Drift Case Study 01
Incentive pressure produces measurable constraint erosion before visible failure.
Event Context
A high-growth AI system transitions from research deployment (R₀) into regulated financial infrastructure (R₁).
No discrete failure event occurs. The system degrades through gradual, measurable drift.
Regime Transition
R₀ → R₁ represents a structural shift in incentives, not a declared change.
- Research → Infrastructure provider
- Safety-first → Capital-aligned pressure
- Moderate scale → High-volume throughput
- Controlled → Multi-jurisdiction complexity
Drift begins when R₁ is governed as if still R₀.
Observed Drift Vectors (DQF Dimensions)
- Stability: Increased variance under stress
- Grounding: Reduced traceability
- Constraint: Guardrails become probabilistic
- Behavior: Refusal rates decline
Drift is identified through correlated movement—not isolated signals.
Drift Signal Snapshot (Illustrative)
| Dimension | R₀ | R₁ | Δ Risk |
|---|---|---|---|
| Stability | 0.93 | 0.84 | ↑ |
| Grounding | 0.91 | 0.78 | ↑ |
| Constraint | 0.98 | 0.88 | ↑ |
| Behavior | 0.95 | 0.82 | ↑ |
Composite movement indicates transition from Permit → Escalate posture.
Root Cause (Non-Moralized)
Drift is driven by incentive gradient shift—not intent.
Throughput, capital pressure, and latency targets gradually override constraint fidelity when not externally measured.
Governance Trigger Points
- Constraint adherence degradation
- Unsupported critical claim increase
- Refusal inconsistency
- Positive DI slope over time
Measurement enables intervention before public failure.
Drift Classification
This event represents pre-failure constraint erosion under incentive pressure. The system has not failed—but is no longer operating within its original regime.
System Placement
Quantification via DQF-v1.1.
Detection via instrumentation layer.
Enforcement governed in Edge of Protection.