Distributed systems spend most of their effort on one problem: agreeing on the order of events across machines. Without synchronized physical clocks you have two options. Logical clocks (Lamport, vector) give you causal order but not wall-clock truth, so you can’t answer “did A really happen before B” for events that don’t have a happens-before relationship. Or you run consensus, which gives total order but costs round trips. At geographic scale that’s tens of milliseconds per decision, and the floor is set by the speed of light.

Tight clock sync collapses this. If clock uncertainty ε is small and bounded, you can timestamp a write, wait ε, and trust the global order without talking to anyone. Spanner’s external consistency works because TrueTime’s ε was a few milliseconds, so commit-wait was tolerable. The latency cost of planet-scale serializability stops depending on how far apart your replicas are and starts depending on how good your clocks are.

That’s the real significance. Time sync converts a coordination problem (bounded by physics) into a local computation (bounded by clock quality). Spanner proved this is possible but required GPS receivers and atomic clocks in every datacenter, which kept the capability inside Google for years. White Rabbit-class sync pushes ε from milliseconds toward sub-nanoseconds over commodity Ethernet hardware, and it’s now in IEEE 1588 as a standard PTP profile. If sub-nanosecond sync becomes baseline network infrastructure, the long-held assumption that strong consistency has to be slow at geographic scale stops holding, and a meaningful chunk of what databases currently work around (HLCs, weak isolation defaults, application-level reconciliation) becomes unnecessary.

Used quite a bit by stock exchanges to ensure consumers and publishers have a reasonably aligned time.

it is useful e.g. to align the phase of signals being sent from different locations