Beyond Model Capability
A System-Level Framework for AI Power
- Wu, Shaoyuan
Global AI Governance and Policy Research Center, EPINOVA LLC
https://orcid.org/0009-0008-0660-8232
Description
This policy brief argues that AI power cannot be reduced to model capability, benchmark performance, parameter scale, or public adoption. It develops a system-level framework in which real-world AI power depends on six interacting dimensions: model capability, data quality, resource access, authority, real-world interface, and rule space. The brief introduces the AI System Power Index (ASPI) as a conceptual and partially quantifiable structure for assessing how AI systems become consequential once embedded in institutions, infrastructure, markets, public services, or security environments.
Abstract
AI competition and capability are often assessed through model performance, benchmark rankings, parameter scale, public adoption, or user reach. These indicators are useful, but they capture only the visible layer of AI capability. In this brief, AI power refers to the capacity of an AI system to shape decisions, mobilize resources, influence behavior, or alter real-world outcomes within a specific institutional, operational, and rule environment. This policy brief argues that AI power depends not only on model capability, but also on data quality, resource access, authority, real-world interfaces, and rule space. A public-facing model may be highly capable but operationally constrained, while a narrower system may have greater real-world impact if it is embedded in institutions, infrastructure, financial systems, or exceptional legal, institutional, or operational environments. The central argument is that AI power is not reducible to model intelligence. It is produced by the broader system that determines what the system can know, access, mobilize, do under authorization, and change.
Files
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Keywords
- AI power
- AI System Power Index
- ASPI
- system-level AI power
- model capability
- data quality
- resource access
- authority
- real-world interface
- rule space
- AI governance
- AI competition
- AI policy
- AI benchmarks
- AI measurement
- institutional embedding
- operational AI
- data governance
- resource control
- authorization
- accountability
- public-facing AI
- enterprise AI
- critical infrastructure
- strategic technology
- EPINOVA
Subjects
- Artificial intelligence
- AI governance
- Technology policy
- Strategic technology
- AI measurement
- AI safety
- Institutional governance
- Public policy
- Digital infrastructure
- Data governance
- Critical infrastructure
- Security studies
Recommended citation
Wu, Shaoyuan (2026), Beyond Model Capability: A System-Level Framework for AI Power, Policy Brief No. EPINOVA–2026–PB–58, Global AI Governance and Policy Research Center, EPINOVA LLC. https://doi.org/10.67037/epinova.pb.2026.058.
APA citation
Wu, S. (2026). Beyond model capability: A system-level framework for AI power. EPINOVA Policy Brief Series, EPINOVA-PB-2026-058. Global AI Governance and Policy Research Center, EPINOVA LLC. https://doi.org/10.67037/epinova.pb.2026.058.
Alternate identifiers
| Scheme | Identifier | Description |
|---|---|---|
| URL | https://epinova.org/policy-brief-1 | Official EPINOVA publication page |
| EPINOVA policy brief number | EPINOVA–2026–PB–58 | Policy brief number printed in the PDF |
| File name | Beyond Model Capability A System-Level Framework for AI Power.pdf | Source PDF file name |
| Short title | Beyond Model Capability | Short form of the policy brief title |
| Analytical concept | AI System Power Index | Quantifiable index structure proposed in the policy brief for assessing system-level AI power |
| Analytical concept | ASPI | Abbreviation for AI System Power Index |
| Analytical concept | System-Level Framework for AI Power | Core framework developed in the policy brief |
Related works
| Relation | Identifier | Type | Description |
|---|---|---|---|
| IsPartOf | https://epinova.org/policy-brief-1 | Publication series | EPINOVA Policy Brief Series |
| IsSupplementedBy | https://github.com/EPINOVALLC/EPINOVA-Research | Repository | Supplementary repository and structural archive |
| References | AI Index Steering Committee, Stanford Institute for Human-Centered Artificial Intelligence. (2026). The 2026 AI Index report. | Report | Referenced for existing AI development, capability, investment, adoption, infrastructure, governance, and responsible AI trend measurement. |
| References | Kim et al. (2025). The AI Power Disparity Index. | Conference paper | Referenced for emerging work on compound measurement of AI actors’ power to shape the AI ecosystem. |
| References | Oxford Insights. (2025). Government AI Readiness Index 2025. | Index report | Referenced for public-sector readiness measurement. |
| References | Tortoise Media. (2024). The Global AI Index. | Index | Referenced for national AI capacity ranking across implementation, innovation, and investment. |
References
- AI Index Steering Committee, Stanford Institute for Human-Centered Artificial Intelligence. (2026). The 2026 AI Index report. Stanford University. https://hai.stanford.edu/ai-index/2026-ai-index-report
- Kim, R. M., Kuehnert, B., Lazar, S., Singh, R., & Heidari, H. (2025). The AI Power Disparity Index: Toward a compound measure of AI actors’ power to shape the AI ecosystem. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 8(2), 1453–1464. https://doi.org/10.1609/aies.v8i2.36645
- Oxford Insights. (2025). Government AI Readiness Index 2025. https://oxfordinsights.com/ai-readiness/government-ai-readiness-index-2025/
- Tortoise Media. (2024). The Global AI Index. https://www.tortoisemedia.com/data/global-ai