Imagine a room that looks like a data center mixed with an ice cream factory. Inside, there are about 100 stainless-steel cabinets, each six feet tall, kept super cold with liquid helium. Inside those cabinets are hundreds of chips, and on those chips, thousands of particles of light called photons fly through a maze of switches and beam splitters. Every single photon has to be tracked because measuring where it ends up could answer questions that today's computers would take millions of years to solve. This machine doesn't exist yet, but a company called PsiQuantum wants to build it.

PsiQuantum was started in 2016 by four physicists from UK universities. In a crowded field of competitors with big dreams, PsiQuantum aims to be the first to build a quantum computer that actually works for real-world problems. Quantum computers are different from regular computers. Normal computers use bits that are either a 1 or a 0. But quantum bits, called qubits, can be both 1 and 0 at the same time, thanks to the weird rules of quantum mechanics. If you put enough qubits together, you get a computer that can solve problems way faster than any normal computer. But so far, the quantum computers we have are too small and make too many errors to be useful.

PsiQuantum thinks it can change that. For example, they want to help drug companies understand how certain enzymes in the body break down medicines. Right now, figuring this out for one drug can take over 10 years. PsiQuantum says its computer could do it in just four minutes. That's a huge claim, and the company has attracted a lot of attention and money because of it. Last year, they raised $1 billion and started building a site in Chicago. They also have plans for a site in Australia that they say will be ready by 2027.

So how does PsiQuantum plan to pull this off? Most quantum computers use things like trapped ions or superconducting circuits as qubits. But PsiQuantum uses photons, which are particles of light. Photons are great because they don't interact much with their environment, which means they stay in their quantum state longer. But that also makes them hard to control. The company has figured out a way to route photons through chips made in regular semiconductor factories, which is a big advantage because they can use existing technology. They've also built special machines to make a material called barium titanate, which is perfect for controlling light particles.

The company is now approaching a make-or-break moment. After years of working behind closed doors and spending hundreds of millions of dollars, we could find out as soon as next year whether their machine actually works. If it does, it could revolutionize everything from medicine to materials science. If it doesn't, it will be a very expensive failure. Either way, it's a bold attempt to build one of the most complex machines ever imagined.

Your brain lives in the dark space of your skull. Yet it knows when the wind lifts the hairs on your skin, when your heart is racing, when your gut tightens with fear. It is also, right now, predicting what you will read next as your eyes move across this page. It is picking up signals that help it make sense of what is happening around you and prepare you to act if you need to stay safe. You are not usually aware that your brain is doing all that.

Our senses take in information at a staggering rate—roughly 11 million bits flood in every second from our skin, eyes, ears, and more. That is nearly three paperback novels worth of data every second. Only a sliver reaches our conscious awareness. Researchers estimate that our conscious minds can process roughly 10 to 60 bits of information per second, about the rate at which you are reading this sentence. That is a ratio of about one conscious bit to hundreds of thousands of unconscious bits.

And that is a mercy. As Moriah Thomason, a neuroscientist at NYU Langone, says, "Thank goodness we are built like this. There is a layer of what we have access to in conscious awareness. And then we have a right-under-the-surface amount. There is only a certain amount we are meant to 'hold in mind' in order to function successfully."

What you are aware of: Your stomach growling when you are hungry. Your palms sweating before you speak in public. The breath you just took, if you pay attention to it. Even your heartbeat, which some people can sense from the inside without feeling their pulse in their wrist.

Scientists have a word for how we sense ourselves from the inside: interoception.

The term was coined in 1906 by the British neurophysiologist Charles Sherrington. For most of the 20th century it remained largely confined to textbooks. Today, thanks to a 2021 Nobel Prize and new tools that can map the interoceptive system across the body, the study of this facility is suddenly quite hot. As researchers decode how signals move between body and brain, a clearer picture is starting to take shape—with implications for how we understand and treat conditions from obesity to chronic pain to anxiety.

The field began to take off in the 1990s. In 1994, the neurologist Antonio Damasio published a book with a pointed title: Descartes' Error. He challenged the historical separation of thinking and feeling, arguing that our ability to choose and act is driven by feelings, and those feelings in turn are shaped by the body's signals, such as your gut clenching or your skin going clammy. When we lose that connection between feeling and thinking, as one of Damasio's patients did after surgery to treat a brain tumor, we may still be able to reason with perfect logic about the pros and cons of traveling on a Tuesday or a Wednesday. But without the emotional signals that help us predict what a choice will feel like, our reason spins and circles, and we cannot decide.

A contemporary of Damasio's, the neuroscientist Bud Craig, spent his career asking one question: How do you feel? He charted how the brain builds an inner map of the body and updates it in real time every moment you are alive.

Think of the captain's bridge on the USS Enterprise, where a live map displays the status of the ship's critical systems: oxygen levels, energy availability, hull integrity, shield strength. Another set of indicators senses things outside the ship: asteroid belts, enemy ships, radiation, life signs, and spatial anomalies not yet understood.

Your brain, only about the size of your two fists pressed together, creates a map like this for your entire body, along with a map of the outside world, from data streaming in through your five senses. Together, they feed into your brain's working model of you in the world, now and across time—where you are, who you are, your expectations for what is about to happen (based on everything you know), and what all that means for you.

When someone asks "How are you doing?" we consult our maps and report back on our status. We might say we are happy, depleted, anxious, or energetic. These feelings are always a braid of emotional and physical sensations. They are what your interoceptive navigational system serves up to your awareness when you sense yourself from the inside.

As we grow up, we learn to interpret what these sensations mean—interpretations that, in turn, can alter our physiology, emotions, and behavior. Research by the psychologist Alia Crum shows that people who embrace a "stress is enhancing" mindset produce more growth hormones than people who have a "stress is debilitating" mindset. They also experience more positive emotions and greater cognitive flexibility.

Language also matters. We learn words for the textures of our feelings—words that then shape how we feel and act. The psychologist Marc Brackett points out that people low in "emotional granularity"—the ability to distinguish between closely related feelings—react more impulsively under stress and are less able to find meaning in difficult experiences. But mindsets and emotional intelligence are malleable. We can learn that "anxious" is different from "terrified," and we can even reframe how we interpret our body's sensations. Instead of thinking of the butterflies in our bellies as annoying, we can welcome them as our body's way of preparing us for a peak performance.

Research by the neuroscientist Lisa Feldman Barrett, who discovered and named emotional granularity, suggests that it is not about describing or labeling emotions. It is about how our brains construct emotions with more or less specificity, often outside of awareness.

Scientists have long understood that the interoceptive information informing these lived experiences travels via two major systems: nerves and humors (blood and lymph). Now they are actively studying a third system—the "interstitium," a network of fluid-filled spaces woven throughout the body's connective fascia that may also play a role in communication.

But until recently, scientific understanding of this interoceptive system looked like a high-level schematic that left out vital details—how information travels from the outside environment in, how it moves from your body to your brain, and how it is integrated and interpreted within your brain. Researchers are now racing to explore what the neuroscientist Catherine Tallon-Baudry calls this "new continent of awareness."

The wandering highway

One of the most active areas of research centers on the vagus nerve, the main component of the parasympathetic nervous system and an information highway carrying news from your organs up to your brain and back down to your body. The vagus has become a celebrity nerve, ubiquitous in wellness podcasts and trauma therapy. "Tone your vagus nerve." "Activate your parasympathetic system." The language suggests a single thing you can target, like a muscle. The reality, as Steve Liberles at Harvard Medical School is discovering, is far more interesting.

Liberles has spent most of his career mapping what he calls "the great wide unknown" of one of our largest and longest nerves. He speaks the way he works—methodically, without overselling. But the questions driving him are huge. How do we sense our body's inner state? What information flows through which channels? And how does the brain decide what to do with it?

Imagine finding out you have hundreds of siblings you've never met. That's the reality for some people conceived through sperm donation, and it's raising big questions about how many families one donor should be allowed to help create.

Ties van der Meer, a 47-year-old from the Netherlands, was conceived using sperm from an anonymous donor. After the Netherlands banned anonymous donations in 2004, the clinic destroyed records that might have identified his biological father. He eventually tracked down one sibling and his father, but he may have others he'll never find. "Children have a right to know their biological parents," he says.

Other donor-conceived people have discovered they have tens or even hundreds of half-siblings. One woman who found 25 half-siblings told the Guardian, "It does make you feel a bit mass-produced."

Now, the European Society of Human Reproduction and Embryology (ESHRE) is calling for international limits on how many children a single sperm or egg donor can contribute to. At a conference in London on July 8, they outlined plans to start with a Europe-wide limit.

Why is this needed? Even in countries where anonymous donation is banned, genetic testing services like Ancestry and 23andMe make it easy to find genetic relatives. Sperm can be frozen for years, so donor-conceived people might discover a parent only after their death, or find siblings of very different ages around the world.

Some donors have been incredibly prolific. Jonathan Meijer, a Dutch man, had his sperm used to conceive between 550 and 600 children before a court ordered him to stop in 2023. Van der Meer's advocacy group, Stichting Donorkind, took him to court.

There are other concerns. Offspring of a prolific donor might unknowingly form romantic relationships with half-siblings. And a donor with a harmful genetic mutation could pass it to many children. This happened in Denmark, where a donor's sperm was used to conceive at least 197 children across Europe before it was discovered he had a mutation linked to multiple cancers. Some of those children developed cancer, and some died.

Many countries already have limits. In Malta and Cyprus, donors can contribute to just one child. In the UK, the limit is 10 families per donor. But these rules are hard to enforce because donated sperm often crosses borders. Denmark, a major sperm exporter, limits donors to 12 families, but more than half of sperm donations in the UK in 2020 were imported, mostly from Denmark or the US.

"The only thing that really makes sense is a transnational limit," says Jackson Kirkman-Brown, a professor at the University of Birmingham. ESHRE is calling for an initial limit of 50 families per donor, with a goal of reducing it to 15. "We may find that 15 is also too high," says Vasanti Jadva, who studies the well-being of donor-conceived people. "We still don't know what the right number is."

Enforcing limits will be tough. If supply drops, some people might turn to unregulated donations from people who haven't been health-screened, which could lead to other problems, like donors seeking parental rights.

International limits are even harder. The American Society of Reproductive Medicine suggests a limit of 25 births per donor for a population of 800,000, but in the US, many sperm banks cap it at around 25 families.

Van der Meer thinks even five families per donor would be high. For international donations, he suggests just two. Still, he calls ESHRE's proposal a "positive first step." He hopes future policies will respect the rights of donor-conceived children to know their genetic relatives. "But," he says, "you have to start somewhere."

Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on the same thing: a weather forecast. While most people glance at forecasts for two seconds, these predictions influence major strategic decisions in industries where real money, livelihoods, and even lives are at stake. Farmers use them to choose which crop variety to sow, when to fertilize, how much to invest in irrigation, and how long livestock should graze. Utilities use them to decide where to build solar and wind farms and how to price wholesale electricity. Predictions warn people about extreme weather and trigger emergency responses. More recently, weather predictions have become relevant for an emerging industry: prediction markets, where people bet money on 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 manageable for now, but as experts, we can foresee scenarios where they snowball into far bigger, systemic problems.

To develop weather predictions, we need accurate observations of current conditions. These come from several sources, including weather stations at airports, utilities, or transport services. Traditional systems like the Weather Research and Forecasting model or the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System combine these observations with numerical approximations to estimate future weather patterns. Sometimes, weather stations have issues due to instrument failures or equipment upgrades. These can be caught 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 been used. This led to big payouts for online prediction-market gamblers who had bet it would hit 22 degrees Celsius (71.6 degrees Fahrenheit) on days when the actual average was around 18 degrees Celsius (64.4 degrees Fahrenheit). 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 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 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. Time works against us; careful checks take hours or days, but forecasts have to go out on schedule.

The shift toward artificial intelligence in weather prediction raises the stakes. These methods are even more dependent on accurate, reliable weather observations; 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 combining geospatial data with large language models and agentic AI to support real-time, autonomous decision-making during extreme events like 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. At the far end, a state actor or saboteur could manipulate one or many stations to set off an early warning system or 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. Here are three ways we can stay ahead. First, 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 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. Human oversight is needed to flag questionable data and model outcomes—after all, it was humans who caught the CDG Airport manipulation. Second, 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 model outputs, identify issues, and become more resilient to attacks. Third, 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 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.