This is actually true for any rank $1$ matrix regardless of the zero rows condition. One way to see this is that $0$ is an eigenvalue of $A$ with geometric multiplicity $n-1$. The remaining eigenvalue must be equal to $\text{tr} (A)$ (since the sum of the eigenvalues is the trace). The eigenvalues of $A$ are $0, 0, \ldots, 0, \text{tr}(A)$, and the eigenvalues of $A+I$ are shifted up by $1$, so: $1,1,\ldots, 1, 1+\text{tr}(A)$. The determinant is just the product of these, which is $1+\text{tr}(A)$.
Alternative solution without eigenvalues: You have probably seen by now that expanding the determinant along a row with only one non-zero entry is particularly easy: instead of a sum, you only get one term. For example, expanding on the first row of the below matrix gives:
$$\left|\begin{matrix}\color{red}0&\color{red}3&\color{red}0 \\ a & b & c \\ d & e & f\end{matrix}\right| = (-1)^{1+2} \cdot 3 \cdot \left|\begin{matrix} a & c \\ d & f\end{matrix}\right|,$$
where the exponent $1+2$ comes from the row/column of the non-zero element, and the $3$ is the value of that element.
Things get even easier when you have a row whose only non-zero element is a $1$ situated anywhere on the main diagonal (i.e. row $i$ and column $i$ for some $i$). In this case, the term $(-1)^{i+j} = (-1)^{i+i} = (-1)^{2i}$ simplifies to $1$, and the value of the non-zero element is $1$, so the determinant of the larger matrix is exactly equal to the submatrix obtained by deleting row $i$ and column $i$, e.g.:
$$\left|\begin{matrix}a & b & c \\ \color{red}0&\color{red}1&\color{red}0 \\ d & e & f\end{matrix}\right| = \left|\begin{matrix} a & c \\ d & f\end{matrix}\right|.$$
Now, think very carefully about the structure of the matrix $A+I$ in the solution you linked to. This contains a large number of rows with the same convenient property as the one we have above: every element is equal to zero except for a single $1$ on the main diagonal. The determinant of this matrix reduces to the determinant of the smaller matrix obtained by deleting the row and column of any of these solitary $1$'s. Now try to picture what happens as you systematically eliminate each one: in the end, only two rows and two columns remain: the rows $i$ and $k$ and the columns $i$ and $k$.
This is how they reach the small matrix $\left|\begin{matrix} \alpha_i+1 & \alpha_k \\ \beta \alpha_i & \beta \alpha_k + 1\end{matrix}\right|$, just by extracting those entries from the larger matrix. If you don't understand where the larger matrix comes from, please state so in the question itself.