They end up having to explain their degrees, which is generally not a good thing. It happens all the time with engineering. In the job market a degree in mechanical engineering is in general worth more than one in robotics.
You also run the risk of graduating into another AI winter, or just deciding you hate AI, and then you really have to try hard to explain that your degree is really just a CS degree.
Imagine graduating now with an undergrad in big data--that sounded like a good idea 5 years ago.
Is that a good analogy? Big data started out as a marketing buzzword, whereas AI always was an academic field of research.
On the other hand, a degree in CS with specialization in Networking, would be more valued than a plain degree in CS.
I'd say if you want to specialize, do a master's. A degree such as above would put the holder in really bad position if that field goes into ice age. In case that happens to master's degree, you always have your bachelor's degree to fall back to.
A consequence of this degree is that it makes CS degrees worth less. Because AI/ML is by definition a subset of CS, this degree implies that you have CS + more. It doesn’t fairly show that there is a tradeoff in a specialized curriculum (less exposure to other areas of CS).
I would say that if you are at CMU or another school offering this degree, you would be a fool not to do it. Even if you are not interested in AI/ML, it will be more hirable than CS because the name implies it is “better” than a CS degree. It’s a similar situation with the new “cybersecurity” degrees being offered. Ideally the accreditation group would bring some sense into this but so far they have greenlit both degrees.