Understanding Machine Knowledge Capital and Its (Geo)political Implications

Throughout history, major revisions of the international order have been based on the emergence of new  productive assets that redistribute power both within and among nations: land in agrarian societies, the machinery of mass production during the industrial revolution, and intellectual property and human capital in the knowledge economy. Data, as a new factor of production, has played such a role. 

The Rise of Machine Knowledge Capital and Its (Geo)political Implications

According to Dan Ciurak, the author of our latest policy paper, we are undergoing a new transition marked by the rise of data and "machine knowledge capital" —a novel form of capital that emerges from applying energy-intensive computing power to massive datasets, generating automated systems of knowledge and decision-making. 

This new paradigm departs sharply from the knowledge economy, which relied on educated human labor as its foundation. Instead, value creation now hinges on access to data, computational scale, and control over digital infrastructure. Nations that can mobilize these inputs are better positioned to dominate AI and platform-driven markets.

Politically, this transition has undermined the social contract that once underpinned liberal democracies—rooted on meritocracy, middle-class stability, and national economic sovereignty—fueling discontent and the rise of populism.

Why Does the Data Revolution Destabilize Smaller Economies?

In the data-driven economy, a handful of dominant hubs have been able to capture disproportionate rewards by training large models, deploying global platforms, and capturing network effects. This concentration of value deepens structural inequalities between digital superpowers and the rest, locking small open economies into peripheral roles marked by dependency and limited influence. 

Key challenges include exacerbated by:

• Economic vulnerability: Small states, with limited markets, few data-rich firms, and smaller populations, struggle to compete in AI-driven sectors. Their reliance on foreign platforms and infrastructure leaves them exposed to external control.
• Weakened state capacity: Competing in the data economy requires coordinated industrial strategy and robust institutional action. Many small open economies lack the tools to respond at the scale of countries like Russia and China, examples of jurisdictions where state control can align platform economies with national goals.
• Outdated global governance frameworks: Today’s global economic architecture was built for trade in physical goods and services— not for the digital age. This limits small open economies’ ability to shape digital rules, leaving them without effective recourse in setting the terms of engagement.

Strategic Pathways to Avoid Digital Dependency

Small open economies largely missed out on the first phase of the data-driven economy, but they now face a chance to recalibrate their position in the emerging economy of data and generative AI.  Our policy paper outlines several strategic pathways: 

• Support domestic firms in the generative AI economy with the capacity to scale model development and platform deployment.
• Mitigate disadvantages in predictive AI by deploying familiar tools of state-led industrial policy to manage the technological gap.
• Reframe how data is valued—not by the cost of datafication, but by the economic rents it can generate and who controls them.
• Prepare Europe’s technical and legal foundations necessary for institutional recovery in the post-conflict stage.
• Foster alignment among small democracies to promote shared standards and strengthen international institutions capable of rebalancing power in global digital governance.

Read the entire policy paper and learn more about our research program on the Digital Revolution and the New Social Contract here.