Category: English

Source: The Conversation – USA – By Shyam Bansal, Associate Professor of Medicine, Penn State

Around 64 million people worldwide suffer from heart failure, and nearly half die within the first five years of diagnosis due to a lack of effective treatments to stop the disease from getting worse.

Heart failure occurs when the heart’s ability to pump blood drops to less than 40%. Most available treatments tend to focus on reducing the effort necessary for the heart to pump blood, rather than tackling the underlying causes of the disease. This is mostly because researchers still don’t know what causes heart failure and what makes it worsen with time.

Turns out the immune system, the body’s protection against infection and disease, may be responsible for worsening heart failure.

T cells heal and harm

For the past 13 years, I have been studying how the T cells of the immune system behave during heart failure.

T cells help the body heal from injuries and fight infections. They do this by making proteins called anti-inflammatory cytokines that help wounds close, and recruiting or modifying other immune cells that kill invading pathogens.

However, when T cells are mistakenly activated against the body’s own cells, it can cause autoimmune diseases. For example, Type 1 diabetes is caused by T cells attacking pancreatic cells, and psoriasis occurs when T cells are activated against skin cells.

T cells and heart failure

If T cells can help heal things like wounds on the skin, why are they unable to heal the heart? My team and I have been working to answer this question.

In initial studies in mice, we found that a type of immune cell called helper T cells makes proteins called pro-inflammatory cytokines that induce more damage to the heart during heart failure, making the disease worse.

In our latest research, we have been studying failing hearts obtained from patients receiving a transplant or an artificial pump. We found that T cells remain active in failing hearts and also turn on pro-inflammatory proteins that worsen heart damage instead of healing the heart.

We also found that the proteins within T cells of failing hearts are similar to proteins seen in T cells involved in autoimmune diseases. These parallels suggest that heart failure induces T cells to behave similarly to those in autoimmune disease rather than those that heal injuries.

Heart failure and autoimmunity

How T cells are activated in the heart might contribute to the worsening heart damage that results in slow and sustained disease progression.

While the underlying mechanisms behind heart failure remain elusive, our findings suggest that viewing heart failure as an autoimmune condition could help lead to new treatments.

Further research on how to stop T cells from damaging the heart could provide a way to stop heart failure from worsening and save the lives of millions of patients.

Shyam Bansal receives funding from the National Heart, Lung and Blood Institute of the National Institutes of Health, the American Heart Association and the W.W. Smith Charitable Trust. He is a member of the American Heart Association and the Society for South Asian Heart Research.

– ref. Overactive immune cells can worsen heart failure – targeting them could offer new treatments – https://theconversation.com/overactive-immune-cells-can-worsen-heart-failure-targeting-them-could-offer-new-treatments-273551

Source: The Conversation – USA – By Bruce Schneier, Adjunct Lecturer in Public Policy, Harvard Kennedy School

In 2023, the science fiction literary magazine Clarkesworld stopped accepting new submissions because so many were generated by artificial intelligence. Near as the editors could tell, many submitters pasted the magazine’s detailed story guidelines into an AI and sent in the results. And they weren’t alone. Other fiction magazines have also reported a high number of AI-generated submissions.

This is only one example of a ubiquitous trend. A legacy system relied on the difficulty of writing and cognition to limit volume. Generative AI overwhelms the system because the humans on the receiving end can’t keep up.

This is happening everywhere. Newspapers are being inundated by AI-generated letters to the editor, as are academic journals. Lawmakers are inundated with AI-generated constituent comments. Courts around the world are flooded with AI-generated filings, particularly by people representing themselves. AI conferences are flooded with AI-generated research papers. Social media is flooded with AI posts. In music, open source software, education, investigative journalism and hiring, it’s the same story.

Like Clarkesworld’s initial response, some of these institutions shut down their submissions processes. Others have met the offensive of AI inputs with some defensive response, often involving a counteracting use of AI. Academic peer reviewers increasingly use AI to evaluate papers that may have been generated by AI. Social media platforms turn to AI moderators. Court systems use AI to triage and process litigation volumes supercharged by AI. Employers turn to AI tools to review candidate applications. Educators use AI not just to grade papers and administer exams, but as a feedback tool for students.

These are all arms races: rapid, adversarial iteration to apply a common technology to opposing purposes. Many of these arms races have clearly deleterious effects. Society suffers if the courts are clogged with frivolous, AI-manufactured cases. There is also harm if the established measures of academic performance – publications and citations – accrue to those researchers most willing to fraudulently submit AI-written letters and papers rather than to those whose ideas have the most impact. The fear is that, in the end, fraudulent behavior enabled by AI will undermine systems and institutions that society relies on.

Upsides of AI

Yet some of these AI arms races have surprising hidden upsides, and the hope is that at least some institutions will be able to change in ways that make them stronger.

Science seems likely to become stronger thanks to AI, yet it faces a problem when the AI makes mistakes. Consider the example of nonsensical, AI-generated phrasing filtering into scientific papers.

A scientist using an AI to assist in writing an academic paper can be a good thing, if used carefully and with disclosure. AI is increasingly a primary tool in scientific research: for reviewing literature, programming and for coding and analyzing data. And for many, it has become a crucial support for expression and scientific communication. Pre-AI, better-funded researchers could hire humans to help them write their academic papers. For many authors whose primary language is not English, hiring this kind of assistance has been an expensive necessity. AI provides it to everyone.

In fiction, fraudulently submitted AI-generated works cause harm, both to the human authors now subject to increased competition and to those readers who may feel defrauded after unknowingly reading the work of a machine. But some outlets may welcome AI-assisted submissions with appropriate disclosure and under particular guidelines, and leverage AI to evaluate them against criteria like originality, fit and quality.

Others may refuse AI-generated work, but this will come at a cost. It’s unlikely that any human editor or technology can sustain an ability to differentiate human from machine writing. Instead, outlets that wish to exclusively publish humans will need to limit submissions to a set of authors they trust to not use AI. If these policies are transparent, readers can pick the format they prefer and read happily from either or both types of outlets.

We also don’t see any problem if a job seeker uses AI to polish their resumes or write better cover letters: The wealthy and privileged have long had access to human assistance for those things. But it crosses the line when AIs are used to lie about identity and experience, or to cheat on job interviews.

Similarly, a democracy requires that its citizens be able to express their opinions to their representatives, or to each other through a medium like the newspaper. The rich and powerful have long been able to hire writers to turn their ideas into persuasive prose, and AIs providing that assistance to more people is a good thing, in our view. Here, AI mistakes and bias can be harmful. Citizens may be using AI for more than just a time-saving shortcut; it may be augmenting their knowledge and capabilities, generating statements about historical, legal or policy factors they can’t reasonably be expected to independently check.

Fraud booster

What we don’t want is for lobbyists to use AIs in astroturf campaigns, writing multiple letters and passing them off as individual opinions. This, too, is an older problem that AIs are making worse.

What differentiates the positive from the negative here is not any inherent aspect of the technology, it’s the power dynamic. The same technology that reduces the effort required for a citizen to share their lived experience with their legislator also enables corporate interests to misrepresent the public at scale. The former is a power-equalizing application of AI that enhances participatory democracy; the latter is a power-concentrating application that threatens it.

In general, we believe writing and cognitive assistance, long available to the rich and powerful, should be available to everyone. The problem comes when AIs make fraud easier. Any response needs to balance embracing that newfound democratization of access with preventing fraud.

There’s no way to turn this technology off. Highly capable AIs are widely available and can run on a laptop. Ethical guidelines and clear professional boundaries can help – for those acting in good faith. But there won’t ever be a way to totally stop academic writers, job seekers or citizens from using these tools, either as legitimate assistance or to commit fraud. This means more comments, more letters, more applications, more submissions.

The problem is that whoever is on the receiving end of this AI-fueled deluge can’t deal with the increased volume. What can help is developing assistive AI tools that benefit institutions and society, while also limiting fraud. And that may mean embracing the use of AI assistance in these adversarial systems, even though the defensive AI will never achieve supremacy.

Balancing harms with benefits

The science fiction community has been wrestling with AI since 2023. Clarkesworld eventually reopened submissions, claiming that it has an adequate way of separating human- and AI-written stories. No one knows how long, or how well, that will continue to work.

The arms race continues. There is no simple way to tell whether the potential benefits of AI will outweigh the harms, now or in the future. But as a society, we can influence the balance of harms it wreaks and opportunities it presents as we muddle our way through the changing technological landscape.

The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

– ref. AI-generated text is overwhelming institutions – setting off a no-win ‘arms race’ with AI detectors – https://theconversation.com/ai-generated-text-is-overwhelming-institutions-setting-off-a-no-win-arms-race-with-ai-detectors-274720

Source: The Conversation – USA – By Brian O’Neill, Professor of Practice, International Affairs, Georgia Institute of Technology

In separate encounters, federal immigration agents in Minneapolis killed Renée Good and Alex Pretti in January 2026.

Shortly after Pretti’s killing, Homeland Security Secretary Kristi Noem said he committed an “act of domestic terrorism.” Noem made the same accusation against Good.

But the label “domestic terrorism” is not a generic synonym for the kind of politically charged violence Noem alleged both had committed. U.S. law describes the term as a specific idea: acts dangerous to human life that appear intended to intimidate civilians, pressure government policy or affect government conduct through extreme means. Intent is the hinge.

From my experience managing counterterrorism analysts at the CIA and the National Counterterrorism Center, I know the terrorism label – domestic or international – is a judgment applied only after intent and context are assessed. It’s not to be used before an investigation has even begun. Terrorism determinations require analytic discipline, not speed.

Evidence before conclusions

In the first news cycle, investigators may know the crude details of what happened: who fired, who died and roughly what happened. They usually do not know motive with enough confidence to declare that coercive intent – the element that separates terrorism from other serious crimes – is present.

The Congressional Research Service, which provides policy analysis to Congress, makes a related point: While the term “domestic terrorism” is defined in statute, it is not itself a standalone federal offense. That’s part of the reason why public use of the term can outpace legal and investigative reality.

This dynamic – the temptation to close on a narrative before the evidence warrants it – seen most recently in the Homeland Security secretary’s assertions, echoes long-standing insights in intelligence scholarship and formal analytic standards.

Intelligence studies make a simple observation: Analysts and institutions face inherent uncertainty because information is often incomplete, ambiguous and subject to deception.

In response, the U.S. intelligence community codified analytic standards in the aftermath of the Sept. 11, 2001, terrorist attacks. The standards emphasize objectivity, independence from political influence, and rigorous articulation of uncertainty. The goal was not to eliminate uncertainty but to bound it with disciplined methods and transparent assumptions.

When narrative outruns evidence

The terrorism label becomes risky when leaders publicly call an incident “domestic terrorism” before they can explain what evidence supports that conclusion. By doing that, they invite two predictable problems.

The first problem is institutional. Once a senior official declares something with categorical certainty, the system can feel pressure – sometimes subtle, sometimes overt – to validate the headline.

In high-profile incidents, the opposite response, institutional caution, is easily seen as evasion – pressure that can drive premature public declarations. Instead of starting with questions – “What do we know?” “What evidence would change our minds?” – investigators, analysts and communicators can find themselves defending a superior’s storyline.

The second problem is public trust. Research has found that the “terrorist” label itself shapes how audiences perceive threat and evaluate responses, apart from the underlying facts. Once the public begins to see the term as a political messaging tool, it may discount future uses of the term – including in cases where the coercive intent truly exists.

Once officials and commentators commit publicly to a version ahead of any assessment of intent and context, confirmation bias – interpreting evidence as confirmation of one’s existing beliefs – and anchoring – heavy reliance on preexisting information – can shape both internal decision-making and public reaction.

The long-term cost of misuse

This is not just a semantic fight among experts. Most people carry a mental file for “terrorism” shaped by mass violence and explicit ideological targeting.

When Americans hear the word “terrorism,” they likely think of 9/11, the 1995 Oklahoma City bombing or high-profile attacks abroad, such as the 2005 London bombings and December 2025 antisemitic attack in Sydney, where intent was clear.

By contrast, the more common U.S. experience of violence – shootings, assaults and chaotic confrontations with law enforcement – is typically treated by investigators, and understood by the public, as homicide or targeted violence until motive is established. That public habit reflects a commonsense sequence: First determine what happened, then decide why, then decide how to categorize it.

U.S. federal agencies have published standard definitions and tracking terminology for domestic terrorism, but senior officials’ public statements can outrun investigative reality.

The Minneapolis cases illustrate how fast the damage can occur: Early reporting and documentary material quickly diverged from official accounts. This fed accusations that the narrative was shaped and conclusions made before investigators had gathered the basic facts.

Even though Trump administration officials later distanced themselves from initial claims of domestic terrorism, corrections rarely travel as far as the original assertion. The label sticks, and the public is left to argue over politics rather than evidence.

None of this minimizes the seriousness of violence against officials or the possibility that an incident may ultimately meet a terrorism definition.

The point is discipline. If authorities have evidence of coercive intent – the element that makes “terrorism” distinct – then they would do well to say so and show what can responsibly be shown. If they do not, they could describe the event in ordinary investigative language and let the facts mature.

A “domestic terrorism” label that comes before the facts does not just risk being wrong in one case. It teaches the public, case by case, to treat the term as propaganda rather than diagnosis. When that happens, the category becomes less useful precisely when the country needs clarity most.

Brian O’Neill does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. A terrorism label that comes before the facts can turn ‘domestic terrorism’ into a useless designation – https://theconversation.com/a-terrorism-label-that-comes-before-the-facts-can-turn-domestic-terrorism-into-a-useless-designation-274790

Source: The Conversation – USA – By Michael J. Socolow, Professor of Communication and Journalism, University of Maine

On Dec. 26, 1926, 16,000 hockey fans packed Madison Square Garden to witness the birth of a rivalry between the New York Americans and the brand-new New York Rangers. The game would later be remembered for establishing a foundation of popularity for the sport in New York City.

The only American playing for the Rangers that night also happened to be the largest player in the history of the NHL up to that point, defenseman Clarence “Taffy” Abel.

Standing over 6 feet tall and weighing 225 pounds, Abel was a brutal behemoth on the ice. Yet off the ice, he was a quiet, personable man who charmed sportswriters.

Despite being a foundational figure in American hockey – an Olympic silver medalist and a two-time Stanley Cup champion – Abel has been largely erased from the national memory. His story is not just one of athletic prowess, but of a secret identity maintained for survival and a career ended by a league that turned against him. As a scholar of Olympic media history, I recognize Abel’s story as an important but overlooked example of how race and labor issues can influence public memory.

Passing as white: Abel’s secret identity

Taffy Abel, who earned his lifelong nickname from his childhood love of candy, was half-Ojibwe, born in 1900 in Sault Ste. Marie, Michigan. One of Abel’s few surviving relatives, George Jones, a nephew by marriage, recalled that his mother, Charlotte, an Ojibwe woman, encouraged Taffy and his sister to “pass” as white to protect them from the era’s rampant racism and the threat of being sent to an Indian boarding school. Though his heritage remained an open secret in his hometown, Abel maintained his whiteness throughout his hockey career.

His mother died in 1939, and it was only after her death – and years after his retirement – that Abel began to speak openly and proudly of his Indigenous roots. This forced silence is a primary reason his legacy remained obscured; for decades, he was categorized simply as a white American athlete, masking his status as a racial trailblazer.

Pioneer on the ice

Abel’s hockey journey was historic. At the 1924 Chamonix Games – the first official Winter Olympics – he was chosen to carry the U.S. flag during the opening ceremony. He led the American team to a silver medal before being recruited by Conn Smythe for the inaugural New York Rangers roster.

Because of his size, and perhaps also because of his biracial identity, which was likely known to many players in the NHL, Abel was forced to fight often in his rookie year. He led the Rangers with 78 penalty minutes, and soon became famous around the league for his jarring and ferocious checking.

In Abel’s second season playing for the Rangers, the team won the Stanley Cup. He became the first American player to win a medal at the Olympic Games and the Stanley Cup, cementing his legacy as one of the finest hockey players in the world. In 1929, he was traded to the Chicago Black Hawks, where he anchored the defense on a team that won the Stanley Cup in 1934.

Hits the wall

The end of Abel’s career was not dictated by age or injury, but by a stand for labor dignity. After the 1934 championship, he held out for a salary that reflected his value as a star attraction. Black Hawks management responded by insulting him in the press, portraying Abel as an ungrateful prima donna.

Around the league, executives mocked Abel’s weight, telling newspapers that Abel walked out because he wouldn’t respect a team-mandated diet. Abel believed a team would sign him for 1935, but it soon became clear he had become effectively banned from the league due to his advocacy for equitable pay.

He had been a star attraction for the Black Hawks, and despite leading the team to the Stanley Cup in his final game, Abel never played another game in the NHL. At age 34, he returned to Sault Ste. Marie, operated a café and coached youth hockey, quietly fading from the national spotlight.

Complicated reckoning

Only recently has the NHL acknowledged Abel’s Native American heritage. However, his story presents a challenge to the league’s historical narrative. To celebrate Abel as a pioneering person of color requires the NHL to confront its own role in the systemic racism that forced him to hide his identity. Only recently has the league’s longtime, historical ban on nonwhite players – dating from its founding in 1917 – been an open and popular subject of public discussion.

Furthermore, the history is messy. Because Abel passed as white during his playing days, some modern observers find it difficult to reconcile his achievements with those of later pioneers who broke the color barrier more overtly.

Ultimately, Clarence “Taffy” Abel was a resilient path breaker who navigated artificial borders – between the U.S. and Canada, and between white and Indigenous identities. He was a charter member of the U.S. Hockey Hall of Fame in 1973, and his memory inspired future Indigenous stars like T.J. Oshie.

Yet his name remains largely unknown because, I believe, his life forces a reckoning with a society that dehumanized him. Even Abel’s U.S. Hockey Hall of Fame biography minimizes his heritage, noting “Thought by some to be the first Native American to play in the NHL.”

Abel fought for fair pay, against racism and through physical pain. He died in 1964, but the issues he grappled with – labor exploitation and racial identity – remain at the forefront of the American story today.

Michael J. Socolow does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. Clarence ‘Taffy’ Abel: A pioneering US Olympic hockey star who hid his Indigenous identity to play in the NHL – https://theconversation.com/clarence-taffy-abel-a-pioneering-us-olympic-hockey-star-who-hid-his-indigenous-identity-to-play-in-the-nhl-275144

Source: The Conversation – USA (2) – By Keith Musselman, Assistant Professor in Geography, Mountain Hydrology, and Climate Change, University of Colorado Boulder

When viewers tune in to the 2026 Winter Olympics, they will see pristine, white slopes, groomed tracks and athletes racing over snow-covered landscapes, thanks in part to a storm that blanketed the mountain venues of the Italian Alps with fresh powder just in time.

But at lower elevations, where cross-country and other events are held, athletes and organizers have been contending with rain; thin, sometimes slushy snow; and icy, machine-made surfaces.

“Most of our races are on machine-made snow,” 2026 U.S. Olympic team cross-country skier Rosie Brennan told us ahead of the Games. “TV production is great at making it look like we are in wintry, snowy places, but this year has been particularly bad.”

As scientists who study mountain snow, water resources and the human impact of warming winters, we see winter’s changes through data: rising temperatures, shrinking snowpack, shorter snow seasons.

Olympic athletes experience changing winter conditions personally, in ways the public and scientists rarely do. Lack of snowfall and more frequent rain affect when and where they can train, how they train and how dangerous the terrain can become.

We talked with Brennan and cross-country skiers Ben Ogden and Jack Young as they were preparing for the 2026 Winter Games. Their experiences reflect what many athletes describe: a sport increasingly defined not by the variability of natural winter but by the reliability of industrialized snowmaking.

What the cameras don’t show

Snowmaking technology makes it possible to create halfpipes for freestyle snowboarding and skiing competitions. It also allows for races when natural snow is scarce – the 2022 Winter Olympics in Beijing relied entirely on machine-made snow for many races.

However, machine-made snow creates a very different surface than natural snow, changing the race.

In clouds, each unique snowflake shape is determined by the temperature and humidity. Once formed, the iconic star shape begins to slowly erode as its crystals become rounded spheres. In this way, natural snow provides a variety of textures and depths: soft powder after a storm, firm or brittle snow in cold weather, and slushy, wet snow during rain or melt events.

Machine-made snow varies less in texture or quality. It begins and ends its life as an ice pellet surrounded by a thin film of liquid water. That makes it slower to change, easier to shape, and, once frozen, it hardens in place.

‘They’re faster, icier and carry more risk’

When artificial snow is being made, the sound is piercing – a high-pitched hiss roars from the pressurized nozzles of snow guns. These guns spew water mixed with compressed air, and it freezes upon contact with the cold air outside, creating small, dense ice particles. The drops sting exposed skin, as one of us, Agnes Macy, knows well as a former competitive skier.

Snow machines then push out artificial snow onto the racecourse. Often, the trails are the only ribbons of snow in sight – a white strip surrounded by brown mud and dead grass.

“Courses built for natural snow feel completely different when covered in man-made snow,” Brennan, 37, said. “They’re faster, icier, and carry more risk than anyone might imagine for cross-country skiing.”

There’s nothing quite like skiing on fresh snow. After a storm brings a blanket of light, fluffy powder, it can almost feel as though you’re floating. The snow is forgiving.

On artificial snow, skiers carry more speed into downhill runs. Downhill racers may relish the speed, but cross-country skis don’t have metal edges like downhill skis do, so step-turning or skidding around fast, icy corners can make an athlete feel out of control. It “requires a different style of skiing, skill sets and strengths than I grew up learning,” Brennan said.

How athletes adapt, with help from science

Athletes must adjust their technique and prepare their skis differently, depending on the snow conditions.

At elite levels, this is science. Snow crystal morphology, temperature, ski base material and structure, ski stiffness, skier technique and environmental conditions all interact to determine an athlete’s speed.

Before cross-country, or Nordic, races, ski technicians compare multiple ski pairs prepared with different base surfaces and waxes. They evaluate how quickly each ski glides and how long it maintains that glide – traits that depend on the friction between the ski and the snow.

Compared to natural snow, machine-made snow generally provides a more durable and longer-lasting surface. In cross-country racing, that allows for more efficient and stronger pushes without skis or poles sinking deep into snow. Additionally, improvements in the machines used to groom snow now provide harder and more homogeneous surfaces that permit faster skiing.

While fast skiing is the goal, ski crashes are also the most common cause of injury in the Winter Olympics. With machine-made snow, ski jump competitors and anyone who falls is also landing on a harder surface, which can increase the risk of injury.

Why winters are changing

Weather can always deal surprises, but long-term climate trends are shifting what can be expected of a typical winter.

In the Alps, air temperature has increased by about 3.6 degrees Fahrenheit (2 degrees Celsius) since the late 1800s, before rising fossil fuel use began increasing the levels of greenhouse gases trapping heat in the atmosphere. Globally, 2025 was the third-warmest year on record, following 2024 and 2023.

For mountain regions, these warmer conditions have consequences. Snow melts earlier and more frequently in midwinter, especially during warm spells that used to be rare.

Midwinter snowmelt events are occurring more often at higher elevations and earlier in the season across many mountain ranges of western North America. At the same time, the snow line – the elevation where precipitation shifts from snow to rain – is moving upslope.

Warming in high mountain environments is also causing the threshold where rain turns to snow to rise by tens of meters per decade in some regions. This means storms that once blanketed entire valleys in snow now may deliver snow only to upper slopes, with rain falling below.

Together, these changes mean that many winter storms produce less snow, over less area, and for shorter durations than they did a generation ago.

Training venues

The changing winter landscape has also transformed how athletes train. Traditional training venues, such as glaciers once used for summer skiing, have become unreliable. In August 2025, the Hintertux Glacier – the only year-round training center operating in Austria – announced its first temporary closure.

“It’s been increasingly hard to make plans for locations to train between races,” Brennan said. “Snow reliability isn’t great in many places. We often rely on going to higher elevations for a better chance of snow.”

Higher-elevation training can help, but it concentrates athletes in fewer places, reduces access for younger skiers due to the remoteness and raises costs for national teams. Some of these glaciers – like Canada’s Haig Glacier or Alaska’s Eagle Glacier – are accessible only by helicopter. When skiers can’t get to snow, dryland training on rollerskis is one of the only options.

Winter athletes see the climate changing

Because winter is their workplace, athletes often notice subtle changes before those changes show up in long-term statistics.

Even athletes in their earlier 20s, like Young, said they have noticed the rapid expansion of snowmaking infrastructure at many racing venues in recent years. Snowmaking requires large amounts of energy and water. It is also a clear sign that organizers see winters becoming less dependable.

Athletes also witness how communities are affected when poor snow conditions mean fewer visitors. “In the Alps, when conditions are bad, it is obvious how much it affects the communities,” Ogden, 25, said. “Their tourism-based livelihoods are so often negatively affected, and their quality of life changes.”

Many winter athletes are speaking publicly about their concerns. Groups such as Protect Our Winters, founded by professional snowboarder Jeremy Jones, work to advance policies that protect outdoor places for future generations.

A wintry look, but an uncertain future

For athletes at the 2026 Olympics, the variability within the Olympic region – snow at higher elevations, rain at lower ones – reflects a broader truth: The stability of winter is diminishing.

Athletes know this better than anyone. They race in it. They train in it. They depend on it.

The Winter Games will go on this year. The snow will look good on television. But at the same time, winter is changing.

Keith Musselman receives funding from the National Science Foundation. He is a member of the Science Alliance for the non-profit Protect Our Winters.

Agnes Macy receives funding from the National Science Foundation.

– ref. What Olympic athletes see that viewers don’t: Machine-made snow makes ski racing faster and riskier – and it’s everywhere – https://theconversation.com/what-olympic-athletes-see-that-viewers-dont-machine-made-snow-makes-ski-racing-faster-and-riskier-and-its-everywhere-274806