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Sex tests for athletes are too little, too late

Lynsey Sharp (R) was beaten by Caster Semenya (C) in Rio. Credit: Getty

Lynsey Sharp (R) was beaten by Caster Semenya (C) in Rio. Credit: Getty

September 22 2025 - 10:00am

Nine years ago, Scottish runner Lynsey Sharp came sixth in the women’s 800m Olympic final in Rio. Canada’s Melissa Bishop-Nriagu finished fourth and Poland’s Joanna Jóźwik fifth. Interviewed by the BBC, Sharp chose her words carefully, saying she had “tried to avoid the issue all year,” that it was “out of our control,” and that she was relying “on people at the top sorting it out.” For this, she was utterly vilified.

What did Sharp have against Caster Semenya, Francine Niyonsaba and Margaret Nyairera Wambui, the runners who took gold, silver and bronze? On social media, Sharp was branded, at best, a sore loser, at worst, a bigot shedding white tears because three black women had beaten three white ones. The backlash was swift and brutal.

Yet the controversy was not about race but about biology. Sharp, Bishop and Jóźwik were competing against “hyperandrogenic athletes” with naturally high testosterone levels — though what that meant was far from clear at the time. Like many, I assumed this referred to disorders of sex development (DSDs) that caused elevated testosterone in females. Only later did I learn it meant something more radical: the medalists in the women’s 800m were, biologically, male.

Almost a decade later the “people at the top” are clarifying the situation. As the Guardian reports, according to a senior World Athletics official “between 50 and 60 athletes who went through male puberty have been finalists in the female category in global and continental track and field championships since 2000”. There is evidence of vast over-representation of athletes who were identified as female at birth but have a 46 XY karyotype with male testes. This is why the introduction of cheek swab sex testing matters. For the past 25 years, exceptional female athletes have been excluded from finals and podiums because unexceptional males took their places.

It is important to frame the introduction of sex testing as a movement for, not against, inclusion. While the situation of athletes with DSDs competing as women is not identical to that of trans-identified males doing the same, there are some similarities. In the case of both, a great deal of attention is paid to how bad those who can no longer compete will feel once sex testing is introduced. There is comparatively little handwringing over the gross injustice female athletes have faced; not only denied places, prizes and potentially life-changing sponsorship, but shamed and ridiculed whenever they dared to complain.

Caster Semenya is not a cheat in the way that someone like Laurel Hubbard, a late-transitioning male who took a women’s Olympic weightlifting spot, might be described as one. Even so, Semenya has displayed zero empathy towards female athletes whose careers have been unjustly hobbled. In a 2024 autobiography, Semenya chose to repeat the “sore loser” line with reference to Sharp. Never is there any acknowledgement that, had they been running against others of their sex, the three winners in Rio would never have ranked near the women behind them.

One can have sympathy for Semenya, but women should not have to sacrifice their own dreams just because other people’s lives are complex. Sex testing has come too late for Sharp, who has since retired, confessing that what happened in Rio tainted her experience of the sport. There is no way of compensating her and countless others. Even awarding a medal years later can’t change the course of a life that was initially denied it. Athletes have short careers, limited chances, a brief window in which to shine. Once your moment has gone, it doesn’t come again.

Sex testing is a step in the right direction. The next step should be to redirect the empathy spotlight toward the women who lost out. We can’t repay them, but we can ensure it never happens again.


Victoria Smith is a writer and creator of the Glosswitch newsletter.

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China is threatening America in the AI race

Reports sugget Zhipu AI  has released a new model that can rival leading US systems. Credit: Getty

Reports sugget Zhipu AI has released a new model that can rival leading US systems. Credit: Getty

July 1 2026 - 10:18am

China is trying to catch up with America on artificial intelligence. The Wall Street Journal has reported that Zhipu AI — one of China’s six “AI tiger” LLMs — has released a new model that can rival leading US systems, including Anthropic’s Mythos, in cybersecurity tasks such as pinpointing security bugs. While this marks a milestone in China’s drive to catch up with Western AI capabilities, strong performance on a single benchmark does not mean it has taken the lead. Chinese models still lag behind their Western counterparts in broader capabilities, such as autonomous operation. Skepticism is therefore warranted before resorting to hysterical conclusions, but complacency about the geopolitical implications of China’s AI advances would be an even greater mistake.

On the infrastructure side, Chinese AI is still constrained by access to advanced chips, with American labs way ahead in computing capacity as well as investment. Analysis from earlier this year suggests that Chinese models are likely to be at least a few months behind those in the US. But they are still continuing to make progress, or that the geopolitical importance of AI will be decided only by whose LLM has ventured deeper into the technological frontier. The practical applications of AI, countries’ to capture foreign markets, and the application of AI into the real economy will matter just as much.

Here, China may hold an advantage. As with its dominance across many critical supply chains, Beijing may not need to produce the most advanced AI systems — only those that are affordable and widely deployable. In doing so, it could consolidate global influence by supplying functional, low-cost AI at scale.

Beijing seems to be pursuing exactly that path, developing an AI “open-source” strategy that offers affordable, widely available AI models for companies and individuals to use and modify as they wish. The production of the DeepSeek AI model, which matched the performance of Silicon Valley tools such as ChatGPT at a fraction of the cost for users, created goodwill among Chinese models with developers.

The four most popular models on OpenRouter, an AI hardware platform for developers, are now all Chinese. The goal for China is not only to win the frontier-model race, but to make its systems the default layer of AI adoption across industries and global markets. For most economies, the choice is increasingly between an affordable tool they can deploy now and a more robust one that may be out of reach.

And while the countries adopting Chinese models may be exposed to political pressure and cyber threats from Beijing, safer and more capable alternatives matter little if they are unaffordable. American AI companies are already under pressure to monetize products whose operating costs are rising. If Chinese open-source models become the cheap default for startups, universities, governments and businesses across the developing world, then America’s AI lead will be eroded from below.

Perhaps more concerning for America in the long run is how AI can give Chinese manufacturing even more strength, through the ongoing integration of AI as a general-purpose technology. China’s new Five-Year Plan mentioned AI more than 50 times and includes an “AI+” action plan aimed at spreading AI across the economy.

Beijing has been pioneering automation of its critical infrastructure for years, with promising recent results in increasing warplane production capacity. In that regard, China’s open-model strategy and manufacturing dominance will reinforce each other. Cheap, adaptable models accelerate deployment across the real economy while those deployments generate real-world data and use cases that can feed back into further model improvement.

The United States should not dismiss the importance of its lead in the AI race. That lead worries Beijing, not least because a more automated Chinese economy would also become more vulnerable to AI-generated cyber threats. But nor should Washington assume that China cannot catch up with American capabilities over time.

This AI competition represents part of a broader struggle over tech supply chains and geopolitical influence. Decisions over whether to adopt US or Chinese models could produce a more fragmented global reality, with different regions relying on different cloud providers, chips and security structures. The result will likely be a global economy which is divided into competing spheres, rather than one which produces a single winner.


Miquel Vila is a political and geopolitical risk consultant focusing on industrial strategy, critical infrastructure and global supply chains.

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