Drug discovery is a high-cost, high-risk endeavor that is under growing pressure from a market increasingly defined by first-mover advantage.
Since the 1950s, the cost of developing new pharmaceuticals has roughly doubled every nine years—a phenomenon known as Eroom’s Law. Today, bringing a new drug to market takes an average of 10-15 years and costs anywhere from $1 billion to $2.5 billion, with failure rates upward of 90%.
AI has become the pharmaceutical industry’s biggest bet on bringing success rates up and timelines down. The faster drug companies can identify, test, and optimize new chemical compounds, the lower the risk of costly failures later in development.
“The main cost in drug discovery is still the clinical phase, so trying to reduce risk and increase your success rates there is obviously hugely beneficial,” says Paul Belcher, director of protein research strategy at global life sciences company Cytiva. “AI is one approach that drug companies hope will not only save time and compress timelines, but enable better quality candidates to reach the clinic.”
Early use of AI in drug discovery shows potential, but also highlights the need for robust and authentic data, as well as integration in lab systems.
AI brings efficiency to the lab
One of the most promising early-stage applications of AI in drug discovery is in hit identification. This involves screening libraries of molecular entities against a disease-related target, such as a protein, to find molecules that bind to it. A successful hit gives researchers a starting point for further testing and refinement, with the aim of eventually developing a viable drug.
Belcher has seen a shift from empirical screening to predictive design: Instead of physically screening libraries, drug companies are now using AI to design drug candidates from scratch and predict how they will interact with disease targets before committing anything to research and development (R&D).
This means companies are no longer limited by how much they can physically screen to identify starting points. “AI does away with that,” says Belcher. “And it can help eliminate low-quality candidates before you have to physically test them, saving time and resources.”
What AI can’t do yet is reliably predict kinetics or developability of new compounds, says Belcher. This means every AI-generated candidate still needs to be validated in the lab.
Traditional screening workflows were built to identify hits at scale, not to profile large numbers of complex candidates in detail. This is placing more pressure on lab teams, who now have to test, characterize, and purify a growing volume of more diverse, AI-generated compounds.
“The current techniques used in hit identification can screen hundreds of thousands, sometimes millions of compounds, using binary or threshold-based techniques producing low-fidelity data—yes-or-no responses,” Belcher explains. “AI can increase the number of hits you get and potentially give you better quality hits as well. That increases demand for higher-throughput, information-rich technologies to then validate and characterize those hits.”
Models need complete, quality data
As AI has accelerated demand for data-rich lab systems, it has also highlighted a fundamental need for better, more complete data.
Many earlier AI models were trained on publicly available datasets and are now hitting what Belcher calls a data wall. Because models have access to the same data, they all reach similar conclusions, with diminishing returns over time. Additionally, the datasets weren’t built with AI in mind, meaning they lack the structure, labeling, and diversity needed to keep models accurate and free of bias.
Publication bias reinforces the problem. “Most publicly available datasets and scientific publications focus exclusively on positive results,” says Belcher. “No one wants to share their failures. This bias is almost like having one hand tied behind your back. AI models can identify patterns associated with success, but they lack the comprehensive understanding of failures that would make predictions more reliable.”
The data Belcher believes would markedly improve models—the failed experiments, the compounds that don’t bind—remains frustratingly difficult to come by. “We often joke that there should be a journal of negative data,” he says. “It’s often buried in lab notebooks, and it’s never used to inform or guide future research.”
This lack of negative data creates a fundamental problem: Without access to a broad range of data, models can’t be adequately trained to avoid bias. “In all machine learning applications, the model’s performance relies heavily on the quality and scope of the training data,” notes Belcher.
Fabrication has also become much easier with AI, compounding concerns around data integrity. Take Western blots, for example. These are part of a standard technique for identifying proteins in blood or tissue samples, and they are among the most common targets for manipulation in biomedical research. Belcher cites research by Dutch microbiologist Elisabeth Bik, who found that almost 4% of biomedical papers contained duplicated or manipulated images. This was back in 2016, before generative AI made fabrication trivial.
“Manipulated or faked data has always been a problem in science, but in the AI world, especially when used to train models, it could have potentially disastrous consequences,” says Belcher. “There needs to be more tools to verify that data is not manipulated.”
Some vendors are starting to tackle this challenge. Belcher points to solutions like Cytiva’s Image Integrity Checker, for instance, which uses secure hash algorithms—the same technology used in blockchain—to detect whether scientific images have been tampered with. “We’re starting to see a lot of interest from publishing houses that want to adopt this as standard because it’s a quick way to ensure that what gets published in the literature is genuine,” he adds.
Autonomous labs could accelerate breakthroughs
Belcher describes the future state of drug discovery as fully autonomous labs that run with minimal human intervention. Foundational to this vision is consistency in data and infrastructure.
These AI-driven dark labs, or labs-in-the-loop, operate around the clock. They cycle through prediction, testing, and optimization, and then feed results back into AI models to guide the next round of experiments. This can improve the success rates of drug candidates entering clinical trials, says Belcher. Better starting points, combined with more rounds of optimization, should result in better candidates with fewer liabilities reaching the clinic.
But automating a lab depends heavily on integration. That means interoperable systems, highly structured and comprehensive datasets, and information flowing easily in and out. Most labs aren’t there yet. “Today, a lot of the instruments in labs are standalone,” Belcher notes. “You can have the best technology in the world, but if it’s a closed ecosystem—if the user can’t get the data out—it doesn’t do any good.”
An integrated infrastructure can enable labs to generate FAIR (findable, accessible, interoperable, and reusable) data at scale. This would not only inform individual lab reports, but could also train subsequent generations of AI models, effectively closing the loop between the computational, AI-driven dry lab and the physical wet lab.
“Our goal is to help scientists and researchers accelerate their breakthroughs and make that future state of autonomous labs a real possibility,” says Belcher. “We want to help them generate reliable data, simplify workflows in discovery, and hopefully enable what they’re working on to become tomorrow’s life-changing therapies, faster and with greater confidence.”
On costs and what comes next
AI-driven drug discovery is still in its early days. Notably, no drug discovered primarily through AI-driven design has yet received full FDA approval—although Belcher expects that to change in the next two to three years.
How big of an impact could AI eventually have on drug discovery? “The holy grail would be full in silico prediction of efficacy and toxicity, eliminating the need for the vast majority of physical wet lab work,” says Belcher. But there are many barriers to this beyond the maturity of the models, including regulatory hurdles and cost challenges.
A Stanford study found that the cost of training frontier AI models has more than doubled every year since 2016, adding more financial pressure to a sector already defined by exceptionally high R&D spend.
Belcher acknowledges the tension, but remains optimistic about what’s ahead. “I think we’ll get to a point where there’s a balance between AI and wet work, from a cost perspective and a risk perspective,” he says. “As long as the cost of compute doesn’t ever outweigh the cost of clinical development, I think AI is going to be an advantage.”
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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.
Scientists have found that damaged myelin sheaths lead to abnormal rhythms in brainwave activity during sleep. These findings could have major implications for the development of biomarkers and therapies for neurodegenerative diseases that affect myelin, such as multiple sclerosis (MS) or Alzheimer’s disease.
“The myelin sheath helps electrical signals travel efficiently through brain circuits,” said Mohit Dubey, PhD, senior scientist at the Netherlands Institute for Neuroscience, who presented the study at the Federation of European Neuroscience Societies (FENS) Forum 2026. “We wanted to understand whether myelin damage could also affect how brain circuits behave during sleep. By studying this link, we hope to better understand what causes sleep disturbances in neurological disease and whether sleep-related brain signals could serve as biomarkers for diseases that are yet to show clinical symptoms, as well as showing disease progression.”
Although sleep is known to play a key role in brain health, its study has often been overlooked in the context of neurodegenerative diseases. In conditions like Alzheimer’s, sleep disruptions are known to contribute to fatigue and cognitive decline, especially during the REM sleep phase, which is critical to preserve healthy cognition and memory.
“Sleep disturbances are extremely common in neurological diseases such as multiple sclerosis and Alzheimer’s disease, but the biological reasons for these problems remain poorly understood,” said Dubey. “Understanding the biological link between sleep and brain circuit dysfunction could help guide future strategies for improving sleep and brain health in these conditions.”
Dubey’s team had previously shown that loss of myelin sheaths cause abnormal spikes of brainwave activity in the brain during sleep that seemed to resemble those observed in epilepsy and Alzheimer’s. In the current study, the researchers compared electroencephalogram (EEG) recordings of MS patients during sleep with mouse models of damaged myelin and Alzheimer’s. In both humans and mice, myelin loss resulted in similar abnormal bursts of brain activity during non-REM sleep phases and slower brainwave rhythms during REM sleep.
“REM is a stage of sleep associated with dreaming and replay of daytime experiences. In this state the brain produces rhythmic electrical patterns called oscillations that help coordinate communication between neurons,” said Dubey. “Our findings show that these rhythms become disrupted and slower when myelin degenerates, and that the electrical spikes seen during sleep are closely linked to the stability of brain circuits affected by neurodegenerative diseases such as MS and Alzheimer’s.”
While there are no treatments that can repair damaged myelin, some MS drugs are able to slow down the immune system’s attack on myelin sheath to reduce or halt disease progression. Further research to determine how exactly myelin damage alters brainwave patterns during sleep could aid the early detection of myelin degeneration and inform the design of therapeutic approaches that target myelination.
“This opens new research directions exploring how sleep rhythms depend upon the myelination status of the brain circuits,” said Dubey. “Sleep recordings may provide a non-invasive way to detect early changes in brain circuit myelination in neurological disease. This could eventually help clinicians monitor disease progression, and we want to investigate whether sleep recordings could be used as biomarkers to detect early changes in brain circuit function.”
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.
Why worms (and microbes) are catching on as a manure pollution solution
Anthony Agueda, a third-generation California dairy farmer, pulls a rake through a bed of dark, wet wood chips to reveal a half-dozen squirming red earthworms. There are likely hundreds of thousands more wriggling just under the surface.
The worms and microbes are part of a “vermifiltration” system that cleans manure wastewater. The approach may dramatically cut methane, nitrous oxide, and water pollution.
Vermifiltration is just one of a variety of methods that farmers, companies, and scientists are employing to drive down manure pollution as the livestock industry faces growing pressure to address the environmental harms from one of the smelliest parts of the business.
MIT Technology Review Narrated: geoengineering gets a reality check
Solar geoengineering, the controversial idea that we could deliberately intervene in the climate system to counteract global warming, is moving beyond computer simulations and into the practical engineering challenges required to make it real.
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 The Trump administration has lifted restrictions on OpenAI’s GPT 5.6 The green light came after additional testing and meetings. (Axios) + OpenAI subsequently said it will launch widely tomorrow. (Bloomberg $) + The rollout had been delayed due to security concerns. (Verge) + Does AI know too much? (MIT Technology Review)
2 China is looking at curbing overseas access to its top AI models Alibaba, ByteDance, and Z.ai attended meetings about the plan. (Reuters $) + Beijing is also weighing the security risks of open-weight AI. (SCMP) + And has issued a “backdoor” security alert over Claude Code. (CNBC)
3 European NATO allies have unveiled a $50 billion high-tech missile plan They will engineer stealth and high-speed hypersonic weapons. (BBC) + Which can strike targets at least 300 km away. (Reuters $) + The Dutch and British are also developing amphibious ships. (Bloomberg $)
4 Meta is testing “super sensing” AI glasses that record every moment It plans to disable privacy LEDs that alert people when they’re “on.” (FT $) + It’s also released an AI image generator. (NYT $) + Which lets anyone use your Instagram photos in AI images. (Wired $)
5 China’s DeepSeek is developing its own AI chip, sources say It could reduce the company’s reliance on Nvidia and Huawei. (Bloomberg $) + DeepSeek V4 was a win for Chinese chipmakers. (MIT Technology Review)
6 Wikipedia is fighting to survive the internet’s next era It’s under attack from MAGA, AI raids, and repressive regimes. (NYT $) + AI has given Wikipedia a language problem. (MIT Technology Review)
7 SpaceX plans to launch its first model coproduced with Cursor The new frontier model could arrive as soon as this week. (Information $) + It’s built with AI startup Cursor, which SpaceX is buying for $60 billion. (FT $)
8 A new academic “humanizer” tool can erase signs of AI-written text But researchers are very divided over its potential impact. (Nature $)
9 Scientists have detected a mystery chemical on Pluto and Titan It appears to absorb light in a way we don’t currently understand. (Wired $)
10 A Waymo robotaxi reportedly called the cops on drinking teens Officers then approached the vehicle with guns drawn. (404 Media)
Quote of the day
“Parents do you know where your teens are? Waymo does!”
—Local police post on Facebook that a Waymo in California called the cops on two teenagers for “drinking and shooting from the vehicle.”
One More Thing
MICHAEL BYERS
Your boss is watching
Dora Manriquez has spent nine years driving for Uber and Lyft, where every ride she accepts or rejects is tracked by the apps she relies on for work. Having found herself unable to score enough better-paying rides, she has had to file for bankruptcy.
App-based employers aren’t the only ones keeping a very close eye on workers today. Jobs today—whether in an office, a warehouse, or your car—can mean constant electronic surveillance with little transparency, and potentially with livelihood-ending consequences if your productivity flags.
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ Literary worlds collide in this marvellous Dr Seuss/Stephen King mashup. + A daring snorkeler saved a dolphin from a suckerfish—and then celebrated with its whole pod. + This clever musical project seamlessly constructs an original song from vocal snippets of 50 artists singing US city names. + A long-lost wallet from 1970 was recently unearthed, creating a cute time capsule from its owner’s high school years.
It has long been known that brain gray matter plays a key role in multiple sclerosis (MS) disease progression and cognitive impairment, but because magnetic resonance imaging (MRI) has only been able to detect lesions in white matter, neither clinicians nor researchers have had a way to detect or monitor gray matter (cortical) lesions. And while many new drugs developed in the past decade can slow disease progression significantly, they primarily work on reducing white matter lesions.
A University at Buffalo (UB)-led team now reports that it has found a way to use artificial intelligence to reveal these otherwise invisible cortical lesions by reviewing existing MRI scans. The researchers say the significance of finally being able to see what has been known as one of the most important indicators in MS disease progression cannot be overstated.
“Detecting previously invisible cortical lesions on conventional legacy MRI scans has major implications for MS research and clinical care,” commented Robert Zivadinov, MD, PhD, SUNY distinguished professor in the Department of Neurology and director of the Buffalo Neuroimaging Analysis Center (BNAC) in the Jacobs School of Medicine and Biomedical Sciences at UB. “The ability to see for the first time these previously hidden indicators of MS disease progression, including cognitive impairment and disability, is an important advance.”
Added Michael G. Dwyer, PhD, associate professor of neurology and biomedical informatics in the Jacobs School and a researcher with BNAC, “What this collaboration has been able to accomplish is a real success story for applying AI in the medical arena. We now have access to these incredibly useful data on MRI scans that were there but you couldn’t see them without using AI to pull them out. The computational methods are finally at the point where we can do this.”
“Multiple sclerosis (MS) affects both the inner, connectivity-oriented portions of the brain (white matter) and the outer layer of the brain (the cortex),” the authors explained. While the involvement of cortical lesions in MS has been known almost since the identification of MS in the late 19thcentury, they weren’t included on diagnostic criteria until the 21st century. And even when they were included, it was noted that their use would be greatly limited due to the current capabilities of clinical MRI.
“Historically, research and clinical care in MS have focused on white matter, where focal demyelinating lesions are a hallmark of the disease,” they continued. And although there are now many therapies that can almost completely halt the incidence of new white-matter lesions in individuals with MS, they haven’t had the same impact on clinical progression, the team continued.
Over more recent decades it’s been found that gray matter is affected from the earliest MS disease stages, and it’s become evident that gray matter pathology is more than secondary to white matter damage. “From a clinical perspective, cortical lesions are strongly associated with clinical disability and cognitive impairment,” the authors stated. “They may also have more prognostic value than white matter lesions for disability and disease course.”
There’s an urgent need for in vivo imaging methods that can show gray matter lesions, they stressed. Dwyer added, “We have all been very frustrated, knowing that these cortical lesions were there but not being able to see them. There’s a lot of ongoing damage that continues to happen in MS that you won’t see with conventional MRI, but that histopathologists have been clearly demonstrating for decades on postmortem tissue.”
For their newly reported study the team applied advanced image processing techniques, including artificial intelligence, to standard MRI scans from a large MS clinical trial. “Recently, several post-processing methods, including synthetic contrasts and artificial intelligence (AI)-based approaches, have shown potential for enhancing cortical lesion detection on conventional MRI data,” they noted. “These methods have the potential to reanalyze existing clinical-trial data to answer key mechanistic questions about both MS development and about treatment effects.”
The AI approaches the researchers used, building on work from co-authors from the Netherlands, were designed to extrapolate vital information from the relationships between multiple images that can’t be seen on a single image.
The researchers combined multiple image-processing techniques, including a new one they developed called MMCLE, or multimodal cortical lesion enhancement. They then applied these techniques to MRI scans from the large, phase III FDA regulatory ORATORIO clinical trial, a study of the MS drug Ocrelizumab that included more than 700 participants.
They found that while individual images of a patient’s brain revealed mostly white matter lesions, once they applied the AI-based image processing methods to multiple different contrast images, they were able to see anywhere from 15 to 20 cortical lesions for each patient, more than 11,000 for the whole dataset. “We confirmed that cortical lesions can be clearly visualized and quantified with these methods,” they stated. “Using deep learning, we also confirmed that the simultaneous use of multiple contrasts improves quantification.”
Dwyer explained further, “If you look on the original scans, you generally can’t see the cortical lesions, but generative AI is very powerful because it can look between the scans and detect tiny differences between them. Because it sees those minor discrepancies, AI can reveal that there’s something going wrong there, that the tissue is not behaving like healthy tissue. The trained models can view multiple MRI images together and synthesize them and synthesize what had been missing.”
Zivadinov added “This work, which has revealed that there is so much invisible pathology in the brain, will have tremendous impact for reviewing data from past clinical trials and also for those going forward,” he says.
A European survey launched at the 42nd Annual Meeting of the European Society of Human Reproduction and Embryology (ESHRE) suggests broad public support for fertility treatment and research, including genome editing in human embryos for specific reasons.
The report, “Fertility, Embryo Research and Genome Editing: Public Attitudes in Europe,” was commissioned by the charity Progress Educational Trust (PET), which aims to improve choices for people affected by infertility and/or genetic conditions, and was supported by ESHRE. It explored public attitudes towards fertility treatment, embryo research, genome editing, surrogacy, and related topics.
The authors say the findings will “inform ESHRE’s ongoing work in Europe, linked to implementation of the European Union’s SoHO (Substances of Human Origin) Regulation and to the ethical considerations that arise in our field.”
The survey included 8688 participants aged 16–75 years across the U.K., Netherlands, Spain and Italy, with more than 2000 respondents in each country.
Across all four countries, a large proportion of respondents supported state-funded fertility treatment for people experiencing infertility and wishing to conceive, ranging from 54% in the Netherlands to 57% in the U.K., 62% in Spain, and 64% in Italy. Support was highest for heterosexual couples (47–59%) and lowest for transgender people (12–18%).
Conversely, most respondents said they did not support people being able to choose the biological sex of their child, based on personal preference. Opposition was strongest in the Netherlands (72%) followed by the U.K. (59%), Italy (55%) and Spain (47%). Nonetheless, there was still a significant minority that expressed support for sex selection. This support was strongest in Spain (32%) followed by the U.K. (26%), Italy (22%) and the Netherlands (18%), and was more common among younger participants than their older counterparts.
The age differential across responses could mean that with time, there will be a shift in views and potential changes in policy, the authors note.
The survey also found public backing for the use of human embryos in research to better understand and develop treatments for congenital diseases. Support ranged from 41% in Italy to 48% in the Netherlands and Spain, substantially exceeding opposition in all four countries (15–24%), including Italy, where research uses of human embryos are currently prohibited.
Respondents were then asked whether they supported or opposed the use of genome editing in human embryos, in three different scenarios:
For scientific and medical research to help understand or develop treatments for congenital disease, without human implantation.
In human embryos that will be transferred to a human to establish pregnancy to help eliminate a severe or life-threatening condition, like cystic fibrosis, in the resulting child.
In human embryos that will be transferred to a human to establish pregnancy to help eliminate a common or medically manageable condition, like asthma, in the resulting child.
In all four of the countries, more respondents supported than opposed all three of the uses of genome editing, with the highest level of support given if the technique helps eliminate a severe or life-threatening condition.
It is important to note that this use of genome editing—which was supported by 55% in the Netherlands, by 53% in Spain, by 52% in the U.K. and by 46% in Italy—is not permitted by law in any of these four countries at present.
PET commented: “It is heartening to see such substantial support for uses of genome editing in human embryos, across all four of the countries surveyed. That said, these findings present an interesting conundrum. Respondents seem to be more ready to countenance the use of genome-edited embryos in treatment—at least, if helps to eliminate a severe or life-threatening condition—than they are to countenance the use of genome-edited embryos in research. Realistically, research must occur first. There is therefore a need for wide-ranging public conversations, where the vital role played by research—in enabling treatment, and ensuring that treatment is safe and effective—can be conveyed.”
Professor Karen Sermon, immediate past chair of ESHRE, said, “Reproductive medicine and embryo research are advancing rapidly, and these findings show the importance of understanding how the public views those developments. It is particularly striking that support for some applications extends beyond what is currently permitted in certain countries. As science advances, it is essential that public awareness keeps pace, so that decisions about future treatments are informed by both evidence and societal values.”
It’s somewhat surprising that he can choose just one. He’s the person spellers rely on to confirm pronunciations and answer questions about the roots of the words they’re given at the Scripps National Spelling Bee—arguably the world’s most prestigious competition of its kind. The story of how the word earned the top spot on his personal list may well mark the beginning of his unique career path as both a linguist and a Greek Orthodox priest.
In third grade, Sietsema ventured to a garage sale at a friend’s house with 50 cents in his pocket and picked out three books that struck his fancy. Although they were priced at 50 cents each, his friend’s mother said the books he’d chosen were on special and sent him home with all three, including a collection of Edgar Allan Poe stories called Masterpieces of Mystery. Knowing it contained macabre tales like “The Tell-Tale Heart,” his own mother told him he’d need to wait a few years before reading it. Naturally, he started it right away.
As he read “The Unparalleled Adventure of One Hans Pfaall,” Sietsema was baffled by the main character’s description of arriving at the moon in a balloon. Pfaall reported tumbling into a crowd of people who were “eyeing me and my balloon askant, with their arms set a-kimbo.” Sietsema had never encountered the word akimbo (with or without a hyphen) and asked his parents what it meant. They didn’t know, and it wasn’t in the family’s dictionary. The question also stumped his teachers, and the dictionaries in his classroom and the school library were no help either. “For years, I didn’t know what this word meant,” Sietsema says. It stuck in his mind that there was a word out there that he, his parents, and his teachers didn’t know. He thinks it wasn’t till he got to college that he finally found a dictionary with the answer: The moon dwellers in Poe’s story had been standing with their hands on their hips, elbows turned outward.
“I credit that puzzle with getting me into dictionaries and being curious about etymology,” he says. It kindled a fascination with words—and an abundance of curiosity—that would shape his life’s trajectory and work.
Growing up in Grand Rapids, Michigan, Sietsema attended a Dutch Reformed Christian school and recalls taking part in only one spelling bee, in second grade. It was in the 1970s, when everyone was hooked on phonics—so he overthought the sounding-it-out implications when asked to spell of. “I spelled it U-V, and of course, I was wrong,” he says.
At the time, he thought he probably wanted to work in the church—when he painted himself as an adult for a class project, he dressed his grown-up self in a cassock. But after taking a class in nuclear chemistry at the local junior college in high school, he decided his backup plan was to become a nuclear engineer. So when he went to the University of Michigan, he enrolled in the school of engineering. While he did well and liked his courses, though, he soon realized he felt called to a career in the church after all.
Sietsema (a.k.a. Father Mark) presides at the 2025 Holy Friday evening service at Holy Trinity Greek Orthodox Church in Lansing, Michigan. As part of that service, he sprinkles the congregation with rose-scented water, which delights the children. “It’s like a one-sided water fight in church,” he says. This service culminates with a procession in which a symbolic tomb is carried around the outside of the church. Everyone who attends takes part and leaves with a flower.
COURTESY OF BRIAN SIETSEMA
Switching to the college of literature, science, and the arts, he chose the studies in religion major, taking advantage of the interdisciplinary freedom it offered to take classes in literature, art, and more. He also tucked in courses that would fulfill seminary prerequisites such as knowledge of the biblical languages, studying ancient Hebrew and ancient Greek as well as modern languages that might come in handy for theological scholarship (Dutch, Swedish, and modern Hebrew).
Being in Ann Arbor gave Sietsema “a different understanding of the wideness of the Christian world,” as he puts it, and he gradually became less sure about which church he wanted to work in. As he neared the end of his fourth year at Michigan, he still needed a few more pre-seminary courses—and it dawned on him that he’d taken an “awful lot” of languages and thoroughly enjoyed them. So he stayed on for a fifth year to study linguistics as well as German, ancient Aramaic, and modern Arabic. One of his professors encouraged him to go to grad school and insisted that he apply to MIT, which was considered the top linguistics program in the country. To his surprise, he got in.
Sietsema calls his four years at MIT a great adventure: “If I could relive them, I would empty out my bank accounts to do so.”
At MIT he worked with Morris Halle, one of the leaders in generative grammar, which Sietsema describes as a working model of the “chemistry” of language—the parts and processes that form the building blocks of verbal communication. Halle and others had developed counting procedures (akin to measured time in music) that help explain stress patterns (that is, which syllables might receive emphasis by varying such things as stress or pitch). Building on that work, Sietsema’s dissertation proposed that the division of words and phrases into metrical units similar to musical measures can be used to predict where high and low tones fall, which he demonstrated in the tonal patterns of four Bantu languages spoken in Tanzania. At the time, research in this area was seen to have implications for creating natural-sounding machine- generated speech.
Sietsema calls Halle “a wonderful mentor,” and the two played well off one another. As he was sweltering in his Central Square apartment while printing the final version of his dissertation, Halle called and asked him to stop by. Knowing that Sietsema read Hebrew, Halle, a Latvian-born Jew who’d learned English as his sixth language, wanted to show him a syllable-counting analysis of the 23rd Psalm he’d just completed; Sietsema answered with his own structural analysis of Psalm 90. “I could tell he was delighted to have this young Gentile boy from Grand Rapids, Michigan, who had the same fascination for biblical Hebrew as he did,” Sietsema says.
Today, he calls his four horizon-expanding years at MIT a great adventure: “If I could relive them, I would empty out my bank accounts to do so.” Beyond embracing the intellectual stimulation of the Institute, he took advantage of Cambridge’s many cultural opportunities and cross-registered at Harvard to study French and Ugaritic. All told, he says, he’s now studied about a dozen languages, including the Latin he took in high school and the modern Greek he would add to his repertoire several years after earning his doctorate. (“I always feel like I’m leaving one out,” he says.)
When Sietsema graduated from MIT in 1989, the job market for linguists was “not great.” As fate would have it, though, Matt Alexander, PhD ’92, his best friend at MIT, had already been hired at the University of Michigan, where a one-year position as a visiting assistant professor of phonology opened up that spring. Alexander recommended Sietsema, who handed in his dissertation and got the job, earning an award for excellence in teaching based on student reviews in his first semester.
Shortly after his one-year gig at Michigan ended, he returned to Massachusetts and landed a job as pronunciation editor at Merriam-Webster in Springfield. Although the work was very different from the theoretical linguistics he’d focused on in grad school, “as the guy who had studied a whole bunch of language back in undergrad, it was kind of coming home to old-school philology,” he says. His main job was to ensure that pronunciations—which can change—were up to date. Fluoride, for example, shifted from floo-o-ride in the early 1900s to flor-ide in the second half of the century.
At Merriam-Webster, he made the call on which pronunciations would go into the 10th edition of Merriam-Webster’s Collegiate Dictionary—and in what order of preference. The dictionary, he explains, takes a descriptivist approach that reflects common word usage, so he kept a radio and a TV on in the background as he worked. He’d listen for interesting pronunciations and record them on index cards, noting how each such word was said, who said it, where the person was from, and what the context was. These went into Merriam-Webster’s “huge files” of index cards containing citations of words in actual usage.
Sietsema also had a hand in identifying new words and usages that appeared in the 10th edition, which was initially released in 1993—and he was responsible for the inclusion of definitions for interjectional uses of like. He recognized three informal uses: to introduce a quotation (“So she was like, ‘Let’s go eat’”); to give an approximation (“There were like 10 people in line”); and to emphasize (“He was, like, gorgeous”) or convey something apologetically or vaguely (“I need to, like, borrow some money”). While not a fan of such usages, he recognized them as real linguistic phenomena that had earned a place in the dictionary.
During his tenure as pronunciation editor, he introduced the use of the International Phonetic Alphabet (a standard phonetic notation for all languages) into Merriam-Webster publications long before it became widely used in American mass-market dictionaries. He also oversaw the recording of pronunciations for digital versions of the dictionary and flew out to a San Diego recording studio to supervise the voice actors. When the actors refused to record certain words that offended them, Sietsema had to step into the breach and do it himself. If you go to www.merriam-webster.com and search for a choice two-part expletive the actor Samuel L. Jackson is famous for delivering, it will be his voice that you hear when you click on the icon of the speaker—offering a decidedly less memorable rendition.
Working at Merriam-Webster gave Sietsema access to what he describes as its “fantastic library of old books on every subject imaginable.” He seized the opportunity to delve into historical questions about the development of Christianity—something he’d been curious about. It struck him that Orthodox Christianity was the most original form of the faith that was still around. Having met Katherine Chapekis, a young linguist raised in the Greek Orthodox tradition, during his year teaching in Ann Arbor also nudged him in the direction of Orthodoxy. In 1991, he converted and they married, and she began working at Merriam-Webster the following year as a definer and researcher who tracked down first usages of English words.
After 15 years of answering etymological queries, when the bee was expanded in 2018 Sietsema began serving as a pronouncer for some of the earlier rounds as well.
AP PHOTO/CAROLYN KASTER
At the Greek Orthodox church in Springfield, Sietsema’s facility for languages proved useful when he served as a volunteer chanter, helping the priest lead services in Greek. “I do a good job with the liturgical Greek because I have the phonological knowledge to know how to make my mouth do the things that it needs to do to sound like authentic Greek speech as opposed to an American just rattling off Greek letters,” he says.
He began taking evening classes in Byzantine chant, and before long the bishop was encouraging him to attend seminary. Merriam-Webster allowed him to work four 10-hour days so he could commute to Brookline to study at the Holy Cross Greek Orthodox School of Theology. And after four years, he earned a master of divinity degree.
Sietsema fully intended to go back to being a lexicographer, perhaps eventually getting ordained so he could serve as a substitute priest on weekends. But he’d made what he jokingly calls “a terrible mistake” at the seminary: He’d embraced his studies so enthusiastically that he became the valedictorian and had to give the commencement speech. The archbishop of America—the head of the Greek Orthodox church in the US—came up from New York to attend the ceremony, and he happened to be in need of a deacon who could also serve as a speechwriter. “A few weeks later, I got a call from the archdiocese saying ‘We want you to be ordained, and we want you to come to New York, and we want you to write for the archbishop,’” Sietsema recalls.
In short order, he and his wife moved to the Upper East Side of Manhattan so he could begin his new post as Father Mark (he used his middle name because Orthodox priests must be ordained with a saint’s name, and there are no Orthodox Saint Brians). As deacon to the archbishop and then to his successor, he wrote their speeches and encyclicals on top of many other duties—including chauffeuring them through New York City traffic—and traveled with them around the country and to Greece, meeting President Clinton, ambassadors, members of Congress, Elie Wiesel, and South Africa’s Anglican Archbishop Desmond Tutu along the way. But after two years, as the father of a newborn, he was eager to move on from a job that required putting in as many as 14 hours six or seven days a week. So in 2000, he returned to Michigan to become pastor of the Holy Trinity Greek Orthodox Church in Lansing.
“The World Series can be a four-game sweep and the Super Bowl can be a blowout, but the National Spelling Bee always comes down to one last word.”
Not long after settling into parish life, Sietsema got an unexpected call from the Scripps Spelling Bee. His wife had served on the event’s word panel from 1997 to 2000, and he had traveled with her to one of the members’ off-site gatherings in 1998. He’d tagged along to dinner one night, and they were pleased to meet the person who was responsible for pronunciations in the bee’s official dictionary. But now, just a few weeks before the 2003 event, there was a crisis: The longtime pronouncer had suddenly died. The veteran associate pronouncer would step into his role and take on the job of giving spellers their words, but a new associate pronouncer would be needed to answer spellers’ questions about word roots, monitor pronunciations, and be prepared to serve as the pronouncer if needed. Could he do it? Honored to be asked, Sietsema got the okay from his bishop and said yes.
Little did he know it would become a permanent gig. After 15 years of answering root-word queries, when the bee expanded in 2018 he began serving as a pronouncer for some of the earlier rounds as well—though never for the finals. Now he’s the head of a team of associate pronouncers. “It’s just wonderful to see these young people blossom right in front of you, asking their questions and analyzing the word on the spot and figuring out how it all goes together,” he says. He dismisses the idea that the kids have photographic memories, saying they’re “really just good little word detectives.”
As a member of the bee’s word panel, Sietsema attends multiple daylong meetings to create and fine-tune each year’s list by mining the 500,000 or so words in Merriam-Webster’s unabridged dictionary. “For an introductory round, you want something that’s an interesting word, a useful word, but something that’s gettable,” he says. “For the later rounds, you really want to find something that’s going to challenge the speller. And it’s nice to have a word that’s analyzable.” “Rooty” words—those with obvious roots—are ideal.
The advent of unabridged online dictionaries has streamlined how students prepare for the bee, which once required wading through the dictionary manually to compile word lists. Today, it’s easy to generate lists of words derived from a particular language to study their roots, for example. Meanwhile, the competition has become increasingly fierce, and once-verboten terms like geographical names are considered fair game. For some of the words in the hardest rounds, “it looks like you’re just taking a spoonful of alphabet soup,” he says. “And that’s for the spellers who really, really are committed to learning just about every word they can in the dictionary.”
When it gets down to the last spellers in the final round, there’s an electric feeling in the room. “It’s always a close competition,” he says. “The World Series can be a four-game sweep and the Super Bowl can be a blowout, but the National Spelling Bee always comes down to one last word, and that’s what makes it exciting each and every time.”
The philosopher Friedrich Nietzsche famously wrote that a characteristic of theologians is their “unfitness for philology,” meaning they can’t be trusted to interpret texts with objective accuracy. He also maintained that a sense of restraint characterizes a good linguist. Sietsema says he’s right on both counts. When linguists analyze texts, “we know what we don’t know, and that’s important because you don’t find meaning where it’s not in the original,” he says. He thinks the well-trained linguist has a mission to the world of theology: to help clarify what is an appropriate interpretation of a sacred text and what is going too far.
He’s put his unique blend of skills into practice. In the early days of the covid pandemic, for instance, a Greek Orthodox scholar defended the practice of continuing to use a single spoon to administer communion. The scholar argued that holy things cannot cause harm and that abandoning them for fear of an earthly disease was far more dangerous than the disease itself, citing a passage from a homily of an archbishop of Constantinople in the fourth century CE saying “nothing is worse than to relegate spiritual things to human reasoning.” Sietsema responded with a thoughtful defense of reason, pointing out that the scholar’s argument relied on a mistranslation of logismoi, which he explained refers not to the faculty of reason but to negative mental habits, such as flawed reckonings, intrusive thoughts, or vain rationalizations. He countered that the church very much values reason and advocated “the exercise of good sense, good science, and compassion,” arguing that “those who pit faith against the faculty of reason end up losing one or the other or both.”
Sietsema’s time at MIT, he says, taught him to pay attention not only to what’s in data sets but also to what’s not there that could be. “That particular muscle gets used in both linguistic analysis and lexicography, as well as in pastoral care,” he says. “When you’re listening to people pour out their hearts, it’s important to notice what they’re saying and what they’re not saying.”
During his time as a deacon at the Archdiocese in New York City, Sietsema stands alongside Archbishop Iakovos, the head of the Greek Orthodox church in the US, at a water blessing service attended by former President George H.W. Bush.
COURTESY OF BRIAN SIETSEMA
As both a priest and a linguist, he’s called on to notice and remember. Attention to detail matters whether he’s gearing up for the celebration of Pascha, or Easter, at Holy Trinity or preparing for the National Spelling Bee, which he calls “the holy week of spelling.”
This spring, before heading to Washington for his 24th National Spelling Bee in May, Sietsema reflected on what words he might add to his list of favorites. A top candidate was one given to Evelyn Blacklock, a speller in his first bee as associate pronouncer in 2003: clepsydra, meaning an old-style water clock. “She didn’t know it, but through a series of questions to me about the Greek roots of the word—from kleptein (to steal) and hydōr (water)—she was able to divine the English spelling,” he recalls. “It was so satisfying to watch this feat of word sleuthing happen in real time, and it gave me a good insight into the importance of my role at the bee.”
It seems unlikely, however, that akimbo will ever lose top billing on his list. It’s easy to imagine Sietsema facing the future with his own hands on hips, elbows out, embracing linguistics, theology, and scientific reason as he shares his joy for life and the words we use to describe it.
Jos Benschop is climbing a ladder to get to the top of his newest machine.
It’s a bit of a schlep. The contraption is the size of a double-decker bus—more than 150 tons of gleaming precision-milled aluminum covered in thousands of snaking tubes, colored cables, and pressurized tanks. From the ground, it looks like a futuristic V8 engine. When I reach the top with Benschop we’re looking down from about 15 feet in the air, with bunny-suited technicians scurrying around below.
It’s more than 200 cubic meters of tech—“mechatronic devices that hold a few mirrors in a position with atomic precision,” he says, gesturing at the gargantuan apparatus. Benschop, a tall and grizzled 66-year-old, has spent over a decade working with his engineers to design this thing, but even so, he’ll sometimes look at it and go: Oh my God.
Benschop is the executive vice president of technology for ASML, a Dutch company that is the linchpin of the microchip industry. If you want to make powerful chips to power phones or AI, a lithography machine like the one we’re standing on is what you need to create increasingly tiny circuitry. Lithography is the art and science of shining light on a silicon wafer to pattern out the transistors, wiring, and other components of the microchips that will be cut from it.
The chipmaking field is essentially controlled by only two big players: ASML, which creates the lithography machines, and TSMC, the chipmaking giant.
Nine years ago, ASML began selling machines that use a daring new way of patterning chip features. These machines employ extreme-ultraviolet light, or EUV—radiation well outside the visible spectrum that they produce by shooting lasers at tiny molten drops of tin, tens of thousands of times a second. Those first machines—the result of an R&D moonshot that lasted 16 years and cost about $10 billion—can craft transistor features with a resolution of 13 nanometers. This new machine can do even better: It has a resolution of just eight nanometers, the width of about 40 silicon atoms. The devices are now shipping to chipmaking factories, or fabs, at an eye-watering price: $400 million each.
But chipmakers will fork that cash over, because they are in a desperate race to produce new and improved chips every year. That means getting their mitts on machines that can make ever smaller components and cram them together ever more densely—part of a long-standing recipe for creating faster and more energy-efficient chips.
For years now, ASML’s tools have been critical to keeping Moore’s Law alive. Without the company’s advanced chipmaking technology it is very possible that chip density—and the ability to perform ever more calculations—would have plateaued.
The AI industry has produced new and ravenous demand for denser chips, as firms like OpenAI and Anthropic scramble to erect server farms that train and deploy new, ever-more-powerful models, which require new, ever-more-powerful hardware. ASML’s latest machine promises to help keep the AI party raging for at least another decade.
“We can allow customers to go to smaller and smaller features, and that opens up the space for whatever we see now today in AI, which is absolutely mind-blowing,” Marco Pieters, ASML’s CTO, told me. “I think we’ve only seen the tip of the iceberg.”
Its relentless push for “shrink”—as they call it in the chipmaking industry—has made ASML a dominant force: The company produces about 90% of all chip-lithography tools worldwide. If you make chips, ASML is unavoidable.
But that monopoly position makes some people, and governments, uneasy. The chipmaking field is essentially controlled by only two big players: ASML, which creates the lithography machines, and TSMC, the chipmaking giant in Taiwan, which uses ASML’s machines to craft the vast majority of all microchips. This duopoly is so powerful that it has geopolitical implications. In an effort to prevent China from developing advanced AI, the US government pressured the Dutch government to impose an embargo in 2019: ASML isn’t allowed to sell high-end machines to any Chinese firm. Geopolitically, “chips are the new oil,” says Marc Hijink, the author of Focus: The ASML Way. Being deprived of them can be as disastrous as being deprived of oil. And in that metaphor, you might say, ASML is the Strait of Hormuz.
James Proud, the cofounder and CEO of the lithography startup Substrate, says the situation is not ideal. The US is “dangerously reliant” on a supply chain that’s overseas and increasingly pricey, Substrate says on its website. “There’s a huge concentration in a small number of players,” Proud says. “And the supply chain is just very expensive.”
Which is why, after two decades of ASML’s dominance, would-be competitors are now gunning for its territory. China is hungrily pouring billions into trying to replicate ASML’s tech. And startups like Substrate are trying to get in the game as well, setting their sights on creating lithography machines that are cheaper, smaller, and even more capable than ASML’s behemoths. Will any of them succeed? The near future clearly belongs to ASML, but as its engineers well know, you can unseat a giant with the right trick of the light.
Making chips is, oddly, a bit like silk-screening a T-shirt. To print a pattern on a silicon wafer, you start with a pattern on a reticle—a mask that carries the design. Shining a light on the reticle transfers that pattern to the wafer. The light interacts with a layer of chemicals on the wafer, fixing the pattern in place.
The size of a chip’s features is partly set by the wavelength of light the machine uses: The smaller the wavelength, the teensier the circuitry you can create. You can stretch the capabilities of a wavelength somewhat; increasing what’s known as the numerical aperture, which usually means swapping in a bigger lens, can further focus the light and thus lay down patterns for smaller and smaller components. Eventually, though, this trick hits its limit, and you need to find a new form of light with a smaller wavelength.
So the history of chipmaking has been a two-step dance. The industry finds a good source of light, eventually increases the numerical aperture, and then finally accepts the need for a smaller wavelength, starting the two-step all over again. Up to the early 1990s, chipmakers used visible light, with a wavelength of about 400 nanometers. By the mid-’90s they’d upgraded to deep ultraviolet, ultimately getting it down to a 193-nanometer wavelength. By the late ’90s they saw the end of the line approaching for deep ultraviolet. But what would come next?
All the options were troublesome. They could shift to x-rays, with a teensy one-nanometer wavelength, but they were devilishly hard to focus. Beams of electrons and ions were equally precise; but they worked like dot-matrix printers, transferring a pattern point by point, which was far too slow. (The chip industry wants a machine to crank out hundreds of wafers per hour.)
“It’s a very engineering-heavy company: Let’s send thousands of engineers and just have them mow down these problems. That’s what they did, and it worked.”
Jeff Koch, analyst, SemiAnalysis
Around 2001, ASML, then a smaller player in the lithography world, placed its bet on another option: EUV, with a wavelength just shy of the x-ray range. Nikon and Canon were working on it as well, but they dropped out—while ASML kept going. The idea was full of unknowns. Nobody knew how to reliably generate that type of light, nor how to focus it; EUV is absorbed by regular glass lenses. It’s even absorbed by air. ASML figured it would take six full years to wade through this R&D nightmare.
In reality it took those 16 years and about $10 billion in research, but it worked. The machine, which works in a vacuum, creates EUV light by vaporizing molten tin and using mirrors to direct it. Zeiss, a historic German optics company, had to invent new techniques for polishing and inspecting the mirrors, using an ion beam to knock off minute imperfections.
“They sort of ignored the buzz of, like, Hey, this is never gonna work, and they just beat their heads against these huge engineering problems,” says Jeff Koch, who used to work for ASML and is now an analyst for the chip-industry research firm SemiAnalysis. “It’s a very engineering-heavy company: Let’s send thousands of engineers and just have them mow down these problems. That’s what they did, and it worked.”
When the first EUV machines went on the market in 2017, they cost well over $100 million apiece. Some observers wondered whether the demand would really be there from the major chipmaking firms—TSMC, Samsung, and Intel. In the years chipmakers were waiting for EUV to happen, the lithography industry had developed clever ways to improve on old-fashioned deep ultraviolet light. (If you put a layer of water on top of the wafer, for example, the light could focus more narrowly.) Maybe EUV wouldn’t be much needed for a while?
But ASML lucked out. Only a few years after EUV debuted, OpenAI released GPT-3 and then ChatGPT. Artificial intelligence burst into the mainstream. Instantly, firms like OpenAI, Google, Meta, and Anthropic were hungry for increasingly high-end chips as they built massive server farms to train and deploy large language models. EUV made it easier and faster to crank out AI-tailored chip designs. Nvidia began producing elite GPUs—processors perfectly suited for AI training—that cost $40,000 a pop; the big companies couldn’t get enough. The AI wars were on, and EUV was in demand. In 2025, ASML says, it sold nearly 50 EUV machines to companies and pulled in nearly $40 billion in revenue. As of press time, the company’s market cap was over half a trillion dollars.
ASML’s new machines have no shortage of potential customers. But there is one in particular, with deep pockets, that can’t buy them for any amount of money: China.
The US wants to hobble China’s ability to create cutting-edge AI chips—or any advanced chips, for that matter. So when ASML began selling its original EUV machines, in 2017, the Trump administration successfully pressured the Dutch government to forbid the company from selling them to any Chinese firms. The US had also imposed export controls on China’s telecom giant Huawei, banning US firms from using its 4G and 5G equipment.
This one-two punch incensed the Chinese government and stirred it to action. China is now pouring billions into catching up and trying to develop its own EUV chip-patterning technology. A Reuters report last winter found that a government skunkworks employing former ASML staffers had cobbled together a machine so huge it filled the entire floor of a lab. It’s unclear how well it works. The experiment may well be making some chips, says Hijink, but he doubts it can do so at an industrial scale.
A mirror is installed in an optical system for the high-NA machine.
COURTESY OF ZEISS
Officially, the government denied it was pushing to develop EUV tech. An editorial in the Global Times—a newspaper closely allied with the Chinese government—pooh-poohed the report, claiming that China was still happy to work with the West to get access to chips. “Our goal has never been to build a self-sufficient ‘technology island’ in isolation,” it stated, “but rather, on the basis of achieving autonomy and control over key technologies, to integrate more deeply and equally into the global innovation network.”
Experts say the reality is in the middle. China definitely craves a domestic ability to make high-end chips. And unlike ASML, it doesn’t need its EUV machinery to be efficient and profitable, cranking out about 200 wafers an hour. Any output would help wean it off reliance on the West.
“They would be very happy to have a tool that does one wafer per hour and it costs them a fortune to run,” Koch says. “They would build a fab with a thousand of those and be super happy with it.”
Still, producing and managing EUV light well is a feat that might take years, some told me. In the meantime, the Chinese will lean hard on deep-ultraviolet lithography, developed in the ’90s, making the most of an alternative but slower approach known as multi-patterning, says David Lin, senior advisor for tech leadership at the Special Competitive Studies Project, a think tank that focuses on security and technology. “They’re going to push DUV to the absolute limits,” Lin says.
The AI race is also pushing China to devise ever cleverer ways of developing LLMs that don’t rely on the fastest AI chips. In the US, OpenAI, Anthropic, and Google are fighting over who can buy the biggest piles of hot Nvidia chips. Since China can’t compete that way, it is innovating not in hardware but in software—building lighter-weight LLMs like DeepSeek.
As China rumbles into action, ASML has remained laser focused on shrink. To go even smaller, Benschop and his engineers decided, they wouldn’t shift to a new form of light. They’d do the second part of the two-step: They’d raise the numerical aperture of the machine by more than half (for those keeping track of the specific numbers, it would be a switch from an NA of 0.33 to an NA of 0.55). That would let them cut the size of the transistors by close to half and nearly triple their density on a chip.
This would also be an easier climb. Without the need to develop an entirely new source of light, the new machine—based on high-numerical-aperture EUV, or “high NA”—would be evolutionary, not revolutionary.
Still, building the new system did present a few gnarly challenges. In an EUV machine, the way you transfer an image onto a wafer is by shining light at the microchip pattern on the reticle and then using an optical system to take the reflected light and demagnify that pattern, shrinking it down to the size you want on the wafer. The light hits only part of the reticle at any given time, so you quickly move the reticle back and forth to expose every part of the pattern to the light.
Going to a higher numerical aperture meant they could have smaller features on the reticle. But this also meant that some of the light would be arriving at the reticle—and reflecting off it—at a steeper angle.
That’s what caused problems. The pattern on the reticle is three-dimensional, so light arriving at such a steep angle caused shadows—much the way slanted sunlight creates shadows in the Grand Canyon. That stood to diminish the machine’s ability to make clear patterns.
The new reticle moves with acceleration up to 22 g, much faster than in the company’s original EUV machine. “Don’t try to sit on it, because you’ll pass out.”
The solution was to change the pattern on the reticle—along with the way the mirrors took the light and shrank it down to impart the pattern to the wafer. The designs on the reticle would now be twice as long as they were wide—stretched, as it were, in one dimension.
But this design came with its own problems. The changes to the mirrors meant the area on the wafer exposed during a single scan was half the size it was with the original EUV machines, reducing the system’s speed. And ASML couldn’t tolerate any slowdown: Chipmakers were paying it for machines with massive throughput, about 200 wafers an hour.
If one part of the system slowed down, another part would have to speed up. The engineers decided the machine should move the reticle faster, which meant making the entire mechanism lighter and dramatically redesigning it. The new reticle moves with acceleration up to 22 g, much faster than in the company’s original EUV machine. “Don’t try to sit on it, because you’ll pass out,” Pieters told me. The wafer stage moves around faster as well, in tandem with the reticle.
Meanwhile, over in Germany, Zeiss’s engineers were busy designing mirrors to accommodate the higher numerical aperture and asymmetric shaping of the light. The new mirrors would be about twice as large as those in the regular EUV machines, and the projection system, which carries light from the reticle to the wafer, weighed fully 12 tons, seven times more than before. Zeiss built a new robot-assisted production line to handle these ponderous new beasts. The company says they’re the smoothest surfaces they’ve ever made.
At the same time, ASML was working on making its EUV light source even more powerful, to help make the wafer-exposing process go faster. The engineers calculated that they could improve the output of EUV if they hit each tin droplet three times with the laser instead of twice, as they do in the first machine. That meant the already-hectic system of firing tin would need to speed up by 50%. “The lasers just keep getting bigger,” says Alex Schafgans, the head of engineering at ASML in San Diego, where the EUV light source is built.
Indeed, the lasers for a single machine now fill an entire room. After Benschop showed me the massive high-NA device, we walked across the hall and entered a chamber filled with hulking six-foot-tall boxes that were part of the laser system. Peering through tiny windows in the sides of the units, we could see the glowing purple plasma used in creating the laser light.
When high-NA machines began to roll off the assembly line, one company was waiting hungrily: Intel. The company purchased the very first high-NA machine put up for sale, and in the spring of 2024, 300 ASML engineers showed up in Oregon at one of Intel’s fabs to begin assembling and testing it.
“ASML actually put a giant ribbon around one of the boxes,” says Mark Phillips, an Intel fellow who is director of its hardware and lithography solutions, laughing. His team has been testing the machine to see how well it performs; Phillips wouldn’t give details other than to say he’s “very pleased at the rapid pace of tool health.” He also wouldn’t give a date for when Intel would start using it to make chips, though observers say that will likely happen next year. The company plans to ease it in, using it for just a few precision components on a chip and then gradually for more and more.
What’s at stake is a chance to recapture its mojo. Intel was once a silicon powerhouse, designing the most cutting-edge CPUs for computers and servers, and building them in its own fabs. But in the 2010s, the big new markets were mobile-phone chips and GPUs for AI and gaming, and Intel rapidly lost ground. Apple designed its own mobile chips (and had TSMC make them), while Nvidia did the same thing with GPUs. Google began banging out its own TSMC-made AI chips called TPUs in 2015, and soon it was stuffing data centers full of them.
Intel fellow Mark Phillips briefs members of the media on the high-NA tool at the company’s Fab D1X in Hillsboro, Oregon. Intel was ASML’s first customer for the new EUV machine.
COURTESY OF INTEL CORPORATION
So in 2021 Intel announced a moonshot. It would aggressively begin building out a foundry business, one that would go toe to toe with TSMC. Instead of creating Intel chips, the Intel foundry would manufacture designs for customers like makers of mobile phones and AI chips.
Intel hopes that being the first to wield high-NA technology will give it an edge in the silicon rat race, making it possible to print tiny patterns faster than anyone else.
It could also make things simpler for customers. Over the years, while waiting for EUV machines to emerge, chip designers used multi-patterning to squeeze more life out of the older forms of light. Every chip is made out of layers, which are laid down to make components like the switches and wiring. If you’re working on one of those layers and need to make features tinier than your machine can normally produce, you can break the pattern for that layer up into several patterns and then expose the wafer to them one at a time. This strategy helped chipmakers keep using older (and cheaper) machines while still creating tinier and tinier components. But multi-patterning is a hassle: It’s more challenging to design the complex overlay of patterns, and much slower to print each chip. Designing a chip is far easier if you know you can do “single patterning,” blasting each layer in one go.
Observers say it won’t be easy to build a foundry business that bests TSMC and Samsung on their own terrain. “Leapfrogging is difficult,” Hijink says. But it’s also true that the high-tech world has such a ravening hunger for better chips that Intel could succeed, simply because even TSMC and Samsung can’t fulfill all that need.
“There’s spillover demand, so Intel can survive off that,” Koch says. “It’s not even scraps now. It’s a meal. It may not be the best foundry, but they can make chips, and there’s only three companies that can do that, right?”
TSMC, for its part, seems to be biding its time when it comes to high NA. “TSMC will deploy high-NA EUV when it is mature and ready to deliver maximum benefit to our customers,” the company wrote to MIT Technology Review. Some suspect it won’t use the machines in serious volume until the 2030s. Part of the reason is cost: TSMC is ruthlessly focused on producing chips as cost-effectively as possible, and the high-NA tools are a blistering $400 million each, far more than the previous EUV rigs. And unlike those, the new machines are not a revolutionary leap upward.
“This is like 30% to 50% better in terms of capability,” says Koch, the analyst and former ASML employee. “This is probably the first tool that hasn’t obviously made business sense right away for ASML.”
It’s not that the industry won’t eventually embrace high NA en masse, Koch says. Most companies will need to, if they want to keep going smaller. But TSMC is more likely to push ahead as far as it can go with its existing EUV tools, using onerous multi-patterning to wring as much as it can out of that generation until it absolutely needs to switch.
“The industry has only shifted paradigms when it just absolutely cannot extend—even one more little bit—out of what it’s been doing,” Koch says.
China isn’t the only party looking to upset the current balance of power. The dominance of ASML, and the swelling cost of its tools, is prompting other upstarts too. But instead of trying to replicate ASML’s breakthroughs in EUV, they’re doing an end run—working on lithography tools that use entirely different forms of light. These will be far cheaper, they promise, and just as powerful.
One is Substrate, a San Francisco–based startup. Founded four years ago, it’s working on a tool that uses x-ray light produced by a particle accelerator. X-rays have a remarkably tiny wavelength, making them a potentially powerful way to create minute features.
Particle accelerators have historically been enormous, making them difficult to fit into a chipmaking process. Substrate says it has harnessed decades of scientific improvements in particle acceleration to produce a light source that’s smaller and suitable for mass production.
Last year the company released images showing that it had created fine patterns, which Proud, the CEO, says are only possible now with a high-NA EUV machine. He says Substrate’s goal is to produce chips at scale by 2030.
But Proud doesn’t intend to sell the tools to TSMC or Intel. Indeed, he doesn’t plan to sell them to anyone. Instead, Substrate wants to create its own fab, building chips using its own tools.
“The amount of chips we’re going to need is going to be many orders of magnitude larger than even the wildest projections you have now.”
James Proud, cofounder and CEO, Substrate
The semiconductor industry, Proud argues, needs new approaches, because it’s become too pricey and too centralized. A single fab today can cost $25 billion to build, up from about $5 billion in the 2010s, the company notes. It’s driving the cost of a single wafer full of advanced chips up toward $100,000, Proud says.
“That is, I think, a prohibitive cost,” he says. There also isn’t enough capacity in the supply chain: “It’s relatively slow and hard to flex to the current increase in demands.” He admires ASML’s EUV tooling—it’s “the apex implementation of that technology”—but new approaches are needed.
That’s partly for national security reasons. Proud and his team think it’s too dangerous for the US to rely on foreign supplies. But he also predicts the current AI boom will go into overdrive, creating a massive demand for chips that the existing ASML/TSMC duopoly won’t be able to deliver: “The amount of chips we’re going to need is going to be many orders of magnitude larger than even the wildest projections you have now.”
ASML’s machines use lasers and molten tin to generate the EUV light.
CHRISTOPHER PAYNE
Substrate predicts it will be able to produce finished wafers at $10,000 a pop—a tenth of where Proud predicts the rest of the industry is heading. Proud says that’s partly because the company’s system will be vertically integrated, so it will control all parts of the chipmaking process, but also because its lithography tooling will be less complex: “We’re able to put together in a sort of simpler package.”
Still, Substrate is playing its cards close to its chest. Unlike ASML, the company isn’t offering nuanced detail on how it generates light, or on how that then translates into making patterns on a wafer.
Substrate’s ambitions give some industry observers pause. Hijink, who thinks it is probably “unachievable and impossible” to simultaneously master both a new form of lithography and high-throughput fab techniques, regards the company’s secrecy as a red flag. “This industry is about open innovation,” he says.
Koch is more impressed by its ambitions and funding. The type of technology it’s pursuing “is really cool,” he says. “It’s interesting.” But “there’s a long road between lab-scale demonstration and high volume,” he adds. “Is this like an imminent disruption to ASML? Probably not.”
Another startup that is aiming to hit the market around the same time as Substrate is Lace Lithography. Based in Norway, it is devising an entirely different approach—one that doesn’t use light at all. Instead, an energized beam of helium atoms is pointed at the pattern on the reticle. When the helium atoms then hit the wafer, the atoms transfer their energy to it, imparting the design to the chip.
The idea dates back a while. Bodil Holst, the CEO, took it up in 2008, when she was a physicist studying the use of atom beams. MIT professor Henry “Hank” Smith, a pioneer in using x-rays for lithography, told her she should explore using atoms as a mechanism for making microchips, because back then he wasn’t sure ASML’s EUV moonshot would work. “Even if it does, we’ll need atoms eventually,” he told her.
Holst did some experiments to investigate the idea further and partnered with a former PhD student—Adrià Salvador Palau, a physicist and expert in machine learning—to found Lace. Like Substrate’s, its tool is completely different from ASML’s massive machinery. The source of the excited atoms “looks a bit like a rocket motor,” says Palau. “It’s very cool.” While EUV’s wavelength is 13.5 nanometers, the helium atoms offer a precision of 0.1 nanometers. The process also requires far less power, and the machine is intended to be far smaller. Holst tells me the company aims to have machines ready to sell to fabs by 2029 or 2030.
“I think everybody’s really looking forward to something that extends a road map beyond light, beyond EUV,” Palau says.
ASML is watching these upstarts with curiosity. Benschop says he can’t assess whether Substrate’s technology will work reliably and affordably, because the company hasn’t explained anything about its processes. But he went to a conference where Holst and Palau did a presentation outlining Lace Lithography’s technology.
“I’m incredibly impressed with how they do it,” he says. The problem, he says, is he doesn’t think the process produces patterns on the wafer that are deep enough to be useful. “I cannot see how they would scale it to a viable volume product,” he told me.
He suspects ASML’s mastery of EUV will keep it on top for the near future. “So far, I have not seen a viable alternative,” he says. He thinks there’s “no serious runner-up” when it comes to volume manufacturing of the most advanced chip generations.
It’s true that major shifts in chipmaking are slow, says Chris Miller, a professor of international history at Tufts University and the author of Chip War, a book about the worldwide struggle for dominance in the industry. “No doubt we’ll eventually have alternatives [to EUV],” he told me via e-mail. “But it’s worth noting that lithography transitions have historically taken years, if not decades.”
ASML’s executives, too, are pondering their future. Benschop expects high-NA technology to dominate chipmaking into the 2030s. Beyond that? The industry has, indeed, tended to shift to a new form of light every decade.
“You may argue it’s time for the next decade,” he told me after we’d stripped off our bunny suits and he was relaxing with a coffee.
But ASML’s executives suspect they can continue to squeeze more capabilities out of EUV by increasing the numerical aperture even further on their existing machine. They’re already toying with a design that would take an NA of 0.55 to an NA of 0.75: “hyper NA.” It could let them pattern wafers with a resolution of six nanometers. They’re also working on standardizing their various optics into a platform of a single size, so customers could order one machine outfitted for either regular EUV, high NA, or hyper NA. If it’s all in the same-sized unit, it would simplify the costs and logistics of integrating each into a fab. If the company goes through with it, Benschop figures, the hyper-NA tool might hit the market seven or eight years from now and be sold in volume during the second half of the 2030s.
For now, the ball is in ASML’s court. “We’re pushing the limits of physics,” Pieters told me. The question now is whether anyone else can push harder.
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Background: University hospital employees face role-specific stressors that can impair mental well-being and work-related vitality. While worksite health promotion programs show potential for improving mental well-being by targeting lifestyle behaviors, most target single professions or hospital subunits, and evidence for mental well-being and work-related vitality remains mixed. Mobile apps offer unique advantages for delivering such worksite health promotion programs hospital-wide. However, accessible interventions tailored to a diverse workforce are lacking. Objective: This study aimed to investigate the feasibility of an app-based worksite health promotion program (the Recharge360 program [The Recharge Company]) targeting multiple lifestyle behaviors, including a team-based competition element, for improving mental well-being and work-related vitality of hospital employees over a 5-month follow-up period by evaluating two objectives: (1) the implementation process of the program, and (2) the preliminary effects of the program on mental well-being and work-related vitality. Methods: We included 532 employees (mean age 43, SD 12 y; n=482, 91% women; n=480, 90% highly educated) from a university hospital in Amsterdam, the Netherlands. The study had a single-arm, longitudinal pretest-posttest design lasting 5 months, during which employees participated in the 5-day Recharge360 program (Recharge week) 3 times—in weeks 1, 9, and 17. At baseline (T0) and after each Recharge week (T1-T3), we assessed mental well-being, work ability, need for recovery, and task performance. The process was evaluated by assessing recruitment, attrition, and survey completion rates, and the degree of participation. Preliminary effects were evaluated by linear mixed model regression analyses to assess changes in mental well-being and work-related vitality between baseline and follow-up. Results: Recruitment appeared feasible, but attrition rates were high (up to 70% in the final Recharge week), and the degree of participation decreased over time. We showed statistically significant, albeit small, increases in well-being at T3 (unstandardized β coefficient=2.08, 95% CI 0.33-3.84), with progressively larger improvements in the analyses among those who started at least 1, 2, and all 3 Recharge weeks (unstandardized β coefficient=3.27, 95% CI 1.09-5.45). Results for work-related vitality were mixed. The need for recovery remained unchanged, task performance increased slightly at T3 (unstandardized β coefficient=0.16, 95% CI 0.07-0.24). Work ability showed a small, but statistically significant, decline across follow-up (unstandardized β coefficient=−0.46, 95% CI −0.64 to −0.29). Conclusions: This app-based worksite health promotion program might be feasible to implement in a university hospital setting and shows potential to slightly improve mental well-being, but primarily for a selective group of highly educated, health-conscious women. While these findings support further investigation in a randomized controlled trial in similar university hospital settings, they also highlight the need for more participatory study designs to improve the tailoring of program components and engagement of underrepresented groups, as well as for a supportive culture and population-based approaches at the organizational level.
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