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.

Introducing: the Engineering issue

We can’t fix everything, but we can be ambitious. We can take on the challenge of making the world better through human ingenuity. That’s what the new Engineering issue of MIT Technology Review is all about. 

Sometimes the challenges we face are giant, like tunneling beneath the seafloor. Some exist at the nanoscale, as with a new ASML machine powering the future of chipmaking. Others represent problems at a planetary scale and in truly unknown territory, like replicating a volcano’s mechanism to cool the Earth on purpose.

These incredible engineering stories show we can come together to get to work and, when the smoke clears, find we’ve made real progress. Subscribe now to read all of them—and more—in the full print issue.

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 is funding a new $500-million nonprofit aiming to prevent both the common cold and the flu. Its eventual goal is to get rid of respiratory viruses altogether.

Anthropic, OpenAI, and Bill Gates have also backed the venture, which will investigate whether modern technologies can counter the common cold and the flu. Dive into the nonprofit’s plans.

—Antonio Regalado

MIT Technology Review Narrated: inside the hunt for the most dangerous asteroid ever

As asteroid 2024 YR4 hurtled toward Earth, astronomers determined that this massive rock posed a higher risk of impact than any object of its size in recorded history. Then, just as quickly as history was made, experts declared that the danger had passed. 

This is the inside story of the network of global scientists who found, followed, planned for, and finally dismissed the most dangerous asteroid ever discovered —all under the tightest of timelines and with the highest of stakes.

—Robin George Andrews

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 China has taken the US’s crown for the world’s fastest supercomputer 
Shenzhen’s LineShine overtook California’s El Capitan. (Axios)
+ China had not had a machine at the top of the list since 2017. (NYT $)
+ But the supercomputer race isn’t geared for AI work. (Reuters $)

2 Mythos reportedly found flaws in classified US government systems
A US official said Anthropic’s model identified certain vulnerabilities. (AP News)
+ The model has now been suspended over US security concerns. (BBC)
+ The NSA has lost access to Anthropic’s tools in fallout. (Engadget)
+ The feud raises new questions about AI safety. (MIT Technology Review) 

3 A US pilot reported seeing Iranian drones swarm in “jellyfish” formation
Which would represent an alarming advance in Iranian drone capabilities. (CNN)
+ The US is heading toward a drone-filled future. (MIT Technology Review)

4 Mark Zuckerberg directed Meta to create a prediction markets app
It will be similar to Polymarket and Kalshi. (NYT $)
+ But won’t let users wager real money. (The Verge) 
+ Another new app, Meta Photos, will create media with AI. (Reuters $)

5 SpaceX’s “Starfall” just launched a secretive test flight
The orbital delivery spacecraft blasted off for the first time yesterday. (Axios)
+ It could also support space manufacturing. (New Scientist $)

6 Alibaba has sued the US for being linked to the Chinese military
It wants to be removed from a Pentagon blacklist. (Reuters $)

7 Nvidia’s banned AI chips have doubled in price on China’s black market
The DGX B300 now costs more than $1.1 million. (Financial Times $)

8 Tesla claims a driver “manually overrode self-driving” in a deadly crash
It said the accelerator was pressed “all the way to 100%.” (The Verge $)

9 The US science retreat has created an opportunity for Europe
But questions about funding and innovation remain. (Nature)
+ Trump has dealt many blows to US science. (MIT Technology Review)

10 Meta’s new smart glasses ditch Ray-Bans for Kylie Jenner 
Meta logos and Jenner designs have replaced the Ray-Ban branding. (Wired $)

Quote of the day

“It’s blasphemy against AI if ‌you say it’s a bubble.”

—SoftBank founder and CEO Masayoshi Son tells shareholders that the AI boom is still in its early stages, Reuters reports.

One More Thing

Video games are dividing South Korea

They say StarCraft was the game that changed everything. When the science fiction strategy game arrived in South Korea in 1998, it wasn’t just a hit—it was an awakening.

Out of 11 million copies sold worldwide, 4.5 million were in the country. The game was so popular that it triggered another boom: “PC bangs,” pay-as-you-go gaming cafés.

StarCraft and PC bangs spoke to a generation of young South Koreans boxed in by economic anxiety and rising academic pressures. But they also sparked arguments about game addiction. They’ve led to feuds between government departments—and a national debate over policy.

Read the full story.

—Max S. Kim

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ This archive lovingly documents the beautiful design of over 1,700 obsolete objects.
+ Classic TV theme tunes like Hey Arnold! Have been revived in a musician’s marvellous samples.
+ Marvel at the mind-boggling geometry of nature and see how bees perfectly construct honeycombs.
+ Hear the ominous, deeply atmospheric tones of a custom string instrument built inside a plastic drainage pipe.

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, 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. The average person will spend 5% of their lifetime fighting a cold or the flu, according to Ransohoff.

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 Vessler. “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 toolkit 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 can infect people.

 “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, anti-viral drugs, and antibodies. 

According to Ransohoff, Intercept’s advisers 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—even all—viruses at one time. That accounts for the group’s interest in air-cleaning technology, such as using strong ultraviolet light to inactivate viruses. The idea, the group says, is to remove viruses 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, 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.”

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.

As soon as August, a giant silver bullet will cut its way through the dry air of the southwestern US and cross the Pacific to reach the coast of Japan. 

Once there, the roughly 200-foot-long craft, built by the New Mexico–based company Sceye, will park some 18 kilometers above the ocean’s surface, in a wispy-thin layer known as the stratosphere. Then it will use a custom-built antenna to supplement Softbank’s 5G network, a test that will include beaming data straight to devices. 

Sceye (pronounced “sky”) is one of several firms building a class of airborne craft called HAPS, or high-altitude platform stations or systems. Such a platform can be a plane or a balloon or, yes, an oblong craft filled with helium and outfitted with solar panels. HAPS companies, including the Airbus subsidiary Aalto, envision them serving a variety of lofty purposes, such as delivering internet service to disaster sites and observing Earth’s surface. 

The stratosphere is a good place to be if you want to cover a large area. It’s also much closer to the ground than even the lowest-orbiting satellites, which means sending down a signal takes far less energy. “What we ultimately offer is space-like conditions, without the cost of going to space and without the complexity of being in orbit,” says Mikkel Vestergaard Frandsen, Sceye’s CEO and founder.

But it’s also not so easy to stay there. Sceye’s aircraft, Frandsen says, has to be light enough to stay aloft but also strong enough to carry the necessary systems. It must soak up and store enough solar energy during the day to provide around-the-clock power to an electric fan that can maneuver the HAPS back into place when winds knock it out of position—mettle it proved in a 2024 test flight.

Since then, Sceye has been preparing for its big Japan test. In the flight pictured here from this spring, for example, the craft stayed aloft for 12 days as it flew to the coast of Brazil and spent more than 88 hours “parked” in various locations. Eventually, the company expects its platform could help satellite operators better serve densely populated areas.

Someday, Frandsen says, spotting a HAPS may be as common as seeing ships at port or trains on the tracks.

When I was 18, I skipped my high school graduation and headed to Kuwait. It was 1991, the first Gulf War had just ended, and the country was in complete chaos. There was little to no electricity, aside from generator power. Rubble and unexploded ordnance were everywhere. Massive oil fires lit up the desert and dimmed the sky overhead. Everything had to be rebuilt, and fast. 

I was there to work on that reconstruction, part of an international effort to fix what war had broken. It was the first time I’d ever seen a truly massive engineering project with my own eyes. The challenge was one that had to be addressed on multiple fronts, simultaneously, to get an entire country back up and running. 

Everywhere you looked, there was something to do. I was mostly working with a labor crew to jump on quick fixes to things like windows and doors blown out in the fighting. But there were, of course, bigger jobs too. Most notably, there were those massive fires to put out. The Iraqi army had set hundreds of oil wells ablaze, the majority of which were still spewing soot and oil smoke into the air. On bad days, the sky would remain dark all day, and the air would burn your eyes and hurt your throat.

It was so apocalyptic that none other than Carl Sagan warned of massive environmental consequences. If smoke from the oil fires reached the stratosphere, he predicted, the result could be akin to the 1815 explosion of the Tambora volcano in Indonesia, which triggered what’s known as “the year without a summer”; global temperatures dropped between 0.4 and 0.7 °C, and crops failed around the world. Fortunately, the plume from Kuwait never made it that high, and although temperatures did decline regionally, there was little effect on a planetary scale. As it turns out, predicting what will or won’t lower global temps is quite hard. (Reader, I am foreshadowing here.) 

Firefighters from companies with monikers like the Red Adair Company or Boots and Coots (as well as less colorfully named outfits, like Bechtel) rushed to Kuwait after the war’s end to figure out how to extinguish the gargantuan blazes and cap the wells. At a hotel in downtown Kuwait City, one of the few places with working phone lines, I would occasionally run into them covered head to toe in black oil and soot. 

Putting out the fires took all sorts of creative thinking. Engineers working in the burning oil fields figured out they could repurpose existing pipelines meant to pump oil out to sea to instead pump water in from the Persian Gulf. One company from Hungary rigged up a firefighting machine called Big Wind by outfitting an old Soviet T-34 tank chassis with two turbines from a MiG-21 fighter jet, each of which could blast 220 gallons of water per second. Sadly, I never got to see it in action (except in the movies). 

Other jobs were less cinematic but no less dire. The retreating Iraqi army had left booby traps all over. They snaked hand grenades into the plumbing (at a facility where I worked, among others). They planted mines everywhere, and those had to be found and removed. Many of them were small plastic “toe poppers” designed not to kill but to maim. Hunting them was a herculean effort. And although it was mostly successful, hundreds of thousands of them, by some estimates, still remain.

Which is to say, we can’t fix everything. But we can be ambitious. We can take on the challenge of making the world better through human ingenuity. 

That’s what the July/August issue of MIT Technology Review is all about. Sometimes the challenges we face are giant, if knowable, like tunneling beneath the seafloor. Some exist at the nanoscale and represent decades of investment and research, as is the case with ASML, a company with the singular ability to produce the machines that make the most advanced computer chips in the world. Others represent problems at a planetary scale and take us into truly unknown territory, like a future where we could engineer the veil of the Tambora volcano to cool the Earth on purpose.

By the end of my 90-day contract in Kuwait, instead of damage everywhere you looked you could see the fruits of a gargantuan international rebuilding effort. The air was not clean, exactly, but inhaling it no longer felt like smoking a pack a day. On the beach, which had been pocked with mines, people swam and splashed at the edge of the gulf. The lights were on. Water ran from the taps. The markets were open. It was a remarkably different place. 

Yes, forces both within and beyond our control will always break things. People will invariably make mistakes, or act out of their own self-interest to the detriment of others. But we can also come together to get to work and, when the smoke clears, find we’ve made real progress. 

The Argentina v. France final of the 2022 Men’s World Cup in Qatar was shaping up to be one of the most epic games in soccer history. With just 12 minutes remaining in the extra time added to the game to break a tie, the referee had a critical decision to make—and fast.

Lionel Messi, the Argentine captain and soccer legend, had just launched the ball past the French goal line, giving Argentina a 3–2 lead. The crowd roared, but a flag was raised. One ref thought that shortly before Messi kicked the ball, the Argentine forward Lautaro Martinez had been closer to the goal than any French players apart from the goalie when he’d received a pass—putting him in an illegal “offside” position. 

If the head referee called Martinez offside, the goal wouldn’t count. If he declared him onside, Argentina would keep its 3–2 lead with minutes left to play. 

The weight of more than just one offside call stood on that referee’s shoulders; it was the weight of the World Cup itself. 

But in 2022, for the first time in the storied competition’s history, referees had access to semi-automated offside technology (SAOT), a system that could rapidly analyze the play and detect an offside player. In this case, it produced an image revealing that a French defender was slightly closer to the goal than Martinez, just barely leaving the Argentine forward in a legal attacking position. 

The referee ruled that the goal counted: 3–2, Argentina.

Argentina eventually emerged as the champion, winning a penalty shootout after a late goal by French forward Kylian Mbappé tied the game at 3–3. Only in a parallel universe will we know how the game—and the tournament—would have played out if the referee had overturned Messi’s goal.

For FIFA, soccer’s international governing body, SAOT is among the latest in a portfolio of innovations used at the World Cup. From goal line technology to video assistant referee (VAR) tools, officiating tech is now commonplace at the top level of the game.

But SAOT is part of a broader sports technology landscape that stretches far beyond soccer. And one of the major players in that landscape is the very team that collaborated with FIFA to bring SAOT to the pitch in the first place: the MIT Sports Lab. Founded in 2015, the lab focuses on using technology and data science to tackle real problems facing athletes, teams, and sports organizations and brands. 

The lab has worked with FIFA, the NBA, the NFL, and Adidas, and it collaborates with a host of other sports organizations and industry players. Some of its work may be hiding in the soles of your running shoes, in the decisions your favorite NBA team makes, or even on soccer’s biggest stage—as was the case in what the AP called “probably the wildest final in the tournament’s 92-year history.”

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The MIT Sports Lab’s origin story begins around 2010, when Anette “Peko” Hosoi, the Pappalardo Professor of Mechanical Engineering, fell in love with downhill mountain biking and needed a new bike. But given the varying linkage systems, shock types, and geometries, she found it difficult to choose the best one. Encountering only minimal information online, she assigned the analysis to her 2.001 class, the introductory course on mechanics. “All of my exams that semester were bike questions,” she says. They proved to be really good engineering questions too. 

Having recently earned tenure, she wondered, What if I actually built this sports thing into something bigger? In 2011, she began conceptualizing a project called STE@M (Sports Technology and Education at MIT), which would assemble students, faculty, athletes, and industry partners to tackle sports engineering challenges. As the effort kicked into gear over the next few years, Hosoi began collaborating with Christina Chase, MIT’s new entrepreneur in residence, and in 2015 the two of them cofounded the MIT Sports Lab. 

“It turned out that we’re the perfect combination for this because my background comes from the math, physics, engineering side,” says Hosoi. “And she comes from the entrepreneurship [and] product development side. To really interface with these different sports companies and leagues, you need to span that whole spectrum.” Chase became the lab’s managing director and Hosoi its faculty director.

“There’s more and more data that’s getting collected,” says Hosoi. “A lot of the teams, leagues, brands don’t necessarily have the in-house manpower to extract the information they need. So that’s where we can give them a boost.”

When MIT researchers looked at early skeletal data representing soccer players in motion, they saw “skeletons” flying above the ground or completely underground, in anatomically impossible positions.

The FIFA partnership has been especially fruitful—and the Sports Lab’s role in validating SAOT has probably had more impact than any other project the organizations have worked on together, says Ferran Vidal-Codina, SM ’13, PhD ’17, a former research scientist at the lab who was part of the team from FIFA, MIT, and third-party data providers that developed the technology. 

The system’s viability depended on the ability to quickly access and analyze what’s known as tracking data—the record of everywhere the players and the ball move throughout a game.  

To collect that information at top-level FIFA tournaments, data providers station about 12 state-of-the-art cameras around the stadium, capturing images at double or more the speed of normal broadcasting cameras. Computer vision algorithms then convert the feeds into what’s called skeletal data—3D representations of the players in motion. 

“It’s a ton of data—22 players, one referee, two assistant referees, [each with] 29 joints with XYZ coordinates, 50 times per second,” says Henry Wang ’23, a former MIT varsity swimmer who earned undergrad degrees in both business analytics and computer science, economics, and data science and is now a Sloan PhD candidate and a FIFA research consultant at the MIT Sports Lab. 

That works out to some 108,900 data points per second for a game that lasts at least 90 minutes. And that’s just the players and referees—a chip embedded in the ball also collects position and velocity data 500 times per second. In total, that’s easily more than a dozen gigabytes of skeletal data and ball-tracking data per game.

FIFA was thrilled to have that much data to work with. But around 2021, when third-party providers started offering skeletal data, the organization did not have the full range of technical skills needed to validate it. “So the data got sent to us,” says Wang.

Right away, the team at the Sports Lab saw some issues. “We saw ‘skeletons’ flying above the ground or completely underground, in anatomically impossible positions,” Vidal-Codina recalls. “We saw skeletons having their bones and limbs stretching from 30 centimeters to a few meters. We saw balls doing weird motions in the air. All sorts of stuff that when you look at it—yeah, that’s definitely not ready to be used.”

Often, when there’s a new idea in the works, “we’re the ones that take the first stab at it,” says Sports Lab researcher and PhD student Henry Wang ’23. “We are the ones that prototype and show it’s possible.”

The lab’s job in tackling this problem was first to validate the data being fed into the system and then to confirm that the SAOT algorithm itself was performing exactly as the third-party vendors claimed it would. 

In 2021 and 2022, FIFA ran a multitude of tests. Renting out a stadium for days at a time, the organization brought the data providers on site, where amateur players, or sometimes even FIFA staff, would run dozens of offside drills while those vendors collected live data.

The lab focused on analyzing that data and relaying the results to FIFA and its providers, which incentivized them to make improvements while casting light on blind spots they sometimes did not know they had, Vidal-Codina says. The lab was able, for example, to analyze how the call might differ if you focused on a player’s whole body, including arms and legs, or just the center of mass.

Before the technology could officially hit the pitch, the Sports Lab had to answer some key questions. First, could FIFA collect live data from the providers fast enough to make game-time assessments feasible? The researchers helped answer this by building a tool in Google Cloud to collect data as it was generated so the lab could later check the latency, allowing FIFA to understand just how “live” its data really was. 

Also important: determining whether two data sets—the skeletal data and the information captured by what’s known as connected ball technology—could be combined to reliably yield a correct offside call. The lab helped do just that, developing a protocol that synched the systems collecting skeletal and connected ball data. 

After validating SAOT, tweaking it, and testing it in many situations, including some official FIFA matches in 2021 and 2022, “FIFA felt it could be used at the biggest stage, which was the World Cup,” says Vidal-Codina. Indeed, FIFA president Gianni Infantino endorsed the tool himself when it debuted in Qatar.

Over the course of the 64-game tournament, SAOT assisted in more than 150 offside calls, some with weighty effects. Eight goals were overturned after a referee declared the scoring team offside; two goals were added to the scoreboard after a referee had incorrectly disallowed a goal that was not, in fact, offside; and in sevencases, an offside call assisted by SAOT changed the game’s outcome. 

These results highlight just how crucial a single offside decision can be, given the low scores typical in soccer—and how tools like SAOT can help improve the game. “Overall, decisions have been made quicker and better. That’s ultimately what we strive for,” Vidal-Codina says. 

The technology also takes some of the pressure off referees. “I would argue that the goal of our work is to make sure that the referee is as informed as possible about the decisions that they make,” says Wang. “It’s an incredibly difficult job.” During World Cup play, SAOT’s animated visuals were shown on stadium screens and available to as many as 5 billion viewers across platforms to help them understand the referees’ calls.  

But the technology is meant to assist referees, not replace them. “We don’t want people to think that we are automating referees. I can guarantee you the referee is not going anywhere,” Wang says. “We want to make sure that the human element is transparent, that it’s informed, and that we are helping referees do their job.”

SAOT may have been the Sports Lab’s highest-profile FIFA project to date, but the lab has had a hand in shaping the organization’s larger innovation pipeline. It’s helped improve the way technology—from hardware like cameras to officiating tools like SAOT—gets tested and certified on its way to the pitch. Since 2021, FIFA’s process for certifying data providers’ systems has included having the Sports Lab assess their data latency from a live data collection event using the same infrastructure it built to validate SAOT. And often, when there’s a new idea in the works, “we’re the ones that take the first stab at it,” says Wang. “We are the ones that prototype and show it’s possible. It’s a call to the industry to say: ‘Hey, this is interesting.’”

FIFA isn’t the only organization interested in the insights that tracking data can offer; the NBA has been collecting it for over a decade. In 2025, Hosoi and the MIT Sports Lab published a paper based on an NBA-MIT collaboration that had a unique focus: Instead of using the tracking data to analyze the game’s physical elements, they sought to understand the mental ones.

“Currently, everything physical about an athlete gets measured,” Hosoi says. “But if you talk to the organizations, they tell you that the mental part of the game is just as important. And we have no tools for measuring the mental part. So the question is, can we use the physical tracking data to extract metrics for mental performance?” 

In basketball, a big part of the mental game comes down to decisions around when to shoot and when to pass. But it’s not so easy to determine which players are making good or bad decisions. So MIT researchers created a metric called expected action value (EAV), which is essentially an assessment of the likelihood of a play’s success. Using a model trained on all 786,208 passes from the 2018–’19 NBA season and all 1.4 million shots from 2013 to 2019, they were able to figure out expected outcomes of different plays. 

EAV takes into account the velocity of the shot and the acceleration of the player making the shot as well as the positions of players on the court. For instance, an uncontested three-point shot from the corner has a higher EAV than a two-point attempt from a player getting double-­teamed closer to the basket (or “in the paint”). This approach can tell you not only the likelihood of a successful shot but also the chances of a successful pass. If the player decides to pass instead of shoot, and the receiver of the pass has a reasonable chance to make the shot, then that was a good decision by the passer.

A consistent record of high-EAV choices—passing at some times, shooting at others—means a player is making good decisions. “You can just calculate: How many times do players make good decisions? How many times do they make bad decisions? And we can rank NBA players by good decision-makers and bad decision-makers,” says Hosoi.

This approach can also help teams see if points are being left on the table. Given that teams averaged about 110 points per 100 possessions in the 2019 NBA season, or 1.1 points per possession, if a player passes up a play option with an EAV of more than 1.25 for a play with a lower EAV, the Sports Lab’s model classifies it as a “missed opportunity.” Flagging these moments saves time for coaches, who have to review video for at least 82 games every season. “If we can point to the time stamps of the different games where your guys might have missed an opportunity, you can take advantage of that, right?” Hosoi says.

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At this point, the MIT Sports Lab doesn’t really need to advertise its services. “If you are good in sports, everybody who needs to know will know,” says Hosoi. The lab’s partners come to it if they need answers to questions—as the NFL did during the covid crisis. 

At the beginning of the 2020 season, some teams had opened their stadiums for limited in-person attendance while others didn’t allow any fans. In March 2021, “there was a paper that was published that said in the cities where NFL stadiums have opened, there are spikes in covid cases,” Hosoi recalls. “And the NFL called us and said, ‘Wait, is this true? Because if this is true, we’re going to stop. Can you guys do an analysis on this?’” 

After investigating, the Sports Lab identified a problem with the original paper. NFL teams made decisions about opening stadiums in conjunction with stadium owners and local governments. What the paper didn’t consider, however, was that some states had stricter covid protocols than others, and it was stadiums in those places that tended to stay closed to fans. 

The lab accounted for the confounding factors involved and found that opening a stadium with distancing and masking protocols had no effect on covid cases. In fact, the analysis found that in some places, in-person attendance was correlated with case totals that were lower than expected. Hosoi hypothesizes that this was not only because the open stadiums required distanced seating and other safety measures but also because if fans were at the stadium, they were usually outdoors—not mingling in a crowded bar or at a friend’s house. Partly on the strength of these findings, the NFL decided to open all stadiums for in-person attendance in the 2021 season.

The Sports Lab’s expertise isn’t limited to data analytics; companies are also welcome to bring their hardware and product quandaries to the lab. Adidas, for example, had announced development of a 3D-printed midsole for running shoes in 2015 and was eager to bring it to market. It partnered with Carbon, a Silicon Valley company specializing in the technology, and by around 2017 the shoe manufacturer had finally figured out a way to produce 3D-printed midsoles at a speed that could match the commercial scale.

Still, it wasn’t quite sure how to use this innovation. Adidas approached the Sports Lab with one big question, which  Sarah Fay ’15, SM ’18, PhD ’21, summarizes as “We know we can do all this cool stuff, but what should we do in order to make a high-performing shoe?”

“A regular running shoe just has a slab of foam in the bottom,” explains Fay, who tackled this project while earning her PhD. “You can only change the stiffness by changing the thickness. The exciting thing about 3D printing is that you can change the stiffness without having to change the shape, the footprint of the midsole—just by changing the lattice architecture.”

But manufacturing a high-performing shoe would be tricky: No two human runners are the same, and there was not much data from the running world at the time. So Fay turned to mechanical models—in particular, the mass-spring-damper model for analyzing a system’s dynamic behavior, which Thomas McMahon, a biomechanics pioneer at Harvard, had used to assess different running surfaces in the 1970s. “Just a simple model can be super powerful,” Fay says.

Fay iterated on this foundation to build a model with a center of mass, a rotating hip, and a leg that stretches. It could predict how runners of a given height, weight, and leg length would adjust their gait in response to different levels of springiness and shock absorption in a simple test shoe. This let Fay and Hosoi test gait response as they varied the stiffness of various parts of the midsole. 

To ensure the accuracy of the model, they also considered that runners typically (and often unconsciously) try to minimize what they called a “biological cost function” of running, such as the impact they feel when their foot hits the ground, or the jerkiness of their gait. In multiple simulations, they optimized their model for various biological cost functions, and they compared the resulting gaits with actual gaits recorded in a previous treadmill study. Upon finding that most runners try to minimize both the impact of their feet and the amount of energy their legs expend, Fay and Hosoi were able to optimize the model for those two factors to deliver highly accurate gait projections. And the ability to predict the gait made it possible to predict how well a shoe would perform. 

Adidas used the model to help evaluate potential lattice-structured midsole designs, selecting the top performer for fabrication to do more formal testing. “Those are the shoes that Adidas ended up making and selling that I wear basically every day,” Fay says. She imagines that one day it could be possible to analyze running videos, determine the best shoe architectures for specific runners, and 3D-print shoes designed just for them.

Fay was able to fill in the mathematics and engineering expertise that the Adidas team was missing. And by giving her a way to couple her technical skills with her experience as a lifelong athlete who played both field hockey and squash at MIT, the Sports Lab may have helped her find her calling. Today, she runs a sports-related research lab of her own at Smith College, where she’s an assistant professor of engineering studying the biomechanics of soccer cleats and their role in players’ risk of knee injury. 

“The big part of sports for me is just that it was a safe space for me to learn how to be a leader, how to be a person, how to be a teammate,” she says. “And I figured that that’s a valid enough reason to make my career path head in that direction.”

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What Vidal-Codina calls the “most magical feature” of the lab is that it meets its partners in the sports industry where they are. As he puts it, its scientists can say, “Okay, what do you need help with? We may have the skills or the methodology to come to a solution. So let’s sit together and try and figure it out.”

“The best thing about the Sports Lab is the community of people we’ve built—a direct connecting line from the industries and the teams to our students and to our faculty.”

Anette “Peko” Hosoi, Pappalardo Professor of Mechanical Engineering, MIT

But its work benefits the MIT community as much as it does the world of pro sports. The Sports Lab hosts an annual MIT Sports Summit, which brings technical and management professionals in sports to campus to help students, faculty, and industry figures make personal connections and share their work. Hosoi and Chase also teach 2.98 (Sports Technology: Engineering & Innovation), a class that involves MIT students in real industry projects. And the lab brings pro-level sports insights to MIT athletes, partnering with the athletics department on projects like analyzing the NCAA Power Index—the metric used to select and seed teams for the Division III national tournament—with an eye toward helping MIT teams maximize their chances of securing spots. Another project involves collecting athletes’ personalized weight-room stats into a dashboard to give coaches a window into their performance and enhance their recovery. The lab also worked with an MIT soccer player to create a tool that automatically tracks the passing sequences leading to goals, shedding light on which players contributed. It’s now widely used by the Institute’s soccer teams.

“The best thing about the Sports Lab is the community of people we’ve built—a direct connecting line from the industries and the teams to our students and to our faculty,” Hosoi says. “That collaboration is better than the sum of the parts.” 

While the lab’s work may take place behind the scenes, its influence will continue to ripple across the world of sports—from the soccer games on our televisions during this year’s World Cup to the shoes on our feet.

And the lab will do it by asking the most important question of all: “How can we help?” 

Here’s a problem you probably didn’t solve in school: You’re an ambitious young plumber from Brooklyn in a world inhabited by violent human-size mushrooms called Goombas. The love of your life has been kidnapped, so you embark on a quest to rescue her, venturing through stretches of pipe-filled and monster-­ridden terrain where your only means of protection are your powers of jumping and stomping. 

It’s a journey so arduous that no computer—real or hypothetical—is powerful enough to figure out if you can reach her. And according to research published by the MIT Hardness Group, determining whether your quest is possible at all is at least as complicated as decoding the encryption behind financial transactions. But if this problem could talk, the first thing it would say is “Hello, it’s a-me, Mario!”

For the love of the game

Though it does have a YouTube channel, the MIT Hardness Group isn’t an official research group. Instead, it’s a placeholder name for theoretical computer science projects—including several related to Super Mario—from Erik Demaine’s class Algorithmic Lower Bounds: Fun with Hardness Proofs.

Demaine, a professor of computer science, received a MacArthur fellowship (also known as a “genius” grant) for his work in computational geometry on protein folding and origami. But he also researches complexity theory, which focuses on organizing problems into categories based on how much time and memory space it takes for computers to solve them.

He happens to be an avid Super Mario fan as well. “I grew up playing NES [Nintendo Entertainment System] games,” Demaine says. “I poured many hours into playing as a kid, so it’s fun to come back to it these many years later and tie it into my research.”

Super Mario takes place on a horizontally scrolling universe of platforms, pipes, and other obstacles. The object of the game is to rescue Princess Peach, the monarch of the Mushroom Kingdom, by racing through this terrain while sidestepping or dueling monsters like Goombas and deadly porcupines called Spinies. The game takes place over several levels; in the original version, each level ends with a flagpole that sends Mario on to the next part of his mission.

Over the last 14 years, Demaine and his collaborators have proved many things about Super Mario, such as that it’s even harder than the infamous traveling-salesman problem (which seeks the most efficient route between many different locations) or the problem of factoring large numbers. But the result that surprised Demaine the most came from four of his students: Hayashi Ani ’21, MEng ’23; Holden Hall ’26; Ricardo Ruiz ’24, MEng ’25; and Naveen Venkat ’23, MEng ’24. For their final project in that 2023 class, the team used a combination of fan-made Super Mario level editors and a platform called Super Mario Maker to create levels so hard that they are undecidable. In other words, it’s impossible to write a computer program that always correctly predicts whether, in those levels, Mario can reach the castle. 

Previously, Demaine had believed that Super Mario belonged in the PSPACE complexity class, which contains problems that are solvable but whose solutions become impractically complex as the problem gets bigger. At the time, he had even said that PSPACE was Mario’s “permanent home.” But the new findings pushed Super Mariointo RE-Complete, the class of undecidable problems. “It’s the hardest complexity class we could imagine for these sorts of games,” Demaine says. 

What computers can’t solve

In 1936, Alan Turing, the father of modern computer science,created a puzzle now known as the Halting Problem to prove it’s not possible to construct a computer that can solve everything.

At the core of the Halting Problem lies a paradox, and it goes like this: Suppose you have a fancy computer, called the Oracle, that looks at any program and correctly determines whether a computer following it will ever come to a stop. For example, if it sees the program “Take 1 and add 3,” the Oracle will say the program halts, but if the program says “Take 1 and add 1 to it until it becomes 0,” the Oracle will say it runs forever. 

Now suppose you have another computer, the Contrarian, and you put the Oracle inside it. When you give the Contrarian a program, it passes it to the Oracle and then does the opposite of whatever the Oracle says the program will do. So if the Oracle assesses the Contrarian’s program and thinks it will halt, the Contrarian will run forever. If the Oracle thinks the program will run forever, the Contrarian will halt. Either way, the Oracle’s assessment is wrong, so the classification problem is undecidable.

The proofs that Super Mario is undecidable rely on a more complex version of this idea. The team’s argument breaks down the video game using a technique called a reduction, in which mathematicians convert a problem they’re trying to solve into a problem they already know something about. “The classic example I remember in a math class is: How do you make a pot of boiling water?” Demaine recalls. “Well, I fill up the pot with water from the sink, and then I put it on the stove, and then it eventually boils. Okay, now I’ll give you a pot of water that’s already filled. How do you make a pot of boiling water? Well, I empty out the pot first and reduce to the previous problem.”

In their particular world of platforms and porcupines, the team broke down their Super Mariolevel into localized parts of Mario’s path called gadgets, which they could use to prove that the level was undecidable.

“A gadget in our sense is anything in your environment that decides whether or not you can go through one pattern [within a level],” explains Jayson Lynch ’12, MEng ’15, PhD ’20, a CSAIL research scientist and head of algorithms at MIT FutureTech. For example, in one gadget Mario might need to jump on a platform to avoid a monster as he makes his way across the screen. As a PhD student mentored by Demaine, Lynch spearheaded the formalization of gadget theory and worked on some of the earlier Super Mario papers but did not study the game’s undecidability.

One of Lynch’s favorite Super Mario gadgets is the door gadget, which works like a door that Mario can open, traverse, and close. The door in question is always either open (when the Spiny is on the right) or closed (when the Spiny is on the left). So if a Spiny is pacing back and forth on the left of the door, Mario has to navigate beneath the moving Spiny and jump up to hit a brick block just as the Spiny reaches it. This bumps the Spiny to the right side, which opens the door and allows Mario to travel across the traverse path and get to the spot where he can close the door. Once there, he must time another jump beneath the pacing Spiny to send it back to the left side of the gadget, closing the door behind him. 

Since a door is always open or closed, its state can be used to simulate a true or false statement, with open being true and closed being false. Earlier Super Mario papers had strung together multiple door gadgets to simulate a true-or-false problem that complexity researchers already knew to be hard. But to show undecidability, the team used Super Mario level editors to put together another device, called a counter gadget, that tallies the game’s monsters and obstacles. 

If you can build a machine with even just a few of those counters, Demaine says, you can simulate an arbitrary computer—one that could essentially do anything a non-quantum computer could do, given enough time and memory. And with no limit on the number of monsters, such a machine could have infinitely expandable memory, even though the level size stays the same, which he calls “pretty wild.” In other words, any theoretical computer can be built in a Super Mario level. “You could use it to solve anything you can use a computer to do,” says Demaine. “You could have it do your taxes, or compile your code, or run an LLM, or optimize your class schedule.” You might even build Super Mario levels that could excel at sudoku, construct optimal chess strategies, or prove any provable mathematical theorem.

The MIT mathematician Marvin Minsky invented counter machines in 1961 to figure out how simple a computer could be while still being “universal” (as powerful as any other computer, given enough time). These theoretical computers each store two numbers and can change them by adding 1, subtracting 1, or doing something special if a number hits a set value. 

In the counter gadgets the students designed for Super Mario, the numbers reflect how many Goombas the levels contain. A number increases when a pipe spits out a Goomba and decreases when Mario stomps on one. Mario dies if he collides with a Goomba without stomping on it, so he can continue along the path only when the counter is at 0. 

Minsky had already proved that counter machines are undecidable because they can run undecidable problems. Since the researchers proved that counter gadgets simulate counter machines, then any level of Super Mario containing a counter gadget will also be unsolvable. “In the future, if someone wants to show a game is undecidable,” explains Holden Hall, one of the students behind the project, “they just have to make one of these gadgets.”

The existence of undecidable problems like the Halting Problem implies that it’s possible to construct an undecidable Super Mario level. Just as the singular undecidable program for the Halting Problem meant thatit’s impossible to figure out if a computer program will run forever, the team’s undecidable level means that it is impossible to determine whether an arbitrary Mario level can be beaten.

Putting the “super” in Super Mario

More than two years after Demaine’s class on hardness proofs, some of his students continue to meet weekly to discuss their Super Marioresearch.

“From the point of view of complexity theory, studying video games is interesting mostly for didactical reasons,” Fabrizio Grandoni, a research professor at the University of Applied Sciences and Arts of Southern Switzerland, told MIT News in 2016. “It’s a simple, natural way to attract students to study this specific topic.”

Hall, who had very little exposure to the ideas of complexity theory before taking Demaine’s class, is a case in point, noting: “I took the class because a bunch of people I knew were taking it. But since I took it, I really enjoyed the class, and so I’ve taken a lot more classes in that realm.”

The applications of the MIT Hardness Group’s work go way beyond stomping on mushrooms and collecting coins. For example, researchers at the University of Texas Rio Grande Valley (including Timothy Gomez, now a PhD student at MIT) have used the gadget theory developed for analyzing games like Super Marioto study the complexity of problems relating to planning robotic motion and modeling chemical reaction networks.

“[Gadget theory] can be used in the negative way to say ‘Oh, well, we should stop searching for algorithms because we know this problem is too hard’—or it can be used in this positive way, because usually, to prove something hard, you’re showing that you can build a computer of a certain type,” Demaine says. 

Though there’s no way of knowing what mark Super Mariowill leave on the future of math and computer science, one thing’s for sure: No matter how many princesses he does or doesn’t save, the legacy of this little plumber is set to extend far beyond video screens. 

Brian Sietsema has a favorite word.

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.

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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. 

 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.  

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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.

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.” 

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. 

The national conversation about the value of education is currently dominated by speculation about the risks and positive potential of AI. 

Whatever your own perspective on that debate, I hope you’ll be glad to know that MIT is also working on a deeply important but comparatively old-fashioned challenge: American high school students’ startlingly uneven access to calculus. According to the National Survey of Science and Mathematics Education, which covers the nation’s more than 13,000 school districts, in almost half of US high schools calculus isn’t even offered. 

As our graduates know better than anyone, preparation in calculus is effectively an admissions requirement at a place like MIT—which means that students in schools with no calculus classes are in practice locked out of an essential route to STEM careers.

Recognizing this glaring need, we set out to find a solution. With support and inspiration from the Siegel Family Foundation, in the fall of 2025 the Institute launched the MIT4America Calculus Project. Developed by the MIT Scheller Teacher Education Program (STEP) Lab, the Calculus Project recruits and trains MIT undergraduates and alumni to provide weekly long-distance calculus tutoring for students in underresourced high schools across the country.

Reflecting the Institute’s longstanding commitment to national service, the MIT4America Calculus Project supplies an innovative answer to a hard practical problem, and it taps the uncommon skill of MIT’s people to create opportunity for others and spread the educational impact of the Institute beyond our walls. 

The project is in its early phases, so far engaging 30 MIT undergraduates and seven alumni tutors. From its initial work with 14 school districts across the country, it’s on track to collaborate with about 20 this summer. 

The demand is clear—and the response from the students we’re reaching makes it all worthwhile. This spring, the first Calculus Project students were prepared for their AP exams, thanks to their own persistence, diligence, and curiosity—and to the generosity, care, and patience of a dedicated group of people from MIT.

Right now, MIT alumni and friends are voicing their support for:

America’s scientific and technological leadership

Merit-based admissions and affordable education

Advances that increase US health, security, and prosperity

Our community is standing up for MIT and its mission to serve the nation and the world. And we need you to join us at this critical moment.

standupfor.mit.edu

Our hands are the nimblest parts of our bodies, coordinating 34 muscles, 27 joints, and over 100 tendons and ligaments to perform countless nuanced movements and gestures. So far, robots have been notoriously bad at mimicking that dexterity, in part because researchers struggle to capture what is actually going on under our skin in order to reproduce it. Now MIT researchers are pioneering a promising new approach.

Mechanical engineering professor Xuanhe Zhao and colleagues at the Institute and the University of Southern California have designed a wristband equipped with an ultrasound “sticker”—a miniaturized version of the transducers used in medical offices, paired with a hydrogel that can safely adhere to the skin. As the wearer’s hand moves, the device produces ultrasound images of the wrist’s muscles, tendons, and ligaments. Then an artificial-intelligence algorithm, trained on ultrasound images meticulously labeled by humans, continuously translates the images into the corresponding positions of the five fingers and the palm. “The tendons and muscles in your wrist are like strings pulling on puppets, which are your fingers,” says Gengxi Lu, a former MIT postdoc and one of the lead authors of a paper on the work. “So the idea is: Each time you take a picture of the state of the strings, you’ll know the state of the hand.”

In demonstrations, the team has shown that a person wearing the wristband can wirelessly control a robotic hand. As the person gestures or points, the robot does the same. In a sort of wireless marionette interaction, the wearer can manipulate the robot to play a simple tune on the piano and shoot a mini basketball into a desktop hoop. With the same wristband, a wearer can also manipulate objects on a computer screen—for instance, pinching the fingers together to enlarge and minimize a virtual object.

The researchers are planning to further miniaturize the wristband’s hardware, which is currently similar in size to a cell phone. They also hope to train the AI software on movements from more volunteers with a wider variety of hand sizes, finger shapes, and gestures. 

They envision building a large data set of hand motions that can be plumbed, for instance, to train humanoid robots in delicate tasks such as surgical procedures. The ultrasound band could also be used to let people grasp, manipulate, and interact with objects in design applications, video games, or other virtual settings. Ultimately, the team is building toward a wearable hand tracker that anyone can use to wirelessly manipulate humanoid robots or virtual objects with high dexterity in real time.

“We believe this is the most advanced way to track dexterous hand motion—through wearable imaging of the wrist,” Zhao says. “We think these wearable ultrasound bands can provide intuitive and versatile controls for virtual reality and robotic hands.” 

With an adaptable fastener designed at CSAIL, pitching a tent or adjusting the cast for a broken bone could be almost as easy as zipping your coat.

The researchers, led by associate professor Stefanie Mueller, were inspired by an abandoned prototype for a three-sided zipper that William Freeman, PhD ’92 (now an MIT professor), patented in the mid-1980s. With software they created, people can design customized versions of this fastener to be 3D-printed in plastic. Its three flexible “arms” can then form different types of rigid objects when zipped together.

Users can select the strips’ length and the direction and angle at which they’ll bend. They can also choose whether the zipped-up device will appear straight, bent, coiled, or twisted. 

A tent making use of the zipper takes just 80 seconds to set up. A wrist cast that incorporates it can be tightened and loosened as desired. A motor can even be added so that, for example, a robot’s leg heights could change with the push of a button.

“A regular zipper is great for closing up flat objects, like a jacket, but Freeman ideated something more dynamic,” says MIT postdoc Jiaji Li, a lead author on a paper presenting the project. “We’ve developed a process that builds objects you can rapidly shift from flexible to rigid, and you can be confident they’ll work in the real world.” 

A technology developed by Professor Sangeeta Bhatia, SM ’93, PhD ’97, and colleagues could offer new hope to the thousands of Americans with chronic liver disease who are waiting for an organ transplant or not strong enough to tolerate one.

The liver is involved in regulating blood clotting, removing bacteria from the bloodstream, metabolizing drugs, and more, and many of these essential functions are performed by specialized cells called hepatocytes. Bhatia’s lab has been working for a decade on ways to introduce functioning hepatocytes without surgically replacing the liver itself. 

In the new technique, these cells are injected directly into the body along with hydrogel microspheres that help them stay together and form connections with nearby blood vessels. The spheres have special properties that allow them to act like a liquid when they are closely packed together, so they can be injected through a syringe and then regain their solid structure once inside the body. “These microspheres provide the hepatocytes with a niche where they can stay localized and become connected to the host circulation much faster,” says MIT postdoc Vardhman Kumar, lead author of a paper on the work.

The researchers have shown that the cells can remain viable in the bodies of mice for at least two months, generating many of the enzymes and other proteins that the liver produces. In the study, they were injected into the fat tissue of the belly, but they could also be delivered to other sites.

“The way we see this technology is it can provide an alternative to surgery, but it can also serve as a bridge to transplantation where these grafts can provide support until a donor organ becomes available,” Kumar says. While patients would likely need to take immunosuppressive drugs, the researchers are exploring how they might get the hepatocytes to evade the immune system or use the microspheres to deliver immunosuppressants locally. 

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.

A startup claims it broke through a bottleneck that’s holding back LLMs

AI startup Subquadratic came out of stealth last month with a huge claim: it had solved a mathematical bottleneck that had held back large language models for almost a decade.

The purported breakthrough comes from slashing the number of computations transformers need to carry out to generate answers. The result is a faster and cheaper LLM that uses far less energy than any other model on the market.

Many experts remained skeptical—but Subquadratic has started to share the receipts. They suggest that their approach might be worth paying attention to.

Here’s how the system works—and why some researchers still aren’t convinced.

—Will Douglas Heaven

Brain-computer interface trials are taking off

—Jessica Hamzelou

This week, I covered the story of Casey Harrell—a man with ALS who is “the first power user” of a brain implant. The device has enabled him to maintain an income, reconnect with friends and family, and read to his daughter. He told me that it’s “nothing short of revolutionary.” 

Over the past couple of years, the number of BCI trial volunteers has soared. This year, China became the first country to approve a BCI for medical use. Advances in technology are allowing engineers to provide more features than ever. BCI research is properly taking off.

Find out how the technology is edging from the lab towards the market.

This story is from The Checkup, our weekly newsletter giving you the inside track on all things biotech. Sign up to receive it in your inbox every Thursday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Amazon workers who backed data center limits may face termination
The engineers say they’re under investigation by the company. (NYT $)
+ And could face discipline, including potential termination. (The Verge)
+They had testified at meetings about pausing data centers. (CNBC)
+ They’ve filed a joint complaint to Seattle’s Office for Civil Rights. (Wired $)

2 A new fossil discovery has rewritten 150 years of evolutionary theory
It suggests early land vertebrates skipped the tadpole stage. (New Scientist $)
+ And raises questions about how vertebrates adapted to land. (404 Media)
+ Sponges may have been the first animals. (MIT Technology Review)
 
3 Bernie Sanders plans to give the public direct ownership of AI firms
He’s unveiled new legislation to create an AI sovereign wealth fund. (AP News)
+ It would be funded through a one-time tax on AI companies’ stock. (Quartz)
+ And make annual payments directly to Americans. (Washington Post $) 
 
4 Investors in China secretly acquired stakes in SpaceX before its IPO
One had ties to Chinese military contractors. (ProPublica)
+ The US fears China has got one of ASML’s top machines. (Reuters $)
 
5 Researchers have figured out Russia’s nuclear-powered missile
They call it “a terrible idea”—but not an impossible one. (NPR)
+ NASA is building a nuclear reactor-powered spacecraft. (MIT Technology Review)
 
6 Longevity medicine faces a do-or-die moment in a landmark trial
It will test whether cellular aging can be safely reversed in humans. (Axios)
+ The next step is “chemical reprogramming.” (MIT Technology Review)
 
7 Studies suggest AI may already be deskilling professionals
Over-reliance appears to weaken doctors’ and engineers’ abilities. (Nature)
 
8 Tech workers who maxed out their AI use are now trying to minimize it
Spiralling costs mean “tokenminning” has replaced “tokenmaxxing.” (NYT $)
 
9 Scientists say the human genome’s structure may confound AI models
Which would constrain AI-based models of biology and disease. (Quanta)

10 A new robotic self-driving toilet brings the bathroom to you
The Xiaoban also cleans up and empties itself all on its own. (The Verge)

Quote of the day

“They hated me. They were doing everything they could to knock me down. And look at them now.” 

—Donald Trump mocks Mark Zuckerberg and Jeff Bezos in a conversation with Elon Musk that’s recounted in a new book, Wired reports. 

One More Thing

Technology can help us feed the world, if we look beyond profit

The pandemic exposed the weak spots in our interconnected food system. They’re the result of decades’ worth of technological advances, from globe-spanning shipping to refrigeration networks. But technology is not inherently opposed to sustainable and resilient food systems.

Powerful technologies like genetic modification can create stronger local agriculture and a healthier food system—but they normally aren’t. The challenge is ensuring they serve food security and human well-being, rather than simply maximizing profits.

Dive into our food system’s problems and the solutions that technology can provide.

—Fabio Parasecoli

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ This intriguing video tracks the covert reality of Japan’s shinobi.
+ Dive into this admirably obsessive archive covering over 100 different ways to tie your shoes.
+ One of the world’s largest digital collections of plants and fungi is now available for free to everyone.
+ A grand orchestra has beautifully covered Michael Jackson’s “Human Nature” at Abbey Road Studios.

Miami-based AI startup Subquadratic came out of stealth mode last month with a huge claim. It announced that it had solved a mathematical bottleneck that had been holding back large language models for almost a decade.

The details were thin, and many people were unconvinced. But Subquadratic has started to bring the receipts, sharing the results of an independent evaluation of its new tech. The results suggest that the company’s claims might be worth paying attention to.

According to Subquadratic, it has developed a new kind of LLM, called SubQ, that is faster and cheaper and uses a lot less energy than any other model on the market. The company also claims that SubQ is able to process up to 12 times as much text at once than most other models, allowing it to carry out a range of data-heavy tasks, such as analyzing hundreds of documents or entire code bases.

What’s more, Subquadratic says, SubQ does this while more or less matching the performance of the best models put out by Google DeepMind, OpenAI, and Anthropic on key tasks like coding.

The problem was that the company at first provided little evidence for its claims beyond a handful of self-published test scores. And it has yet to make SubQ widely available for people to try out themselves.

So it’s no surprise that Subquadratic’s claims were met with skepticism. Dan McAteer, an artificial intelligence engineer, captured the overall response on X: “SubQ is either the biggest breakthrough since the Transformer … or it’s AI Theranos.”

A month on, the company has published more information about its model, including the results of additional independent tests run by third-party firm Appen.

“We expected healthy skepticism,” says Subquadratic cofounder and chief technology officer Alex Whedon. “In hindsight, releasing the third-party benchmarks alongside the initial announcement would have preempted much of the skepticism, which is why we’re taking the time to make sure any future results are fully verified before putting them out.”

Subquadratic asked Appen, which evaluates other companies’ models, to run its tests on SubQ. The results seem to back up a lot of Subquadratic’s claims. “That was really exciting to me, it validated their architecture,” says Jeanine Sinanan-Singh, Appen’s director of generative AI research.

“I was like, ‘Wow, this could be a game changer,’ because models struggle with speed and inefficiency,” she adds. “But when you have kind of shocking results, it’s really not as credible when you say it yourself.”

SubQ won’t replace existing top models across the board, but it could offer huge increases in speed at a fraction of the typical cost for certain tasks. Subquadratic insists that in the long run, though, its breakthrough could change how LLMs are built. “We hope we’re kicking off a new age of efficiency,” says Justin Dangel, the firm’s cofounder and CEO. “We don’t think anybody will be building on transformers in a few years.”

Attention!

To understand why Subquadratic’s claims are a big deal, let’s dig into how most LLMs work. The key mechanism inside an LLM is a type of neural network called a transformer, which runs a process known as dense attention. Today’s LLMs typically chain together multiple transformers. (The foundational paper of the LLM era, published by researchers at Google in 2017, was titled “Attention Is All You Need.”)

Dense attention works like this: When a transformer processes a chunk of text, it first encodes each word (or part of a word, known as a token) with a number. To capture the meaning of the full text, it then multiplies each of those numbers with every other number for that text. For example, a piece of text 10,000 words long would kick off almost 50 million individual multiplications. That’s a lot of computation and the main reason that LLMs are notorious power hogs.

“If you want to summarize The Great Gatsby, you have to look at the first word and the last word together, and then you have to look at every other combination,” says Dangel.

As the length of the text increases, the number of computations skyrockets. That’s because each additional number must be multiplied by all other previous numbers. Double the number of words, and you roughly quadruple the number of computations, a rate of increase known as a quadratic expansion.

(You can picture this yourself: Draw a circle and mark dots around its edge. Each dot is a token. Then draw lines between pairs of dots to represent the multiplication of those two tokens. A circle with five dots will have 10 lines crossing it. Make it 10 dots and you will have 45 lines, 20 dots and you will have 190 lines, and so on.)

Slashing costs

Subquadratic’s solution is to ditch dense attention, the core operation of a transformer, in favor of what’s known as sparse attention, which slashes the number of computations needed. Instead of multiplying the number assigned to each token by every other number, sparse attention selects just some of the numbers to multiply. The idea is that not all relationships between words in a piece of text matter.

“Sparse attention says not all of those relationships are important, because they’re not,” says Whedon. “If you’re reading a book, you’re not going to look at the first and second words, first and third—that’s insane.”

It’s a simple approach, and Subquadratic is not the first to try it. “Pretty much everything under the sun has been attempted,” says Will Depue, an independent AI researcher who previously worked at OpenAI. “It’s not impossible, but it’s akin to running a four-minute mile.”

Previous techniques for selecting which numbers to multiply and which to ignore have not produced a mechanism that can capture the meaning of a document as well as dense attention can.

Subquadratic claims to have cracked the problem at last. It pitches SubQ as the first sparse-attention LLM that rivals mainstream dense-attention models in performance.

“Historically, most mechanisms have used fixed patterns, like always comparing the first word to the fifth,” says Whedon. “That’s pretty limiting. Language is too sophisticated for that. And so, one of the things that makes our mechanism unique is that we dynamically select which ones are important.”

The firm won’t say exactly how SubQ chooses which words to focus on, but the selection is calculated on the fly and differs for each piece of text the model is given. “That’s kind of where the secret sauce is,” says Whedon.

Testing, testing

The upshot is that for certain tasks, SubQ may be faster and cheaper to run than most other models. Appen evaluated SubQ on a handful of standard tests. In a straight-up speed test, which sets a baseline for how fast a model can operate in theory rather than assess what a model can actually do, Appen found that SubQ was 56 times faster than models using FlashAttention, a previous sparse-attention technique. 

On LiveCodeBench, a test that looks at how well models perform on competitive coding problems taken from real contests, SubQ scored 89.7%, putting it in the same ballpark as other top coding models. “This model continues to provide frontier-level performance in coding,” says Appen’s Sinanan-Singh.

Subquadratic’s claims about cost are harder to verify because SubQ is not yet widely available. According to Dangel, it costs $2600 to run Anthropic’s LLM Opus 4.6 through RULER 128, a test developed by Nvidia to assess a model’s ability to retrieve information from large data sets. And SubQ? “It cost us eight dollars,” he says.

SubQ does seem to be able to handle very large data sets. The model has a context window (roughly akin to a working memory) up to 12 million tokens long. Most top models today have context windows one million tokens long. In a demo that Whedon ran for me, he asked SubQ to perform a task that required it to reason about information contained in 400 documents. It responded in seconds. When he gave Perplexity—a popular LLM-powered search engine—the same task, it failed to load all 400 documents. 

Appen also ran the needle-in-a-haystack test, which assesses how well a model can retrieve specific information buried in a large amount of data. In its report, Appen states that SubQ scored 98% with context windows six million and 12 million tokens long, “sustaining near-perfect long-context retrieval at scales few models are tested at.”

Too good to be true?

Despite the high scores, benchmarks paint an incomplete picture of what a model can and cannot do. Testing under very specific conditions is not a substitute for running a model on a wide range of real tasks.

Subquadratic is offering SubQ as a model tailored to coding and to searching very large data sets. It says that tens of thousands of potential users have already signed up for early access, including more than 500 enterprise customers. But there’s a long waitlist, and the firm has given very few people access so far. Subquadratic’s response is that it is a new, small company with limited resources and cannot serve too many people at once.

Until more people get their hands on the model and try it out for themselves, some skepticism is justified. One nagging issue is that Subquadratic reused the weights (values set within a model during training that determine how it will behave) from a version of the Chinese open-source model Qwen to bootstrap SubQ, rather than training it from scratch. That’s a common thing for model makers to do, but it cuts across Subquadratic’s claim that it has fully reinvented how LLMs work.  

“They may have built something real and useful,” says Depue. “But the public evidence does not yet justify the stronger claim that they have solved the quadratic attention bottleneck.”

In the meantime, Subquadratic cofounder Whedon insists that making something different was his only option. If you want to build a competitive model, you have to have new ideas, he says: “We’re more up against it than OpenAI is.”

There are plenty of useful things a metric can reveal. There are even more it can obscure or corrupt. It took me well over a decade of tracking my own life in ever greater detail to fully appreciate this duality, which probably reveals something about both me and the nature of measurement.

Like a lot of people bitten by the self-quantifying bug, I initially started gathering personal data to pursue a nebulous collection of goals and desires. As a sedentary technology journalist, I wanted to feel better physically and emotionally, to get outside more, and—where possible—to bring order to some of the messiness and uncertainty of my daily existence. These all seemed to be things that could be improved with the cool clarity of numbers.

Self-quantifiers often get stereotyped as obsessive self-optimizers (and many of them are), but my reasons for producing and collecting personal data were less about life-maxxing and more about life meaning—at least at first. As most people who know me will attest, I do not have now, nor have I ever possessed, a “productivity mindset.” I’m also not all that interested in life hacks, shortcuts, or new ways to compare myself with other people. Instead, what I wanted out of metrics—what I hoped I could divine from a never-ending stream of numbers about my health, work, and social life—was something more elusive: self-knowledge. This was my first mistake. 

The idea that the more we know, the better is so profoundly embedded in our culture that it feels weird to even point it out. Since at least as far back as the Enlightenment, the primary way we’ve all agreed to go about knowing more has been through measurement and quantification. After all, more knowledge—more data—leads to better decisions, which leads to happier, more fulfilled people. Or so we’re told, and with increasing frequency in the era of AI. 

When two Wired magazine editors, Gary Wolf and Kevin Kelly, coined the term “quantified self” in 2007 and helped launch the movement we are all now helplessly a part of, they were essentially selling this very idea. “Unless something can be measured, it cannot be improved,” wrote Kelly in an early blog post, doing his best impression of Lord Kelvin. “So we are on a quest to collect as many personal tools that will assist us in quantifiable measurement of ourselves.” Almost 20 years later, that quest is easier than ever thanks to a flood of devices, apps, and websites all designed to help us build our self-­knowledge through numbers. 

My first tool was a small, plastic clip-on Fitbit I started using in 2011. It did one thing: count the number of steps I took in a day. As a lifelong video game player, I was already well acquainted with the motivational power of simple scoring systems, and I hoped my new gadget would offer the gentle numerical nudge I thought I needed to step away from my Twitter feed and, if not touch grass, at least walk next to some. Walking also seemed to be one of the few times I had what could charitably be called intelligent ideas, which seemed like another promising by-product of doing more of it.

Alas, that was short-lived. I can’t tell you precisely when “getting out into nature more” or “thinking smarter thoughts” stopped mattering to me as goals, but I suspect it took no more than a few weeks. What I can say with certainty is that my initial goal of 6,000 daily steps quickly turned into 10,000, which then jumped to 15,000 and eventually settled at 20,000 for years. Stories about becoming a “steps guy” are clichéd at this point, and they’ve earned that status for a reason.  

It didn’t take long for me to trade in pedometers for heart-rate monitors (I also started running), smartwatches, sleep-tracking rings, and an embarrassing number of macronutrient-­tabulating apps. Outside the health and fitness realm, my early career as a journalist also happened to coincide with the rise of social media and web analytics tools like Chartbeat, which promised to further quantify ­difficult-to-measure aspects of my life, like “job success” and “impact,” by tracking things like page views, followers, retweets, likes, and all sorts of other attentional metrics that now carry great weight.

Metrics inevitably redefine your core sense of what’s important, whether you’re aware of the trap or not.

Ultimately, during the 10-plus years I diligently tracked my heart rate, steps, active calories, sleep, story engagement time, stress levels, and other metrics, I gained virtually nothing in terms of greater self-knowledge. (I suppose I did learn that I liked to make numbers go up and down, but who doesn’t?) The swirl of data that followed me everywhere did not lend additional meaning or insight to the way I relate to myself, my work, or the important people in my life. In fact, the more I used numerical proxies, the worse I felt about pretty much everything. 

What I did learn were two important lessons about what happens when you try to quantify the minutiae of your life. First and foremost, whatever the amount of data you’re currently collecting about yourself, it will never feel sufficient. There’s always a new metric around the corner, a better way for a tracker to remix its readings and more accurately measure what’s “important”: heart rate variability, daily stress, exercise “readiness,” cardiovascular or “fitness” ages. Measurement begets more measurement. You can count on it. 

The second lesson was less obvious but no less significant. The more personal or nuanced your goals are when you set off on your self-quantifying journey, the more likely it is you will ultimately replace them with some simplified metric or ranking. Want to become a better journalist? Why not use page views and leaderboards as a proxy for success? Enjoy cooking and want to improve? Foodie metrics dictate that more complicated recipes with longer ingredient lists are the answer. Even when we know that the value of good journalism isn’t reflected in how many people read a given story or that the joys of cooking are as much about improvisation and experimentation as about successfully following some complex recipe, it’s hard to resist the allure of a simple score or stat. Metrics inevitably redefine your core sense of what’s important, whether you’re aware of the trap or not. 

Over the years, people have invented various terms to describe this phenomenon. In his recent book The Score: How to Stop Playing Somebody Else’s Game, the philosopher C. Thi Nguyen calls it “value capture.” Value capture happens, he says, when you adopt external sources of measurement and then let them rule you without adapting them to suit your life. “In value capture, you’re essentially outsourcing your values,” Nguyen writes. “You’re letting an external metric or ranking set what’s important for you.” Crucially, you’re also outsourcing the process of figuring out your own sense of meaning. It’s why my walks quickly shifted from feeling meditative to prioritizing miles. 

Individuals, institutions, and indeed entire societies can fall prey to value capture. In fact, once you start noticing it, you start seeing it everywhere—in journalism, education, and business, but also in our food, our hobbies, and, yes, the way we measure our health and happiness. Here’s how Nguyen puts it:

Value capture happens when a restaurant stops caring about making good food and starts caring about maximizing its Yelp ratings. It happens when students stop caring about education and start caring about their GPA. It happens when scientists stop caring about finding truth and start caring about getting the biggest grants. It even happens in religion. A pastor recently told me that his church had become completely obsessed with baptism rates. The higher-ups had established an internal leaderboard in which the pastors competed on monthly baptism rates, and it was starting to dominate everybody’s attention. He’d found himself caring less about the long-term spiritual development of his flock and focusing more on trying to deliver popular sermons that would up his baptism rates and move him up that leaderboard.

At its core, The Score is trying to untangle a mystery that Nguyen, a specialist in the philosophy of games at the University of Utah, has been thinking about for a long time: Why is it that numbers and scoring systems in games can be the source of so much joy and fluidity and play, but public measures and institutional metrics (i.e., scores that apply to the real world) seem to drain the life out of everything and thrust us all into a bleak mindset of grinding optimization?

To begin to answer this question, he turns to one of the foundational inquiries into the limits of data and quantification, Theodore M. Porter’s 1995 book Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. 

Porter, a historian of science who specializes in the social power of numbers, has spent his career looking at why quantification has become so dominant, not just in political and bureaucratic life but everywhere. One of his key insights about the inherent attractiveness of quantification, which he calls “a technology of distance,” is that it “minimizes the need for intimate knowledge and personal trust.” Put another way, metrics travel extremely well between different contexts and are easy to grasp and aggregate. 

Whether it’s a student’s GPA or a country’s GDP, these measures are understood by pretty much everyone. But that understanding comes at a price, Porter reminds us: To arrive at a clear metric, you inevitably need to simplify what you’re attempting to measure, often jettisoning heaps of nuanced, qualitative, or open-ended information so that others can find the resulting number legible. 

No one (hopefully) believes that a GPA captures in any meaningful way a student’s entire educational experience or aptitude for learning, but we’ve agreed to use it because more qualitative assessments are onerous to wade through and require expertise to decipher and compare. Ditto for the economic metric of GDP, which politicians and societies are now compelled to drive higher and higher because a group of economists once concluded that this figure correlates with general economic well-being.  

This is the essential tension at the heart of all data, argues Nguyen. Any institutional quantification, he says, requires that the evaluation procedure and its product be comprehensible across contexts. That profoundly limits what the metric can actually measure. “In value capture, you’re ultimately taking that decontextualized nugget and internalizing it,” he writes. “You’re guiding your life using an evaluative technology that has been engineered to travel between contexts, by stripping it of nuance.” 

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Every so often I’ll find myself in friendly debate with a “numbers person”—a statistician, an economist, or a friend who’s still a committed self-quantifier. After patiently listening to my measurement-gone-awry examples—the disastrous attempt to quantify pain as “the fifth vital sign” in the mid-1990s (which exacerbated the opioid epidemic), or any of the countless examples of the McNamara fallacy, where decisions in academia, medicine, and politics are based solely on what’s easily measured—many will insist that I’m misunderstanding or misinterpreting the whole point of measuring. Metrics, they’ll say, are simply a means, and the important questions concern the ends for which they are used. In other words, these unfortunate outcomes amount to user error, not something inherently dangerous or misleading about the nature of measurement. 

At some point during these conversations, Goodhart’s Law will invariably come up, usually as an explanation the metrics-minded deploy for why the ends get all mucked up. The principle, which is attributed to the British economist Charles Goodhart, is often expressed as the following: “When a measure becomes a target, it ceases to be a good measure.” I have a profound dislike for Goodhart’s Law, not because I think it’s untrue, but rather for the way it gets interpreted.

As Nguyen notes, Goodhart’s Law says very little about why metrics fail to capture what’s important—or what to do about it. Find better measures, some will conclude. Don’t let metrics become targets, others will insist. These are not helpful takeaways. All measurements, I would argue, are in fact targets, whether you intend them to be or not. Metrics inevitably present one direction or option as better, Nguyen writes in The Score—“longer lifespans, faster student graduation rates, more page views, higher customer satisfaction scores.” What people are talking about when they bring up Goodhart’s Law isn’t human error; it’s actually a fundamental problem with measurement itself. 

I want to be clear here: Measurement can and does serve a number of vital functions. It has in a very literal sense made the modern world possible, with all its life­-saving, suffering-reducing, and awe-
inspiring scientific breakthroughs. When used with care and diligence, metrics can make our progress (or lack of it) clearer and more transparent. Are we decreasing carbon dioxide emissions or not? They can also introduce accountability into formerly opaque systems, such as by measuring whether a company is complying with state and federal regulations. They can even make us more objective, reduce biases, and galvanize us to act. 

But as Nguyen points out throughout The Score, the fundamental weakness of metrics comes when we use them to pursue subtler, more personal goals. What I think many of us miss—what I know I certainly missed—is that there are always trade-offs when you try to distill something important down to a data point. When we turn to metrics to understand ourselves, our social world, and culture as a whole, they will never come close to capturing what matters. Even worse, they’ll often actively obscure it. 

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Today, I find that numbers have very little to offer when it comes to my daily work, my physical or mental fitness, my relationships, or any other part of my life I consider important. Granted, I’m lucky enough to be in relatively good health at the moment. I don’t have to track my glucose levels or monitor my blood pressure. As a freelance writer, I also have the luxury of not having numbers foisted on me in the form of key performance indicators (KPIs), objectives and key results (OKRs), or any of the endless quantitative evaluations that come baked into pretty much every corporate and gig economy job. 

Still, in a very real sense, there is no escaping metrics or, especially, the logic that accompanies them. Knowing has become numeric, and we all live in a world that increasingly sees us as a collection of numbers—as “data subjects.” The first and most urgent challenge, I’d suggest, is finding a way to keep us from seeing ourselves and each other that way. 

This won’t be easy. As Porter, Nguyen, and countless other philosophers, anthropologists, and historians have already observed, the language of numbers is largely how we ascribe value today—as well as how we digest and metabolize our relationships to ourselves, to others, and to the world around us. Indeed, many of us have accepted not only that metrics have a natural existence in human affairs but that there are in fact no aspects of human life that cannot be somehow translated into data.

Knowing has become numeric, and we all live in a world that increasingly sees us as a collection of numbers— as “data subjects.”

So how do we push back? Nguyen’s book offers a useful first step. As he notes again and again in The Score, believing that numbers say something real or useful about human needs and desires gives them power. We can, at the very least, start to seriously question that belief, to ask what meaning and pleasure we might be giving up in pursuit of a metric.

Doing so will hopefully lead to another realization: that playing the numbers game is ultimately a losing proposition for humans. If we insist on expressing our worth through attentional metrics and productivity scores, if we continue to turn intelligence and creativity into a series of benchmarks for AI to surpass, we’ve already lost. Of course machines will surpass us in a world built around metrics. That is literally what we create them to do. The answer is not to turn ourselves into machines too. 

If there’s one thing that keeps me up at night, it is that we’ve become so accustomed to seeing and understanding the larger world and ourselves through numbers that it has deprived us of the language to express what’s fundamental and valuable about our own humanity. We need this ability now more than ever, especially if we’re going to adequately answer two of the most important questions of our era: What are humans for? And what is AI for?

As part of my own attempts to disentangle myself from a life of numbers—efforts that started shortly before covid—I’ve abandoned most of the tools of measurement I spent a decade collecting. I’ve largely given up on social media. I stopped using apps to track my health and well-­being. The watch I currently wear tells me the time and the date and nothing else. 

In fact, the only holdover from my days of obsessive self-quantification is a dogmatic devotion to walking—without all the step counting, of course. These days, I walk when I’m feeling disillusioned or overwhelmed; I walk when I can’t figure out how to finish an essay; I also walk because I enjoy spending time outdoors with my dog and catching up on the details of my neighbors’ lives. The benefits of pursuing this daily activity are as clear and obvious to me as anything could be in life. I just can’t express them in a number. 

Bryan Gardiner is a writer based in Oakland, California.

This week, I covered the story of Casey Harrell—a man with ALS who is “the first power user” of a brain implant, according to the researchers who worked with him. Harrell is paralyzed and unable to speak coherently without the device. He has now spent almost three years using a brain-computer interface (BCI) that enables him to “speak,” surf the web, and perform his job as a climate activist, largely independently.

Since Harrell was implanted with the device, in July 2023, a team at the University of California, Davis, has worked with him to adjust and improve its offerings. They’ve refined its accuracy, for example. And they’ve introduced settings including a privacy mode and a “profanity filter” that lets Harrell talk to his daughter without risking accidental swearing.

Harrell told me that, for him, the device is “nothing short of revolutionary!” It has enabled him to maintain an income, reconnect with friends and family, and read to his daughter. 

The team that developed his BCI is one of several working on ways to use technology to allow people with paralysis to communicate, engage with the online world, and regain some independence. And Harrell is one of a growing number of people volunteering their brains to, as he puts it, “pay it forward and do the scientific research … [and] get some personal benefit.”

Over the past couple of years, the number of BCI trial volunteers has soared. This year, China became the first country to approve a BCI for medical use. Advances in technology are allowing engineers to provide more features than ever. BCI research is properly taking off.

I should first point out that BCIs come in different forms. Harrell’s device includes a set of electrodes embedded in his brain that pick up the electrical activity associated with speech. Those electrodes are connected to two docking ports on top of his head that can be plugged into a computer.

That computer is loaded with software trained to decode his brain signals into phonemes (units of sound in speech) and predict what Harrell wants to say. He can then use an eye gaze tracker to make any corrections before the speech is played out loud.

But some BCIs don’t need to be “plugged in”—they’re fully implanted and wireless. Others are less invasive; they might involve placing wired electrodes on the surface of the brain or simply wearing a cap of electrodes, for example. There are trade-offs—the closer you get to the neurons you want to record from, the better your signal will be. But generally speaking, the more invasive the surgery, the higher the risk of complications.

BCIs can also have different functions. Harrell has ALS, but most BCIs in use today are sitting in the brains of people with spinal cord injuries. Typically, these individuals have some degree of paralysis; for example, they may be unable to move their arms and legs, but their face and ability to speak are unaffected. In those cases, BCIs can be used to control other kinds of devices that might help with mobility.

In 2024, Michelle Patrick-Krueger, then at the University of Houston, and her colleagues published a roundup of all trials of BCIs conducted between 1998, which is when they believe the first device was implanted, and the end of 2023. They identified 21 research groups that, among them, had trialed BCIs in a total of 67 volunteers.

“Since then, that number has increased a lot,” says Mariska Vansteensel, a BCI researcher at University Medical Center Utrecht. In January, Neuralink (the BCI company founded by trillionaire Elon Musk) announced that it has implanted 21 people with its device in the past two years.

Synchron, another BCI company, is currently testing its devices in trials in North America and Australia. Shanghai-based Neuracle has been trialing a BCI since November 2024, and it recently obtained approval for the device to be used outside of clinical trials. Precision Neuroscience, cofounded by a former co-creator of rival Neuralink, is also trialing its BCI, which sits on the surface of the brain.

At the same time, academic research has continued. The UC Davis team that worked with Harrell is part of BrainGate—a BCI research effort that has been running for the past two decades. Other academic teams are exploring a variety of devices, from the fully implanted to the minimally invasive.

Since 2024, when Patrick-Krueger’s paper was published, the number of people who have been implanted with a brain electrode has more than doubled, according to Vansteensel. “My current estimation would be around 150 people,” she says.

The technology is improving too. Take the BrainGate trial, for example. The first 17 years of that trial focused on the use of what researchers call “point-and-click” communication—allowing users to control a cursor and “click” with their brain activity. But in recent years the team has pivoted toward decoding speech, says David Brandman, the lead investigator on the team (and the person who implanted Harrell’s electrodes). Today, Harrell’s device uses a voice clone—the speech it produces is based on previous recordings of Harrell’s voice.

But BCIs are still experimental. And plenty of questions remain about who might benefit from them—and how long the devices will last. So far, most BCIs have been implanted in people with spinal cord injuries. We know even less about how they might benefit other people who have ALS, for example. In some cases where the devices initially helped people with ALS—even someone who was completely locked in—the BCIs eventually stopped working. And scientists don’t really know why.

The only way they’ll find out is through more research—and the participation of volunteers like Harrell. So it’s exciting to see trials truly take off. And I promise I’ll update you on where they stand two years from now.

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.

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.

The search for dark matter has been blown wide open

For decades, physicists have hunted for weakly interacting massive particles (WIMPs), a leading candidate for dark matter. But their search has run into a new problem: neutrinos. 

These tiny particles from the sun and other stars can create a “neutrino fog” that drowns out any signal of dark matter. Hitting the neutrino fog does not, however, mean an end to the search. Researchers just have to shift the focus of their hunt.

They’re now casting a much wider net. New proposals include quantum sensors, liquid-helium detectors, and even searches in Jupiter’s atmosphere.

Find out how the search for dark matter has entered entirely new territory.

—Dan Garisto

This story is from the next edition of our magazine, which is all about engineering. Subscribe now to get a copy when it lands!

Entrepreneurs in Nairobi are making the case for going solar

Shops with diesel-powered grain mills are common in Nairobi. Milcah Wanjiru’s is different: it runs on either solar energy or the grid.

About a quarter of Kenya’s population still lacks centralized electricity, and off-grid solar is being promoted as a route to universal access by 2030. In Wanjiru’s case, it cuts operating costs and can improve profits once the upfront investment is recovered.

Read the full story on the rise of solar milling systems across Kenya and beyond.

—Geoffrey Kamadi

Geoengineering still faces major practical challenges

—Casey Crownhart

Solar geoengineering is often portrayed as a sort of emergency brake. Something along the lines of “Pull in case of climate emergency to scatter light-reflecting particles to bounce sunlight out of the atmosphere and cool the planet.”

But it might be less like a simple brake the more like a complicated, entirely unsolved puzzle. My colleague James Temple dug into these engineering challenges in his latest feature story. My biggest takeaway? This all looks a lot harder than I thought.

Read the full piece to find out why.

This article is from The Spark, our weekly newsletter giving you the inside track on all things climate. Sign up to receive it in your inbox every Wednesday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The Pentagon says it used Grok in strikes on Iran
Its AI chief said it helped fire over 2,000 munitions. (Le Monde)
+ He spoke in defense of xAI in a data center pollution lawsuit. (NYT $)
+ Officials claim the company is essential to national security. (AP News)
+ Conversational AI has entered the war room. (MIT Technology Review)  
 
2 Apple will raise prices due to the memory chip shortage
Tim Cook said price increases are “unavoidable.” (WSJ $)
+ AI’s demand for data centers has led to dwindling supplies. (Reuters $)
+ iPhone prices could rise by $200 or more. (WSJ $)
 
3 Strikes beyond battlefields are pumping demand for counter-drone tech
The market for airport and infrastructure defenses is booming. (Reuters $)
+ Worried by China, Taiwan is teaching its citizens to fly drones. (Guardian)
+ Europe has a drone-filled vision for future war. (MIT Technology Review)
 
4 Anthropic and DeepMind’s CEOs have called for a US-led AI coalition
They want the alliance to shape AI rules and standards. (CNBC)
+ Anthropic’s CEO told G7 leaders to “resist the temptation to splinter.” (FT $)
 
5 American developers are turning to cheaper Chinese AI
They say DeepSeek is good enough for a fraction of the cost. (Rest of World)
+ What’s next for Chinese open-source AI? (MIT Technology Review)
 
6 Two-thirds of Americans think AI is advancing too quickly
Pew Research found increasing use but negative views. (The Verge)
+ AI is sprinting, and we’re struggling to keep up. (MIT Technology Review)
 
7 Elon Musk’s next move may be a megamerger of SpaceX and Tesla
Shareholders might object, but there’s little they could do. (NYT $)

8 White House aims for Anthropic to block jailbreaks may be impossible
Security experts say it simply isn’t technically feasible. (Wired $)
 
9 Ancient DNA is rewriting the history of plague
Genomic data suggests it emerged thousands of years earlier. (Economist $)

10 AI image generator Midjourney is shifting to full-body ultrasound scans
It also plans to build a spa in San Francisco. (The Verge)

Quote of the day

“We had a great meeting with AI.” 

—President Trump says negotiations with Anthropic over restoring access to the company’s latest AI models are going well, the Wall Street Journal reports.

One More Thing

Why can’t tech fix its gender problem?

Women remain grossly underrepresented in the technology industry. At the core of the problem is money: tech has generated enormous personal fortunes, and most of that wealth has gone to men.

White and Asian men manage 93% of venture dollars. In 2021, only 2% of venture capital funding went to startups founded solely by women.

The lack of investor and founder diversity doesn’t only determine who gets rich. It also shapes the kinds of problems technology companies set out to solve.

Discover why tech’s gender problem has proved so hard to fix—and why a new generation of activists believes change is finally possible.

—Margaret O’Mara

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Meet one of the world’s most wonderfully weird animals: the aardwolf.
+ Escape into this dreamy, minimalist photo collection about the Pacific surf.
+ Admire the casual football skills of this Venetian gondolier executing a stylish backheel while on the job.
+ Explore an interactive prehistoric globe simulation and browse an endless timeline at The Dinosaur Database.

Solar geoengineering is often portrayed as a sort of emergency brake. Something along the lines of Pull in case of climate emergency to scatter light-reflecting particles to bounce sunlight out of the atmosphere and cool the planet.

But it might be less like a simple brake and more like a complicated, entirely unsolved puzzle.

Some researchers are starting to look into how nations or companies would go about trying to cool the planet—and there’s a lot to figure out. My colleague James Temple dug into these engineering challenges in his latest feature story. My biggest takeaway? This all might be a lot harder than I thought.

I’ll admit, I’ve always thought of geoengineering as a relatively low-tech solution. That’s partly because over the years we’ve seen some companies do their own low-cost guerrilla “experiments,” tossing balloons up into the atmosphere and claiming to have made some small dent in climate change.

But to actually actively cool the planet in a significant way, and to make sure we understand exactly what effect we’re having, there’s a lot that researchers still need to learn. 

First, there’s the problem of getting up into the atmosphere. Generally, the target for solar geoengineering efforts is the stratosphere, since the air there is drier and more stable, so particles deposited there would stay aloft and move around the planet, lowering temperatures over a wider area and for a longer time.

You can release the particles in balloons, but balloons may not go where you want them to. And at a large scale, you’d be leaving a lot of litter all over the planet. That leaves aircraft, but conventional planes aren’t suited to fly around in the stratosphere. (Commercial aircraft generally fly at around 12 kilometers above the Earth’s surface, while geoengineering would require reaching roughly 20 kilometers.) The air is thinner higher up, so aircraft with massive wings would probably fare better than more conventional designs.

One design, from a startup called Iris Aero, shows just how much rethinking of our current flight technologies might be needed—the plane is almost unsettling in its proportions. Its wings are so long, on a stubby little body. It reminds me of a water strider, those bugs that have super-long legs to scurry around on a pond’s surface.

And that’s just the beginning. There’s also the question of what, exactly, would be best to scatter up in the stratosphere. The idea behind geoengineering comes from volcanoes—after an eruption, sulfuric acid ends up floating around in the atmosphere, and it can temporarily cool the planet. But that chemical is sticky and would be heavy to carry, so scattering some sort of precursor to sulfuric acid would probably be better. Researchers, including some at the University of Chicago, one of the leading institutions in this field, are working to figure out the best formula. 

I’m struck by how complicated this turns out to be, and I’m also left with a big question: As research turns from modeling and simulations to the practical aspects of this incredibly controversial technology, what does it mean to be doing this work?

There are major concerns about what effects might come from large-scale attempts to cool the planet. The effects could be positive for some parts of the globe and negative for others. Established weather patterns, like the monsoon season in South Asia, could shift. There are major questions about what the governance for the use of geoengineering should look like, and who gets to decide whether to go ahead. 

Experts who champion research in geoengineering often draw a line between a desire to support learning more about the technology and a call to deploy it. Many would argue that we should understand it better, so we can make informed decisions.

But to me, there’s a clear difference between atmospheric modeling and detailed engineering work on an aircraft. If there’s public research that essentially amounts to a set of practical instructions, I can’t help but feel like it could enable any number of individual actors or nations to take geoengineering into their own hands. It also might normalize the idea of using the technology. 

Some experts shared concerns along these lines with James, arguing that the shift to practical engineering work requires more oversight. Some called research in this area dangerous.

One alternative perspective I found interesting came from Shuchi Talati, executive director of the nonprofit Alliance for Just Deliberation on Solar Geoengineering.

Rather than further practical research making a slippery slope slipperier, it could have the opposite effect, she told James. “The actual practice of R&D will be a sticky slope, because there will be more real-world problems that come up that we haven’t even thought of yet,” she says. Engineering research could challenge the “idealized notions” of how easy the technology would actually be, she adds.

It’s hard to argue against better understanding potential tools to address climate change. But if we draw a map towards a potential future, it might become difficult to control who follows it. 

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

Underneath an Apennine massif, below the Jinping Mountains of Sichuan, and at the bottom of a South Dakota mine, there is a cosmic hunt afoot.

Isolated deep beneath these rocky shields, massive detectors filled with liquid xenon aim to make the first direct detections of dark matter, the long-sought invisible substance whose gravity has sculpted our universe.

The hope is that someday, a bit of dark matter called a weakly interacting massive particle (a WIMP, for short) will collide with a xenon atom, creating a burst of light and electric charge. After running for years, these experiments have recently begun seeing infrequent blips from a particle that glides ethereally through ordinary matter until it crashes into the detectors. Unfortunately, the new signal is not produced by dark matter. Instead, the detectors are picking up on something similarly insubstantial but much more mundane: neutrinos, the featherweight subatomic particles that the sun and other stars produce in massive quantities.

Physicists’ failure to find dark matter where they thought it was has led to a cornucopia of proposals for new ways to search: quantum sensors, liquid-helium-based detectors, searches in Jupiter’s atmosphere, and more.

Physicists have known for decades that this neutrino background was there; they were just hoping to discover WIMP dark matter first. Now the chance is looking slim. Some of today’s WIMP detectors are simply so large and sensitive that they are entering the so-called “neutrino fog,” in which the ordinary particles are likely to drown out any signal from the main target. There is no shielding these detectors from neutrinos, which easily slip through the Earth itself. That means the next experiment to use this long-standing approach for seeking WIMP dark matter may be the last. 

Hitting the neutrino fog does not, however, mean an end to the search for dark matter. Researchers just have to shift the focus of their hunt. “We haven’t seen WIMP dark matter,” says Kathryn Zurek, a theoretical particle physicist at the California Institute of Technology. Nor, she says, have scientists found new particles in the Large Hadron Collider (LHC), the powerful proton-smashing facility that straddles the border between France and Switzerland. “And so people naturally broaden their scope,” Zurek says. As they do, there are plenty more candidates waiting in the wings

In other words, the hunt is transforming from a narrow probe into a kind of free-for-all. It’s a big shift. Today, particle physicists are less sure about dark matter’s identity than when they began looking for it. They’ll freely admit that they cannot presume the basics—for example, if the stuff that makes up dark matter is heavier than the Earth or lighter than a radio wave, or if dark matter is one kind of particle or a dozen. 

The uncertainty can be frustrating, even humbling. “The potential range where the candidates could be is so enormous that the odds of any one small experiment finding it are very, very small,” says Hugh Lippincott, a dark matter experimentalist at the University of California, Santa Barbara. 

But physicists’ failure to find dark matter where they thought it was has also led to a cornucopia of proposals for new ways to search: quantum sensors, liquid-helium-based detectors, searches in Jupiter’s atmosphere, and more. “Now there’s a great deal of excitement. And finally, there’s technology there,” says Gray Rybka, a University of Washington physicist who co-leads an experiment looking for axions, an ultra-lightweight dark matter candidate. 

Still, with so many places to look, where does it make sense for physicists to begin again? 

Astronomical ignorance

For starters: the birth of the universe. Dark matter has been with us since the beginning, and there’s much to learn from those early eons. Maps of the cosmic microwave background—the first light from the universe’s early years—are full of fluctuations caused by the clumpiness of under­lying matter. Reading these cosmic dregs, researchers can tell that only 17% of the matter in the universe is made of ordinary particles like protons and neutrons. The remaining 83% is dark matter, which has little to no interaction with light or ordinary matter other than through gravity.

We can tell quite a bit about dark matter from those gravitational effects. We know that the Milky Way contains a halo of the stuff. Our own solar system orbits the galactic center far too quickly to be bound by the tug of ordinary matter alone: without dark matter’s gravitational tether, we would be flung off into intergalactic space. We can also see how the heft of a galaxy’s dark matter bends the path of light as it makes its way to Earth’s telescopes. And on the grandest scale, we can see how superclusters of galaxies are distributed in space like dewdrops on a spiderweb. No cosmological theory without dark matter can explain all these phenomena. 

But all the astronomical and cosmological evidence has little to say about what dark matter is actually made of. “It does not tell you anything about the individual constituents. It just tells you the effect of a bunch of them together,” says Lippincott, who has led the LZ experiment, a WIMP dark matter detector currently in operation at the former Homestake Mine in South Dakota.

The idea of WIMPs emerged during the 1980s. At the time, theorists were exploring add-ons to the standard model, the overarching theory of particle physics that describes all the universe’s fundamental particles and their interactions. The standard model is powerful but doesn’t account for everything—notably, it omits gravity—so some adjustments seemed necessary. The most popular idea, a class of theories called supersymmetry (SUSY, informally), called for pairing each known particle type in the universe with an as-yet-unseen “super­partner.” To have avoided detection, superpartners would have to have a lot of mass (putting them outside the reach of existing colliders) and be weakly interacting, able to pass ghostlike through matter. That is to say, they would be WIMPs. It didn’t take too long for physicists to realize that the WIMP was also an excellent dark matter candidate: two problems, one particle. 

The appeal of SUSY was so strong that many particle physicists expected to see WIMPs as soon as the LHC turned on in 2008. Instead, as the data came in from the LHC, the most promising SUSY theories were largely ruled out. 

WIMPs, though, have lived on, no longer tied to the theory that birthed them. And the latest generation of dark matter detectors have kept the hunt alive. After all, Lippincott says, “the motivation to look for dark matter has not gotten any weaker, right?” 

Now it’s looking as if those WIMPs—if they exist—may be beyond our current powers of detection. There are a range of difficulties, but the most pervasive is that when you’re looking for a needle in a haystack, even a few other needle-shaped objects can cloud the search. Interactions between neutrinos and the xenon inside the detectors, while astronomically rare, do just that.

A future, final WIMP experiment would investigate the rest of the places WIMPs could be hiding, even peering into the neutrino fog. An effort called XLZD (a somewhat ungainly acronym reflecting a mashup of existing collaborations) would use 60 to 80 metric tons of liquid xenon, which is about the yearly global production of that rare element and at least six times more xenon than the biggest current detector contains. But it may already have been scuttled, for reasons unrelated to the neutrino fog: At a particle physics meeting in December 2025, the US Department of Energy announced that the US would neither host XLZD nor pay its share of the price tag, which could be well over $300 million. “It may be that the project doesn’t happen at all,” Lippincott says. “And then the US pulling out would have effectively killed it.” 

In the meantime, the hunting ground for dark matter has been expanding dramatically. In 2022, researchers developed an enormous plot showing various candidates for what dark matter is made of and their possible masses. The options fell mainly into two ranges that span about 50 orders of magnitude (that’s 10⁵⁰, or 10 with 49 more zeroes). At the heavy end of the scale are primordial black holes, hypothetical asteroid-size objects that formed shortly after the Big Bang and might still be floating about the universe. 

But many physicists are most interested in the lightest option: the axion.

Listen closely

Like the WIMP, the axion first emerged as a solution to problems with the standard model. In the axion’s case, it was to address an outstanding question about the strong nuclear force, the fundamental force that holds atomic nuclei together. Axions were proposed by theorists in the 1970s as a mathematical tweak. By adding a particle with a trillionth to a millionth the mass of an electron, they could explain why the strong force seems to behave precisely the same way when it comes to both matter and antimatter even though it doesn’t need to—an unanswered question known as the strong CP problem. 

Axions would be abundant and interact infrequently with ordinary matter, two necessary features for dark matter. Detecting axions is no walk in the cosmic park, though. The delicate particles carry only a hint of energy—about as much as a radio wave. That makes them imperceptible to traditional particle detectors. (Proton collisions at the LHC, for example, pack about a quintillion times more energy.)

Physicists have developed strategies they hope will bear fruit. One of the most promising ideas is to use an ultracold chamber suffused with a strong magnetic field like a radio, tuning it to specific wavelengths. If an axion happens to be resonant, meaning it has the same wavelength as the chamber, it stands the chance of transforming into something far easier to detect: a particle of light. The first full-size detector, called a haloscope, was built at Lawrence Livermore National Laboratory in 1994; today there is a coterie of detectors around the world, with quirky acronymic names like MADMAX and ABRACADABRA.

Fiddling with a cosmic radio to listen for a rare invisible particle is tricky and requires quantum sensors cooled to millikelvin temperatures, a fraction of a degree above absolute zero. Even that doesn’t entirely negate background noise. “At one point, we detected a ‘message from God’ in our experiment,” Rybka tells me wryly. “We looked it up in the FCC allocation of spectrum. It was a religious programming station.” 

So far, particle physicists have combed somewhere between 10% and 20% of the parameter space—the area on a chart showing how massive and interactive dark matter might be—where an axion that solves the strong CP problem might exist. But the search for axions might not end there. Physicists are also looking for axions that don’t address the strong CP problem but could still be dark matter. “More and more people are thinking about models specifically for dark matter, even without connecting that to any other problem,” says Stefania Gori, a theorist at the University of California, Santa Cruz. “Of course,” she says, “if one can solve more than one problem at once, it’s even nicer.”

Quiet revolution

As dark matter has continued to go undetected, physicists have dropped some of their theoretical desiderata. Modern candidates don’t need the convenience of WIMPs or the clean simplicity of axions; they need only fulfill the requirements for dark matter.

The poster child for these unpretentious candidates is low-mass dark matter, so named because it is somewhere between an electron and a proton in weight. If a WIMP is a billiard ball, low-mass dark matter is a ping-pong ball, explains Rouven Essig, a theorist at Stony Brook University. Like a ping-pong ball hitting bowling pins, low-mass dark matter wouldn’t have enough heft to produce a clear signal from a collision with atomic nuclei. “One needed to really come up with new ideas for how one could detect the signals, and then, of course, also new technologies,” says Essig. 

Researchers have designed novel detectors and installed them in underground labs around the world—often right next to their WIMP-detecting forebears. Some of the new machines look for evidence that particles have collided with electrons and ionized them. Others peer into crystals whose lattices should jiggle subtly after a bump from such a particle. There are even proto­types that look for hints in liquid helium, a sensitive superfluid that should throw up a splash when hit by incident dark matter.

There is, however, a rub: noise. While all experiments in dark matter detection are hampered by noise from the outside world, low-mass searches also suffer from the intrinsic din of their detector mediums. Atomic lattices like those inside crystals are like a crowded subway car, naturally prone to shaking and jostling their electron passengers. It’s not the quiet space you want if you’re looking for dark matter. 

This noise has created challenges. In 2020, a surprising number of what physicists call “excess events” cropped up across a range of detectors looking for low-mass dark matter. Could they, some physicists wondered, be a signal of dark matter? Unfortunately, the noise causing most of these readings has been identified, and the answer is a pretty firm “no.” 

Background noise can come from anywhere: impurities in silicon-based detectors, materials that have spent too long on Earth’s surface (cosmic rays make them lightly radioactive). In one experiment, a crystalline detector was clamped too hard; the extra pressure caused vibrations that looked like evidence of dark matter. “It’s always been true that understanding those backgrounds has been difficult,” says Dan McKinsey, an experimentalist at Lawrence Berkeley National Laboratory. “But we’ve shifted our regime so quickly that suddenly we don’t understand, as a community, what the key backgrounds are.”

Getting a handle on that background noise is key, especially as experiments to detect low-mass dark matter get bigger. In a few years, for example, McKinsey and his colleagues plan to install a number of tabletop experiments in Modane, France, underneath 1,700 meters of solid rock on the Italian border. One of them is a vessel with about a tablespoon of ultracold liquid helium. If a particle of low-mass dark matter impacts the liquid, it will generate a vibration that sprays thousands of helium atoms upward, where silicon detectors will look for microscopic changes in voltage. Other setups will work with sapphire-silica crystals and gallium arsenide, a semiconductor. 

These experiments will help researchers identify the best approaches to use for bigger, more sensitive—and more expensive—detectors. “There’s still lots of ideas, and it’s still not quite clear what’s going to be the best one to really scale up,” Essig says. For now, if it fits on a table and can plausibly detect dark matter, physicists are willing to try it.

The utmost gravity 

The hunting ground for dark matter extends past the surface of any table, or even Earth itself. Some researchers have suggested we look for the stuff not in underground laboratories but on planets, stars, and moons. If dark matter particles occasionally annihilate when they encounter one another, they could ionize hydrogen in the atmosphere of a planet, creating an ultraviolet aurora visible from space. This self-annihilation could also be a strong source of heat—enough to melt a planet’s core. The fact that Earth’s core is solid provides limits for dark matter’s characteristics, and measuring the temperatures of planetary cores more precisely could set even more stringent constraints.

Searching for astrophysical signs of dark matter is not new, but in recent years physicists have become almost artistic with their proposals. One particularly evocative suggestion: looking at the icy ocean of Jupiter’s moon Ganymede. If dark matter is indeed really heavy—possibly a primordial black hole—it could punch through the surface and leave a crater that looks very different from one caused by an asteroid.

Some particle physicists think it would be better to drop all the existing assumptions and refocus. “Everything we know about dark matter has come from its interaction with gravity,” says Caltech’s Zurek. If you concentrate more specifically on that interaction, she says, at least “you’re guaranteed to learn something.” We know how dark matter behaves on the scale of the observable universe as far down as galaxies. “Below that, we really know very little about how dark matter collapses under the weight of gravity,” Zurek says. How, for example, does it clump up on the level of a solar system? 

In recent years physicists have become almost artistic with their proposals. One particularly evocative suggestion: looking at the icy ocean of Jupiter’s moon Ganymede.

This isn’t a “next year” or “next five years” project. The technologies physicists have now are simply not sensitive enough to search for these gravitational interactions. So Zurek is thinking long term. Really long term. “It’s going to take decades, like probably 100 years,” she acknowledges. “It may not be something that I see in my lifetime.” 

Someday—perhaps by monitoring the timing of distant pulsars (the spinning remnants of dead stars) or measuring slight perturbations in gravitational interaction between atoms suspended in lasers—physicists could learn more about dark matter’s true nature. 

For now, the scale of the problem seems daunting, especially compared with the ones particle physicists have tackled in the past. Before the LHC even turned on, for example, those hunting for the Higgs boson had hemmed their quarry in. They knew, from rigorous theoretical calculations and robust experimental measurements, that if the boson existed, it had to weigh between 120 times and 150 times the mass of a proton. Soon after the LHC began smashing protons, the Higgs popped out of the data, right at 133 times the mass of a proton.

Dark matter, by contrast, remains an almost total mystery. Rybka likens guessing about its mass or its interaction strength to “drawing numbers out of a hat.” “We literally don’t even know what the hat looks like,” he adds. 

With this many unknowns, success is far from certain, and the researchers have no illusions. “There’s no discovery guaranteed. You might be wasting your time completely,” Essig says. 

None of this has dissuaded him or others. “That’s just the nature of the problem. We have to search far and wide and explore lots of things,” Essig says. “If you don’t like it, do something else.” 

Dan Garisto is a freelance physics journalist based in Syracuse, New York.