Adoption (AI Capabilities)
Adoption measures how widely AI systems are integrated into economic activities, daily life, and critical infrastructure. This encompasses not just the deployment of AI tools but the depth of integration—from surface-level assistance to foundational dependence where systems and organizations cannot function without AI.
Broader adoption amplifies both the benefits of AI systems and the potential harms from failures, misalignment, or misuse. Deep adoption creates path dependencies that make reversal difficult—once critical infrastructure depends on AI systems, removing those systems becomes increasingly costly. Managing adoption trajectory while maintaining human capability and reversibility represents a key challenge.
| Metric | Score | Notes |
|---|---|---|
| Changeability | 40 | Policy and market forces can influence adoption speed |
| X-risk Impact | 45 | Affects vulnerability but not primary risk sources |
| Trajectory Impact | 70 | Shapes societal dependence and reversibility |
| Uncertainty | 40 | Adoption trends observable but future pace unclear |
Related Content
Section titled “Related Content”Related Risks:
- Enfeeblement — Gradual loss of human capabilities due to AI dependence
- Economic Disruption — Labor market impacts of AI automation
- Expertise Atrophy — Loss of domain knowledge needed to evaluate AI
Related Responses:
- Labor Transition — Policies to manage workforce disruption
Related Models:
- Expertise Atrophy Progression — Modeling skill degradation over time
Key Debates:
- How quickly will AI be integrated into critical systems, and is there time for adequate safeguards?
- Does widespread AI adoption create dangerous dependencies and correlated failure modes?