[R] Identifying the “Complexity Kink”: An Econometric Analysis of AI Marginal Productivity Collapse in Multi-Asset Tasks

I’ve been quantifying the structural limits of LLM productivity beyond standard benchmarks. Using the recently released Scale AI Remote Labor Index (RLI), I modeled the interaction between inference density and coordination complexity to identify where AI marginal productivity collapses relative to human experts.

Information-Theoretic Variables: * Inference Density (E): A scale-invariant MDL expansion ratio (zlib-based proxy) measuring the “inference gap” between instruction and solution. * Coordination Complexity (kappa): A normalized reference-density metric quantifying symbolic state-dependency across multi-asset architectures.

Methodology (Exploratory Pilot): To address the “Benchmark Paradox,” I implemented a Heckman Two-Stage Correction to account for selection bias. Stage 2 utilizes a Mean-Centered Translog Production Function with Wild Cluster Bootstrap estimation to generate robust inference from the finite project clusters (G=10, N=57).

Findings: The primary finding is significant evidence of Benchmark Curation Bias (p=0.03). The data demonstrates that existing “gold-standard” benchmarks are non-randomly curated toward modular, low-coordination tasks, masking the true boundaries of the human labor floor.

While the exploratory sample size is currently insufficient to definitively confirm the non-linear coordination penalty (p=0.22), the results identify a clear High-Entropy Regime where coordination costs begin to outpace the value of autonomous execution. I’ve honestly reported the null result for the coordination penalty in this pilot pass—it indicates a trend but requires a larger N to confirm.

I’m looking for feedback on the Instruction Quality Paradox—specifically, how to better utilize MDL ratios to isolate task complexity from the human “orchestration labor” required to generate expert-level instructions.

Repo: [https://github.com/XxCotHGxX/Instruction_Entropy]