TP-06 CEM Technical Papers Technical Paper

The Drift Tax

What AI actually gets wrong — measured

Structural error rates in AI-native execution. AI doesn't fail catastrophically — it drifts. Here's what that actually costs and how to manage it.

CTOs · Researchers · Methodology

12–15%

Structural drift rate

2,561

Commits classified

Cost to eliminate (you can't)

The Problem

AI outputs present with uniform confidence. A correct implementation and an incorrect implementation arrive with identical formatting, identical tone, identical certainty. The AI does not flag its own uncertainty reliably. Roughly 12–15% of what AI reports as complete requires correction. This is not a deficiency to eliminate — it's a structural property to manage. The operator who expects 100% accuracy is perpetually surprised. The operator who budgets for the Drift Tax absorbs corrections as routine.

What This Establishes

AI drift is structural, not exceptional.

12–15% of AI output requires correction. This rate held across 2,561 commits and multiple project types. It's a property of the tool, not a failure of the operator.

The entire recovery architecture exists because of this.

Stop/Pause/Reset, Micro-Triage, Regroup — the CEM recovery stack exists because drift is permanent. You build systems to manage it, not to prevent it.

CTOs need this number.

Any organization evaluating AI adoption needs to budget for the Drift Tax. The 12–15% rate is the first empirically documented measurement from production-scale AI-native execution.

Validated by these builds

CS-01 The Flagship Build CS-04 The Recovery Build CS-06 The Full Portfolio

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Key Data

Theoretical basis Tversky & Kahneman (1979)

Extended by Parasuraman's complacency research

Measured drift rate 12–15%

Signal detection Rasmussen's framework (1983)

AI confidence calibration Unreliable (uniform)

Commits analyzed 2,561

Trust failure mode Over-reliance on AI output

CEM response Budget, don't eliminate

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Complete drift taxonomy, signal detection analysis, trust calibration framework, and commit-level classification data.

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