That's the weird thing with LLMs, they know just enough about a topic to fool anyone without much knowledge on the subject, seemingly even frontier labs.
They are trying to sell this false myth of machines so good they can replace any human expert, and they buy this BS themselves, so, instead of actually learning something and/or confirming results with institutions of experts (what's left of them anyway) they just rush out a PR piece knowing the unwitting public and VCs won't care enough to look into the validity of anything themselves anyway.
The whole thing is a gamble on whether or not they can convince the world that a fancy word machine substitutes for deep experience and expertise.
(To be clear, the fancy word machines are still very useful tools, but they are not adequate replacements for knowing humans).
There are humans like this and they are some of the most catastrophic people to have on a project, I actively avoid them.
I'm sure LLMs are different.
How do I know I didn't learn hallucinations?
Because based on that knowledge, I've produced code that works as intended.
Or at the very least, you were interested in the subjects in question and just never had the time or other resources to learn them.
As such, I'm learning through motivated, self-directed, focused exploration, using the AI to answer questions, run experiments, explain findings, investigate surprises, and explore new directions to research, all grounded in concrete data related to those goals.
The data and our results on it are what keep us honest. The data is also what helps understanding nuances and building intuition because the AI can explain outcomes in the context of samples data points that you can reason about independently.
The difference between YOLO vibe-coding and engineering is understanding. I think this is the future of learning and skill-building.
If that's the case, that's the primary example that I've seen in many places.