No, that's not really true these days.
LLMs are designed to effectively do weighted word prediction, yes, and stringing together plausible sentences was an early metric, for sure. This is research going back decades. This is done based on both syntax and knowledge.
But the real benefits of AI are not this alone, the real benefits of AI are when this ability to "understand" a statement or request is leveraged in order to carry out other specific commands.
To highlight a really simple example - an LLM can respond to "tell me how to cook eggs" because that's easy and trivial information. But where an LLM provides additional value is when it can contextualize a prompt to do other actions beyond a simple description. In the case of an LLM running with a search engine it might be able to determine that "cook eggs" might mean frying, boiling, poaching, scrambling etc. While "cook eggs in water" might mean boiling or poaching, but probably not frying. By trying to understand (or more correctly, simulate understanding of) the query it can then leverage other tools, including research tools. And when the LLM is searching the web for information on cooking eggs, it is also able to analyze the information that it found to see if it's relevant. LLMs/AI today are fundamentally not just stringing together plausible sentences, and that hasn't been for quite a while. It's the ability to use an LLM as a part of a toolset, not purely as a hammer, where the value and benefits lie.
LLMs do regurgitate information, and when it comes to trivia it can be poor. Right tool for the right job still applies though. Don't expect your downloaded 8GB model to match a cloud based AI that is also scraping the Internet in real time.
We've already seen material medical breakthroughs come from AI. It could be argued that every step forwards in technology is a step towards techno-geddon. I get it. But do we just stop trying to advance?