There are some cute applications of the fundamental theorem of calculus, and I'm sure some of the others answers will dig them up. But for the most part I agree with you: the FTC, in one dimension, isn't all that exciting! But it's just the tip of the iceberg.
You have focused on one form of the fundamental theorem of calculus; there is a second form, namely that
$$\int_a^b \frac{df}{dx}\,dx = f(b) - f(a)\tag{1} $$
I wouldn't blame you for thinking this is even more obvious than the first form! But the FTC is one special case, and the most boring one at that, of a general principal called Stokes's Theorem that is much more deserving of the "fundamental" moniker.
Let's take a closer look at what equation 1 is saying. The left-hand side asks you to take some (possibly horrible and complicated) function $f$, take its derivative, and then sum the values of that derivative up over the entire interval $[a,b]$. FTC says you can get the same answer just by looking at $f$ at two values. The key points, here, are that
- The LHS requires knowing $f$ (and its derivative) everywhere along $[a,b]$. The RHS needs $f$ only at the boundary of the interval.
- The LHS requires being able to take the derivative of $f$. The right-hand side requires only knowing $f$, and no derivatives.
Well duh, you're thinking. Isn't that the entire point of anti-derivatives. Yes, indeed. But it turns out that both of these benefits carry over, in a beautiful way, to higher dimensions, where an equivalent property holds. Let's say you have a region of the plane $\Omega$, and its boundary curve $\partial \Omega$. Then:
$$\int_{\Omega} \nabla \cdot v \,dV = \int_{\partial \Omega} v\cdot \hat{n}\,dA.$$
There is some fairly elementary intuition about what these terms mean, but going into it without at least a bit of vector calculus knowledge would take us too far astray... the key point though is that the above lets you turn integrals over areas in the plane to integrals over their one-dimensional boundaries, just like the FTC turns a one-dimensional integration into a zero-dimensional difference of values. It doesn't always work, but it works enough of the time to be extremely powerful.
For example, let's say you draw a closed curve $\gamma(s)$ in the plane. What is the area enclosed by the curve? Maybe you've learned some tools for computing this area: some slicing techniques, perhaps. You've also seen that these techniques are a huge pain; even moreso when the curve $\gamma$ is complicated with lots of loops and concavities. It turns out you can compute the area enclosed by only integrating around $\gamma$:
$$\textrm{Area} = \int \frac{1}{2} \gamma(s) \cdot \gamma'(s)^{\perp}\, ds.$$
The two-dimensional problem has become a one-dimensional problem, and much more tractable both analytically and computationally. You can find similar nice formulas for many other geometric quantities of interest, such as the center of mass of a region $\Omega$, its moment of inertia, etc -- all quantities that nominally depend on the entire interior of $\Omega$ -- using only integration around the boundary.
One final example: let's say I give you a point $p$ in the plane, and a super-complicated closed curve $\gamma$. How can you tell if the point is inside, or outside, the region enclosed by $\gamma$? People use various tricks to do this, for example by drawing a ray from $p$ to a point at infinity, and counting how many times the ray intersects $\gamma$... but you can do it robustly and easily using a boundary integral,
$$\int \frac{-1}{4\pi\|\gamma(s) - p\|^2}[\gamma(s)-p]\cdot \gamma'(s)^{\perp}\,ds$$
which will be equal to $1$ if $p$ is inside the region, or $0$ if $p$ is outside (assuming I've not made any mistakes in my calculation).
There's more: recall the second key point about the FTC above: the LHS requires computing derivatives, while the RHS does not. This comes up all of the time when doing numerical calculations and simulations. For example, let's say you want to simulate the way that your clothing wrinkles and folds as you dance. It turns out that if you represent your shirt as a surface parameterized by $r(s,t): \mathbb{R}^2 \to \mathbb{R}^3$, the bending energy of the shirt, which is required to correctly compute shirt physics, is given by
$$E \propto \int (\Delta r \cdot \hat{n})^2\,dA.$$
Again I won't go too much into the details of the math; the important part is that computing $\Delta r$ requires knowing two derivatives of $r$. This means that $r$ must be twice-differentiable for the formula to make sense; that's fine in an ideal setting, but what if the geometry of your shirt comes from a Microsoft Kinect, or is inferred form video footage? The shirt surface will be "chunky," or have lots of noise, and you often won't even be able to compute first derivatives, never mind second derivatives. It turns out that Stokes's Theorem can be used to reduce the number of derivatives that are needed, and is behind the cloth animation you see in video games and movies effects. In fact, countless physical simulations, from how the galaxy formed, to how wind flows around an airplane wing, to how your cheek deforms when you get punched in the face, rests on a foundation made up of the "pointless trick" that is the (generalized) FTC.