Incentives for Digital Twins: Task-Based Productivity Enhancements with Generative AI

2025-09-10

AI Summary

This study addresses the “training–replacement paradox” confronting human workers amid the proliferation of generative AI—particularly AI digital twins: how to actively train AI to enhance productivity while avoiding displacement. We develop a novel analytical framework integrating principal–agent theory with task characteristics—specifically editability and verifiability—and employ a hybrid methodology combining theoretical modeling and empirical synthesis. Our analysis identifies critical incentive boundaries: workers are most motivated to train AI twins for tasks exhibiting high verifiability and low editability. Results show that productivity gains are more pronounced in highly editable tasks, whereas non-experts benefit disproportionately in highly verifiable tasks. Crucially, realizing long-term technological dividends depends on institutional designs that safeguard workers’ bargaining power and ensure equitable returns. The study contributes foundational theoretical insights and actionable policy implications for human–AI co-governance in the age of generative AI.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: AI & Jobs/Labor
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Generative AI is a technology which depends in part on participation by humans in training and improving the automation potential. We focus on the development of an "AI twin" that could complement its creator's efforts, enabling them to produce higher-quality output in their individual style. However, AI twins could also, over time, replace individual humans. We analyze this trade-off using a principal-agent model in which agents have the opportunity to make investments into training an AI twin that lead to a lower cost of effort, a higher probability of success, or both. We propose a new framework to situate the model in which the tasks performed vary in the ease to which AI output can be improved by the human (the "editability") and also vary in the extent to which a non-expert can assess the quality of output (its "verifiability.") Our synthesis of recent empirical studies indicates that productivity gains from the use of generative AI are higher overall when task editability is higher, while non-experts enjoy greater relative productivity gains for tasks with higher verifiability. We show that during investment a strategic agent will trade off improvements in quality and ease of effort to preserve their wage bargaining power. Tasks with high verifiability and low editability are most aligned with a worker's incentives to train their twin, but for tasks where the stakes are low, this alignment is constrained by the risk of displacement. Our results suggest that sustained improvements in company-sponsored generative AI will require nuanced design of human incentives, and that public policy which encourages balancing worker returns with generative AI improvements could yield more sustained long-run productivity gains.

Problem

• Analyzing trade-offs between productivity gains and worker displacement from AI twins
• Modeling strategic investment in AI twins based on task editability and verifiability
• Designing incentive structures to balance worker returns with AI productivity improvements

Innovation

• Developed AI twin framework for productivity enhancement
• Proposed task model based on editability and verifiability
• Designed incentive alignment for human-AI collaboration