AI Capability Doubling Time
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An AI Capability Doubling Time is an exponential domain-specific AI progress measure that can quantify AI capability doubling periods across AI domains (through empirical performance tracking).
- AKA: AI Doubling Time, AI Capability Doubling Metric, AI Performance Doubling Period, AI Progress Doubling Rate.
- Context:
- It can typically measure AI System Performance Growth through AI benchmark progressions and AI capability evaluations.
- It can typically track AI Domain-Specific Progress via AI task-specific metrics and AI performance indicators.
- It can typically quantify AI Capability Acceleration using AI exponential growth patterns and AI improvement trajectorys.
- It can typically support AI Investment Decisions through AI progress forecasts and AI timeline projections.
- It can typically enable AI Research Prioritization via AI capability gap analysis and AI development bottleneck identification.
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- It can often vary across AI Task Domains with AI coding tasks showing shorter AI doubling periods than AI reasoning tasks.
- It can often reveal AI Hidden Exponential Patterns beneath AI incremental progress appearances.
- It can often inform AI Strategic Planning through AI capability milestones and AI development thresholds.
- It can often correlate with AI Compute Scaling and AI model size increases.
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- It can range from being a Short-Term AI Capability Doubling Time Measure to being a Long-Term AI Capability Doubling Time Measure, depending on its AI capability measurement horizon.
- It can range from being a Task-Specific AI Capability Doubling Time Measure to being a General AI Capability Doubling Time Measure, depending on its AI capability scope coverage.
- It can range from being a Empirical AI Capability Doubling Time Measure to being a Projected AI Capability Doubling Time Measure, depending on its AI capability data source.
- It can range from being a Conservative AI Capability Doubling Time Measure to being an Aggressive AI Capability Doubling Time Measure, depending on its AI capability growth assumptions.
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- It can integrate with AI Benchmark Systems for AI capability measurement standardization.
- It can connect to AI Progress Tracking Platforms for AI capability trend analysis.
- It can support AI Investment Frameworks through AI capability return projections.
- It can inform AI Safety Protocols via AI capability emergence detection.
- It can enhance AI Research Agendas through AI capability priority setting.
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- Example(s):
- METR AI Capability Doubling Time Measures, such as:
- METR Coding Capability Doubling Time (2025), measuring AI coding performance doubling at approximately 6-month intervals.
- METR Reasoning Capability Doubling Time (2025), tracking AI reasoning task doubling over 12-18 month periods.
- METR Multimodal Capability Doubling Time (2025), assessing AI vision-language doubling across 9-month cycles.
- Benchmark-Specific AI Capability Doubling Time Measures, such as:
- MMLU Benchmark Doubling Time, tracking AI knowledge assessment doubling from GPT-3 to GPT-4 era.
- HumanEval Coding Doubling Time, measuring AI code generation doubling across model generations.
- ARC-AGI Benchmark Doubling Time, quantifying AI abstract reasoning doubling with cost-performance ratios.
- Domain-Specific AI Capability Doubling Time Measures, such as:
- AI Medical Diagnosis Doubling Time, tracking AI diagnostic accuracy doubling in clinical applications.
- AI Language Translation Doubling Time, measuring AI translation quality doubling across language pairs.
- AI Game Playing Doubling Time, assessing AI strategic capability doubling from chess to Go to StarCraft.
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- METR AI Capability Doubling Time Measures, such as:
- Counter-Example(s):
- Linear AI Progress Measures, which assume constant rather than exponential AI capability improvement.
- Static AI Benchmarks, which measure point-in-time performance without AI temporal progression.
- AI Deployment Metrics, which track adoption rather than AI capability advancement.
- See: AI Progress Measure, AGI Performance Measure, AI Benchmark System, Exponential Growth Pattern, Moore's Law, AI Scaling Law, AI Development Timeline, AI Investment Strategy, METR Organization, AI Safety Framework.