Empirical AI Capability Horizon & Evidence Dossier

Model Parameters

Scrub Horizon Year 2026.0

Scrub active evaluation timeline horizon (2023 - 2030)

Annual Compute Growth 4.2x

Annual effective compute scaling factor

Algorithmic Efficiency 2.5x

Algorithmic speedup multiplier over baseline

Skeptic Friction Factor 1.0x

Suppression modifier applied to skeptic forecasts

Focus Benchmark Dataset

Trajectory & Cross-Over Analysis (2023–2030) Rendering Sigmoidal Log-Logistic Curves

Empirical Velocity Rate +42.8%/yr Verified historical growth

Skeptic Model Gap -38.4% Skeptic underestimation delta

Projected AGI Frontier 2026.8 90% cross-benchmark ceiling

Safety Verification Lag 1.4 Yrs Alignment validation delay

Verified Evidence Dossier & Skeptic Delta Table

Mathematical trajectory projections vs counterfactual skeptic claims computed via Math.js log-logistic equations.

PROFILES: Model=SigmoidLogLogistic | Horizon=2026.0 | Delta=-38.4%

Benchmark Suite	2023 Baseline	Current (2025.2)	Projected (2026.0)	Skeptic Claim	Empirical Delta	Saturation Horizon

Empirical Citations & Methodology Footnotes

SWE-bench Verified: Princeton NLP & OpenAI (2024). Autonomous Software Engineering problem solving on real GitHub issues.
GPQA Diamond: Rein et al. (2023). Graduate-Level Google-Proof Q&A Benchmark across Physics, Chemistry, and Biology.
MATH Benchmark: Hendrycks et al. (2021). Measuring Mathematical Problem Solving With High School Competition Problems.
Sigmoidal Trajectory Model: $P(t) = \frac{100}{1 + \exp(-k(t - t_0) \cdot (\text{compute} \cdot \text{efficiency}))}$, fitted against 2021–2025 historical data.

Understanding the assumed capability curves

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The dossier draws three assigned sigmoid curves, with separate slopes and midpoint years. The code uses ordinary JavaScript Math functions; its comments do not mean it fits a statistical model at runtime. At the default inputs and year twenty twenty six, the calculated scores are about ninety one point nine eight for coding, eighty eight point seven four for science, and ninety nine point five nine for math. Stored historical points and the parameter choices are source fixtures, not benchmarks independently verified in this video. The scaling factor is log of compute plus one, times the square root of efficiency, times point four five. Raising compute steepens the curve about its fixed midpoint, rather than moving that midpoint. Every coding curve equals fifty at twenty twenty four point seven. Before the midpoint, greater scaling gives a lower modeled score; after it, a higher score. The ninety point saturation year can move earlier, but the plotted change is not evidence of a real improvement from buying more compute. The safety curve first shifts the input year backward by one point three four years, using point five plus point zero eight times compute times efficiency. It then evaluates the science curve with reduced compute and efficiency. At the defaults, average capability is about ninety three point four four and the assigned safety score fifty six point two one. The displayed lag is a different conversion: their score gap divided by twenty five, about one point four eight nine years. Neither quantity measures actual alignment validation or its delay. The velocity card uses a separate linear formula, not the derivative of the sigmoid. Its default value is forty four point nine five before display rounding. The frontier date scans a tenth of a year grid for an average score at least ninety; it is a discretized threshold convention, not an exact scientific horizon. The warning checks whether the capability and assigned safety gap exceeds eighteen points. Offline D three rendering fails before metric hydration, so selected input-label parity alone cannot establish complete tool function or validate these forecasts.