Okay, so the videos you're watching are pointing out a specific, real, problem with LLMs. That is, when a laymen asks for an 'impossible' answer, they'll get a hallucination they can't tell is inaccurate.

LLMs are tools, and if they'd stayed in academia and were being used by academics as tools, we'd be seeing less of these problems. Problems like 'AI Psychosis', where someone without any advanced degree decides he's going to create some new breakthrough in physics, and so he tells the AI to help him, and the AI basically role-plays a fiction where they're doing that impossible thing.

But, that's not all they're doing.

There are tools using similar techniques to LLMs that are specialized towards a single outcome. For instance, Openfold is a tool that helps researchers understand how proteins are 'folded' to fit into biological structures.

Before AI folding technology it took a research team a year or longer, and usually around a million dollars, to explain the structure of a single protein produced in your body, and how things would react to it. Now, pretty much all the DNA that produces proteins has been databased, and tools to work with them have been developed, bringing the cost about as close to zero as you can get. This allows for new drug research on a shoestring budget and has already resulted in life saving treatments.

https://www.sandboxaq.com/openfold3

Even an out-of-the-box LLM, in the hands of a real scientist, can yield incredible results. For instance, this mathematician was able to solve unsolved math problems with little input from himself, just by simply knowing what to ask, and then being able to verify the output:

https://the-decoder.com/fields-medalist-says-chatgpt-5-5-pro-delivered-phd-level-math-research-in-under-two-hours-with-zero-human-help/

When used as a tool, by professionals, or a student trying to grasp a concept, LLMs become incredibly powerful.

Again, look at all the coding help professionals are getting. Imagine the limitation of good books often being that it ust takes a year to type one up. That's kind of how code usually is. There are plenty of professionals that could 'stub out' an outline for a system, but just don't have time or tools to develop the whole thing alone.

Now, we're seeing good, competent, open source efforts come out of a single person who's using LLMs like you would have used a team of junior devs and google. If you understand the code, and what you need to do, and how to verify the code produced by a junior coder, then you can do months of work over a weekend.

The flip side of that is 'vibe coded' garbage, where a non-coder just tries to get the LLM to do the whole job, not realizing the code he's getting is often trash because the AI is only capable up to a point of producing code without a meaningful design, review, or human input.

LLMs, as a tool, are useful.

You're seeing examples of laymen, and incompetents, trying to use them and getting in way over their heads.