arXiv:2603.27049v2 Announce Type: replace-cross Abstract: AI-assisted task delegation is increasingly common, yet human effort in such systems is costly and typically unobserved. Recent work by Bastani and Cachon (2025); Sambasivan et al. (2021) shows that accuracy-based payment schemes suffer from incentive collapse: as AI accuracy improves, sustaining positive human effort requires unbounded payments. We study this phenomenon in a budget-constrained principal-agent framework with strategic human agents whose output accuracy depends on unobserved effort. Our first contribution is a general im
This research is emerging as AI-assisted task delegation becomes increasingly prevalent, highlighting critical economic challenges in managing human-AI collaboration.
It directly addresses the sustainability of human involvement in AI-driven workflows, which is crucial for workforce planning and the design of AI systems.
The understanding of incentive structures required for human effort in AI-augmented systems shifts from simple accuracy-based payments to more complex, budget-constrained frameworks.
- · AI platform developers (integrating human feedback)
- · Consultants specializing in human-AI workflow design
- · Organizations adopting advanced principal-agent models
- · Companies using simplistic accuracy-based payment for human annotation
- · Human task forces paid purely on output volume or accuracy
- · Platforms without sophisticated incentive mechanisms
The design of AI-human hybrid systems will increasingly incorporate sophisticated economic models to optimize human effort under budget constraints.
This could lead to new frameworks for valuing human cognitive input within highly automated pipelines and potentially new labor market structures for human-in-the-loop tasks.
Long-term, this research may influence policy around fair compensation and worker protections in AI-driven economies, as traditional payment models become unviable.
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