AI aging isn't entropy. It's a redundancy problem.

Every company that runs a model in production eventually asks the same question: does running AI for a long time slowly break it? An arXiv preprint argues the answer depends on something specific: how much internal redundancy the system carries, meaning how many overlapping components can absorb changes without losing consistency. Under the right formal conditions, the way a system's internal makeup shifts across repeated use cycles (retraining, updating, repeated queries) does not compound without limit. Under stronger ones, the per-cycle burden shrinks toward zero.

The reframe matters because "AI aging" has mostly been talked about as cumulative damage, framed as entropy, drift, decay. The paper, anchored to a measure it calls the Artificial Age Score (AAS), treats it as a design problem with measurable bounds. Same phenomenon, different question: not "will this system eventually rot," but "does its structure preserve enough redundancy to keep age from compounding."

The conditions doing the work are narrow. The result is theoretical. It does not address data drift, hardware wear, or adversarial inputs, the failure modes that actually show up in production. If those conditions don't describe deployed systems, the bound does not carry over. But the framing survives: AI longevity is now a measurable property a system either has or lacks, not a sentence handed down by entropy.