TURKIYE AS A MIDDLE POWER IN AI GOVERNANCE
“From Bridge Diplomacy to Decisive Leverage: The Rise of Middle Powers in AI Geopolitics“
AI is reshaping global power paradigms. Türkiye can emerge as a strategic middle power by leveraging its demographic strengths, expanding technology capabilities, and unique geopolitical and diplomatic position. Türkiye can advance AI security, promote ethical governance frameworks, and bridge regional normative divides. However, achieving this role requires implementing focused and sustained efforts, such as improving internal governance mechanisms, investing in AI infrastructure, and developing a stronger voice in international norm-setting arenas. This report explores how Türkiye can transform its structural advantages as a middle power into strategic influence and secure a decisive role in shaping the future of AI governance.
The Rise of The Middle Power Paradigm in Ai
Since the 1990s, the concept of a middle power has been used to describe countries that can carve out niches and conduct bridge diplomacy in the competition between superpowers. In the age of AI, this idea is gaining new meaning. As Eric Schmidt suggests, multi-aligned middle powers can leverage their specific advantages—such as talent, infrastructure, or regulatory credibility—to play a meaningful role in shaping AI's future.
As a universal power multiplier, AI has become the main factor in shaking the geopolitical balances in the newly forming global order. The US and China are intensifying the competition by placing AI at the centre of their national priorities. However, several middle power countries with high situational awareness are increasingly engaging in AI governance, believing that this process should benefit from broader international collaboration rather than be shaped solely by the leading superpowers.
These countries see AI as an opportunity for economic growth, military capacity, political effectiveness, and sociocultural competition. We are talking about the AI value chain as complex and multilayered, encompassing not only upstream components such as data, computation, and energy, but also downstream outcomes such as autonomous systems, fintech solutions, or regulatory tools. This supply chain allows middle powers to exert strategic leverage without frontier AI capabilities. This layered structure allows for differentiated influence points, from setting standards and hosting sovereign datasets to piloting context-specific applications.
In this context, I propose to define middle powers as “decisive powers,“ which are beginning to shape global governance debates at key leverage points by aligning technological capacity with normative leadership. While this term is specific to this analysis, it aligns with broader arguments about the emerging role of swing states and middle AI powers in global geopolitics and governance.
Provisioning weaknesses, such as support for access to GPU/HPC resources and the fragmented nature of AI regulation, present three critical governance areas for middle powers. Critical areas include securing inputs, shaping interoperable norms, and building agile coalitions. These manoeuvre areas are particularly complex due to the three-layered regulatory heterogeneity frameworks proposed by Fritz and Giardini, which include differences in priority, instrument, and scope. They emphasize how different national practices address AI risk, how means of transportation and sectoral focus are coordinated, and how policy is enhanced.
Three key areas of manoeuvre are emerging for middle powers navigating today’s AI ecosystem:
· Input Diplomacy: As access to edge computing is increasingly constrained by geopolitical tensions and export controls, middle powers can strengthen their strategic positions by negotiating multilateral swap agreements to engage in joint GPU or HPC infrastructure projects or pool resources with like-minded states. This is diplomacy through inputs—compute, data, or talent—used as bargaining chips to generate influence without dominating model development.
· Regulatory Export: Inspired by the so-called “Brussels Effect,“ this strategy aims not to create sweeping legislation but to design narrow, enforceable regulatory packages. Targeted certification schemes or risk metrics for specific AI use cases—for example, UAVs and medical triage systems—can provide these. These compact yet robust frameworks can gain traction internationally by offering interoperability and legitimacy without being overly involved.
· Mini-lateral Coalitions: Middle powers can form small coalitions around a shared normative goal or technical focus instead of pursuing large, consensus-based blocs. These coalitions can act as “testbeds for norm innovation,“ driving risk metrics, compliance tools, or trust-building protocols that can be scaled outward. Such alliances' agility and thematic precision offer a high-impact path for countries lacking unilateral leverage.
Yet even within these promising manoeuvre areas, governance challenges remain. As Fritz and Giardini point out, countries often diverge in how they approach AI governance—what they prioritize, which instruments they use, and which domains they target.
· Priority heterogeneity refers to different national focuses: while some countries emphasize innovation, others may prioritize ethics, human rights, or security.
· Instrument heterogeneity captures preferences in how to regulate: from binding legislation to voluntary codes, public-private partnerships, or market incentives.
· Scope heterogeneity relates to variation in which regulations target technologies, sectors, or actors.
These divergences can lead to policy fragmentation and higher coordination costs. However, through mini-lateral cooperation and norm-piloting, middle powers can stabilize by bridging different regulatory cultures and promoting interoperable approaches that scale.
Without coordination across these three layers, standards fragment, compliance costs increase, and the risk of global digital fragmentation grows. Therefore, balancing this heterogeneity becomes important in mini-lateral coalitions established by middle powers.
AI, which we can liken to Prometheus’ fire, can catalyse economic development, public service optimisation, and social transformation when managed correctly. However, when left unchecked, it can create threats such as algorithmic bias, data exploitation, and democratic weaknesses. By directing this fire responsibly, middle powers can accelerate their development and rise to the position of decisive power in shaping global norms.
Global Trends - Windows of Opportunity - Geopolitical Value Chains
The AI race is redefining not only countries’ technological production capacities but also their geoeconomic positions. Value chain analysis is where value-added is generated in the flow of raw materials, products, and services, while the geopolitical value chain questions how these links translate into strategic power. The basic requirements in an AI ecosystem—human capital, data, computation, energy, financing, regulation, connectivity, partnerships, and trust—each constitute a separate geopolitical lever. However, some points stand out in the narrowing time window:
· Computational Pressure: AI systems are increasingly computationally demanding, making it harder for countries without strong infrastructure to keep up. While this trend raises barriers, generative AI has the potential to increase global productivity by as much as $7 trillion over the next decade.
· Energy and Infrastructure Shifts: By 2040, electricity demand from data centres could triple. Countries like Saudi Arabia are investing in solar-oil hybrid systems to support high-performance computing and reduce dependency on fossil fuels.
· Access Gaps in AI Tech: There is a growing divide in access to key AI components like chips and model weights. The US’s 2025 AI Diffusion Framework introduced export controls that now force middle powers to ask: “How much compute can I actually get—and from whom?“
· The Regulation Race: As AI adoption accelerates, countries compete on who sets the rules. At the 2025 Paris AI Action Summit, the EU’s AI Act shifted toward “innovation-friendly simplification,“ sparking debate on how far the Brussels Effect can go.
These dynamics open windows of opportunity in a short period defined as the inter-AI years (2025-2030). When decisions are delayed, costs will increase exponentially. The following table outlines the key building blocks of a national AI ecosystem, showing how each component offers a different kind of strategic value and time-sensitive opportunity for middle powers.
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“From Bridge Diplomacy to Decisive Leverage: The Rise of Middle Powers in AI Geopolitics“
AI is reshaping global power paradigms. Türkiye can emerge as a strategic middle power by leveraging its demographic strengths, expanding technology capabilities, and unique geopolitical and diplomatic position. Türkiye can advance AI security, promote ethical governance frameworks, and bridge regional normative divides. However, achieving this role requires implementing focused and sustained efforts, such as improving internal governance mechanisms, investing in AI infrastructure, and developing a stronger voice in international norm-setting arenas. This report explores how Türkiye can transform its structural advantages as a middle power into strategic influence and secure a decisive role in shaping the future of AI governance.
The Rise of The Middle Power Paradigm in Ai
Since the 1990s, the concept of a middle power has been used to describe countries that can carve out niches and conduct bridge diplomacy in the competition between superpowers. In the age of AI, this idea is gaining new meaning. As Eric Schmidt suggests, multi-aligned middle powers can leverage their specific advantages—such as talent, infrastructure, or regulatory credibility—to play a meaningful role in shaping AI's future.
As a universal power multiplier, AI has become the main factor in shaking the geopolitical balances in the newly forming global order. The US and China are intensifying the competition by placing AI at the centre of their national priorities. However, several middle power countries with high situational awareness are increasingly engaging in AI governance, believing that this process should benefit from broader international collaboration rather than be shaped solely by the leading superpowers.
These countries see AI as an opportunity for economic growth, military capacity, political effectiveness, and sociocultural competition. We are talking about the AI value chain as complex and multilayered, encompassing not only upstream components such as data, computation, and energy, but also downstream outcomes such as autonomous systems, fintech solutions, or regulatory tools. This supply chain allows middle powers to exert strategic leverage without frontier AI capabilities. This layered structure allows for differentiated influence points, from setting standards and hosting sovereign datasets to piloting context-specific applications.
In this context, I propose to define middle powers as “decisive powers,“ which are beginning to shape global governance debates at key leverage points by aligning technological capacity with normative leadership. While this term is specific to this analysis, it aligns with broader arguments about the emerging role of swing states and middle AI powers in global geopolitics and governance.
Provisioning weaknesses, such as support for access to GPU/HPC resources and the fragmented nature of AI regulation, present three critical governance areas for middle powers. Critical areas include securing inputs, shaping interoperable norms, and building agile coalitions. These manoeuvre areas are particularly complex due to the three-layered regulatory heterogeneity frameworks proposed by Fritz and Giardini, which include differences in priority, instrument, and scope. They emphasize how different national practices address AI risk, how means of transportation and sectoral focus are coordinated, and how policy is enhanced.
Three key areas of manoeuvre are emerging for middle powers navigating today’s AI ecosystem:
· Input Diplomacy: As access to edge computing is increasingly constrained by geopolitical tensions and export controls, middle powers can strengthen their strategic positions by negotiating multilateral swap agreements to engage in joint GPU or HPC infrastructure projects or pool resources with like-minded states. This is diplomacy through inputs—compute, data, or talent—used as bargaining chips to generate influence without dominating model development.
· Regulatory Export: Inspired by the so-called “Brussels Effect,“ this strategy aims not to create sweeping legislation but to design narrow, enforceable regulatory packages. Targeted certification schemes or risk metrics for specific AI use cases—for example, UAVs and medical triage systems—can provide these. These compact yet robust frameworks can gain traction internationally by offering interoperability and legitimacy without being overly involved.
· Mini-lateral Coalitions: Middle powers can form small coalitions around a shared normative goal or technical focus instead of pursuing large, consensus-based blocs. These coalitions can act as “testbeds for norm innovation,“ driving risk metrics, compliance tools, or trust-building protocols that can be scaled outward. Such alliances' agility and thematic precision offer a high-impact path for countries lacking unilateral leverage.
Yet even within these promising manoeuvre areas, governance challenges remain. As Fritz and Giardini point out, countries often diverge in how they approach AI governance—what they prioritize, which instruments they use, and which domains they target.
· Priority heterogeneity refers to different national focuses: while some countries emphasize innovation, others may prioritize ethics, human rights, or security.
· Instrument heterogeneity captures preferences in how to regulate: from binding legislation to voluntary codes, public-private partnerships, or market incentives.
· Scope heterogeneity relates to variation in which regulations target technologies, sectors, or actors.
These divergences can lead to policy fragmentation and higher coordination costs. However, through mini-lateral cooperation and norm-piloting, middle powers can stabilize by bridging different regulatory cultures and promoting interoperable approaches that scale.
Without coordination across these three layers, standards fragment, compliance costs increase, and the risk of global digital fragmentation grows. Therefore, balancing this heterogeneity becomes important in mini-lateral coalitions established by middle powers.
AI, which we can liken to Prometheus’ fire, can catalyse economic development, public service optimisation, and social transformation when managed correctly. However, when left unchecked, it can create threats such as algorithmic bias, data exploitation, and democratic weaknesses. By directing this fire responsibly, middle powers can accelerate their development and rise to the position of decisive power in shaping global norms.
Global Trends - Windows of Opportunity - Geopolitical Value Chains
The AI race is redefining not only countries’ technological production capacities but also their geoeconomic positions. Value chain analysis is where value-added is generated in the flow of raw materials, products, and services, while the geopolitical value chain questions how these links translate into strategic power. The basic requirements in an AI ecosystem—human capital, data, computation, energy, financing, regulation, connectivity, partnerships, and trust—each constitute a separate geopolitical lever. However, some points stand out in the narrowing time window:
· Computational Pressure: AI systems are increasingly computationally demanding, making it harder for countries without strong infrastructure to keep up. While this trend raises barriers, generative AI has the potential to increase global productivity by as much as $7 trillion over the next decade.
· Energy and Infrastructure Shifts: By 2040, electricity demand from data centres could triple. Countries like Saudi Arabia are investing in solar-oil hybrid systems to support high-performance computing and reduce dependency on fossil fuels.
· Access Gaps in AI Tech: There is a growing divide in access to key AI components like chips and model weights. The US’s 2025 AI Diffusion Framework introduced export controls that now force middle powers to ask: “How much compute can I actually get—and from whom?“
· The Regulation Race: As AI adoption accelerates, countries compete on who sets the rules. At the 2025 Paris AI Action Summit, the EU’s AI Act shifted toward “innovation-friendly simplification,“ sparking debate on how far the Brussels Effect can go.
These dynamics open windows of opportunity in a short period defined as the inter-AI years (2025-2030). When decisions are delayed, costs will increase exponentially. The following table outlines the key building blocks of a national AI ecosystem, showing how each component offers a different kind of strategic value and time-sensitive opportunity for middle powers.
Click here to continue.
