The null hypothesis isn't just the opposite of whatever your opposition believes.
For LLMs the null hypothesis would be that there is no relationship between the input and output tokens. Something that is so obviously not true that it's not even worth calculating the number of sigmas away from the null hypothesis that LLMs are.
So clearly we discarded the null hypothesis sometime in 2017. Now we have a system that is really really good at pattern matching and seems to understand consequences. Is that "seeming" just a ruse or does it really understand stuff? A proper scientists would look at that evidence and put forward the hypothesis that maybe it really does understand stuff and begin working on experiments that would disprove that alternative hypothesis, moving forward with the assumption that the hypothesis is true until disproven or a better hypothesis is proposed that explains previous evidence more accurately. Naysayers saying "you haven't proven that pattern matching becomes understanding to my satisfaction" is not a rebuttal. They need an alternative hypothesis that can make predications that better fit the model and can be tested.
The only rebuttals I've heard are "AI can't actually understand stuff and therefore can't do X" which is a testable hypothesis at least. But Invariably AI eventually does X, just in a different way than anyone really expected.
They naysayers didn't come around claiming they invented a form of intelligence in a program, AI companies/advocates did. Burden on proof is on them.