The moment you define human experience or human consciousness through language is the moment you open the door to emergent properties for that very experience or consciousness. Learning through science that we think or that we act a certain way deterministically is also giving us the means to self-correct or condition ourselves to act or to have a different viewpoint. This is the difference between us and machines: we collectively defined the perimeters of our experiences (phenomenologically or through a science-based approach) and therefore anything that changes that perimeter also changes the context of the human experience in an infinitely recursive way.

We also use language in ways that make us able to collectively conceptualize the world and its phenomenas, although we individually interpret words differently, idiosyncratically from our individual experience and value systems. This is something else that differentiate us from machines: we don’t need the outputs and inputs to be mathematically the same to minimally understand each other. Our way to communicate, although imperfect and imprecise, has made us able to cooperate on a higher level than any other species (as of known) despite those epistemological hurdles.

I would posit that LLMs have no such emergent capabilities, as they’re bound by their training datasets and limited by their model’s parameters. The burden of proof for those emergent properties are on the data scientists, and as of yet I have seen no such proof.