With few exceptions, the motivation for scale that isn't driven by a real need in the real world is because someone wants to experiment with distributed systems

With few exceptions, the motivation for scale that isn't driven by a real need in the real world is because someone wants to experiment with distributed systems and is looking for an excuse. I think it comes from a good place -- wanting to learn something new, or add a new skill to your toolbox. However, what happens is most of the time the product or company will never even reach that scale so that effort will be wasted. If you're not able to close the loop and actually support a running system at scale, with a real workload (not simulated or ab stats) it actually does the opposite: you don't learn what really works and what doesn't. It's easy to convince yourself you have built a system that can handle such scale based off back of the envelope calculations, synthetic testing, or cargo-culting. Most of what actually happens with scale in the real world has to do with old & boring things like appropriate choice of data structures, cache design, load balancing, locality, etc. and has nothing to do with distributed computing, serverless, etc. which are just tools that have specific characteristics that might make them good choices or not.