Why do we need to flip the kernel in 2D convolution? Why do we need to flip the kernel in 2D convolution in the first place?
What's the benefit of this?
So, why can't we leave it unflipped? What kind of terrible thing can happen if you don't flip it?

SEE:
"First, flip the kernel, which is the shaded box, in both horizontal and vertical direction"

http://www.songho.ca/dsp/convolution/convolution2d_example.html
At the risk of repeating what has been said elsewhere, this is how convolution in two dimensions is defined:

$$y(n_1,n_2) = \sum_{k}\sum_{l}x(k,l)h(n_1-k,n_2-l))$$

$x(n_1,n_2)$ is your data and $h(n_1,n_2)$ is the kernel. Note that in order to compute the double sum above, you need to flip $h(n_1,n_2)$ with respect to $x(n_1,n_2)$. Just take $n_1=0$ and $n_2=0$: in this case you need to multiply $x(k,l)$ with the flipped kernel $h(-k,-l)$. In order to compute the output for other values of $n_1$ and $n_2$ you need to move the flipped kernel across the input data.