An article in Wired on the challenges of using a natural language (ChatGPT) search engine:

wired.com - The race to build a chatgpt powered search engine

Two salient points:

1) It's very expensive to retrain a natural language bot on a new or expanded data set

2) "AI" chatbot search may be 10x more expensive to run than an indexed search because of the amount of computing power required, while delivering questionable results.

Excerpt:

Gary Marcus, a professor emeritus at New York University and a vocal critic of AI hype, believes ChatGPT is unsuited to search because it has no true understanding of what it says. He adds that tools like ChatGPT may cause other problems for search companies by flooding the internet with AI-generated, search engine-optimized text. 'All search engines are about to have a problem,' he says.

Alex Ratner, an assistant professor at the University of Washington and cofounder of Snorkel AI, which works on training AI models more efficiently, calls ChatGPT 'legitimately an inflection' in what software can do. But he also says that it may take a while to figure out how to prevent language models like GPT from making things up. He believes that finding a way to keep them up to date with new information to keep search fresh will most likely involve new approaches to training the underlying AI models.

How long those fixes will take to invent and prove out is unclear. It may be some time before the technology can radically change the way people search for answers, even if other use cases come to pass, such as dreaming up new recipes or serving as a study or programming buddy. 'It's amazing, and I told my team that people are going to see years as pre- and post-ChatGPT,' says Chen of Moveworks. 'But whether it will replace search is a different question.'