Yes, I find the biggest issue in discussing the present state of AI with people outside the field, whether technical or not, is that "machine learning" had only just entered popular understanding: i.e. everyone seems ready today to talk about the limits of training a machine learning model on X limited data set, unable to extrapolate beyond it. The difference between "learning the best binary classifier on a labelled training set" and "exploring the set of all possible programs representable by a deep neural network of whatever architecture to find that which best generates all digitally recorded traces of human beings throughout history" is very far from intuitive to even specialists. I think Ilya's old public discussions of this question are the most insightful for a popular audience, explaining how and why a world model and not simply a Markov chain is necessary to solve the seemingly trivial problem of "predicting the next word in a sequence."