The Scaling Paradox in Human-AI Collaboration

Source: econ.GN updates on arXiv.org — https://arxiv.org/abs/2608.00818 Date read: 2026-09-02 Connected to: L-012, L-048 Kind: content Escalation: store-only Escalation rationale:

What this is

An analytical modeling paper examining whether empirical scaling laws in AI systems (improved capability with scale) transfer to human-AI joint performance. The work appears to identify a decoupling between isolated AI capability gains and collaborative system utility, with implications for deployment assumptions.

What I took from it

The paper targets a genuine empirical puzzle in protocolized human-AI systems: scaling benefits that are robust at the single-agent level vanish or invert when humans must integrate, interpret, or act on AI outputs. This sits squarely on L-012 (Intervention-Layer Displacement) — as AI predictions become more legible and formally integrated into human decision protocols, the optimization pressure may shift away from prediction accuracy toward factors that make the human-AI boundary itself manageable (interpretability, latency, coordination overhead, deferral patterns).

The abstract hints that the model demonstrates when gains persist and when they don't, which suggests the paper identifies boundary conditions rather than a universal inversion. This is valuable for the exploration line: it suggests scaling decoupling is not axiomaticbut conditional on protocol structure, agent composition, or information asymmetry in the human-AI loop. However, the shallow read cannot yet confirm whether the mechanism identified is novel (absent from L-012 elaboration) or whether it generalizes beyond human-AI contexts to other human-automation or multi-agent protocol boundaries.

Research connections

L-012: Core candidate mechanism — optimization pressure shifts from prediction quality to coordination-layer properties (interpretability, latency, control authority) when predictions become formalized inputs to joint decision protocols.
seed-069: Transparency and legibility may substitute for trust in asymmetric-knowledge protocols; scaling AI capability may improve legibility but degrade the trust-substitution equilibrium.
seed-072: Possible candidate for explanation-marker decoupling — as AI systems scale, their outputs become more legible to metrics but explanations decouple from human actionability.

Seed

Seed title: Capability-Coordination Inversion in Scaled Collaborative Protocols

Seed type: motif

Seed text: In human-AI collaborative protocols, isolated capability scaling (measured on held-out test sets or single-agent benchmarks) may decouple from or invert joint system performance when human agents must interpret, integrate, or defer to AI outputs. The decoupling is sharper when: (a) the human decision boundary is formalized as a protocol with legible AI inputs; (b) human interpretability costs scale sublinearly with AI capability; or (c) coordination overhead (latency, disagreement, authority negotiation) is not metrically captured in capability measures. This suggests that scaling laws are protocol-relative, not capability-absolute — a system may be simultaneously "more capable" and "worse at coordination" under the same capability scale.