OpenAI recently released a new product designed to support medical care: ChatGPT Health. They describe this innovation as an “experience that securely brings your health information and ChatGPT’s intelligence together, to help you feel more informed, prepared and confident navigating your health” (Introducing ChatGPT Health, 2026). They claim that health is already one of the most common ways people use ChatGPT, and that this platform will increase users’ knowledge, preparation and confidence in managing their health (Introducing ChatGPT Health, 2026). However, while AI-powered health tools may reduce information overload and improve accessibility, their integration into help-seeking processes raises significant ethical questions regarding misinformation, autonomy and bias.

For decades, human-computer interaction was built on a comfortable assumption: people click, systems respond and the boundaries are clear. A button is a button, a menu is a menu. When things go wrong, you can often spot the missing step, the frozen cursor, the bad modal window.

For years, the industry has focused on making models bigger. This training time scaling (Kaplan et al., 2020) made models highly fluent, similar to a student who memorised the entire textbook. But fluency is not the same as reasoning. Large language models (LLMs) still struggle with complex logic, maths or coding tasks because they respond too quickly, predicting the next word without truly thinking (McCoy et al., 2023).

For decades, human-computer interaction was built on a comfortable assumption: people click, systems respond and the boundaries are clear. A button is a button, a menu is a menu. When things go wrong, you can often spot the missing step, the frozen cursor, the bad modal window.

For years, the industry has focused on making models bigger. This training time scaling (Kaplan et al., 2020) made models highly fluent, similar to a student who memorised the entire textbook. But fluency is not the same as reasoning. Large language models (LLMs) still struggle with complex logic, maths or coding tasks because they respond too quickly, predicting the next word without truly thinking (McCoy et al., 2023).

Trigger Warning/Disclaimer: This blog post mentions suicide. If you or someone you know is experiencing suicidal thoughts or a crisis, please reach out immediately for help. A hotline in your country can be found on befrienders.org.

AI is reshaping not only our social practices but also the foundations of regulatory thinking. The transformative power of AI has compelled regulators to adopt a regulatory learning process, shifting from static legal doctrine to an adaptive, learning-driven regulatory approach (Hadfield & Clark, 2023). This shift is driven by both the emergent challenges of AI and the motivation to devise laws that enable AI innovation while protecting against its potential risks (Smuha, 2019). As a result, we present some doctrine examples to argue that AI does not merely challenge existing legal rules but disrupts the obsolete assumptions underlying traditional regulations, making regulatory learning a structural necessity rather than a policy choice.