Forecasting AI-Era Productivity: The Intellectually Converged Human Framework and a Missing Cognitive Mediator in Production Function Theory

Forecasting AI-Era Productivity: The Intellectually Converged Human Framework and a Missing Cognitive Mediator in Production Function Theory

Source: cs.CY updates on arXiv.org — https://arxiv.org/abs/2606.19794 Date read: 2026-06-24 Connected to: none Escalation: escalate-to-deep Escalation rationale: Introduces a structural mechanism (cognitive mediation layer / convergence capacity) absent from production function theory; claims this explains a systematic empirical paradox; proposes formalized framework (ICH) with stage-dependent productivity dynamics that generalizes beyond AI specifically to any technology requiring human cognitive integration.

What this is

This is a theoretical economics paper proposing that standard production function models fail to explain AI productivity underperformance because they treat AI as a separable input without accounting for the cognitive capacity required to integrate it into existing work systems. The authors introduce the Intellectually Converged Human (ICH) framework—a five-stage model of cognitive development—to argue that productivity gains require prior investment in worker convergence capacity (C), not just in AI deployment.

What I took from it

The paper identifies a category error in how we model artificial-human systems: productivity is mediated by an unmeasured, prior variable (cognitive convergence). This is directly relevant to the "new nature" research agenda because it suggests protocolized systems don't generate value by simple factor addition; they require a structural phase of human-system cognitive alignment that is prerequisite, not parallel. The framework proposes this is stagewise and non-linear—productivity remains flat or negative until convergence capacity crosses threshold, then accelerates. This implies deployment timelines and investment ROI curves for artificial systems are systematically mispredicted when C is unmodeled.

The work also gestures toward a general pattern: any complex technology imposing new cognitive demands (not just AI) will exhibit this productivity paradox until convergence capacity is developed. This suggests a deeper law about protocolized system adoption curves.

Research connections

• [New law candidate]: Productivity of human-artificial systems is not monotonic in deployment but threshold-dependent on prior cognitive integration capacity.

Candidate laws or signals

• CL-2606-1: Artificial system productivity exhibits a convergence-capacity phase transition—productivity remains suppressed until human cognitive mediation infrastructure reaches domain-specific threshold; deployment without prior convergence investment generates negative or zero return, explaining systematic paradoxes in technology adoption.