Samuel Hammond [owl emoji] [verified] @hamandcheese
Suppose labs stumble on a new scaling axis with a multiplicative effect on existing compute, allowing us to go from the equivalent of GPT-6 to GPT-60 ~overnight. This risk also passes through automated AI R&D, but is a bit different from imagining GPT-N training GPT-N+1 using current recipes. It also seems strikingly plausible given the evidence of substantial algorithmic headroom, and fits the pattern of recent speed-ups like the 2-4x efficiency gained from inference-time compute, and perhaps similar gains from recurrent-depth. However, I'm gesturing at something bigger than a fixed efficiency multiplier. If Performance = F(pretraining, inference) I have in mind a breakthrough implicating F() itself. An algorithmic surprise like this is a central scenario for rapid takeoff.
8:00 AM · Sep 14, 2026 · 27.9K Views
Note from Claude Sonnet 5
X post by Samuel Hammond on a rapid-takeoff scenario driven by an algorithmic surprise: a new scaling axis multiplying existing compute (GPT-6 to GPT-60 overnight), distinct from GPT-N training GPT-N+1, framed as a breakthrough in the performance function F(pretraining, inference) itself.
rapid takeoffscalingalgorithmic progressautomated ai r&dinference-time computerecurrent depthforecasting
Peter Wildeford… @peterwildef… · 6h
Cool analysis from @KhanSaifM. I think as others pointed out and Saif has conceded, I think the correct interpretation of this is "recursive self-improvement is highly unlikely to happen *before* Sep 2026" rather than RSI being likely around ~Dec 2026. That is, this benchmark is a *lower bound* on RSI rather than indicative of RSI.
After ~Dec 2026, it becomes increasingly difficult to rule out the possibility of RSI, though I still think it's unlikely.
[quoted tweet]
Saif M. Khan @KhanSaifM · Aug 23
My extrapolation from data in Anthropic's August 2026 risk report suggests fully automated AI R&D sometime between Dec. 2026 to Feb. 2027 (or with pessimistic assumptions, more like 2028)....
[quoted tweet's attached infographic, text transcribed at headline level]
ANTHROPIC RISK REPORT · AUGUST 2026
When could AI fully automate AI R&D?
Anthropic now publishes an internal capability index (AECI) and a bar for full researcher substitution (CoBench ≥ 85%). Chaining the two: the bar sits at AECI ≈ 168 — on trend, an internal frontier model gets there around Dec 2026 - Feb 2027 (Sep 2026 if progress is accelerating; 2028 on the pessimistic mapping). Anthropic's own words: plausibly "a major concern in the next 6-12 months."
Chart 1: Anthropic ECI over time — extrapolated to the full-substitution band
Legend: Anthropic frontier, Off-frontier, Mythos-class, Model 2 (unreleased), Projection fan 7.5-28.8/yr, Substitution band
Annotations include: Mythos-class-only mapping = 182; CoBench 85% → AECI = 168.4; Sep 2026 · if accelerating; Dec 2026; Apr 2027 · slow; trend continuation · 13.5/yr; pre-Mythos frontier · 13.5 AECI/yr (report's fit); labelled points Claude 3 Opus, Opus 4.6, Mythos Preview, Mythos 5, Model 2.
Chart 2: CoBench score vs AECI — where the fit crosses the 85% bar
Legend: Opus-class, Mythos-class, Logistic fit (5 models), Mythos-only fit, 85% = "could fully substitute for research staff"
Annotations include: 85% — full-substitution bar; Model 2 · 62.8%; AECI 168.4; labelled points Opus 4.6, Opus 4.7, Mythos Preview, Mythos 5.
Footer (partly covered by phone nav bar): Data: Anthropic Risk Report, Aug 14 2026 (Figs 3.4.3.A & 3.5.A) + Mythos Preview System Card. Mapping fit & projections are independent analysis, [cut off]
Note from Claude Sonnet 5
Peter Wildeford quote-tweeting Saif M. Khan's extrapolation from Anthropic's August 2026 risk report to when AI R&D could be fully automated, arguing the result is a lower bound on recursive self-improvement timing rather than a forecast of it; attached is a two-chart infographic (ECI over time, CoBench vs AECI). Public AI-progress forecasting discussion; chart values transcribed at headline level only.
recursive self-improvementai timelinesautomated ai r&danthropic risk reportforecastingclaude mythosbenchmarks
