Idempotent Equilibrium Analysis of Hybrid Workflow Allocation: A Mathematical Schema for Future Work

TL;DR

This paper models the division of work between humans and AI as an iterative process that converges to a stable equilibrium, revealing insights into automation limits and the emergence of a new human role in AI-assisted workflows.

Contribution

It introduces a formal mathematical framework for analyzing hybrid human-AI work allocation, proving existence and uniqueness of equilibrium, and providing a dynamic model with policy implications.

Findings

Long-run automation share approaches 65% by 2045

A persistent one-third of tasks remain human-led

Residual human tasks form a new profession of workflow conductors

Abstract

The rapid advance of large-scale AI systems is reshaping how work is divided between people and machines. We formalise this reallocation as an iterated task-delegation map and show that--under broad, empirically grounded assumptions--the process converges to a stable idempotent equilibrium in which every task is performed by the agent (human or machine) with enduring comparative advantage. Leveraging lattice-theoretic fixed-point tools (Tarski and Banach), we (i) prove existence of at least one such equilibrium and (ii) derive mild monotonicity conditions that guarantee uniqueness. In a stylised continuous model the long-run automated share takes the closed form $x^* = \alpha / (\alpha + \beta)$, where $\alpha$ captures the pace of automation and $\beta$ the rate at which new, human-centric tasks appear; hence full automation is precluded whenever $\beta > 0$. We embed this analytic result in three complementary dynamical benchmarks--a discrete linear update, an evolutionary replicator dynamic, and a continuous Beta-distributed task spectrum--each of which converges to the same mixed equilibrium and is reproducible from the provided code-free formulas. A 2025-to-2045 simulation calibrated to current adoption rates projects automation rising from approximately 10% of work to approximately 65%, leaving a persistent one-third of tasks to humans. We interpret that residual as a new profession of workflow conductor: humans specialise in assigning, supervising and integrating AI modules rather than competing with them. Finally, we discuss implications for skill development, benchmark design and AI governance, arguing that policies which promote "centaur" human-AI teaming can steer the economy toward the welfare-maximising fixed point.