True, for large-scale problems Hessian-vector products are often the way to go (or completely ignoring second order information). However, computing first an expression for the Hessian symbolically and then taking the product with a vector is still more efficient than using autodiff for Hessian-vector products. It is not in the paper though. But the gain is rather small (just a factor of two or so). But true, only for problems involving up to a few thousand parameters computing Hessians or Jacobians is useful.