genuine question because i can't tell anymore. i used to spend hours reading through raw customer feedback, reddit threads, amazon reviews, forum posts, manually pulling out patterns and organizing them into themes. it was slow and boring but by the end i knew the data cold. like i could tell you from memory which complaints came up the most and which ones were edge cases.
now i dump everything into an LLM and get a summary in 30 seconds. the output looks great, clean categories, ranked by frequency, sometimes even with example quotes. and i catch myself just... accepting it. moving straight to the next step without actually reading the source material. which means i'm making decisions based on a summary i never verified, written by a model that optimizes for coherence not accuracy.
the weird part is my output looks better now. cleaner reports, faster turnaround, more structured thinking. but i genuinely don't know if the quality of my conclusions has improved or if i've just gotten better at producing professional-looking work that's built on a shakier foundation. like the packaging upgraded but the ingredients might have gotten worse.
a few things i've noticed in my own workflow since leaning on AI for research: i read less raw data than i used to. i question patterns less when they come pre-organized. i spend more time prompting and less time thinking. and when the model gives me something that confirms what i already suspected, i almost never push back on it.
the counterargument is that AI handles the grunt work so i can focus on higher level thinking. and sometimes that's true. but "higher level thinking" can also just mean "skimming the summary and calling it strategy." hard to tell the difference from the inside.
has anyone else felt this? did you find a way to use AI for research without it quietly replacing the part of the process where you actually learn something
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