The challenge of “continual learning” in artificial intelligence (AI) remains significant, as current models lack the ability to learn and adapt during user interactions. While developers strive to enhance AI capabilities, models are typically limited to fixed contexts during sessions, which complicates AI regulation. Ongoing innovations, like Cursor’s daily updates and Claude’s memory features, indicate progress, but no general-purpose AI model can self-update like the human brain. This presents regulatory challenges, as evolving AI could blur liability lines and disrupt existing frameworks designed for static models. With models potentially becoming dynamic and user-modifiable, regulatory bodies may need to rethink how they monitor, assess, and hold entities accountable. Anticipating these developments is crucial, as regulations based on traditional models may not suffice for AI that learns continuously, highlighting the need for adaptable legal frameworks that ensure safety without stifling innovation.
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