Jon Hoeksma examines what two former health secretaries, Andy Burnham and Wes Streeting, running the country might mean for NHS IT investment.
IBM has unveiled chip technology that could help extend Moore’s Law another decade
IBM has built a new prototype chip with around 100 billion transistors on an area the size of a fingernail, which is twice the density of the company’s previous state-of-the-art technology announced in 2021. The design could pave the way for faster and more energy efficient computers for years to come.
For more than half a century, chipmakers have been able to make ever more powerful computers by following the key principle of Moore’s Law: Cram more transistors onto the chip. To do this, they shrank transistors—the tiny switches that perform computations—to incrementally smaller sizes. But in the last 15 years, transistors have gotten close to the point where quantum mechanics starts to interfere with their function: just a few dozen nanometers in size. They can’t get smaller.
So to fit more transistors on a chip, engineers across the industry are eyeing a pivot to an approach familiar to urban planners: build up. On Thursday, IBM announced it has created a chip that uses this strategy. The new architecture, known as a nanostack, vertically stacks transistors in two layers on a silicon chip.
“It’s not just an incremental step,” Jay Gambetta, the director of IBM Research, said during a press conference on Tuesday. “It’s a meaningful leap forward.” Within a decade, Gambetta expects, chips with nanostacking will be widely used in data centers, where their improved efficiency could help the facilities better manage their energy consumption.
“Absolutely, it’s transformational,” says Dan Hutcheson, vice chair of TechInsights, a technology analysis company. “This puts another 10, 15 years on the roadmap.”
Compared with IBM’s previous state-of-the-art architecture, the company reports, chips built with this new approach can do as much as 50% more work in the same amount of time and be up to 70% more energy efficient.
The architecture offers a general way of laying out transistors, and IBM will partner with semiconductor manufacturers to make the actual chips. It anticipates that chip designers will deploy the design in many different types of chips, including GPUs and CPUs. “I expect to have many conversations with designers about how they can use this technology,” Huiming Bu, IBM’s vice president of global semiconductor R&D, said in the press conference announcing the new design.
A layer cake
Engineers created IBM’s new chip layer by layer, like a cake. They start by fabricating transistors on one layer of silicon. Then they place a silicon layer on top of these devices, and they fabricate another layer of transistors directly on top of that. Finally, they create the electrical connections between the two layers of transistors. This kind of vertical stack, which combines two types of transistors, is known as a complementary field-effect transistor, or CFET, explains Qing Cao, a professor of materials science and engineering at the University of Illinois at Urbana-Champaign, who was not involved with the work.
The company isn’t the only one pursuing this general approach. The biggest chip manufacturers—Intel, Samsung, and TSMC—and the competing research lab Imec in Belgium have been investigating CFETs. IBM says its design is distinguished by the fact that the transistors in the second layer do not sit directly on top of the first layer’s transistors; rather, they are staggered, which the company says simplifies wiring, among other advantages.
CFETs like those in IBM’s nanostack architecture contrast with another common approach to making two-tiered chips, such as AMD’s 3D V-Cache and Huawei’s forthcoming LogicFolding technology, Cao says. In those approaches, engineers fabricate the transistors on each layer of the chip independently before bonding the two together. IBM’s new method allows for more precise alignment of the layers, which is important for performance because transistors are so tiny, says Cao.
Nanostacking builds on an approach called nanosheet technology, which has been used to make current state-of-the-art transistors since around 2022. A transistor is essentially a hose through which electrons flow, with a valve that can turn the flow on or off. Inside the transistor, electrons move through a patch of the silicon called a channel. In IBM’s nanostack approach, the channel consists of three nanosheets that are each 15 atoms thick, spaced nine nanometers apart.
Every chip generation gets a name. IBM refers to its nanostack technology as “sub-nanometer” or “0.7 nanometer,” following a longtime industry convention where each generation is named for a smaller and smaller length. But “0.7 nanometer” is a marketing term and does not correspond to any physical characteristics of the chip. The distance between transistors “has been staying at about 40 nanometers for quite a long period of time,” says Cao.
Putting it into production
Looking ahead, chipmakers can try increasing transistor density by building on more tiers, as Bu suggested in the press conference. However, they will face practical challenges, according to Cao. Manufacturing introduces errors, which means a certain number of chips are faulty upon creation. “Here you’re building another layer on top, so if either top layer or bottom layer fail, your entire chip is going to fail,” says Cao. The resulting failure rate will be higher than for single-layer chips, and that will be costly.
Another central challenge is what Cao calls “the thermal budget.” Essentially, it means that engineers need to figure out how to build each layer without melting the connections to the one underneath. This means keeping manufacturing processes below 400 °C. IBM figured out how to make the second stack at low enough temperature, although the company is mum about its methods.
Academics are also on the case. Cao’s group, for example, has created a method for stacking transistors layer by layer where the second layer is created with processes below 200 °C. They manage this by using a type of transistor known as the junctionless transistor, which can be created without a typically required step called doping—a process that injects non-silicon atoms into silicon to tune the material’s properties. Doping is usually the hottest part of fabricating transistors. Cao thinks from a thermal management perspective, his approach could be easier to scale up to multiple tiers, although his demonstration is just a proof of principle.
But Cao thinks IBM’s work is “transformative” because it demonstrates how to stack transistors “on a full wafer using a state‑of‑the‑art manufacturing line.” The new approach pushes the industry forward, he says: “I’m interested in what’s their killer application.”
What Europe’s heat wave means for the power grid
It’s been hard to look away from headlines about the European heat wave this week. Temperatures are breaking records across the continent, and the weather is threatening lives, shutting down schools, and in one particularly ironic case, forcing the cancellation of a London Climate Action Week event about extreme heat.
As the summer ramps up and we see this kind of weather sweep around the Northern Hemisphere, I’m always keeping my eye on the power grid. And one notable update that caught my attention this week was news that a nuclear power plant in the south of France had to close down because of the heat.
Climate change is squeezing the grid from all sides, affecting both supply and demand. Heat can affect power availability, from generation to transmission infrastructure, as I covered in my latest story. But climate change is also helping push electricity use higher—and countries in Europe and around the world will need to adapt.
In the US, nearly 90% of homes have air-conditioning. That means many grids see their highest demand in the summer months, and the risk of brownouts and blackouts is at its worst.
People are often quick to cast air-conditioning as a villain, and it’s true that the technology will account for a major chunk of the globe’s rising energy demand in the future. But the reality is that heat waves can be incredibly dangerous, and as climate change pushes temperatures higher, that risk is becoming more real in parts of the world that haven’t historically had to worry quite so much about heat.
In Europe, air-conditioning is historically much less common, with about 20% of homes across the continent using it. Some countries, including those getting hit by this heat wave, have even lower rates—the UK comes in at about 5%, and Germany is around 3%.
But those numbers are starting to tick up as people adapt to increasingly brutal summers. As they do, we should expect higher electricity demand, and stress for the grid—just as in the US. And utilities often have to look across borders to buy more power, driving prices up for everyone.
“The main pressure comes from a triple squeeze: Cooling demand rises sharply, while power plants and grids become less efficient, and some thermal and nuclear plants must cut output because cooling water is too warm or scarce,” says Simone Tagliapietra, senior fellow at Bruegel, an economic and policy think tank, via email.
Grid planning in the age of climate change generally means that we need a lot more supply, and quickly. But one interesting facet to this challenge is that in some places, seasonal patterns are shifting, compounding the difficulty of meeting demand.
Generally, grid operators plan maintenance and outages at power plants around expected peaks in demand. Take nuclear power, for example. In the US, planned outages for maintenance and refueling tend to come in the spring and fall when demand falls below the summer and slightly smaller winter peaks.
Europe, however, has historically seen its grid peak in the winter, because electric heating is more common than air-conditioning. So some planned outages happen in the spring and into the summer, which is affecting the supply right now.
At the Golfech power plant near Toulouse in France, for example, unit two had to shut down this week because of the water temperatures in the nearby river, which is used to cool the reactor. But unit one was already offline because of planned maintenance and refueling, according to EDF, the plant’s operator.
We’re going to continue to see record-high temperatures around the world because of climate change. Communities are adapting, and utilities will have to follow. And if you thought this summer was hot, just wait until next year. With the El Niño weather pattern, 2027 could very well blow these heat waves out of the water.
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
Bipartisan bill seeks to allow direct prescribing of methadone for opioid addiction
Two senators are renewing their push to greatly expand access to methadone treatment for opioid addiction.
On Thursday, Sens. Ed Markey (D-Mass.) and Rand Paul (R-Ky.) are introducing an updated version of legislation that would allow doctors who hold board certifications in addiction medicine to prescribe methadone directly to patients for pickup at a pharmacy.
Opinion: My adult daughter has Down syndrome. Her life is entirely different from what experts expected
The genetic counselor handed me a tissue.
“Don’t worry, Mrs. Whitten,” she said. “Eighty to ninety percent of people terminate these pregnancies. You can, too.”
STAT+: Facing a brutal run, battered vaccine makers still see cause for hope
SAN DIEGO — It is an unusual time to be in the vaccine business. But in the view of those gathered here at the BIO international conference this week, it’s not altogether a bleak one.
In fact, some vaccine makers are feeling more optimistic than they were a year ago — considering the circumstances, at least.
In interviews with STAT, they acknowledged that Health Secretary Robert F. Kennedy Jr., a longtime vaccine critic, has brought once-unthinkable disruptions — the cancellation of major mRNA vaccine contracts, the dismantling of universal vaccine recommendations, cuts to government funding for research — and that Washington could deliver more ahead.
Oxford AI studies secure NIHR funding to tackle NHS waiting times
Two studies have received funding from the National Institute for Health and Care Research (NIHR) as part of an £8 million initiative supporting AI projects aimed at reducing NHS waiting times and improving patient care. Through its Invention for Innovation (i4i) programme, the NIHR awarded £8,136,409 to six projects testing a range of AI and […]
The emergence of the web data infrastructure layer for AI
AI is booming. New use cases are emerging each day. To capitalize on the technology’s potential, enterprises require data at scale. In many cases, though, the relevant information is blocked or unstructured, which limits its use by AI models.
To understand this challenge, consider the foundation of the web itself. The web was not designed for the automated discovery and retrieval that new AI applications demand. Overcoming this inherent design constraint requires infrastructure.
The next frontier in AI may depend on a new web data infrastructure layer that can enable models to discover and map this ever-expanding digital realm. This layer must be able to navigate hundreds of millions of existing web domains and billions of new URLs created each week, delivering real-time information and overcoming technical barriers.
“The data suggests there’s far more data out there,” says Or Lenchner, CEO of Bright Data, a web data collection platform. “Think of the universe: It’s out there, but you don’t know what you don’t know.”
Enabling access to fresh, relevant, and trustworthy data
While early AI breakthroughs were driven by scaling training data and model size, organizations are now encountering a fundamental bottleneck: They need to keep pace with the dynamic, unstructured, and constantly evolving nature of web data in order to ground outputs in current and verifiable information. AI performance increasingly depends not just on model architecture but on a system’s compute, networking, retrieval, and data engineering capabilities—that is, the system’s ability to quickly and reliably retrieve data that is fresh, relevant, and trustworthy.
Traditional model training relies on snapshots of information collected at a particular point in time. Training AI on such static data is no longer sufficient. To track fluctuations such as competitor pricing, consumer sentiment, and market trends, companies need a constant feed of new information, pulling data in real time along with relevant context. Their infrastructure must therefore be able to handle millions of simultaneous interactions across websites that vary by geography, language, format, and access rules.
“If it can’t retrieve real-time information, it lacks context,” Lenchner says. “In a business setting, that’s not acceptable anymore. Stale answers lead to bad decisions and disappointed consumers.”
Speed is not merely a matter of convenience; it’s a matter of necessity. Today’s organizations operate in environments where prices, inventory, markets, security threats, and customer behavior change continuously. Delayed data retrieval can reduce the usefulness of an otherwise sophisticated model.
Using live, high-quality web data can also reduce AI hallucinations because the model has a more relevant knowledge base. This builds user trust. In fact, one survey found that 56% of AI practitioners said businesses need access to real-time web data to improve trust in AI outputs. To ensure the model runs efficiently and effectively, the information must also be pared down to the appropriate essentials.
Despite the introduction of retrieval-augmented generation (RAG), where models pull in external data at the moment of a query, many AI systems still struggle to deliver outputs that are current, contextually relevant, and trustworthy in operational settings. According to Gartner, 60% of AI projects that are not supported by AI-ready data—accurate, structured, organized, and contextualized—will be abandoned by the end of the year.
This is because large-scale retrieval alone does not solve the problem. As Lenchner puts it, “You need to retrieve data at scale, but also in real time. Latency becomes an issue because of the end user who is waiting for the output.”
Accessing fresh, AI-ready data at scale introduces technical and structural challenges. In practice, many enterprise systems combine public web retrieval with APIs, licensed datasets, and proprietary internal data in their AI applications. Integrating these fragmented sources into a timely and usable knowledge layer requires specialized capabilities. Some research has found that 97% of AI organizations depend on real-time web data infrastructure, but 90% feel boxed in by various restrictions. Companies are increasingly developing technical approaches to navigate these constraints.
Lenchner draws this metaphor: “Think of the trained model as intelligence and relevant data as knowledge. A powerful intelligence layer sitting on top of a hollow knowledge layer is like a genius who knows nothing—useless in practice. Intelligence and knowledge have to come together.”
The promise of new infrastructure
A new layer of web data infrastructure can address this developing need for stronger AI inputs by enabling discovery of data, real-time access, and tailoring to a specific context. As Lechner describes it, “It’s all about collecting data at scale, super-low latency, without being blocked.”
Rather than relying on increased computing power, this type of platform emulates human browsing behavior to access available content and transform raw code into structured data feeds. It can work with websites that might not interact with traditional scraping tools, such as those heavy in JavaScript, or with aggressive antibot software.
As Lenchner explains, “It’s basically having infrastructure that can mimic a web user with identifying information—IP address, location, and 1,000 more parameters. And at scale. Think of doing that 80 billion times a day for millions of websites. And every single time, you are looking exactly as the website expects you to look.”
Of course, continuous retrieval introduces new data governance challenges. To address them, platforms can enforce strict compliance protocols aligned with global privacy frameworks, such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). They can also be limited to openly accessible, public information, avoiding paywalls or private logins. Any networks used can be vetted and consent-based, and incentives can be provided to owners of IP addresses. In this way, systems can be designed to comply with tightening regulation.
Such complex capabilities do not come easy. “When this is critical infrastructure for a company,” Lenchner says, “doing it in-house becomes a full-time engineering problem that competes with the actual AI work.” Addressing this complexity requires organizations to commit significant resources, leading many to seek specialized platforms designed specifically for data retrieval, orchestration, and observability.
Infrastructure for the real world
Real-time data retrieval is changing what AI systems can do inside organizations. For example, a retail company can use public information to enable a dynamic pricing engine, and global brands can track trademark infringements.
As the ecosystem matures, organizations that invest in this emerging data infrastructure layer will be better positioned to build AI systems that are more responsive, reliable, and aligned with real-world conditions—AI systems that can continuously adapt using current web data. Over time, the distinction between AI models and the infrastructure that feeds them may even begin to disappear.
As Lenchner says, “The world is changing. And everything that is happening in the world is being uploaded to the public web. The amount of new data that is being generated is growing and accelerating.”
To learn more from Bright Data, read the Data for AI 2026 report.
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.
Stripe, Anthropic, and OpenAI are backing an effort to stop respiratory infections
The common cold comes for us all—often more than once a year. And there is no way to prevent it. The best you can do is take vitamin C and stay away from people with the sniffles.
Now the payment company Stripe, founded by brothers Patrick and John Collison, says it will fund a new $500 million nonprofit whose goal is preventing both the common cold and the flu. Its eventual aim is to get rid of respiratory viruses altogether.
The new organization, called Intercept, will use grants and investments to back prevention approaches, including vaccines, as well as large-scale air-cleaning systems for schools, offices, and other public spaces.
In addition to Stripe, other funders include Anthropic, Flu Lab, and the OpenAI Foundation, as well as Bill Gates and several traders at the quantitative investing fund Jane Street Capital, according to an Intercept spokesperson.
“I think we treat respiratory infections as a minor nuisance, but have really underweighted the burden that they impose on society,” says Nan Ransohoff, the Stripe executive leading the initiative along with Charlie Petty, a venture capitalist who joined Stripe this year. On average, people spend 5% of their lifetime fighting a cold or the flu, according to Ransohoff.
Despite that, drug companies put relatively little effort into preventing colds. Part of the problem is that the sniffles are caused by more than 200 different viruses, according to the American Lung Association, with rhinoviruses being the most common culprits. There are so many that it typically doesn’t pay to try to stop any one of them with a vaccine. “When pharma companies look at it, it’s not as attractive as other things they could work on,” says Ransohoff. “So it hasn’t attracted the resources.”
Stripe previously organized a $1.8 billion program called Frontier to encourage the development of carbon removal technology, as a way of countering climate change. Ransohoff says removing carbon from the atmosphere and getting rid of respiratory viruses are similar in that each is “technically possible” but they “lack commercial incentives.”
The concept for Intercept took shape after Ransohoff started talking to David Veesler, a structural biologist and vaccine designer at the University of Washington, who argued that it’s possible to come up with broad countermeasures that work against many viruses at once.
“He effectively sort of nerd-sniped me,” Ransohoff says of Veesler. “He convinced me that this is technically possible. He also helped me understand that some of the reasons that this hasn’t been done before was sort of an incentive problem.”
Veesler says the growing tool kit available to scientists includes RNA drugs, antibodies, and computational protein design. For instance, one idea is to engineer virus-grabbing proteins that people could spray in their nasal passages, to catch viruses before they cause infection.
“Most people just accept these viruses as a fact of life, and that got us thinking: Do we have to accept it?” says Veesler. “The more we thought about it, the more we realized that many of these problems have not been worked on with modern technologies.”
The project takes inspiration from efforts to fight the covid-19 virus, where Veesler’s group was among those involved in the speedy development of vaccines, antiviral drugs, and antibodies.
According to Ransohoff, Intercept’s advisors will include Peter Marks, a former top FDA official, as well as Moncef Slaoui, the pharmaceutical executive who led the US coronavirus vaccine effort, Operation Warp Speed.
A key challenge for Intercept will be coming up with ways to counter many viruses at one time. That accounts for the interest in air-cleaning technology, such as using strong ultraviolet light to inactivate viruses. The idea, the group says, is to remove them from the air in the same way municipalities remove impurities from the water supply before it’s piped to people’s homes.
The US funds about $6.5 billion a year in virus research through the National Institute of Allergy and Infectious Disease, or NIAID. But that agency’s budget hasn’t grown in recent years, leaving more room for private philanthropy.
And Stripe’s Collison brothers have become some of the most reliable philanthropists in viral research. After giving away “fast grants” to help labs during the covid-19 pandemic, they later joined other donors who committed $650 million to establish the Arc Institute in Palo Alto, California, which has developed AI models for biological research.
“The diversity of viruses is just too large and seems daunting, so people don’t even try,” says Veesler. “I’m happy that someone is ready to help scientists, not accepting the status quo, and doing something different.”
Associations between childhood trauma, intolerance of uncertainty, and symptom severity in obsessive-compulsive disorder
BackgroundChildhood trauma (CT) has been associated with obsessive-compulsive disorder (OCD), but its relationship with obsessive-compulsive symptom (OCS) severity remains inconsistent. Intolerance of uncertainty (IU) may represent one of the cognitive processes underlying this association. The present study aimed to examine differences in CT and IU between patients with OCD and healthy controls (HCs), and to test whether IU mediates the relationship between CT and OCS severity.MethodsThis study included 82 patients with OCD and 82 healthy controls (HCs) matched on age and sex. CT was assessed using the Childhood Trauma Questionnaire-33 (CTQ-33), IU using the Intolerance of Uncertainty Scale–Short Form (IUS-12), and OCS severity using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS).ResultsPatients with OCD had significantly higher scores than HCs on all CTQ-33 subscales and on IU measures. In particular, the patient group showed higher IUS-12 total scores than the HC group (39.30 ± 10.42 vs. 32.11 ± 8.62, p < 0.001), with higher prospective anxiety (22.11 ± 5.13 vs. 20.11 ± 4.59, p = 0.009) and inhibitory anxiety scores (17.19 ± 5.99 vs. 12.00 ± 4.82, p < 0.001). Within the patient group, physical abuse was the only CT dimension significantly associated with total Y-BOCS scores (r = 0.248, p = 0.025), whereas IU was positively associated with symptom severity (IUS-12 total: r = 0.346, p = 0.001). Path analysis showed that CT was associated with IU (β = 0.238, p = 0.023), IU was associated with OCS severity (β = 0.329, p = 0.007), and the direct effect of CT on OCS severity was no longer significant after IU was included in the model (c′ = 0.209, p = 0.093), supporting partial mediation.ConclusionCT appears to be elevated in patients with OCD, although its association with symptom severity is not uniform across trauma dimensions. IU may represent an important cognitive mechanism linking CT to OCS severity. These findings suggest that assessing and addressing IU may contribute to more individualized clinical approaches in OCD.