For $x\in \mathbb{R}^d$, an elementary computation yields that $$\frac{d}{dp}\log \|x\|_p =\frac{1}{p^2}\sum_{i=1}^d \frac{|x_i|^p}{\|x\|_p^p}\log \frac{|x_i|^p}{\|x\|_p^p}=-\frac{1}{p^2}\operatorname{Ent}(\mu_{x,p}),$$ where $\mu_{x,p}$ is the law of a random variable taking values in $\{1,\ldots,d\}$ that takes the value $i$ with probability proportional to $|x_i|^p$ and $\operatorname{Ent}(\mu_{x,p}) = -\sum \mu_{x,p}(i) \log \mu_{x,p}(i)$ is its entropy.
I've been curious about the following question: Is there a good/conceptual reason for an entropy to appear here?
Obviously the computation is very easy, but could I have predicted this equality without any computation?