Search “detransition” on the New York Times website and you’ll find 276 results, compared to more than 10,000 for “transgender”. To those who believe in “gender ideology” — that each person has a gender identity, which, should it not match one’s sex, needs medicating — that might seem fair. After all, we often hear that the detransition rate is “less than 1%“.

But the reality of those who transition and then try to return to living in their natal sex is much more complicated — as a recent New York Times article on the subject highlights.

In a piece entitled “The Truth About Detransitioning“, Kinnon MacKinnon, a trans researcher focused on improving trans medicine through the study of detransition, explores this complex phenomenon. Unlike much of the gender-affirmation industry, MacKinnon acknowledges the reality of detransition and does not attribute it solely to external factors, as gender psychiatrist Jack Turban’s flawed research has suggested.

Rather, MacKinnon shifts us from “it’s not happening”, past “it’s happening, but not because treatment is bad”, to “it’s happening and sometimes treatment is bad, but not as bad as Trump says”. Movement? Yes. But the truth? Doubtful.

MacKinnon writes that: “My personal experience, that of most trans people I know and a large body of research, show medical transition can help many resolve their gender dysphoria and improve their quality of life.” But some of that research, especially relating to minors, has been declared “very low certainty” by multiple systematic evidence reviews, which examines not only outcomes of studies but their reliability. Low or very low certainty claims can’t be relied upon, and the true effect could be the opposite of what’s asserted.

So while it’s true that some people are very happily transitioned, it can be devastating for others. MacKinnon notes that recent studies — conducted during a period when the number of people transitioning sharply increased — suggest a higher-than-expected rate of such negative outcomes, ranging from 5 to 10%.

According to MacKinnon’s own study, only 29% of those who detransitioned did it for “external” reasons: “lack of familial support, feeling discriminated against or an inability to get the treatment they need”. They didn’t regret — they just couldn’t keep going. Meanwhile, 20% blamed external reasons as well as “changing gender identity” and “mental health challenges”. They fared worse after transition, and didn’t consider themselves trans in the same way.

That leaves two more cohorts. The next group, making up about 20%, “cited changing gender identity, but generally did not feel regret about their earlier transition”. These may be the people who shift from trans to non-binary as an off-ramp, but we don’t know. Some studies suggest regret can take up to a decade to set in. Maybe they’ll feel worse later, or maybe they won’t. Besides, is that how we measure the treatments’ success: whether or not someone feels bad about it later?

This brings us to the largest group — an astonishing 33% who detransitioned due to “identity changes, mental health-related factors, and dissatisfaction with treatment”. Stories elsewhere have shown how these tend to be the most troubling cases: individuals left with permanent physical damage, often realizing too late that their reasons for transitioning masked deeper issues needing attention, ranging from internalized homophobia to autism.

Given this significant percentage, one might expect greater caution regarding the scale and rapid pace of gender-affirming surgeries. Yet MacKinnon remains firm on one point: “Nothing in my team’s research, or any other studies on detransition, should lead to the conclusion that policymakers ought to issue blanket bans on gender-affirming care.”

That’s a hard assertion to make based on MacKinnon’s own data. If only 29% detransitioned for external reasons, and the majority reported worsening mental health and regret, why wouldn’t policymakers consider banning treatments that led to those results? After all, gender-affirming care proponents haven’t offered alternatives or modifications, suggested stricter guidelines or more rigorous follow-up.

While I have never publicly supported outright bans, I understand why many view them as a response to a regulatory vacuum left by the rapid expansion of the gender-affirmation industry without sufficient oversight. The lack of effective safeguards creates a perfect storm where vulnerable individuals may undergo irreversible medical interventions without comprehensive evaluation or adequate support systems.

Researchers like MacKinnon must move beyond simply asking why people detransition. If their goal is to provide genuine alternatives to sweeping bans, they need to listen carefully to those who regret their transitions. The critical question should be: how can the medical community prevent these devastating outcomes from happening in the future? Without addressing this, research risks becoming an academic exercise that fails those it is meant to serve.

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Lisa Selin Davis is the author of Tomboy. She writes at Broadview on Substack.

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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.

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

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