Tabuga Think Tank

Three outcomes would signal success. First, explicit recognition that AI governance must address the structural asymmetry between AI-producing and AI-consuming nations. Current frameworks focus on regulating systems that developing countries import but do not build. The Dialogue should name this gap and commit to addressing it. Second, a concrete commitment to develop shared indicators for domestic AI production capacity — not just adoption metrics. Countries need governance tools that measure whether AI investments translate into local knowledge, patents, and technical talent retention. Without this, governance frameworks will inadvertently consolidate dependency. Third, a mechanism for structured evidence contributions from independent research institutions in developing economies. The Dialogue's credibility depends on incorporating empirical analysis from the countries most affected by AI governance decisions, rather than relying exclusively on inputs from governments and multinational technology firms. Success means governance that builds capacity, not just compliance.

• Open-source software, open data and open AI models
• Transparency, accountability, and human oversight
• Interoperability of governance approaches
• AI capacity-building

1. **AI capacity-building** - the core of the Input Trap thesis. Countries investing heavily in connectivity and digital infrastructure yet failing to convert those investments into domestic innovation, knowledge production, or technical talent retention. 2. **Interoperability of governance approaches** - directly connected to our finding from 417 public technology procurement processes in the Dominican Republic: over 80 government institutions acquiring AI and digital systems independently, with no shared standards. Interoperability is the most concrete governance problem our research documented. 3. **Open-source software, open data and open AI models** - the pathway in the Dominican Republic to breaking the 99% dependency on external solutions. Without open models, countries caught in the Input Trap remain permanent consumers of AI rather than participants in its development. 4. **Transparency, accountability, and human oversight** - supports the case for AI procurement governance and the estimated USD 180-450 million fiscal exposure from fragmented, opaque technology acquisition in the Dominican Republic. Transparency in how governments purchase and deploy AI systems is the operational argument grounded in our evidence.

The seven thematic areas address AI systems once they exist - their safety, transparency, openness, and human rights implications. A critical gap remains in how governments in developing economies acquire these systems in the first place. Our research analyzed 417 public technology procurement notifications in the Dominican Republic over ten months. The finding: more than 80 state institutions purchase AI and digital systems independently, with no interoperability requirements, no shared technical standards, and no consolidated oversight. The estimated fiscal exposure from this fragmentation ranges between USD 180 and 450 million over a decade, accounting for functional duplication, vendor lock-in, incident remediation, and lost competitiveness. This pattern is common across the Caribbean and Latin America. It represents a governance failure that precedes and conditions every other thematic area listed. Transparency requirements mean little if procurement processes lack the technical capacity to evaluate what is being purchased. Capacity-building stalls when public budgets flow to external vendors without knowledge transfer obligations. Interoperability becomes impossible when each institution acquires closed, incompatible systems. AI procurement governance sits at the intersection of all seven themes, yet none of them captures it directly. It requires specific attention: standardized technical evaluation criteria for public AI acquisition, mandatory interoperability clauses, fiscal impact assessments, and domestic capacity requirements embedded in procurement frameworks. A second emerging issue is the absence of metrics for domestic AI production capacity. Current international indices measure adoption and readiness but fail to track whether countries are building or merely consuming AI. Governance frameworks that rely exclusively on adoption metrics risk reinforcing the structural asymmetry between AI-producing and AI-consuming nations. Both gaps - procurement governance and production metrics - are measurable, actionable, and urgently relevant for developing economies engaging with this Dialogue.

The Dominican Republic operates the most advanced connectivity infrastructure in the Caribbean and Central America, anchored by the NAP del Caribe, 85% internet penetration, and carrier-grade availability above 99.999%. The infrastructure foundation is world-class. The innovation outcomes are not. The country dropped seven positions on the Global Innovation Index between 2020 and 2025, reaching rank 97. On the Latin American AI Index, it fell three positions in a single year. Annual patent production remains at four. An estimated 99% of the roughly USD 300 million spent on AI solutions in 2025 went to external providers. The investment flows in; the capacity flows out. This input-output disconnect creates three concrete governance challenges. First, fiscal fragmentation. Our analysis of 417 public procurement processes revealed over 80 institutions acquiring technology independently, generating an estimated fiscal exposure of USD 180–450 million over ten years through duplication, vendor dependency, and lack of interoperability. The absence of procurement governance standards converts public investment into structural waste. Second, talent erosion. The region trains technical professionals who migrate to markets where compensation and career development match their skills. Without governance frameworks that incentivize domestic retention and production, capacity-building investments subsidize other economies. Third, governance asymmetry. International AI governance discussions focus on regulatory frameworks designed for AI-producing nations. Caribbean and Central American countries risk adopting compliance obligations without the institutional capacity to implement them or the productive base to benefit from them. The opportunity is equally concrete. The Dominican Republic's infrastructure, geographic position, and demonstrated innovation cases — Eco Mensajería's recognition by Mastercard among the 25 most innovative firms globally — confirm that the enabling conditions exist. The missing layer is governance architecture that converts inputs into outputs.

The AI Dialogue occupies a unique position: it is the first UN platform designed to bring governments and all stakeholders into a structured, ongoing exchange on AI governance. Its value lies in what existing mechanisms cannot do individually — connect regulatory design with implementation reality across asymmetric economies. Three roles would maximize the Dialogue's impact on international cooperation. First, establishing a structured evidence mechanism. Current AI governance discussions rely heavily on policy positions and expert panels. The Dialogue should create a pathway for independent research institutions in developing economies to contribute empirical data — procurement analyses, capacity assessments, fiscal impact studies — directly into the preparatory process. Governance grounded in evidence from affected countries produces frameworks that work beyond the capitals where they are drafted. Second, bridging the governance design gap between AI-producing and AI-consuming nations. Existing cooperation mechanisms — the OECD AI Principles, the Global Partnership on AI, the Hiroshima Process — were designed primarily by and for technologically advanced economies. The Dialogue can complement these by introducing governance dimensions that matter most to developing countries: procurement standards, knowledge transfer requirements, domestic production metrics, and fiscal accountability for technology investments. International cooperation advances when frameworks address both sides of the AI divide. Third, creating accountability through measurement. The Dialogue should promote the development of indicators that track whether governance frameworks actually build capacity in developing economies or merely impose compliance costs. Measuring domestic AI production, talent retention, and public procurement efficiency would give the Dialogue a concrete function that distinguishes it from declarative processes. The greatest contribution this Dialogue can make is demonstrating that AI governance and AI capacity-building are inseparable. Governance without capacity produces dependent compliance. Capacity without governance produces fragmented waste. The Dominican Republic's experience illustrates both risks simultaneously.

Several mechanisms have advanced AI governance at the international level. The OECD AI Principles established foundational norms for trustworthy AI. The Global Partnership on AI created a multi-stakeholder research infrastructure. The Hiroshima Process addressed frontier AI risks among advanced economies. UNESCO's Recommendation on the Ethics of AI provided the first global normative instrument. The Global Digital Compact embedded AI governance within the broader digital cooperation agenda. Each addresses a necessary dimension. None addresses the structural gap between governance frameworks designed in AI-producing economies and implementation realities in developing countries. The AI Dialogue's added value lies in three specific connections. First, linking governance to national digital readiness processes already underway. The UNDP Digital Readiness Assessment, currently active in the Dominican Republic through the Ministry of Public Administration, represents exactly the kind of implementation-level process where governance frameworks meet institutional capacity. The Dialogue should systematically connect with these national assessments to ensure that global principles translate into actionable institutional reforms, procurement standards, and capacity benchmarks. Second, incorporating regional knowledge infrastructure. Organizations like the NAP del Caribe, the Cámara TIC Dominicana, and independent research institutions such as the Tabuga Think Tank produce empirical analysis on digital transformation that rarely reaches global governance discussions. The Dialogue should establish formal channels for regional evidence to inform its thematic tracks, particularly on capacity-building and interoperability. Third, connecting AI governance with public financial management mechanisms. The DGCP procurement framework in the Dominican Republic, and equivalent systems across Latin America and the Caribbean, represent the operational layer where AI governance succeeds or fails. Partnerships with public procurement authorities would ground the Dialogue in measurable institutional outcomes rather than declarative commitments. The Dialogue adds value by being the platform where global principles meet national implementation evidence.

The Dialogue's multi-stakeholder mandate requires format design that produces actionable inputs, rather than sequential three-minute statements that accumulate without synthesis. Three structural recommendations. First, create a dedicated evidence track alongside the policy track. Governments contribute policy positions; stakeholders should contribute structured data. Independent research institutions, civil society organizations, and technical communities in developing economies possess empirical analysis — procurement data, capacity assessments, implementation case studies — that rarely enters global governance processes. The Dialogue should establish a formal submission mechanism for evidence-based contributions, with clear criteria and a commitment to integrate findings into the Co-Chairs' summary. The web platform announced for written inputs is a starting point; it should be designed to receive structured datasets alongside narrative positions. Second, organize thematic sessions by governance gap rather than by stakeholder type. The current format separates governments from stakeholders. A more productive structure would convene mixed sessions around specific governance challenges — AI procurement standards, capacity metrics, interoperability frameworks — where different actors contribute from their respective expertise. A procurement governance session, for instance, benefits simultaneously from government budget authorities, independent researchers, private sector technology providers, and international organizations with comparative benchmarks. Third, establish a developing-economy caucus with a specific function. Small and developing economies share structural conditions — the Input Trap, talent erosion, fiscal fragmentation — that differ fundamentally from the governance priorities of AI-producing nations. A dedicated mechanism ensures these perspectives shape the agenda rather than appear as commentary on frameworks designed elsewhere. Regarding stakeholder roles: governments should contribute implementation data, the private sector should disclose procurement and deployment practices, academia and think tanks should provide independent analysis, and international organizations should facilitate evidence comparison across national contexts. Format determines substance. The Dialogue's credibility depends on producing governance frameworks informed by the countries they most affect.

Global AI governance discussions are shaped primarily by two poles: advanced economies that produce AI systems and advocate for safety-focused regulation, and the least developed countries that participate through digital inclusion narratives. A critical group remains structurally underrepresented — middle-income developing economies that have built significant digital infrastructure, invest substantially in technology adoption, and yet fail to convert those investments into domestic innovation capacity. These countries are the most directly affected by governance design decisions. They absorb compliance costs from regulatory frameworks they did not shape. They consume AI systems under terms defined elsewhere. They train technical talent that migrates to producing economies. The Caribbean, Central America, Southeast Asia, and parts of North and West Africa share this structural position. Their absence from governance discussions produces frameworks that address AI production risks and digital exclusion but ignore the conversion gap between the two. Three inclusion mechanisms would address this. First, the Dialogue should actively identify and invite independent research institutions from these economies. Government delegations alone cannot represent the full complexity of national digital ecosystems. Think tanks, technical communities, and applied research organizations produce the empirical analysis that governance frameworks require. The Tabuga Think Tank's experience in the Dominican Republic demonstrates that rigorous, policy-relevant research exists in these countries but lacks formal channels into global processes. Second, the Scientific Panel's inaugural report should incorporate data from middle-income developing economies as a distinct analytical category, separate from both OECD members and least developed countries. Third, consultation formats should allocate dedicated time for evidence-based interventions from underrepresented regions, distinguishable from general stakeholder commentary. Structured evidence carries more weight than sequential position statements in shaping governance outcomes. Representation requires new institutional designs.

The most common format in global governance dialogues — sequential statements followed by a summary drafted by organizers — produces diminishing returns. Stakeholders speak, co-chairs synthesize, and the connection between input and output remains opaque. The AI Dialogue has an opportunity to demonstrate that governance innovation applies to its own process. Three format innovations would produce substantively different outcomes. First, structured evidence workshops before the plenary. The July session should include pre-Dialogue working sessions where researchers, practitioners, and government officials co-examine specific datasets — procurement analyses, capacity assessments, implementation case studies — and produce shared findings. These workshops would feed concrete, jointly validated evidence into the plenary discussion rather than leaving synthesis entirely to the Secretariat. The format shifts participation from commentary to co-production. Second, asymmetric response sessions. Instead of uniform three-minute interventions, the Dialogue should design sessions where a developing-economy institution presents a ten-minute evidence-based case study, followed by structured five-minute responses from a government, a private sector actor, and an international organization. This creates genuine exchange rather than parallel monologues. The procurement governance gap documented by the Tabuga Think Tank, for instance, would benefit from direct response by multilateral development banks and technology providers rather than appearing as one statement among dozens. Third, a live governance simulation. Select one concrete governance challenge — public AI procurement in a developing economy, for example — and convene a mixed group of stakeholders to draft governance principles in real time during the Dialogue. The exercise produces a tangible prototype that demonstrates the Dialogue's capacity to generate actionable outputs, while revealing where stakeholder interests converge and diverge under practical constraints.

Three practices from our direct research offer transferable governance models. First, procurement-based governance. The Tabuga Think Tank's analysis of 417 public technology procurement processes in the Dominican Republic identified a concrete governance lever: embedding interoperability requirements, knowledge transfer clauses, and fiscal impact assessments directly into public procurement frameworks. This approach converts existing institutional infrastructure - procurement authorities operate in every country - into AI governance mechanisms without requiring new legislation. The Dominican Republic's DGCP framework already provides the regulatory basis; what is missing are technical standards specific to AI and digital systems acquisition. This model is immediately replicable across Latin America and the Caribbean through existing public procurement harmonization agreements. Second, independent ecosystem diagnostics as governance tools. The Tabuga Think Tank's strategic assessment methodology - combining quantitative procurement analysis, structured interviews with ecosystem actors, and international benchmarking adapted to local institutional conditions - demonstrates that developing economies can produce policy-grade evidence with modest resources. The methodology was developed in the context of the UNDP Digital Readiness Assessment process with the Ministry of Public Administration, proving that independent research and institutional cooperation can be complementary rather than adversarial. Third, community-anchored infrastructure governance. The NAP del Caribe, documented through our research, operates as a regional connectivity hub that achieved carrier-grade reliability through a governance model balancing private investment with public infrastructure principles. Its approach to redundancy, open access, and regional interconnection offers a practical template for governing shared AI infrastructure - computing resources, training datasets, model repositories - at a regional scale appropriate for small economies. Each example shares a common principle: effective AI governance emerges from institutional practices that already exist, adapted with technical specificity rather than invented through new multilateral frameworks. The Dialogue should prioritize identifying and scaling these existing governance mechanisms.