Guest academic & practitioner Angel Talamona, Professor of AI Audit at the University of Buenos Aires, explores the landscape of global data storage and calls for a data commons which transforms data from a private asset into a collectively managed infrastructure that can be used by industry, regulators, researchers, and the public sector.

Data Commons, the New Frontier by Angel Talamona. With kind acknowledgments to Prof. Adrian Zicari, ESSEC Business School.

A growing asymmetry

From energy production to industrial processing, the data that powers our economies is increasingly concentrated in the hands of a few corporations. Moreover, training and operating large-scale artificial intelligence require enormous datasets, highly specialized talent, and energy-intensive computing infrastructure.

These resources are concentrated in hyperscale cloud providers. Independent trackers show that the top three global providers now capture roughly two-thirds of enterprise spending on cloud infrastructure services. Their dominance has increased alongside the explosion of artificial intelligence workloads.

In addition, a second source of asymmetry arises from industrial equipment manufacturers. Connected machines used in mining, oil and gas, agriculture, and construction constantly generate telemetry data. Today, most of this information is collected, stored, and monetized by the manufacturers themselves, with operators who purchase the machines often having no real control over the data produced in their own operations. In response to this imbalance, the European Union adopted the Data Act, which gives users of connected products the right to access and share machine-generated data and requires data holders to make it available under fair conditions. These provisions apply directly to industrial equipment and related services.

The result is a dual concentration of power. On one side, cloud providers centralize computing and talent. On the other, equipment manufacturers control the operational data on which whole industries depend. Both forms of concentration raise fundamental questions of sovereignty, responsibility, and innovation. In a world where data is increasingly becoming the new oil, the question of who controls data becomes a central issue for countries, companies, and society.

Data commons as structural response

A data commons is a shared and governed data resource with clear access rules, privacy and security safeguards, and oversight by multiple stakeholders. It transforms data from a private asset into a collectively managed infrastructure that can be used by industry, regulators, researchers, and the public sector.

This model already exists in practice. For instance, Norway’s national petroleum data repository, DISKOS, has grown from about one petabyte in 2014 to more than thirteen by 2022, with seismic and well data stored in a secure cloud environment managed by the Norwegian Offshore Directorate. Public evidence shows that this repository reduces exploration costs and improves efficiency by enabling coordinated access to geological information.

In electricity, the ENTSO-E Transparency Platform publishes real-time operational data across European transmission systems. It includes hourly generation and load figures, as well as cross-border flows, supporting coordination and transparency at continental scale.

Brazil provides another example through the platforms developed by ANEEL, the national electricity regulator. These services make operational and commercial data available to companies, researchers, and consumers. They support competition, integration of renewable generation, and public oversight of the sector.

Agriculture provides another example of sectoral data commons, with the United States Department of Agriculture operating the Ag Data Commons, a curated repository of thousands of datasets produced with public funding. The system ensures that research results are accessible, interoperable, and reusable.

These cases demonstrate that data commons can operate securely at national or continental scale, deliver measurable efficiency gains, and support public interest objectives. They also show that commons are feasible in sectors as diverse as petroleum, electricity, and food production.

Commons as prerequisite infrastructure for autonomous systems

As a definition, a sovereign autonomous system is one whose design and operation remain under the jurisdictional and institutional control of the societies that depend on it. Achieving this requires strategic datasets to be governed in the public interest and shielded from extraterritorial claims or unilateral vendor control. Without commons, sovereignty over autonomous systems is fragile.

Indeed, autonomous systems in strategic industries cannot be designed, validated, or regulated without access to large, diverse, and high-quality datasets. If these datasets remain proprietary, autonomous systems become black boxes: opaque to regulators, inaccessible to local firms, and untrusted by citizens. Data commons are therefore not optional complements but mandatory infrastructure for sovereign and responsible autonomous systems.

Moreover, a responsible autonomous system can be trusted because it is transparent, auditable, and aligned with safety and ethical standards. This requires datasets that are shared, documented, and open to independent testing, bias detection, and lifecycle monitoring. Without commons, responsibility remains a promise rather than a verifiable property.

Finally, an autonomous system can only be effective if local ecosystems are able to build, deploy, and improve automation without negotiating case-by-case access to proprietary datasets. Data commons make this possible and also allow regulators to measure safety and performance continuously rather than depending solely on vendor disclosures.

Why energy industries sharpen the stakes

Artificial intelligence itself is becoming a significant energy consumer, which ties the future of digital innovation to energy planning and to the supply of critical minerals. The International Energy Agency projects that data center electricity demand could more than double by 2030, requiring the equivalent of several additional medium-sized countries worth of generation capacity. This raises pressing questions of grid adequacy, siting, and decarbonization that only public institutions can address.

In parallel, the supply chains for minerals such as lithium, nickel, and rare earths, essential both to clean energy and to advanced electronics, are tightening. The International Energy Agency’s 2024 review shows that refining is highly concentrated in only a few countries. This creates vulnerabilities that directly affect the ability to deploy autonomous and digital technologies at scale.

Nuclear energy illustrates how data commons could shape emerging strategic projects. Small modular reactors, defined as advanced nuclear reactors with a capacity of up to about 300 megawatts per unit, are under development in France and Argentina. France is advancing the NUWARD program and Argentina is constructing the CAREM-25 prototype. These projects are significant both for low-carbon energy supply and for national technological capability. A data commons for nuclear operations would allow rigorous safety analytics, transparent performance benchmarking, and international collaboration while keeping sensitive data under strong governance.

Data Commons: Governance, funding, and inclusion

Creating a production-grade commons is not only a technical task. It requires a governance framework that defines membership, decision rights, access rules, privacy safeguards, and procedures for redress. Sector regulators and public bodies should hold veto power over decisions that affect safety or public interest.

Economic sustainability is also crucial. National repositories like DISKOS in Norway are funded by a consortium of industry participants under legal obligations, complemented by oversight from the regulator. Similar blended models that combine public contributions and member fees tied to usage can ensure stability. Interoperability and security must be ensured through open standards, strong identity and access management, and reliable cloud infrastructure. Partnerships with global cloud providers can accelerate deployment, but contractual safeguards are necessary to guarantee portability, auditability, and jurisdictional control. Otherwise, one form of dependency is merely replaced by another.

Inclusion matters as well. Commons should provide access programs for universities, small and medium enterprises, and public research laboratories. Training and re-skilling initiatives can allow local workforces to develop the competencies needed to use the data and to participate in emerging ecosystems. In this way, commons do not only redistribute access to information but also help open new opportunities for employment and capability building.

Policy pathways

Policymakers and industry leaders have several tools at their disposal. They can require access to machine-generated data under fair conditions, as the European Union has already legislated. They can create sectoral commons with statutory backing, starting in high-impact domains such as energy exploration and grid operations.

Further, they can attach data-sharing obligations to public funding, concessions, or exclusive rights. They can invest in trusted computing infrastructure co-located with the commons, with strict guarantees of portability. Most importantly, they can use commons as the evidence base for assessing autonomous systems under three dimensions: whether they are responsible, whether they remain under sovereign control, and whether they allow for safe and effective autonomy with accountable human oversight.

Data Commons: The choice

Either societies design data commons that broaden access, anchor public oversight, and enable sovereign and responsible autonomous systems, or they allow a small number of corporations, based in a few countries, to define how critical infrastructures operate. The empirical record already shows that the commons model can work at scale, in petroleum, electricity, and agriculture. The legal frameworks already exist to require fair access to machine-generated data. What remains is to apply these principles to the sectors where the stakes are highest.

Useful links:

• Link up with Angel Talamona on LinkedIn
• Read a related article: How do humans interact with AI to ensure the common good?
• Visit the Universidad de Buenos Aires website.

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