Epoch AI

Quick Assessment

Overview

Epoch AI is a research organization founded in 2022 that provides rigorous, data-driven empirical analysis and forecasting of AI progress. Based in San Francisco with 34 employees, their work serves as critical infrastructure for AI governanceParameterAI GovernanceThis page contains only component imports with no actual content - it displays dynamically loaded data from an external source that cannot be evaluated. and timeline forecastingConceptAGI 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. Their public database tracks over 3,200 machine learning models from 1950 to present—the largest resource of its kind.

Their research documents three key scaling dynamics: training compute growing at 4.4x per year since 2010, high-quality training data stock of approximately 300 trillion tokens with exhaustion projected between 2026-2032, and algorithmic efficiency doubling every 6-12 months. As of June 2025, they have identified over 30 publicly announced AI models exceeding the 10^25 FLOP training compute threshold.

Unlike organizations developing AI capabilities or safety techniques directly, Epoch provides the empirical foundation that informs strategic decisions across the AI ecosystem. Their databases and forecasts are cited by policymakers designing compute governance frameworks, safety researchers planning research timelines, and AI labs benchmarking their progress against industry trends. In 2024, they were featured on The New York Times' "2024 Good Tech Awards" list.

Their most influential finding is the exponential growth in training compute for frontier models—with training costs growing 2-3x annually and projected to exceed $1 billion per model by 2027. This analysis has become foundational for understanding AI progress and informing governance approaches focused on compute as a key chokepoint.

2025 Research Impact

Key Findings Summary

Organizational Risk Assessment

Key Research Areas

Compute Trends Analysis

Epoch's flagship research tracks computational resources used to train AI models, revealing exponential scaling patterns. Their Machine Learning Trends dashboard provides real-time tracking of these dynamics.

Critical findings from Epoch's compute database:

• Exponential growth faster than Moore's Law: While chip performance doubles every ~2 years, AI training compute grows 4.4x annually—driven primarily by larger training clusters, longer training runs, and improved hardware
• Economic scaling: Training costs growing 2-3x annually, reaching $50-100M+ for frontier models; the largest AI supercomputers (like Anthropic's 750MW Indiana facility) cost billions to build
• Concentration effects: Only 12 organizations have trained models above 10^25 FLOP; companies now own 80% of all AI supercomputers while government share has declined

Training Data Constraints

Epoch's "Will We Run Out of Data?"↗🔗 web★★★★☆Epoch AI"Will We Run Out of Data?"ai-forecastingcompute-trendstraining-datasetsSource ↗ research revealed potential bottlenecks for continued AI scaling. Their analysis estimates the total stock of human-generated public text at approximately 300 trillion tokens.

Key implications:

• Pressure for efficiency: Data constraints may force more efficient training methods; Epoch research shows algorithmic efficiency doubling every 6-12 months
• Synthetic data boom: Investment in AI-generated training data accelerating to extend runway
• Multimodal shift: Language modelsCapabilityLarge Language ModelsComprehensive analysis of LLM capabilities showing rapid progress from GPT-2 (1.5B parameters, 2019) to o3 (87.5% on ARC-AGI vs ~85% human baseline, 2024), with training costs growing 2.4x annually...Quality: 60/100 increasingly incorporating image/video data
• Overtraining risk: If models are "intensely overtrained," high-quality text could be exhausted even earlier than 2026

Timeline Forecasting Methodology

Epoch employs multiple complementary approaches to estimate transformative AI timelines:

Impact on AI Safety and Governance

Policy Integration

Epoch's data directly informs major governance initiatives. Their research has been particularly influential in establishing compute thresholds for regulatory frameworks.

FrontierMath Benchmark

Epoch's FrontierMath benchmark represents a significant contribution to AI evaluation infrastructure:

Research Community Influence

Current State and Trajectory

2024 Developments

Database expansion:

• Added 200+ new model entries to Parameter Database
• Enhanced tracking of Chinese and European models
• Improved cost estimation methodologies
• Real-time updates↗🔗 web★★★★☆Epoch AIReal-time updatesai-forecastingcompute-trendstraining-datasetsSource ↗ for new releases

Research breakthroughs:

• Refined algorithmic efficiency measurement showing 6-12 month doubling times
• Updated data exhaustion projections with synthetic data considerations
• New economic modeling of AI investment trends
• Bioweapons AI uplift analysisModelAI Uplift Assessment ModelQuantitative assessment estimating AI provides modest knowledge uplift for bioweapons (1.0-1.2x per RAND 2024) but concerning evasion capabilities (2-3x, potentially 7-10x by 2028), with projected ...Quality: 70/100

2025-2026 Projections

Key Uncertainties and Debates

Forecasting Limitations

Methodological Debates

Trend extrapolation reliability:

• Optimists: Historical trends provide best available evidence for forecasting
• Pessimists: Sharp left turnsRiskSharp Left TurnThe Sharp Left Turn hypothesis proposes AI capabilities may generalize discontinuously while alignment fails to transfer, with compound probability estimated at 15-40% by 2027-2035. Empirical evide...Quality: 69/100 and discontinuities make extrapolation unreliable
• Epoch position: Multiple methods with explicit uncertainty bounds

Information hazards:

• Security concern: Publishing compute data aids adversaries in capability assessment
• Racing dynamics: Timeline estimates may encourage competitive behavior
• Transparency advocates: Public data essential for democratic governance

Leadership and Organization

Key Personnel

Organizational Structure

Funding Profile

Additional funding sources:

• Future of Humanity Institute↗🔗 web★★★★☆Future of Humanity Institute**Future of Humanity Institute**talentfield-buildingcareer-transitionsrisk-interactions+1Source ↗ (historical support)
• Government contracts for specific projects
• Research grants from academic institutions

Comparative Analysis

vs. Other Forecasting Organizations

Relationship to Safety Organizations

Future Directions and Challenges

Research Roadmap (2025-2027)

2026 Plans (from Impact Report):

• Development of 2 new benchmarks beyond FrontierMath
• Continued expansion of the Epoch Capabilities Index
• Deeper analysis of decentralized training feasibility (10 GW training runs across thousands of kilometers)
• Enhanced AI Chip Sales data explorer across Nvidia, Google, Amazon, AMD, and Huawei

Expanding scope:

• Multimodal training data analysis beyond text
• Energy consumption and environmental impact tracking (largest data centers now approaching gigawatt scale)
• International AI development monitoring (enhanced coverage of Chinese and European models)
• Risk assessment frameworks for different development pathways

Methodological improvements:

• Better algorithmic progress measurement via Epoch Capabilities Index
• Synthetic data quality and scaling analysis
• Economic impact modeling of AI deployment
• Scenario analysis for different development paths

Scaling Challenges

Sources & Resources

Primary Resources

Key Publications

External Coverage