It's also misleading because nothing optimal is faster than A* . Quote from AIMA:
"A∗ is optimally efficient for any given consistent heuristic. That is, no other optimal algorithm is guaranteed to expand fewer nodes than A∗ (except possibly through tie-breaking among nodes with f(n) = C∗). This is because any algorithm that does not expand all nodes with f(n) < C∗ runs the risk of missing the optimal solution."
In the general setting A-star is optimal. JPS (and JPS+) can speed it up if you have additional information about the topology of the search space: 1) search is on a rectangular grid, and 2) movement between adjacent grid squares has uniform cost. In that case, you can take advantage of certain properties/symmetries of a rectangular grid to safely "skip" parts of the expansion that A-star would normally do, while still being guaranteed to find the optimal solution.
For what task? The speaker seems to be concerned with the problem of answering repeated path-finding queries for a single static map. With that problem in mind A* is indeed sub-optimal since it redoes all the computations for each query.
In that sense nothing is faster than Dijkstra's. There are problems for which A* heuristic fails completely, and Dijkstra's is better. JPS exploits a more specific structure of pathfinding just like A* exploits structure given by an heuristic. We have to be very careful to the setting when we say something is 'optimal'.
"A∗ is optimally efficient for any given consistent heuristic. That is, no other optimal algorithm is guaranteed to expand fewer nodes than A∗ (except possibly through tie-breaking among nodes with f(n) = C∗). This is because any algorithm that does not expand all nodes with f(n) < C∗ runs the risk of missing the optimal solution."