Because research requires precision. To do background reading, I have to throw out everything that isn’t relevant to my particular study. My study is new, so the main driver is my concept of what I want to do. The AI is good at finding things with the same keywords, but not at excluding what isn’t relevant to my thought process.
I use Google Scholar for searching for literature. I put in some keywords, then scan through the titles. Most of them are not relevant, but I can click through hundreds of titles in a few minutes to focus my attention where it matters, then use the references/citations of those papers to build out what I need to focus on.
AI will give me a massive block of text that includes 5-6 references, with long explanations of what each is about. It is an attempt to do the same thing, but I have to throw out most or all of them, re-prompt, and then get another similar answer. Sometimes it throws things out that aren’t peer reviewed.
It also loses the happenstance of stumbling on something irrelevant but important. That’s why I still look at the monthly journal publication lists to see what is happening.
Writing is similar. I need to condense several peer reviewed papers to the core ideas that are relevant to what I am saying, which might mean 1-2 sentences. AI will always give paragraphs that are not really getting to what I want, so I still have to do the work myself anyway. It is just adding steps to do the same process.
I recently rejected an AI written paper, and left a nasty note to the authors about how they were wasting my time.