As a CMU professor, in case it wasn't clear: We, in general, felt fairly strongly that we did not want to radically alter the meaning of a CMU CS degree, i.e., that our B.S. in C.S. students
do come out with the degree having hit some depth in systems, in PL, in algo and theory.
We also felt fairly strongly that supporting students who wanted something different from their education, which still falls under the broad CS umbrella but is different in some important ways -- was important.
These things are surprisingly hard to juggle. Eight semesters, four courses per semester, juggling cross-university requirements, some amount of personal electives and fun, and an intensive set of major courses, doesn't actually leave much wiggle room, especially when you include the prerequisite dependency graph of those courses.
Hence, it's a different major, because it reflects a quite different set of skills that {employers, grad schools, whatever} can count on the graduate knowing.
(There's also value in providing a roadmap for sequencing these things, again because of the prerequisite chains, but I concur that that alone isn't a reason for a major.)
And yes, of course it's all a continuum. We call AI a separate major, alongside things like HCI and computational biology. We don't have an "operating systems" major. If you like systems a lot, you still have to take all the normal algo/PL/etc. breadth requirements.
It's all a judgement call. The feeling here is that AI/ML are starting to contain a sufficiently different set of core skills that it was worth breaking them out into their own major instead of just saying "eh, go take some electives, and try to fit in all of your interests while _still_ taking all the other CS classes." Because that's what we used to suggest, and the students rightly pointed out that it wasn't possible to do it right within the existing degree framework, at least, if you wanted to sleep.