Downside is a lot of those that argue, try out some stuff in ChatGPT or other chat interface without digging a bit further. Expecting "general AI" and asking general questions where LLMs are most prone for hallucinations. Other part is cheap out setups using same subscription for multiple people who get history polluted.

They don't have time to check more stuff as they are busy with their life.

People who did check the stuff don't have time in life to prove to the ones that argue "in exactly whatever the person arguing would find useful way".

Personally like a year ago I was the person who tried out some ChatGPT and didn't have time to dabble, because all the hype was off putting and of course I was finding more important and interesting things to do in my life besides chatting with some silly bot that I can trick easily with trick questions or consider it not useful because it hallucinated something I wanted in a script.

I did take a plunge for really a deep dive into AI around April last year and I saw for my own eyes ... and only that convinced me. Using API where I built my own agent loop, getting details from images, pdf files, iterating on the code, getting unstructured "human" input into structured output I can handle in my programs.

*Data classification is easy for LLM. Data transformation is a bit harder but still great. Creating new data is hard so like answering questions where it has to generate stuff from thin air it will hallucinate like a mad man.*

Data classification like "is it a cat, answer with yes or no" it will be hard for latest models to start hallucinating.