Open-source AI could widen opportunities for innovation and participation across APEC economies, but its specific economic return has not been measured in the evidence available here. APEC’s 2026 statements endorse trusted open-source approaches with strong security assurance; regional investment and adoption figures show momentum in AI overall, not the value generated by open-source AI in particular.
What APEC’s 2026 statements endorse
In the Chengdu Statement, issued on 23 July 2026, APEC ministers said: “We note the important role of trusted open-source approaches in unlocking the potential of digital technologies and fostering innovation in the digital economy.” The statement encourages member economies to support open-source models and projects that use strong security assurance in development and deployment, while respecting security, data protection and intellectual property rights. It also encourages cooperation with open-source communities.
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The APEC High-Level Forum on AI, held the following day, likewise encouraged support for open-source models and projects with strong security assurance. Its broader agenda included responsible AI applications, AI literacy and skills, trusted cross-border data flows, secure development and deployment, and wider participation.
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How open-source AI might create economic value
The potential value is not simply that a model’s code or weights can be accessed. Open-source approaches may give more organizations and developers the ability to inspect, adapt or build on AI systems. That could support locally tailored products and services, experimentation, collaboration, and participation by actors that might otherwise have fewer routes into AI development. These are plausible value pathways, not measured outcomes established for APEC economies.
Experimentation and adaptation
Access to models that can be adapted may let businesses, researchers and public institutions test applications against their own needs. For example, an organization might explore a model’s suitability for a local language, a sector-specific workflow or a public service. Whether that produces a useful result depends on factors such as data rights, technical expertise, compute access, integration work and the cost of operating the system.
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Collaboration and participation
Open-source communities can provide a way for developers and organizations to contribute improvements and share tools. This may broaden who can take part in building AI applications. But access alone does not ensure meaningful participation: reliable connectivity, infrastructure, skills and institutional capacity affect who can develop, deploy and benefit from AI.
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APEC’s 2025 Policy Support Unit brief notes that the release of open-source models such as Llama and DeepSeek further contributed to widespread AI adoption. It does not estimate how much adoption those releases caused, or how much economic value the models generated.
What the regional figures do—and do not—show
APEC’s 2025 Policy Support Unit brief provides useful context for the scale of AI activity. The figures below concern AI investment or adoption overall; they are not open-source-specific measures.
| Measure | Reported figure | What it represents |
|---|---|---|
| Equity investment in private AI firms in APEC economies | USD 164.9 billion in 2024; up 156.9% since 2018 | Regional private-firm AI investment overall, not investment in open-source AI specifically. |
| AI application investment in data and analytics | USD 77.7 billion in 2024 | An AI application category reported by the APEC Policy Support Unit; not an open-source figure. |
| AI application investment in software | USD 74.7 billion in 2024 | An AI application category reported by the APEC Policy Support Unit; not an open-source figure. |
| AI application investment in general-purpose applications | USD 73.0 billion in 2024 | An AI application category reported by the APEC Policy Support Unit; not an open-source figure. |
| Organizations integrating AI into business processes | 47% in 2018 and 78% in 2024 | Global survey estimates from McKinsey, as reported by the APEC Policy Support Unit—not an APEC-only adoption rate. |
The investment categories are indicators of activity, not evidence of returns, productivity gains or how benefits are distributed among firms and communities. None of these figures establishes open-source AI’s market share or its net economic contribution in the region.
What determines whether the opportunity is realized
APEC’s policy statements and regional context point to conditions that can shape the value of any AI approach. Open-source availability does not remove the need to assess these factors for a particular use case.
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- Infrastructure and connectivity: Models need suitable computing capacity, energy, networks and deployment environments. APEC’s statements emphasize resilient and accessible infrastructure; access to a model does not itself provide the resources to run it.
- Skills and capacity: Developing, adapting, evaluating and maintaining systems requires technical talent and AI literacy. Economies and organizations with different levels of capacity will not necessarily capture the same benefits.
- Security assurance: The open-source or open-weight label alone does not establish that a system is secure. Development and deployment controls, vulnerability response and clear operational responsibility matter.
- Data protection and intellectual property: Organizations need to consider what data they can lawfully use, how personal or sensitive information is protected, and whether model and data licenses permit the intended use.
- Trust and responsible adoption: APEC’s 2025 ministerial statement emphasizes human-centered workforce and education policy, trust, safety and risk management. Economic use should account for potential harms and the people affected by deployment.
- Local fit and interoperability: A model must suit the language, sector, service or business context, and work with relevant systems. Cross-border regulatory cooperation is still at an early stage, according to the APEC Policy Support Unit’s 2025 brief, whose desk research runs through March 2025. Limited clarity about interoperability among domestic frameworks could contribute to fragmentation and raise adoption costs, with possible effects on trade and investment.
How to compare open-source and proprietary approaches
Neither approach is automatically the better economic choice. The relevant comparison is how well each option meets a specific need, including its full operating responsibilities—not just whether access to the model is free or restricted.
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| Decision axis | Questions to ask |
|---|---|
| Control and adaptability | Can the organization inspect, adapt or self-host the system if needed, and does it have the expertise to manage those choices? |
| Security assurance | What controls apply during development and deployment, who handles vulnerability response, and who is accountable for oversight? |
| Data and intellectual-property governance | Are the relevant licenses, data rights, privacy protections and intended uses compatible? |
| Infrastructure and total cost | What are the costs of compute, energy, integration, maintenance and staffing over the system’s life? The evidence cited here does not establish that open-source is always cheaper. |
| Skills and inclusion | Are the necessary talent, AI literacy and connectivity available to deploy the system and enable affected groups to participate? |
| Local fit and interoperability | Does the system work for the relevant language, sector or service, and can it operate with the systems and rules it must interact with? |
This comparison is a practical synthesis of APEC’s stated concerns, not an official APEC scoring framework. The answer may differ by use case and economy.
What evidence would make the economic case clearer
To judge open-source AI’s contribution rather than AI activity in general, evaluations would need to distinguish model approach from other factors and track outcomes in real deployments. Useful evidence would include:
- Comparable measures of adoption, deployment costs and ongoing operating requirements, including compute, energy, integration and staffing.
- Outcome evaluations across sectors, firm sizes and economies, with attention to productivity, service quality, innovation and who receives the gains.
- Economy-level assessments of readiness, including infrastructure, skills, responsible-use capacity and the ability to manage data, security and intellectual-property obligations.
- Transparent comparisons of open-source and proprietary options for similar tasks, while accounting for differences in licensing, operational control and risk.
Until such evidence isolates open-source-specific effects, the sound conclusion is conditional: APEC sees trusted open-source AI as a potential contributor to innovation and participation, while the size and distribution of any economic return remain unquantified.
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