Ultimate Scenarios

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---
numericId: E674
title: Ultimate Scenarios
description: The intermediate pathways connecting root factors to ultimate outcomes—AI Takeover, Human-Caused Catastrophe, and Long-term Lock-in.
sidebar:
  label: Overview
  order: 0
lastEdited: "2026-01-04"
subcategory: scenarios
---

import {ScenariosTable, FullModelDiagram, EntityLink} from '@components/wiki';

Ultimate Scenarios are the intermediate pathways that connect <EntityLink id="E667">root factors</EntityLink> to <EntityLink id="E669">ultimate outcomes</EntityLink>. They describe *how* parameter changes lead to catastrophe (or success)—the specific mechanisms and pathways that determine what kind of future we get.

The <EntityLink id="__index__/ai-transition-model">AI Transition Model</EntityLink> uses **three main ultimate scenarios**:

1. **<EntityLink id="E670">AI Takeover</EntityLink>** — AI gains decisive control
2. **<EntityLink id="E671">Human-Caused Catastrophe</EntityLink>** — Humans use AI for mass harm
3. **<EntityLink id="E673">Long-term Lock-in</EntityLink>** — Permanent entrenchment of values/power

Each ultimate scenario has sub-variants that describe more specific pathways (e.g., "rapid" vs "gradual" AI takeover, "state" vs "rogue actor" catastrophe).

---

## The Three-Layer Model

<FullModelDiagram />

**Color coding:**
- **Red**: Ultimate negative outcome (<EntityLink id="E130">existential catastrophe</EntityLink>)
- **Green**: Ultimate trajectory measure (could be good or bad)
- **Pink**: Negative ultimate scenarios (catastrophes)
- **Orange**: Symmetric ultimate scenario (could entrench good or bad values)

---

## Ultimate Scenarios Summary

<ScenariosTable />

---

## How Ultimate Scenarios Differ from Other Concepts

| Concept | What It Is | Example |
|---------|-----------|---------|
| **Root Factors** | Aggregate variables that shape scenarios | "Misalignment Potential" |
| **Parameters** | Specific measurable factors | "<EntityLink id="E20">Alignment Robustness</EntityLink>" |
| **Risks** | Things that could go wrong | "<EntityLink id="E93">Deceptive Alignment</EntityLink>" |
| **Ultimate Scenarios** | Intermediate pathways connecting factors to outcomes | "AI Takeover" |
| **Ultimate Outcomes** | High-level goals we care about | "Existential Catastrophe", "<EntityLink id="E194">Long-term Trajectory</EntityLink>" |

**Key distinction**: A *risk* like "deceptive alignment" is a mechanism that could happen. An *ultimate scenario* like "AI Takeover" is the outcome that results if such mechanisms play out. Multiple risks can contribute to a single ultimate scenario.

---

## Why This Layer Matters

### 1. Clarifies Causal Chains

Without this layer, the connection between "Misalignment Potential increasing" and "Existential Catastrophe increasing" is abstract. Ultimate scenarios show the specific pathway: alignment fails → AI develops misaligned goals → AI takes over → catastrophe.

### 2. Enables Different Intervention Strategies

Different ultimate scenarios require different interventions:
- **AI Takeover**: Technical alignment, capability restrictions
- **Human-Caused Catastrophe**: <EntityLink id="E171">International coordination</EntityLink>, misuse prevention
- **Long-term <EntityLink id="E189">Lock-in</EntityLink>**: Power distribution, institutional design

### 3. Supports Scenario Planning

Ultimate scenarios map directly onto scenarios that organizations can plan for. Rather than asking "what if Existential Catastrophe increases?", planners can ask "what if we're heading toward a Human-Caused Catastrophe?"

### 4. Connects to Existing Threat Models

Each ultimate scenario corresponds to threat models discussed in the AI safety literature:
- <EntityLink id="E54">Carlsmith's six-premise argument</EntityLink> → AI Takeover scenarios
- Christiano's "What Failure Looks Like" → <EntityLink id="E619">Gradual AI Takeover</EntityLink>
- Ord's "The Precipice" risk categories → Multiple ultimate scenarios
- Kasirzadeh's decisive vs. accumulative → Rapid vs. Gradual takeover

---

## Using This Section

### For Analysts
- Map specific risks to the ultimate scenarios they could produce
- Estimate which ultimate scenarios are most likely given current parameter trends
- Identify which parameters to prioritize based on which ultimate scenarios concern you most

### For Policymakers
- Design interventions targeted at preventing specific ultimate scenarios
- Coordinate across domains (a single ultimate scenario may require multiple types of intervention)
- Track early warning signs for each ultimate scenario

### For Researchers
- Use ultimate scenarios to frame research priorities
- Connect technical work to concrete scenarios it addresses
- Identify gaps in our understanding of specific pathways

---

## Related Sections

- <EntityLink id="E667">Root Factors</EntityLink> — The parameter groupings that feed into ultimate scenarios
- <EntityLink id="E669">Ultimate Outcomes</EntityLink> — The high-level goals ultimate scenarios affect
- <EntityLink id="__index__/ai-transition-model">Interactive Model</EntityLink> — Full interactive visualization
- Models — Analytical frameworks for understanding pathways