AI Resilience is the only LLM-scored module in the framework. Claude Sonnet evaluates each company across five dimensions using earnings transcripts, thesis narratives, financial data, and peer context. Scores are calibrated against reference anchors to prevent drift.
| Dimension | Weight | What It Measures |
|---|---|---|
| Revenue Catalyst | 25% | Degree to which AI directly drives incremental revenue. Direct AI product sales, AI-enabled upsell, AI-driven customer acquisition. |
| Moat Durability | 25% | Switching costs, data moats, network effects, system-of-record status. How defensible is the business against AI-native competitors? |
| Operating Leverage | 15% | AI's impact on margin improvement, revenue-per-employee, cost structure. Can the company use AI to scale without proportional cost increases? |
| Pricing Resilience | 20% | Vulnerability to seat compression, pricing disruption, commodity risk. Will AI force price reductions or enable premium pricing? |
| Obsolescence Shield | 15% | Protection from AI-native replacement. Regulatory barriers, physical infrastructure requirements, domain expertise moats. |
The scoring prompt includes:
Reference scores are provided in the prompt to ground the LLM's output. On a 1–5 scale (mapped to 0–100):
| Company | Type | Rev Cat | Moat | Op Lev | Pricing | Obsol |
|---|---|---|---|---|---|---|
| PLTR | Software | 5 | 4 | 4 | 3 | 3 |
| U (Unity) | Software | 2 | 2 | 2 | 1 | 1 |
| ORCL | Software | 3 | 5 | 3 | 4 | 4 |
| CEG | Infrastructure | 5 | 5 | 4 | 5 | 5 |
| NEE | Infrastructure | 3 | 4 | 3 | 4 | 5 |
| BE | Infrastructure | 3 | 2 | 2 | 2 | 2 |
Scale mapping: 1→20, 2→40, 3→60, 4→80, 5→100
Soft cap at 92: scores above 92 require identified vulnerabilities in the rationale. Every dimension must cite at least one vulnerability. This prevents score inflation from overly optimistic LLM outputs.
For companies in Energy, Utilities, Materials, Industrials, and Real Estate, the dimensions are reinterpreted:
| Dimension | Infrastructure Meaning |
|---|---|
| Revenue Catalyst | % revenue from AI/data center customers |
| Moat Durability | Contracted backlog depth, supply constraints, switching costs |
| Operating Leverage | Execution track record, margin expansion from AI demand |
| Pricing Resilience | Supply-constraint pricing power |
| Obsolescence Shield | Secular demand duration (10–20yr structural theme) |
US data center power demand is projected to 3x to 134.4 GW by 2030, with $720B in grid spending and 7–15 year capital cycles. Infrastructure companies are scored against this structural backdrop.
| Label | Score Range | Meaning |
|---|---|---|
| Fortress | ≥80 | AI is a major tailwind; business model is strengthened by AI adoption |
| Defensible | ≥60 | Well-positioned with manageable risks; AI is net positive |
| Moderate | ≥40 | Mixed picture; some AI benefit but meaningful vulnerabilities |
| At-Risk | ≥20 | Significant AI disruption risk; business model under pressure |
| Vulnerable | <20 | High probability of AI-driven disruption to core business |
AI Resilience flows into conviction scoring in two ways: