Earlier quoted context omitted.
You can’t reason about inference (or training) of LLMs on the semantic level. You can’t predict the output of an LLM for a specific input other than by running it. If you want the output to be different in a specific way, you can’t reason with precision that a particular modification of the input, or of the weights, will achieve the desired change (and only that change) in the output. Instead, it’s like a slot machin…
That’s like saying that the human brain is based on simple physical field equations, and therefore its behavior is easy to understand. Right, which is the point: LLMs are much more like human coworkers than compilers in terms of how you interact with them. Nobody would say that there's no point to working with other people because you can't predict their behavior exactly.
"...there's no point to working with other people because you can't predict their behavior exactly."
Because you CAN predict coworker behavior to a useful point. Ex, they'll probably reply to that email on Monday. They'll probably show you a video that you find less amusing than they do.
With LLMs you can't be quite sure whether they will make something up, forget a key detail, hide a mistake that will obviously be found out when everything breaks, etc. Stupid things that most employable people wouldn't do, like building a car and forgetting the wheels.