Let $P_n(x) = x^n - x^{n-1} - \dots - 1$. Note that $P_n(1) = 1 - n \le 0$ when $n \ge 1$. On the other hand, if $r$ is real and $r \ge 2$, then
$$r^n > r^n - 1 \ge \frac{r^n - 1}{r - 1} = r^{n-1} + \dots + 1.$$
Hence any real root $r$ of $P_n(x)$ must be less than or equal to $2$. Since $P_n(2) = 2^n - (2^n - 1) = 1$, by the intermediate value theorem $P_n(x)$ must have a real root between $1$ and $2$, and this is where its largest real root must be.
To estimate this largest real root $r$ (dependence on $n$ suppressed because I hate writing $r_n^n$), we can rearrange the identity $r^n = \frac{r^n - 1}{r - 1}$ to
$$2 - r = \frac{1}{r^n}.$$
Write $\varepsilon = 2 - r$. Once we know, for example, that $r \ge 1.5$ for $n \ge 2$ (which is true), we immediately conclude that $\varepsilon \le \frac{1}{1.5^n}$, and hence this identity lets us bootstrap bad lower bounds on $r$ into much better upper bounds on $\varepsilon$. In fact, once we know that $\varepsilon$ decays exponentially in $n$ at all it follows immediately that
$$\lim_{n \to \infty} \frac{r^n}{2^n} = \lim_{n \to \infty} \left( 1 - \frac{\varepsilon}{2} \right)^n = 1$$
(in fact to get this we just need to know that $\varepsilon$ decays faster than $\frac{1}{n}$) and the desired result follows. I used this argument explicitly for the purpose of analyzing the behavior of getting runs during coin tosses here.
Here is another argument which uses fewer of the special features of the problem, although it is not very careful. Below $\approx$ means two things are equal up to an error of $O(\varepsilon^2)$, and (mea culpa!) I am not tracking the dependence of the error on $n$, which I really should be. We have
$$r^n = (2 - \varepsilon)^n \approx 2^n - n \cdot 2^{n-1} \varepsilon$$
and
$$\frac{r^n - 1}{r - 1} \approx ((2^n - 1) - n \cdot 2^{n-1} \varepsilon)(1 + \varepsilon) \approx (2^n - 1) - (n \cdot 2^{n-1} - (2^n - 1)) \varepsilon$$
and hence equating the two we get
$$1 \approx (2^n - 1) \varepsilon.$$
More generally, suppose $f(x)$ is a smooth function and $r_0$ is a real number such that $f(r_0)$ is small. You want to estimate the size of a root $r$ close to $r_0$. Writing $r = r_0 - \varepsilon$, we have
$$f(r_0 - \varepsilon) \approx f(r_0) - f'(r_0) \varepsilon$$
and hence
$$\varepsilon \approx \frac{f(r_0)}{f'(r_0)}.$$
This approximation is the basis of Newton's method.