Digital Brain Archive: Bit-Rot vs Redundancy

Archive Parameters

Scan resolution 14³ voxels

Redundancy (replicas / shard) 3×

Medium failure rate / yr 2.0%

Maintenance re-replication / yr 20%

Time acceleration 25 yr/s

Archive Status

Elapsed

0 yr

Fidelity

100.0%

Archive size

— PB

Shards lost

Fully redundant shard Degraded (partial replicas) Data lost — unrecoverable

How it works

The uploaded connectome is split into shards, each stored as k independent replicas on separate physical media. Every replica has an annual failure probability λ, so a single replica survives t years with:

P_replica(t) = exp(-λ·t)
P_shard_lost(t) = (1 - exp(-λ·t))^k   (no maintenance)

Each simulated year, every surviving replica independently rolls against λ. A shard is only truly gone once all k of its replicas have failed — that's why redundancy matters so much more than raw storage. A shard with at least one surviving replica can be repaired: with probability equal to the maintenance rate, its lost replicas are re-copied from the survivor back up to k, restoring full redundancy. Once every replica of a shard is gone, there is nothing left to copy from — that fraction of the mind is permanently unrecoverable.

Scan resolution — finer voxel sampling of the connectome means more shards and a larger archive, at higher fidelity per shard.
Redundancy — more replicas per shard exponentially cuts the odds of total shard loss, at k× the storage cost.
Failure rate — how quickly the storage medium itself degrades (bit rot, disk failure, decay of the physical substrate).
Maintenance — how often a repair cycle re-replicates degraded-but-not-lost shards back to full redundancy.

This is the real bottleneck the article's "long-term data storage" section gestures at: mind uploading is not just a scanning problem, it's an indefinite, unbroken maintenance obligation — skip enough repair cycles and the archive erodes to nothing no matter how good the original scan was.

t = 0 yrfidelity = 100.0%

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