How is a point triangulated with SVD with 2 correspondences?

According to these slides, if you have 2 correspondences for one 3D point, is is possible to build a system of linear equations (slide 27):

AX = 0


And solve for X (the 3D point coordinates, 4D because it is homogeneous coordinates in this case). They propose to solve using SVD (singular value decomposition). SVD can be used to solve linear systems of the form AX=b However, SVD seems to fail when b=0 (as in the triangulation case), providing a degenerate solution X = 0

How does one solve the triangulation system using SVD then?

EDIT: I found this other deck which says that X is the last column of V matrix in SVD. But I would like to know why.

PS: I know in practice there are other methods which also consider more points/observations. But I am interested in this particular example