A common mistake in probability is to think that a distribution is uniform because it looks visually flat when all its values are near zero. This is because we tend to see that $f(x)=0.001 \approx 0.000001=f(y)$ and yet $f(x)/f(y)=0.001/0.000001=1000$, i.e. a small interval around $x$ is 1000 times more likely than a small interval around $y$. 

It's definitely not uniform on the entire real line in the limit, as there is no uniform distribution on $(-\infty,\infty)$. It's also not even approximately uniform on $[-2\sigma,2\sigma]$. 

You can see the latter from the 68-95-99.7 rule you seem to be familiar with. If it were approximately uniform on $[-2\sigma,2\sigma]$, then the probability of being in $[0,\sigma]$ and $[\sigma,2\sigma]$ should be the same, as the two intervals are the same length. But this is not the case: $P([0,\sigma])\approx 0.68/2= 0.34$, yet $P([\sigma,2\sigma])\approx (0.95-0.68)/2 = 0.135$. 

When viewed over the entire real line, this sequence of normal distributions doesn't converge to any probability distribution. There are a few ways to see this. As an example, the cdf of a normal with standard deviation $\sigma$ is $F_\sigma(x) = (1/2)(1+\mbox{erf}(x/\sqrt{2}\sigma)$, and $\lim_{\sigma\rightarrow\infty} F_\sigma(x) = 1/2$ for all $x$, which is not the cdf of *any* random variable. In fact, it's not a cdf at all. 

The reason for this non-convergence boils down to "mass loss" is the limit. The limiting function of the normal distribution has actually "lost" probability (i.e. it has *escaped* to infinity). This is related to the concept of [tightness of measures][1], which gives necessary conditions for a sequence of random variables to converge to another random variable. 

  [1]: https://en.wikipedia.org/wiki/Tightness_of_measures