Early leads in artificial general intelligence (AGI) capabilities lead to either durable economic dominance or temporary gains. But AGI may differ from past general-purpose technologies in ways that would allow a leader to turn an early advantage decisive into economic advantage. This report offers a framework for distinguishing temporary first-mover gains from structurally self-reinforcing advantages.

Decisive Economic Advantage

Modeling the Transition from Temporary First-Mover Leads to Economic Dominance in Artificial General Intelligence

Tobias Sytsma

ResearchPublished Feb 23, 2026

When do early leads in artificial general intelligence (AGI) capabilities translate into durable economic dominance rather than temporary gains? In one view of the economics of AGI, rivals can also invest, imitate, and reallocate resources, so early advantages tend to erode over time. A competing view is that AGI may differ from past general-purpose technologies in ways that would allow a leader to permanently entrench its position, and an early advantage could translate into an enduring constraint on a rival's ability to compete, translating to decisive economic advantage (DEA).

These debates often rely on competing intuitions without a shared framework for distinguishing temporary first-mover gains from structurally self-reinforcing advantages. This gap matters because strategic choices, such as research and development investment, supply chain policy, diffusion controls, and international coordination, might implicitly assume different answers to the question. Misdiagnosis of the underlying regime risks either overreacting to transient leads or underestimating conditions under which early advantages become durable.

In this report, the author provides a framework for analyzing when economic feedback converts temporary technological leads into durable constraints on a rival's ability to compete, with implications for strategic economic and geopolitical priorities. This report is intended for researchers and analysts concerned with the strategic dynamics of AGI development, including those working on artificial intelligence (AI) competition, economic security, and long-run technological advantage.

Key Findings

This report introduces the concept of DEA

A DEA is defined by an economic regime in which asymmetries widen over time, progressively limiting the follower's ability to contest the leader's position. This reframes strategic advantages as an emergent property of interacting economic systems rather than a threshold cross by a single technological metric.

Dominance can emerge in multiple ways; intelligence explosion is sufficient but not necessary for a DEA

Although self-reinforcing AI capability gains offer one pathway to dominance, distinct accumulation-driven pathways operate without recursive self-improvement. Specifically, development flywheels (in which deployment generates learning data) and reinvestment loops (in which economic gains finance infrastructure moats) can drive divergence through economic feedback alone.

DEA is a robust structural possibility but not inevitable

Across a simulation space spanning deep parameter uncertainty, the results identify two robust competitive regimes. In the first, natural equilibrating forces (such as technology diffusion or capital adjustment frictions) successfully dampen early leads, leading to convergence. In the second, feedback mechanisms exceed critical thresholds, causing even modest initial leads to compound into extreme economic dominance.

The leverage of strategic intervention decays as economic asymmetries widen

Acting while the economic gap is still small offers the follower substantially higher returns than would attempting to stop the leader's momentum after more-significant asymmetries have emerged. Intervention effectiveness depends on the underlying mechanism driving the DEA.

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Sytsma, Tobias, Decisive Economic Advantage: Modeling the Transition from Temporary First-Mover Leads to Economic Dominance in Artificial General Intelligence. Santa Monica, CA: RAND Corporation, 2026. https://www.rand.org/pubs/research_reports/RRA4444-1.html.
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