On the other hand, where I remain a skeptic is this constant banging-on that somehow this will translate into entirely new things - research, materials science, economies, inventions, etc - because that requires learning “in real time” from information sources you’re literally generating in that moment, not decades of Stack Overflow responses without context. That has been bandied about for years, with no evidence to show for it beyond specifically cherry-picked examples, often from highly-controlled environments.
I never doubted that, with competent engineers, these tools could be used to generate “new” code from past datasets. What I continue to doubt is the utility of these tools given their immense costs, both environmentally and socially.
Personally I hope this will materialize, at the very least because there's plenty of discoveries to be made by cross-correlating discoveries already made; the necessary information should be there, but reasoning capability (both that of the model and that added by orchestration) seems to be lacking. I'm not sure if pure chat is the best way to access it, either. We need better, more hands-on tools to explore the latent spaces of LLMs.
That said, yes, it could be highly beneficial for identifying patterns in existing research that allows for new discoveries - provided we don’t trust it blindly and actually validate it with science. Though I question its value to society in burning up fossil fuels, polluting the atmosphere, and draining freshwater supplies compared to doing the same work with Grad Students and Scientists with the associated societal feedback involved in said employment activities.