Provided bySyensqo The conversation about AI often centers on algorithms, computing power, or huge investments in new semiconductor fabrication plants and hyperscale data centers. But beneath each of these advances is another layer of innovation that makes them possible: advanced materials. Every new generation of AI technology demands more processing power, more memory, greater energy efficiency, and higher reliability. Every increase in computing performance increases the physical demands placed on the systems that make and run AI. Delivering these gains depends not only on advances in chip design and system architecture, but on advances in the materials that enable them to perform under extreme conditions. As AI continues to push the physical limits of semiconductors and data center infrastructure, advanced materials are no longer simply supporting innovation in this area; they are defining the limits of what is possible.
Performance first Advanced materials exist to solve performance challenges. As AI raises the bar, these challenges are becoming more demanding. Manufacturing a semiconductor chip today requires thousands of tightly controlled process steps, with almost no room for error. Tiny variations in temperature or chemical instability can create defects that reduce yield and drive up manufacturing costs. With every new generation of semiconductor chips, manufacturers seek advanced materials that can deliver greater purity, higher chemical and plasma resistance, and better stability under increasingly harsh operating conditions.
These are familiar engineering challenges being pushed to new extremes. And it’s here that materials innovation makes the difference with continuous advances in polymers, elastomers, specialty fluids, and other advanced materials that make each new generation of technology possible. For materials companies, it’s not about reinventing semiconductor manufacturing but about ensuring the materials supporting the industry continue to evolve alongside it. This same principle applies beyond the semiconductor fabrication floor. As AI workloads become more demanding, the physical infrastructure that powers them is evolving rapidly. Increasing computing density is transforming data center design, driving the need for more sophisticated thermal management, higher-voltage power architectures, increased data storage, and faster, more reliable data transmission. Every part of the system is under greater pressure, from cooling and power management to critical electronic components, such as connectors, capacitors, and hard disk drives. At Syensqo, we’re building on our expertise in electronic and electrical components, along with insights from other markets, to meet these emerging needs. For example, as data centers shift to higher-voltage architectures and greater power density, many of the materials challenges we face closely mirror those of electric vehicles. Fluid-circulation know-how from semiconductor and automotive coolant systems, for instance, can be adapted to direct liquid-cooling designs for AI servers. By transferring knowledge across markets, we can accelerate new power and thermal management solutions while supporting the reliability required by next-generation AI infrastructure. Whether we’re talking about semiconductor fabrication or hyperscale server farms, the challenge for materials science companies is the same: enabling greater performance without compromising reliability. A new definition of what performance means While performance remains the first priority, the way performance is defined is changing. In addition to meeting the increasingly demanding technical requirements of next-generation semiconductors and data centers, there is now an expectation that these materials are developed and manufactured more responsibly.

Perfluoroelastomers, for example, are used to seal semiconductor manufacturing equipment. These materials operate under extreme temperatures, aggressive plasma, and highly reactive chemicals. To make the process more sustainable, at Syensqo, our next generation of perfluoroelastomers use a fluorosurfactant-free manufacturing process. Our goal was to make a better-performing material, produced in a better way, ensuring manufacturers no longer have to choose between higher performance and a more responsible way of producing the materials that enable it. This approach reflects a broader reality across the industry. New materials aren’t adopted simply because they are new. Qualification can take years, and manufacturers only make changes when a material solves a genuine engineering challenge or enables new technology. Performance remains the price of entry. The difference today is that the definition of performance has expanded. Success increasingly depends on delivering technical excellence through more responsible manufacturing from the outset. Accelerating the pace of discovery As the performance bar rises, the way we innovate must evolve with it. Developing advanced materials has traditionally involved a lengthy process of hypothesis, synthesis, testing, and iteration. While this process remains unchanged, new digital tools are helping researchers move through these cycles faster. By helping researchers identify the most promising candidates earlier, AI can reduce the number of physical experiments required and accelerate the earliest stages of materials discovery. AI isn’t replacing scientific expertise. It’s helping scientists apply that expertise more effectively, allowing them to spend less time searching for answers and more time solving the industry’s toughest challenges.
At Syensqo, we’re putting this approach into practice through use of several AI tools, including the Microsoft Discovery platform, which are helping researchers identify and evaluate promising molecular candidates for next-generation heat transfer fluids, used in semiconductor manufacturing and data centers. AI helps our researchers rapidly identify and evaluate promising molecular candidates based on the properties they need to achieve. This allows us to focus laboratory work where it has the greatest potential to deliver results, accelerating discovery and reducing the time needed to turn promising materials into solutions customers can qualify and deploy.
The journey from laboratory discovery to a qualified material will always require scientific expertise, rigorous testing, and close collaboration with customers. But by accelerating the earliest stages of discovery, AI can help materials innovation keep pace with the evolving needs of industries such as semiconductors, electronics, and data centers. Progress is earned The future of artificial intelligence will depend on better algorithms, more powerful chips, and larger computing infrastructure. But sustaining that progress will also require advances in the materials that make those technologies possible. Whether in semiconductor manufacturing or AI infrastructure, progress is earned. Every new generation of technologies raises the bar, and every new material must prove it can deliver the performance, reliability, and efficiency needed before it earns its place. For materials companies, that remains both the challenge and the opportunity. This content was produced by Syensqo. It was not written by MIT Technology Review’s editorial staff.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Over the weekend, several current and former advisors to President Donald Trump on AI publicly lobbed insults at the country’s leading AI companies. David Sacks, the president’s AI and crypto “czar” until March, branded Anthropic’s models as “lobotomized” and “woke.” Emil Michael, a top Pentagon official, called OpenAI’s new head of strategic futures a “supreme village idiot.” It began because no one can agree on what to do about Kimi, a free, open source model that Chinese AI company Moonshot launched last week. It appears to rival the intelligence of models from OpenAI and Anthropic, which are very much not free.  Kimi and other Chinese models like it pose a real problem for Trump. And they’re dividing the top AI strategists in his orbit into factions. Every time a new smart, free model from China like Kimi gets released, US companies see less reason to fork out money to access models from Anthropic or OpenAI. Given that enthusiasm for these and other AI companies is driving an outsized share of economic growth, China’s AI models create both economic and political problems for the president. They are “a threat for an administration that really doesn’t want more economic bad news,” Anton Leicht, a fellow at the Carnegie Endowment, wrote on X. They’ve already rattled US stocks. 
What is Trump to do? First, consider that this is all happening just a week after New York imposed the country’s first state ban on new data centers. There is growing distrust of AI companies, and I imagine a not-insignificant share of Americans would have little sympathy for OpenAI or Anthropic as they fend off cheaper competitors, and would say it’s not the government’s job to protect their interests. On this point, they’d see a sliver of agreement (and really just a sliver) with David Sacks, who on July 19 criticized top AI companies that “want the government to eliminate their open source competition.” He has also argued that Chinese AI models have become popular because they come with fewer restrictions on how people can use them (putting aside the built-in state censorship). 
Sacks, however, is out of a job. He no longer has a formal role advising Trump, and his position that more open AI is better has been largely replaced in the administration by one that sees a larger role for government intervention. The thinking behind this view is that because AI models have gotten strong enough to pose threats to national security, the government must control how they’re used.  This position has fueled the new White House review process that aims to vet AI models’ security before they’re released. Dean Ball, a former Trump AI advisor who now works for OpenAI, criticized it over the weekend as a “de facto licensing regime for frontier AI.” Ball predicted Trump may solve his Chinese open source problem with a bit of soft power, perhaps by making US companies afraid to use models like Kimi. That drew a response from Michael, who, with Secretary of Defense Pete Hegseth, has been the agency’s main liaison with AI companies. Michael called Ball the AI industry’s “supreme village idiot,” bristling at the suggestion that the government would quietly strong-arm companies rather than, as Michael put it, go through “the democratic process not some Deep State scheme.” Left out of the conversation has been how a model like Kimi got so good in the first place. For much of the Biden administration and even the beginning of Trump’s second administration, keeping China from getting top chips was a priority. Those export controls have loosened—Trump made the controversial decision to allow Nvidia to sell more chips to China, in exchange for the US government taking a cut—and the government has alleged that some chip smuggling has taken place. But China nonetheless has limited computing power, and it’s not clear what chips the company behind Kimi used to train the model.  It’s possible that the process involved some distillation, a practice in which AI models are trained on the outputs of existing AI models. OpenAI and Anthropic have long complained that Chinese AI companies do this, and they have requested government help to put a stop to it. In April, they got it, when the Trump administration announced a series of efforts to curb the practice.   But Kimi is out there and free, and it is nearly as good as the Anthropic model the US government deemed so powerful that it was briefly shut down because it threatened national security. The weekend’s sparring suggests many in Trump’s orbit see that as a wake-up call. But nobody can agree on what for.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. AI is more likely than humans to form biases when hiring The next time you apply for a job, AI may screen your résumé before any human sees it. But there’s good reason to question whether AI will judge you fairly.  We already know that LLMs pick up human biases from their training data. New research suggests they can also develop their own biases from experience—and stereotype job applicants more than humans do. As AI companies race to build agentic models that remember the tiniest details about users, they may be handing them ammunition for forming those biases. 
Read the full story on AI’s alarming potential to stereotype job applicants. —Michelle Kim
The risk of weather data sabotage is rising Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on weather forecasts. More recently, the forecasts have become relevant for another industry: prediction markets, where people bet money on all kinds of real-world events, including the weather.  The temptation to manipulate weather data to get an edge in these markets, combined with a collective move toward data-driven AI weather forecasting, is starting to put the accuracy of weather predictions at risk.  As experts in the field, we can foresee scenarios where the risks snowball into far bigger, more systemic problems.  Find out why the threats to weather data are growing—and how to stay ahead of them. —Monique Kuglitsch, Jesper Dramsch, Franz G. Kuglitsch, & Andrea Toreti The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 SpaceX is negotiating to sell the Pentagon AI computeIt would provide data center capacity worth billions of dollars. (WSJ $)+ Deepening ties between Elon Musk’s company and the DoD. (Reuters $)+ Meanwhile, Anthropic is in talks with Meta to acquire compute. (CNBC)+ The compute explosion is only just beginning. (MIT Technology Review)2 Trump Media wants $100,000 a month for early access to Trump’s postsThe premium feed is being pitched to trading firms and banks. (FT $)+ It aims to monetize Trump’s market-moving social media posts. (Reuters $)+ Critics described the plan as “brazen corruption.” (Guardian) 3 ICE shared Medicaid data it wasn’t supposed to have with PalantirCourt filings show the data reached the contractor before being deleted. (NPR)+ ICE is using data broker tools to identify “unaccompanied minors.” (Wired $)  4 Apple briefly overtook Nvidia as the world’s most valuable companyThe iPhone maker’s earnings durability has impressed investors. (Reuters $)+ While Nvidia’s rise has stalled amid shifting AI bets. (CNBC) 5. Politicians are trying to change what chatbots say about themA new industry has sprung up to help them edit AI outputs. (NYT $)+ Chatbots can sway voters better than political ads. (MIT Technology Review) 6 Washington is opening the door to armed robotsThe Pentagon is accelerating AI weapons development. (WP $)+ “Humans in the loop” in war is an illusion. (MIT Technology Review) 7 China’s Moonshot has paused new subscriptions amid surging uptakeDemand for the headline-grabbing Kimi ​K3 has strained capacity. (SCMP)+ China’s open-source AI is challenging US models. (MIT Technology Review)8 Lab-grown teeth could soon replace fillings and implantsScientists believe regenerative medicine could transform dentistry. (BBC)+ Humanlike “teeth” have been grown in mini pigs. (MIT Technology Review) 9 AI slop on birdwatching forums is putting research at riskIt could contaminate records of species. (Guardian)10 Heart experts have good news for your coffee habitRoughly five cups per day is fine—and may even be beneficial. (Gizmodo)

Quote of the day “The most authoritarian government is producing the most egalitarian models, and what should be the most democratic government is breeding companies that are the most authoritarian.”  —Rayan Krishnan, CEO of Vals AI, a company that evaluates AI performance, gives the New York Times his take on the competition between Chinese and American models. One More Thing RICHARD CHANCE The curious case of the disappearing Lamborghinis A new wave of theft is rocking the luxury car industry—mixing high tech with old-school chop-shop techniques to snag vehicles while they’re in transport. 

EXECUTIVE SUMMARY The next time you apply for a job, AI may screen your résumé before any human sees it. But there’s good reason to question whether AI will judge you fairly. Researchers already know that LLMs pick up human biases from their training data. New research suggests that LLMs can also develop their own biases from experience—and stereotype job applicants more than humans do. As AI companies race to build agentic models that remember the tiniest details about users, they may be handing them ammunition for forming those biases.  Researchers at Princeton University and the University of Chicago ran LLMs, including ChatGPT, Claude, and Gemini, through a simulated hiring game, adapted from a psychology study that explored how humans can form stereotypes. Each model was told it had been hired as a consultant by the mayor of a fictional city and was then asked to help hire people for 20 jobs, including doctors, lawyers, child-care aides, and janitors. Candidates came from four fictional ethnic groups: Tufa, Aima, Reku, and Weki.  In each round, there was a new job opening and four candidates, one from each group. After the model hired a candidate, it learned whether they succeeded at their job and moved onto the next round. The model was told to make as many successful hires as possible over 40 rounds. Unbeknownst to the models, all candidates were equally likely to succeed at every job.
The models quickly started segregating candidates from different groups into different jobs on the basis of early observations of hiring outcomes. For example, when a model was told an Aima had failed as a doctor, a job considered to require high levels of warmth and competence, it veered away from hiring all Aimas as doctors. Instead, it started hiring Aimas as janitors, which the model classified as being less warm and competent than doctors.  The models were even more likely to stereotype people by demographic group than the human participants in the original study. On the study’s segregation scale, where 2 means every group has been completely confined to its own job niche, human participants scored 0.84. The models scored roughly 65% higher, with OpenAI’s reasoning model o3 scoring 1.83, close to the maximum possible.
That’s because LLMs “really are eager to create generalizations from limited data,” says Ryan Liu, a PhD student at Princeton University and a coauthor of the study, which was published in a paper at ICML in Seoul in July. “That’s literally a lot of what they’re optimized for.” Every decision-maker, human or machine, faces a trade-off between sticking with what worked before and trying something new that might work better—a phenomenon psychologists call the “exploration-exploitation dilemma.” It’s like choosing between a new restaurant and your reliable favorite.  Because LLMs are trained on math, coding, and science problems—tasks that reward generalizing from just a few examples—they can settle on a hunch too early. And the same instinct that helps LLMs crack logic puzzles also makes them quick to stereotype. In the experiment, newer models with higher reasoning capabilities, such as OpenAI’s o3 and DeepSeek’s R1, showed even stronger biases. When LLMs rush to generalize in social settings, “that’s when things tend to go wrong,” says Liu. OpenAI and Anthropic did not respond to requests for comment. The finding is especially relevant now that chatbots are gaining improved memory and personalization features, says Angelina Wang, a computer scientist at Cornell University who did not work on the study. When a chatbot draws on its previous conversation history, it can “over-index on the same kinds of behaviors it’s experienced before” and form biases, she says. Simply having chatbots remember less isn’t a fix, though, because users want chatbots to remember what they say. “We still are trying to figure out just the right amount that isn’t too much or too little,” says Wang. Telling the model to be fair didn’t change its behavior much. “Either it can’t put these values into action or that process is being submerged under the tendency to try to optimize for the goal of getting the most correct hires,” says Liu. But promising the models an additional bonus for diverse hiring made them far less biased. The trick, then, is to design goals that “incorporate desirable social values in order to make the large language model act in socially desirable ways,” says Liu. The models also became less biased when they were told more personal information about individuals. In another experiment in the same study, the researchers asked the models to resettle members of different ethnic groups in cities across Canada. When the models were told personal information relevant to the ability to adapt to a new city, such as age and education, they were less likely to segregate people by their ethnicity. But when they were given irrelevant information, such as hair color and tattoo shape, the models largely fell back to sorting people by their ethnicity again.  To what extent AI systems will stereotype job applicants in the real world is still an open question. While the models in the experiment immediately learned whether they’d made successful hires, a model screening résumés in the real world doesn’t get an instant report card. Companies can take a long time to find out whether a new hire is any good. But when feedback does trickle in, a model could still read too much into those results when making future hires. As companies increasingly deploy LLMs to screen résumés and even conduct interviews, the finding that models can form biases from their hiring experience “is a really serious implication that they should grapple with,” says Wang.  As LLMs learn from experience to make decisions about who gets hired, who gets a loan, or who gets parole, the biases we should worry about may include ones no human ever taught them. “These novel biases—they’re sort of ever present,” says Liu.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. There’s a lot of hype around perimenopause. Don’t buy it. Perimenopause used to be considered taboo, but not anymore. Thanks at least in part to TV doctors and social media influencers, conversations about the sometimes years-long period before menopause are now more open than ever. But the conversation is increasingly shaped by misinformation. Despite what some marketers will claim, there is no test for perimenopause. That doesn’t mean women should have to put up with symptoms, but treatment suggestions often lack scientific evidence. And not all the symptoms women experience in midlife can be blamed on hormones. Read the full story on the hype and misinformation surrounding perimenopause.
—Jessica Hamzelou This article is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday.
The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 China’s AI gap with the US may have just narrowedA Chinese startup has released the world’s largest open AI model. (Reuters $)+ It competes with some Anthropic and OpenAI models. (Gizmodo)+ The model’s launch sent AI and semiconductor stocks sliding. (Bloomberg $)+ Chinese Nvidia alternatives are also gaining traction. (SCMP)+ Xi Jinping pitched China as an AI partner to the developing world. (CNBC)+ The country is betting big on open-source. (MIT Technology Review)2 Trump Media is selling instant access to “market-moving’ social postsIt’s developed a new way to monetize the president’s posts. (Quartz)+ And Trump could profit directly from selling access to his statements. (BBC)+ Kalshi says it caught Trump’s teleprompter operator insider trading. (Verge) 3 Astronomers have found an atmosphere on a nearby Earth-like planet It’s the first potentially habitable world known to host an atmosphere. (NYT $)+ Making it a top contender in the search for aliens. (404 Media)+ But you need to know how to spot one. (MIT Technology Review) 4 A brain implant has restored feeling in a paralysed hand The recipient can now feed himself and drink from a cup. (Guardian)  + Movement continued when the stimulation was turned off. (New Scientist $)+ China has approved a world-first brain chip. (MIT Technology Review) 5 The EU has told Google to share search data and open up AI on AndroidIt will be forced to share data with competing search providers. (Ars Technica)+ And open Android phones to rivals’ AI bots. (WP $) 6 Period trackers are hiding privacy problemsNew research uncovers how they’re sharing users’ health data. (BBC) 7 The Tesla driver in a fatal Texas crash overrode FSD, investigators sayHe bypassed the tech by pressing the gas pedal to 100%. (Verge)8 A new stealth drone spins so fast that it disappearsThough its creators admit it can still be easily heard. (New Scientist $)9 A space-station study suggests why astronauts’ bodies waste awayMicrogravity disrupts mitochondria, reducing protein production. (Nature)10 “Adversarial clothing” that confuses facial recognition is all the ragePrivacy could be the next big trend. (Guardian) Quote of the day “Xi’s message is clear: China is not going to follow anyone on both AI technology and ​standards. Instead, China is going ⁠to lead the world in both aspects.”  —George Chen, chair in digital practice at The Asia Group consultancy, gives Reuters his take on Xi Jinping’s speech at the World Artificial Intelligence Conference (WAIC) in Shanghai. One More Thing BRYN NELSON How poop could feed the planet A new industrial facility in suburban Seattle is giving off a whiff of futuristic technology. It can safely treat fecal waste from people and livestock while recycling nutrients that are crucial for agriculture but in increasingly short supply across the nation’s farmlands.  It’s among a range of systems reframing feces, urine, and their ingredients as invaluable natural resources to reuse instead of waste products to burn or bury. Several companies are now showing how to safely scale up the transformation with energy-efficient technologies.

Find out how human waste is being transfomed into agricultural solutions. —Bryn Nelson 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.) + Soccer icons have received the Ghanaian movie poster treatment.+ A captivating cosmic construction project is July’s Picture of the Month from the James Webb Space Telescope.+ Sir David Attenborough recently turned 100. Here’s everything he’s ever worked on, all in one place.+ “Desire paths” are the trails made by people walking contrary to defined routes. This video explains what they mean about psychology and design.

Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on the same thing: a weather forecast. While these forecasts are something that most people glance at for two seconds, weather predictions influence major strategic decisions in many industries, with real money, livelihoods, and even actual lives at stake. Farmers use them to determine which crop variety to sow, when to fertilize, how much to invest in irrigation infrastructure, and how long livestock should graze. Utilities use them to decide where to build solar and wind farms, as well as how to price wholesale electricity. Predictions are used to warn people about extreme weather and to trigger emergency response measures. More recently, weather predictions have become relevant for an emerging industry: prediction markets, where people bet money on all kinds of real-world events, including the weather. However, the temptation to manipulate weather data to get an edge in these markets, combined with a collective move toward data-driven AI weather forecasting, is starting to put the accuracy of weather predictions at risk. These risks are relatively manageable for now, but as experts in the field, we can foresee scenarios where they snowball into far bigger, more systemic problems.  To develop weather predictions, we need accurate observations of current conditions. These are collected from several sources, including weather stations at airports, utilities, or transport services. Traditional operational systems like the Weather Research and Forecasting model or the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecasting System combine these observations with numerical approximations in order to estimate future weather patterns. 
Sometimes, weather stations have issues because of, for example, instrument failures or upgrades in equipment. These can be caught either in real time (through checking and correction) or retroactively. Traditional forecasting systems also have a built-in safeguard called data assimilation: Every incoming measurement is weighed against what the physical model says should be happening and against readings from nearby stations. Together, these mechanisms help keep weather observations reliable and predictions robust. However, new threats are putting observational accuracy at risk. Earlier this year, news outlets reported that the weather station at Paris Charles de Gaulle Airport (CDG) had been manipulated to record suspicious temperature spikes on April 6 and April 15, 2026. Authorities speculate that a hand-held hairdryer or lighter might have come into play. Either way, it led to some big payouts for online prediction-market gamblers who had bet it would hit 22 °C (71.6 °F) on days when the actual average was around 18°C (64.4°F). One individual won $20,000.  
Fortunately, tampering with a single station like this can usually be caught by human monitoring or current statistical methods. In this case, members of a French climate nonprofit association noticed the anomalies by chance and raised the alarm. But what if there are no human monitoring systems in place? And what about other types of manipulation? What if, instead of tampering with one station, someone remotely nudged the readings at many stations at once—making each change small enough to look plausible on its own? Existing quality controls struggle to catch this kind of coordinated manipulation. And time works against us; careful checks of data and metadata take hours or days, but forecasts have to go out on schedule, whatever the weather is doing. The shift toward artificial intelligence in weather prediction raises the stakes. These methods are even more dependent on accurate, reliable weather observations; in fact, they are known as “data-driven models.” For example, researchers at ECMWF are exploring whether high-quality weather forecasts can be produced directly from raw observations, skipping the assimilation step that currently acts as a quality filter. Other researchers are going one step further; combining geospatial data (including weather station data) with large language models and agentic AI to support real-time, autonomous decision-making during extreme events such as storms.  Possible benefits are improvements in accuracy, efficiency, and speed. But removing humans from the equation introduces a vast range of new risks. At the low end of the risk scale, an individual speculator manipulates a weather station for personal gain—that is the CDG Airport case. One step up: A group of traders could coordinate to bias forecasts of renewable energy output, moving wholesale electricity prices and leaving whoever is on the other side of the trade holding the loss. And at the far end, a state actor or saboteur could manipulate one or many stations to set off an early warning system or even keep one silent when it should sound. Step by step, the risk grows, from fraud to compromised disaster preparedness to a matter of national security.   As long as there are financial (or other) incentives to manipulate observational data, adversaries will search for new opportunities, and it is our task to stay one step ahead. Here are three ways. 1. Watch the stations. Data quality controls should include station security, anomaly detection and correction, and human oversight. Weather stations should be monitored continuously to deter tampering. Data homogenization methods that clean up weather records also need to get faster, with the goal of catching problems in real time. This will become increasingly important as agentic AI systems use these data to deliver real-time decisions. Finally, human oversight is needed to flag questionable data and model outcomes. After all, it was humans who caught the CDG Airport manipulation. 2. Protect the data to safeguard the AI. Data defense mechanisms must be positioned throughout the AI pipeline. AI explainability and adversarial robustness tools can help us understand the underlying data and the AI model outputs, help us identify data- or model-related issues, and potentially  make us more resilient to adversarial attacks. 

3. Ensure continuous accountability along the chain. Observational data passes through many hands: the operators who run the stations, the national weather services that steward the records, and the forecasting centers that turn them into predictions. No single one of them can protect data integrity alone—each guards its own link, and any anomaly needs to be communicated along the whole chain, from station operators to the people acting on the forecast. It is fortunate that the situation at CDG Airport was caught, but it should serve as a wake-up call. As the role of observational data grows in weather forecasting, we need to adapt to evolving threats. This means protecting our data and models by strengthening existing oversight and accountability structures, and improving coordination among key partners. This op-ed was written by: Monique Kuglitsch — Innovation Manager at Fraunhofer Heinrich Hertz Institute and Chair of the UN Global Initiative on Resilience to Natural Hazards through AI Solutions Jesper Dramsch — Scientist for Machine Learning at the European Centre for Medium-Range Weather Forecasts (ECMWF), where they work on AIFS (Artificial Intelligence Forecasting System), ECMWF’s data-driven weather prediction model Franz G. Kuglitsch — Climate Scientist and Executive Secretary of the International Union of Geodesy and Geophysics (IUGG) at the GFZ Helmholtz Centre for Geosciences in Potsdam Andrea Toreti — Senior Scientist at the European Commission’s Joint Research Centre (JRC), where he coordinates the European and Global Drought Observatory under the Copernicus Emergency Management Service