I was defending generative AI recently when an article came up about Gemini misidentifying a toxic mushroom:
https://news.ycombinator.com/item?id=40682531. My thought there was that nearly everyone I know knows that toxic mushrooms are easily misidentified, and there have been lots of famous cases (even if many of them are apocryphal) of mushroom experts meeting their demise from a misidentified mushroom.
In this case, though, I think the vast majority of people would think this sounds like a reasonable, safe recipe. "Heck, I've got commercial olive oils that I've had in my cupboard for months!" But this example really does highlight the dangers of LLMs.
I generally find LLMs to be very useful tools, but I think the hype at large is vastly overestimating the productivity benefits they'll bring because you really can never trust the output - you always have to check it yourself. Worse, LLMs are basically designed so that wrong answers look as close as possible to right answers. That's a very difficult (and expensive) failure case to recover from.