It's about the immutability of the network at runtime. But I really don't think this is a big deal. General-purpose computers are immutable after they are manufactured, but can exhibit a variety of useful behaviors when supplied with different data. Human intelligence also doesn't rely on designing and manufacturing revised layouts for the nervous system (within a single human's lifetime, for use by that single human) to adapt to different settings. Is the level of mutability used by humans substantially more expressive than the limits of in-context learning? what about the limits of more unusual in-context learning techniques that are register-like, or that perform steps of gradient descent during inference? I don't know of a good argument that all of these techniques used in ML are fundamentally not expressive enough.