I think AI skeptics have a strong bias to assume that human intelligence fundamentally functions differently from LLMs. They may be correct, but we don't have a strong enough understanding of human cognition to make the claim in as uncertain terms as the skeptical argument is unusually made. The training methods between human learning and machine learning are obviously fundamentally vastly different as are the infrastructure-level mechanics. These elements are likely never going to align, though with time the machine infrastructure may start to increasingly resemble human bio hardware. I bring this up because these known vast differences may account for a significant portion of the differences in expected output from human and machine processing. We don't understand the fundamental conceptual "black box" portions of either form of processing well enough to state definitely what is similar or dissimilar about those hazy areas. Somewhere within that not-well-understood area is what we collectively have vaguely defined "intelligence." But also within that area are all the other aspects that both humans and now machines are quite good at - prediction, fluency, translation. The challenge of lexicon and definition is potentially as difficult a task as is sharpening the focus of our understanding of the hazy black-box portion of both machine processing as well as human processing. Until all those are better defined I don't think we have a good measure for answering the question of machine intelligence either way.