I agree that a hybrid could work, although ChatGPT struggles to keep context with more than a dozen paragraphs, so I doubt it could summarize millions of results.

And in either case, you're not solving the problem that commenters here are trying to solve: Google's bad results will still affect your search, so ChatGPT will be fed largely by dubious blogspam.

Even in a small number of paragraphs language models hallucinate. For example Barack Obama, it’s not hard for me to trick it into saying he was born in Chicago. He was not. But there’s a ton of text about Barack Obama in Chicago. Plus, Michelle was born there. When predicting the most likely next token for “Barack Obama was born in ______” there’s a lot of confounding text.

No, there’s a fundamental issue in that statistical language models don’t start with facts to form sentences, they form sentences based on previous tokens and statistical weights. The weights are not auditable or easily updatable.

And any kind of "tuning" of the weights to get better results is going to be so bespoke that you might as well just curate the response yourself

So LLM's are pretty much Mark V Shaney at heart, only with enough weights to get to the far side of Eliza?

Though for that matter a ChatGPT based product would be even more vulnerable to the same thing that made Google search suck: content sources gaming its process

This was my immediate thought as well. I don’t need millions of searches done, I need 20, then a language model to look through them and collect text that helps me find what I’m looking for.

The lucky thing for Google if they got it working, is they are half there already. As much as I despise Google, I have to admit GPT won’t be their death.

Google already does this it is called Knowledge Graph[0].

[0] https://support.google.com/knowledgepanel/answer/9787176?hl=...