Redundancy often looks inefficient at first glance. Extra copies. Duplicate paths. Systems doing the same work more than once. From a distance, it feels like waste.
In many systems, it is.
But in infrastructure, redundancy plays a different role. It is not there to optimize for normal operation. It exists for the moments when normal operation disappears.
Most efficiency arguments assume stable conditions. They focus on average performance, predictable load, and clean dependencies. Redundancy assumes the opposite. It assumes components will fail, networks will fragment, and behavior will drift in ways that cannot be fully anticipated.
Under those assumptions, duplication stops looking wasteful.
A redundant system does not try to prevent failure. It accepts failure as routine and plans around it. When one path breaks, another continues. The goal is not elegance, but continuity.
This is why redundancy feels unnecessary right up until it isn’t.
In centralized designs, redundancy is often treated as a cost center. It increases operational expense without improving the visible experience. When everything works, redundant components sit idle. That makes them easy to remove under pressure to optimize.
The problem is that their value is invisible by design.
Redundancy does not improve performance in good conditions. It limits damage in bad ones. Metrics rarely capture that distinction well. It shows up only when something goes wrong, and by then, it is either there or it isn’t.
There is also a difference between redundancy and duplication. Duplication copies the same assumptions. Redundancy spreads assumptions. True redundancy introduces diversity: different operators, different locations, different failure characteristics.
Without that diversity, redundancy becomes fragile. All copies fail the same way, at the same time.
This is where decentralization changes the equation. In decentralized systems, redundancy is not an add-on. It is part of the baseline design. Multiple participants hold data, process requests, or validate outcomes independently. No single component is expected to be reliable on its own.
As a result, redundancy becomes a functional feature.
Failures are absorbed rather than escalated. The system degrades unevenly instead of collapsing. Users may experience slower responses or partial availability, but total failure becomes less likely.
This does not mean redundancy is free. It introduces coordination overhead, inconsistency, and inefficiency. Systems built this way are rarely optimized for peak performance. They trade speed and simplicity for survivability.
That trade-off is intentional.
Redundancy also changes how trust forms. Instead of trusting a single component to behave correctly, users trust the aggregate behavior of many imperfect ones. This kind of trust is statistical rather than absolute. It is less comfortable, but more durable.
The mistake is evaluating redundancy using the wrong lens. When judged by day-to-day efficiency, it looks like waste. When judged by how a system behaves under stress, it often looks essential.
Redundancy becomes a feature when failure is not an exception, but a condition to design for.
In systems that need to persist through uncertainty, redundancy is not excess capacity. It is the capacity that matters.