Not really. A model (after training) continues to be as plastic as it was before. Correspondingly, it can be made to "forget" what it has "learned" and learn something else. [At least architecture-wise. Whether the learned parameter values reduces plasticity is a non-trivial statement that needs to be demonstrated]

If contemporary AI/ML models behaved like what Alison Gopnik says, then their tendency to overfit would be even more of a problem -- you couldn't even transfer them from simulation domain to reality -- since they would lose all their plasticity overfitting to simulation!

Also, this article contains lots of other interesting ideas to think about. Highly recommend reading all of it.