Autonomous replication failure modes across generations

Background

The capacity cost model from rq-0-28 assumes self-replicating manufacturing foundries that produce approximately 25 copies per 12-month replication cycle, growing from 1,000 seed foundries to 10^6 in roughly 10 generations. However, each generation of replication introduces potential quality degradation — manufacturing tolerances compound, sensor calibration drifts, and material impurities accumulate. Unlike biological replication, which has error-correction mechanisms refined over billions of years, engineered self-replication must address quality drift explicitly.

Why This Matters

If quality degrades by even 1% per generation:

• After 10 generations, cumulative degradation could reach 10%
• Later-generation units may fail to meet specifications
• Failure rate escalation could halt exponential growth
• The capacity cost model's assumptions break down
• Remediation requires human intervention at scales that defeat the purpose of autonomy

Understanding and bounding generational failure modes is essential for validating the economic model that justifies Phase 2-3 budgets.

Key Considerations

• Manufacturing tolerance stack-up across generations (each copy is slightly worse than its parent)
• Sensor and measurement system calibration drift
• Software/firmware integrity across replication (digital is easier than analog)
• Material composition drift as feedstock varies between asteroid bodies
• Quality assurance becomes harder as the number of units exceeds inspection capacity
• Biological analogy: error correction codes (DNA repair) vs. error accumulation (aging)

Research Directions

1. Tolerance analysis across generations: Model how manufacturing tolerances compound over 10+ generations of self-replication, identifying which dimensions and parameters are most sensitive to drift.

2. Self-calibration architecture: Design systems that can verify and correct their own calibration using reference standards, without requiring external measurement equipment.

3. Digital twin verification: Develop approaches where each manufactured unit is compared against its digital specification to detect degradation before the unit enters service.

4. Graceful degradation boundaries: Establish performance thresholds below which a replicated unit should not itself replicate, creating a natural quality firewall.

5. Biological error correction analogs: Study DNA replication error correction mechanisms for principles applicable to engineered self-replication (checksums, redundancy, repair cycles).