> You have to go read it yourself afterwards
^ this is important.
Otherwise you may very well be missing anything really surprising or novel.
See for example https://www.programmablemutter.com/p/after-software-eats-the... , an experience report of NotebookLM where
> It was remarkable to see how many errors could be stuffed into 5 minutes of vacuous conversation. What was even more striking was that the errors systematically pointed in a particular direction. In every instance, the model took an argument that was at least notionally surprising, and yanked it hard in the direction of banality.
On one hand 2024 in AI time was a decade ago.
On the other, Google might not have done much to upgrade the podcast feature since them.
This regression towards the mean is still very much a feature of the newer models, in my experience. I don't see how a model that predicts the most likely word based on previous context + corpus data could possibly not have some bias towards non-novelty / banality.
It’s gotten somewhat better over time though clearly not their top priority.