This specific tech you seem to be emotionally invested in is no different from the rest in this sense because it still faces in the real world the very same kind of risks and pitfalls as the rest - there are possible internal pitfalls inherent to every new technology (i.e. a problem we never knew about because we never used it with so many people in the real world before, becomes visible with widespread use) and there are possible external pitfalls inherent to how it fits in the complex world we live in (i.e. it turns out the use cases don’t make quite as much economic sense as was first tought or it indirectly generates more problems than it solves).
Such Process and Fit risks are true for every early stage “revolutionary” tech (i.e. we never did it before, now that we do it, we discover problems we were not at all aware of before) and is why the bean counters rarelly put money in revolutionary and instead go mainly for incremental improvements on proven tech. At times one or more of such “we had no idea this could happen problems” turn out to be surmountable, sometimes they’re not.
In the case of LLMs, the two risky problems from what I’ve heard are in how LLMs being trained in material which includes LLM-generated material actually get worse and the other is the so-called Hallucinations, which are really just the natural side effect of them being Language Models hence all that they do is generate compositions of language tokens that pass for human generated language, with no reasoning involved hence cannot validate through inductive or deductive reasoning said “compositions of language tokens”.
Unless you want to deny decades of History in Tech, you can’t logically extrapolate from an early “looks light it migh be a success” to “it will be a success”, especially the era of overhype we live in.