AI Safety Atlas Ch.1 — Appendix: Forecasting
Source: Appendix: Forecasting | ai-safety-atlas.com/chapters/v1/capabilities/appendix-forecasting/
A quantitative deep-dive into the effective-compute components — hardware efficiency, software efficiency, semiconductor production — plus training cost trends and power consumption.
Effective Compute Components
Hardware Efficiency
- AI chip performance grew ~1.35× annually since 2010
- Hardware costs declined ~30% per year at fixed performance
- Headroom — current analysis suggests potential for 50–1,000× efficiency gains before hitting fundamental CMOS limits
- 50% likelihood gains plateau before reaching ~200× improvement on existing technology
The chapter’s bottom line: “we have significant headroom to scale using current silicon paradigms through 2030 and beyond.”
Software / Algorithmic Efficiency
- Algorithmic improvements reduce required compute by ~3× annually
- Performance achieved with given compute doubles roughly every 8 months
- Since 2014, algorithmic progress accounts for ~35% of language modeling improvements; the remaining 65% from compute increases
Semiconductor Production
- NVIDIA GPU computing capacity grew ~2.3× yearly since 2019
- TSMC dominates fabrication; only 5% of advanced production went to AI accelerators in 2024
- New cutting-edge fabs require 4–5 years plus billions in investment
- ASML extreme-UV lithography machines: $150–380M each, long waitlists
This last point — chip-fab and EUV bottlenecks — is the wiki’s existing ai-population-explosion discussion concretized. ASML appears here as a non-trivial supply-chain chokepoint, relevant to Todd’s geographic-positioning recommendation (the Netherlands is a TAI-relevant ally precisely because of ASML).
Investment and Training Costs
- Frontier model training costs grew 3.5× annually since 2020
- Costs approaching $1 billion by 2027
- Cost breakdown: hardware 50–65%, R&D staff 30–45%, electricity 2–6%
- Hardware acquisition costs grew 2.5× yearly since 2016
- Grok-4 (July 2025) development: ~$480M total
- Grok-3 hardware acquisition: ~$3 billion alone
The structural caveat: “Uncertainty about productivity returns and structural constraints — particularly lengthening lead times and investment hurdles — may encourage incremental rather than massive upfront scaling investments.”
Power Consumption
- GPT-4 training: ~50 megawatts
- Continuing 4–5× annual compute scaling implies frontier training will require 4–16 gigawatts by 2030
- Electricity is currently 2–6% of training costs; this proportion may rise significantly at larger scales
- A data center supporting 2×10²⁹ FLOP training would require ~6 gigawatts; 1–5 GW facilities by decade’s end
- Large-scale power plant construction: 2–3 years typical
Why This Appendix Matters
This is the quantitative backbone behind the main subchapter’s “scaling can continue through 2030” thesis. Each constraint is examined and shown to have headroom — but only just:
- Hardware: 50–1,000× headroom OK
- Software: 3×/year, ~8-month doubling OK
- Production: 2.3×/year via TSMC + ASML — bottleneck but expanding
- Training cost: $1B by 2027 — reachable but punishing
- Power: 4–16 GW by 2030 — small-city scale, infrastructure-limited
Whether this trajectory continues depends on infrastructure (chips, fabs, power plants) catching up before economic returns from scaling start to flatten.
Connection to Wiki
This appendix is the most quantitatively concrete chapter section so far and feeds directly into:
- scaling-laws — adds 2025 numbers to Aschenbrenner’s OOM framework
- sa-ch3a-trillion-dollar-cluster — Aschenbrenner’s “trillion-dollar cluster” thesis is partially priced here at “$1B per training run by 2027”
- sa-ch3b-lock-down-labs — chip-fab and weight-protection concerns share the same supply-chain map
- ai-population-explosion — power and chip availability limit how many parallel AI workers can run
- effective-compute — the new concept page draws core numbers from here
- summary-substack-benjamin-todd — Netherlands/ASML positioning is grounded here