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.