No, it's capable of reasoning, but it gained its knowledge of how the world works through analyzing things that were written or drawn by (mostly) humans.
To oversimplify a bit, it's a next-word-in-the-sentence prediction engine.
Turns out that when you ask LLMs to predict the next word in the sentence, and then train billions of parameters on billions of sentences, they realize the best way to improve at predicting the next word is to understand why the world works the way it does.
GPTs are similar. They can tell you that the sky is blue and they can tell you why the sky is blue, but they can't translate that to a hypothesis about how the wavelengths of light also influence the properties of lasers. If they've ingested information about these different topics, they can make the connection, but otherwise they can't.
/s
My point was that it should be.
The training is on images and text, and the output is text. But nothing constrains the model in the middle of those two ends to only model based on language features, as opposed to world features discoverable from the language and images.
So if you make your model through slow thinking and discovery chatgpt just builds it to a billion words.
The result is the same
A LLM (that hasn't been trained on any data regarding this topic) is unlikely to demonstrate the same bouba/kiki effect seen among humans. https://en.wikipedia.org/wiki/Bouba/kiki_effect
People continue to claim LLMs are just parrots. But then there is never a solid rebuttal to how humans are different.