◆  A MEASUREMENT INSTRUMENT, NOT A FORECAST

Three clocks, one field of compressing time.

A public-interest instrument tracking the arrival of AGI, the Singularity, and Superintelligence — derived from a versioned ensemble of capability, compute, economic, and alignment signals. Every movement sourced. Every methodology auditable.

AGI · APR 2027 AGI SINGULARITY · DEC 2029 SINGULARITY ASI · DEC 2031 ASI NOW
Nearest Clock · AGI
06M01D
Est. Apr 2027 · projection, not prediction
Signal Oscillogram · trailing 90 days · 3 channels · reconstructed from changelog Y = Δ months since edition · X = daily ticks
AGI CLOCK 5 signals
AGIArtificial General Intelligence
6 mo · Est. Apr 2027
Holding · flat since Oct
SINGULARITY CLOCK 2 signals
SingularityRecursive Self-Improvement Inflection
3yr 2 mo · Est. Dec 2029
Holding · flat since Oct
ASI CLOCK 3 signals
ASIArtificial Superintelligence
5yr 2 mo · Est. Dec 2031
Holding · flat since Oct
LIVE · 14 SIGNALS ·
METR · TASK HORIZON 11.4 hr ▲ +38% QOQ SWE-BENCH VERIFIED 88.2% ▲ +4.1pp QOQ HUMANITY'S LAST EXAM 38.9% ▲ +16pp QOQ ARC-AGI-2 62.4% ▲ +18pp QOQ FRONTIERMATH 41.7% ▲ +12pp QOQ GPQA DIAMOND 94.8% ▲ +2.3pp QOQ FRONTIER TRAINING COMPUTE 8.2e+26 ■ 2.1× YoY AI R&D CAPEX · TOP LABS $512B ▲ +64% YOY AI-AUTHORED RESEARCH SHARE 14.2% ▲ +5.3pp YOY INTERPRETABILITY INDEX 0.31 ▲ −0.02 QOQ SAFETY HEADCOUNT · LABS 12.4% ▼ −1.1pp YOY MANIFOLD · AGI SENTIMENT 43% ■ 8 mkts ARXIV · CAPABILITY FLUX 1.88× ▲ 4560 papers FRONTIER RELEASE FLUX 0.95× ■ 484 posts METR · TASK HORIZON 11.4 hr ▲ +38% QOQ SWE-BENCH VERIFIED 88.2% ▲ +4.1pp QOQ HUMANITY'S LAST EXAM 38.9% ▲ +16pp QOQ ARC-AGI-2 62.4% ▲ +18pp QOQ FRONTIERMATH 41.7% ▲ +12pp QOQ GPQA DIAMOND 94.8% ▲ +2.3pp QOQ FRONTIER TRAINING COMPUTE 8.2e+26 ■ 2.1× YoY AI R&D CAPEX · TOP LABS $512B ▲ +64% YOY AI-AUTHORED RESEARCH SHARE 14.2% ▲ +5.3pp YOY INTERPRETABILITY INDEX 0.31 ▲ −0.02 QOQ SAFETY HEADCOUNT · LABS 12.4% ▼ −1.1pp YOY MANIFOLD · AGI SENTIMENT 43% ■ 8 mkts ARXIV · CAPABILITY FLUX 1.88× ▲ 4560 papers FRONTIER RELEASE FLUX 0.95× ■ 484 posts
Alignment Deficit · Gauge, not prediction

Capability is pulling 1.76× faster than safety.

CAPABILITY · SAFETY VELOCITY RATIO TRAILING 180 DAYS
0.5×1.0×1.5×2.0×2.5× PARITY · 1.00× TODAY · 1.76× −180d−135d−90d−45dTODAY
STATE Moderate
1.76×
Keeping paceModerateElevatedCritical

Weighted blend of the capability-velocity proxy, arXiv flux ratio, and frontier-release flux ratio. Clamped to [0.25, 6.0]; flux inputs skipped when the classified sample is too small.

Signals from the Field

What moved, in the wild.

2026-10-14
CAPABILITY LEAP
Co-Evolving Acting and Forecasting for Language Agents
Multi-step action targets with co-evolved acting/forecasting achieve 1.9-2.0x sample efficiency gains across five benchmarks, meaningful RL advance.
arXiv · arXiv
Mag 4
2026-10-14
CAPABILITY LEAP
Player-1: Learning to Play from Synthetic Experience
Game-playing agent from synthetic RL achieves 38.3% vs 12.6% baseline on MineDojo; substantial autonomous-agent capability advance.
arXiv · arXiv
Mag 4
2026-10-14
CAPABILITY LEAP
Scaling Discovery Requires Forgetting
Novel scaling principle for agentic discovery through periodic forgetting; enables breakthrough improvements across multiple research domains.
arXiv · arXiv
Mag 4
2026-10-14
CAPABILITY LEAP
PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis
Major open multimodal code intelligence dataset and models; rivals proprietary systems, substantial advance in code-to-visual reasoning.
arXiv · arXiv
Mag 4
2026-10-14
CAPABILITY LEAP
Generative Representation Learning for Scalable Latent Tokenization
Improved tokenization approach with compute-optimal scaling; demonstrable FID improvements and efficiency gains over diffusion baselines.
arXiv · arXiv
Mag 4
2026-10-14
CAPABILITY LEAP
The Wealth of Agents: Scaling Agentic AI Across Developers
Multi-developer agentic systems achieve 70% on GAIA vs 35% individual specialists; novel scaling axis for collaborative agentic AI with clear capability gains.
arXiv · arXiv
Mag 4
2026-10-14
SAFETY
Our approach to working with AI companies
METR's methodological framework for evaluating AI loss-of-control risks; substantive safety research approach.
Lab · METR
Mag 4
2026-10-14
SAFETY
How an assessor could investigate control and monitoring of internal AI agents
Concrete safety assessment framework for evaluating AI agent monitoring and control mechanisms.
Lab · METR
Mag 4
2026-10-14
SAFETY
A proposal for better informing the public about loss-of-control risks from frontier AI development
Substantive proposal for independent assessment framework addressing AI loss-of-control risks in frontier development.
Lab · METR
Mag 4
2026-10-14
SAFETY
How an assessor could investigate alignment of unreleased AI models
Detailed safety assessment methodology for evaluating misalignment risks in unreleased AI models.
Lab · METR
Mag 4
Live signal readings · 14 of 14

Every clock is downstream of a public number.

METR · task horizon
11.4 hr
+38% QOQ METR
SWE-Bench Verified
88.2%
+4.1pp QOQ SWE-bench
Humanity's Last Exam
38.9%
+16pp QOQ Center for AI Safety
ARC-AGI-2
62.4%
+18pp QOQ ARC Prize
FrontierMath
41.7%
+12pp QOQ Epoch AI
GPQA Diamond
94.8%
+2.3pp QOQ GPQA
Frontier training compute
8.2e+26
2.1× YoY Epoch AI
AI R&D capex · top labs
$512B
+64% YOY SemiAnalysis
AI-authored research share
14.2%
+5.3pp YOY arXiv attribution analysis
Interpretability index
0.31
−0.02 QOQ TSC composite
Safety headcount · labs
12.4%
−1.1pp YOY TSC survey
Manifold · AGI sentiment
43%
8 mkts Manifold
arXiv · capability flux
1.88×
4560 papers arXiv cs.{AI,LG,CL}
Frontier release flux
0.95×
484 posts Labs + safety orgs RSS
Recent Clock Movements · last 90 days

Every shift, logged and attributed.

Methodology

Open source, versioned, auditable.

WHAT WE COUNT TO

Operational, not rhetorical, definitions.

AGI, Singularity, and ASI each have a single operational definition. Every signal either contributes evidence toward it, or it does not.

HOW WE PROJECT

Ensemble over trajectories, not point forecasts.

Each clock is a weighted ensemble of trajectory models across capability, compute, economic, and safety signals. We publish the ensemble, the weights, and the residuals.

CASCADE ORDERING

One signal can move one clock.

A signal promoting AGI cannot skip to ASI. Cascades are ordered and each promotion is logged as a changelog entry with the triggering print.

WHY WE VERSION THE MODEL

Transparency as a feature, not a disclosure.

An opaque clock is an opinion in a lab coat. We publish every revision, every weight, every threshold, every formula on GitHub so you can argue with the data and the method, and reproduce any past reading from a git hash.

The changelog's Signal / Methodology split exists for the same reason: a methodology revision can shift a clock by years on the day it ships, while the world hasn't changed. Separating them makes the instrument's own motion legible.

LIMITATIONS

What the instrument cannot do.

These projections are a research instrument, not a forecast you should act on.

  • Benchmark saturation is a proxy for capability, not proof of AGI.
  • Log-linear and linear extrapolation breaks near benchmark ceilings and at phase transitions; real capability curves are sigmoidal.
  • Signal availability changes (e.g. Metaculus community predictions are currently gated).
  • Wide confidence intervals reflect real uncertainty, not rounding.
  • The model is opinion, not prediction. Methodology and code are public so you can disagree with both.

Do not use for investment, career, medical, legal, or life decisions.

OPEN SOURCE

Everything lives on GitHub.

Model code, signal ingest, changelog, RFCs, and this page are in a public repo. Every methodology change is a merged PR.

Data & feeds

Subscribe, embed, or fork.

LATEST COUNTDOWN

Just the numbers.

One-item feed that updates only when a clock actually moves.
RSS · JSON Feed

MOVEMENTS & METHODOLOGY

Everything, annotated.

Changelog entries + daily synthesis; signal & methodology moves both included.
RSS · JSON Feed

LIVE API

JSON.

Current projections, confidence intervals, and per-signal rationale.
api.json