Thanks for a sober look without the hysteria that's sweeping tech forums right now in actually using more precise terms than conflating "AI" with the subset of "language models". AI/ML has been around in various forms with various subcategories for decades. It's just the LLM evangelists/demonizers are currently sucking all the oxygen out of the room with no middle room. Indeed, many advanced signal processing systems incorporate some form of machine learning, it's just hasn't historically been called that because of the backlash from the late 80s' cycle of AI hype. We'll probably end up in a similar marketing down cycle at some point where the marketing shifts away from the current conflation trend and call the technologies and techniques that survive the current cycle something else yet again.
But yeah, that ML caveat is the same one that's always been there: a learning algorithm learns from what it's seen before, but usually can't intuit an outlying event's severity. The tendency is to underestimate the problem or, in some cases where there's a circuit breaker somewhere in the code, bail out in the computer algorithmic equivalent of a wail of "I DON'T KNOW! DADDY HELP!" (aka they exit simulation with an error equivalence).
