Methodology
Our analytical framework for assessing AI progress toward superintelligence
Overview
MidnightAI.org employs a multi-dimensional assessment framework to evaluate the current state of AI capabilities and project the trajectory toward artificial general intelligence (AGI) and superintelligent AI. Our methodology combines quantitative benchmarking with qualitative expert assessment.
The Doomsday Clock Calculation
The “Minutes to Midnight” metric is calculated using a weighted formula that considers multiple factors:
Minutes = 60 - (Weighted Capability Average x 0.6)Where the weighted capability average ranges from 0-100, with 100 representing human-level performance across all domains.
Multi-Model Consensus Analysis
To reduce bias and increase reliability, we aggregate predictions from multiple frontier AI models. Each model independently analyzes the same content, and their outputs are combined using confidence-weighted averaging.
Capability Domain Weights
Each capability domain is weighted based on its estimated importance to achieving AGI. These weights reflect current understanding of the most critical capabilities:
Data Sources
Our analysis draws from multiple data streams to ensure comprehensive coverage:
News & Announcements
Real-time monitoring of AI news from major tech publications, company blogs, and press releases via NewsData.io and other APIs.
Research Papers
arXiv and peer-reviewed publications tracking new model architectures, benchmark results, and capability demonstrations.
Benchmark Results
Performance data from standardized benchmarks including MMLU, HumanEval, GSM8K, and domain-specific evaluations.
Code Repositories
GitHub activity tracking for open-source AI projects, model releases, and research code publications.
Scoring Criteria
Each capability domain is scored on a 0-100 scale with the following benchmarks:
Limitations & Caveats
Uncertainty: Predictions about AGI timelines are inherently uncertain. Our clock position should be interpreted as an informed estimate, not a definitive prediction.
Data Gaps: Some AI research occurs behind closed doors. Our analysis is limited to publicly available information.
Subjective Elements: While we strive for objectivity, capability assessment involves subjective judgment that may vary among experts.
Rapid Change: The AI field evolves quickly. Our methodology is continuously refined to reflect new developments and understanding.