AI Transition Model
Overview
Section titled “Overview”The AI Transition Model is a causal framework for understanding how various factors influence the trajectory of AI development and its ultimate outcomes for humanity. The interactive diagram above shows how Root Factors flow through Ultimate Scenarios to determine Ultimate Outcomes.
This model helps identify:
- Leverage points: Which factors have the most influence on outcomes
- Intervention targets: Where effort can most effectively shift trajectories
- Key uncertainties: Which causal relationships are most uncertain
- Scenario dependencies: How different pathways interact
How Parameters, Risks, and Interventions Connect
Section titled “How Parameters, Risks, and Interventions Connect”Both risks and interventions connect to root factors:
- Risks (like deceptive alignment, racing dynamics) tend to increase harmful factors or decrease protective ones
- Interventions (like interpretability research, compute governance) work to counteract risks
Interactive Views:
- Parameter Table - Sortable tables with ratings (changeability, uncertainty, x-risk impact, trajectory)
- Interactive Graph - Clickable flow diagram with expandable nodes
- Graph View - Visual causal diagram showing relationships between factors, scenarios, and outcomes
- Outline View - Hierarchical text-based navigation with detailed descriptions
Why This Framing Matters
Section titled “Why This Framing Matters”Traditional Risk Framing
Section titled “Traditional Risk Framing”- “Trust erosion is a risk we must prevent”
- “Concentration of power threatens democracy”
- Focus: Avoiding negative outcomes
Parameter Framing
Section titled “Parameter Framing”- “Trust is a parameter that AI affects in both directions”
- “Power distribution is a variable we can influence through policy”
- Focus: Understanding dynamics and identifying intervention points
The parameter framing enables:
- Better modeling: Can estimate current levels, trends, and intervention effects
- Clearer priorities: Which parameters matter most for good outcomes?
- Strategic allocation: Where should resources go to maintain critical parameters?
- Progress tracking: Are our interventions actually improving parameter levels?
Relationship to Other Sections
Section titled “Relationship to Other Sections”| Section | Relationship to Parameters |
|---|---|
| Risks | Many risks describe decreases in parameters (e.g., “trust erosion” = trust declining) |
| Interventions | Interventions aim to increase or stabilize parameters |
| Metrics | Metrics are concrete measurements of parameter levels |
| Models | Analytical models often estimate parameter dynamics and trajectories |
How to Use This Section
Section titled “How to Use This Section”For Researchers
Section titled “For Researchers”- Understand which underlying variables matter for AI outcomes
- Identify gaps between current and optimal parameter levels
- Design studies to measure parameter changes
For Policymakers
Section titled “For Policymakers”- Prioritize interventions based on which parameters are most degraded
- Monitor parameter trends to assess policy effectiveness
- Coordinate across domains (a single parameter may affect multiple risks)
For Forecasters
Section titled “For Forecasters”- Use parameters as input variables for scenario modeling
- Estimate how different interventions would shift parameter levels
- Identify tipping points where parameter degradation becomes irreversible