They did not learn anything. Their engines started outputting different statistically probable output based on changed input parameters.
On the other extreme we tend to give our human-exceptionalism way too much weight and contrast its behaviour with "mere statistical parrot rumination" as if our brains deep down were not just a much (much) more sophisticated machine, but nevertheless a machine.
That is one position, but materialism is not universally taken as a foregone conclusion.
I guess the dualistic viewpoint can be graded on a spectrum.
Some people may hold the "absolute dualist" position, where no matter how much advances we make in reproducing cognitive abilities, they will always move the goalpost and claim that what makes _us_ different is that little extra bit that hasn't been reached yet. This leads to accepting the p-zombies as a logical possibility.
On the other side of the spectrum is the "Bayesian dualist" who could in principle change their minds but are not at all convinced that a naturalistic and mechanistic implementation of the brain can possibly explain the utter complexity and beauty of the human condition and thus are unmoved by the crude first steps in that field.
This category would consider a p-zombie to not be logically coherent but they may just state that in practice we stand no chance in producing a mechanism that would truly behave not even close to a human being.
Those people may contemplate a possibility that we may eventually explain how cognitive behaviour arises from nature but lean towards the necessity of discovering some brand new science or some quantum effects to account for the mysterious beauty of the human condition (e.g. Penrose)
Does this categorization make sense? Are there some other points in that spectrum I'm missing?
I figure brains are amazing and we may never understand how they work very well. But nobody needed to understand intelligence for it to occur in humans. Evolution is a crude optimization mechanism that brute-forced the problem over millions of years with biology as a substrate. Computers are a worse substrate in some important ways. However, we have some incredibly fast optimization mechanisms available to us when we train models (and of course we aren’t limited to a single chip).
I’m hand-waving at the inner workings of intelligence and don’t claim to understand it at all. Given my belief that meat computers can do it without any grand design, I don’t see any reason to think that silicon computers won’t be able to do the same.
Now, if you believe that consciousness and/or intelligence requires some kind of intangible soul, and such souls are hard to come by, of course you won’t find one on the rack in an H100.
Rather living-systems-exceptionalism
There's other reasons to consider this particular model "not learning", but that ain't it, it's too generic and encompasses too much.