The thing to know about LLMs

(I'm mostly describing LLMs here, but many of the principles apply in their own way to other forms of generative AI.)

LLMs generate text. But there's a world of difference between "generating text" and "thinking", or even just "reporting information". LLMs generate text that is statistically plausible based on the patterns discovered in the mountains of text they were trained on. The text they were trained on, and the text they generate, are just words to the LLM; it learns how they are used together and in what patterns, but it doesn't understand any of them. "Thinking", or even "reporting information", implies a way to represent that information meaningfully, some kind of cognitive capacity, but LLMs don't have that, they just have words with no meaning and patterns for how to combine them. To the extent that their output appears to reflect thought or carry meaningful information, it's only because the training text was meaningful and some of that meaning was accidentally reproduced by similar word combinations in the generated text. In other words, they aren't as smart as they look; they're really just parlor tricks.

Here are some major reasons not to use LLMs: Author Lionel Dricot has published his personal policy on generative AI, and I adopt it as mine also.