Эксперты предложили реформу налоговой системы для защиты занятости в эпоху искусственного интеллекта

Tax Policy Options to Promote Employment and Mitigate Federal Revenue Impacts in the Age of AI Expert InsightsPublished Aug 31, 2026 Estimates suggest that artificial intelligence (AI) could displace approximately 10 to 15 percent of labor hours over a ten- to 15-year horizon. Among the many profound implications of AI displacing workers is a systemic threat to the U.S. government’s source of revenue: About two-thirds of federal revenue comes from labor in the form of payroll taxes and federal income tax. Furthermore, portions of the tax system may exacerbate the labor risks from AI by penalizing employment. In this paper, the authors propose a six-category framework to identify which parts of the tax code exacerbate this risk, which parts are resilient to it, and what reforms could stabilize revenue while protecting the demand for human labor. Policy options include reducing the business share of payroll taxes, implementing a progressive corporate tax on per-capita profits, taxing long-term capital gains at ordinary income rates, introducing a federal value-added tax, exploring a federal wealth tax, and reforming inheritance tax provisions. Each option involves trade-offs in revenue, growth, and feasibility. The authors conclude that a broad-based tax reform package—targeting outcomes rather than specific technologies—can help maintain fiscal stability and support employment in an AI-augmented economy. This work was supported by the Diller-von Furstenberg Family Foundation and Pershing Square Philanthropies and conducted by the RAND von Furstenberg Family Budget Model Initiative within RAND Education, Employment, and Infrastructure. This publication is part of the RAND expert insights series. The expert insights series presents perspectives on timely policy issues. This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions. RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.