I think you're vastly underestimating how little of human intent is really encoded in language in a strict sense, and how much nontrivial inference of intents LLMs do every day with simple queries. This used to be an apparently insurmountable barrier in pre-LLM NLP, and now it is just not a problem.
Suppose I'm in a cold room, you're standing next to a heater, and I say "it's cold". Obviously my intent is that I want you to turn on the heater. But the literal semantics is just "the ambient temperature in the room is low" and it has nothing to do with heaters. Yet ChatGPT can easily figure out likely intent in situations like this, just as humans do, often so quickly and effortlessly that we don't notice the complexity of the calculation we did.
Or suppose I say to a bot "tell me how to brew a better cup of coffee". What is encoded in the literal meaning of the language here? Who's to say that "better" means "better tasting" as opposed to "greater quantity per unit input"? Or that by "cup of coffee" I mean the liquid drink, as opposed to a cup full of beans? Or perhaps a cup that is made out of coffee beans? In fact the literal meaning doesn't even make sense, as a "cup" is not something that is brewed, rather it is the coffee that should go into the cup, possibly via an intermediate pot.
If the bot only understands literal language then this kind of query is a complete nonstarter. And yet LLMs can handle these kinds of things easily. If anything they struggle more with understanding language itself than with inferring intent.