That's generally true for all LLMs, but as with any tool - you can actually use it in a way that produces actual, verifiable results. Obviously it requires a lot of work that 99% of people won't put into what is essentially seen as a 'magic black box', but I do agree with GP that Gemini is quite useful in actually breaking things down in an understandable way, compared to most other models.

My experience with it is mostly using it against an actual, academic source, where I can easily tell if it starts making things up. I wouldn't rely on it for anything that I can't confirm myself.

I had a research question in my area of expertise. Tried it with ChatGPT, Claude, and Gemini. I think all three could have solved the question, but ChatGPT was an unreadable mess with too many sections and headings, Claude got stuck thinking and never produced an output. Gemini gave succinct explanation that I think was similar to ChatGPT conceptually but that I was able to follow and verify. The answer was actually fairly trivial to understand; I just hadn't gone at the problem from that perspective.

I know it is common for people to try AI in domains they aren't experts in, but it is also common for people to use AI in domains they are experts in.