Effective Compute
Effective compute (eFLOPs) is the multiplicative measure of total computational capability available to AI training, decomposed into three independent factors. It is the core variable underlying scaling-laws extrapolation: when AI capability is plotted against “compute,” what is meant is effective compute — not raw FLOPs from a single chip.
The Decomposition
Effective compute = Software efficiency × Hardware efficiency × Number of chips
Each factor improves independently, so the total compounds much faster than any single input. As of mid-2025 (per the AI Safety Atlas):
| Factor | Annual growth | Mechanism |
|---|---|---|
| Hardware efficiency | 1.35× / year | Better GPU performance per dollar; ~30% cost decline at fixed performance |
| Software / algorithmic efficiency | ~3× / year | Better training methods cut required compute for a given result; doubling roughly every 8 months |
| Chip production | 2.3× / year | Semiconductor fab expansion, capital investment in NVIDIA/TSMC |
Combined: roughly 9× per year of effective compute growth — the empirical basis for “5× annual training compute” trajectories projected to 2030.
Compatibility with Aschenbrenner’s OOM Framework
The Atlas’s effective-compute decomposition is compatible with Aschenbrenner’s framework in situational-awareness, which decomposes scaling laws into:
- Compute scaling — investment-driven, ~5× / year (subsumes “number of chips” in the Atlas decomposition)
- Algorithmic efficiency — ~0.5 OOMs/year (matches “software efficiency”)
- Unhobbling gains — chain-of-thought, agentic scaffolding, tool use (a fourth factor not captured in the simple multiplicative formula)
The Atlas also adds a third hypothesis explicitly recognizing unhobbling: the “Scale + Techniques + Tools” scaling hypothesis (see scaling-laws).
The Constraints
Each factor has structural limits the wiki should track:
Hardware Efficiency
- Headroom — 50–1,000× efficiency gains potentially possible before fundamental CMOS limits
- 50% likelihood that gains plateau before reaching ~200× improvement on existing technology
- “We have significant headroom to scale using current silicon paradigms through 2030 and beyond.”
Software Efficiency
- ~3× / year, ~8-month performance doubling at fixed compute
- Since 2014, algorithmic progress accounts for ~35% of language modeling gains; remaining 65% from compute scaling
Chip Production
- TSMC dedicated only 5% of advanced production to AI accelerators in 2024
- Building new cutting-edge fabs: 4–5 years plus billions in investment
- ASML extreme-UV lithography machines: $150–380M each, long waitlists
- This is the supply-chain chokepoint underlying Benjamin Todd’s “Netherlands as TAI-relevant ally” geographic-positioning argument
Power
- GPT-4 training: ~50 megawatts
- Frontier 2030 training: 4–16 gigawatts (small-city scale)
- Large-scale power plant construction: 2–3 years typical
Why This Frame Matters
Effective compute clarifies several debates:
- “Will scaling continue?” — depends on which factor stalls first. Software hits no hard wall but may slow; hardware has known headroom; chip production is real-physical-infrastructure-constrained.
- “Compute governance” — see ai-governance. Compute is an attractive lever because it’s physical, observable, and globally concentrated. The factor decomposition shows compute governance only constrains one of three factors.
- “Sanctions / export controls” — the chip-production factor is what the US-China chip export controls aim at; software and hardware-efficiency factors are not export-controllable.
- “Trillion-dollar cluster” feasibility — see sa-ch3a-trillion-dollar-cluster. The Atlas confirms the trajectory ($1B per training run by 2027) but flags lengthening lead times and investment hurdles as drag.
Connection to Wiki
Effective compute is the technical core variable connecting:
- scaling-laws — what scaling laws plot capability against
- transformative-ai — TAI compute requirements operationalize through this number
- ai-population-explosion — number of parallel AI workers depends on inference compute, which scales with the same chip-production factor
- situational-awareness and sa-ch3a-trillion-dollar-cluster — the trillion-dollar-cluster thesis prices effective compute over a decade
- summary-substack-benjamin-todd — the geographic-positioning argument hinges on chip-production constraints
Related Pages
- scaling-laws
- bitter-lesson
- transformative-ai
- ai-population-explosion
- foundation-models
- ai-governance
- ai-safety-atlas-textbook
- atlas-ch1-capabilities-05-forecasting-timelines
- atlas-ch1-capabilities-09-appendix-forecasting
- situational-awareness
- sa-ch3a-trillion-dollar-cluster
- summary-substack-benjamin-todd
Sources cited
Primary URLs harvested from this page’s summary references. Auto-generated by scripts/backfill_citations.py; edit by re-running, not by hand.
- AI Safety Atlas Ch.1 — Appendix: Forecasting — referenced as
[[atlas-ch1-capabilities-09-appendix-forecasting]] - AI Safety Atlas Ch.1 — Forecasting Timelines — referenced as
[[atlas-ch1-capabilities-05-forecasting-timelines]] - Summary: Situational Awareness — The Decade Ahead — referenced as
[[situational-awareness]]