Let $$ I_m=\int_0^\infty (1+s)^m \, {\rm e}^{-ns} \, {\rm d}s \, .$$
I've been thinking some time on the following inequality $$I_{m} \left( I_{m+1} + I_{m-1} \right) \geq 2 \, I_{m+1} I_{m-1}$$ or in somewhat interesting parallel circuit fashion $$\frac{1}{I_{m+1}} + \frac{1}{I_{m-1}} \geq \frac{2}{I_m} \, .$$ Is there any clever way to tackle it?
It also looks somewhat like AMGM $$I_{m+1}^2 + I_{m-1}^2 \geq 2 \, I_{m+1}I_{m-1}$$ in a sense, where one $I_{m+1}$ and $I_{m-1}$ on the LHS is replaced by $I_m$.
$I_m$ is monotonically increasing in $m$ and log-convex (i.e. $I_{m+1} I_{m-1} \geq I_m^2$, without proof though).
It seems to be a rather tight inequality, since e.g. $$I_{m+1} I_m + I_m I_{m-1} \geq I_{m+\frac{1}{2}}^2 + I_{m-\frac{1}{2}}^2 \geq 2 \, I_{m+\frac{1}{2}} I_{m-\frac{1}{2}} \leq 2 \, I_{m+1} I_{m-1}$$ or $$I_m \, \frac{I_{m+1} + I_{m-1}}{2} \geq I_m^2 \leq I_{m+1} I_{m-1} \, .$$
I will show that the inequality is true for $m$ large compared to $n$.
$\begin{array}\\ I_m &=\int_0^\infty (1+s)^m e^{-ns}ds\\ &=\int_0^\infty \sum_{k=0}^m{m\choose k}s^k e^{-ns}ds\\ &=\sum_{k=0}^m{m \choose k}\int_0^\infty s^k e^{-ns}ds\\ &=\sum_{k=0}^m{m \choose k}\frac{k!}{n^{k+1}}\\ &=\sum_{k=0}^m\frac{m!}{(m-k)!n^{k+1}}\\ &=\sum_{k=0}^m\frac{m!}{k!n^{m-k+1}}\\ &=\frac{m!}{n^{m+1}}\sum_{k=0}^m\frac{n^k}{k!}\\ &<\frac{m!}{n^{m+1}}e^n \\ \end{array} $
If we treat this as an equality, and it is close for $m >> n$, then $\frac1{I_m} \approx \frac{n^{m+1}}{m!e^n} $ so that $\frac{1}{I_{m+1}} + \frac{1}{I_{m-1}} \geq \frac{2}{I_m} $ becomes $ \frac{n^{m+2}}{(m+1)!e^{n}}+ \frac{n^{m}}{(m-1)!e^n} \geq \frac{2n^{m+1}}{m!e^n} $
Dividing by $\frac{n^m}{(m-1)!e^n} $ this becomes $ \frac{n^2}{m(m+1)}+ 1 \geq \frac{2n}{m} $ which is certainly true for $m \ge 2n$.