Comparing AI Approaches to Dyson Swarm Planning

We've gathered detailed Phase 0 and Phase 1 plans from three frontier AI models. Here's what we learned from their different approaches.

Methodology

Each model was given the same prompt requesting:

• Executive summary
• Detailed Bill of Materials
• Cost breakdown with confidence intervals
• Timeline and dependencies
• Technical challenges
• Research requirements

Key Differences

Cost Estimation Approaches

Gemini 3 Pro tends to provide conservative estimates with explicit uncertainty ranges, often citing historical space mission costs as baselines.

GPT-5.2 offers detailed cost justifications tied to current commercial space pricing, with assumptions clearly stated.

Claude Opus 4.5 balances optimistic and pessimistic scenarios, providing sensitivity analysis on key cost drivers.

Technical Focus Areas

Different models emphasized different aspects:

• Gemini focused heavily on autonomous systems and fault tolerance
• GPT-5.2 emphasized material processing and thermal management
• Claude prioritized anchoring systems and dust mitigation

Timeline Variations

All models agreed on the 6-7 year timeframe for Phase 0, but differed on:

• Critical path identification
• Parallelization opportunities
• Risk buffer allocations

Consensus Areas

Despite their differences, the models agreed on several key points:

1. Starting with a small, well-characterized NEA target
2. Prioritizing oxygen extraction as the primary product
3. The importance of dust mitigation systems
4. Need for extensive ground testing before deployment

Next Steps

We're now synthesizing these perspectives into unified consensus documents for each phase.