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AI Overviews lead to fewer clicked links, study finds as web traffic falls – Google disagrees

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Google Search has been going all-in on AI over the past few years, but it seems like that’s not really benefitting anyone but the user as a new study shows that searches with AI Overviews are more likely to see fewer clicked links out of Google, and also fewer further searches within Google.

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bogorad
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ai overviews are absolutely useless! compare do grok it's just laughable.
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Aeneas transforms how historians connect the past - Google DeepMind

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  • AI Model Introduction: Aeneas is presented as the first artificial intelligence (AI) model specifically designed for contextualizing ancient inscriptions.
  • Purpose and Functionality: The model aims to assist historians in interpreting, attributing, and restoring fragmentary ancient texts by finding textual and contextual parallels.
  • Acceleration of Research: Aeneas significantly speeds up the traditional, labor-intensive process historians use to identify similar inscriptions, retrieving relevant parallels in seconds.
  • Multimodal Capabilities: It is the first model capable of determining an inscription's geographical origin by analyzing both textual and visual (image) data.
  • Restoration of Gaps: Aeneas can restore texts with gaps of unknown length, a feature offering enhanced versatility for damaged historical materials.
  • Dataset and Training: The model was trained on a curated dataset of over 176,000 Latin inscriptions, harmonized from major epigraphic databases.
  • Performance Benchmarks: Aeneas demonstrates state-of-the-art performance in restoring damaged texts, predicting their origin date and location, and handling unknown restoration lengths.
  • Accessibility and Collaboration: An interactive version of Aeneas is freely available for researchers and the public, and the code and dataset are open-sourced to support further research.

Research

Aeneas transforms how historians connect the past

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The Aeneas team

Introducing the first model for contextualizing ancient inscriptions, designed to help historians better interpret, attribute and restore fragmentary texts.

Writing was everywhere in the Roman world — etched onto everything from imperial monuments to everyday objects. From political graffiti, love poems and epitaphs to business transactions, birthday invitations and magical spells, inscriptions offer modern historians rich insights into the diversity of everyday life across the Roman world.

Often, these texts are fragmentary, weathered or deliberately defaced. Restoring, dating and placing them is nearly impossible without contextual information, especially when comparing similar inscriptions.

Today, we’re publishing a paper in Nature introducing Aeneas, the first artificial intelligence (AI) model for contextualizing ancient inscriptions.

When working with ancient inscriptions, historians traditionally rely on their expertise and specialized resources to identify “parallels” — which are texts that share similarities in wording, syntax, standardized formulas or provenance.

Aeneas greatly accelerates this complex and time-consuming work. It reasons across thousands of Latin inscriptions, retrieving textual and contextual parallels in seconds that allow historians to interpret and build upon the model’s findings.

Our model can also be adapted to other ancient languages, scripts and media, from papyri to coinage, expanding its capabilities to help draw connections across a wider range of historical evidence.

We co-developed Aeneas with the University of Nottingham, and in partnership with researchers at the Universities of Warwick, Oxford and Athens University of Economics and Business (AUEB). This work was part of a wider effort to explore how generative AI can help historians better identify and interpret parallels at scale.

We want this research to benefit as many people as possible, so we’re making an interactive version of Aeneas freely-available to researchers, students, educators, museum professionals and more at predictingthepast.com. To support further research, we’re also open-sourcing our code and dataset.

Aeneas’ advanced capabilities

Named after the wandering hero of Graeco-Roman mythology, Aeneas builds upon Ithaca, our earlier work using AI to restore, date and place ancient Greek inscriptions.

Aeneas goes a step further, helping historians interpret and contextualize a text, give meaning to isolated fragments, draw richer conclusions and piece together a better understanding of ancient history.

Our model’s advanced capabilities include:

  • Parallels search: It searches for parallels across a vast collection of Latin inscriptions. By turning each text into a kind of historical fingerprint, Aeneas identifies deep connections that can help historians situate inscriptions within their broader historical context.
  • Processing multimodal input: Aeneas is the first model to determine a text's geographical provenance using multimodal inputs. It analyzes both text and visual information, like images of an inscription.
  • Restoring gaps of unknown length: For the first time, Aeneas can restore gaps in texts where the missing length is unknown. This makes it a more versatile tool for historians dealing with heavily damaged material.
  • State-of-the-art performance: Aeneas sets a new state-of-the-art benchmark in restoring damaged texts and predicting when and where they were written.

How Aeneas works

Aeneas is a multimodal generative neural network that takes an inscription’s text and image as input. To train Aeneas, we curated a large and reliable dataset, drawing from decades of work by historians to create digital collections, especially the Epigraphic Database Roma (EDR), Epigraphic Database Heidelberg (EDH) and Epigraphic Database Clauss Slaby (EDCS-ELT).

We cleaned, harmonized and linked these records into a single machine-actionable dataset that we refer to as the Latin Epigraphic Dataset (LED), comprising over 176,000 Latin inscriptions from across the ancient Roman world.

Our model uses a transformer-based decoder to process the textual input of an inscription. Specialized networks handle character restoration and dating using text, while geographical attribution also uses images of the inscriptions as input. The decoder retrieves similar inscriptions from the LED, ranked by relevance.

For each inscription, Aeneas’ contextualization mechanism retrieves a list of parallels using a technique called “embeddings” — encoding the textual and contextual information of each inscription into a kind of historical fingerprint containing details of what the text says, its language, when and where it came from, and how it relates to other inscriptions.

State-of-the-art performance

Aeneas groups inscriptions by date of writing far more clearly than other general-purpose models also trained on Latin, as shown in the visualization below.

Aeneas restores damaged inscriptions with a Top-20 accuracy of 73% in gaps of up to ten characters. This only decreases to 58% when the restoration length is unknown - itself an incredibly challenging task. It also shows its reasoning in an interpretable way, providing saliency maps that highlight which parts of the inputs influenced its predictions. Thanks to its use of visual data, our model can attribute an inscription to one of 62 ancient Roman provinces with 72% accuracy. For dating, Aeneas places a text within 13 years of the date ranges provided by historians.

A new lens on historical debates

To test Aeneas’ capabilities on an ongoing research debate, we gave it one of the most famous Roman inscriptions: the Res Gestae Divi Augusti, Emperor Augustus’ first-person account of his achievements.

Historians have long-argued about the dating of this inscription. Rather than predicting a single fixed date, Aeneas produced a detailed distribution of possible dates, showing two distinct peaks, with one smaller peak around 10-1  BCE and a larger, more confident peak between 10-20 CE. These results captured both prevailing dating hypotheses in a quantitative way.

Aeneas based its predictions on subtle linguistic features and historical markers such as official titles and monuments mentioned in the text. By turning the dating question into a probabilistic estimate grounded in linguistic and contextual data, our model offers a new, quantitative way of engaging with long-standing historical debates.

Most importantly, Aeneas also retrieved many relevant parallels from imperial legal texts tied to Augustus’ legacy, highlighting how the ideology of empire was reproduced across media and geography.

Advancing historical research collaboratively

To assess Aeneas’ impact as an aid for research, we conducted a large-scale Historian and AI collaborative study. We invited twenty-three historians who regularly work with inscriptions to restore, date and place a set of texts using Aeneas.

Our evaluation, summarized in the table below, shows how the most effective results were achieved when historians used Aeneas’ contextual information alongside its predictions for restoring and attributing Roman inscriptions.

Aeneas helped the historians in our study identify new parallels and increased their confidence when tackling complex epigraphic tasks. Historians consistently highlighted Aeneas’ value in accelerating their work and expanding the range of most relevant parallel inscriptions.

Sharing the tools, shaping the future

Aeneas is designed to integrate within historians' existing research workflows. By combining expert knowledge with machine learning, it opens up a collaborative process, offering interpretable suggestions that serve as valuable starting points for historical inquiry.

As part of today’s release, we’re upgrading Ithaca, our ancient Greek model, to be powered by Aeneas and include the contextualization function, restorations of unknown length and better performance overall.

We’ve also co-designed a new teaching syllabus for bridging technical skills with historical thinking in the classroom. This syllabus aligns with AI literacy initiatives, including the European Commission's Digital Competences Framework for Citizens (DigComp 2.2), UNESCO’s AI Competency Framework for Students, and the preview of European Commission and the Organization for Economic Cooperation and Development (OECD) AILit Framework.

The Aeneas team is continuing to partner with diverse subject matter experts, using Aeneas to help shed light to our ancient past — with more to come.

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bogorad
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denubis
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Ewan... we should poke at this.

Newly discovered photos and video shed fresh light on Trump’s ties to Jeffrey Epstein | CNN Politics

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  • New Footage and Photos: CNN's KFile has uncovered previously unreported video footage and photographs offering new details on Donald Trump's past interactions with Jeffrey Epstein.
  • Epstein at Trump's Wedding: Photos from 1993 confirm for the first time that Jeffrey Epstein attended Donald Trump's wedding to Marla Maples at the Plaza Hotel.
  • Victoria's Secret Fashion Show: Raw footage from a 1999 Victoria's Secret fashion show in New York captures Trump and Epstein seen laughing and chatting together.
  • Pre-Legal Issues: These newly surfaced images pre-date the widely known legal troubles that later emerged concerning Epstein.
  • Trump's Reaction: When asked about the wedding photos, Trump responded by calling them "fake news" and hanging up the phone.
  • White House Statement: A White House spokesperson dismissed the footage as "out-of-context frame grabs" and stated that Trump had previously distanced himself from Epstein.
  • Past Interactions: The article outlines a history of interactions between Trump and Epstein dating back to the 1980s, including shared events and travel on Epstein's jets, noting no law enforcement has accused Trump of wrongdoing.
  • Summarizer Insight: The article focuses on visually documenting Trump's association with Epstein prior to Epstein's legal issues becoming public, highlighting specific events and interactions. Readers should note the timing of these revelations in relation to ongoing scrutiny of Epstein's associates and the political context.

CNN  — 

Newly uncovered archived video footage and photos reveal fresh details about Donald Trump’s past relationship with Jeffrey Epstein.

Photos from 1993 confirm for the first time that Epstein attended Trump’s 1993 wedding to Marla Maples. Epstein’s attendance at the ceremony at the Plaza Hotel was not widely known until now.

In addition, footage from a 1999 Victoria’s Secret fashion event in New York shows Trump and Epstein laughing and chatting together ahead of the runway event. CNN’s KFile uncovered the raw footage during a review of archival video of Trump at events in the 1990s and 2000s. Trump and Epstein appeared together in at least one video among the limited archival footage reviewed.

CNN’s KFile discovers video of Trump and Epstein at 1999 Victoria’s Secret fashion show
01:13 - Source: CNN
CNN’s KFile discovers video of Trump and Epstein at 1999 Victoria’s Secret fashion show
01:13

The new footage and photos, which have not been widely reported and pre-date any of Epstein’s known legal issues, come amid renewed scrutiny of Trump’s past relationship with Epstein. The Justice Department’s recent decision not to release long-promised files related to Epstein has spurred outrage in some corners of Trump’s MAGA movement, where people developed an expectation for bombshell revelations into Epstein’s alleged co-conspirators.

In a brief call with CNN on Tuesday, President Trump, asked about the wedding photos, responded, “You’ve got to be kidding me,” before repeatedly calling CNN “fake news” and hanging up.

In a statement to CNN, White House Communications Director Steven Cheung said, “These are nothing more than out-of-context frame grabs of innocuous videos and pictures of widely attended events to disgustingly infer something nefarious.

“The fact is that the President kicked him out of his club for being a creep. This is nothing more than a continuation of the fake news stories concocted by the Democrats and the liberal media.”

Allegations that Epstein sexually abused underage girls first surfaced in 2005, leading to his arrest a year later. He was arrested again in 2019 on federal sex trafficking charges and later died in jail, fueling numerous conspiracy theories. The medical examiner ruled his death a suicide by hanging.

A past relationship

Trump’s relationship with Epstein dates back to the 1980s and included regular appearances at social events in Palm Beach and New York. No law enforcement authorities have ever accused Trump of wrongdoing in relation to Epstein.

The two had a falling-out in the mid-2000s, according to the Washington Post, stemming from a dispute over a high-profile real estate deal in Palm Beach.

Before then, photos and video repeatedly showed the two were friendly. In 2019, NBC posted footage of a party showing Trump socializing with Epstein in 1992.

A year later in October 1993, high-society photographer Dafydd Jones took photos at the opening of the Harley Davidson Cafe in New York, capturing Trump and Epstein together.

“There was this guy there who struck me — the way he was looking — and he gave me his card. It said: Jeffrey Epstein, financial advisor,” Jones recalled in an interview with CNN this week.

Jones captured photos of Trump with his arm around his two young children as he stands next to Epstein, leaning on a railing.

Two months later, in December 1993, Jones was assigned by a media organization to photograph Trump’s wedding. Among the photos he took was one of Epstein entering the event.

“I must have recognized him going in [to the event],” Jones said to CNN, adding he only took select photos of attendees he thought looked interesting.

“I wish now I took more of him with Trump,” he said. “I had the job of photographing the Trump wedding, so I stood with the press and photographed him. The image you have is from the contact sheet — the negatives were lost.”

Another photo captures Epstein at Trump’s wedding, part of LIFE’s archive that was reviewed by CNN. It shows Epstein smiling in the background — his head just visible between other guests and shock jock Howard Stern and Robin Leach of “Lifestyles of the Rich and Famous,” who were taking a group picture.

LIFE’s collection of dozens of photos of Trump’s wedding are available online through Google Images and Shutterstock, and a CNN review of photos found multiple photos with Epstein.

Together at a fashion show

The 1999 fashion show wasn’t the first Victoria’s Secret event the pair attended together. Two photos from Getty Images show Trump and Epstein appearing at a 1997 Angels party in New York, two years before the footage uncovered by CNN.

Epstein’s presence at the 1999 fashion show also reflects his longstanding ties to Leslie Wexner, the billionaire founder of Victoria’s Secret’s parent company. Epstein managed Wexner’s finances from 1987 to around 2007. The two later severed ties, and Wexner has said he was unaware of Epstein’s alleged crimes during their association.

In 2002, Trump was quoted in a New York Magazine profile of Epstein — “Jeffrey Epstein: International Moneyman of Mystery” — describing him as “a terrific guy,” saying he’s known Epstein for 15 years. “It is even said that he likes beautiful women as much as I do, and many of them are on the younger side,” Trump said.

Trump flew on Epstein’s jets between Palm Beach and New York, at least seven times according to flight logs.

In his 2004 book, “Trump: How To Get Rich,” Trump wrote about taking a call from a man he named “the mysterious Jeffrey.”

“As mysterious as Jeffrey is, he’s one of the few people I know who can get by on just a first name. My staff never asks for a last name in his case, which in a way puts him up there with Elvis. Not that Elvis calls in much these days, but you never know,” Trump wrote.

It’s unclear if the “mysterious Jeffrey” is Epstein and White House did not address it in a comment to CNN.

Images published in the Palm Beach Post in 2000 also show Trump, Epstein associate Ghislaine Maxwell — who is currently serving a 20-year prison sentence for sex trafficking — and Prince Andrew in attendance at a charity fundraiser at Mar-a-Lago.

Last week, the Wall Street Journal reported on a birthday message sent bearing Trump’s name for Epstein’s 50th birthday in 2003. According to the Journal, it contained an outline of a naked woman and a typed note that ended with the line: “Happy Birthday—and may every day be another wonderful secret.”

Following the report, the Trump administration pledged to release grand jury materials related to Epstein. The federal judge overseeing Maxwell’s case set a deadline for the Justice Department to provide information so he can determine whether to unseal the transcripts.

The Department of Justice also said Tuesday it has reached out to Maxwell for a meeting amid backlash over the administration’s handling of files related to Epstein.

Maxwell’s attorney told CNN they “are in discussions with the government” on the matter. “Ghislaine will always testify truthfully. We are grateful to President Trump for his commitment to uncovering the truth in this case,” attorney David Oscar Markus said.

Trump has denied authoring the note and drawing, calling the report false. On Friday, he sued the newspaper for libel in federal court in Florida.

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AI Comes Up with Bizarre Physics Experiments. But They Work. | Quanta Magazine

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  • AI Designing Experiments: Artificial intelligence software is creating novel experimental protocols that offer improvements over human-designed experiments, though human oversight is still significant.

  • LIGO Enhancement: AI designed a new configuration for the LIGO gravitational-wave detectors, potentially increasing their sensitivity by 10-15% by incorporating counterintuitive, theoretical principles that physicists had not experimentally explored.

  • Entanglement Swapping: AI developed a simpler experimental design for entanglement swapping, a crucial quantum technology, which was later experimentally confirmed to be effective.

  • Data Pattern Recognition: AI is being used to analyze experimental data, identifying patterns that might be missed by humans, such as deriving a formula for dark matter clump density and finding symmetries in Large Hadron Collider data.

  • Future Potential: While AI has not yet led to new physics discoveries, its role in experimental design and data analysis is growing, with potential for AI to assist in hypothesis generation and lead to future breakthroughs.


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Ozempic Works Wonders Until You Stop. Then, the Weight Starts to Come Back

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  • Weight Rebound: On average, individuals regain 3.3 pounds within eight weeks of stopping weight-loss drugs like Ozempic.

  • Physiological Resistance: The body naturally resists weight loss by increasing hunger hormones, slowing metabolism, and attempting to return to a baseline weight.

  • Stubborn Biology: Even with lifestyle support, weight regain occurred, suggesting that biology plays a significant role in weight regulation.

  • Short-Term Fixes?: The drugs are effective while used, but their benefits diminish upon discontinuation, raising questions about long-term necessity.

  • Obesity Misunderstanding: Obesity is often viewed as a personal failure, but research indicates it is primarily a biological issue influenced by hormones and complex bodily feedback loops.


Eight weeks. That’s how long it takes, on average, for the pounds to begin creeping back after someone stops taking the world’s most promising weight-loss drugs.

Medications like semaglutide (Ozempic) and liraglutide have been hailed as “game-changers” in the fight against obesity, helping people shed 10, 15, even 20% of their body weight. But we don’t know as much about what happens when the prescription runs out. According to a new study published by researchers at Peking University People’s Hospital, there’s a big rebound effect.

In just a few years, weight-loss drugs like Ozempic and Wegovy have become a global sensation. They promise to do something millions of people dream of: rapid, effortless weight loss without surgery or starvation. Backed by dramatic trial results and viral celebrity endorsements, they’ve become so popular that 1 in 8 adults in the US have already taken them at some point.

Yet, as so often happens with weight loss, shedding extra pounds is one thing — keeping them off is another.

The team, led by endocrinologist Xiaoling Cai, analyzed 11 randomized controlled trials, tracking more than 2,400 adults who had taken FDA- or EMA-approved anti-obesity drugs. Their question was simple: What happens after the medication stops?

The simple answer is that the weight gradually crept back, though not entirely. On average, participants regained 1.5 kilograms (about 3.3 pounds) within eight weeks of stopping treatment. At twelve weeks, it was 1.8 kilograms. By twenty weeks, 2.5 kilograms. That might not sound like much. But it reveals a consistent trend, a steady reversal of the drug’s effects.

The weight stabilized after six months. It settles at a plateau, higher than the low point reached with the medication, but lower than where people started. The rebound effect wasn’t full, but it was definitely there.

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The body is stubborn

For decades, researchers have known that the human body resists weight loss. When we lose fat, our levels of leptin (the hormone that signals fullness) fall. At the same time, ghrelin, the hunger hormone, rises. Our metabolism slows, our cravings grow more intense, and our bodies burn fewer calories even at rest. Recent studies have also shown that our cells have a “memory” and they try to push you back to a baseline weight.

In other words, our weight has more inertia than we thought. It’s a tug-of-war with physiology, and our physiology is stubborn.

GLP-1 drugs short-circuit some of those defenses. But once withdrawn, the body mounts a counterattack. It doesn’t forget where it started from and it tries, with impressive efficiency, to get back there. It’s important to keep in mind that it doesn’t revert quite to the baseline, but it doesn’t stay at the minimum weight either.

This study doesn’t say anti-obesity drugs don’t work. Quite the opposite: they work extremely well — while you’re on them.

But it does seem to show that these are short-term fixes. Like glasses for near-sightedness, their benefits vanish when you stop using them. For a chronic condition like obesity, that means we may need to think about these drugs as long-term treatments, perhaps lifelong ones. But this raises tough questions for doctors, patients, and policymakers. Should people stay on GLP-1 drugs indefinitely? Are they safe for long-term use? Who pays the bill, and what happens when access ends?

We keep misunderstanding obesity

The study also examined what happened when patients received lifestyle support like diet plans, exercise regimens, and coaching, either during or after drug therapy. Surprisingly, weight regain happened even when lifestyle interventions continued. That came as a surprise and runs counter to conventional wisdom.

It does show, however, that there’s much we still don’t understand about how our body deals with extra pounds. It also suggests we keep falling into the same obesity management traps.

Obesity is still too often framed as a personal failure. If you’re overweight, it’s your fault, a matter of weak will or bad choices. But studies like this underscore what obesity researchers have long argued: that weight regulation is mostly biological. It’s the result of complex feedback loops between the brain, gut, hormones, and fat tissue. Of course, all of this is overlaid on our modern lifestyle and often processed, unhealthy foods.

But the idea that people can simply ‘choose’ to be thin ignores how our bodies actually work. We’re not machines. We’re ecosystems, and ecosystems resist change.

The rebound problem doesn’t mean we’re stuck. Researchers are already testing combination therapies that pair GLP-1 drugs with other molecules to blunt hunger even more effectively. Perhaps, after a longer prescription period, the rebound effect would be slower.

This new generation of weight-loss drugs is promising. But they’re still just tools, not magic bullets. To make them work long-term, we may need to keep using them long-term.

The study “Trajectory of the body weight after drug discontinuation in the treatment of anti‑obesity medications” was published in the journal BMC Medicine. 10.1186/s12916-025-04200-0

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A Stark Reminder That Sex Differences Matter in Elite Sport

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  • :Faith Kipyegon's Mile Attempt: The elite runner's attempt to break the 4-minute mile, supported by Nike, resulted in a 4:06 time.

  • :Physiological Differences Highlighted: Kipyegon's performance, while exceptional, underscored the inherent physiological performance gaps between elite male and female athletes.

  • :Male vs. Female Performance: Even with advanced pacing and equipment, Kipyegon's time was slower than top high school boys, illustrating significant biological advantages.

  • :Debate on Transgender Inclusion: The event's context is framed within the ongoing discussion about fairness in women's sports and the inclusion of transgender athletes.

  • :Preserving Women's Sports: The article argues that maintaining sex-based categories in sports is essential for ensuring a level playing field for biological females.


Faith Kipyegon is the greatest female middle-distance runner in history. The Kenyan star is a three-time Olympic gold medalist and the current world record holder in both the women’s mile and 1500 meters. In a high-profile event last month, she gave us something rare: a transparent and widely-broadcast reminder of the physiological differences that separate male and female athletic performance—differences that are too often downplayed or denied in today’s debate.

It was a Nike-orchestrated, globally livestreamed spectacle—complete with supershoes, aerodynamic pacers, custom speedsuits, and a team of ten male runners tasked with pacing her to history. The goal was for Kipyegon to become the first woman ever to run a sub-4 minute mile. Nike set her up with the very best conditions that any athlete could ever expect.

Kipyegon ran a mile in 4:06—a remarkable performance by any measure, and a personal best, but well short of the sub-4 minute goal. While Kipyegon wasn’t directly racing her pacers, they were there to pull her to a time that hundreds of male athletes have already achieved. Rather than charging down the final straightaway alone, leaving the best women in her wake, as she so often does, we saw Kipyegon straining to hang on behind a group of male runners who weren’t even near their limit, as they turned around to cheer her on.

This race matters because it offered something exceedingly rare: an honest, direct comparison of male and female performance at the highest level. It was a window into the physiological differences that shape athletic performance, raising critical questions about how to ensure fairness in women’s sports while respecting all athletes’ identities.

What the Race Revealed

The most talented female runner of our time still wasn’t close to the standard that elite high school boys can now achieve. The 25th fastest U.S. high school boy this year has run 4:04—faster than any woman in history—and seven of them have run it in under 4 minutes. Sam Ruthe, a 15-year-old boy in New Zealand, joined the sub-4 club this year. Even with Nike’s full arsenal of technological support behind her, Kipyegon still couldn’t match the time he achieved. That’s not a knock on her—she’s amazing. It’s a reality check, rooted in biology.

We’re living in a moment when the boundaries of sex-based categories in sport are increasingly contested, with sports allowing the inclusion of transgender athletes in women’s competitions. These include cases from swimming, track and field, weightlifting, cycling, and fencing, and other sports where biological males are competing with biological females—often following testosterone suppression, but sometimes not.

Why does this matter?

Sex-based sport categories exist to contain predictable physiological systems—not to eliminate all individual-level advantage, but to manage how advantage is distributed across shared developmental contexts. The female category is not meant to ensure equal outcomes. It’s meant to exclude one massive system-level advantage: male-pattern development shaped by testosterone exposure through puberty.


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The advantage males carry, especially after puberty, is not confined to just one or two variables—it is the result of an entire developmental system affecting multiple traits simultaneously: limb proportions, bone density, cardiovascular capacity, neuromuscular function, and more. These are not isolated attributes that can be easily matched or offset, even if testosterone is suppressed.

And while the most significant performance gaps between males and females emerge after puberty due to testosterone-driven physiological changes, research shows that athletic differences begin even earlier. Studies have found that biological males often demonstrate 1-5% advantages in speed, strength, and power beginning in childhood. Even pre-pubertal testosterone suppression may not eliminate all male-performance advantages—though current evidence is limited and not sufficient to draw definitive conclusions.

Some critics of biological sex-based categories argue that all elite athletes have biological advantages which makes them champions—Michael Phelps’ long arms and large lung capacity are often cited as examples of such an advantage. But those are individual anomalies within a sex category. They are not the result of massive biological system-level advantages—ones which cannot be overcome by even the best training, coaching, or other resources.

Others argue that defending biological sex-based categories risks reinforcing sexist stereotypes, as if acknowledging male athletic advantage implies all biological women are somehow socially inferior. Yes, sexism remains a serious and persistent problem in the world of sports, from unequal pay to lack of media coverage and support. But recognizing physiological differences between males and females isn’t sexist; it’s a necessary step toward fairness. The biological differences between males and females reflect average, well-documented physiological traits that significantly influence performance outcomes. It’s precisely because sex-based categories exist that women—of all shapes, sizes, and sexual orientations—can compete on a level playing field, without being overwhelmed by the systematic performance advantages typically conferred by male development.

Some point to examples of cisgender women who have defeated transgender athletes—such as Lia Thomas falling short of NCAA records or Laurel Hubbard failing to medal at the Olympics. But aside from the fact that Hubbard was much older than her competitors, these examples miss a crucial distinction. A truly elite, world-class female athlete can often outperform well-trained, non-elite biological males (such as Thomas or Hubbard), but not national-level or world-class biological males. In Kipyegon’s case, she can comfortably outrun more than 99.999% of men, including many highly-trained male runners. But against the very best men (and boys), the gap is simply unbridgeable. Telling women they should simply be “training harder” is naïve and condescending to those already giving their very best.

Advocates of identity-based participation often highlight the many sociocultural benefits of sport—building confidence, fostering belonging, and promoting health and well-being. The idea that sport is about more than just podium finishes and medals is a valid and valuable perspective. Indeed, all athletes should be able to compete in the sports that they love, regardless of their gender identity.

But when it comes to competitive sport, performance does matter. Victories lead to scholarships, prize money, records, and endorsements. In these arenas, fairness isn’t just symbolic—it’s material. And that’s precisely why sex-based categories were created in the first place: to ensure that female athletes have a level playing field in the competitions where winning carries real-world consequences.

The Hard Question We Need To Ask

That doesn’t preclude transgender inclusion in sport, but it does raise a pressing question: At what level of competition does fairness need to take precedence over inclusion? Is it only at the Olympic level, or does it also matter in college and high school championships, where scholarships and future opportunities are on the line?

These are difficult questions, but if we want to maintain competitive integrity in women’s sports, we need to be willing to ask them—and answer them honestly.



This isn’t about demonizing transgender or intersex individuals or denying the value of women’s athletic achievements. It’s about acknowledging that, at every competitive level, biological males are more likely to reach the upper limits of athletic performance—and that preserving fairness in women’s sport depends on recognizing that reality.

Nike may have missed the mark with its marketing campaign, but the event inadvertently proved something far more important than any world record—that even the most gifted female athletes cannot overcome the male physiological advantage. Any policy that ignores that will inevitably render the playing field uneven, particularly at the expense of women who’ve trained their entire lives, not to simply participate in sports, but to win at the highest level. If fairness is truly the basis for women having their own sports leagues and competitions, organizations must anchor those competitions in biological reality.

James Smoliga is a professor in the Department of Rehabilitation Sciences at Tufts University School of Medicine and writes the Substack Beyond the Abstract.


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