Smart power reframes influence: states and non‑state actors now marry coercion and attraction into a legitimacy‑based strategic model, using diplomacy, development and digital technologies — especially AI — to shape global economic and political orders. This shifts economic levers (trade, investment, standards, industrial policy) and raises coordination, market-structure and distributional risks for global AI ecosystems.
Smart Power and the Transformation of Contemporary International Relations: Connecting Coercion and Persuasion in Global Politics
Smart power reconceptualises state influence as a legitimacy-centred integration of hard and soft tools, with diplomacy, development, and technology (including AI) as core operational vectors shaping economic and strategic outcomes in the digital era.
This paper discusses?smart power as a transformative paradigm of international relations, conceptualising coercion and persuasion within a strategic framework of global influence. Popularised by Joseph Nye, smart power is the combination of using both hard and soft power resources, such as military, economic, and cultural tools, required to achieve?lasting influence. This paper investigates how classical and current theories relate to the concept of smart power and demonstrates how states?and non-state actors employ smart power via diplomacy, development, and technology to pursue both national and global aims. The operationalisation of smart power in a multipolar world is demonstrated through case studies of the United States, China, the European Union, and Russia. It claims?that Smart Power goes beyond traditional binaries of compulsion and attraction, and develops a legitimacy-based model of global governance, conflict resolution, and co-operative security in the era of digital. Received: 15 January 2026 / Accepted: 26 February 2026 / Published: March 2026
Summary
Main Finding
Smart power is a transformative strategic paradigm that integrates coercion (hard power) and attraction (soft power) into a single legitimacy‑based model of global influence. In the digital era, states and non‑state actors operationalise smart power through diplomacy, development, and technology to pursue national and transnational objectives; this approach transcends simple compulsion/attraction binaries and foregrounds legitimacy, cooperative security, and governance as central mechanisms for durable influence.
Key Points
- Definition: Smart power = deliberate combination of hard power (military, economic coercion) and soft power (culture, norms, attraction) deployed in a coordinated strategic framework.
- Theoretical contribution: Extends classical IR debates by arguing smart power is not merely a mix of tools but a distinct, legitimacy‑centred logic of influence appropriate for a multipolar, digitally networked world.
- Operational vectors: Emphasises three primary channels — diplomacy (bilateral/multilateral engagement, public diplomacy), development (aid, infrastructure investment, capacity building), and technology (digital platforms, AI, surveillance, standards).
- Actors: Both states and non‑state actors (tech firms, NGOs, international organisations) can exercise smart power; the balance and instruments vary by polity and strategic aims.
- Case studies: Applied to the United States, China, the European Union, and Russia to show different mixes and institutional practices of smart power in practice.
- Normative shift: Advocates a legitimacy‑based model of governance and conflict resolution—where legitimacy (domestic and international) is the key currency of sustainable influence.
- Digital dimension: The digital/AI era changes both tools (new technological instruments of influence) and targets (information environments, data infrastructures), raising novel governance and collective‑action problems.
Data & Methods
- Approach: Conceptual/theoretical analysis built from a systematic literature review of classical and contemporary IR and strategic studies on power, persuasion, and coercion.
- Methods: Comparative qualitative case studies of four major international actors (United States, China, EU, Russia) used to illustrate how smart power is operationalised in different political and institutional contexts.
- Model building: Development of a legitimacy‑based conceptual model for governance, conflict resolution, and cooperative security in the digital age.
- Evidence: Policy documents, public diplomacy examples, development initiatives, technology export and standards behaviour, and secondary empirical studies are synthesised; the paper is primarily qualitative and interpretive rather than relying on new quantitative datasets.
Implications for AI Economics
- AI as strategic economic asset: AI technologies are core instruments of smart power—affecting productivity, industrial competitiveness, and the ability to project influence through platforms, surveillance systems, and information controls.
- Trade and investment policy: Export controls, sanctions, investment screening, and tech diplomacy become economic levers of smart power; these measures reshape global AI supply chains, FDI flows, and comparative advantage.
- Standards and platform competition: Competition over AI standards, data governance norms, and platform rules is an economic contest with long‑run market structure implications (network effects, winner‑take‑most outcomes).
- Labour and distributional effects: Smart power strategies that promote domestic AI champions (via procurement, subsidies, industrial policy) affect labour markets, inequality, and international labour arbitrage.
- Information economics & marketplace of ideas: AI‑driven information operations, recommendation systems, and content economies alter market incentives, advertising revenues, and the political economy of attention—raising externalities not priced in markets.
- Development and technology transfer: Aid and infrastructure investment (digital public goods, AI capacity building) are economic channels of influence; they shape recipient countries’ technological trajectories and participation in AI value chains.
- Governance and coordination failure risks: Multipolar competition in AI increases risks of fragmented regulations, export control cascades, and inefficient duplication of standards—raising global coordination and collective‑action problems with large economic costs.
- Research avenues and empirical measures for AI economists:
- Quantify cross‑border flows: AI‑related FDI, venture capital, patent filings, personnel migration, and trade in AI‑enabled goods/services.
- Policy event studies: Assess economic impacts of export controls, sanctions, and tech diplomacy initiatives on firm performance and sectoral outcomes.
- Market structure analysis: Study platform concentration, standards adoption, and network effects induced by state‑backed AI industrial policies.
- Externalities measurement: Estimate economic costs of misinformation, cyber enabled coercion, and data‑driven surveillance on markets and public goods.
- Microdata sources: patent databases, trade and investment statistics, firm financials, app/platform usage metrics, and datasets on regulatory actions and sanctions.
- Policy relevance for economists: Incorporate geopolitical strategic incentives into models of technology adoption, trade, and industrial policy; evaluate welfare trade‑offs of defensive measures (controls, decoupling) versus cooperative governance (standard‑setting, data sharing).
Assessment
Paper Typetheoretical
Evidence Strengthn/a — Conceptual and interpretive synthesis without new empirical causal analysis or identification strategy; provides theoretical argumentation and illustrative case studies rather than causal inference.
Methods Rigormedium — Systematic literature review and comparative qualitative case studies across four major actors provide coherent, well‑sourced conceptual development, but the approach lacks formalized causal tests, pre-registered coding, or new quantitative data to validate claims empirically.
SampleSystematic review of classical and contemporary IR and strategic studies literature; qualitative comparative case studies of the United States, China, the European Union, and Russia using policy documents, public diplomacy outputs, development initiatives, technology export/standards behavior, and secondary empirical studies; no original micro- or macro-quantitative datasets.
Themesgovernance innovation adoption labor_markets
GeneralizabilityQualitative case selection limited to four major powers — findings may not generalize to smaller states or developing countries., Interpretive framework depends on contemporary geopolitical context; applicability may change with shifting global power dynamics or technological advances., Normative legitimacy focus reflects theoretical framing and may not capture actor-specific domestic political constraints or private-sector incentives., No empirical quantification — economic magnitudes, causal channels, and heterogeneity remain untested.
Claims (14)
| Claim | Direction | Confidence | Outcome | Details |
|---|---|---|---|---|
| Smart power integrates hard power (coercion) and soft power (attraction) into a single legitimacy‑based model of global influence. Governance And Regulation | positive | medium | form and logic of international influence (legitimacy‑centred integration of coercion and attraction) |
0.01
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| In the digital era, states and non‑state actors operationalise smart power through three primary channels: diplomacy, development, and technology. Governance And Regulation | positive | medium | channels/vectors used to project smart power (diplomacy, development, technology) |
0.01
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| Smart power transcends simple compulsion/attraction binaries by foregrounding legitimacy, cooperative security, and governance as central mechanisms for durable influence. Governance And Regulation | positive | medium | durability of influence mediated by legitimacy, cooperative security, and governance |
0.01
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| Both states and non‑state actors (tech firms, NGOs, international organisations) can exercise smart power; balance and instruments vary by polity and strategic aims. Governance And Regulation | positive | medium | who exercises influence (state vs non‑state actors) and variation in instrument mix |
0.01
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| The paper demonstrates different mixes and institutional practices of smart power in practice by applying the framework to the United States, China, the European Union, and Russia. Governance And Regulation | null_result | high | variation in smart power mixes and institutional practices across four named actors |
n=4
0.02
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| The digital/AI era changes both the tools (new technological instruments of influence) and the targets (information environments, data infrastructures), creating novel governance and collective‑action problems. Governance And Regulation | negative | medium | emergence of new governance/collective‑action problems related to digital/AI tools and targets |
0.01
|
| AI technologies are core instruments of smart power, affecting productivity, industrial competitiveness, and the ability to project influence via platforms, surveillance systems, and information controls. Firm Productivity | positive | medium | productivity, industrial competitiveness, and capabilities to project influence |
0.01
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| Export controls, sanctions, investment screening, and tech diplomacy function as economic levers of smart power and reshape global AI supply chains, FDI flows, and comparative advantage. Market Structure | negative | medium | structure of AI supply chains, cross‑border FDI flows, and comparative advantage |
0.01
|
| Competition over AI standards, data governance norms, and platform rules is an economic contest with long‑run market structure implications (network effects, winner‑take‑most outcomes). Market Structure | negative | medium | market concentration and distributional outcomes in platform/AI markets (network effects, 'winner‑take‑most') |
0.01
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| Smart power strategies that promote domestic AI champions (via procurement, subsidies, industrial policy) affect labour markets, inequality, and international labour arbitrage. Employment | mixed | low | labour market outcomes, income inequality, cross‑border labour arbitrage |
0.01
|
| AI‑driven information operations, recommendation systems, and content economies alter market incentives, advertising revenues, and the political economy of attention—creating externalities not priced in markets. Firm Revenue | negative | medium | market incentives, advertising revenue distribution, and unpriced externalities (misinformation, attention harms) |
0.01
|
| Aid and infrastructure investment (digital public goods, AI capacity building) act as economic channels of influence that shape recipient countries' technological trajectories and participation in AI value chains. Adoption Rate | positive | medium | recipient countries' technological trajectories and participation in AI value chains |
0.01
|
| Multipolar competition in AI increases risks of fragmented regulations, export control cascades, and inefficient duplication of standards, producing large economic coordination and collective‑action costs. Governance And Regulation | negative | medium | regulatory fragmentation, standard duplication, and associated economic costs |
0.01
|
| The paper's empirical approach is primarily qualitative and interpretive: a systematic literature review plus comparative qualitative case studies, using policy documents, public diplomacy examples, development initiatives, technology export and standards behaviour, and secondary empirical studies as evidence. Other | null_result | high | nature of evidence and methodological approach (qualitative, interpretive case study design) |
n=4
0.02
|