Governance

A regulatory package as a long-term political strategy

The European Union’s recent digital laws are often described as a regulatory package. The AI Act, the Data Act, and the emerging Data Union Strategy form a wide experiment in using transparency as infrastructure for the digital economy.

The underlying idea is that digital markets cannot be governed well if users, businesses, regulators, and affected individuals cannot understand how systems work, who controls data, where risks arise, and who is responsible for intervention. Therefore, transparency is becoming a condition for accountability, market access, innovation, and long-term trust that falls under what appears as a long-term strategy to regain data sovereignty.

The EU’s policy bet

The EU regulatory approach is founded on the premise that greater transparency can enhance the governability of complex digital systems. However, the mere disclosure of information does not result directly in a greater understanding of the data available; a company can disclose large amounts of technical material while leaving users no better able to assess risk, compare alternatives, or challenge decisions.

Accordingly, the success of the EU’s transparency framework should not be measured by the sheer volume of regulatory obligations it imposes. Rather, its effectiveness depends on whether those obligations generate information that is genuinely useful in practice. The relevant benchmarks are whether disclosures are meaningful, accessible, timely, and comparable, thereby enabling users and regulators to make informed decisions.

The AI Act’s goal to make AI legible

The AI Act shows the EU’s approach most clearly. Its stated purpose is to improve the functioning of the internal market, promote human-centric and trustworthy AI, protect health, safety, and fundamental rights, and support innovation (Regulation (EU) 2024/1689).

In policy terms, the AI Act tries to make AI systems legible. It assumes that AI risks should not be addressed only after harm occurs. They should be identified, documented, and managed before systems are placed on the market or deployed in sensitive settings.

This is why transparency is linked to risk. High-risk systems face more demanding documentation, monitoring, and information obligations. Lower-risk systems face lighter duties. The European Commission describes the AI Act as the first comprehensive legal framework on AI, designed to address AI risks while fostering trustworthy AI in Europe (European Commission, “Regulatory framework for AI”).

The policy logic is fundamentally pragmatic. Effective regulatory oversight depends on access to adequate information. Likewise, deployers require sufficient information to make informed decisions regarding whether and under what conditions to implement AI systems. Individuals affected by AI-assisted decisions must also have access to relevant information in order to understand how such decisions have been made and, where appropriate, to question or challenge them.

The Data Act attempts to rebalance informational power.

The Data Act uses transparency for a different purpose. Where the AI Act focuses on risk and trust, the Data Act focuses on access, fairness, and economic value. Its objective is to create harmonized rules on fair access to and use of data (Regulation (EU) 2023/2854).

The challenge is that data generated by connected products and digital services is often controlled by a small number of firms. Users may generate valuable data through their use of products but still lack practical access to it. Businesses may need data to innovate, repair products, or offer competing services but face legal, technical, or contractual barriers.

The Commission presents the Data Act as a way to address the challenges and opportunities created by data in the EU, with emphasis on fair access, user rights, and personal data protection (European Commission, “Data Act”).

In this context, transparency functions as a mechanism for redistributing information. Where users are unaware of what data is generated, how it can be accessed, or the conditions under which it may be shared, formally recognized rights of access are unlikely to translate into meaningful practical control. Effective data rights therefore depend not only on their legal recognition but also on the transparency necessary to enable individuals to exercise them.

The Data Union Strategy: From Control to Usable Data

The Data Union Strategy shows the broader direction of EU policy. The Commission frames it around increasing the availability of data for AI development, simplifying EU data rules and strengthening Europe’s position on international data flows (European Commission, “European Data Union Strategy”).

This is significant because it seems that the European Union seeks to pursue two complementary goals simultaneously. On the one hand, it aims to protect fundamental rights and mitigate the risks associated with digital technologies. On the other, it seeks to facilitate greater access to data in order to foster innovation, support the development of artificial intelligence, and enhance European competitiveness. In this way, transparency serves as the connecting principle between these objectives. In fact, by increasing the visibility of how data is collected, processed, and shared, it is intended to strengthen trust in data flows while making them more accessible and capable of supporting innovation.

Why meaningfulness matters most

Meaningfulness is the anchor test. Transparency is useful only if it reveals something that can change decisions or enable scrutiny.

In the AI context, this means information about a system’s purpose, limitations, performance, and risk profile must be specific enough to support procurement, oversight, and challenge. In the data context, it means users must receive information that helps them understand what data exists and how it can be used.

Generic compliance language is not enough. A disclosure that says a system is “risk managed” or that data is “available upon request” may be formally correct but still unhelpful. The real question is whether the information helps someone act.

Information must arrive before decisions are locked in.

Transparency is most useful when it arrives early enough to affect decisions. AI information matters most before procurement and deployment. Data-access information matters most before users become dependent on a particular product, service, or cloud provider.

Post-event transparency can still support audit and enforcement. But it is weaker as a prevention tool. A regime that informs users only after they have lost practical freedom of choice will have limited effect.

Accordingly, comparability occupies a central role in the European Union’s internal market strategy. If transparency is intended to promote competition, facilitate public procurement, and strengthen trust in cross-border digital markets, disclosures must be presented in a manner that enables users, businesses, and regulators to meaningfully compare systems, services, and contractual arrangements.

This objective is particularly relevant in the context of AI procurement, connected product ecosystems, and cloud switching, where informed comparisons are essential to reducing information asymmetries and preventing vendor lock-in. Nevertheless, pursuing comparability inevitably involves trade-offs. While standardized disclosure frameworks can improve the accessibility and consistency of information, they may also obscure sector-specific risks and contextual nuances. Consequently, a uniform template may enhance market discipline and regulatory oversight while simultaneously limiting a more nuanced understanding of the particular risks associated with individual technologies or markets.

The risk of regulatory complexity

The EU’s approach is ambitious, but it is also complex. The AI Act does not operate alone. It sits alongside the GDPR, the Data Act, the Digital Services Act, the Digital Markets Act, the Cyber Resilience Act, and sector-specific rules.

A European Parliament study notes that the AI Act interacts with other digital laws, including the GDPR, Data Act, and Cyber Resilience Act, and that this interplay creates significant regulatory complexity (European Parliament, “Interplay between the AI Act and the EU digital legislative framework”).

Secondary analysis makes a similar point. CEPS has argued that the AI Act may overlap with several horizontal and sector-specific rules, creating possible gaps, inconsistencies, and legal uncertainty (CEPS, “The AI Act and emerging EU digital acquis”).

Competitiveness and the SME problem

The burden of complexity is not shared equally. Large technology firms are better able to absorb compliance costs, hire specialists, and shape standards. Smaller firms may struggle.

Bruegel has warned that EU AI regulation risks imposing disproportionate burdens on smaller firms and may contribute to market concentration if compliance demands are not properly balanced (Bruegel, “The right balance: how to fix European Union artificial intelligence regulation”). This is a key policy tension. The EU wants trustworthy digital markets, but it also wants innovation and technological sovereignty. Transparency can support both goals, but only if it is designed in a way that smaller firms can use and implement.

From disclosure to governance

The EU’s digital strategy should be judged by a practical standard. The question is not whether Europe has created the world’s most elaborate digital rulebook. The question is whether that rulebook produces usable knowledge, enables timely intervention, supports meaningful comparison and redistributes informational power.

If it does, transparency may become genuine governance infrastructure. If it does not, the EU risks building a sophisticated compliance architecture that documents the digital economy without effectively governing it.

Ukraine has appointed veteran energy executive Sergii Koretskyi as its new prime minister, marking a significant leadership change as the country continues to battle Russia’s invasion and prepares for another difficult winter.

Parliament approved the 48-year-old on Thursday as part of a wider government reshuffle announced by President Volodymyr Zelenskiy. Koretskyi becomes Ukraine’s third wartime prime minister and takes office at a time when Kyiv faces mounting military, economic and energy challenges.

A Political Outsider Takes Office

Unlike many of his predecessors, Koretskyi arrives in government without a political background. An engineer and economist by training, he has never held elected office or served in government and is not affiliated with any political party.

Analysts say that independence could work in his favour. Volodymyr Fesenko, director of the Penta think tank, has described Koretskyi as an experienced manager whose political neutrality makes him well suited to lead a technocratic government focused on wartime priorities rather than party politics.

More Than Two Decades in Ukraine’s Energy Sector

Koretskyi is best known for his extensive career in Ukraine’s energy industry, where he has spent more than two decades working across oil production, refining, retail fuel operations, wholesale energy management and international financing.

Since May 2025, he has served as chief executive of Naftogaz, Ukraine’s state-owned oil and gas company that oversees much of the country’s natural gas production, imports and distribution. Before taking over Naftogaz, he led Ukrnafta, Ukraine’s largest oil producer and a subsidiary of the Naftogaz Group.

Earlier in his career, Koretskyi headed Western Oil Group, served as chief executive of the Continuum Group, and managed WOG, one of Ukraine’s largest fuel station networks. Outside the energy sector, he also founded a coffee chain business in his hometown of Lutsk in western Ukraine.

Winter Energy Security Will Be the First Test

His appointment comes as Ukraine’s energy sector remains under relentless pressure from Russian missile and drone strikes. Last winter, Russia launched its most extensive campaign against Ukraine’s power infrastructure since the war began, damaging power plants, substations and transmission networks across the country.

Preparing the energy system for another winter has become one of the government’s most urgent priorities. President Zelenskiy has said ensuring stable electricity and heating supplies while strengthening protection for critical infrastructure will be among the new government’s immediate tasks.

Leading Ukraine During Wartime

Koretskyi also takes office as Ukraine faces continued battlefield pressure despite making gains through long-range strikes against Russian energy facilities and military logistics. At the same time, Kyiv continues to rely heavily on international military and financial assistance while confronting shortages of air defence interceptors needed to counter Russian missile attacks.

His background suggests the government will place a strong emphasis on energy security, infrastructure resilience and economic management as the war enters another challenging phase.

Whether Koretskyi’s experience in managing some of Ukraine’s largest energy companies translates into effective wartime leadership will likely become one of the key tests for Zelenskiy’s newly reshuffled government in the months ahead. His ability to secure Ukraine’s energy network, maintain economic stability and coordinate with international partners will be closely watched as the country prepares for another winter under the shadow of war.

With information from Reuters.

In May 2026, just hours before President Donald Trump met President Xi Jinping, OpenAI’s Vice President of Global Affairs Chris Lehane floated the idea of a US-led global governance body for artificial intelligence that would include China as a member. The model, according to media reports, was compared to the International Atomic Energy Agency (IAEA), a familiar reference for managing strategic technologies with global consequences.

One month later, at the G7 summit in Évian-les-Bains, a different tone emerged. Several influential AI executives joined leaders from advanced economies to discuss AI governance, online safety, and global security. According to Axios, Anthropic’s Dario Amodei and Google DeepMind’s Demis Hassabis leaned towards a more selective framework among democratic countries, while OpenAI’s Sam Altman used broader language, calling for an international forum to develop shared testing standards and risk assessments.

These two moments reveal something important: the meaning of “global AI governance” remains unsettled. In one setting, global means including China for legitimacy. In another, it can mean a trusted coalition designed to manage access, capability, and strategic risk. AI governance is becoming part of the architecture of global power.

Three Voices, Different Emphases

Their presence at the G7 showed how quickly AI firms have moved from building systems to helping shape the politics around them. The leaders of OpenAI, Anthropic, Google DeepMind, Mistral, Cohere, and other firms were not simply observers of geopolitics. They were part of the conversation about how technological power should be governed.

Their positions were not identical. Amodei reportedly urged democratic countries to coordinate more closely so that AI governance would not fragment. Hassabis stressed the strategic importance of frontier capability. Altman, by contrast, used more institutionally neutral language, suggesting that advanced AI should not be shaped only by the companies building the most capable systems.

Even among frontier AI developers, there is no settled imagination of global governance. Should it include all major AI powers, including strategic rivals? Should it be built around trusted coalitions? Should it prioritize safety, democratic values, geopolitical advantage, or public legitimacy?

The question became more complicated because the G7 discussions came shortly after the US government imposed export controls that forced Anthropic to suspend foreign access to its Fable 5 and Mythos 5 models. Reuters reported that the order required Anthropic to block access to the models for foreign nationals, leading the company to disable them more broadly to ensure compliance. The episode showed how frontier AI governance can move from abstract principles to abrupt restrictions. Even among democratic allies, technological solidarity has limits. When AI becomes strategic infrastructure, every country begins to think about its own room for maneuver.

The Asymmetry of “Global”

The deeper issue lies in who has the power to define the word “global” in the first place. In May, global governance could mean a US-led institution that includes China. In June, it could mean coordination among democracies to manage frontier capability and strategic access. The definition changed because the political room changed.

This reveals a double asymmetry. The first is technical: only a small number of firms can define what counts as a frontier model, how its capabilities should be tested, and who should be allowed to access it. The second is narrative: the same ecosystem also helps frame the language through which the world discusses governance.

For countries outside the frontier AI circle, they may be invited to conversations but not always to the stage where categories, thresholds, and governance priorities are first shaped. They may be asked to adopt best practices whose assumptions were formed elsewhere. They may be told that risks are global, even when preparedness remains highly unequal.

G7 outreach to partner countries such as India, Brazil, Kenya, South Korea, and Egypt is important. It recognizes that AI governance cannot remain a conversation among advanced economies alone. Yet there remains a difference between being present in a forum and helping design the architecture of the forum itself. The question is who defines the table, the agenda, the risk categories, and the meaning of global governance itself.

When the AI Frontier Moves Towards the Market

There is another reason why a broader governance imagination is necessary. Frontier AI innovation is no longer centered primarily in universities or public research institutions. It is increasingly shaped by private firms with the capital, compute, talent, data access, and infrastructure required to train and deploy the most capable models.

Stanford’s AI Index 2025 noted that nearly 90 per cent of notable AI models in 2024 came from industry, up from 60 per cent in 2023. A report prepared for the European Economic and Social Committee on generative AI and foundation models also described significant US dominance across the value chain. These findings point to a structural shift: the frontier is becoming more concentrated, more expensive, and more closely tied to corporate and geopolitical capacity.

Much of AI’s progress has come from companies willing to take risks, scale products, and build technical capability at extraordinary speed. But the center of gravity has shifted. When frontier AI is largely financed, defined, and deployed by market actors, the default imagination of AI development can tilt towards commercial viability, platform advantage, user growth, and strategic positioning.

Public interest does not disappear in such a system. It risks becoming secondary unless other actors are strong enough to bring it back into the room.

Open Future, a European digital policy organization, has warned that concentrations of power in AI can make public activities dependent on “a narrow group of monopolists.” The phrase matters because infrastructure-level dependency can weaken society’s ability to negotiate the terms of the technologies it relies on.

A Wider Public-Interest Layer

In a multiplex digital world, power does not flow only through states or markets. It also moves through universities, civil society organizations, professional associations, media, labor groups, open-source communities, public-interest technologists, and moral institutions. Together, these actors form the society layer often missing from discussions dominated by states and markets.

States define security priorities. Companies define technical possibility. Society must help test legitimacy. Who bears the risk? Who benefits from deployment? Who is excluded from design? What harms are being normalized because they are commercially convenient or geopolitically useful?

This is why Pope Leo XIV’s recent intervention on AI is politically relevant beyond its religious context. In his encyclical Magnifica Humanitas, he argues that protecting the human person in the age of AI requires renewed reflection on the common good, solidarity, social justice, and human dignity. Such interventions will not replace regulation or technical standards. They help recover a truth easily lost in frontier AI politics: governance is also about preserving the human meaning of technological progress.

The same question of authorship is beginning to appear in empirical research. Ongoing fieldwork-based research at the University of Oxford has started to examine whether countries in the Global South are developing approaches to AI governance that are neither simple copies of Western regulatory templates nor rejections of international cooperation but pragmatic syntheses shaped by local institutional capacity, regulatory sequencing, and historical experience with technology transfer. Indonesia has appeared as one of the country cases in this line of inquiry.

Governance models worth studying are not only those negotiated in Évian, Brussels, Washington, or New York. They are also being improvised, often informally, by mid-sized digital economies navigating dependency and ambition at the same time.

The United Nations’ Global Digital Compact (GDC), adopted in September 2024, offers a useful multilateral reference point. It frames digital cooperation and AI governance around inclusion, human rights, open standards, interoperability, digital public goods, and multi-stakeholder cooperation. The Compact does not resolve the power asymmetries of frontier AI by itself, but it gives societies, alongside states and firms, a language for claiming a legitimate role in digital governance.

The practical task is to strengthen public-interest evaluation: the ability to test social impact, language bias, local risks, institutional misuse, and deployment consequences in different societies. The aim is to preserve enough room for public reasoning so that the future of AI is not defined only by those with the largest models, the biggest markets, or the strongest strategic leverage.

Imagining a More Inclusive AI Governance

The lesson from the IAEA analogy and the G7 discussions is not that one model is right and the other is wrong. Both reflect real concerns. A broadly inclusive governance arrangement may be necessary for legitimacy, especially when AI risks cross borders. A trusted coalition may also be necessary when capability access raises genuine security concerns. The problem begins when either model claims to be global while leaving too many societies downstream of decisions made elsewhere.

For emerging economies, the strategic challenge is not simply to wait for a better invitation to the next summit. Participation matters, but it is not enough. Countries and societies need stronger capacity to evaluate AI systems, understand their dependencies, articulate local risks, and negotiate governance terms with greater confidence.

This is a call for a more plural architecture of governance, where states, markets, and society all have meaningful roles. The uncomfortable question is not whether AI requires international coordination. It clearly does. The harder question is whether that coordination can remain open enough for societies, not only states and companies, to shape the terms of technological power.

In the age of frontier AI, the future will not be determined only by who builds the largest models. It will also be shaped by who gets to define risk, test systems, question assumptions, and decide what counts as progress.

Every era that has tried to govern a transformative technology eventually learns the same lesson: legitimacy borrowed from power is not the same as legitimacy earned through participation. The IAEA’s own history shows that global trust is rarely built at the moment institutions are created; it is earned over time, through broader representation, credible restraint, and shared accountability. The real question for AI governance is whether it can shorten that distance by design, rather than waiting for legitimacy to arrive only after contestation.