Epoch AI
node
The research group that maintains the public databases this Journey depends on for anything quantitative about inputs: a catalogue of notable machine-learning models with reconstructed training-compute estimates, a benchmarking hub, and from 2025 a database of large AI data centres built from satellite imagery and permit filings. It matters to the argument of this Journey structurally rather than substantively. Almost every widely quoted figure about how fast AI compute is growing traces back to one small organisation reconstructing numbers that the developers themselves do not publish, and its own documentation is explicit that the estimates are reconstructions with stated uncertainty and partial coverage. A field whose central quantitative claim rests on third-party estimation of undisclosed inputs is in a different evidential position from one whose numbers are filed, audited or independently reproducible.
A node is not a place. Drawing it on a map would assert something about the world that no stored fact supports.
Read in · 1
Evidence · 1
Timeline
No dated observations are stored for this object. Atlas shows what was observed and when — it does not infer a history.
Connections · 1
Assembled narrative · 1
Assembled from 15 blocks · 1 evidence · 12 related
- Story
- The research group that maintains the public databases this Journey depends on for anything quantitative about inputs: a catalogue of notable machine-learning models with reconstructed training-compute estimates, a benchmarking hub, and from 2025 a database of large AI data centres built from satellite imagery and permit filings. It matters to the argument of this Journey structurally rather than substantively. Almost every widely quoted figure about how fast AI compute is growing traces back to one small organisation reconstructing numbers that the developers themselves do not publish, and its own documentation is explicit that the estimates are reconstructions with stated uncertainty and partial coverage. A field whose central quantitative claim rests on third-party estimation of undisclosed inputs is in a different evidential position from one whose numbers are filed, audited or independently reproducible.
- Knowledge
- Growth in frontier training compute
- Epoch AI
- Connections
- Growth in frontier training compute
- The transformer architecture
- Training compute
- Data-centre electricity demand
- Northern Virginia data-centre corridor
- Epoch AI
- The IEA publishes its first global analysis of energy and AI
- Evidence
- Training compute for frontier AI models grew by roughly 4–5× per year from 2010 to 2024, with frontier language models growing at about 5× per year since 2020; Epoch also maintains a database of large AI data centres built from satellite imagery and permit filings, with concentrations of covered sites in Texas, Virginia, Ohio, Nebraska and Iowa. V55 verification basis: epoch.ai was not fetched. The 4–5× and 5× growth rates were carried in search results attributing them to Epoch's own published trends analysis of that title, and the data-centre database and its state-level site concentrations were carried with attribution to Epoch. Forward projections of compute growth, coverage percentages for the data-centre database, and GPU-equivalent counts appearing in secondary summaries were NOT confirmed and are excluded from this pack.
Observed changes · 0
No public Signals are attached to this object. Signals show what changed and when it was observed — never a direction or a rank.
Actions
/atlas?object=ORG_EPOCH_AI&experience=ORG_EPOCH_AI