Training power draw growth (doubling time)
Doubling time of notable models' estimated training power draw, fitted log-linearly from 2020 on Epoch's notable-model table. Derived metric training_power_doubling_days.
Reason through thisThe reading
Estimated from hardware counts and thermal design power; a proxy for the buildout's physical footprint, not a metered figure.
1 reading drawn from the derived metric training_power_doubling_days; 216 observations in the evidence.
Derived (training_power_doubling_days) from: Epoch AI notable and frontier models. Epoch AI, Notable AI Models (CC BY 4.0).
The numbers
| as of | reading | value | record |
|---|---|---|---|
| 2026-10-15 | 340.5 171 0.116 | 419 days | estimate |
Why it matters
Power is the physical constraint that compute growth eventually hits; the brief's reading is 2.2x a year.
What cuts against it
Power draw is estimated for nearly every model; the notable set skews to models whose hardware is public.
How it is measured
- derived metric
training_power_doubling_days(formula in the semantic layer) - source Epoch AI notable and frontier models · default tier 6 · CC BY 4.0
- proxy model_release · cadence monthly · valve invention to product
Normal = doubling no faster than yearly (grid-connection pace); fast = every 230 days or less (3x a year, the pace a runaway buildout needs). Between is emerging.
Applied to metric:training_power_doubling_days.
Latest readings
- 2026-10-15 · 419 daysas of 2026-10-15(216 obs)
Why this status, and its history
Log-linear fit on 168 notable models' estimated training power draw since 2020: doubling every 506 days (CI 332-1067), inside the normal band (yearly or slower), but the fit explains almost none of the variance (r2 0.08) because the notable set mixes small and frontier models. Single tier-6 source and a weak fit: emerging. Initial seed.
- 2026-09-10unmeasured to emergingconf — → 50 · the evaluator (automatic)
Log-linear fit on 168 notable models' estimated training power draw since 2020: doubling every 506 days (CI 332-1067), inside the normal band (yearly or slower), but the fit explains almost none of the variance (r2 0.08) because the notable set mixes small and frontier models. Single tier-6 source and a weak fit: emerging. Initial seed.
Why this confidence
Rests on 216 observations from Epoch AI notable and frontier models; best evidence tier 6, grade C; current.
Confidence is independent of status: 90–95 multiple strong independent sources; 70–89 good evidence, some ambiguity; 50–69 mixed or hard to operationalise; 0–49 limited or vague evidence.
Predictions that lean on this
- Happening
- Too early to tell
- Too early to tell
- Too early to tell