German Institute of Global and Area Studies

The Dialogue will have succeeded if it moves interoperability, Objective 4(d), upstream of regulatory design. Across Europe and MENA, the defining risk is not that governance frameworks disagree in good faith, but that divergent-yet-legitimate frameworks interact with concentrated, rent-financed sovereign procurement to produce inefficient markets and architectural fragmentation that no downstream audit can detect. Three outcomes would make this tractable. First, a shared diagnostic. The Dialogue should register that in rentier political economies such as the GCC, Algeria, and partially Iraq, Libya, and Iran, hydrocarbon-funded AI procurement decouples compliance cost from fiscal discipline, thus producing sticky sovereign prices and vendor accommodation equilibria that formal convergence cannot price. Between 2024 and 2026, announced GCC sovereign AI commitments and investments have exceeded USD 500 billion against fiscal breakeven oil prices already sitting above the Brent forward curve through 2030. Interoperability, here, is a fiscal-architecture question. Second, instrumentation. The Dialogue should endorse two transparency standards achievable within its two-day structure: (i) compliance-cost disclosure in sovereign AI procurement and (ii) vendor architectural-fragmentation reporting including through open procurement, extending the Microsoft-G42 Intergovernmental Assurance Agreement template into a multilateral disclosure norm covering jurisdiction-specific model weights, inference stacks, and integration layers. Third, early warning. The Independent International Scientific Panel can carry deployment-depth, sources-of-finance, and procurement diversification indicators for critical-infrastructure AI (grid, water, border, sovereign cloud) so that lock-in thresholds are detectable before fiscal stress forces ex-post acceptance of external governance terms. Linking the Secretary-General's proposed Global Fund for AI Capacity Development to interoperability-convergence assistance would give Objective 4(b) operational leverage over Objective 4(d). Success, therefore, ought not be a communiqué. It is whether the Dialogue leaves Geneva with the upstream instruments needed to prevent predictable fragmentation across EMEA.

Interoperability of governance approaches;

Interoperability, Objective 4(d) is the structural prerequisite for the Dialogue's other mandated areas. Without it, capacity-building (4(b)) fragments across incompatible architectures, safety frameworks (4(a)) remain jurisdiction-bound, and human rights protections (4(e)) apply unevenly. The Global Digital Compact itself recognizes that "coordination and compatibility of emerging AI governance frameworks" is not a secondary objective but a foundational enabler. Interoperability is, analytically, the connective tissue of global AI governance. My selection reflects two considerations rooted in my research and operational portfolio. First, my professional trajectory spans public financial management, macro-fiscal risk assessment, and institutional performance diagnostics across EMEA. As Research Fellow at the German Institute of Global and Area Studies and Programme Coordinator at the Basil Fuleihan Institute prior, I have consistently observed that formal convergence on principles, while necessary, is insufficient where underlying fiscal architectures and institutional incentive structures remain misaligned. Interoperability failures in AI governance mirror those I documented in public procurement, civil service reform, and SDG implementation: divergence emerges not from bad faith but from the interaction of legitimate frameworks under distinct macroeconomic constraints. Second, the EMEA region presents the highest-stakes test case for Objective 4(d). Europe's AI Act establishes the world's most comprehensive regulatory framework. Simultaneously, MENA rentier economies, the GCC, Algeria, and others, are deploying sovereign AI procurement at unprecedented scale, funded by hydrocarbon rents that decouple compliance costs from fiscal discipline. The empirical record from 2024-2026 shows architectural fragmentation accelerating precisely where interoperability is most urgent. My contribution leverages transaction-cost economics, institutional utility theory, and quantitative macro-fiscal modeling to diagnose why this divergence is structural, not transitory, and to propose upstream instruments including compliance-cost disclosure, vendor architectural-fragmentation reporting, and critical-infrastructure contract lock-in monitoring, that the Dialogue could discuss operationalizing. Interoperability is where the economics meets the governance. That is where I can contribute most substantively.

The thematic clusters treat AI governance as a regulatory-design problem downstream of procurement decisions, when in fact the macroeconomic and fiscal-architecture conditions under which states procure AI systems determine whether regulatory convergence is enforceable. Three cross-cutting gaps are visible. First, sovereign AI procurement transparency. The listed themes address capacity-building (4(b)) and interoperability (4(d)) but do not engage the upstream fiscal mechanisms that structure market access and vendor incentives. Between 2024 and 2026, MENA rentier economies announced over USD 500 billion in sovereign AI commitments (both enacted or committed to) funded by hydrocarbon rents rather than tax-financed budgets. This rent-financing wedge decouples compliance costs from fiscal discipline, producing sticky procurement prices and vendor accommodation equilibria that fragment global AI architectures regardless of formal interoperability commitments. In that respect, the Dialogue could incorporate a transparency standard requiring disclosure of compliance-cost components in sovereign AI contracts above a given threshold. This makes the fiscal-architecture distortion visible to the Independent Scientific Panel and to markets. Second, vendor-side architectural fragmentation. The themes focus on state-level governance frameworks but omit the vendor strategies that translate divergent frameworks into actual architectural incompatibility. The Dialogue should encourage multilateral reporting norm that is vendor-specific in order to iron out architectural divergences, thereby hard-wiring human security commitments into cross-framework audit mechanisms that should nonetheless remain multi-stakeholder driven and market-friendly. Third, critical-infrastructure lock-in monitoring. The themes address safety (4(a)) and human rights (4(e)) but not the non-linear switching costs that emerge when sovereign AI procurement is deployed in grid, water, border, and sovereign cloud infrastructure. When switching costs exceed the acceptance price of external governance terms, fiscal stress forces ex-post acceptance rather than ex-ante negotiated convergence, a worse interoperability outcome. The Scientific Panel should therefore carry deployment-depth indicators for critical-infrastructure AI, enabling early-warning detection before the lock-in threshold binds. These gaps will determine whether the Dialogue's interoperability mandate is structurally achievable across EMEA.

The most significant challenge is structural fragmentation disguised as formal convergence. Across EMEA, states are signing onto interoperability frameworks while simultaneously deploying sovereign AI procurement strategies that produce architectural incompatibility at the vendor level. This rent-financing wedge decouples compliance costs from fiscal discipline: formal convergence is cheap, substantive implementation is not. The Dialogue risks producing communiqués that declare interoperability achieved while the underlying architecture diverges irreversibly. When hydrocarbon revenues eventually contract, fiscal stress will force ex-post acceptance of external governance terms, thereby concentrating bargaining power in alternative providers at the moment of the accepting state's greatest weakness. This is a worse outcome than the Dialogue currently anticipates. The most significant opportunity is that the Dialogue has the institutional infrastructure and the mandate to address these upstream conditions before lock-in binds. Three instruments are immediately achievable within the two-day format. First, encouraging compliance-cost disclosure in sovereign AI contracts. Second, promoting vendor architectural-fragmentation reporting as a multilateral transparency standard, extending bilateral assurance agreements into a cross-framework audit mechanism and encouraging stakeholder consultation and participation. Third, incorporating deployment-depth indicators for critical-infrastructure AI into the Panel's annual report, in order to enable early-warning detection of non-linear switching-cost thresholds.

The Dialogue's essential role is to prevent a fragmentation outcome that no party wants but every party tends to produce. Across EMEA, AI governance is fragmenting not because frameworks disagree (the EU AI Act, OECD Principles, and MENA sovereign strategies are each defensible) but because their interaction with competitive vendor markets creates architectural incompatibility as an emergent property. This coordination failure is invisible to any single framework. Only knowledge transfers and platform-driven market-signaling can flag it. The Dialogue can therefore function as the system's early-warning mechanism for emergent divergence that risks producing sticky prices, monopolies, inefficient markets, human security-related divergences, and procurement lock-ins. Existing frameworks regulate what states and vendors do individually, while the Dialogue can smoothen what they produce collectively. When a supplier signs jurisdiction-specific commitments with G42 in the UAE, with PIF in Saudi Arabia, and operates under the EU AI Act, each is legitimate. The fragmentation emerges from their sum. The Independent Scientific Panel can therefore synthesize vendor-specific architectural data across jurisdictions in ways no bilateral agreement can replicate, mirroring Voluntary National Reviews regarding SDG-16. Second, the Dialogue addresses the temporal dimension existing frameworks miss. The EU regulates at deployment; the OECD coordinates at policy-design. Neither tracks the approach to irreversibility, when switching costs in critical infrastructure exceed the acceptance price of external governance terms. For MENA rentier economies funding AI through hydrocarbon rents, this lock-in threshold will bind under fiscal stress, forcing ex-post acceptance rather than ex-ante negotiation. The Dialogue can detect this trajectory before it becomes irreversible and generates monopolies and barriers to entry. Third, the Dialogue links interoperability (4(d)) to capacity-building (4(b)) through the proposed Global Fund. Existing frameworks treat these separately. The Dialogue can make capacity financing conditional on interoperability-convergence commitments, thereby giving developing states an ex-ante alternative to vendor lock-ins. That way, the Dialogue makes visible what is otherwise emergent, measurable what is otherwise diffuse, and actionable what is otherwise inevitable all through a market-signaling approach.

The Dialogue can build on three clusters of existing mechanisms. First, regulatory frameworks: the EU AI Act, OECD AI Principles, G7 Hiroshima AI Process, and regional efforts in ASEAN and the African Union establish substantive governance norms. Second, bilateral assurance agreements: the Microsoft–G42 Intergovernmental Assurance Agreement (April 2024) and analogous PIF-Microsoft sovereign cloud commitments create jurisdiction-specific compliance architectures. Third, development and financing mechanisms: IMF macro-fiscal diagnostics, World Bank digital development programs, and the ITU's AI for Good Summit provide capacity-building infrastructure and technical assistance. The Dialogue's added value is systemic synthesis and market/stakeholder signaling. Existing frameworks each operate within bounded scope, the EU regulates its jurisdiction, bilateral agreements govern two parties, the IMF assesses national fiscal trajectories, but none can detect or address the emergent fragmentation that arises and market distortions that arise when these legitimate frameworks interact with concentrated sovereign procurement and non-competitive, rentier vendor markets. The Dialogue, anchored by the Independent Scientific Panel's annual assessments, can synthesize vendor-specific architectural data, deployment-depth indicators, and compliance-cost structures across jurisdictions and transmit them across in ways that are not achieved yet by any other actor. Concretely, the Dialogue could extend multilateral transparency standards encouraging all major AI vendors to disclose jurisdiction-specific pricing and broad-based architectural divergences. This transforms bilateral commitments into cross-framework audit infrastructure. Second, the Dialogue should link IMF fiscal diagnostics on MENA hydrocarbon breakevens with deployment-depth monitoring in critical infrastructure, thereby creating an early-warning system to prevent barriers to entry and lock-in thresholds that neither the IMF nor other bilateral agreements currently track.

Stakeholder contributions can map the upstream conditions mentioned previously. Governments should be encouraged to disclose compliance-cost components in sovereign AI procurement contracts above a materiality threshold, making the rent-financing wedge visible to the Scientific Panel in order to circumvent sticky-price formation and market rigidities. MENA rentier economies can provide fiscal-architecture data, hydrocarbon breakeven prices, sovereign wealth fund deployment schedules, data-center capital commitments, to enable the forecasting of model lock-in trajectories before they become irreversible. Vendors can consider reporting jurisdiction-specific architectural divergences to enable convergence towards transparency and AI usage standards. Multilateral institutions can integrate AI deployment-depth indicators into existing macro-fiscal diagnostics and development frameworks, therefore linking their assessments to the Dialogue's interoperability monitoring and allowing for long-term evaluation of the impact of AI integration on productivity. Format recommendations: Day 1 can open with the Scientific Panel's annual report presented in plenary, to establish shared empirical grounding. Thematic breakouts could be structured as diagnostic workshops rather than general exchanges, for participants to exchange concrete case studies. Day 2 could feature a "Dialogue of Architectures" session where vendors present at their discretion jurisdiction-specific commitments side-by-side. The Co-Chairs' summary could incorporate quantitative benchmarks, deployment-depth thresholds, compliance-cost disclosure rates, vendor reporting compliance, and other procurement indicators. On the structural principle, the Dialogue could resist the temptation to replicate OECD or G7 formats (principles-based declarations). Its comparative advantage is measurement, pooling and knowledge transfer infrastructure.

Three underrepresented perspectives are critical for Objective 4(d). First, public financial management (PFM) practitioners and fiscal policy analysts in MENA and other developing regions. Current discussions privilege computer scientists, ethicists, and regulatory lawyers. Missing are the finance ministry officials, sovereign wealth fund managers, and PFM technical advisors who structure the procurement decisions that determine whether interoperability incentives bind. The Dialogue can convene a PFM-AI governance working group from across MENA region ministries and agencies. These actors understand the transaction-cost dynamics, Dutch Disease mechanisms, and fiscal architecture constraints that upstream governance fragmentation. Second, critical infrastructure operators in developing and middle-income countries, i.e. national grid managers, water authorities, border control agencies, sovereign cloud administrators, who face non-linear switching costs once AI systems are deployed at scale. Their operational perspective on vendor lock-in, migration risk, and continuity-of-service constraints is absent from current frameworks. Third, economists specializing in rentier state theory, institutional economics, and comparative political economy. Current AI governance discourse is dominated by technologists and lawyers; the macroeconomic and institutional conditions under which governance commitments succeed or fail remain undertheorized. The Scientific Panel can include representation from scholars working on resource curse dynamics, sovereign procurement, and fiscal-architecture political economy, particularly those with MENA and Gulf expertise.

First, live vendor architecture mapping: Major AI vendors can be encouraged to present pilots regarding jurisdiction-specific architectural commitments. This makes accommodation understandable as a system property rather than bilateral compliance and can therefore be market-driven information sharing rather than top-bottom. Second, fiscal-architecture stress-testing workshops: Small groups receive anonymized case studies of sovereign AI procurement under different hydrocarbon price scenarios. Participants-mixing finance ministry officials, vendors, civil society, Panel members work through switching-cost calculations, deployment-depth trajectories, and ex-post acceptance conditions. This builds shared understanding of why institutional convergence commitments fail under fiscal stress, and surfaces the instrumentation needed to detect trajectories early. Third, compliance-cost decomposition sessions: Participants reverse-engineer the compliance-cost components of actual sovereign AI contracts. This makes the rent-financing wedge measurable and exposes where divergence is cheap (formal commitments) versus expensive (substantive implementation) and can prevent lock-in before it is enacted.

Effective AI governance emerges where information asymmetries are reduced and transaction costs for competitive choice are lowered, not where compliance is mandated. Three approaches demonstrate this principle. First, open-source foundation model ecosystems (Llama, Mistral, Falcon) reduce vendor lock-in by eliminating proprietary model weights as a switching barrier. When Abu Dhabi's Technology Innovation Institute released Falcon as open-source, it created competitive pressure on closed vendors and gave states genuine procurement alternatives. The governance insight is not that open-source should be mandated but that supporting open-source infrastructure through compute credits, shared research facilities, technical training reduces the transaction costs that create bilateral dependency. Markets discipline vendor accommodation when exit is cheap, while rentier-driven procurement and sticky-prices, opaque PPP arrangements and monopolistic lock-ins distort markets and lead to fragmentation, black boxes and echo chambers. Second, fiscal transparency frameworks enable comparison of sovereign AI procurement costs across jurisdictions. When Saudi Arabia's fiscal breakeven oil price is published alongside its sovereign AI capital commitments, markets and domestic stakeholders can assess sustainability independently. This doesn't prevent rent-financed procurement but makes its costs visible, therefore allowing informed adjustment. Governance through information, not prohibition. Third, modular API standards developed by industry consortia (not regulators) reduce architectural lock-in. When vendors compete on model performance rather than proprietary integration layers, switching costs fall naturally. The Linux Foundation's AI projects demonstrate how collaborative standard-setting, voluntary, technically grounded, market-driven achieves interoperability without regulatory imposition. The common thread is this: governance approaches that enable competitive discovery outperform those that mandate convergence. The governance challenge is not designing the optimal global framework but reducing the costs that prevent markets and states from discovering efficient arrangements themselves and flagging/signaling conditions that are likely to create Public-Private nexuses that lead to inefficient markets.