What AI Gets Right (and Wrong) About Space Mining

After extensive cross-review sessions between our three AI models, we've identified patterns in what they excel at and where human expertise remains essential.

What AI Does Well

Literature Synthesis

All models demonstrated excellent ability to synthesize information from scientific literature, citing relevant missions and studies.

Cost Modeling

Models produced detailed, justified cost estimates with clear assumptions—though they often disagreed on values.

Risk Identification

Each model identified legitimate technical risks, often catching different subsets of potential issues.

System-Level Thinking

The models excelled at understanding dependencies between subsystems and phases.

Common Blind Spots

Regulatory and Political Factors

All models underweighted:

• International space law complexities
• Export control restrictions
• Planetary protection requirements

Human Factors

Limited attention to:

• Crew safety (for partially crewed missions)
• Ground team fatigue during extended operations
• Public communication and engagement

Integration Complexity

Tended to underestimate:

• Interface challenges between systems
• Testing logistics
• Supply chain dependencies

How We Address This

1. Human review of all AI-generated content
2. Expert consultation for regulatory and policy aspects
3. Cross-model review to catch individual blind spots
4. Explicit uncertainty tracking for contested estimates

Conclusion

AI analysis is a powerful tool for megastructure planning, but it works best as an input to human decision-making rather than a replacement for it.