You don't understand how the tech works, then.

LLMs aren't as good as humans at understanding, but it's not just statistics. The stochastic parrot meme is wrong. The networks create symbolic representations in training, with huge multidimensional correlations between patterns in the data, whether its temporal or semantic. The models "understand" concepts like emotions, text, physics, arbitrary social rules and phenomena, and anything else present in the data and context in the same fundamental way that humans do it. We're just better, with representations a few orders of magnitude higher resolution, much wider redundancy, and multi-million node parallelism with asynchronous operation that silicon can't quite match yet.

In some cases, AI is superhuman, and uses better constructs than humans are capable of, in other cases, it uses hacks and shortcuts in representations, mimics where it falls short, and in some cases fails entirely, and has a suite of failure modes that aren't anywhere in the human taxonomy of operation.

LLMs and AI aren't identical to human cognition, but there's a hell of a lot of overlap, and the stochastic parrot "ItS jUsT sTaTiStIcS!11!!" meme should be regarded as an embarrassing opinion to hold.

"Thinking" models that cycle context and systems of problem solving also don't do it the same way humans think, but overlap in some of the important pieces of how we operate. We are many orders of magnitude beyond old ALICE bots and MEgaHAL markov chains - you'd need computers the size of solar systems to run a markov chain equivalent to the effective equivalent 40B LLM, let alone one of the frontier models, and those performance gains are objectively within the domain of "intelligence." We're pushing the theory and practice of AI and ML squarely into the domain of architectures and behaviors that qualify biological intelligence, and the state of the art models clearly demonstrate their capabilities accordingly.

For any definition of understanding you care to lay down, there's significant overlap between the way human brains do it and the way LLMs do it. LLMs are specifically designed to model constructs from data, and to model the systems that produce the data they're trained on, and the data they model comes from humans and human processes.