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Forecasting
Overview
This section compiles forecasts about AI development trajectories, capability timelines, and safety outcomes. Tracking predictions over time helps calibrate expectations and identify who has good track records.
Forecast Categories
AGI DevelopmentE604Comprehensive synthesis of AGI timeline forecasts showing dramatic compression: Metaculus aggregates predict 25% probability by 2027 and 50% by 2031 (down from 50-year median in 2020), with industr...Quality: 52/100
When might transformative AI arrive?
Expert surveys and estimates
Compute-based projections
Historical accuracy of past forecasts
AGI TimelineConceptAGI TimelineComprehensive synthesis of AGI timeline forecasts showing dramatic acceleration: expert median dropped from 2061 (2018) to 2047 (2023), Metaculus from 50 years to 5 years since 2020, with current p...Quality: 59/100
Detailed timeline predictions:
2025-2030 scenarios
2030-2040 scenarios
Post-2040 considerations
Key Forecast Sources
Source
Focus
Method
Track Record
MetaculusOrganizationMetaculusMetaculus is a reputation-based forecasting platform with 1M+ predictions showing AGI probability at 25% by 2027 and 50% by 2031 (down from 50 years away in 2020). Analysis finds good short-term ca...Quality: 50/100
AI milestones
Prediction market
Good calibration
Epoch AIOrganizationEpoch AIEpoch AI maintains comprehensive databases tracking 3,200+ ML models showing 4.4x annual compute growth and projects data exhaustion 2026-2032. Their empirical work directly informed EU AI Act's 10...Quality: 51/100
Compute trends
Trend extrapolation
Strong on hardware
Expert surveys
Timeline estimates
Elicitation
Variable
Superforecasters
Specific questions
Tournament forecasting
Best overall
Current Consensus Ranges
Question
Low
Median
High
P(AGI by 2030)
10%
25%
50%
P(AGI by 2040)
40%
65%
85%
P(Catastrophe
AGI)
5%
15%
Estimates represent rough synthesis of public expert views; wide disagreement exists.
Forecasting Best Practices
Track calibration - Did predictions come true at stated probabilities?
Decompose questions - Break complex questions into more tractable components
Update regularly - Revise forecasts as new information arrives
Acknowledge uncertainty - Use ranges, not point estimates