Artificial intelligence (AI) represents the most significant economic and societal transformation since the industrial revolution, with the potential to unlock trillions of dollars in global productivity and help solve humanity's greatest challenges from climate change to disease.

This Hinrich Foundation concept note addresses what we term “the AI Participation Imperative,” framing the urgency for equitable sharing and inclusive participation in the global development of this transformative technology. Failure to do so risks significantly widening the global economic divide between those with access to advanced AI capabilities and those without.

The current trajectory of AI development is characterized by the extreme concentration of computational power, proprietary data, and elite talent in a few regions and corporations. It poses an extreme risk of creating and embedding a two-tiered global economy. This divergence will not merely widen existing income gaps; it risks establishing a new form of systemic structural inequality, where nations, or communities within them, that don’t or cannot keep up with AI development face economic stagnation and dependency on AI services controlled by a powerful few. It is equally important to ensure an adequate return on the capital invested in providing AI as an innovative and highly productive enterprise.

The AI Participation Imperative calls on policymakers and corporate executives worldwide to recognize that equitable sharing and inclusive participation are not simply ethical ideals, but fundamental prerequisites for future global stability, open markets, and world peace. Without such principles, AI services risk becoming a coercive device that could easily be used by those who control its supply, among other purposes, to undermine ambitions for democratic sovereignty and the evenness of socioeconomic development.

This concept note joins a dialogue on ongoing discussion across policy, corporates, academia, and international organizations on the subject of AI development. The proposed AI Participation Imperative would focus on four pillars for critical action:

Data sovereignty and access;
Capacity and talent-building;
International cooperation on AI development; and
Non-coercive AI governance.

An AI Participation Imperative is critical for both developed and developing countries.

The rise of general-purpose AI is a tectonic shift fundamentally restructuring the ways economic value is created. AI drastically alters traditional factors of production in the way it enhances or replaces labor, coordinates capital, and accelerates intellectual discovery. Estimates vary but the consensus is clear: AI will drive massive and concentrated wealth creation.

This monumental change demands a proactive global response. The central thesis of the AI Participation Imperative is that the future of the global economy has to be founded on stability, openness, and capacity for shared growth. This will be determined by how the international community handles equitable and inclusive participation in the AI value chain.

The discussion must urgently shift from discrete national AI governance strategies to a global collaborative framework. This is not a plea for international aid or philanthropic transfers. It is a call to action for global coordination to avert a fractured, unequal AI-dominated world shackled to market fragmentation, geopolitical friction, and reduced innovation.

The speed of AI development is already outpacing global governance. As evidenced by current trends, the economic and structural advantages of early leaders are compounding rapidly, creating self-reinforcing loops of wealth and power concentration. The window for initiating global participation frameworks is closing. The issue is critical to maintain open markets and geopolitical stability especially for developed economies, and secure development pathways and avoid systemic marginalization especially for developing economies.
That AI will displace jobs in developing countries is a given. The larger threat is that it will bypass these economies entirely, reinforcing the dominance of a few nations in high-value sectors and making it exponentially harder for poorer nations to catch up. Neither are developed economies immune from within; AI already has hollowed out and has the potential to further hollow out entry-level knowledge roles, such as in legal and finance industries.

AI capability is currently predicated on four scarce resources with increasingly concentration in supplier control in the hands of the few:

Compute: Access to cutting-edge chip technology and data center infrastructure.
Data: Ownership of proprietary, high-quality, and often language-specific datasets.
Talent: A small, globally mobile pool of elite AI researchers and engineers.
Energy: A dependable and consistent source of power.

This concentration funnels the development of core AI models and services toward a small fraction of the world’s population that produce and control supply for a global market. This handful of suppliers and their controllers would fundamentally determine economic policy and societal equity. This is digital sovereignty risk playing out in real time, where the majority of national economies and their public services become structurally dependent on a handful of foreign-controlled AI monopolies.

The environmental instability caused by AI development will likely worsen climate change at a time when many countries are struggling with basic power generation and transmission or the cost of those services. The measurement of data center capacity in gigawatts presumes that these gigawatts can be sustainably sourced, a presumption which should be questioned.

The lack of participation in AI development leads to a crippling loop:

Low AI readiness in infrastructure skills, and the granularity and timeliness of national-level data, among other factors, leads to limited adoption of productivity-boosting AI;
Limited adoption leads to stagnant productivity and slower economic growth relative to AI leaders;
Stagnation leads to decreased resources to invest in foundational AI assets and education.

The disproportionate allocation of the benefits of AI exacerbates the wealth gap both within and between nations, according to recent economic analysis, increasing inequality.

The control over mission-critical AI services, from financial risk modeling to essential-infrastructure management and military defense, creates a chokepoint of vulnerability for national security. Dependence on a small set of providers for services critical to economic growth and stability can and will be weaponized through direct technical denial (e.g., cutting off access to a crucial service) and also through more subtle systemic pressure applied by controlling data standards, algorithmic bias, technology dependencies, and access to personal data.

The following principles should guide policy and corporate strategy:

Shared global data pool: Frameworks must be established to encourage the ethical sharing of diverse, interoperable, non-personal aggregated national data, such as those on climate, health, and agriculture, to prevent bias and ensure global model relevance, balanced with a recognition of data sovereignty. An example would be AI developers helping economies develop technical capacity to keep national records such as for healthcare on the basis of ethical sharing.

Open science and open models: Governments and industry should consider risk-based open-sourcing of foundational models and related research, perhaps through “global public AI goods” initiatives, to reduce entry barriers especially for developing nations and foster local innovation.

Local capacity and ownership: Encourage targeted investment in digital literacy, technical education, and the computational infrastructure such as regional AI centers and public cloud access needed to enable effective use of AI. Even a relatively limited investment could go a long way in enhancing the value and quality of national-level data that less-developed economies are able to gather.

Risk-based, non-discriminatory governance: International governance should establish universal human rights and safety guardrails, but avoid prescriptive, one-size-fits-all regulations that disproportionately burden smaller nations and stifle their nascent AI ecosystems. The rules should strike a balance with the need to avoid hindering innovation on AI development which does help businesses reduce costs and raise efficiency.

The following recommendations are for consideration, guidance, and discussion:

For policymakers:

Invest in foundational compute as a public good: Consider establishing and funding regional AI compute centers accessible to universities, startups, and other researchers, with appropriate guardrails, to democratize the resource bottleneck.
Advocate for global data interoperability and data flows: Work through multilateral institutions such as the United Nations (UN), the World Trade Organization (WTO), the G20, the Organisation for Economic Co-operation and Development (OECD), the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC), and digital trade agreements to enable secure, ethical, cross-border data access and usage that respect data sovereignty while enabling international model training.
Implement an "AI dividend" or shared investment models: Explore mechanisms to fund global capacity-building directed toward developing nations' AI infrastructure.
Prioritize AI for public service: Encourage the use of AI in public services including in agriculture, education, and healthcare, but with the precondition that the underlying model architecture and data governance remain transparent and locally auditable to deter overdependence on foreign suppliers.

For corporates and AI developers:

Adopt a "Global Inclusivity by Design" approach: Ensure AI models are trained on globally representative data and rigorously tested to prevent bias across socio-economic, linguistic, and cultural contexts.
Commit to international knowledge transfer: Create and fund open-access curricula, internships, and partnerships to co-develop local AI talent and foster an equitable dialogue, recognizing that a wider talent pool benefits the industry and the global market. Promote the delivery of low-cost or free online education services in less-developed markets.
Develop non-coercive AI licensing models: Design licensing, pricing, and service access agreements that prohibit terms that could be used for economic or political leverage. Such terms should aim to promote transparency and predictability in AI service provision to all customers, regardless of national origin.
Support open-source initiatives: Dedicate resources to supporting the development of high-quality, multilingual, open-source foundational models worldwide that can be fine-tuned and owned by local actors.

The current inflection point in AI development offers humanity a choice: a future of extreme concentration in AI technology by a few gatekeepers, or one of shared access and equitable participation.

The shared prosperity challenge for AI access requires urgent action. It requires global participation to strike a balance between the benefits of a transformative technology and its potential for imbalance. By framing this challenge around effective sharing and inclusive participation, we can harness AI to be a catalyst for global development and a foundation for a more equitable world.

We welcome your comments and suggestions at aiparticipation@hinrichfoundation.com