Epoch AI

Quick Assessment

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Organization Details

Overview

Epoch AI is a research institute dedicated to tracking and forecasting AI development through rigorous empirical analysis. Founded in April 2022 by Jaime SevillaPersonJaime SevillaFounder and Director of Epoch AI, a research institute tracking AI development trends through comprehensive databases on training compute, model parameters, and hardware capabilities. Founded Epoch... and six co-founders, Epoch has become the authoritative source for data on AI training compute, model parameters, hardware capabilities, and development timelines. Their research directly informs policy discussions, corporate planning, and academic research on AI trajectories. The New York Times praised their work for bringing "much-needed rigor and empiricism to an industry that often runs on hype and vibes," and featured them in their 2024 Good Tech Awards.

The organization's core contribution is maintaining comprehensive databases that enable quantitative analysis of AI progress. Their public database tracks over 3,200 machine learning models from 1950 to present, documenting the training compute, parameters, and capabilities of each system. By cataloging these metrics, Epoch provides the empirical foundation for discussions about AI timelines, resource constraints, and capability trajectories. Their work bridges the gap between speculative AI forecasting and evidence-based analysis.

Epoch's research has been directly cited in major policy documents including the EU AI Act (which adopted their 10^25 FLOP compute threshold) and US Executive Order 14110. Their data informs Congressional hearings, and leading AI labs use their metrics for planning. As director Jaime Sevilla stated, "We want to do something similar for artificial intelligence to what William Nordhaus, the Nobel laureate, did for climate change. He set the basis for rigorous study and thoughtful action guided by evidence." The organization represents a critical piece of epistemic infrastructureApproachAI-Era Epistemic InfrastructureComprehensive analysis of epistemic infrastructure showing AI fact-checking achieves 85-87% accuracy at $0.10-$1.00 per claim versus $50-200 for human verification, while Community Notes reduces mi...Quality: 59/100 for understanding where AI development is headed and what constraints may shape its trajectory.

History and Evolution

Founding (2022)

Epoch AI emerged from a collaborative research effort that began when Jaime Sevilla, a Spanish researcher, put his Ph.D. on pause and issued a call for volunteers to systematically document the critical inputs of every significant AI model ever created. The initial team that responded went on to become Epoch's founding members: Jaime Sevilla, Tamay Besiroglu, Lennart HeimPersonLennart HeimCompute governance researcher. Affiliated with RAND, Epoch AI, and GovAI. Leading expert on AI compute supply chains and governance. Co-authored RAND working papers on hardware-enabled mechanisms (..., Pablo Villalobos, Eduardo Infante-Roldan, Marius Hobbhahn, and Anson Ho. Collectively, they brought backgrounds in Machine Learning, Statistics, Economics, Forecasting, Physics, Computer Engineering, and Software Engineering.

During Epoch's first retreat in April 2022, the members decided to formalize as an organization and chose the name "Epoch" through a Twitter poll. When they published their findings in the paper "Compute Trends Across Three Eras of Machine Learning" in early 2022, the reaction was overwhelmingly positive, with the paper going viral in AI research communities. The paper documented the 10-billion-fold increase in training compute since 2010 and identified three distinct eras of ML development with different scaling dynamics.

Growth and Fiscal Sponsorship (2022-2024)

From its founding, Epoch was fiscally sponsored and operationally supported by Rethink PrioritiesOrganizationRethink PrioritiesRethink Priorities is a research organization founded in 2018 that grew from 2 to ~130 people by 2022, conducting evidence-based analysis across animal welfare, global health, and AI governance. Th...Quality: 60/100, whose Special Projects team provided critical infrastructure for the growing organization. At founding, Epoch had a staff of 13 people (9 FTEs). Coefficient GivingOrganizationCoefficient GivingCoefficient Giving (formerly Open Philanthropy) has directed $4B+ in grants since 2014, including $336M to AI safety (~60% of external funding). The organization spent ~$50M on AI safety in 2024, w...Quality: 55/100 (then Coefficient GivingOrganizationCoefficient GivingCoefficient Giving (formerly Open Philanthropy) has directed $4B+ in grants since 2014, including $336M to AI safety (~60% of external funding). The organization spent ~$50M on AI safety in 2024, w...Quality: 55/100) provided early support with a $1.96M grant for general support, followed by additional grants totaling over $6M through 2025.

During this period, Epoch expanded its database from the initial 123 models documented in their founding paper to over 3,200 models. They launched new data products including the AI Chip Sales tracker, Parameter Counts database, and AI Supercomputer Tracker. The team grew to approximately 34 employees with headquarters established in San Francisco.

Independence and Expansion (2025-Present)

In early 2025, Epoch spun out from its fiscal sponsor and began operating as an independent 501(c)(3) nonprofit organization. This transition marked their maturation from a research project to a fully independent institution. Key 2025 developments included:

• Launch of the Epoch Capabilities Index (ECI) in October 2025, a unified metric combining scores from 37 benchmarks
• Completion of FrontierMath Tier 4, commissioned by OpenAI, featuring 50 research-level mathematics problems
• Publication of more plots and visualizations in 2025 than in all previous years combined
• Development of the GATE model for forecasting AI's economic impact

Team

Leadership and Key Researchers

Team Composition

The current team of approximately 34 employees includes researchers with diverse backgrounds:

• Machine Learning researchers: Core technical expertise in model architectures and training
• Economists: Analyze AI's economic implications and build forecasting models
• Statisticians: Develop rigorous methodologies for trend analysis
• Policy analysts: Translate research findings for governance contexts
• Data engineers: Maintain and expand the organization's databases

Key Research Areas

flowchart TD
  EPOCH[Epoch AI] --> DATA[Data Infrastructure]
  EPOCH --> RESEARCH[Research Analysis]
  EPOCH --> FORECAST[Forecasting]
  EPOCH --> BENCH[Benchmarking]

  DATA --> MODELS[ML Model Database<br/>3,200+ models tracked]
  DATA --> COMPUTE[Training Compute<br/>Estimates]
  DATA --> HARDWARE[AI Chip Sales<br/>15M+ H100-equiv]
  DATA --> SUPER[AI Supercomputers<br/>Capacity mapping]

  RESEARCH --> TRENDS[Trend Analysis]
  RESEARCH --> CONSTRAINTS[Constraint Analysis]
  RESEARCH --> ECON[AI Economics]

  TRENDS --> GROWTH[4.4x compute<br/>growth/year]
  CONSTRAINTS --> DATAWALL[Data Wall<br/>2026-2032]
  CONSTRAINTS --> POWER[Power Constraints<br/>9GW by 2030]

  BENCH --> ECI[Epoch Capabilities<br/>Index]
  BENCH --> FMATH[FrontierMath<br/>Benchmark]

  FORECAST --> GATE[GATE Economic<br/>Model]
  FORECAST --> POLICY[Policy<br/>Implications]

  style EPOCH fill:#e6f3ff
  style GROWTH fill:#ffddcc
  style DATAWALL fill:#ffddcc
  style POWER fill:#ffddcc
  style ECI fill:#d4edda
  style FMATH fill:#d4edda

Training Compute Estimation Methodology

A critical contribution of Epoch AI is their rigorous methodology for estimating the compute used to train machine learning models. This methodology enables accurate comparisons across models and time periods, forming the foundation for their scaling analysis.

Two Primary Estimation Strategies

Epoch uses two complementary approaches to estimate training compute:

Architecture-based estimation: Epoch maintains detailed tables of common neural network layers, estimating parameters and FLOP per forward pass. For many layers, forward pass FLOP approximately equals twice the parameter count. A backward pass adds approximately 2x the forward pass FLOP, yielding the common heuristic: total training FLOP approximately equals 6 times parameters times tokens.

Hardware-based estimation: When architecture details are unavailable, Epoch calculates compute from GPU training time multiplied by peak GPU performance, adjusted by utilization rate. Their empirical analysis found utilization rates typically range from 0.3 to 0.75 depending on architecture and batch size.

Key Methodological Insights

• The backward/forward FLOP ratio is "very likely 2:1" after correcting for common counting errors
• The "Theory method" multiplies forward pass FLOP by 3.0 to account for backward pass
• Larger batch sizes yield more consistent utilization rates
• Parameter sharing (as in CNNs) and word embeddings require special handling

Training Compute Trends

Three Eras of Machine Learning

Epoch's foundational 2022 paper identified three distinct eras with different compute scaling dynamics:

This analysis corrected earlier estimates (Amodei and Hernandez 2018) that suggested 3.4-month doubling, finding the actual rate closer to 5-6 months with approximately 10x more data points.

Historical Compute Scaling

Epoch's database reveals the dramatic scaling of AI training compute:

The first model trained at the 10^25 FLOP scale was GPT-4, released in March 2023. As of June 2025, Epoch has identified over 30 publicly announced AI models from 12 different developers that exceed this threshold.

Hardware Trends

AI Chip Sales Database

Epoch's AI Chip Sales data explorer is the most comprehensive public dataset tracking global AI compute capacity across vendors:

Key finding: Global computing capacity has been growing by 3.3x per year, equivalent to a doubling time of approximately 7 months.

AI Supercomputer Projections

Epoch's analysis of AI supercomputer trends projects significant scaling challenges:

The 9 GW power requirement for 2030 frontier training represents the equivalent of 9 nuclear reactors—a scale beyond any existing industrial facility. This represents a potential binding constraint on AI scaling.

Geographic Distribution of AI Compute

The shift from academic to industry dominance has been dramatic:

Data Constraints Research

Epoch's influential research on training data constraints (the "data wall") has become central to discussions of AI scaling limits. Their paper "Will We Run Out of ML Data?" projects when AI development may exhaust human-generated training data.

Key Projections

The exact date depends on scaling assumptions. According to researcher Tamay Besiroglu: "There is a serious bottleneck here. If you start hitting those constraints about how much data you have, then you can't really scale up your models efficiently anymore. And scaling up models has been probably the most important way of expanding their capabilities."

Overtraining Factor Analysis

Updated Estimates (2025)

Epoch's analysis has evolved based on new evidence:

• The effectiveness of carefully filtered web data and multi-epoch training has substantially increased estimates of available high-quality data
• After accounting for data quality, availability, multiple epochs, and multimodal tokenizer efficiency, Epoch estimates 400 trillion to 20 quadrillion tokens available for training by 2030
• This allows for training runs from 6x10^28 to 2x10^32 FLOP

Mitigating Factors

Epoch identifies three categories of innovation that could extend the scaling runway:

While Sam AltmanPersonSam AltmanComprehensive biographical profile of Sam Altman documenting his role as OpenAI CEO, timeline predictions (AGI within presidential term, superintelligence in "few thousand days"), and controversies...Quality: 40/100 noted OpenAI experiments with "generating lots of synthetic data," he expressed reservations: "There'd be something very strange if the best way to train a model was to just generate, like, a quadrillion tokens of synthetic data and feed that back in." Research shows training on AI-generated data can produce "model collapse" with degraded outputs.

Epoch Capabilities Index (ECI)

The Epoch Capabilities Index (ECI), launched in October 2025, represents a major methodological advance in measuring AI progress. As individual benchmarks saturate, ECI provides a unified scale for comparing models across time.

Methodology

ECI combines scores from 37 distinct benchmarks into a single "general capability" scale, similar to how IQ tests capture broad underlying capability:

ECI scores function like Elo ratings: absolute values are less meaningful than relative comparisons. The scale is linear, so a 10-point jump should be equally significant whether moving from 100 to 110 or from 140 to 150.

Key Finding: 90% Acceleration

Epoch's analysis reveals a significant acceleration in AI capabilities progress:

This acceleration is corroborated by METR's Time Horizon benchmark, which found a ~40% acceleration in task completion capabilities starting around the same period.

FrontierMath Benchmark

FrontierMath is Epoch's benchmark of original, expert-crafted mathematics problems designed to evaluate advanced reasoning capabilities—problems that typically require hours or days for expert mathematicians to solve.

Development and Structure

Tier Structure

Model Performance

While leading AI models achieve near-perfect scores on traditional math benchmarks (GSM-8k, MATH), FrontierMath reveals substantial gaps:

The discrepancy between o3's announced 25% and measured 10% reflects differences in model versions and benchmark composition over time. Both the model and benchmark changed between December 2024 and April 2025.

Significance

FrontierMath addresses two critical challenges:

1. Benchmark saturation: Traditional math benchmarks no longer differentiate frontier models
2. Data contamination: Using entirely new, unpublished problems with automated verification

GATE Economic Model

The GATE model (Growth and AI Transition Endogenous) is Epoch's integrated assessment model of AI's economic impact, published in 2025 (arXiv:2503.04941).

Core Dynamics

GATE models an automation feedback loop: investments drive increases in compute for training and deploying AI, which leads to gradual task automation, which generates returns enabling further investment.

Key Predictions

Interpretation Caveats

Epoch explicitly cautions against treating GATE outputs as precise quantitative predictions. The model illustrates key dynamics rather than providing forecasts. According to their analysis: "These findings suggest that those who are confidently either extremely skeptical or extremely bullish about an unprecedented growth acceleration due to AI are likely miscalibrated."

A public GATE playground allows users to modify parameters and explore scenarios.

Policy Impact

Epoch's research has directly influenced major AI governance frameworks. Their compute trend data provides the empirical foundation for regulatory thresholds.

Compute Thresholds in Regulation

The EU AI Act explicitly references the statistical relationship between training compute and model capabilities documented by Epoch, noting that "performance of 231 language models (measured in log-perplexity) against scale (measured in FLOP)" shows clear trends.

Government Engagements

Model Count Projections

Epoch's analysis of how many models will exceed compute thresholds directly informs regulatory planning:

Key Publications

Funding

Epoch AI has raised approximately $7 million through September 2025, primarily from Coefficient GivingOrganizationCoefficient GivingCoefficient Giving (formerly Open Philanthropy) has directed $4B+ in grants since 2014, including $336M to AI safety (~60% of external funding). The organization spent ~$50M on AI safety in 2024, w...Quality: 55/100 grants.

Coefficient Giving Grants

Other Funding

• Carl Shulman: $100,000 individual donation
• Various individual donors
• Contract revenue from clients including AI labs and government offices

Coefficient Giving has cited Epoch as producing "world-class work that is widely read, used, and shared."

Comparison with Other Forecasting Organizations

Our World in Data directly incorporates Epoch's compute trend data in their AI visualizations, extending Epoch's reach to a broader audience.

Strengths and Limitations

Strengths

Limitations

Critical Assessment

What Epoch Does Well

Epoch fills a crucial gap in the AI ecosystem by providing rigorous empirical grounding for discussions that previously relied on intuition and speculation. Before Epoch, claims about AI progress rates were often based on anecdotes or marketing materials. Epoch's systematic data collection enables evidence-based analysis of trends that matter for policy and planning.

Their methodology for compute estimation has become the de facto standard, cited by academics, policymakers, and industry alike. The FrontierMath benchmark addresses a genuine problem—traditional benchmarks saturating—with a thoughtful approach using novel problems from credentialed experts.

Key Uncertainties

Perspectives on Epoch's Role

Timeline

Sources and External Links

Official Resources

• Epoch AI Website
• ML Trends Dashboard
• Epoch AI Database
• Epoch Substack
• 2025 Impact Report
• GitHub: epoch-research
• Team Page
• Funding Information

Key Data Products

• AI Models Database - 3,200+ models from 1950-present
• AI Chip Sales - Global compute capacity tracking
• Epoch Capabilities Index - Unified capability measurement
• FrontierMath Benchmark - Advanced mathematical reasoning
• GATE Model Playground - Interactive economic modeling

Academic Publications

• Compute Trends Across Three Eras (arXiv:2202.05924)
• FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI (arXiv:2411.04872)
• GATE: An Integrated Assessment Model for AI Automation (arXiv:2503.04941)

Methodology Documentation

• Estimating Training Compute
• How to Measure FLOP Empirically
• Interpreting the Epoch Capabilities Index

Coefficient Giving Grants

• Epoch General Support (2022)
• Epoch AI General Support (2025)
• Epoch AI Worldview Investigations

Policy Documents Citing Epoch

• EU AI Act Technical Documentation (JRC)
• Training Compute Thresholds in AI Regulation (arXiv:2405.10799)

Media Coverage

• The Epoch AI Researcher Trying to Glimpse the Future of AI (TIME)
• Announcing Epoch (EA Forum)