Signals, Scoring Method & Expert Layer
We believe that understanding our future with artificial intelligence requires neither breathless optimism nor paralyzing dread. It requires what science has always demanded: honest observation, humble hypotheses, and transparent math.
1. Epistemic Foundations & Humility
Our numbers are not prophetic decrees. They represent a calibrated probability distribution that reflects our current state of evidence across computing, labor economics, safety research, and governance.
The Complete Horizon (Sum = 100%)
The five levels form a mutually exhaustive spectrum of human outcomes. The sum of all probabilities always equals exactly 100%: Σ P(Li) = 100%. When evidence raises the likelihood of one future, another must yield.
No Dogma / Non-Zero Floors
We reject the illusion of absolute certainty. We enforce a strict floor constraint (P(Li) ≥ 2.0%) so no plausible future is prematurely discarded or declared inevitable.
Evidence Over Narrative Noise
Social media rumors and sensationalist press releases do not move our numbers. Only reproducible technical benchmarks, SEC capital filings, peer-reviewed labor studies, or enacted statutory laws trigger updates.
Dedicated to Human Agency
This observatory exists not as passive academic theater, but to provide everyday people with clear, practical guidance for their careers, families, and communities.
2. Two-Tier Scoring Formulation & The Expert Layer
Our scoring engine distinguishes between Primary Empirical Signals (unambiguous technical benchmarks, SEC energy/compute filings, government labor statistics, and statutory laws) and our secondary Expert Layer (rigorous analysis from vetted technical researchers and institutional economists).
Δi = ∑ [ Direction(Signal, Li) × MaterialityWeight(1..5) × ConfidenceIndex(0.5..1.0) × LayerMultiplier ]
P'raw(Li) = Pprior(Li) + Δi
P'bounded(Li) = max(2.0, P'raw(Li))
Pfinal(Li) = [ P'bounded(Li) / ∑ P'bounded ] × 100
Why Damping Matters (0.35x): Even the most brilliant researchers can suffer from cognitive blind spots or groupthink. By damping expert commentary to a 0.35x multiplier, we ensure that subjective analysis can refine the probabilities, but can never overpower cold empirical reality (actual compute purchases, measurable labor displacements, and enacted legal statutes).
3. Curated Expert & Institutional Roster
We screen expert sources strictly. We exclude social media pundits, hyper-partisan influencers, and anonymous speculators. Our roster includes only researchers with direct technical proximity to frontier models or proven empirical research track records:
Foundational deep learning pioneer analyzing biological vs. digital compute scaling and catastrophic agency risks.
Pioneer of deep learning advocating for world models (JEPA) and open-source foundational AI, grounding autoregressive hype.
Direct builder of frontier systems driving AI-accelerated science (AlphaFold) while establishing pre-deployment evaluations.
Frontier lab leader publishing transparent scaling laws, mechanistic interpretability, and bio/cyber defense frameworks.
Core architect behind modern scaling breakthroughs, now focused entirely on mathematical alignment and provably safe superintelligence.
Deep learning pioneer focusing on non-profit, internationally inspected safety architectures and avoiding monopolization.
Exceptional technical practitioner providing clear, hype-free assessments of current agentic capabilities and engineering bottlenecks.
Renowned educator and builder emphasizing iterative agentic patterns, practical enterprise utility, and open-source democratization.
Creator of ImageNet and champion of human-centered AI, advocating for democratic public compute and human augmentation.
Leading technical researcher providing transparent, code-backed analysis of open-source LLM architectures, fine-tuning, and quantization.
Pioneering empirical workplace researcher conducting controlled experiments on human-AI cognitive collaboration and transition friction.
Macroeconomic research on technological power distribution, measuring whether automation concentrates rent or lifts wages.
Technomoral philosopher formulating rigorous frameworks for human virtue, practical wisdom, and preserving agency.
Grounding technical analyses separating real benchmark leaps from commercial salesmanship.
Creator of major academic test suites measuring complex reasoning, safety bounds, and catastrophic failure modes.
Gold-standard empirical tracking of training FLOPs, hardware cost deflation, and model efficiency milestones.
Objective testing of autonomous task execution, sandbagging, and exfiltration risks in sandbox environments.
Rigorous technical scenario models analyzing the speed of cognitive automation and verifiable governance mechanisms.
4. Open Science & Public Data Feeds
Science flourishes in the open sunlight. All observatory data is publicly accessible in machine-readable JSON formats: