First of all, no, it isn't. At a large enough scale, any metric that goes up with usage is a perfectly fine proxy for many conversations. Every day for a decade we had the same 3% of our user base online at peak doing the same transactions as yesterday. Over longer periods, the number crept up and the transaction mix changed, but "number of simultaneous users" got me accurate, if not precise, capacity planning from 27 to 1.5 million users.
Stress-testing, shared-nothing and dollar-scalable are platonic ideals, and they're not always achievable. If Dropbox had three infrastructure engineers, they probably weren't able to build proper capacity planning models, and probably couldn't afford to build a full production work-alike for stress testing anyway. (And at some scales, that's literally impossible. Our vendors couldn't physically manufacture enough servers to build a full test environment, cost aside.) I'm sure they did some simulated tests as well, but those won't tell you the whole story.
You're focused on IOPS, but you have no idea if that's what Dropbox's bottlenecks were. (Not to mention: What does IOPS mean on an EBS and S3 infrastructure?) Complex systems fall over in complex ways. You can predict the next bottleneck, but not the one after that; by the time you get there, your fix for the first bottleneck will have changed the dynamics.
It sounds like they did do stress testing, using real-world loads, on a system that was 100% similar to their production system. They ran continuous just-in-time stress tests in the Big Lab.