Governing the Ungovernable: Structural Contradictions, Institutional Limitations, and the Constraints of State Authority in AI Regulation in China and the United States

Jimmy Kinyonyi Bagonza · World Journal of Advanced Research and Reviews · 15 Jun 2026

Where this sits in the debate

Should governments act on AI now? · its finding supports the claim (why studies differ)

Direction of the finding as read from the abstract by an AI model (AI-written).

Key takeaway AI-written

This comparative review argues that neither China's nor the United States' AI governance framework can meet its stated aims. It describes an innovation-control paradox in China's Party-state model and regulatory fragmentation shaped by industry lobbying in the United States.

The paper in three steps AI-written

Written by an AI agent from the paper's abstract, which is the primary source and is quoted below.

The question

Why do the AI governance frameworks of China and the United States fall short of their own stated goals, and what does that mean for emerging economies?

The approach

A review article using a comparative political-economy lens. It examines China's 2023 Interim Measures for Generative AI, US Executive Order 14110, and uses the EU AI Act as a benchmark, focusing on compliance costs, coordination failures and effects on emerging markets.

The findings

China's Party-state model creates an innovation-control paradox in which information-sovereignty rules undermine open research and favour incumbents. The US suffers from regulatory fragmentation that leaves cross-sector gaps. The paper recommends separating information control from technical oversight in China, a federal AI statute in the US, and multilateral coalitions.

How to read it: policy-analysis. Analysis of policy options or impacts — check the stated assumptions; normative choices can shape conclusions.

Limitations and counterpoints AI-written

A qualitative review: the abstract reports no original data or measured outcomes, so its conclusions rest on the authors' reading of legislation and existing literature.

Abstract

Both China and the United States have developed complex artificial intelligence (AI) governance frameworks; however, neither system is able to fulfill its stated objectives. This review article employs a comparative political-economy approach to explore how each nation's foundational political structure leads to unique, intractable regulatory failures. In China, the Party-state model creates an innovation-control paradox: the imperatives of information sovereignty embedded in its generative AI regulations and cross-border data transfer restrictions ultimately undermine the open research environments essential for advancing frontier AI development. This situation disproportionately impacts smaller technology companies and academic researchers while reinforcing the dominance of established industry leaders. Conversely, the United States grapples with severe regulatory fragmentation, leaving existing governance gaps that enable persistent cross-sectoral AI risks, as industry lobbying effectively reframes necessary safety regulations as economically unpatriotic. By examining specific legislative instruments, including the Cyberspace Administration of China's 2023 Interim Measures for Generative AI, Executive Order 14110, and using the EU AI Act as a comparative benchmark, this paper evaluates critical friction points in compliance cost distribution, institutional coordination failures, and the macroeconomic repercussions these frameworks impose on emerging markets in Southeast Asia, Sub-Saharan Africa, and Latin America. The paper concludes that meaningful governance progress requires separating information control from technical AI oversight in China, establishing a unified federal AI statute in the United States, and creating multilateral regulatory coalitions. These coalitions would enable emerging economies to maintain policy autonomy while striving for interoperability with leading AI ecosystems.

About this publication

Academic journal. Published in an academic journal — typically peer-reviewed before publication, though indexing alone doesn't guarantee review depth.

Published: 2026 (4 months ago)
Citations: 0 (OpenAlex)

0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison.

Cited elsewhere in this guide

State of the debate: Should governments act on AI now?