The whole point is that an LLM is not a search engine and obviously anyone who treats it as one is going to be unsatisfied. It's just not a sensible comparison. You should compare working with an LLM to working with an old "state of the art" language tool like Python NLTK -- or, indeed, specifying a problem in Python versus specifying it in the form of a prompt -- to understand the unbridgeable gap between what we have today and what seemed to be the best even a few years ago. I understand when a popular science author or my relatives haven't understood this several years after mass access to LLMs, but I admit to being surprised when software developers have not.

Hosted and free or subscription-based DeepResearch like tools that integrate LLMs with search functionality (the whole domain of "RAG" or "Retrieval Augmented Generation") will be elementary for a long time yet simply because the cost of the average query starts to go up exponentially and there isn't that much money in it yet. Many people have and will continue to build their own research tools where they can determine how much compute time and API access cost they're willing to spend on a given query. OCR remains a hard problem, let alone appropriately chunking potentially hundreds of long documents into context length and synthesizing the outputs of potentially thousands of LLM outputs into a single response.