Major news outlets began reporting this week that Meta’s new AI chatbot has been automatically publishing users’ private conversations to a public feed, exposing information ranging from embarrassing to outright criminal. The feature, which launched earlier this year, defaults to making all interactions public unless users actively change their privacy settings — a decision that has resulted in elderly users and children unknowingly broadcasting their most intimate questions to the world.

The results are exactly what you’d expect: clueless baby boomers asking about genital injuries, very young people seeking help with gender transitions, and even one user who requested assistance with cooperating with authorities to reduce their penal sentence. Others have posted equally compromising queries, such as questions regarding the safety of masturbating while driving or the application of high heat to one’s genitals. These posts often include usernames and profile pictures that trace directly back to social media accounts, turning private medical anxieties and legal troubles into permanent public records.

Did Meta know this would happen? Decades of user experience research shows that virtually no one changes default settings. When you make “public” the default option, you’re effectively choosing to broadcast the vast majority of user interactions. Meta even included a pop-up warning that “Prompts you post are public and visible to everyone… Avoid sharing personal or sensitive information.”

But warnings are useless when users don’t understand they’re publishing to a feed in the first place and most people haven’t been conditioned to expect their AI chatbot interactions to appear on a feed normally associated with social media. The company’s press release cheerfully announced “a Discover feed, a place to share and explore how others are using AI,” as if turning private conversations into public entertainment was a feature rather than a catastrophic bug.

The Meta debacle is merely the most visible symptom of a broader ongoing crisis in AI privacy. According to the Electronic Frontier Foundation, AI chatbots can inadvertently reveal personal information through “model leakage.” A 2024 National Cybersecurity Alliance survey found that 38% of employees share sensitive work information with AI tools without employer permission. The Dutch Data Protection Authority has received multiple breach notifications from companies whose employees fed patient medical data and customer addresses into AI chatbots.

Even AI services that promise better privacy protections offer cold comfort. Anthropic’s Claude claims stronger default protections, while ChatGPT requires paid subscriptions to guarantee data isn’t used for training. But there’s nothing preventing these companies from changing their policies tomorrow and retroactively accessing years of stored conversations. We’re essentially trusting profit-driven corporations to resist the temptation of sitting on goldmines of intimate user data.

Recent breaches underscore this vulnerability. OpenAI suffered a data breach that exposed internal discussions, while over one million DeepSeek chat records were left exposed in an unsecured database. The MIT Technology Review warns we’re heading toward a security and privacy “disaster” as these tools become essential for daily life. Every day, millions of users pour their medical anxieties, work secrets, and intimacy challenges into AI chatbots, creating permanent records that could be exposed, sold, or subpoenaed at any moment.

Meta’s public feed disaster simply makes visible what every AI company is doing behind closed doors: harvesting intimate conversations for profit while users bear all the risk. GDPR violations can result in fines up to €20 million or 4% of global revenue, but actual enforcement against AI companies remains virtually non-existent there or in the United States. Even when companies try to comply, the documentation required by GDPR and CCPA doesn’t address how personal information is handled in AI training data or model outputs.

Put simply, nothing you tell an AI chatbot today is safe from future exposure, whether through corporate policy changes, security breaches, or legal demands. Meta’s ham-fisted episode helps us strip away the comforting illusion of privacy that other companies maintain. At least Meta’s users can see their embarrassing questions posted publicly and try to delete them. The rest of us have no idea what’s happening to our conversations.

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Oliver Bateman is a historian and journalist based in Pittsburgh. He blogs, vlogs, and podcasts at his Substack, Oliver Bateman Does the Work

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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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