Global South at risk of digital dependency without decentralized AI governance

Poverty alleviation requires more than technology. It demands institutional reform, capacity building and equitable economic structures. AI should be deployed only when paired with investments in digital infrastructure, community training, regulatory protection and governance innovation.

A new study warns that current AI systems, dominated by centralized digital infrastructures, may reinforce economic dependence and digital inequality across the Global South unless new governance and economic models emerge. At the same time, the study finds growing momentum behind community-owned, decentralized digital ecosystems that aim to redistribute value and strengthen local control.

The research, titled “Decentralizing AI Economics for Poverty Alleviation: Web3 Social Innovation Systems in the Global South,” published in AI, concludes that technical innovation alone cannot tackle poverty unless paired with structures that ensure fair value distribution and community participation. The author compares established centralized AI systems with emerging Web3 models that promise local empowerment through blockchain, data cooperatives and decentralized governance.

The author argues that these two ecosystems are on a collision course. One is powerful but extractive. The other is inclusive but fragile. Achieving genuine poverty reduction, he writes, will require hybrid systems that blend the strengths of both while avoiding their pitfalls.

Centralized AI4SI Systems Deliver Services but Entrench Dependency

The paper analyses centralized AI systems used in social innovation, often described as AI4SI. These systems are common in health monitoring, crop management, public service delivery, educational platforms and humanitarian aid. They rely on large datasets, corporate partnerships and government institutions, usually based in the Global North.

These platforms have delivered real benefits such as easier access to public services, improved health diagnostics and streamlined educational tools. However, the study shows that this centralization creates structural weaknesses. Data often flows outward from the Global South into servers and companies located elsewhere, limiting local ownership. Value created by AI systems tends to concentrate in institutions that design, maintain and control the platforms rather than in local communities that generate the data.

The author highlights the risk that these centralized systems, while effective in the short term, may reinforce patterns of digital colonialism. Local communities depend on external providers for software, cloud infrastructure and algorithmic decisions. This dependence limits their ability to shape AI outcomes, negotiate terms or benefit financially from the data economy.

This model, the author explains, is efficient but unequal. It scales quickly, but it does not redistribute value or governance authority. As a result, the Global South may continue to play the role of data supplier rather than active participant in the digital economy.

The study also notes that centralization can undermine long-term sustainability. Projects often rely on donor funding, foreign expertise and inflexible technological frameworks. When funding cycles end, tools become outdated or local institutions struggle to maintain them.

Web3 Ecosystems Offer Data Sovereignty but Face Maturity and Inclusion Challenges

The study examines a growing movement toward decentralized Web3 systems. These models use blockchain, data wallets, decentralized autonomous organizations (DAOs), community currencies and peer-to-peer networks to give communities greater control over their digital assets and governance.

The author describes a series of real-world experiments where these systems support environmental protection, community-led financial networks, transparent supply chains and decentralized philanthropic initiatives. Examples include community currencies in Kenya, DAOs supporting sustainable fisheries and blockchain tools for land rights protection.

These ecosystems aim to support data sovereignty, local decision-making and transparent value distribution. Communities can own and monetize their data. Governance rules are encoded into shared digital systems. Resources flow through peer-managed networks rather than external intermediaries.

However, the author points up that decentralization is not a simple solution. Web3 environments can be technically complex, fragmented and vulnerable to governance failures. Many communities lack the digital skills needed to navigate these systems. Without careful design, decentralized tools may be captured by elites or fail to gain public trust.

The study identifies four major challenges facing Web3 social innovation:

• High technical complexity creates barriers for community use.
• Governance vulnerabilities can allow small groups to dominate decision-making.
• Uneven digital literacy limits who benefits.
• Limited institutional integration prevents large-scale impact.

Despite these challenges, the author argues that decentralized systems offer a promising alternative to extractive digital economies. Their strength lies in transparency, shared control and inclusive value distribution.

Web3 models align more closely with local empowerment and community resilience. They can support local currencies, cooperative ownership structures and participatory development processes. But they require stable governance, long-term support, digital infrastructure and clear regulatory oversight.

Hybrid AI economics and polycentric governance

After examining both innovation ecosystems, the study concludes that neither centralized nor decentralized models can fully support poverty alleviation on their own. Centralized systems provide scalability and reliability but often lack fairness and local ownership. Decentralized systems offer empowerment but lack maturity and stability.

The author calls for a hybrid AI economics model grounded in polycentric governance. This model would combine the institutional strength of centralized frameworks with the inclusive structures of decentralized networks. Local communities would gain control over data through cooperative ownership, while governments and expert institutions would provide regulatory oversight, standards and infrastructure.

The study outlines several principles for building such hybrid systems:

• Data cooperatives that allow communities to manage collective data rights.
• Shared governance models that distribute authority across institutions and community groups.
• Community participation in development, deployment and monitoring of AI systems.
• Transparent value flows to ensure benefits return to local populations.
• Interoperable tools that connect Web3 innovations with public service infrastructure.

This approach aims to avoid the weaknesses of both ecosystems. Centralization alone risks extraction. Decentralization alone risks fragmentation. A hybrid, polycentric model allows AI systems to be effective and inclusive at the same time.

Poverty alleviation requires more than technology. It demands institutional reform, capacity building and equitable economic structures. AI should be deployed only when paired with investments in digital infrastructure, community training, regulatory protection and governance innovation.

If the Global South becomes locked into external AI infrastructures without control over data and economic value, inequality could deepen, the study warns. But if communities gain meaningful participation in AI governance, new paths toward digital justice and sustainable development may emerge.