How can policymakers effectively manage automation policy to maintain or accelerate economic growth while mitigating wealth distribution inequality in an era characterized by rapidly advancing artificial intelligence (AI) capabilities? The author examines policy strategies for managing economic growth and inequality across a variety of AI futures, paying particular attention to implications for technological competition between major powers.

Managing AI's Economic Future

Strategic Automation Policy in an Era of Global Competition

Tobias Sytsma

ResearchPublished May 13, 2025

How can policymakers effectively manage automation policy to maintain or accelerate economic growth while mitigating wealth distribution inequality in an era characterized by rapidly advancing artificial intelligence (AI) capabilities? This question takes on heightened urgency within the context of global technological competition, particularly between the United States and China, in which competitive dynamics may precipitate rapid AI deployment throughout the economy before coherent policies can be formulated.

In this report, the author confronts AI's economic uncertainty through robust decisionmaking analysis of thousands of potential futures. An economic model is developed that distinguishes between horizontal automation (displacing human labor) and vertical automation (enhancing existing automated processes) while endogenizing policy choices through variable automation incentives.

The methodology simulates outcomes across thousands of scenarios for 81 unique policy packages of automation incentives and disincentives, systematically varying key uncertain parameters, including automation rates, productivity improvements, and the degree of complementarity between tasks in production. By exploring this extensive parameter space, the analysis identifies which policy approaches perform robustly across a wide variety of potential futures rather than optimizing for a single forecast.

Policy performance is evaluated through multiple complementary metrics, including compound annual growth rates of income and inequality, policy regret (measuring opportunity costs relative to optimal choices), and robustness (the probability of achieving desired outcomes across scenarios). Threshold effects and critical parameter values that determine policy success under different objectives are identified.

Key Findings

  • Strongly incentivizing vertical automation growth appears to be a highly robust policy strategy; these strong positive incentives are part of all of the best-performing policy packages regardless of policy objective.
  • Although multiple policy approaches can achieve moderate growth objectives, the pathways to transformative growth that could fundamentally reshape global economic leadership narrow significantly as growth targets increase. Even when accepting greater inequality growth, achieving transformative economic growth outcomes requires increasingly specific technological conditions and policy choices, suggesting the need for careful strategic planning in automation policy design.
  • The choice between comprehensive automation support (which strongly encourages both productivity improvements and new automation) and targeted policies (which favor enhancing existing automated processes while moderating the expansion of new automation) depends primarily on two factors: how difficult it is to automate new tasks and how quickly automation naturally spreads through the economy.

Recommendations

  • Policymakers may wish to consider an asymmetric approach to automation policy that strongly incentivizes vertical automation while moderately constraining horizontal automation expansion, particularly if the goal is to at least maintain historical growth patterns while managing inequality.
  • Future research should develop metrics that better distinguish between vertical and horizontal automation at the firm or industry level, potentially through tax policy, research funding, or regulatory frameworks that explicitly account for this distinction.
  • Policymakers could leverage existing institutional mechanisms — including labor organizations, professional standards, and tax structures — to implement optimal automation policy packages without necessarily creating new restrictions.

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Sytsma, Tobias, Managing AI's Economic Future: Strategic Automation Policy in an Era of Global Competition. Santa Monica, CA: RAND Corporation, 2025. https://www.rand.org/pubs/research_reports/RRA3764-1.html.
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