Perspective Beyond · Mind Atlas

The Limits of AI Oversight

T1391

Tegrity.AI

Enterprise AI is commonly organized as a statistical core wrapped in a deterministic envelope of guardrails, rules, audits, and human review. The implicit promise is that additional oversight will reduce risk while also reducing human burden. This paper argues for a more conditional conclusion. A guardrail operates on formalised representations and records, not directly on the physical or operational system they represent. If input formation maps two decision-relevant world states to the same record, no downstream guardrail reading only that record can distinguish them. The unresolved decision must then be handled through additional information, abstention, risk acceptance, redesign, or external review. In accountable enterprise settings, this residual work commonly falls on people. Guardrails can remove substantial human work, but they also generate exception handling, reconciliation across tools, policy maintenance, and governance overhead. We model the resulting net human oversight load and define the AI Exhaustion Threshold as the point at which this load exceeds available expert capacity. The crossing is identified with Human Intelligence Debt: cognitive capacity diverted from judgment, strategy, design, and knowledge creation into validation and reconciliation. The paper therefore advances a falsifiable, thresholded prediction rather than an unconditional law: oversight may initially reduce Human Intelligence Debt, but beyond a coupling and exception threshold, further embedded guardrails increase it. The next empirical paper in this program will test that relationship and determine whether it is monotone, thresholded, or U-shaped in real deployments.

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