This paper develops a unified framework for evaluating the optimal degree of task automation. Moving beyond binary automate-or-not assessments, we model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation. On the supply side, we estimate an AI production function via scaling-law experiments linking performance to data, compute, and model size. Because AI systems exhibit predictable but diminishing returns to these inputs, the cost of higher accuracy is convex: good performance may be inexpensive, but near-perfect accuracy is disproportionately costly. Full automation is therefore often not cost-minimizing; partial automation, where firms retain human workers for residual tasks, frequently emerges as the equilibrium. On the demand side, we introduce an entropy-based measure of task complexity that maps model accuracy into a labor substitution ratio, quantifying human labor displacement at each accuracy level. We calibrate the framework with O*NET task data, a survey of 3,778 domain experts, and GPT-4o-derived task decompositions, implementing it in computer vision. Task complexity shapes substitution: low-complexity tasks see high substitution, while high-complexity tasks favor limited partial automation. Scale of deployment is a key determinant: AI-as-a-Service and AI agents spread fixed costs across users, sharply expanding economically viable tasks. At the firm level, cost-effective automation captures approximately 11% of computer-vision-exposed labor compensation; under economy-wide deployment, this share rises sharply. Since other AI systems exhibit similar scaling-law economics, our mechanisms extend beyond computer vision, reinforcing that partial automation is often the economically rational long-run outcome, not merely a transitional phase.

翻译：本文构建了一个统一框架，用于评估任务自动化的最优程度。我们突破二元"自动化与否"的评估模式，将自动化强度建模为连续选择，企业通过选择AI准确度水平（从无自动化、部分人机协作到完全自动化）来最小化成本。在供给侧，我们通过规模定律实验估计AI生产函数，建立性能与数据量、算力和模型规模之间的关联。由于AI系统对这些投入要素呈现可预测但边际递减的回报，更高准确度的成本呈现凸性：良好性能可能成本低廉，但接近完美的准确度需要不成比例的高昂代价。因此完全自动化往往并非成本最小化选择，企业保留人类工人处理剩余任务的部分自动化模式常成为均衡状态。在需求侧，我们引入基于熵的任务复杂度度量，将模型准确度转化为劳动替代率，量化各准确度水平下的人类劳动力替代程度。我们利用O*NET任务数据、3778名领域专家调查及GPT-4o任务分解对该框架进行校准，并在计算机视觉领域实施验证。任务复杂度塑造替代模式：低复杂度任务呈现高替代率，而高复杂度任务更倾向有限的部分自动化。部署规模是关键决定因素：AI即服务和AI智能体通过分摊固定成本，显著扩展经济可行的任务范围。在企业层面，成本有效型自动化约占计算机视觉相关劳动力薪酬的11%；当经济整体部署时，该份额急剧上升。由于其他AI系统具有相似的规模定律经济学特征，我们的机制分析可延伸至计算机视觉之外，进一步印证部分自动化往往是经济理性的长期结果，而非单纯过渡阶段。