Do you not have any imagination?
Who is going to read the whisper transcripts of mass surveillance to make decisions on who to target for repression? That's what LLMs are good for, it allows mass surveillance to scale. You can feed it the transcript from millions of flock cameras (yes they have highly sensitive microphones) for example. Or you hack or supply chain compromise smartphones at scale and then covertly record millions of people. The LLM can then sift through the transcripts and flag regime critical language, your ideological enemies or just to collect compromat at scale. The possibilities are endless!
For targeting it's also useful, because you want to indiscriminately destroy a group of people you still need to decide why a hospital or school full of children should be targeted by a drone, if a human has to make that decision it gets a bit dicy, people have morals and are accountable legally (in theory), if you leave the decision up to an AI nobody is at fault, it serves as a further separation from the violence you commit, just like how drone warfare has made mass murder less personal.
The other factor is the amount of targets you select, for each target you might be required to write lengthy justifications, analysis on collateral damage and why that's acceptable etc. You don't want to scrap those rules because that's bad optics. But that still leaves you with the problem of scalability, how do you scale your mass murder when you have to go through this lengthy process for each target? So again AI can help there, you just feed it POIs from a map with some GPS metadata surveillance and tell it to give you 1500 targets for today with all the paperwork generated for you.
It's not theoretical, that's what Israel did in their genocide of the Palestinians, "the most moral army“ "the only democracy in the middle east":
https://en.wikipedia.org/wiki/AI-assisted_targeting_in_the_G...
And here is the best part: none of this has to actually work 100%, because who cares of you accidentally harm the wrong person, at scale, the 20% errors are just acceptable collateral damage.