I agree that the Boolean matrix multiplication given on that slide is not work efficient. It has a parallel runtime of O(1) and work O(N3) which would be work efficient if the best sequential algorithm would have runtime O(N3). It is true that there are better ones as mentioned above, and compared to these, it is worse. It is therefore not work efficient.
Note however that Strassen's algorithm assumes operations + and * of a ring which is not the case for ⋁ and ⋀. Still, we can use Strassen's algorithm if we simply use the Boolean matrices as integer matrices (and if you want modulo n+1 for nxn matrices). Note that the elements of the product matrix are computed as c[i][j] = sum_{k=0..n-1} a[i][k]*b[k][j] and that a[i][k]*b[k][j] is the same as a[i][k]⋀b[k][j], i.e., 1 iff both a[i][k] and b[k][j] are 1. Hence, we have ⋁_{k=0..n-1} a[i][k]⋀b[k][j] iff sum_{k=0..n-1} a[i][k]*b[k][j] >0. For this reason, we can use any matrix multiplication algorithm like Strassen's algorithm or Coppersmith-Winograd also for Boolean matrix multiplication.
For this reason, there are sequential algorithms for Boolean matrix multiplication with a runtime of less than O(N3), and therefore the considered Boolean matrix algorithm with a parallel runtime of O(1) and work O(N3) ist not work efficient (in the sense of PRAMs, i.e., Brent's scheduling principle).
It can be shown that (Boolean) matrix multiplication can be implemented by a PRAM algorithm that runs in parallel time O(log(n)) with work O(M(n)) where M(n) is the runtime of the best sequential algorithm for matrix multiplication (see Jájá92, page 248-249). It would be interesting to combine both algorithms!