An upper bound on Gaussian mean width The following is an excerpt from this blog post on Talagrand's Generic Chaining:
"Suppose that we have a subset $T \subseteq {\mathbb R}^n$, we pick a random Gaussian vector $g$ from $N({\bf 0}, I)$, and we are interested in the random variable $\sup_{t \in T} \langle g,t \rangle $.
A first observation is that each $\langle g,t \rangle$ is Gaussian with mean zero and variance $|| t||^2$. If $T$ is finite, we can use a union bound to estimate the tail of $\sup_{t\in T} \langle t,g \rangle$ as   $$\displaystyle \Pr \left[ \sup_{t\in T}\ \langle g, t \rangle > \ell \right] \leq |T| \cdot e^{-\ell^2 / 2 \sup_{t\in T} \lVert t\rVert^2} $$
and we can compute the upper bound
$$\mathbb{E}_{g \sim N({\bf 0},I)} \left( \sup_{t \in T} \langle g,t \rangle \right) \leq  O \left( \sqrt{\log |T|}\cdot \sup_{t \in T}\lVert t\rVert 
 \right) ."$$
I don't see how we get the last bound on expectation. We can get a lower bound on the cdf of $\sup_{t \in T} \langle g,t \rangle $ from the tail bound. But this doesn't seem to help in getting an upper bound on the expectation.
 A: A useful result from Talagrand's book is that for any r.v. $X \ge 0$ satisfying for any $t\ge 0$
$$
P(X \ge t) \le A \exp (-t^2/B^2)
$$
for constants $A\ge 2$ and $B>0$. Then $E X \le CB \sqrt{\log A}$, where $C$ is a universal constant.
The proof is straightforward:
\begin{align*}
        E X
        &= \int_0^{\infty} P(X \ge t) dt\\
        &= \int_0^{t_0} P(X \ge t) dt + \int_{t_0}^{\infty} P(X \ge t) dt\\
        &\le t_0 + \int_{t_0}^{\infty} P(X \ge t) dt\\
        &\le t_0 + \int_{t_0}^{\infty} A\exp \left (-\frac{t^2}{B^2}\right)dt\\
        &\le t_0 + \frac{1}{t_0}\int_{t_0}^{\infty} t A\exp \left (-\frac{t^2}{B^2}\right)dt\\
        &= t_0 + \frac{AB^2}{2t_0} \exp \left ( -\frac{t_0^2}{B^2} \right),
    \end{align*}
and minimizing with respect to $t_0$ yields the choice $t_0 = B\sqrt{\log A}$.
See also this post for a similar result going from high probability to expectation.
What if $X$ is not non-negative?
If $X$ cannot be assumed non-negative, as in the case for example where $X := \sup_{t \in T} \langle g, t\rangle$, then we can use instead the fact:
$$
    E X
    = \int_0^{\infty} P(X \ge t) dt - \int_{-\infty}^0 P(X < t)dt 
\le \int_0^{\infty} P(X \ge t) dt
$$
and reason as above.
How do we get $t_0 = CB\sqrt{\log A}$?
Since as you say the minimizer in closed form is hard/impossible to compute, the idea here is to reason about the correct order of the minimizer. In our case, we have
$$
t_0 + \frac{AB^2}{2t_0} \exp \left ( -\frac{t_0^2}{B^2} \right)
\le 
t_0 + \frac{AB^2}{2} \exp \left ( -\frac{t_0^2}{CB^2} \right)
$$
is always true for a constant $C$ large enough. Now we have two terms that move in opposite directions, as $t_0$ grows bigger, the first term grows but the second term is smaller, and vice versa. The idea then is to find a $t_0$ such that both terms are of constant order. Note that the choice of $t_0$ mentioned before does exactly this. This is not an exact minimizer but it is a minimizer up to constants which is good enough for our purpose. Note that universal constants $C$ can differ from line to line.
