preparedness to a matter of national security.
As long as there are financial (or other) incentives to manipulate observational data, adversaries will search for new opportunities, and it is our task to stay one step ahead. Here are three ways.
1. Watch the stations. Data quality controls should include station security, anomaly detection and correction, and human oversight. Weather stations should be monitored continuously to deter tampering. Data homogenization methods that clean up weather records also need to get faster, with the goal of catching problems in real time. This will become increasingly important as agentic AI systems use these data to deliver real-time decisions. Finally, human oversight is needed to flag questionable data and model outcomes. After all, it was humans who caught the CDG Airport manipulation.
2. Protect the data to safeguard the AI. Data defense mechanisms must be positioned throughout the AI pipeline. AI explainability and adversarial robustness tools can help us understand the underlying data and the AI model outputs, help us identify data- or model-related issues, and potentially make us more resilient to adversarial attacks.
3. Ensure continuous accountability along the chain. Observational data passes through many hands: the operators who run the stations, the national weather services that steward the records, and the forecasting centers that turn them into predictions. No single one of them can protect data integrity alone—each guards its own link, and any anomaly needs to be communicated along the whole chain, from station operators to the people acting on the forecast.
It is fortunate that the situation at CDG Airport was caught, but it should serve as a wake-up call. As the role of observational data grows in weather forecasting, we need to adapt to evolving threats. This means protecting our data and models by strengthening existing oversight and accountability structures, and improving coordination among key partners.
This op-ed was written by:
Monique Kuglitsch — Innovation Manager at Fraunhofer Heinrich Hertz Institute and Chair of the UN Global Initiative on Resilience to Natural Hazards through AI Solutions
Jesper Dramsch — Scientist for Machine Learning at the European Centre for Medium-Range Weather Forecasts (ECMWF), where they work on AIFS (Artificial Intelligence Forecasting System), ECMWF’s data-driven weather prediction model
Franz G. Kuglitsch — Climate Scientist and Executive Secretary of the International Union of Geodesy and Geophysics (IUGG) at the GFZ Helmholtz Centre for Geosciences in Potsdam
Andrea Toreti — Senior Scientist at the European Commission’s Joint Research Centre (JRC), where he coordinates the European and Global Drought Observatory under the Copernicus Emergency Management Service
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Meet GPT-Red: an LLM super-hacker OpenAI built to make its models safer
OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other models boost their defenses against cyberattacks.
It automates a type of safety evaluation for software systems known as red-teaming, which is typically done by a team of human testers. The aim is to find as many different ways to break or hijack a system as possible.
It feels as if it should be illegal to even think about heating appliances during the height of summer, but we need to talk about heat pumps.
The appliances use electricity for heating, they’re incredibly efficient, and they’re on the rise. In the US, their sales have doubled over the past 15 years, according to a new report. They’re also winning the heating race against fossil fuels, outpacing natural-gas furnaces by 32% during the first quarter of 2026.
These stats are especially striking at this moment, because a key tax credit for heat pumps just ended. So why are heat pumps still so hot? Read the full story for the answer.
This article is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Elon Musk discreetly bought a $1 billion gas turbine firm to power Grok He acquired fossil fuel company APR Energy in May. (Electrek) + The most likely application will be powering AI data centers. (Engadget) + The deal was revealed through an FTC filing. (Gizmodo) + What will power AI’s growth? (MIT Technology Review)
2 A hack shows the Suno AI music generator scraped YouTube, Deezer It scraped decades’ worth of music to train its models. (404 Media) + The hacked is a unique look into the black boxes powering GenAI. (CNET) + AI is coming for music, too. (MIT Technology Review)
3 Thinking Machines has launched an open-weight AI model Inkling offers a US alternative to China’s open-source models. (Reuters $) + It’s the first AI model built by Thinking Machines. (WSJ $) + The startup was founded by former OpenAI CTO Mira Murati. (Axios)
4 Europe is narrowing its ambitions for tech independence Manufacturing and research show promise, but funding is a problem. (NYT $) + Earnings are strong, but an AI gap persists. (Reuters $) + India is also scrambling for AI independence. (MIT Technology Review)
5 Earth is absorbing energy at a rate that’s alarming climate scientists The planet is taking in more heat than models predicted. (Economist $) + The legal case for climate justice is growing. (MIT Technology Review)
6 The AI backlash has tech executives fearing for their lives Violent threats against AI firms are spilling into the real world. (WSJ $) + An anti-AI movement is growing globally. (MIT Technology Review)
7 A Moroccan intelligence insider exposed widespread Pegasus use Including to target journalists, activists, and foreign politicians. (Guardian)
8 AI is powering citizen-led disaster relief from afar for Venezuela It’s helping to locate missing people and coordinate relief. (Rest of World)
9 Thermodynamic computers could turn noise into useful calculations They may offer a cooler, more efficient way to process information. (Quanta)
10 An engineer has explained every ’90s computer in Jurassic Park Fans have debated the technology in the film for decades. (Ars Technica)
Quote of the day
“We hit pause because the communities powering AI should share in its success. Maybe that’s a novel concept in Washington.”
—New York Gov. Kathy Hochul responds on X to President Donald Trump’s criticism of her state’s new data center moratorium.
One More Thing
Will we ever trust robots?
Robotics firm Prosper is developing a humanoid called Alfie to perform tasks in homes, hospitals, and hotels. The company’s founder, Shariq Hashme, has identified trustworthiness as the top design priority—and first hurdle to clear before humanoids can live up to their hype.
Hashme believes one essential tactic to get people to put their trust in Alfie is to build a detailed character from the ground up—something humanlike but not too human. But the robot’s reliance on remote human operators raises broader questions about privacy, labour, and whether society will truly accept humanoids in our private spaces.
It feels as if it should be illegal to even think about heating appliances during the height of summer—seriously, these heat waves in New York have been brutal—but we need to talk about heat pumps.
The appliances use electricity for heating, they’re incredibly efficient, and they’re on the rise. (For what it’s worth, many heat pumps can also be run in reverse to cool buildings.) In the US, heat pump sales have doubled over the past 15 years, according to a new report. And they’re winning the heating race against fossil fuels, outpacing natural-gas furnaces by 32% during the first quarter of 2026.
These stats are especially striking at this moment, because a key tax credit for heat pumps just ended with the close of 2025. But you wouldn’t know it from looking at the data. Why are heat pumps still so hot?
In case you need a quick refresher, heat pumps use electricity to essentially move heat from one spot to another. A refrigerant moves around a loop in the device, expanding and compressing, gathering and releasing heat at different points in the cycle. (For a more in-depth look at the thermodynamics, this explainer I wrote in 2023 still holds up.)
The result is an appliance that can be incredibly efficient. Once you pay for and install a heat pump, it’s generally significantly cheaper to run than a gas or oil furnace or other types of electric heating systems. And because they’re more efficient and don’t involve burning fossil fuels, heat pumps can be a major help in decarbonizing buildings.
One of the major hurdles to wider use of heat pumps is the appliances’ cost: They tend to be more expensive to buy and install than gas furnaces. For this reason, many governments offer incentives to encourage their adoption. In the US, people who installed heat pumps between 2023 and 2025 were eligible for up to $2,000 in tax credits.
Last year, though, the Trump administration slashed those tax credits, along with many of the other incentives that were part of the 2022 Inflation Reduction Act. Effective January 1, 2026, no more financial help for heat pumps.
I think I’ve seen this film before, and I didn’t like the ending. Tax credits of up to $7,500 for new EVs ended on September 30, 2025. In the quarter leading up to that deadline, sales spiked as people rushed to take advantage of the incentive. Then they fell off a cliff. Things are starting to normalize now, but clearly the tax credit’s sunset had a major effect.
But as it turns out, heat pumps are an entirely different story. In the first few months of 2026, sales have actually gone up, as Lucas Davis, an energy economist and UC Berkeley professor, points out in a new analysis.
Heat pump shipments were flat from December to January and have seen a gradual rise since then, according to data from the Air Conditioning, Heating, and Refrigeration Institute, a trade group that represents about 90% of the US market. This increase from winter into spring follows a seasonal trend seen in previous years—and it’s actually a bit stronger in 2026.
This data isn’t what you’d expect to see if losing the tax credit were hurting demand. As Davis lays out in his post, it seems the credit wasn’t really convincing people to install heat pumps, or at least the case for doing so was sufficient without the added incentive.
“It appears that the U.S. market for heat pumps is strong enough that it does not depend on tax credits,” Davis writes.
In 2024, MIT Technology Review put heat pumps on our annual list of breakthrough technologies. “We’ve entered the era of the heat pump,” I wrote at the time.
While heat pump sales have been up and down over the last few years, the era is going strong. The appliances have outsold gas furnaces in the US for the last four years. It’s not just the US, either. Countries including China and Germany have seen strong movement to heat pumps in recent years.
There’s rarely a straight path to adoption for new technology, especially something that requires so many individual households to make a significant change. But it’s encouraging that a major decarbonization tool is going strong, even when roadblocks pop up.
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other models boost their defenses against cyberattacks. Last week the company released the latest version of its flagship LLM, GPT-5.6. OpenAI says that training it against GPT-Red made the model its most robust release yet.
GPT-Red automates a type of safety evaluation for software systems known as red-teaming, which is typically done by a team of human testers. The aim is to find as many different ways to break or hijack a system as possible. The weak spots can then be patched before the final version of the software is released.
As LLMs become more complex and get used in a wider variety of tasks—especially in the form of agents, which can interact with computer files, websites, and third-party code as well as other agents—it’s hard for teams of people by themselves to keep up with all the types of attacks that might take place. “The risk surface grows and the blast radius also grows,” says Nikhil Kandpal, a research scientist at OpenAI who co-created GPT-Red.
OpenAI built GPT-Red to future-proof its safety testing process. “As more capable models become available, we will have already designed the system that can discover new modes of attack,” says Dylan Hunn, a research scientist at the company and fellow co-creator of GPT-Red. The researchers say it has already come up with new types of attack that had not been seen before.
OpenAI focused most of its efforts on a type of attack known as a prompt injection, where a hacker slips an LLM instructions to make it do things its developers or users do not want it to, such as copy confidential information, sabotage a company’s code base, or generate embarrassing or harmful output. In theory, such instructions can be hidden in any text that the LLM might encounter—in code or on a website, for example.
Training dojo
To build GPT-Red, OpenAI’s researchers took an LLM that had not been trained as a hacker and set it up in what’s known as a self-play loop with several other models. Its goal was to try to attack the other models; their goal was to try to defend themselves. Over many rounds of play, GPT-Red became better and better at attacking other LLMs, and those LLMs became better and better at fending off the attacks.
The training took place in a kind of dojo that OpenAI had designed to mimic a range of scenarios in which LLMs might be deployed in the real world, including browsing the web, reading emails or calendar apps, and editing code.
When GPT-Red found a new kind of attack, it would explore multiple different versions of it to find the most efficient one for specific scenarios. “Compared to a human red-teamer, the model is very, very good at finding exactly what will work, exactly what’s most effective,” says Hunn. “It’s extremely persistent about drilling down into an attack that it has discovered.”
In particular, OpenAI claims that GPT-Red found a type of prompt injection attack that the researchers had not seen before, which they call a fake chain of thought. A chain of thought is a kind of diary in which an LLM makes notes to itself and keeps track of partial results as it works through problems. GPT-Red found a way to insert a fake entry into another model’s chain of thought that would trick that model into acting on spoofed information.
“It’s like if I told you that 1+1=3 and that you have verified this already,” says Chris Choquette-Choo, another research scientist on the team. “The model’s like, ‘Oh, okay, of course,’ and it just spits out 3.”
Jessica Ji, a senior research analyst who works on AI security at Georgetown University’s Center for Security and Emerging Technology (CSET), thinks the self-play loop that OpenAI used is a good approach. “The results look very promising,” she says.
OpenAI tested how good an attacker GPT-Red was by rerunning an experiment from 2025 in which human red-teamers tried to find weaknesses in an earlier version of GPT-5. When GPT-Red was set the same task, it was more successful at finding effective attacks than the humans had been.
OpenAI also tested GPT-Red against Vendy, a vending machine agent developed by Andon Labs, a company that assesses how well agents perform real-world tasks. GPT-Red was able to hack Vendy to make it change the prices of items on sale and cancel a customer’s order.
Defensive behavior
OpenAI says that when it tried out some of the strongest attacks that GPT-Red had come up with on its models, more than 90% of them worked against GPT-5 (released in August last year), and fewer than 23% worked against the new GPT-5.6.
GPT-Red isn’t perfect. It is not great at figuring out attacks that involve a back-and-forth conversation between hacker and target, something that human attackers would have few problems with. It is also not yet that great at using images, which can be used to pass text to models in prompt injection attacks.
The company says that GPT-Red supplements the work of its human red-teamers. People can still find attacks it misses. One approach OpenAI is taking is to give GPT-Red an attack that humans came up with and ask it to find all the variations.
“I think human expertise will still be very important,” says CSET’s Ji. “It would be really useful to be able to distinguish where human testing is most needed.”
Unsurprisingly, OpenAI will not be releasing GPT-Red. The company is also confident that the super-hacker is stronger than any copycat model someone might try to create. The researchers say they have been working on the model for more than a year, backed by the compute resources of one of the richest companies in the world.
“It’s not a trivial thing that someone could easily do—you know, just go and train a super-attacker using this idea,” says Choquette-Choo.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
PsiQuantum has a plan to make a massive quantum computer out of light
The machine that could change the world will be housed in a room that looks like a data center crossed with an ice cream factory.
Inside, some 100 stainless-steel cabinets each hold hundreds of chips. On those chips, thousands of light particles will fly through a maze of optical switches and beam splitters. Each photon must be accounted for, because precisely measuring where it ends up will help answer questions that current computers might take millions of years to solve.
This computer, as described, does not exist. It’s the brainchild of a company called PsiQuantum, founded in 2016 by four physicists from UK universities. In a crowded field of deep-pocketed competitors with similarly fantastical visions, the company aims to be the first to build a useful quantum machine.
MIT Technology Review Narrated: inside the world’s deepest and longest subsea road tunnel
—Niall Firth
I’m currently around 1,000 feet beneath the North Sea, in a dark, dank cave. It smells weird. And I’m increasingly aware of the pressure from millions of tons of seawater just above my head.
I’m under the iconic fjords of Norway to visit what will soon become the world’s longest and deepest subsea road tunnel—an exceptional engineering feat that will carry drivers deep beneath the North Sea.
I’m here to understand how you make a 16.6-mile highway that sits 1,280 feet below the sea at its deepest point. And also—at a time when it can feel hard to get anything done—to reassure myself that ambitious engineering is still possible. That we can still make things.
This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Meta allegedly used AI to target workers with health issues for layoffs Their lawsuit says Meta relied on AI to create a termination list. (Guardian) + And pinpointed staff who took maternity or disability leave. (Reuters $) + One was allegedly informed the day before her water broke. (Ars Technica) + The layoffs aimed to offset Meta’s AI spending. (Gizmodo) + AI agents are not your “coworkers.” (MIT Technology Review)
2 OpenAI’s first consumer device will be a mobile smart speaker The screenless device will serve as an “AI companion.” (Bloomberg $) + It’ll let you talk with ChatGPT. (Verge) + And use a camera and sensor to understand your environment. (Reuters $) + It’s set to launch next year. (Engadget)
3 The US military sent explosive drone boats into combat for the first time They attacked an Iranian midget submarine and naval port. (Ars Technica) + Underwater drones may shape a war in Taiwan. (MIT Technology Review)
4 DeepMind’s CEO has called for a US-led body to test frontier AI models Demis Hassabis wants the watchdog to vet national security threats. (FT $) + If dangers mount, it would coordinate an industry-wide slowdown. (Axios)
5 Data centers are set to add billions in power costs in 13 states A power auction is slated to produce $6.3 billion in new charges. (NYT $) + Australia plans to govern the use of water and power for AI. (WSJ $)
6 xAI’s unpermitted power pollution hits Black communities hardest Elon Musk’s xAI has been installing gas turbines without permits. (Reuters $) + We need to focus on Big Tech’s energy footprint. (MIT Technology Review)
7 Stripe and Advent have offered to buy PayPal for more than $53 billion The payments giant and private equity firm have made a joint bid. (Reuters $) + Apple and Google Pay have eroded PayPal’s market share. (Bloomberg $)
8 DeepSeek plans to file for IPO as soon as this year The Chinese AI pioneer is likely to list in Shanghai. (WSJ $) + Here’s why DeepSeek’s latest model matters. (MIT Technology Review)
9 A hard, lightweight “bio-metal” has been discovered in sea worm jaws It could have applications in engineering. (New Scientist $)
10 A new $3,000 fitness suit electrocutes you to boost your gains Celebrities love it—but not everyone’s a fan. (404 Media)
Quote of the day
“By economic and engineering measures, generative AI might be the worst technology ever deployed.”
—Alex Reisner, a staff writer at The Atlantic, explains why GenAI’s scaling problem is an engineering disaster.
One More Thing
FRANZISKA BARCZYK
Hackers made death threats against this security researcher. Big mistake.
In April 2024, an anonymous hacker began posting death threats on Telegram and Discord channels aimed at a cybersecurity researcher named Allison Nixon. It wasn’t long before others piled on. Someone shared AI-generated nudes of her.
They targeted Nixon because she had become a formidable threat. As chief research officer at the cyber investigations firm Unit 221B, named after Sherlock Holmes’s apartment, she had built a career tracking cybercriminals and helping get them arrested.
For years, Nixon had lurked quietly in online chat channels or used pseudonyms to engage with perpetrators and bring them to justice. Now, she resolved to unmask the people behind the death threats—and take them down for crimes they admitted to committing.
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ A musician has discovered the true masters of metal breakdowns: birds. + Photographer Fontanesi’s surreal photo splits transform everyday images into spectacular hybrid scenes. + Over 30 actors, filmmakers, and friends recount how Steven Spielberg infiltrated Hollywood in this terrific article. + Who would win the World Cup if less important things than soccer decided it, like life expectancy and happiness? A new game tests your knowledge.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
What Anthropic’s latest AI discovery does—and doesn’t—show
—James O’Donnell
When Anthropic announced last week that it had found a new window into its models’ “internal thoughts” as they reason through answers, there was one colleague I had to talk to: senior editor Will Douglas Heaven.
Aside from having a PhD in computer science, Will has spent a lot of time digging into what we can say about how AI models work. I spoke with him about what we should take from Anthropic’s new (and typically quirky) research. Here’s what he had to say.
This article is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday.
How will AI understand the real world?
Today’s AI systems can generate text, images, and code with impressive skill, but they still struggle with the complexities of the physical world. To bridge this gap, many researchers believe you need something called a world model.
At a LinkedIn Live event today, MIT Technology Review will investigate how this technology could transform robotics and help unlock a new generation of intelligent machines. Join Will Douglas Heaven, our senior editor for AI, and Sam Sinha, founding AI researcher and head of world models at 1X Technologies, for the discussion.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 New York has become the first state to enact a data center moratorium Its governor banned large data-center construction for up to a year. (WSJ $) + A bill passed by state lawmakers could go even further. (Verge) + Everyone hates data centers. (MIT Technology Review)
2 Smartphone shipments have hit a 13-year low due to the memory crunch They fell 11% in the second quarter of 2026. (Reuters $) + The memory chip shortage has increased prices. (Gizmodo) + And threatens the promise of Moore’s Law. (MIT Technology Review)
3 Sugar molecules have been found in interstellar space for the first time It hints that life on Earth may have been seeded from space. (Nature) + And boosts the odds of living organisms existing elsewhere. (New Scientist $) + Researchers used radio telescopes and data to spot the molecules. (NYT $)
4 Nvidia has halved its Asia buyer list to stop AI chips reaching China It introduced a “white list” of companies that passed tougher checks. (FT $) + It moved amid tighter chip controls from the Trump administration. (Reuters $)
5 Russian state hackers are targeting routers to spy and steal, the US warns The government has warned users to secure their devices. (Ars Technica) + Now is a good time for doing crime. (MIT Technology Review)
6 Trump moved his crypto gains into stocks while urging people to buy more His crypto projects earned him a fortune—but steep losses for retail buyers. (Reuters $) + He’s called for Congress to pass a new crypto bill to honor Lindsey Graham. (CNBC)
7 A new cell therapy has saved four children with terminal brain cancer They were treated with an experimental immunotherapy. (New Scientist $) + Access for older children will also be limited. (Bloomberg $)
8 The LAPD has halted use of Flock surveillance cameras due to privacy issues Flock’s automated license plate readers have caused concerns. (LA Times $) + It’s also been criticized for sharing data with state and federal officials. (Engadget)
9 The US has approved launching a space mirror that reflects sunlight onto Earth As part of a controversial plan to power solar panels round the clock. (Wired $) + But geoengineering faces many practical challenges. (MIT Technology Review)
10 Anthropic says Claude’s values vary depending on your language It’s most cautious in English and most deferential in Arabic. (Gizmodo)
Quote of the day
“The age when humans are the highest life form on earth will end. For better or for worse, it will happen and it can’t be stopped.”
—SoftBank CEO Masayoshi Son predicts that AI will overtake human intelligence by 2040 in a speech at his company’s annual corporate conference in Tokyo, Reuters reports.
One More Thing
Inside the strange limbo facing millions of IVF embryos
Millions of embryos created through IVF sit frozen in time, stored in cryopreservation tanks around the world. Many are left in a peculiar limbo, with no clear path forward.
UK residents can discard them, make them available to other prospective parents, or donate them for research. People in the US can also opt for “adoption,” “placing” their embryos with families they get to choose. In Germany, people aren’t typically allowed to freeze embryos at all. And in Italy, unused embryos must remain frozen, ostensibly forever.
While these embryos remain in suspended animation, patients, clinicians, embryologists, and legislators must grapple with the essential question of what to do with them. What do these embryos mean to us? Who should be responsible for them?
The machine that could change the world will be housed in a room that looks like a data center crossed with an ice cream factory. Inside will be some 100 stainless-steel cabinets, each about six feet tall and connected to a supply of liquid helium that keeps them only a few degrees above absolute zero. Inside those cabinets will be hundreds of chips, and on those, thousands of particles of light flying through a maze of optical switches and beam splitters. Each photon must be accounted for, because precisely measuring where it ends up will help answer questions that current computers might take millions of years to solve.
This computer, as described, does not exist. It’s the brainchild of a company called PsiQuantum, founded in 2016 by four physicists from UK universities. In a crowded field of deep-pocketed competitors with similarly fantastical visions, the company aims to be first to fulfill its promise.
In the years since the physicist Richard Feynman first envisioned them in 1981, quantum computers have promised to speed up everything from medical research to AI by harnessing the qualities of quantum particles. Unlike normal computer bits, which can be either a 1 or 0, quantum bits can exist in multiple states at once. And combining enough of those quantum bits together could produce a computer capable of tasks well beyond the reach of today’s conventional machines. But even today’s best quantum prototypes are too small and error-prone to do anything useful.
That makes PsiQuantum’s promises for what its computers will ultimately do all the more bold. Consider the company’s hopes for predicting the effects of cytochrome P450 enzymes, which often break down drugs in the body. If pharma companies knew more precisely how they would work on a particular molecule, they could design more effective medications faster. Estimating this for a specific drug can take over 10 years with today’s methods, says Philipp Ernst, vice president of quantum applications for PsiQuantum, but “we aim to get it down to four minutes.”
The company’s chips will be contained in large cabinets. A quantum computer powerful enough to be commercially useful is expected to require roughly 100 of these cabinets connected together.
COURTESY OF PSIQUANTUM
In a field full of such claims, PsiQuantum has attracted unusual investment and scrutiny for two reasons: It is one of the few companies aiming directly at building a large and useful machine, and it is already working with a major chip manufacturer to build its systems using existing semiconductor fabs. Its vision has attracted momentum: Last year, PsiQuantum raised $1 billion in funding and broke ground in Chicago on a site it’s building in partnership with local governments. It also has a second site in the works in Australia, which it promises will be operational—meaning hardware-ready—in 2027. And it’s one of just two companies (along with Microsoft) to reach the third stage of an intensive government evaluation program to see which quantum companies might succeed.
Evaluating whether PsiQuantum will do what it says is harder than, say, judging a drugmaker by its clinical trial results: Advances in quantum computing are incremental, opaque, and tough to verify from the outside. But the company is now approaching its prove-it moment, when years of closed-door work and hundreds of millions in investment will either culminate in a useful quantum computer or fall short. We could start to know which as soon as next year.
A new kind of machine
Terry Rudolph, one of PsiQuantum’s four founders, is soft-spoken and shaggy-haired. He was born in Malawi and learned only after earning his first physics degree that he is a grandson of the famed physicist Erwin Schrödinger. He later self-published a 150-page book to explain quantum computing to teenagers (my PR contact gave me a signed copy with a wink that said “We never expect anyone to actually read this,” but I can report that it is a funny and helpful book).
Around 2014, Rudolph and his cofounders became increasingly convinced that the quantum breakthroughs they were finding to be possible in theory might also be possible in a real machine. They eventually left their academic positions and divided the tasks before them: Rudolph worked on theory, Mark Thompson on engineering, Pete Shadbolt on scaling the technology up, and Jeremy O’Brien on articulating the vision and finding investors (O’Brien served as CEO until February; he’s been replaced by Victor Peng, a veteran of the semiconductor industry).
To understand why the quantum computer the company is building would be a big deal, consider how imprecise much of modern science remains. We cannot reliably predict, for example, which lithium-ion battery will catch fire or how quickly a critical aircraft component will corrode.
This isn’t just because these systems are complex, though they are. It’s that, at their core, they are governed by quantum mechanics. Subatomic particles don’t have well-defined properties—this location and that velocity—but instead occupy quantum states spread across many possibilities. And that in turn influences a range of atomic and molecular behavior. Schrödinger (Rudolph’s grandfather, remember) showed how to describe this haziness mathematically a century ago this year, but precisely carrying out the calculations on real-world systems quickly becomes unfeasible even for the best computers. Scientists cope with this gap using approximations, imperfect simulations, or experiments on animals.
WINNI WINTERMEYER
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PsiQuantum co-founder and chief scientific officer Pete Shadbolt (left), and machinery the company has built to manufacture its own barium titanate, a material with the perfect qualities for routing light particles (right).
Feynman, David Deutsch, and other physicists in the 1980s wondered if we could do better. Maybe such complexity could instead be modeled using a new kind of machine. Rather than using transistors that are only ever on or off, this one would use particles held in quantum states, manipulate them to perform calculations, and then measure them at the end for an answer. Using quantum systems to simulate quantum systems would for the first time allow a simulation of physics and chemistry that directly reflected reality. It would be an invaluable tool for designing new drugs, materials, or really anything affected by quantum mechanics. Revolutionary, in other words.
Humankind’s leaps in understanding how nature works have often resulted in the invention of powerful new tools, Rudolph told me. “I don’t think it’s a coincidence that the Industrial Revolution coincided with our ability to calculate and simulate the laws of Newtonian mechanics, the laws of thermodynamics,…the laws of classical electromagnetism,” he says. “Whenever we have more power to calculate and simulate and understand things, we build incredible machines that come from it.” He sees something similar coming with quantum computers.
Chasing photons
One mystery has always been which quantum thing—ions, atoms, or something entirely new engineered with quantum properties—could be made stable and controllable enough to use as a qubit, the basic unit in the quantum computing world. Quantum systems are delicate, and observing any particular particle causes it to collapse into one state rather than a superposition of multiple states. If this happens during the computation rather than at the end, it produces an error that must be corrected for. Too many of these means the computer fails to produce a useful answer.
Just as engineers in the early days of aviation weren’t sure whether airplane wings would be fixed or flap like a bird’s, we’re not yet sure which of these quantum things will work best. Google and IBM are betting on superconducting qubits, superconducting circuits made of aluminum or other metals. Intel is using electrons. PsiQuantum is using photons, the particles that make up light.
“Photons have lots of nice things going for them,” Rudolph says. They can maintain quantum states for a long time; indeed, the photons in the universe’s cosmic microwave background may have done so for billions of years. But photons also move fast and scatter easily. More importantly, two photons are more likely to pass through one other than interact. That makes them a challenging candidate for quantum computation, in which qubits need ways to influence one another.
For a while, this last flaw seemed to doom the idea of quantum computing with light. But in 2001, researchers from the Los Alamos National Laboratory and the University of Queensland found a loophole. They discovered they could essentially fake interactions between photons by sending the light particles through a network of beam splitters and detectors. Their paper changed everything. PsiQuantum was created to make the theory a reality.
Size was the first problem; previous plans would have required a computer as large as California. Mercedes Gimeno-Segovia, who was a PhD student of Rudolph’s in the early 2010s (after almost becoming a professional violinist instead), thought of a way for the machine to be smaller.
The basic process since then has been this: First create photons with lasers and then “entangle” them, exploiting a quantum phenomenon in which the particles no longer have individual states but instead share one. Next, route them through a maze of gates that perform computations, and finally read out details of their quantum state at the end, all while tracking and correcting for the errors that occur. Succeeding at each of these steps millions of times is not so much an engineering hurdle as a brick wall. And building the supply chain—like manufacturing new materials with the qualities to route individual photons around—is arduous.
A sizable chunk of PsiQuantum’s funding is being spent on custom cooling machinery that uses tanks of liquid helium to cool the company’s chips. Shown here is part of the PsiQuantum’s cooling system at a facility in Milpitas, California.
COURTESY OF PSIQUANTUM
To get a sense of it all, last year I joined Shadbolt at the SLAC National Accelerator Laboratory, in Menlo Park, California. The center has helped produce several Nobel Prizes and played a role in the 1968 discovery of quarks, fundamental building blocks of matter that make up protons and neutrons. But PsiQuantum set up shop there essentially to siphon liquid helium from SLAC’s giant cryoplant. This is what the company uses to cool its computing cabinets down to deep-space temperatures.
Right now the cabinets operate at 2 K, or -456 °F, but the goal is to be able to run them slightly warmer—at a balmy -452 °F. Most quantum approaches require the whole machine to be cooled to superconducting temperatures, so that much of the expense in running it will actually be spent on refrigeration. But photonic computers require only one piece to be this cold—the detectors that measure single photons at the end of the computation. And the required temperature can be a bit higher. (PsiQuantum said in May that it will spend some of the $100 million award in CHIPS Act funding it’s slated to get on these detectors).
The siphoning setup was a temporary solution; PsiQuantum now has its own cooling system at its testing facility in Milpitas, California, and is setting up a larger one at its production site in Australia next year. These helium systems represent some of the biggest capital expenditures for any quantum company and will consume a significant chunk of PsiQuantum’s $1 billion funding round.
In the afternoon we drove to a lab in San Jose, where I donned a cleanroom suit—a head-to-toe covering that keeps dust at bay—to watch the manufacture of a blueish crystal called barium titanate.
It’s prized by PsiQuantum because it quickly and reliably routes light particles with very little electrical input, keeping the precious photons undisturbed as they move through the circuit. But for all barium titanate’s theoretical value to the company, its structure makes it a pain to manufacture, and the material wasn’t available at scale when PsiQuantum got its start. The company, in what Rudolph told me was an agonizing decision, opted to make it in-house, requiring a massive investment. I saw a technician—operating at what looked like a giant pressure cooker—adding the base elements to several hoppers; then I watched through a porthole as the elements got heated, vaporized, and finally crystallized into a thin layer on a wafer disc. At that time each disc took about 12 hours to make; the company now says several are produced each day. The discs then get shipped to the chipmaker GlobalFoundries in Malta, New York, where PsiQuantum’s chips are made.
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The company has invested heavily in making its own barium titanate, a material whose delicate crystalline structure is tedious to manufacture.
PsiQuantum’s bet is that this entire supply chain, byzantine as it might sound, will make the company more efficient than its competitors. That’s because, if you squint, it looks like a souped-up and high-precision version of the existing supply chain for silicon photonic chips, another type of technology that transmits information with light—one that’s already used in data centers. If PsiQuantum produces its chips at scale, it can take advantage of tools and infrastructure that already exist.
But it’s not a given that one working chip can easily be wired up to thousands more. That’s why the company is testing in phases: Its Milpitas site has connected three cabinets together, with 250 chips in each, but the next step is to scale the systems up and see whether the company’s techniques for correcting errors can keep up. Once the cooling system arrives at the Australian site late next year, the company says, it aims to connect about 100 cabinets together. Then PsiQuantum will work up to running the world-changing algorithms it has promised.
The timeline for this, it’s worth noting, is up for debate. News articles have said that 2027 is the year that PsiQuantum aims to have its first full-scale quantum computer come online at its Australian site, but the company insists the deadline has been misread, and that it only intends for its facility to be “operational” by the end of next year. That means cooling systems in place and ready for hardware to be installed, but no promises about what size computer will be ready. In an industry where timelines are perpetually in flux yet central to how companies are judged, that distinction isn’t trivial.
Into the unknown
The outsider with perhaps the best guess of whether PsiQuantum will succeed is the Pentagon. The US Defense Advanced Research Projects Agency—the Pentagon’s research and development arm—has been running an initiative to determine which of the boastful quantum companies might actually deliver. In the last year and a half, the heads of the program have been sounding more confident. Joe Altepeter, who ran the program until last year and proudly described himself as a “quantum skeptic,” told me in March 2025: “I am more optimistic now than I have been at any point in the past 10 years.” And in a statement earlier this year, his successor, Micah Stoutimore, said “it now seems likely that someone will build a utility-scale quantum computer by 2033,” referring to a machine that generates more value from its calculations than it costs to build and operate.
The program has been scrutinizing PsiQuantum’s systems since 2023, and last year placed the company into the third stage of a benchmarking initiative meant to determine whether the technology will actually work. But to the rest of the industry, PsiQuantum is sort of a black box.
PsiQuantum has broken ground at the Illinois Quantum and Microelectronics Park outside Chicago, pictured here, and on another site in Moreton Bay, Australia. It aims to build large-scale quantum computers at each site.
COURTESY OF PSIQUANTUM
“It is very hard for an outsider to evaluate,” says Scott Aaronson, a theoretical computer scientist at the University of Texas at Austin who runs a popular blog that often covers the industry. Other companies, like Google and Quantinuum, have regularly published results over the years demonstrating chips and systems with incremental improvement, publicly laying the engineering groundwork needed to eventually build large machines.
PsiQuantum has instead focused squarely on a commercial goal—a computer with one million qubits, which is the scale that researchers expect to unlock research currently not possible on normal computers. PsiQuantum often differentiates itself with this industrial-scale goal, but IBM, which debuted a development road map in 2020, has been progressively building bigger and bigger systems. It initially targeted 2028 for a large-scale, error-corrected system, a deadline that now appears to have been pushed out to 2030.
Making it useful
On top of actually building the machine, a major focus for PsiQuantum is getting the rest of the world to develop a plan for how to use it. PsiQuantum has announced partnerships with customers including the defense giant Lockheed Martin, which intends to use it for materials design; the automaker Mercedes, which wants it for battery design; and the aerospace manufacturer Airbus.
That these companies don’t have a computer to experiment with is not a problem, according to Ernst at PsiQuantum. “There’s a PlayStation 6 probably coming up from Sony next year or the year after, and people are programming those games right now,” he says. “This is, in principle, very similar.” (It’s a glib analogy but not an entirely empty one; the quantum algorithms for solving a research problem can be cracked even if there is not yet hardware to run them on.)
The idea is that experts in quantum information from both PsiQuantum and its customers will be able to translate design problems—say, the requirements for a battery in a Mercedes electric vehicle—into algorithms the computer could solve. The company offers a software package called Construct, which companies can use to design their own algorithms that might one day run on the computer.
The future of quantum computing hinges on these algorithms. Quantum computers get painted as a speedup for everything, but in reality, they’re suited to a subset of problems, and answering a question with this sort of machine requires the question to be formulated with very specific types of algorithms. People spend entire careers working on such algorithms, even if the computers to run them don’t exist yet. At their core, they use the rules of quantum mechanics to manipulate probabilities in ways that ordinary computers can’t.
The most famous example, and a reason the government is so interested in quantum computers, is Shor’s algorithm. It was developed in 1994 by the theoretical computer scientist Peter Shor and could effectively break many forms of encryption used online, for everything from credit card numbers to military intelligence. The thing keeping the world together, for now, is that nobody has a computer to run the algorithm on (and security experts are already launching new encryption methods that could withstand attacks from a quantum computer). PsiQuantum is researching how long its systems might take to run Shor’s algorithm.
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PsiQuantum’s chips are manufactured at GlobalFoundries in Malta, New York, and tested at company headquarters in California. Both PsiQuantum and GlobalFoundries have been awarded federal CHIPS Act funding.
The company also published a paper in December in collaboration with Airbus, essentially seeing if a new algorithm developed by the authors could beat a classical computer in modeling fluid dynamics, like the turbulence around an airplane wing. Andrew Childs, an expert in quantum simulation, told me PsiQuantum achieved only a moderate speed increase over what today’s computers can do. “It’s probably unlikely that speedups like this will have a significant practical impact until we have very large-scale quantum computers,” he said in an email. (When I asked Ernst, he agreed the improvement was modest.)
Some of the algorithms PsiQuantum is working on are not expected to be perfected or even used in the first applications of its computer. Instead, its initial tasks might be more along the lines that Feynman envisioned way back in 1981: simulating the smallest particles of our world.
The company’s most significant research in this realm is in modeling quantum chemistry. Take those pesky P450 enzymes. More precisely understanding how they operate, PsiQuantum says, would allow for faster drug development and testing.
Last year, PsiQuantum published methods for doing these sorts of chemistry calculations on a quantum computer, along with another paper demonstrating an algorithm that can simulate the collision of two molecules and estimate the likelihood of different outcomes femtosecond by femtosecond (there are one quadrillion femtoseconds in a second). It’s a remarkable amount of detail not currently possible with today’s technology, and it would allow drug and materials researchers to simulate new chemical interactions.
Dominic Berry, who developed some of the core techniques used in the collision paper but isn’t involved in PsiQuantum, says the company made impressive improvements, but to do the simulations scientists are most curious about would require the algorithm to be made even faster and PsiQuantum’s early computer to have fewer errors than currently expected.
Until PsiQuantum’s computers are up and running, the breakthroughs that these research papers tease remain in the realm of theory. It’s a space where Rudolph operates quite comfortably. He told me that Alan Turing created the theory of classical computing with pen and paper, imagining how the 1s and 0s would be represented in the machine, and how with the right approach to logic you could compute almost anything.
“But there is no way that by hand, with a pen and paper, Turing was ever going to produce—you know—Minecraft and Facebook,” he says. That took more than 70 years of tinkering (during which we fortunately created more useful things than Minecraft and Facebook).
For all the time Rudolph spends dreaming up things quantum computers might do, in other words, people working on those problems are still stuck with pen and paper for now: “Until you have the actual machine in hand, you don’t have the opportunity to really explore its potential.”
This story was updated on July 14 to clarify how long DARPA’s quantum program has been evaluating PsiQuantum.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
Anthropic—currently the world’s most valuable AI company, with a nearly $1 trillion valuation—has a reputation for publishing strange and heady research. It’s looking into whether AI models can feel pain, for example, and will sometimes cut off chatbot conversations if it suspects users are “abusing” the model.
One niche that Anthropic spends more time and money on than other AI companies is called mechanistic interpretability, which means looking inside the complex math of an AI model to learn why it comes up with one particular output and not another. It’s complicated stuff; there are millions of data points that might contribute to any result, and wading through them can look more like word salad than anything useful. It’s also controversial. Describing AI models with terms borrowed from psychology and neuroscience can make their behavior seem more sophisticated than we might otherwise judge it to be.
That’s why, when Anthropic announced last week that it had found a new window into its models’ “internal thoughts” as they reason through answers, there was one colleague I had to talk to. Senior editor Will Douglas Heaven, aside from having a PhD in computer science, has spent a lot of time digging into what we can say about how AI models work. I spoke with him about what we should take from Anthropic’s new (and predictably quirky) research.
What did Anthropic learn here, exactly?
Anthropic has been trying to understand how large language models (LLMs) work for a few years now. Anthropic isn’t the only one looking at this, but I think the company has made it part of its core mission more than most. Anthropic’s CEO, Dario Amodei, has said we won’t be able to control LLMs fully unless we learn more about how they work.
So this new research is very much in that context. It goes deeper into the weird mechanisms inside LLMs than ever before. What Anthropic learned was that LLMs have a space inside them—which Anthropic calls the J-space—filled with words that don’t appear in their output but that seem to influence the way they puzzle through problems. All this was hidden until Anthropic developed a new technique to probe its model Claude, so it’s a genuine discovery.
Sometimes these words keep track of where the LLM has got to in a particular task, sometimes they look more like flashes of recognition (for example, “protein” might pop up when you give an LLM only the letters of a protein sequence), and sometimes they represent a kind of internal commentary on the model’s decision-making. In my favorite example, Claude decided to cheat on a coding test when the word “panic” appeared.
Anthropic also found that LLMs are able to describe and manipulate the words in this space. So somehow they seem to be making use of it.
Let’s step back for a second. I don’t think of large language models as simple, but they’re also not magic. There’s a bunch of math that learns relationships between words, right? So why is it so hard to “peer” into an LLM to know what’s going on?
Yeah, they’re not magic! I think the fact we don’t fully understand them plays into the mythmaking. And it’s worth noting that the whole narrative that Anthropic is leaning into here—that they’ve built this really mysterious technology, but don’t worry, because they’re also the ones to figure it out—very much fits with the company’s vibe. [See how Anthropic warned that its new models were so good at coding they posed a global cybersecurity risk, only for the US government to shut them down shortly thereafter.]
So yes: LLMs are just math. And yet it’s vastly complex math. Not only are today’s LLMs made out of hundreds of billions of numbers, but running them triggers a cascade of millions and millions of calculations. I wrote last year that if you printed out even a medium-size LLM on pieces of paper, it would cover a city the size of San Francisco.
It’s impossible to make sense of any of that math without specialist tools that highlight specific parts of an LLM at specific times. You need to know where to look and how to look. And building those tools requires understanding something of that complex math in the first place.
You’ve written elsewhere about this concept of studying LLMs the way one might study an organism’s brain. Is it fair to use “brain-like” terms when talking about how an LLM works?
I don’t love using those kinds of terms. LLMs are not brains. Talking like this is misleading because it can suggest that LLMs are capable of more human-like things than they are or that we can make assumptions about how they might behave that we shouldn’t. The whole anthropomorphization thing is also tied up with a bunch of strong ideological positions about what this technology is and what it’s going to be.
But at the same time, we lack a good alternative vocabulary for talking about what these models are doing. I can understand why people reach for words like “think” and “understand” and “brain-like”—they’re convenient shorthand.
Anthropic compares this new space it found inside LLMs to the space that some neuroscientists think our brains use to keep track of conscious thoughts. I asked the company how seriously we should take that comparison and it said in a statement: “Drawing these analogies was helpful to us in designing our experiments, as they allowed us to make many non-obvious experimental predictions about the J-space that turned out to be true. At the same time, it’s important to note that there are some important differences between the J-space (and language models in general) and the human brain, so we don’t mean to claim there’s a perfect correspondence.”
What’s a problem in AI that this new concept of the J-space might be used to solve?
Anthropic has said that monitoring the J-space could be a way to catch models doing something they shouldn’t. Because words pop up in this space that don’t appear in a model’s output, they can tell you things about its behavior that you might not have noticed otherwise—such as when it is giving biased responses or when it is weighing the pros and cons of cheating.
That’s the theory, at least. I think it’s better to think of this result as one more step on the path to understanding this technology overall than as something that will be useful by itself.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Sperm donors need limits, says a European fertility group
Ties van der Meer doesn’t know how many siblings he has. The 47-year-old was conceived at a private fertility clinic using sperm from an anonymous donor. He eventually tracked down one sibling, but he may have others he’ll never find.
Other donor-conceived people have found they have tens or even hundreds of them. “It does make you feel a bit mass-produced,” said one who discovered they had 25 half-siblings.
In response, a European fertility organization says we need international limits on the number of children a single donor can contribute to.
This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.
How will AI understand the real world?
LLMs have transformed what AI can do with language, but helping machines understand and operate within physical spaces presents a different challenge. In response, researchers are developing a new form of artificial intelligence: world models.
At a LinkedIn Live event tomorrow, MIT Technology Review will explore how this technology could shape the future of robotics and open one of AI’s next major frontiers. Join Will Douglas Heaven, our senior editor for AI, and Sam Sinha, founding AI researcher and head of world models at 1X Technologies, for the conversation on Tuesday, July 14.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Apple has sued OpenAI for allegedly stealing trade secrets OpenAI purportedly stole IP to develop its own consumer hardware. (CNBC) + The suit claims OpenAI poached Apple staff to access the information. (BBC) + And requested trade secrets in job interviews with Apple workers. (Guardian) + Apple also sued two former employees, Chang Liu and Tang Tan. (Reuters $)
2 A Nobel-winning chemist is leaving the US to lead an AI lab in China Omar Yaghi will head an institute using AI to discover new materials. (LA Times $) + He won a Nobel Prize in Chemistry for creating “molecular sponges.” (NYT $) + His departure comes as China tries to woo US scientists. (Nature) + The White House has slashed science spending. (MIT Technology Review)
3 The EU is moving closer to banning children from social media It’s proposed barring under-13s unless supervised by an adult. (NYT $) + And limiting access for older children. (Bloomberg $) + The EU has also told Meta to disable autoplay and infinite scroll. (Politico $)
4 Meta scrapped an AI image feature on Instagram after a backlash It allowed users to generate images based on public accounts. (TechCrunch) + And automatically opted in any Instagram user with a public account. (NYT $) + AI memories are privacy’s next frontier. (MIT Technology Review)
5 Phoebe Gates’ shopping app claimed credit for sales it didn’t drive Phia claimed unearned affiliate sales through fake clicks. (Bloomberg $) + Cofounder Gates is the daughter of Microsoft cofounder Bill. (Engadget)
6 Leaked police drone footage exposes the new reality of surveillance Hours of San Francisco Police video were accidentally released. (Wired $) + Surveillance from drones is on the rise in the US. (MIT Technology Review)
7 Over two-thirds of Americans back a Sanders-style AI ownership plan A poll found strong support for public ownership of AI stock. (Gizmodo) + Tech firms have their own takes on the idea. (MIT Technology Review)
8 AI may soon make campaign text messages more potent—and irritating AI platforms are training bots to sound like political candidates. (NPR)
9 An orbiting disco ball gave Einstein’s theory its most precise test yet It measured Earth’s twisting of space-time more precisely. (Rest of World)
10 Australia’s biggest radio hit may be the product of GenAI Musicians are questioning how the song was made. (Guardian)
Quote of the day
“LOL, I found out I can access the [network storage], so funny.”
—A text message sent by former Apple engineer Chang Liu to a colleague, which a new lawsuit alleges was part of a scheme to steal hardware IP for OpenAI.
One More Thing
Colombian military officials intercepted this 40-foot-long uncrewed fiberglass “narco sub” in the ocean just off Tayrona National Park.
CARLOS PARRA RIOS
How uncrewed narco subs could transform the Colombian drug trade
On a bright April morning in 2025, a surveillance plane operated by the Colombian military spotted a 40-foot-long “narco sub” idling in the Caribbean Sea. The stealthy vessel, used by drug cartels to move cocaine north, could sail with its hull almost entirely underwater.
After seizing the boat, the coast guard noticed something unusual: there was no one on board. This was Colombia’s first confirmed uncrewed narco sub, operable by remote control, but also capable of a degree of autonomous travel.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Anthropic found a hidden space where Claude puzzles over concepts
The AI firm Anthropic has got the clearest glimpse yet at what’s really going on inside large language models as they answer questions or carry out tasks. What they found ranges from the mundane to the unnerving.
Researchers at the company built a tool called the Jacobian lens (or J-lens) and used it to uncover a hidden area, which they named the J-space, inside its flagship LLM, Claude.
The J-space contains words related to the response a model is working on but may not ultimately produce. If Claude were a person (which it is not), you might say these hidden words reveal what’s on its mind before it actually speaks.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 OpenAI has unveiled its long-awaited “super app” ChatGPT Work blends its chatbot, coding tool, and new models. (Reuters $) + It’s designed to do your work for you and with you. (Ars Technica) + And arrived the same day as OpenAI’s GPT 5.6 models. (NYT $) + It’s also developing a fully automated researcher. (MIT Technology Review)
2 Humanoids have performed teleoperated surgery on living animals In the world-first, they removed gallbladders from pigs. (Ars Technica) + The human work behind humanoids is hidden. (MIT Technology Review)
3 SK Hynix has landed the largest US listing by a foreign company The South Korean chip giant raised $26.5 billion. (CNN) + Demand for AI data centres has led its profits to skyrocket. (Guardian) + But its jumbo share sale may be a sign of overheated times. (FT $) + South Korea’s hottest bachelors are chip workers. (MIT Technology Review)
4 Tencent is leading a deal to unwind Meta’s $2 billion Manus acquisition It’s in talks to become the Chinese AI startup’s largest shareholder. (FT $) + Tencent will reportedly buy Manus for no less than $2 billion. (Reuters $) + Beijing had ordered Meta to unwind the acquisition. (Bloomberg $)
5 Resuscitated human retinas responded to light 10 hours after death It’s a big step towards eye transplants that restore vision. (New Scientist $) + As is a new device that revives dead eyeballs. (MIT Technology Review)
6 Meta has started charging for AI access A new version of Muse Spark has a paid tier for developers. (Quartz) + Meta also plans to start producing an AI chip in September. (Reuters $)
7 OpenAI and Google have sold AI models to blacklisted China groups Via Singapore-based subsidiaries of Alibaba, Baidu and Tencent. (FT $)
8 A daughter tested an AI “death bot” of her father The technology provided both comfort and unease. (New Yorker $)
9 An astronomer says the hunt for alien life needs more statistics He wants to replace speculation with mathematical frameworks. (Quanta)
10 Pokémon Go players turned Times Square into a giant battlefield More than 1,500 fans finally fulfilled the game’s 2016 launch promise. (Wired $) + Pokémon Go is also training world models. (MIT Technology Review)
Quote of the day
“When we’re talking about AI, we love the hype, we get excited about it. The damn thing never actually lands in practice.”
—Vijay Janapa Reddi, an engineering professor at Harvard University, tells Wired why he’s skeptical about grand plans for AI.
One More Thing
B.F. SKINNER FOUNDATION
Why we should thank pigeons for our AI breakthroughs
In 1943, psychologist B.F. Skinner led a secret government project to make bombs more precise. His idea: teach pigeons to guide missiles by pecking at targets on a screen inside a warhead. To train them, Skinner rewarded the birds with food when they made the right decisions, using trial and error to shape their behavior.
Unsurprisingly, the military never deployed Skinner’s kamikaze pigeons. Yet his experiments convinced him that pigeons were “an extremely reliable instrument” for studying learning.
Decades later, those same principles would help power reinforcement learning, the technology behind some of today’s most advanced AI systems.
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ Here’s a splendid selection of this year’s NSW architecture award winners. + Photographers have captured the Strawberry Moon’s golden glow in stunning detail. + Idiocracy is the film that best exemplifies the “American experience,” according to a new poll. Look back at the prescient comedy with this Screen Junkies trailer. + Get ready for the weekend with this psychedelic house journey from Jamie xx b2b Caribou.
Ties van der Meer doesn’t know how many siblings he has.
The 47-year-old was conceived at a private fertility clinic in the Netherlands using sperm provided by an anonymous donor. After the Netherlands banned anonymous donation in 2004, the doctor who ran the clinic destroyed records that might have identified those donors, he says.
He describes the situation as “problematic.” Children have a right to know their biological parents, he says. While he did ultimately track down one sibling, who helped him identify his father along with other genetic relatives, he may have others he’ll never find.
Other donor-conceived people who have been able to track down siblings have found they have tens or even hundreds of them. One donor-conceived woman who found 25 half-siblings over the course of seven years told the Guardian, “It does make you feel a bit mass-produced.”
We need international limits on the number of children a single donor can contribute to, a European fertility organization argued yesterday. At a conference in London, members laid out plans to start with a Europe-wide limit.
Today many countries, including the UK, have banned anonymous egg and sperm donation. But anonymity can’t be guaranteed even in places where it is technically allowed. Genetic tests offered by companies like Ancestry and 23andMe, along with genetic registries, have made it much easier for donor-conceived people to find parents and siblings who share their genes.
And because sperm can be frozen and stored for years before it is eventually used, the current set-up can result in situations where donor-conceived people discover the identity of a genetic parent only after the person’s death. They might also find that they have siblings of very different ages, all around the world.
Stories like these can be distressing for donor-conceived people. And there are other reasons why limits are considered important. The offspring of a prolific donor might be at risk of unknowingly forming romantic or sexual relationships, for instance. And some people are concerned that a donor with a harmful genetic mutation might pass that down to many children.
This is unlikely, given the level of screening that most donors undergo. But it has happened. A man who donated his sperm to a sperm bank in Denmark was found to have a genetic mutation that significantly increased the risk of multiple cancers. But his sperm had already been used to conceive at least 197 children across Europe. Some of those children developed cancer. Some died.
Many countries already have legal limits for donors. In Malta and Cyprus, for example, both egg and sperm donors are allowed to contribute to the birth of just a single child, according to data presented at the European Society of Human Reproduction and Embryology (ESHRE) meeting in London on July 8.
Other countries set limits based on the number of families a single donor can contribute to, allowing recipients to have children who share a genetic link. In the UK, that limit is set at 10 families per donor.
But these limits are difficult to enforce, partly because donated gametes don’t necessarily stay in their original country. In Denmark, the national limit is set at 12 families. But the country is a major exporter of sperm. In the UK, for example, more than half of sperm donations in 2020 were imported—with most of those coming from either Denmark or the US.
“The only thing that really makes sense is a transnational limit,” Jackson Kirkman-Brown, a professor of reproductive biology at the University of Birmingham, said at the meeting.
Kirkman-Brown and his colleagues have spent months putting together a document that represents ESHRE’s position on these limits. After consulting with fertility specialists, clinics, sperm and egg banks, donors, and donor-conceived people, the team has developed a plan to start with a Europe-wide limit on sperm and egg donations.
ESHRE is calling on sperm and egg banks, as well as fertility clinics, to respect an initial limit of 50 families per donor. That’s still very high, according to a handful of people I spoke to at the meeting. But at least it’s a start.
Europe should move toward setting limits at 15 families per donor, Kirkman-Brown said. “We may find that 15 is also too high,” says Vasanti Jadva, who studies the psychological well-being of people conceived using donated eggs, sperm, and embryos at City St George’s in London. “We still don’t know what the right number is.”
It will be difficult to enforce these limits, too. And if they end up limiting the supply of donor sperm, there’s a chance that some people will turn to unregulated sperm donations from people who do not undergo health screening. Unregulated donations can lead to other problems for prospective parents, including the possibility that donors will seek parental rights over the children conceived using their sperm.
And it will be even harder to establish international limits. When I asked the American Society of Reproductive Medicine for its thoughts on ESHRE’s proposed limits, a representative directed me to a guidance document saying “it has been suggested” that for a population of 800,000, single donors should be limited to “no more than 25 births” in order to avoid the risk that relatives will have children together. (Considering the US has a population of over 340 million, the total figure could be pretty high, but many sperm banks opt to limit the number of families contributed to by a single donor at around 25.)
Van der Meer thinks that even a limit of five families from a single donor would be high. International donation makes it even harder for donor-conceived people to connect with genetic relatives, so the limit for international contributions should be set at two families, he says.
Still, he thinks ESHRE’s suggested limit is a “positive first step.” Van der Meer has managed to track down a sibling, his father, and nephews, aunts, and uncles. He hopes that future policies respect the rights of donor-conceived children to know, and be in contact with, their genetic relatives.
“But,” he says, “you have to start somewhere.”
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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