Radically different
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Good morning, everyone. Now that we’ve all narrowly survived a workday without the World Cup, we can at last return to our new normalcy this afternoon for France 3-1 Morocco.
Less than a month after its stock roller-coastered on safety signals associated with its late-stage ulcerative colitis (UC) drug candidate obefazimod, shares of Abivax (Euronext Paris and Nasdaq: ABVX) enjoyed smoother sailing this past week—namely a 63% surge in Europe and a 50% leap in the United States over four trading days, following more positive data that appeared to reassure investors.
Abivax declared that obefazimod “delivered meaningful clinical benefit” to adults with moderately to severely active UC in the ABTECT Maintenance Part 2 supplemental portion of its Phase III UC maintenance program, with 37.2% of induction nonresponders achieving clinical remission and 34.5% achieving endoscopic remission at Week 44 following continued 50 mg treatment. Of those patients, 61.5% also showed clinical response, 48.0% endoscopic improvement, and 44.6% Histologic-Endoscopic Mucosal Improvement (HEMI).
In patients whose doses were escalated to 50 mg, clinical remission was recaptured in 45.5% of patients who relapsed during ABTECT Maintenance Part 1—a result Abivax said supported a practical dose-escalation strategy for regaining and sustaining disease control over time.
Of special interest to investors, no new safety signals were seen since earlier this month, when Abivax disclosed various malignancies in nine patients among the 580 enrolled in the study. The earlier disclosure triggered price plunges of 44% for both Abivax’s ordinary shares traded on Euronext Paris and the company’s American depositary shares (ADSs) traded on the Nasdaq Global Market.
The latest data from ABTECT Maintenance Part 2 showed four total cases of non-melanoma skin cancer (NMSC)—two in the study’s 25 mg arm, two in the 50 mg arm: “All occurred in patients with established NMSC risk factors including advanced age, thiopurine use, prior skin cancer history, and failure of multiple prior advanced therapies,” Abivax stated.
Significantly, incidence rates of malignancies (including NMSCs) when adjusted for patient-year exposure were well within the pre-defined background reference ranges based on previous UC studies.
Exposure-adjusted incidence rates (EAIRs) for malignancies excluding NMSC were 0.48 and 0.69 events per 100 person-years (PYs) in the all-active combined (50 mg + 25 mg) and 50 mg cohorts, respectively, and for NMSC were 0.95 and 0.69 events per 100 PYs, in the all-active combined (50 mg + 25 mg) and 50 mg cohorts respectively, all consistent with expected UC background rates
EAIRs for malignancies excluding non-melanoma skin cancer (NMSC) were 0.48 per 100 PYs in the all-active combined (50 mg + 25 mg) cohort, and 0.69 events per 100 PYs in the 50 mg cohort. For NMSC, EAIRs were 0.95 in the all-active combined cohort and 0.69 in the 50 mg cohort. All those results were consistent, Abivax said, with expected UC background rates ranging from 0.30–0.70 for malignancies excluding NMSC, and 0.70–1.40 for NMSC.
“The expanded cumulative safety data further strengthens our confidence in the long-term safety profile of obefazimod and reinforces the favorable benefit-risk profile for our program as we prepare for our planned NDA [New Drug Application] submission later this year,” Abivax CEO Marc de Garidel stated. “We believe this growing body of evidence positions obefazimod, if approved, to become a paradigm-defining treatment option for patients living with ulcerative colitis.”
Investors appeared to share de Garidel’s optimism. The data sparked a buying surge among investors, who sent Abivax shares traded on Euronext Paris soaring 39% the day after the announcement, from €83.30 ($94.71) to €115.50 ($131.31) on Tuesday. The shares rose another 1.7% Wednesday, closing at €117.50 ($133.59), then climbed another 9% Thursday to €127.80 ($145.28) before finishing the week with a 6% increase, to €135.80 ($154.38) and a 63% one-week gain.
On Nasdaq, Abivax ADSs surged 50% for the week, consisting of a roughly 39% leap Tuesday from $96.15 to $133.26. From there, shares dipped 0.5% the following day to $132.56, before rebounding 9% Thursday, finishing the Independence Day holiday-shortened week at $144.65.
Wednesday was a shorter trading day than usual since the company requested a temporary, single-day halt. Abivax requested the halt to price an upsized offering of its U.S. American depositary shares (ADSs), which increased from the originally announced $600 million to $800 million—6.4 million ADSs at $125 per ADS, which the company expected would extend its cash runway into the second quarter of 2029.
The offering closed Thursday at $920 million, of which approximately $874.1 million consisted of net proceeds, after underwriters exercised in full their option to purchase 960,000 additional ADSs representing 15% of the total number initially sold in the offering.
The size of the offering appeared, based on investor chatter cited by Stocktwits, an effort to dampen speculation about Abivax being a prime candidate for a buyout; the company appears in GEN’s most recent A-List of Top 10 Takeover Targets of 2026. But the upsizing of the offering rekindled buyout speculation by individual or “retail” investors, the same outlet reported Thursday.
Abivax said it intends to use the net proceeds toward expenses relating to potential commercialization of obefazimod in the United States; clinical R&D expenses, primarily related to UC and Crohn’s disease; and the remainder, if any, for general corporate purposes.
Leerink Partners, Morgan Stanley, Piper Sandler, and Guggenheim Securities were joint bookrunning managers for the offering, while LifeSci Capital acted as a passive bookrunning manager and Van Lanschot Kempen as the lead manager.
“Response rates (clinical & endoscopic remission) in this portion also appear compelling, especially given the refractory nature of patients in this subset, reaffirming obe’s best-in-disease efficacy,” Thomas J. Smith, senior managing director, immunology and metabolism, and a senior research analyst with Leerink Partners, commented in a research note.
“We believe this update further de-risks obe’s profile in UC and Crohn’s and should allay investor concerns following Part 1 maintenance data released earlier this month,” Smith added.
Smith raised his firm’s 12-month price target on Abivax shares 6%, from $140 to $148.
Two other firms also raised their price targets on Abivax stock:
Even more positive feedback on the latest data came from Faisal Khurshid, a managing director and equity research analyst with Jefferies. Khurshid upgraded his firm’s rating on Abivax’s stock from “Hold” to “Buy,” and boosted Jefferies’ price target 46%, from $108 to $158.
On June 1, Khurshid downgraded Jefferies’ rating on Abivax from “Buy” to “Hold,” citing the safety concerns he said have since been addressed.
“Mgmt. did a nice job addressing investor concerns w/ how they presented the safety data [June 29] vs. the Part 1 update. On top of that, the efficacy profile strengthens w/ each add’l piece of data,” Khurshid observed.
He also cautioned: “There is still risk on cash runway, catalyst path, and commercial needs for a pot’l standalone launch. But ultimately, good data should generate value.”
The biggest outstanding risk for Abivax, Khurshid wrote, is the need for significant resources associated with a commercial launch for an indication in inflammatory bowel disease (IBD): “We still think pot’l of asset better realized w/ a strategic partner.”
Obefazimod is a small molecule upregulator of miR-124, an anti-inflammatory microRNA. It enhances the selective splicing of a single long noncoding RNA to generate miR-124, which downregulates cytokines and chemokines shown to promote inflammation, including tumor necrosis factor (TNF) alpha, IL-6, monocyte chemoattractant protein-1 (MCP-1), and IL-17, as well as Th17+ cells.
Under its former name ABX464, obefazimod was initially developed against HIV but was repurposed to fight inflammatory conditions based on its anti-inflammatory effect.
The post StockWatch: New UC Data Sparks Smoother Sailing for Abivax appeared first on GEN – Genetic Engineering and Biotechnology News.
Background: Safety planning is recognized as one of the most effective interventions for reducing suicidal behaviors. The quality of safety plans strongly depends on professional training, and traditional methods, such as role-playing, are time-consuming and offer limited opportunities for repetition across diverse patient profiles. Generative artificial intelligence (GenAI) may provide innovative solutions by offering accessible, flexible, and realistic training environments. Objective: This pilot study aimed to evaluate the acceptability and feasibility of a GenAI-based simulator designed to train mental health professionals in safety planning. Methods: Twenty nurses and nursing assistants from psychiatric units in a French university hospital participated in a pre-post, single-session evaluation. After self-rating their ability, competence, and willingness to manage patients experiencing suicidal ideation, participants interacted individually with the text-based simulator for 20 minutes to perform a safety plan with a chatbot, then completed postsimulation acceptability items, and open-ended feedback. Composite scores were computed: acceptability (eg, helpfulness; 0‐40), realism (eg, looking like real interaction with patient; 0‐20), and challenge (eg, emotional challenge; 0‐30). Pre-post changes were tested (Wilcoxon signed-rank test), and age-group comparisons were performed. Results: Acceptability was high (mean 31.9/40, SD 5.3; median 32, IQR 7), realism moderate-to-high (mean 15.1/20, SD 4.1; median 15, IQR 5.25), and challenge manageable (mean 17.0/30, SD 8; median 18, IQR 12.5). Participants rated usefulness (mean 7.65/10, SD 1.57; median 8, IQR 1.57), perceived learning (mean 7.6/10, SD 1.79; median 8, IQR 2), recommendation to use the chatbot for training (mean 8.3/10, SD 1.59; median 9, IQR 2.25), and feedback quality (mean 8.35/10, SD 1.27; median 8.5, IQR 1.25) favorably. Willingness to actively manage patients experiencing suicidal ideation significantly increased postsimulation (.03). Younger participants reported higher acceptability (.04) and realism (.03). Participants reported minimal concerns regarding the simulator’s use. Conclusions: This pilot study demonstrates that a GenAI-based simulator for safety planning is feasible and highly acceptable among experienced mental health professionals. The findings are promising and warrant larger, controlled trials to assess impacts on training effectiveness and patient outcomes.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/662a4ae716b1ea24241b30b661b4ecb7" />
Apple. Anthropic. Disney Research. Google. Meta. Microsoft. NVIDIA. OpenAI. Few places outside Silicon Valley can claim R&D hubs from all of these companies. Fewer still are concentrated in a city of just over 400,000 people—roughly half the size of San Francisco.
Over the past two decades, however, many of the world’s most influential technology companies have established R&D operations in and around Zurich, Switzerland. What began with Google’s decision to build its largest R&D hub outside the United States has evolved into one of the world’s most concentrated centers for AI research, talent, and commercialization, in certain areas at a higher density than Silicon Valley.
The question is why so many technology leaders keep choosing the same place to build and scale.
Located at the center of Europe, Greater Zurich Area, a region spanning the cantons of Glarus, Graubünden, Schaffhausen, Schwyz, Solothurn, Tessin, Uri, Zug, and Zürich, the region of Winterthur, and the city of Zurich, combines access to major markets with political stability, regulatory predictability, and strong intellectual property protection. And Zurich Airport connects the region directly with key business hubs across Europe, North America, and Asia, making it an efficient base for international operations.
The country’s innovation performance reinforces this position. Switzerland has ranked first in the Global Innovation Index for more than a decade, leads the world in patents per capita, and invests over 3.3% of GDP in research and development. Earlier this year, google.org pledged a $1 million grant to the Swiss National AI Institute, a joint effort to advance AI research for the public good.
Switzerland’s venture ecosystem reflects a similar focus. Over 60% of Swiss venture capital is invested in deep tech—the highest share globally by a large margin and nearly twice the share of major economies like Germany, France, and the UK. And, according to the Swiss Deep Tech Report 2026, at $1,470 invested per capita, Switzerland commits more to deep tech per capita than any other country in Europe.
While Switzerland is one of Europe’s most expensive locations for talent and operations, salaries remain at a fraction of those in Silicon Valley. The talent pool is small by global standards. Scaling a team quickly is harder in Zurich than in London, Paris, or Amsterdam. For early-stage companies that need to hire fast and burn lean, that trade-off is real. For companies building specialized AI capabilities, however, the equation works: The objective is to assemble the right team, not the largest one.
Switzerland’s economy is built around high-value, knowledge-intensive work. Productivity is among the highest in the world, and companies concentrate on functions that depend on specialized expertise rather than large workforces. For companies developing advanced AI capabilities, cost is often weighed against factors that are harder to replicate elsewhere: direct access to leading universities and research institutions, regulatory stability, and a quality of life that helps attract and retain skilled international talent.
Within Switzerland, the Greater Zurich Area concentrates many of the ingredients required to build and deploy AI systems.
The defining characteristic of this region is density. Many of the world’s leading AI companies, research institutions, investors, and startups operate in close proximity, creating connections between talent, capital, and ideas.
For example, Google engineers teach at ETH Zurich. ETH graduates join companies such as Anthropic. Researchers launch startups, while former employees of global technology firms go on to found new ventures of their own. Investors, founders, academics, and corporate teams encounter each other repeatedly through shared networks, industry events, and professional circles. In a region of this size, collaboration often happens less through formal introductions than through proximity. While talent flows freely, it rarely leaves the ecosystem.
One indicator of the region’s maturity is its ability to convene. Events such as the Zurich AI Festival will bring together more than 6,500 guests this September 28 to October 3. With more than 35 confirmed events across AI and the arts, AI literacy, health, technology, and policy, it is designed as a platform for cross-sector exchange. Its flagship events, the AI + X Summit, AI + Environment, and the AI + Policy Summit, will bring together internationally recognized leaders alongside researchers, policymakers, venture capitalists, and entrepreneurs, convening international voices and fostering dialogue across sectors.
At the center of the country’s AI capabilities are institutions such as ETH Zurich, the University of Zurich, École Polytechnique Fédérale de Lausanne (EPFL), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), and Zürcher Hochschule für Angewandte Wissenschaften (ZHAW).
ETH Zurich ranks among Europe’s leading universities for deep tech commercialization, generating more than 40 spin-offs and startups in 2025 alone, helping create some of the continent’s most valuable technology companies.
The Stanford AI Index 2026 reinforces that picture: Switzerland ranks first globally for AI researchers and inventors per capita, with 110.5 per 100,000 inhabitants—ahead of Singapore (109.5), Sweden (80.6), and the United States (64.8). And the IMD World Talent Ranking ranked Switzerland as number 1 for the 10th consecutive year, leading globally in investment, development, and talent appeal.
Engineers, researchers, and founders move frequently between universities, startups, and established technology firms, creating strong knowledge flows across organizations. That density is increasingly attracting companies from outside the region too. Even before formally announcing their Zurich office, Exa.ai received a strong pipeline of candidate applications. ‘To assemble the greatest search team in the world, you’ve got to meet people where they are,’ says Will Bryk, the company’s CEO and co-founder. ‘And many are in Greater Zurich.’
Former Google Switzerland employees alone have founded approximately 210 companies and created around 2,600 jobs over the past two decades. For a country of around nine million inhabitants, the multiplier effect is significant. Large technology firms contribute not only through direct employment, but also through the creation of new companies and the transfer of expertise.
For many technology companies, Switzerland is not a substitute for Silicon Valley. The two serve different functions within the AI value chain.
Silicon Valley remains unmatched in scale, venture capital, and frontier model development, but for global technology companies, an R&D presence in Switzerland has increasingly become a strategic complement: a way to access specialized talent, stay close to leading research, and build capabilities that will shape the next generation of products and services.
This is particularly relevant for companies working at the intersection of AI and the physical world. Switzerland offers direct access to leading universities, industrial partners, and sectors such as healthcare, finance, manufacturing, and robotics, where reliability, compliance, and precision are often as important as raw model performance.
Global AI leaders came to the Greater Zurich Area because the region concentrates capabilities that are often distributed across multiple locations: world-class research, specialized talent, industrial partners, capital, and pathways to deployment. Those advantages were built over decades, not years.
For companies evaluating where to build the next generation of AI products, the answer may not be another larger ecosystem. It may be one where the distance between research, talent, capital, and deployment is measured in minutes rather than hours.
Learn more about the Greater Zurich Area.
This content was produced by the Greater Zurich Area. It was not written by MIT Technology Review’s editorial staff.
It’s been hard to look away from headlines about the European heat wave this week. Temperatures are breaking records across the continent, and the weather is threatening lives, shutting down schools, and in one particularly ironic case, forcing the cancellation of a London Climate Action Week event about extreme heat.
As the summer ramps up and we see this kind of weather sweep around the Northern Hemisphere, I’m always keeping my eye on the power grid. And one notable update that caught my attention this week was news that a nuclear power plant in the south of France had to close down because of the heat.
Climate change is squeezing the grid from all sides, affecting both supply and demand. Heat can affect power availability, from generation to transmission infrastructure, as I covered in my latest story. But climate change is also helping push electricity use higher—and countries in Europe and around the world will need to adapt.
In the US, nearly 90% of homes have air-conditioning. That means many grids see their highest demand in the summer months, and the risk of brownouts and blackouts is at its worst.
People are often quick to cast air-conditioning as a villain, and it’s true that the technology will account for a major chunk of the globe’s rising energy demand in the future. But the reality is that heat waves can be incredibly dangerous, and as climate change pushes temperatures higher, that risk is becoming more real in parts of the world that haven’t historically had to worry quite so much about heat.
In Europe, air-conditioning is historically much less common, with about 20% of homes across the continent using it. Some countries, including those getting hit by this heat wave, have even lower rates—the UK comes in at about 5%, and Germany is around 3%.
But those numbers are starting to tick up as people adapt to increasingly brutal summers. As they do, we should expect higher electricity demand, and stress for the grid—just as in the US. And utilities often have to look across borders to buy more power, driving prices up for everyone.
“The main pressure comes from a triple squeeze: Cooling demand rises sharply, while power plants and grids become less efficient, and some thermal and nuclear plants must cut output because cooling water is too warm or scarce,” says Simone Tagliapietra, senior fellow at Bruegel, an economic and policy think tank, via email.
Grid planning in the age of climate change generally means that we need a lot more supply, and quickly. But one interesting facet to this challenge is that in some places, seasonal patterns are shifting, compounding the difficulty of meeting demand.
Generally, grid operators plan maintenance and outages at power plants around expected peaks in demand. Take nuclear power, for example. In the US, planned outages for maintenance and refueling tend to come in the spring and fall when demand falls below the summer and slightly smaller winter peaks.
Europe, however, has historically seen its grid peak in the winter, because electric heating is more common than air-conditioning. So some planned outages happen in the spring and into the summer, which is affecting the supply right now.
At the Golfech power plant near Toulouse in France, for example, unit two had to shut down this week because of the water temperatures in the nearby river, which is used to cool the reactor. But unit one was already offline because of planned maintenance and refueling, according to EDF, the plant’s operator.
We’re going to continue to see record-high temperatures around the world because of climate change. Communities are adapting, and utilities will have to follow. And if you thought this summer was hot, just wait until next year. With the El Niño weather pattern, 2027 could very well blow these heat waves out of the water.
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
Europe is in the middle of a record-breaking heat wave, and the grid is being pushed to its limits as people turn to fans and air-conditioning to try to stay cool. Some power plants won’t be online to help handle the load.
On June 23, France saw its hottest day since record-keeping began in 1947. Temperatures climbed to over 44 °C (111 °F), and overnight temperatures remained unusually high. This prolonged hot weather warmed up the water in some rivers across the country, a problem for the many nuclear plants that rely on those bodies of water for cooling. One reactor has already shut down, and others are being ramped down or will see limitations later in the week.
Unit two at the Golfech nuclear power plant in southern France shut down at about 11:45 p.m. on June 22 when the river used to cool the plant got too hot. The move was a precautionary measure, according to Brid Nelligan, a spokesperson for EDF, the plant’s owner and operator.
The power plant takes in water from the Garonne River and then returns most of it to the river at slightly higher temperatures after using it to cool equipment. French regulations limit the temperature of that return stream, so the warm water (it was expected to reach 28 °C, or around 82 °F) forced the operator to shut down the plant.
EDF, which operates France’s entire nuclear fleet, is also limiting the output of other reactors across the country—one reactor at the Nogent-sur-Seine power plant was ramped down as of Tuesday, and more will follow later in the week, Nelligan says.
Extreme heat has affected France’s nuclear industry before. At least seven gigawatts’ worth of nuclear energy was forced to shut down across the country during a heat wave in July 2025, according to data from Ember Energy. That’s more than the entire grid of Ireland.
This time, power plant outages and limitations aren’t expected to be drastic enough to affect the ability to meet demand in France, according to RTE, operator of the national electric grid.
Nuclear power has made most of the headlines during this heat wave, but other forms of electricity generation face similar challenges. Hydropower plants frequently run into problems when dry conditions lower the amount of water available to generate energy and force them to decrease or shut off operations. In the first five months of 2025, high temperatures and low water conditions cut hydropower supplies in Europe by 13% compared with the year before.
Even established coal and natural-gas plants can be challenged by high temperatures. Hot weather can stress equipment and limit the efficiency of cooling towers. Five gas plants across the UK have reported output reductions due to the conditions, cutting a total of about 2.5 gigawatts from the power supply.
Increased demand, largely driven by cooling, is the main factor stressing Europe’s power grid, says Jean-Paul Harreman, director of Montel, an energy intelligence provider, via email. Even countries that haven’t historically relied much on cooling technologies are turning to them now—the number of UK homes that use air-conditioning has roughly doubled since 2022.
Around the world, the challenges heat presents for the grid are only expected to get worse as climate change brings more frequent and intense heat waves. Globally, energy use for cooling is set to double by 2050 relative to 2023 levels, according to the International Energy Agency.
“Utilities can adapt by planning for summer peaks, making cooling demand more flexible, reinforcing grids for high temperatures, deploying batteries and demand response, and climate-proofing power plants’ cooling systems,” says Simone Tagliapietra, senior fellow at Bruegel, an economic and policy think tank, via email.
But those changes could be expensive. Earlier this year, EDF shared a climate-change vulnerability assessment for its business, including nuclear and hydropower operations across France. Upgrades are expected to cost about €600 million per year (about $680 million) over the next 15 years.
Meanwhile, high temperatures are expected to continue across much of Europe through the end of the week.
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.”
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.
For over a decade, the Sports Lab has grown as interest in sports tech has skyrocketed—and it’s accumulated what younger fans would call some elite ball knowledge in the process.
This depth is exactly what its partners need.
“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.
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.”
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?”
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
For those of you enjoying your summer unaware of Anthropic’s latest feud with the US government, here’s a recap: In April the company said it had built an AI model called Mythos that was so good at working with code it could pose a global cybersecurity threat. Anthropic gave access to a small group of cybersecurity experts so they could see what they were up against. Then it released a modified version called Fable which it said was safer to the public on Tuesday, June 9. That Friday, the federal government told the company it was a threat to national security and placed export controls on the new release. Anthropic revoked access to both models hours later.
People worried about catastrophic effects of AI—broadly labeled “doomers”—have said for years that the technology poses a threat to humanity and published proposals for how the government should intervene in its development. The doomers just got their government intervention—not over a bioweapon or rogue AI, but in response to an AI model that’s basically just really good at coding. And the result so far looks less like a safety plan than like a superficial reaction.
There’s plenty to dissect about what happened in those few days that led to such drastic action from the government, and it’s notable that Amazon CEO Andy Jassy was the one who told government officials that Fable would be dangerous (Amazon is both invested in Anthropic and building its own competing AI models). It’s also possible this will be a short-lived ban from the government that doesn’t survive legal scrutiny (it’s not clear that Anthropic’s offering access to Fable really counts as “exporting” it, for example).
But there are ripple effects happening already.
For one, this is making a whole lot of people not want to rely on American AI companies. TheFrench politician Bruno Retailleau described it as a “wake-up call” that should motivate Europe to build more AI. But any vision of turning Paris into Silicon Valley—touted by many other European leaders following the shutdown of Anthropic’s models—is complicated by one big thing: China.
Open-source models from China are very capable and incredibly cheap, and they can be downloaded to run on anyone’s servers with no rules or guardrails. (This makes them attractive to companies that don’t want access turned off on the basis of a decision from the White House—but equally attractive to cybercriminals, the type that Anthropic hoped to fend off by building safety guardrails into its models.)
It’s possible that companies, including those in the US and Europe, will decide that working with Chinese models is just easier, as the skyrocketing of shares in the Chinese startup Zhipu suggests. Playing this forward, is it possible the government’s next drastic decision will be to say that US companies using models from China pose a threat to national security? I wouldn’t write it off.
Second, it’s possible that shutting off access to Anthropic’s models will leave the country morevulnerable to cybersecurity attacks, not less. Leading cybersecurity experts have said as much in an open letter to the government, writing that access to Anthropic’s models was helping researchers prepare defenses, and that the company’s models are no more dangerous than other leading models that are widely available. Such is the risk of applying the concept of nonproliferation to software—trying to control and restrict dangerous AI models in the manner of the uranium used for nuclear weapons.
The third thing worth watching is how US lawmakers will react. Remember that following Anthropic’s last feud with the government over how the Pentagon could or could not use its models, a slate of new bills was introduced that would define the limits of military AI.
Right now, the biggest players shaping how AI gets used are the companies and the White House. There’s been much talk about more federal AI regulation, and polling suggests most Americans want it. Lawmakers are still figuring out whether to form rules on how kids use chatbots and are far from a clear answer on the extent to which the government should vet the safety of AI models. But with every drastic action from the White House, the pressure for regulations rises.
To state the obvious, predictions are hard when the administration’s attitudes toward AI change with the wind. When President Trump took office, he threw out the restrictive rulebook for how to make AI safe and promised to get out of the way of tech companies. The White House has now called the most valuable AI startup a risk to national security once in the spring, and again in summer. What will fall bring?